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Vol. 7
No. 2
July 2019
ISSN 2321-3388
© Mizoram University, Aizawl–796004, India
Science and Technology Journal
Vol. 7, No.: 2, July–December 2019
ISSN: 2321–3388
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Science and Technology Journal
Editorial Board
Patron
Professor K.R.S. Sambasiva Rao
Vice Chancellor, Mizoram University, Aizawl
Advisors
Professor Lalnundanga
Registrar, Mizoram University, Aizawl
Editor-in-Chief
Professor Diwakar Tiwari
Department of Chemistry, Mizoram University, Aizawl
Editorial Board
Professor N. Senthil Kumar Dr. H.T. Lalremsanga
Professor Zaithanzauva Pachuau Dr. K.C. Lalthlamuani
Professor S.K. Mehta Dr. N.P. Maity
Professor Lalnuntluanga Dr. T.K. Hazarika
National Advisory Board
Professor R.P. Tiwari Professor Harsh Gupta
Vice Chancellor, Dr. Hari Singh Gour University, Sagar Member-NDMA, New Delhi
Professor G.D. Yadav (FNA) Professor K.L. Chopra
Vice Chancellor, ICT, Mumbai Former Director, IIT Kharagpur
Professor Anil K. Gupta Professor M.M. Salunkhe
WIHG, Dehradun Former Vice Chancellor, CUOR, Kishangarh
Professor A.N. Rai Professor T.P. Singh
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Dr. Shailesh Nayak Dr. T. Ramaswami
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Professor G.D. Sharma Dr. T.K. Chakraborty
Former Vice Chancellor, Bilaspur University, Bilaspur Director-CDRI, Lucknow
From the Editorial Desk
Contents
1.
Poonam 7
2. Incorporation of Carbon Fiber and Silica Fume in the Production of Conductive
Concrete
M. Purushothaman and K. Natarajan 17
3. Smart Automated Farming System using IOT and Solar Panel
Anita Shukla and Ankit Jain 22
4. A Comprehensive Review on Molecular Characteristics and Food-Borne Outbreaks of
Listeria monocytogenes
Dipika Malakar, Probodh Borah, Leena Das and Nachimuthu Senthil Kumar 33
5. Potential of Iron Pillared Clay as Active Nanocatalyst for Rapid Decolorization
of Methylene Blue
Lalhmunsiama and Seung-Mok Lee 46
6.
R. Raja Aswathi, K. Pazhani Kumar and B. Ramakrishnan 54
7. A Pressure-based Compressible-Liquid Flow Model for Computation of
Instantaneous Valve Closure in Pipes
R. Jishnu Chandran and A. Salih 60
8. Electrodes for Estimation of Nimesulide Drug using Voltammetry Technique: A Revisit
Abhik Chatterjee 67
9. Synergistic Effect of Thiourea-Zn2+ and L-Phenylalanine on the Inhibition of Corrosion
of Mild Steel in Acid Medium
M.B. Geetha, J. Sathish and S. Rajendran 72
10.
Kunal Roy, Kaushik Mukherjee, Dilip Kumar Hajra and Santanu Banik 78
11. Fuzzy Logic: An Easiest Technique to Predict Celiac Disease
Sunny Thukral and Jatinder Singh Bal 89
12.
K. Kavitha and L. Palaniappan 95
13.
Mukesh Upadhyay, Ashok Kumar Thakur and Mohan Daimary 102
AUTHOR INDEX 105
INSTRUCTION FOR AUTHORS 107
7
A Novel Framework to Secure CBWSNS Against
Poonam
CE Department, J.C. Bose University of Science & Technology, YMCA, Faridabad, India
E-mail: poonamgarg1984@gmail.com
Abstract—Dynamic and cooperative nature of sensor nodes in Wireless Sensor Networks raises question on security.
which most nodes transmit to cluster heads, and the cluster heads aggregate and compress the data and forward it to the
base station (sink). Each node uses a stochastic algorithm at each round to determine whether it will become a cluster
operation at CH, IN and MNs beside their usual activities in cluster based wireless sensor networks. This paper mentioned
various security level processes of wireless sensor networks. Results implies that in a cluster-based protocol such as LEACH
or the entire network disabled, in the worst-case scenario, if these cluster heads are compromised. Our main contribution
in this paper is our novel approach in maintaining trusted clusters through a trust-based decision-making cluster head
election algorithm.
Keywords:
Science and Technology Journal Vol. 7 Issue: 2 July 2019 ISSN: 2321-3388
INTRODUCTION
Wireless Sensor Networks (WSN) are vulnerable to
internal and external attacks as a result of collaborative
and dynamic nature of the network having sensor nodes
with less memory and low power devices (Yick et al.
2008; Akyildiz 2002). Many crypto-logical algorithms
were accessible for generic enhanced securities but
most of them are not appropriate for wireless sensor
networks. As cryptography mechanisms are not enough
to prevent any internal attacks, as well as not able to
differentiate between malicious node or selfish, behavior
of nodes (Mittal et al. 2015; Schaffer et al. 2012; Dong et
al. 2009). But this mechanism is not capable to secure
the complete network (no improvement of distributed
knowledge gathering and cooperative data processing
in the network). The main objective of the security
framework for cluster based wireless sensor networks
is to enhance the general performance by monitoring
network activities like like information gathering and
information processing and minimizing the risk (Thein et
al. 2008). A security framework for Cluster- Based Wireless
Sensor Networks (CBWSNs) was introduced (Ishaq et al.
2015) to deal the security issue as shown in figure 1.
1
A Novel Framework to Secure CBWSNS against the
Selfishness Problem
Poonam
CE Department, J.C. Bose University of Science & Technology, YMCA, Faridabad, India
poonamgarg1984@gmail.com
________________________________________________________________________________________________________
Abstract: Dynamic and cooperative nature of sensor nodes in Wireless Sensor Networks raises question on security.
Various researchers work in this direction to spot malicious, selfish and compromised nodes. Various mechanisms
followed are uniqueness of clustering, reputation system and an operation at specific nodes. LEACH is a
hierarchical protocol in which most nodes transmit to cluster heads, and the cluster heads aggregate and compress the
data and forward it to the base station (sink). Each node uses a stochastic algorithm at each round to determine whether it
will become a cluster head in this round. Clustering process carried out in two stages takes the role of the reputation
scheme and reveals specific operation at CH, IN and MNs beside their usual activities in cluster based wireless
sensor networks. This paper mentioned the final structure of the security framework, corresponding attacks and
defense mechanism of the model. It also discusses various security level processes of wireless sensor networks.
Results implies that in a cluster-based protocol such as LEACH in which optimally 5% of the nodes are cluster heads
it is likely that a significant portion of the network can be paralyzed or the entire network disabled, in the worst-
case scenario, if these cluster heads are compromised. Our main contribution in this paper is our novel approach in
maintaining trusted clusters through a trust-based decision-making cluster head election algorithm.
Index Terms - WSNs, Selfish Attack, Security Schemes, Trust, Cluster, LEACH
________________________________________________________________________________________________________
I. INTRODUCTION
Wireless Sensor Networks (WSN) are vulnerable to internal and external attacks as a result of collaborative and
dynamic nature of the network having sensor nodes with less memory and low power devices (Yick et al. 2008;
Akyildiz 2002). Many crypto-logical algorithms were accessible for generic enhanced securities but most of
them are not appropriate for wireless sensor networks. As cryptography mechanisms are not enough to prevent
any internal attacks, as well as not able to differentiate between malicious node or selfish, behavior of nodes
(Mittal et al. 2015; Schaffer et al. 2012; Dong et al. 2009). But this mechanism is not capable to secure the complete
network (no improvement of distributed knowledge gathering and cooperative data processing in the network).
The main objective of the security framework for cluster based wireless sensor networks is to enhance the
general performance by monitoring network activities like like information gathering and information processing
and minimizing the risk (Thein et al. 2008). A security framework for Cluster- Based Wireless Sensor Networks
(CBWSNs) was introduced (Ishaq et.al 2015) to deal the security issue as shown in figure 1.
In this secured framework two special nodes per cluster are appointed: investigation node and cluster head node.
In every cluster three types of nodes are formed CH, IN, and MNs (member nodes) and these nodes are one
hop apart from CH as shown in Figure 1. In order to control the selfishness attack (Nagpal 2016; Kanchan et al.
2014; Yoo et al. 2006), a security mechanism is provided by using a reputation system at every node. The IN node
exploits the packet overhearing scheme, that is one among the characteristics of wireless communication
and utilized by several previous researches to supply security against the selfishness attack ensures entity as
secure and reliable, so security model is used to differentiate trust-worthy and unreliable nodes in a network. It
N6
N2
N1
N7
N4
N5
N9
N8
CH
M
N
MN
M
N
MN
MN
I
N
Figure
-
1 WSN: before and after cluster formation
Fig. 1: WSN-before and after Cluster Formation
In this secured framework two special nodes per cluster
are appointed: investigation node and cluster head node.
In every cluster three types of nodes are formed CH, IN,
and MNs (member nodes) and these nodes are one hop
apart from CH as shown in Figure 1. In order to control
the selfishness attack (Nagpal 2016; Kanchan et al. 2014;
Yoo et al. 2006), a security mechanism is provided by
using a reputation system at every node. The IN node
Poonam
8
exploits the packet overhearing scheme, that is one
among the characteristics of wireless communication
and utilized by several previous researches to supply
security against the selfishness attack ensures entity
as secure and reliable, so security model is used to
differentiate trust-worthy and unreliable nodes in a
network. It encourages trustworthy nodes (Nagpal 2016)
to speak and discourages unreliable nodes to participate
within the network.
Also, it increases the network life time, throughput and
resilience of the wireless sensor network. There are three
1. It drops information packets rather than
forwarding to sink nodes.
2. It stops overhearing CH or sends deliberate
accusing messages on CH.
3. It does not properly participate within
the CH and IN election method. It means it does not
present itself for the IN nomination and additionally
does not reply to CH selection method deliberately.
they do not perform their roles regularly or intermittently.
forwarding of CH, overhearing of IN, and participation of
MNs in election method can be stopped intermittently.
On the opposite hand, if these activities are stopped for a
protracted while, then nodes can be thought of as absolutely
existing schemes for the safety of cluster head election,
specializing in the schemes. The common goal of these
schemes is to produce security for cluster head node
election against active attacks by using various technologies.
However, they met with many limitations. Recent security
proposals were discussed in the following section.
VARIOUS SECURITY SCHEMES IN
WIRELESS SENSOR NETWORKS
Brief of various security mechanisms to secure CBWSNs is
discussed in Table 1.
Table 1: Literature Review
Title Methodology Objective Performance Matrix
Impact of a simple load balancing approach
and an incentive-based scheme on MANET
performance (Yoo et al. 2010).
Incentive Scheme Resolve the selfishness
attack
They participated in a
cooperative environment
A lightweight and dependable trust system
for clustered wireless sensor networks
(Li et al. 2013)
Trust System Providing collaboration
among trustworthy nodes
An identifying misbehavior
nodes
A survey of trust and reputation
management systems in wireless
communications (Yu et al. 2010)
Reputation and Trust
System
To avoid beings a victim of
inside attacks
Encourage the nodes to
be honest by giving some
credits
Trust among strangers in internet
transactions: empirical analysis of eBay’s
reputation system (Resnick and Zeckhauser
2002)
Reputation and Trust
System
To avoid beings a victim of
inside attacks
Encourage the nodes to
be honest by giving some
credits
Using overhearing technique to detect
malicious packet-modifying attacks in
wireless sensor networks (Ssu et al. 2007)
Centralize Scheme Mitigate the selfishness
problem in CBWSNs
Maximizing the life time
and minimizing selfishness
attack.
SecLEACH-on the security of clustered
sensor networks, Signal Processing (Oliveira
et al. 2007)
Distributed Scheme Avoid the single point of
failure
Excessive use of resources
Performance evaluation of wireless sensor
network under black hole attack (Gulhane
and Mahajan 2014)
Overhearing Scheme Captured black hole region
and blocked
Easily monitored and
controlled by IN
Queuing the trust: Secure backpressure
algorithm against insider threats in wireless
networks (Lu et al. 2015)
Data overhearing
scheme
Resolve selective
forwarding attack
Detected and controlled
transmission of CH
A Novel Framework to Secure CBWSNS Against the Selfishness Problem
9
The common goal of these schemes is to produce security
for cluster head node election against active attacks by
using various technologies. However, these techniques met
with many limitations. First, they can handle only active
or external attacks; second, they are centralized schemes,
employing a base station to form a decision about the head
nodes. Hence, they are not appropriate for WSNs. Third, the
3 election protocols in (Chowdhury et al. 2014) use light-
weight crypto-graphical algorithms, but they are vulnerable
to various attacks. Lastly, the protocols in [27] using digital
signatures involve considerable computation overhead and
area unit susceptible to DoS attacks, being not appropriate
for resource restricted little WNS nodes. So there is a
strong need to deal the severe issues of WSN security
discussed above.
PROPOSEDWORK
Aim of this protocol is to choose trusted CH i.e. nodes with
less trust value or less energy should not be selected as CH.
Proposed work can be divide into two main modules that is
trust based routing module and trust management module.
Figure 2 represents overall architecture of proposed
algorithm.
●: This module calculates
trust based upon remaining energy, PDR and distance.
● : It is almost same
as basic LEACH protocols with some changes in it.
Trust-based routing module uses trust management
module to perform secure routing.
3
Cluster
Joining
Steady
State Phase
Advertisement
Phase
Schedule
Creation
The common goal of these schemes is to produce security for cluster head node election against active attacks
by using various technologies. However, these techniques met with many limitations. First, they can
handle only active or external attacks; second, they are centralized schemes, employing a base station to form a
decision about the head nodes. Hence, they are not appropriate for WSNs. Third, the 3 election protocols in
(Chowdhury et al. 2014) use light-weight crypto-graphical algorithms, but they are vulnerable to various attacks.
Lastly, the protocols in [27] using digital signatures involve considerable computation overhead and area
unit susceptible to DoS attacks, being not appropriate for resource restricted little WNS nodes. So there is a strong
need to deal the severe issues of WSN security discussed above.
III. PROPOSEDWORK
Aim of this protocol is to choose trusted CH i.e. nodes with less trust value or less energy should not be selected as CH.
Proposed work can be divide into two main modules that is trust based routing module and trust management module.
Figure 3.1 represents overall architecture of proposed algorithm.
Trust Management module: This module calculates trust based upon remaining energy, PDR and distance.
Trust-based routing module: It is almost same as basic LEACH protocols with some changes in it. Trust-based
routing module uses trust management module to perform secure routing.
Figure 3.1: System Architecture
An improvement in clustering protocol has been proposed, while maintaining the routing of original LEACH protocol. The
scheme used to calculate trust is described below:
Inputs
Network area
Number of nodes
3.1 Assumptions
For executing reputation mechanism for sensor networks environment, following assumptions have been made:
There are some selfish nodes present in the network
BS has unlimited source of energy and it is free from any kind of attack
If a node is performing some selfishly then it will be penalized and its reputation value will decrease.
If a node is showing good behaviour, it will be rewarded and its reputation value will be increased.
Selfish nodes present in network are consuming more energy and dropping smore packets than normal nodes.
Nodes will be randomly distributed in given area. Every node runs with an energy watcher, PDR calculator, distance
estimator and trust supervisor. Energy Watcher will calculate remaining energy of neighbor nodes and CHs, PDR
Trust
-
based routing
Trust
Management Module
Energy
Watcher
PDR
Calculator
Distance
Estimator
Trust based
CH joining
Trust based
CH selection
Fig. 2: System Architecture
Poonam
10
An improvement in clustering protocol has been proposed,
while maintaining the routing of original LEACH protocol.
The scheme used to calculate trust is described below:
Inputs:
●Network area.
●Number of nodes.
For executing reputation mechanism for sensor networks
environment, following assumptions have been made:
●
●BS has unlimited source of energy and it is free from
any kind of attack.
●If a node is performing
penalized and its reputation value will decrease.
●If a node is showing good behaviour, it will be
rewarded and its reputation value will be increased.
●
more energy and dropping more packets than normal
nodes.
Nodes will be randomly distributed in given area. Every
node runs with an energy watcher, PDR calculator, distance
estimator and trust supervisor. Energy Watcher will
calculate remaining energy of neighbor nodes and CHs,
PDR calculator will calculate PDR of every node based upon
number of packets dropped by node, Distance Manager
will calculate and maintain distance between node and
neighbors node along with CH distance with node Trust
Supervisor will maintain trust level of neighboring nodes
and CHs elected by considering three factors that are
remaining energy, PDR and distance between nodes. For
calculating trust value three factors will be considered that
are remaining energy, PDR and distance i.e. nodes with high
remaining energy, high PDR, and less distance between
nodes will have more trust value and thus have high chances
of becoming CH as compared to those nodes with low trust
value, low PDR and high distance between nodes. These four
components will work as follows:
It will keep track of remaining energy
of nodes. Energy model for the network is discussed as:
To transmit a k-bit message with a distance of d, energy
consumption can be calculated by:
Et = Ee (k, d) + Ea (k,d) (1)
Where Et is the transmitting energy, Ee is energy required
to run transmitter and receiver circuitry, Ea is transmitter
can be calculated by:
Er = K * Ee (2)
Hence energy will be consumed while transmitting or
receiving packets in the network. As sensor networks
are deployed in area where it is not possible to charge
these nodes timely, so protocol designed should be energy
network lifetime.
● This component will keep track
of PDR. From the past records PDR calculator will
maintain total number of packets sent to BS and how
many of them are actually received by BS. Packets
●Another reason for packets drop may be poor
network connectivity. Node with high PDR will have
high trust value and node with low PDR will have less
trust value. Formula for PDR can be given as:
Packet_Delivery_Ratio = Packets_Rcvd/Packets_TO_
BS (3)
Where, Packets_Rcvd are total number of packets
received by BS
Packets_TO_BS are total number of packets sent to BS.
● This component will keep
track of distance between nodes. If distance between
evaluated node and subject node is less, a high trust
value will be assigned to evaluated node otherwise
if distance between subject node and evaluated
node is high, then low trust value will be assigned
to node. Hence trust value is inversely proportional
to distance between nodes. Also this component will
keep track of distance between nodes and CH.
● This component will maintain
trust values of nodes that will be used by routing
module for trusted CH election and secure routing.
The working of trust supervisor is being discussed in
trust management module.
For calculating trust, trust supervisor will calculate both
A Novel Framework to Secure CBWSNS Against the Selfishness Problem
11
aggregating both trust values. Direct trust is that trust which
is calculated by nodes itself. Direct trust will be calculated
based on past and present interactions of nodes. Sometimes
it is not possible for a node to calculate direct trust of other
nodes in order to save energy; in that case nodes will take
recommendations from other nodes which will result in
indirect trust. Indirect trust is also called second hand trust.
In this model trust is calculated by considering energy,
distance and PDR as trust metric. Nodes with high remaining
energy, high PDR, less distance between nodes will have
more trust value as compared to those nodes with less
remaining energy, less PDR, more distance between nodes.
calculate trust of other node, evaluated node is one whose
trust value is to be calculated, recommendation nodes are
those whose opinions are considered for calculating in
direct trust.
Fig. 3: Trust Relationship
An initial trust of 0.5 is assigned to every node. For
calculating direct trust, trust supervisor will interact with
energy watcher, PDR calculator and distance estimator. For
remaining energy, and then a series of if-then rules will
be applied to remaining energy, by comparing remaining
energy with threshold value trust values will be assigned to
nodes. Threshold values are selected by analyzing remaining
energy after a particular round. Nodes will be awarded or
penalized based upon the results after comparing remaining
energy with threshold value. A node will be rewarded if its
remaining energy is high after a particular round and if at
the same round node is having less energy as compared to
threshold then it will be penalized.
Once remaining energy has been checked, next trust
supervisor will check PDR of nodes. PDR of nodes is
compared with thresholds and then accordingly reward or
penalty will be given. A node with high PDR will be rewarded
and the nodes which drop more number of packets will have
less PDR and hence penalized.
Further trust is dependent on another factor that is distance
between nodes. If distance between nodes is high then
corresponding trust of the node will be more and vice-versa.
Hence direct trust can be calculated based upon aggregation
of three factors.
Next, indirect trust will be calculated based on
recommendations considered from other nodes. Indirect
trust is the sum of trust values calculated by other nodes
and given by equation 4.
Trust_Calculation ( )
Input: Remaining energy, Packet_delievery_ratio,
Distance between nodes
1. Every node is assigned with initial direct trust of
0.5
2. if (R.E >Th1)
3. DT= DT+5%of DT//node will be rewarded
4. elseif (Th2<R.E <Th1)
5. DT=DT //trust will remain same
6. elseif (R.E<Th2)
7. if (PDR>Th3)
8. DT=DT+5%of DT//node will be rewarded
9. elseif (Th4<PDR<Th3)
10. DT=DT //trust will remain same
11. elseif (PDR<Th4)
12. DT=DT-5%ofDT //node will be penalized
13. End
14. if (D>Th5)
15. DT=DT+5%of DT//node will be rewarded
16. elseif (Th6<D<Th5)
17. DT=DT//trust will remain same
18. elseif (D<Th6.)
19. DT=DT-5%ofDT//node will be penalized
20. End
21. Indirect trust will be calculated from
recommendation nodes.
22. TT= w *DT + (1-w)*IT
23. DT=DT-5%of DT//node will be penalized
24. End
Notation: DT = Direct Trust
IT = Indirect Trust
TT = Total Trust
Th = Threshold Value
D= Distance between subject node and evaluated
node
Fig. 4: Pseudo Code for Trust_Calculation ( ) in CBWSNs
ITC
C DT× DT (4)
Where, ITC
is indirect trust of B calculated by A considering
Poonam
12
are the direct
trust value calculated by A for C and C forB.
trust will be aggregated as given below by 3.5:
TT= wDT+ (1–W) ITC
(5)
TTis the total trust of node A on node B, w is the weight
associated with direct and indirect trusts. A higher value
nodes has more trust on recommendations provided by
other nodes. Final trust values of nodes will be stored by
Trust Supervisor.
Routing module consists of two main phases: Set-up
phase and Steady-state phase. In Set-up phase clusters are
arranged and selected followed by steady-state phase where
nodes will transmit data to BS.
Set-up Phase
a. Advertisement Phase: This phase is same as in
original LEACH protocol but for increasing lifetime of
network energy factor is considered while selecting
CH, so that the nodes with less energy should not get
selected as CH. The number of nodes elected as CH
with low energy will be less thus increasing network
lifetime. To start procedure of CH election, node will
select a random number between 0-1. If the number
chosen is less than threshold node, node will be
selected as CH otherwise not. The threshold value can
be given by equation 6.
( ) ( )
p
nG
Tn=
I - pxr mod 1 / p
(6)
Where p is the desired percentage of CHs, G is set of nodes
that have not been elected as cluster-heads in the last 1/p
rounds and r is the current round, is remaining energy of
node and is initial energy of node. After this phase, nodes
has list of all eligible CH members. After CH has been
all information regarding CH neighbor will be collected from
energy watcher, trust supervisor and distance manager. CH
will maintain information of neighbor CH in form of a table.
Each node will maintain an entry corresponding to every
attribute mentioned in table 2.
Table 2: Neighbor CH Information
Attribute Description
ID ID of neighboring CH
Remaining energy of CH
Final trust of neighboring CH
Minimum distance of neighboring CH from BS
EC How many times Neighboring CH is elected as CH
Whether nearest neighbor or not
Now, CH will examine whether neighbor is nearest neighbor
or not and this will be decided by comparing distance of
nodes with D. Equation 7 gives the value of D. Distance
between CHs will be calculated with signal strength. If
distance calculated is less than D then
N nearest = 1else it is 0.
Where L is the side length of the square area where sensor
nodes are deployed, K is the number of cluster-heads; is an
adjusting factor. This will uniformly cover whole area CHs.
If number of nearest neighbor CH is greater than 0 then CH
will calculate trust weight associated with every nearest
neighbor CH and trust weight, is calculated by equation 8.
WT REM / EINT node /
node + EC (8)
As for this thesis energy is already considered as attribute
for trust calculation, so for simulation a lower value of
have higher value as trust value already considered energy
factor and EC is number of times node is selected as CH. Tnode
is the trust value of neighboring CH obtained from trust
node is aggregation of trust of all
nearest neighbor CH. CH with heaviest trust weight value is
selected as new CH and will broadcast this information to
other nodes and CH selected earlier will vanish. In addition,
minimum distance of node from BS is also considered. CH
distance to BS is compared with others nodes CH distance
to BS and if difference between CH and BS is greater than
BS) is distance calculated between CH and BS and AD
is the acceptable distance between CH and BS. Hence CH
selected with this procedure will be trusted, with better
energy and will help in saving energy as transmitting
energy cost will be less.
A Novel Framework to Secure CBWSNS Against the Selfishness Problem
13
a. Cluster Joining: In original protocol non-CH
nodes join cluster based on signal strength
received from CH but here nodes will select
their CH based on trust values of cluster nodes.
b. Schedule Creation: CH receives all messages from
nodes that would like to join cluster. Based upon
strength of nodes in the cluster, CH begins to create a
TDMA schedule and assign slots to non-cluster nodes
to send data as well as to calculate trust.
Steady State Phase
In steady state phase nodes will transmit sensed data to CH
along with calculating trust. This phase can be divided into
After this phase every other round begins with set-up phase.
●Data Slots: Nodes will keep their transmitter on
during their time slot only and will sense the data
in the same time slot and send sensed data to CH
selected meanwhile other nodes transmitters are
off in order to save energy. It is assumed that CHs
are having more energy than normal nodes so they
keep their receivers always on to receive data from
non-CH nodes.
●Trust Slots: During this slot trust supervisor will
calculate trust associated with their neighbors based
upon considered factors as well as CH. Nodes update
trust value regularly. In addition, CH will calculate
trust of neighbour CHs in this slot and updates
their table.
Fig. 5: TDMA Schedule
For communication within a cluster i.e. an intra-cluster
a inter-cluster communication that is a communication
between CH and BS [22]. The reason behind this is within
a cluster distance between nodes is less, so less of energy is
needed to transmit a message as compared to inter-cluster
communication. Therefore more energy could be saved.
Data Slots and trust slots
1. Nodes will be randomly placed in an area.
2. forr=1:1:n
3. fori=1:1:n
4. temprand =rand // a random value
between 0-1 well be chosen
5. if (temp_rand <=((p/(1-p*mod(r, round(1/
p))*Erem/Eint))))
6. Then CH will be selected
7. Total_trust=Trust_Calculation()// nodes
will calculate trust of other nodes
8.
9. D=phi*sqrt(1/pi/K)*L;
10. if ((distance between CH and close
neighbour CH)<D)
11. then Nclose=True
12. else Nclose=False
13. N=count number of close CH neighbour
14. if(N>0)
15. Then Compute weight of each close
neighbour
16. Node with heaviest weight factor will be
selected as trusted CH
17. Nodes will join the CH with maximum
weight value
Notations:
r= Number of rounds
n= Total number of nodes
Fig. 6: Pseudo Code for Routing Module in CBWSNs
IMPLEMENTATION
The proposed algorithm CBWSNs has been designed in
MATLAB [15]. It is considered that 100 nodes are randomly
distributed over area of 100*100 m2. Firstly basic LEACH
is implemented. Sensor nodes send data to CH, CH after
aggregating the data from cluster members further route it
to BS. To study better results of trust management scheme
will drop packets that were supposed to send to BS i.e.
forwarding attack. After implementing trust management
scheme, chances of selecting malicious CH is almost
negligible which will enhance network performance. Hence,
improved with this scheme. In addition CH with heaviest
trust weight is selected, so probability of packets drops ratio
is decreased.
Steady Sate Phase
Frames
1 2 3 4
Poonam
14
Evaluation is done based upon following metrics:
●Network life time.
●PDR
Simulator parameters are mentioned in table 3.
Table 3: Simulation Parameters
Network Parameters Values
Network Size 100X100m2
Number of nodes 100
Packet Size 4000 bits
Routing Protocol LEACH
Initial battery power of node 0.5 J/node
Energy to run transmitter and
receiver
50 nJ/bit
Data aggregation energy 5 nJ/bit
Amplification Energy
(Cluster to BS)
Efs =10pJ/bit/m2
Amplification Energy (Intra Cluster
Communication)
Efs/10 = Efs1
Selection of CH
Figure 7 shows random distribution of sensor nodes in an
area of 100*100 sq. units and LEACH protocol is simulated
for routing purpose.
Fig. 7: CH Selection in LEACH
There are some malicious nodes present in the network.
Malicious nodes are represented by a plus (+) sign, normal
nodes are represented with a circle (o). In addition selection
that are selected as CH are represented with dark blue
asterisk. It can be easily analyzed that if no security practices
selected as CH. Hence as a result malicious CH selected
would drop packets received from cluster members which
in result reduce network performance. After implementing
as CH are almost negligible. In CBWSNs, CH is selected based
upon trust values of nodes. Therefore selected CH will not
Trusted CH selected is represented by Green asterisk.
Fig. 8: CH Selection in CBWSNs
Trust Evolution
W
is selected chosen to be 0.5 in equation 6 which concludes
that node is equally considering direct trust as well as
0.2, 0.6, 0.2 in equation 9.
trust will constitute to 0.5. Similarly at 10th round direct
trust is 0.4800, indirect trust is 0.2438 and thus total trust
is 0.3619 for this round. In the proposed model calculated
trust is directly proportional to remaining energy and PDR,
as malicious node consumes more energy, drops more
packets therefore its trust value decreases as number of
round increases.
A Novel Framework to Secure CBWSNS Against the Selfishness Problem
15
Analysis of PDR
It could be observed that average PDR is 96% in CBWSNs
and 91% in LEACH when there is no malicious node present
in the network. There would be some packet loss because
of poor network connectivity. Therefore PDR would not be
has PDR value of 0.9400 and LEACH has 0.8390, hence after
implementing CBWSNs PDR increases by 12%. Similarly
21.5%. Hence it could be concluded that after implementing
CBWSNs average PDR ratio is increased by 15.8%. CBWSNs
nodes are not selected as CH and hence there are less packet
drop in the network. Moreover CBWSNs can help in avoiding
selective forwarding attack.
Fig. 10: PDR vs.
Network Lifetime Comparison
While comparing network lifetime it has been observed
that CBWSNs has better lifetime as compared to LEACH.
As in LEACH there are many retransmissions as compared
to CBWSNs, in addition in CBWSNs less of malicious nodes
would be selected as CH so less consumption of energy as it
is assumed that malicious nodes are consuming more energy.
Moreover, consumption of less energy while intra-cluster
communication as compared to inter-cluster communication
and consideration of energy factor while selecting CH makes
th rounds as
th round.
Figure 11 shows network life time comparison.
Fig. 11: Network Lifetime
CONCLUSION
which is combination of trust-based routing module and
trust management module. In trust management module,
trust supervisor calculates trust for nodes as well CH that
can be used for trusted CH selection and secure routing.
Total trust value is a combination of direct trust that is
calculated by node itself and indirect trust which is trust
from recommendation nodes. Trust-based routing module
further four phases that are advertisement phase, cluster
joining, schedule creation and steady state phase. Nodes
as CH because their computed trust value will be less. In
addition, routing module uses less energy for intra-cluster
communication as compared to inter-cluster communication
which would help in improving network life time.
Pro
CH and trust value of a malicious node decreases with
time. Simulation results proved that proposed algorithm
consumes less energy and improves PDR as there are
less number of retransmission. Average PDR is improved
by 15.8%. In addition with implementation of CBWSNs,
th round
700th round.
FUTURE WORK
In future, this work could be extended by considering other
types of WSNs e.g. dynamic WSNs, heterogeneous WSNs.
Other social trust attributes such as privacy, intimacy,
Poonam
16
number of interaction could be considered in future to
extend this work. Beside this trust model could be further
extended to develop lightweight algorithm to further reduce
energy consumption. Memory requirement for this protocol
are bit high as past records has to be stored by energy
watcher, trust supervisor, PDR calculator. So challenging
problem is to reduce memory requirements. In this thesis,
nodes that have less PDR are considered as malicious
without considering the fact that less PDR could be less
because of poor network connectivity, this also motivates
future work.
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17
Incorporation of Carbon Fiber and Silica Fume in
the Production of Conductive Concrete
M. Purushothaman1* and K. Natarajan2
1,2Department of Civil and Structural Engineering,
Annamalai University, Annamalai Nagar–608002, Tamilnadu, India
E-mail: *emp4624@gmail.com
Abstract—Concrete is regarded as a composite material that has good mechanical and durability properties for construction.
However normal concrete is poor in electrical conductivity. An endeavour has been made with concrete to have all these
causes an increase in strength and durability properties as well as electrical conductivity. In this paper, experimental results
of compressive strength and electrical resistivity are presented. This Concrete technology can be applied with low voltage
cost and environmental issues of roads in snow fall region.
Keywords:
Science and Technology Journal Vol. 7 Issue: 2 July 2019 ISSN: 2321-3388
INTRODUCTION
The development of infrastructure necessitates the
technological improvement in concrete. The current
researches are therefore trying to improve or modify the
property of the concrete to meet the needs of today’s world.
The conventional concrete has the desirable properties of
strength and durability, according to the mix design and
materials used. But it is very poor in electrical conduction
property (Purushothaman M, et. al., 2008). In addition to
or replacement of the conventional materials used in the
production of concrete by mineral and chemical admixtures,
can change or improve the property of the concrete. Many
experimental studies focussed on mechanical and durability
properties but the studies on electrical properties of
concrete are very limited.
The electric conductive property can be achieved by adding
some conductive material to conventional concrete. This
Conductive concrete can be widely used for melting snow
of road, bridges, and airport (Xie P, et. al. 1995; Yehia S,
et. al. 1999) and also used in radio frequency interference
shielding, electromagnetic defense, protecting the building
from lightning, radiation heating, corrosion protection,
automated high way systems, etc (Xin Tian, 2012). America,
Canada, and Northern European countries researched
problem. China started the research of conductive concrete
in the late 1980s (Xie Xin, et. al, 2015). But in India, a
countable number of studies have been done.
A major portion of Kashmir in the western Himalayan
region of India experiences heavy snowfall between
days in the National Highway of 270-km, which is the single
road linking Kashmir with the rest of the country, due to
issue for the Indian government. In India, de-icing salts
and mechanical equipment are used for snow clearance of
roads. These methods are uneconomical and also affect the
environment. In the Previous studies of conductive concrete
in India, preliminary research has been made to study on
electrically conductive concrete made with bottom ash
(Purushothaman M, et. al., 2008), Steel residue(Abid Ahmad
et. al
et. al, 2016).
component than other types of materials for improving the
conductive property of the conductive (Xin Tian, 2012-b).
Recent studies on conductive concrete have also proved
that ed to conventional concrete to
improve both mechanical and electrical behaviors (Mashudi,
2014; Xin Tian, 2012; Oscar Galao, 2016). Short length
Purushothaman and Natarajan
18
mix (Ohama Y,1985).
concrete mix is not uniform, the resistivity will be different
concrete (Wu J, et. al, 2015). The addition of silica fume
as an admixture in concrete will help for dispersion of the
matrix, thus enhancing the properties of the concrete
(Chung D.D.L, 2000; Wang Chuang, 2018). Well-dispersed
compressive strength (Mashudi, 2014) and decrease
the resistivity and becomes an electrically conductive
cement composite (Oscar Galao, 2016; Wen S, et. al. 2003).
Therefore, in this experimental study, the effect of carbon
resistivity property was studied.
MATERIALS AND METHODS
Ordinary Portland cement of 43 grade Portland according
to IS: 8112-1989; potable tap water; locally available river
sand; crushed hard blue granite stone of size 20mm and
below; Silica fume (SF) obtained from Elkem. India private
Ltd., Mumbai, India; Polyacrylonitrile (PAN)-based short
SRJ composites, Hyderabad, India, was used to prepare
the concrete specimens.
aggregate were determined as per IS:2386-3,1963 and the
collected from the supplier. Table 1. and Table 2 shows the
properties of CF and SF.
Three groups of concrete namely conventional concrete
(CC), concrete with silica fume (CSF) and electrically
conductive concrete (ECC) were considered in this study.
Table 1: Properties of Carbon Fiber
Property Value
Diameter 7µm
Length 5mm to 7mm
Carbon content 93%
Tensile strength 2800 MPa
Resistivity 1.43 ×10-3
The proportions of the ingredients for CC of grade M30 was
arrived by following IS:10262-2009. To know the optimum
dosage of silica fume, the volume of the cement of the CC mix
was replaced by 0% to 20% of Silica fume and the volume of
other ingredients kept constant. The 28 days compressive
strength test results for 150 mm cube specimens prepared
from each mix and the corresponding dosages of silica fume
was plotted as shown in Fig.1. from the experimental results,
10% in volume of cement is the optimum for the concrete
mix. Beyond this level of replacement, the reduction in
strength of concrete was observed. Strength and resistivity
are the two important parameters considering in the
production of ECC.
As strength is concerned, the experimental results of
various researchers (Andrey Nevsky, et. al., 2015, Manivel,
et. al,.
is the optimum. Considering the percolation threshold, the
is 1% by Volume (Alireza Sassani, 2018). Therefore, in this
experiment, the electrical resistivity of ECC made with 1%
aggregate content. The other ingredients were adopted as
same as the CSF mixture. Mix proportions of these three
mixes are shown in Table 3. The high range water reducing
used in the mix. Methylcellulose of 0.4% by weight of binder
was used as FDA in all the three mixes for consistency.
The ECC mix was prepared as per the procedure followed
by HOU zuofu (HOU zuofu, et. al. 2007). Methylcellulose in
then added and stirred gently again. The SF and cement
were mixed well in a dry state; HRWR has added with the
rest of the mixing water stir well for uniformity. HRWR
the binder mix was added and allowed to rotate for 3 min.
Finally, the CF mix was poured into the mixer and allowed to
rotate for 1 min, then the FA aggregate followed by CA was
added and allowed to run for 3 min each. The admixtures
D.D.L. Chung 2001; Yunsheng Xu and DDL Chung, 2001).
Six numbers of 150 mm × 150 mm × 150 mm cube
specimens were cast from each mix. Three specimens
used for compressive strength test (IS: 516,1959), and the
remaining three was used for resistivity test. Water curing
for 28 days was adopted for all the specimens.
Incorporation of Carbon Fiber and Silica Fume in the Production
19
Table 2: Properties of Cement and Silica Fume
Property Specific Gravity SiO2Al2O3MgO CaO Fe 2O3
Cement 3.1 21.94 5.04 2.08 63.61 3.16
Silica fume 2.2 93.2 0.48 0.4 0.1 0.36
Table 3: Mix Proportions-kg/m3
Materials Cement SF (10%) FA CA CF (1%) w/b
Mix
CC 394 --- 776 1082 --- 0.5
CSF 394 28 776 1082 --- 0.47
ECC 394 28 749 1082 18 0.47
RESULTS AND DISCUSSIONS
Compressive strength test results from the mean value of
three cubes for CC, CSF, and ECC mixes are given in Table.4.
From the test results, it is known that the strength of ECC
mixes is greater than other mixes. This may be due to the
mixture. Incorporation of a pozzolanic material, Silica fume
thus enhancing the properties of the concrete; improved
durability; and the mechanical properties of concrete
(Shetty. M.S, 2007; Chung D.D.L, 2000; Mashudi, 2014).
The value of resistivity depends on the properties of the
conductive material. Resistivity and conductivity are
inversely proportional. On reducing the resistivity of the
concrete, the conductivity will be more. The electrical
resistivity of the concrete mixes was determined using the
Two-probe method. In this test, a thin copper plate is pasted
on the side surfaces of the cube specimen as an electrode.
It should be checked for no gap between the plate and the
concrete surface. Then only a uniform current will pass
through the side surfaces. The (Direct current) DC source of
50v applied to the electrode. A voltage difference between
the electrodes (V) and current (I) was measured using a
multimeter. The following expression is used to calculate
the resistivity.
Resistivity,
AV
IL
ρ
= (1)
Where,
V = Voltage drop across the specimen; A = Area of cross
section, cm2
I = Direct current taken by the conductive specimen (amps)
L = Length of the specimen between the two electrodes, cm.
The resistivity of the conventional concrete under dry
condition varies from 6.54 × 1033
hence it acts as a pure resistor (Whittington et. al., 1981)
When the resistivity is reduced, the electrical conductivity
will be more. The use of conductive materials in concrete
reduced the resistivity considerably. The resistivity of
per Chinese national standard (Xin Tian, 2012; Mashudi,
et. al., 2018).
Fig. 2: Schematic Diagram and Test Setup for Electric
Resistivity
The average resistivity of the cube specimens of each mix
is tabulated in Table 4. The high electrical resistivity was
observed with CSF cube, comparing with CC cubes, whereas
the reduced resistivity was observed with ECC mixture
when compared to CC and CSF mixtures. Generally, if the
compressive strength of concrete and the resistivity are
directly proportional. But, the test results of ECC mixture
Purushothaman and Natarajan
20
de-icing (Hou, et. al.; Oscar G, 2016; Tang, et. al. 2005). From
is well dispersed in the concrete system and can be used to
produce conductive concrete. Anyhow, further experimental
study with slab specimens of different size, test for surface
heating is to be done.
Table 4: Test results of Concrete Cube Specimen
Property CC CSF ECC
Comp. strength, MPa 31.2 34.9 38.7
9.2×1061.12×107801
CONCLUSION
1.
concrete reduces the resistance and improves the
as a good ingredient for making conductive concrete.
2. The resistivity of Electrically conductive concrete
required for de-icing.
3. The electrical resistivity of concrete with silica fume is
higher when compared to the conventional concrete.
It is due to the pozzolanic effect of silicafume.
4. Nowadays the whole world is facing a major problem
of environmental pollution. Utilization of an industrial
waste silica fume as cement replacement material in
concrete for construction, will help to protect our
environment.
5. This study may helpful for future experiments on
electrically conductive concrete and could be an eco-
friendly solution to the de-icing problems of snow fall
regions of India.
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Mohmmad Vekas Wani and Aeijaz Masoodi (2016).
Electrically Conductive Concrete Formed by Using Waste
Mechanical and Civil Engineering (IOSR-JMCE). 13(1) Ver. II:
pp. 07-12.
Andrey Nevsky, Konstantin Kudyakov, Ilia Danke, Aleksandr
Kudyakov and Vitaly Kudyakov (2015). Improvement
of Cement Concrete Strength Properties by Carbon
Fiber Additives. Advanced Materials in Technology and
Construction (AMTC-2015), AIP Conf. Proc. 1698. Pp.
070005-1–070005-5.
Chung D.D.L. (2000). Cement reinforced with short carbon
pp. 511-526.
HOU Zuofu, LI Zhuoqiu and WANG Jianjun (2007). Electrical
Conductivity of the Carbon Fiber Conductive Concrete.
Journal of Wuhan University of Technology-Mater. Sci. 22(2):
pp. 346-349.
IS: 516-2004. Methods for Tests for Strength of Concrete. Bureau of
Indian Standards, New Delhi, India. 2004.
IS: 2386 (Part III)-1963. Methods of Test for Aggregates for
bulking, Bureau of Indian Standards. New Delhi. India. 1963.
ordinary Portland cement. Bureau of Indian Standards. New
Delhi. India. 2005.
IS: 10262-2009, Recommended guidelines for concrete mix Design.
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Cement and Concrete Research. 31: pp. 1633-1637.
Manivel S., Susmita Suresh Kakkanattu, Satya Ramesh Pott V. (2019).
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in Light Weight Concrete. International Journal of Recent
Technology and Engineering (IJRTE). 8(3), pp. 517-521.
Mashudi, Wicaksono S.T., Ardhyananta H. Effect of surfactants on
composite for self-monitoring applications. International
Conference on Science and Applied Science (ICSAS) 2018; AIP
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and Pedro Garcés (2016). Highly Conductive Carbon Fiber
Reinforced Concrete for Icing Prevention and Curing.
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Electricity. Second International Conference on Resource
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Incorporation of Carbon Fiber and Silica Fume in the Production
21
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22
Smart Automated Farming System using
IOT and Solar Panel
Anita Shukla1* and Ankit Jain2
1Dept. of Applied Sciences and Humanities,
Pranveer Singh Institute of Technology, Kanpur, U.P. (India)
2Department of Electronics and Communication Engg., MPEC, Kanpur, U.P. (India)
*E-mail: 1shukla.anita27@gmail.com, 2ankit.jain@mpgi.edu.in
Abstract—Present work focuses on need of automation in farming by using IOT technology. Automation of farming envisages
monitoring and controlling of various parameters which could be helpful in increasing productivity. The proposed system
provides a technological solution to the various problems like, maintenance of water requirements, humidity level,
sensors like humidity sensor,
used for monitoring and controlling of various problems technologically. In proposed system a Wi-Fi module has been used
alert and about the unwanted occupancy or encroachment by displaying real time data which can be seen and accessed
over internet using IOT technology from anywhere in the world. System is equipped with solar panel which provides power
different environmental conditions and the performances of different sensors are found to be upto the desired expectations.
Keywords: Automated Farming, Node MCU Wi-Fi Module, Humidity Sensor, Soil Moisture Sensor, PIR Sensor, Solar Panel
INTRODUCTION
The history of Agriculture in India dates back to Indus
Valley Civilization Era and even before that in some parts
of Southern India[1].Today, India ranks second worldwide
in farm output. The economic contribution of agriculture to
India’s GDP is steadily declining with the country’s broad-
based economic growth. Still, agriculture is demographically
the overall socio-economic fabric of India. India exported
$38 billion worth of agricultural products in 2013, making it
the seventh largest agricultural exporter worldwide and the
sixth largest net exporter [2] .Most of its agriculture exports
serve developing and least developed nations [2]. Indian
agricultural/horticultural and processed foods are exported
to more than 120 countries, primarily in the Middle East,
Southeast Asia, SAARC countries, the EU and the United
States(3; Yalla et al. 2013).
We live in a world where everything can be controlled and
operated automatically, but there are still a few important
sectors in our country where automation has not been
monitoring and controlling of the climatic parameters
which directly or indirectly govern the plant growth and
hence their production.
Problems of over exploitation of ground water in India
are bound to become more acute and widespread in the
years to come unless corrective mechanisms are put in
place before the situation becomes worse. Other problems
related to farming like maintenance of humidity level,
maintenance of proper temperature, proper availability of
light for sophisticated plants,keeping a check on unwanted
of technological solution. On the other hand, the most
Science and Technology Journal Vol. 7 Issue: 2 July 2019 ISSN: 2321-3388
Smart Automated Farming System using IOT and Solar Panel
23
important parameter for the agriculture is regular and
adequate supply of electricity. Highly unreliable power
supplies with frequent power cuts have not only lowered
who cannot start their work without it.Apart from the
problems mentioned here there are lots of problems in the
using technology.
The proposed system uses a hardware which provides
in Indian farming system. The solution provided is eco-
friendly, economical and electronically operated, making
Indian farming system more farmers friendly. The proposed
system is connected with internet using node MCU Wi-Fi
module capable of displaying real time data which can be
seen and accessed over internet using IOT technology from
anywhere in the world.
Satya Prasanth Yallaet al.(2013)discussed about automatic
with modules and soil moisture sensor, the source to generate
electricity through renewable resources, prefer sunlight as
the main source. Pradeep et al. (2011), havedeveloped and
atomized the PV farmers pump by considering the power
supply, direct current (dc),alternating current (ac), inverter
frequency, GSM technology, awell, water level in the well,
submersible pump. Herethe investigators have introduced
an advanced technique using GSM module. JiaUddin et al.
(2012)have proposed a model of variable rate automatic
microcontroller based irrigation system. They have used
solar power as the only source of power to control the
these sensors continuously sense the water level and keep
farmer informing about the water level through messages.
Binoy seal et al. (2014)in their work, have discussed the
design of solar tracking system to harness maximum
solar energy that is converted into electrical energy which
is used further to power the irrigation system. Ishwar
Kumar et al. (2014)have discussed solar powered auto
a moisture sensor. They have proposed a solution for the
present energy crises for the Indian farmers. Nilesh R.
Patel et al. (2013)have discussed different monitoring and
controlled systems in order to increase the yield. Since the
disease in crops is one of the key factors that causes the
degradation of yield. So they have developed a monitoring
system which mainly focuses on predicting the onset of the
germination of the diseases. They have employed a sensor
module to detect different environmental conditions across
the farm and the sensed data is displayed on LCD using
microcontroller. Shiraz Pasha B.R. et al. (2014)have made an
attempt to automate farm or nursery irrigation that allows
farmers to apply the right amount of water at the right time,
regardless of the availability of labor to turn valves on and
off. The Microcontroller based automated irrigation system
consists of moisture sensors, analog to digital converter,
microcontroller, relay driver, solenoid valve, solar panel
and a battery. The system can be used in the areas where
friendly and it uses a renewable source of energy. Basava
Sidramappa Dhanne et al. (2014) in their work focuses
system is done with modules,soil moisture sensor and the
source to generate electricity through renewable resources.
through solar powered water pump and automate the
system for better management of resources.M Chetan
Dwarkani et al. (2015) have proposed a novel methodology
for smart farming by linking a smart sensing system and
smart irrigator system through wireless communication
technology. They also demonstrated the detailed modeling
and control strategies of a smart irrigator and smart
farming system. Nikesh Gondchawar and R.S. Kawitkar
(2016) aims at smart agriculture by using automation
and IOT technologies. The highlighting features of their
system includes smart GPS based remote controlled robot
to perform tasks like weeding, spraying, moisture sensing,
human detection and keeping vigilance. N.Suma et al.(2017)
in their work have discussed smart agriculture techniques
using IOT including various features like GPS based remote
controlled monitoring, moisture & temperature sensing,
intruders scaring, security, leaf wetness and proper
irrigation facilities. Kayode E. Adetunji and Meera K. Joseph
(2018)in their work have discussed the development of a
cloud-based monitoring platform to monitor agricultural
resource. Soil moisture (percentage volumetric water
content), humidity, ambient temperature, dew point and soil
temperature were used as variables for monitoring. They
discussedhow the cloud computing could be implemented
to the agricultural sector. Shivang et al.(2018)have
discussed about IOT (Internet of Things) which is basically
a network of objects enabled by internet coupled with the
web services. They have designed aIOT network divided in
three subsystems which could be employed to resolve three
major problems like supply-demand anomaly, the irrigation
problem including data acquisition using the different
Shukla and Jain
24
sensor nodes and designing of a model that would reduce
the transportation costs of the farmers considerably by
and reduce their expenses.Sercan et al.(2018)have studied
the regression trees to obtain the sensor data relations from
8 different data related to light, temperature, humidity, rain,
soil moisture, atmospheric pressure, air quality, and dew
point. A test prototype of the hardware together with the
software design is created for data monitoring and sensor
fusion in different combinations. In their work, different
input devices are synchronized by using a microcontroller
system and each data, obtained from the sensors, are sent
wirelessly by an (Internet of Things) IoT device to the
cloud, by recording and monitoring from the graphical user
interface on the web as a real-time environment to apply
data mining algorithms thereafter.
Shanmugasundaram et al. (2018) have developed an
optimized irrigation system which collects database from
threshold values. Outcome of this system would be helpful in
increasing groundwater table. System has an added feature
that it does not require any human interventions.
Based upon the knowledge that we extracted by thorough study
of literature survey we have proposed this system. From [6]
we came to know that how water level sensor could be used
in sensing water level and the information regarding water
level could be oat through message. To meet out energy
crisis in farming we can use solar energy[7],[8].Reference[9]
was helpful in deciding to incorporate LCD display panel
while [14],[16] were useful in understanding the use of IOT
in proposed system in order to make it a smart device. Idea of
Automatic water supply and use of soil moisture sensor was
understood well by [11].
In the proposed system, there are many features which have
been incorporated to enhance its utility, and thus make it
more and more farmers friendly. This system is connected
with internet using node MCU Wi-Fi module which can
display real time data that could be seen and accessed over
internet using IOT technology from anywhere in the world.
The features are enlisted as below:
●This model has a provision of water pump which
starts automatically when soil needs water and the
real time information could be accessed from display
from anywhere in the world using IOT.
●For sophisticated crops, if light decreases below
ting
system switches ‘ON’ automatically. The real time
information regarding intensity of light could be
accessed from anywhere in the world using IOT.
●For humidity, if the humidity increases above a
automatically and information regarding humidity
level percentage could be accessed over internet.
●On temperature rising, the system automatically
turns ‘ON’ the fan for sophisticated crops and updates
the temperature values over internet.
●If any alarming situation like unwanted occupancy or
the update over internet.
●The system is also equipped with solar panel which
provides power backup to the system in the absence
of power supply.
●There is a real time display system which displays
information collected by all sensors along with
decision/action taken by microcontroller over
internet.
●It has a provision of automatic and manual operation
of relay using switches, provided on control panel
over internet which can be accessed from anywhere
in the world.
The hardware developed for the present system consists of
the following sensors/modules and circuitry:
●ESP 8266 node MCU Wi-Fi module.
●LDR (Light Dependent Register) sensor.
●Flame sensor.
●Soil moisture sensor.
●PIR sensor.
●HT11 Temperature and Humidity sensor.
●Multiplexer module.
●Buzzer.
●Relay section.
●Solar panel with power backup battery.
As we have already explained in features of the project that
the heart of the system is Node MCU Wi-Fi module:
●We need soil moisture sensors to detect the humidity
of the soil, LDR sensor for monitoring the intensity
of the light. DHT 11 sensor for monitoring the
temperature and humidity, Fire sensor and PIR
happens or unwanted occupancy.
●This system has power backup system with solar
panel which provides power to the system in the
Smart Automated Farming System using IOT and Solar Panel
25
absence of power supply. We can monitor the real
time data of all the sensors in graphical form over web
page from anywhere in the world and take necessary
action whatever needed according the sensor value.
It has a provision of automatic and manual operation
of relay using switches, provided on control panel
over internet which can be accessed from anywhere
in the world.
Block diagram of the system is shown in Fig. 1
Fig. 1: Block Diagram of the Proposed Model
Circuit diagram of the system is shown in Fig. 2
Fig. 2: Circuit Diagram of the Proposed Model
Shukla and Jain
26
ALGORITHM AND IMPLEMENTATION
We propose to design a model by using control engineering
to overcome the problem dened and also to add other useful
features. The step by step methodology is adopted to
develop hardware of the overall proposed system. The main
objective is to build a general system to obtain data from
external devices (sensors) and to manipulate it to achieve a
DEVELOPMENT OF ALGORITHIM AND
DISPLAY PANEL OVER IOT
A program is developed in Arduino IDE to run the hardware
system which is based on IOT and node MCU Module. The
on the basis of which the program has been developed is
shown in Fig. 3 (a), (b), (c), (d), (e) and (f).
Fig. 3: Flow Charts for the System
Smart Automated Farming System using IOT and Solar Panel
27
a. Light Sensor System (b) Temperature Sensor System
(c) Humidity Sensor System.
b. Soil Moisture Sensor System (e) Occupancy Sensor
System (f) Fire Sensor System.
The display panel which we have prepared under the
io.adafruit.com is shown in Fig. 4. This can be accessed over
internet from anywhere in the world using IOT technology.
We can also get the real time monitoring system in gauge
form. We are monitoring six sensors here i.e. temperature
moister sensor, motion (PIR) sensor. This display system
has automatic and manual operation of relays. If any
sensor reading goes beyond the threshold level value then
relay triggers automatically. We can also operate the relay
manually, if we wish.
Fig. 4: IOT Based Control
IMPLEMENTATION OF PCB AND CIRCUIT
Fig. 5: (a) and (b) shows Top and Bottom View of PCB Used in this System. This PCB was Prepared using Dip Trace.
(b) Bottom view
(a) Top view
Fig. 5: PCB Prepared using Dip Trace
Shukla and Jain
28
The original PCB is shownin Fig. 6.
Fig. 6: Original Picture of Implemented Hardware
IMPLEMENTATION OF SOIL MOISTURE
SENSOR
Out of all necessities for farming, irrigation has the most
important role to play that is why it becomes important to
concentrate over water retention by soil. For this soil moisture
sensor has been used. For the successful implementation and
to get fruitful results series of experiments using soil moisture
sensor have been performed.
In this experiment a set of three different soils and plant
combinations are taken say A, B and C as shown in Fig. 7.
Smart Automated Farming System using IOT and Solar Panel
29
Fig. 7: Three Different Soil and Plant Combinations A,B
and C
different water levels i.e., low water content, normal water
content and excess water content. It is observed during
experiment that sensor is smart enough to detect the water
level and information is sent through controller which is
received on display panel over IOT and the necessary action
is also performed like;
1.
water) then water pump is automatically switched
“ON” until it reaches to the required level (Fig. 7.1).
Fig. 7.1: Soil and Plant Combination A (Without Water)
2.
water) then it remains in passive mode (Fig. 7.2).
Fig. 7.2: Soil and Plant Combination B (Average Water)
3.
water) then water pump is automatically switched
“OFF” (Fig. 7.3).
Fig. 7.3: Soil and Plant Combination C (Excess Water)
In order to test the compatibility of sensor with different soil
and plant combinations, we have used it with combination
B also, which is the second combination of plant and soil as
shown in Fig. 7.4.
Fig. 7.4: Soil and Plant Combination B (a) Without Water
(b) Average Water (c) Excess Water
The results obtained with plant A were found to be true with
with plants A and B, third and last combination of soil and
plant i.e., plant C, is used as shown in Fig. 7.5.
Fig. 7.5: Soil and Plant Combination C (a) Without Water
(b) Average Water (c) Excess Water
The results found with combinations A and B are repeated
hence it was conrmed that sensor is sensitive enough to
produce the same results with different soils and different plant
combinations using different water level conditions. Similarly,
the other sensors were tested and it was found that their working
Shukla and Jain
30
was upto the expectations.It may please be noted that all the
activities as referred to in the manuscript have actually been
carried out and keenly observed by the author himself.
RESULTS, DISCUSSION AND ANALYSIS
We have designed and developed a system which provides
electronic and eco-friendly solution to the problems related
to Indian farming system. The cost analysis (table 1) of
this system shows that implementation of this system is
very cheap. This will result in reducing problems faced by
Table 1: Cost Analysis of the Hardware Model
Sr.
No. Component(S) Quantity Rate Amount
In Rs.
1 NODE MCU 1 450 450
2 Soil Moisture Sensor 1 250 250
3 DHT11 Sensor 1 150 150
4 Flame Sensor 1 120 120
5 PIR Sensor 1 150 150
6 LDR 1 10 10
7WATER PUMP (230V,
50Hz AC) 1 250 250
8DC Motor (12V,
100RPM) 1 225 225
9 MUX IC 1 230 230
10 Fan (12V, DC) 1 300 300
11 IC ULN2003A 1 20 20
12 1 10 10
13 Relay 12V 4 20 80
14 AC Bulb with Holder
(White) (0W) 1 130 130
15 Buzzer 1 20 20
16 Transistor BC547 1 8 8
17 DC Socket (Male &
Female Type) 1 Each 15 30
18 12V/1A DC Adapter 1 250 250
19 LED (2 Red, 1 Green,
1 Yellow and 1 Blue) 5 1 5
20 Diode (IN4007) 2 2 4
21
6 ---- 6
22 Burg (Male and
Female Type) 4 Each 15 60
23 Connectors (6 pin, 4
pin, 3 pin and 2 pin) 4 10 40
24 PCB (Glass Epoxy) 1 350 350
25 DC (12V/2.5mA)
Battery 1 600 600
26 Solar PV Panel 1 800 800
27 Connecting Wires
and Jumper Wires ---- ---- 40
28 IC Base (16 Pin) 1 2 2
TOTAL 4590
This system leads to various pleasant results. These are;
Proposed model is equipped with a water pump which
automatically starts to function when soil needs water.
Hence it is helpful to meet out the water requirements.
Problem of unwanted occupancy and vandalization of crops
is resolved by using an automatic buzzer system which
provides information about any unwanted entry/occupancy
if detected in farmer’s land.
Problem of maintenance of humidity level is resolved by
using water spreading jet motor which starts automatically
Problem of maintenance of temperature for sophisticated
crops is resolved by using fan which operates automatically
when the temperature. This feature is useful for sophisticated
crops.
For uninterrupted power back up and supply the system
is equipped with solar panel and battery which provides
power backup to the system even in the absence of
power supply.
Smart Automated Farming System using IOT and Solar Panel
31
The requirement of proper light for sophisticated crops is
crops.
IOT based module is used in system which keeps updating the
real time monitoring over internet which can be accessed
from anywhere in the world.
CONCLUSIONS
The proposed system is equipped with useful features
and leads to various pleasant results. The system provides
agriculture like water problem, unwanted occupancy,
humidity level maintenance, temperature maintenance,
electricity supply, and light arrangement for sophisticated
system which automatically informs the farmer about the
temperature, humidity, moisture, light, unwanted occupancy
all over the world. We can operate the relay manually as
well as automatically from internet. Implementation of such
of the crops and overall production.
FUTURE SCOPE
The utility of the proposed hardware could be enhanced by
system can be used for the purposes such as;
As the system has a solar panel which charges battery
and provides DC supply which is used to provide back
up to microcontroller in absence of power supply. By
incorporating inverter in the system one can convert DC into
AC so that ac appliances like water pump, automatic lighting
system etc. could be run easily.
Though the system is equipped with number of sensors but
still there is a scope to add other sensors. By adding one
important sensor like gas sensor the utility of the system
could be enhanced. That will be helpful in saving crops from
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33
A Comprehensive Review on Molecular
Characteristics and Food-Borne Outbreaks of
Listeria monocytogenes
Dipika Malakar1, Probodh Borah2, Leena Das3 and Nachimuthu Senthil Kumar4*
1,4Department of Biotechnology, Mizoram University, Aizawl–796 004, Mizoram, India
2,3Department of Animal Biotechnology, College of Veterinary Science,
Assam Agricultural University, Khanapara, Assam, India
E-mail: 1dipikamalakar88@gmail.com, 2borahp61@gmail.com,
3dasleena63@gmail.com, *4nskmzu@gmail.com
Abstract—Listeria monocytogenes is an important foodborne pathogen which causes serious invasive illness, and affects
mostly elderly and immune-compromised people, pregnant women, newborns and infants leading to listeriosis. L.
monocytogenes
public health. The pathogen has been isolated from food, human and animal samples world-wide. Neonatal listeriosis is
most commonly reported incase of humans, where as in animal populations, spontaneous abortions, meningoencephalitis
and endometritis are the most common. The purpose of this review is to enumerate Listeria epidemiology world-wide by
using publicly available data from CDC, FDA and ProMED and by describing the details such as countries involved, source,
of bacteriological characteristics, taxonomy, virulence determinants, typing methods, a detailed account of listeriosis in
human and in animals and an up-to-date information of the recent outbreaks of L. monocytogenes.
at the prevalence and epidemiology of L. monocytogenes globally, since it is a major food-borne pathogen and is the third
leading cause of death due to food poisoning. This review paper provides information on L. monocytogenes to understand the
better management of the infection, the source of infection and route of transmission of the disease. Most of the listeriosis
cases were linked with the consumption of contaminated food and it is important to identify the type of food materials to
mitigate the risk of Listeriosis in the high-risk populations.
Keywords: Listeria monocytogenes,
Science and Technology Journal Vol. 7 Issue: 2 July 2019 ISSN: 2321-3388
INTRODUCTION
Listeria monocytogenes is a foodborne pathogen that causes
severe invasive illness, mainly in elderly and immuno-
compromised persons, pregnant women and infants.
Listeriosis, a disease caused by an opportunistic food-
borne pathogen Listeria monocytogenes causes invasive
syndromes with more than 30% fatality rate. Listeriosis
occurs in animals and humans mainly from the consumption
of contaminated food (Barbuddhe et al. 2012). The genus
Listeria is closely related to the genus Bacillus,,
Streptococcus, Staphylococcus and Clostridium. Being
facultatively anaerobic, the Listeria spp. are 0.4 by 1-1.5
mm in size, does not form spores, have no capsule and show
motility at 10-25°C (Rocourt 1999).
Listeria monocytogenes
Corynebacteriaceae in Bergey’s Manual of Determinative
Bacteriology (Stuart and Pease 1972), but later it was
listed in a section named Regular, Nonsporing Gram-
Positive Rods together with Lactobacillus,
and (Seeliger and Jones 1986). Till 1961, it
was the only recognized species within the genus Listeria,
and , L. grayi and L. murrayi were added to
the genus between 1961 to 1971 (Rocourt et al. 1982). The
non-pathogenic strains of L. monocytogenes belonging to
serovar 6 were separated into a new species, L. innocua in
Malakar, Borah, Das and Kumar
34
1983 and later L. welshimeri and L. seeligeri were also added
(Seeliger and Jones 1986). The species is
quite different from other Listeria (Jones 1988) and it
base composition and DNA-DNA hybridization (Stuart and
Weishimer 1974). The taxonomic position of the genus
Listeria now includes the species L. monocytogenes, L.
innocua, L. ivanovii, L. seeligeri, L. grayi, L. welshimeri and L.
murrayi in one group and the other in based
on numerical taxonomy as well as DNA homology and 16S
rRNA cataloging (Rocourt 1988).
The ISO Standard 11290, part 2 (ISO 1998) and optional
protocols introduced by FDA and USDA methods are used for
enumeration of L. monocytogenes. Samples are incubated in
enrichment broths supplemented with antibiotics, such as
Buffered Listeria Enrichment Broth, Half-Fraser Broth and
Fraser Broth for all the methods. The cultures are streaked
on selective agars such as PALCAM Agar where Listeria spp.
show small brown/black colonies with black halos, Listeria
Selective Agar (Oxford) with the same characteristic as in
PALCAM and Chromogenic Listeria Agar (ALOA) where
Listeria spp. show blue/ green colonies (Jeyaletchumi et
al
The colonies show a characteristic blue/green colour
illuminated by obliquely transmitted light, a technique
called Henry’s lamp technique. Listeria monocytogenes also
shows CAMP reaction: the ability to haemolyse in horse or
sheep red blood cells cultured with Stapphylococcus aureus.
It is catalase positive and oxidase negative (Low and
Donachie 1997).
Denmark (Nyfelt 1929) and L. monocytogenes was
cultured from a patient with meningitis (Seeliger 1988).
Reports on isolation of Listeria was persistent between 1970s
and 1980s, and a sequence of epidemic outbreaks started in
humans in North America and Europe, and listeriosis was
clearly recognized as an important food-borne infection
(Bille 1990) from 1983 onwards, The foods mainly involved
are the industrially produced, refrigerated ready-to-eat
products that are eaten without cooking or reheating such
and salads (Rocourt 1996). In case of ruminants, Listeriosis
can be transmitted by consumption of spoiled silage where
(Vazquez et al. 1992). Both outbreaks and sporadic cases of
listeriosis in European Union (EU) were reported in 2013
(EFSA 2013) -an increase by 8.6% compared to that in 2012.
In all, 99.1% of the cases were hospitalized and listeriosis
was recorded as the most prevalent among all zoonoses
under EU surveillance (Robert et al. 2017). The large scale
outbreak that occurred in South Africa during 2017 - 2018
brought to light the potential of Listeria to cause widespread
disease. This outbreak is the largest till now with more than
et al. 2019).
This review has been prepared by using publicly available
data to report trends in Listeria epidemiology through
an analysis of CDC, FDA and ProMED reports for better
understanding the public health importance of L.
monocytogenes. This review addresses detailed information
relating to bacteriological characteristics of the organism
with its virulence determinants, the listeriosis disease
in human and animals, and also provides the up to date
information of recent outbreaks till date from food materials
associated with L. monocytogenes.
MOLECULAR CHARACTERISTICS AND
STRAIN TYPING
Being an intracellular pathogen, L. monocytogenes has
evolved over a long period of time with unique properties
and functions. It enters into the host by ingestion of
contaminated food and can resist to the host proteolytic
stress-response genes (opuCA, lmo1421 and bsh) and the
related proteins (Sleator et al. 2003). With the help of a
family of surface proteins called internalins (InlA and InlB), it
adheres and enters into host cells (Gaillard et al. 1991). With
the 88 kDa protein encoded by inlA and the 65 kDa protein
encoded by inlB, L. monocytogenes recognizes E-cadherin
and C1q-R (or Met) receptors on cell surface to enter easily
and epithelioid cells, and elude host immune monitoring
functions (Vazquez-Boland et al. 2001). Primarily L.
monocytogenes is located in single-membraned vacuoles
following its uptake by host cells. Listeriolysin O (LLO) and
phosphatidylinositol-phospholipase C (PI-PLC) are the two
virulence associated molecules that are responsible for lysis
of the primary single-membraned vacuoles and successive
escape by L. monocytogenes. LLO is a pore-forming, thiol-
activated toxin; a 58 kDa protein (encoded by hlyA) essential
for L. monocytogenes virulence (Portnoy et al. 1992). PI-
PLC, a 33 kDa protein encoded by plcA, interacting with a
29 kDa protein encoded by plcB, the phosphatidylcholine-
A Comprehensive Review on Molecular Characteristics and Food-Borne
35
phospholipase C (PCPLC), assist LLO in the lysis of the
primary vacuoles (Vazquez-Boland et al. 2001). Listeria
monocytogenes is released into the cytosol and then
undergoes intracellular growth and multiplication after
lysis of the primary single-membraned vacuoles. ActA, a 67
kDa surface protein aids in the intracellular adaptability and
cell-to-cell spread of L. monocytogenes, co-transcribed with
PC-PLC and helps in the formation of polarized actin tails to
propel it toward the cytoplasmic membrane. The bacteria
structures engulfed by the adjacent cells which result in
the formation of secondary double-membraned vacuoles.
The starting of a new infection cycle depends on PC-PLC
activated by Mpl, a 60 kDa metalloprotease (Vazquez-Boland
et al. 2001). The virulence-associated proteins LLO, Mpl,
ActA, PIPLC and PC-PLC are placed in a 9.6 kb virulence gene
cluster (Gouin et al. 1994) regulated by a 27 kDa protein
(PrfA) encoded by prfA (pleiotropic virulence regulator).
The prfA gene, co-transcribed with the plcA gene, activates
the transcription of L. monocytogenes virulence-associated
genes. In addition to these, an invasion-associated protein,
Iap is also associated with L. monocytogenes virulence and
pathogenicity (Vazquez-Boland et al. 2001).
Based on the results of different genotyping methods, L.
monocytogenes was divided into three genetic lineages
(Wiedmann et al. 1997a). Serotypes 1/2b, 3b, 4b, 4d and
4e were added into Lineage I (Cheng et al. 2008). Among
strains of different serotypes, 4d and 4e strains are scarce
among clinical and food samples and have similarity with
4b strains (Cheng et al. 2008). Therefore, serotypes 4b,
4d and 4e describes the subcluster in Lineage I known as
‘‘serotype 4b complex” (Eifert et al. 2005). Serotypes 1/2a,
1/2c, 3a and 3c were added in Lineage II and serotypes 4a,
4c and some strains belonging to serotype 4b were added
in Lineage III (Cheng et al. 2008). Lineage III isolates were
divided into three distinct subgroups, IIIA, IIIB, IIIC (Liu et
al. 2006). A fourth phylogenetic lineage was also added later
distinct group and also shows phylogenetic differences from
other lineages (Orsi et al. 2011). The Lineages I and II have
been mainly found in sporadic and outbreak cases of human
listeriosis (Cheng et al. 2008). Lineage II and some Lineage
I strains (serotypes 1/2b and 4b) are widely distributed
in natural and farm environments, and also found in foods
biodiversity but are rare (Ward et al. 2010), associated with
non-primate mammals and ruminants (Cheng et al. 2008).
Since L. monocytogenes contains numerous strains, it
is important to have a vigorous system of subtyping to
track individual strains, to examine the epidemiology and
population genetics, and to control outbreaks associated
with Listeriosis. The subtyping methods also help to track the
source of contamination in food processing environments
which is essential to develop control strategies. Since the
phenotypic subtyping methods have low differentiation
ability, the genotypic methods are more reliable.
Although plasmid typing was used in combination with
L.
monocytogenes, it seems that it has not much value as a
typing tool, because most strains of L. monocytogenes do not
contain plasmids (Farber and Peterkin 1991).
(PFGE)
PFGE uses restriction enzymes which cut the genomic DNA
resulting in many different fragments ranging between 40
and 600 kb. PFGE has been considered as the gold standard
tool to study epidemiology in foodborne outbreaks. It is a
sensitive method which tracks genetic changes like point
mutations, deletions, insertions and transpositions (Jadhav
et al. 2012). A study was carried out in China by Luo et al.
(2017) to investigate the prevalence of L. monocytogenes in
raw pork from open markets where they performed PFGE
by using AscI restriction enzyme and MLST. They found
Simpson’s index of diversity as 0.8793 and 0.7842 for PFGE
and MLST, respectively. The 262 L. monocytogenes isolates
generated 39 different pulsotypes in PFGE in their study.
Oliveira et al. (2018) from Brazil isolated L. monocytogenes
from chicken samples at industrial slaughterhouse and
studied genetic relationship among the isolates by PFGE
12 different pulsotypes. They found high diversity in
pulsotypes among the isolates from carcass and drumettes.
Nadia et al. (2018) screened L. monocytogenes in 1.5%
food samples from Tetouan, North-Western of Morocco. The
isolates belonged to serogroups 4b and 2a, and revealed
eight different pulsotypes in (AscI/ApaI) combined
MLST based on DNA sequencing is used for genetic
subtyping of L. monocytogenes which targets multiple
genes or gene fragments to determine the subtypes and
Malakar, Borah, Das and Kumar
36
the genetic relatedness among isolates. MLST is less
ambiguous and easy to interpret compared to PFGE. MLST
plays an important role in subtyping of L. monocytogenes
and in phylogenetic studies due to the lower cost of DNA
sequencing and the results can be exchanged easily between
different laboratories giving reliable and unambiguous
data (Jeyaletchumi et al. 2010). A study was carried out by
Abbey et al. (2018) in the Upper Great Plains states of US to
evaluate the genetic diversity of clinical listeriosis isolates
from ruminants using MLST.
using only one DNA primer which gives polymorphic DNA
Listeria monocytogenes contains a genome
which has randomly disseminated repetitive sequence
elements, such as repetitive extragenic palindromes (REPs)
of 35–40 bp length, an inverted repeat, and enterobacterial
repetitive intergenic consensus sequences (ERICs) with
124–147 bp length, a highly conserved central inverted
repeat. In order to discriminate species and strains, REP
and ERIC sequences exhibit functional primer binding sites
L. monocytogenes genome.
REP-PCR gives similar level of discrimination with PFGE;
hence it can be used as an alternative method for the fast-
track subtyping of L. monocytogenes (Jeyaletchumi et al.
2010). Hadjilouka et al. (2014) carried out REP-PCR for
121 L. monocytogenes strains isolated from food products
and found better differentiation among isolates. REP-PCR
possesses a discriminatory power similar to PFGE and
ribotyping. It is also faster and cheaper than other typing
techniques. Hence, it is considered as an important typing
method for L. monocytogenes. Soni et al. (2015) carried
out a study to characterize L. monocytogenes isolated from
pregnant women using ERIC- and REP-PCR methods. Both
these methods collectively showed that the isolates from
except a few isolates with identical patterns.
LISTERIOSIS IN HUMANS
With the help of a variety of virulence factors, L.
monocytogenes can invade the host and survive within the
host cells. It can colonize the human hosts using phagocytic
cells. After adhesion and invasion of the intestinal
epithelium, it proliferates throughout the body and infects
macrophages, epithelial cells, endothelial cells, hepatocytes,
nd neurons. With the help of the surface protein
internalin A (encoded by InlA gene), it translocates from
the lumen into the intestinal epithelial cells. InlA protein
binds to glycoprotein E-cadherin present on the host cell,
which helps in the entry of L. monocytogenes into the cell.
After internalization, a pore-forming cytolysin listeriolysin
O (LLO) encoded by hlyA gene and two phospholipases
C encoded by the genes plcA and plcB, help the pathogen
to escape from phagosomes. With the help of an actin
polymerizing protein ActA, it spreads to the neighbouring
cells. Listeria monocytogenes can proliferate by the
bloodstream to mesenteric lymph nodes, liver, spleen and
organs affected. These organs release the bacterium into
the blood stream resulting in septicaemia. The organism
can cross the placental barrier which leads to abortion
or generalized neonatal infections such as pneumonia,
sepsis and meningitis, and can also cross the blood–brain
barrier causing meningitis, meningoencephalitis and
rhombencephalitis (Anderson et al. 2016).
Different serotypes of L. monocytogenes have been
isolated from foods, while serotypes 1/2a, 1/2b and 4b
are responsible for majority of clinical cases worldwide
(Kathariou 2002). Many listeriosis outbreaks in the past
have been associated with closely related clones of serotypes
1/2a and 4b (Lomonaco et al. 2013). The annual incidence
of listeriosis ranges between 0.1 and 11.3 cases per million
population worldwide (Swaminathan and Gerner 2007).
During 1980–2000, serotype 4b strains were responsible for
several outbreaks. In Europe and North America, serotype
1/2a was more frequently associated with the outbreaks
of listeriosis (Cartwright et al. 2013). The rate of incidence
was higher during the 1980s, but the number of human
listeriosis cases reported was lower during the 1990s in
Europe. The cases of listeriosis have increased since 2000
in the European Union (EFSA 2007). In France from 2005
to 2007, 46% increase in listeriosis incidence was observed,
mainly in people of >60 years of age. Similar cases were also
reported in France (Goulet et al. 2008).
Listeriosis predominantly affects immuno-compromised
individuals of older age group (EFSA and ECDC 2014). It
can result either in a non-invasive gastrointestinal form or
an invasive clinical syndromes (Swaminathan and Gerner
2007). Risk of listeriosis has been found to increase among
women of reproductive age and pregnant women (Pouillot et
al. 2012). Materno-fetal listeriosis usually leads to abortion
at a rate inversely proportional to the month of gestation
et al. 2015).
A Comprehensive Review on Molecular Characteristics and Food-Borne
37
Sporadic and epidemic forms of listeriosis have been
reported throughout the world. In India, the epidemiological
extent of infection in human and animals. Due to the lack of
reliable and rapid diagnostic test and also lack of required
and under-reported (Barbuddhe et al. 2004). About one-
third of human listeriosis cases in USA were reported
during pregnancy resulting in spontaneous abortion in
second or third trimester (CDC 2005). In England and
Wales, 10–20% of cases were associated with pregnancy
and neonatal disease that lead to abortion and stillbirth
with 15–25% of infection (McLauchlin et al. 2004). In
India, genital listeriosis has been reported most commonly.
Listeria monocytogenes was found to be one of the causes
of abortion and premature births (Bhujwala et al. 1973).
There was no report of isolation of L. monocytogenes from
the cervix of unsuccessful pregnancy, and of unhealthy
cervical and vaginal discharges (Dhawan and Dhall 1963).
However, L. monocytogenes was isolated 14% of 150
patients from the cervix with past history of abortions and
miscarriages (Krishna et al. 1966), 1.34% of women with a
bad obstetric history (Bhujwala and Hingorani 1975) and
10% of women with a record of abortions (Stephen et al.
1978). Moreover, 3.3% cases of spontaneous abortions
was due to L. monocytogenes with a history of abortion in
earlier pregnancy (Kaur et al. 2007), and cases of meningitis
and hydrocephalus in children born from infected mothers
was also reported (Gogate and Deodhar 1981). Listeria
monocytogenes was isolated from the blood sample taken
from a 4 hours old newborn, one and a half month old child
having congenital heart disease and digestive failure, and
a 1.5 year old child severely ill-fed (Gupta et al. 1997). The
organism was also isolated from a 5 year old malnourished
child with Perinephric abscess having abdominal pain
(Gomber et al. 1998). It was detected in one out of 43
(Pandit et al. 2005). Listeriosis case was reported as late
onset type with characteristics such as hepatosplenomegaly,
lymphadenopathy, cutaneous haemorrhages and meningitis
(Raghuraman and Rupnarayan 1988). A meningitis case
was reportedly caused by L. monocytogenes in a 17 year
old immuno-compromised patient, in which it was isolated
treatment with ampicillin (Kalyani et al. 2006). Perinatal
listerial infection including abortion, stillbirth, neonatal
sepsis and meningitis are the common clinical syndrome
caused by L. monocytogenes. In neonatal listeriosis,
symptoms develop within 7 days or classically within 1
or 2 days (Klein 2001). Previously, a number of cases of
spontaneous bacterial peritonitis in cirrhosis caused by L.
monocytogenes were reported worldwide. In India, a greater
need extent use of temporary dialysis catheters in Catheter
related bacteraemia (Nirni et al. 2002). The incidence of
listeriosis cases varies in different countries between 0.1
and 11.3/1,000,000 (Anon 2002) but no such information
are available in India because of negligence of active
surveillance system for human listeriosis. Such surveillance
system is necessary for proper estimation of the disease.
To keep lowering the morbidity and mortality as much as
possible, the government should take steps for educating
the consumers, especially the high risk groups.
PREVALENCE AND OUTBREAKS OF
LISTERIA
Listeria monocytogenes is listed as the third leading cause
of death due to food poisoning (den Bakker et al., 2010).
At least, 90% of listeriosis cases are linked to the ingestion
of contaminated food products (Liu et al. 2015). The
prevalence of L. monocytogenes is summarised in Table 1.
In India, isolation of L. monocytogenes was reported from the
meat samples of 6.6 to 7.0% goats (Rekha et al. 2006), 7.4%
sheep (Barbuddhe et al. 2000), 3 to 6 % buffalo (Barbuddhe
et al. 2002), and 8.l% poultry meat samples (Barbuddhe et
al
products worldwide and the prevalence rate was reported
2000). Listeria monocytogenes was involved in numerous
outbreaks of listeriosis associated with the consumption of
milk and milk products (Lyytikainen et al. 2000). Isolation
of pathogenic L. monocytogenes was reported from goat milk
(1.56%) (Barbuddhe et al. 2000) and buffalo milk (6.25%)
(Barbuddhe et al. 2002) samples.
Soni et al. (2013) isolated L. monocytogenes from Ganges
water (8%), human clinical samples (1.7%) including
placental bit (5.3%), and vaginal swab (1.3%), and cow milk
(5.8%) in Varanasi, India. However, L. monocytogenes could
not be recovered from pasteurized milk and milk products
(cheese, butter and ice-cream) in their study. Biswas et al.
(2018) reported isolation of L. monocytogenes from cattle
faeces, raw milk and lassi, and dahi and ice-cream samples,
respectively. Prevalence of Listeria spp. in chevon, mutton
and swab samples was reported to be 1.82%, 3.21% and
6.66%, respectively (Alka et al. 2019). Pegu et al. (2017)
reported prevalence of L. monocytogenes from 2.31% of
Malakar, Borah, Das and Kumar
38
region of India and reported isolation of L. monocytogenes
samples. Biswas et al. (2018) collected a total of 200
samples from West Tripura district, Tripura including 50
each of cattle faeces and raw milk and 100 samples of milk
products. The overall occurrence of L. monocytogenes was
8.50% including in cattle faeces (6.0%), raw milk (8.0%),
lassi (12.0%), dahi (16.0%) and ice-cream (12.0 %) samples.
Although in US, regulatory initiatives and industry actions
were executed to reduce outbreaks during 1998 to 2008,
listeriosis outbreaks from dairy products were still reported
(Cartwright et al. 2013). A number of listeriosis outbreaks
from celery, lettuce, cantaloupe, sprouts, stone fruit, ice
cream and caramel apples were reported in the U.S since
2010. As indicated by European Food Safety Authority
and European Center for Disease Prevention and Control,
frequencies of L. monocytogenes contamination was found
in the period 2004–2006 (EFSA and ECDC 2014). A EU-wide
baseline survey was performed to estimate the prevalence
of L. monocytogenes
cheese) in 2010–2011 (EFSA 2013). In a study carried
out in UK, human listeriosis was attributed mainly to RTE
f
and beef (Little et al. 2010). Four EU listeriosis outbreaks
corresponded to the consumption of contaminated cheese,
acid curd cheese in 2006–2007, quargel cheese in 2009–
2010, hard cheese made with pasteurized milk in 2011 and a
fresh cheese in 2012 in Germany, Austria, Germany, Belgium
and Spain, respectively (Yde et al. 2012). Around 50% of the
outbreaks reported in the US have also been linked to cheese
during 2009-2011 indicated in the Foodborne Outbreak
Online Database (FOOD) from CDC (Lomonaco et al. 2015).
A multistate outbreak took place by ricotta salata cheese
produced in Southern Italy in 2012 spread over 14 States
et al. 2015) leading a worldwide recall since no single case
was reported outside the US with the same cheese which was
sold in Canada, Egypt, Europe, Australia, Japan and Mexico.
Many outbreaks have also been linked with community food
service where mainly elderly or people with underlying
conditions were infected in hospitals (Gaul et al. 2013) and
home-delivered meal programs (Smith et al. 2011).
Table 1: Prevalence of Listeria monocytogenes Strain from Different Sources from India
Source of Sample Prevalence (%) References
Fish and fishery products 50 Karunasagar and Karunasagar (2000)
Goat milk 1.56 Barbuddhe et al. (2000)
Sheep meat 7.4
Buffalo meat 3 to 6 Barbuddhe et al. (2002)
Buffalo milk 6.25
Poultry meat 8.l Barbuddhe et al. (2003)
Goat meat 6.6 to 7.0 Rekha et al. (2006)
Ganges water 8Soni et al. (2013)
Placental bit 5.3
Vaginal swab 1.3
Cow milk 5.8
Fish intestine 1.03 Pegu et al. (2017)
Fish gill 0.85
Flesh fish 0.43
Cattle faeces 6.00 Biswas et al. (2018)
Raw milk 8.00
Lassi 12.00
Dahi 16.00
Ice-cream 12.00
Chevon 1.82 Alka et al. (2019)
Mutton 3.21
A Comprehensive Review on Molecular Characteristics and Food-Borne
39
As of 2016, data collected on L. monocytogenes infections
from European countries reported 2555 listeriosis cases in
the European Union, highest (1.3 per 100000 population)
people (1.6 per 100000 population). In 2016, 375 cases
were reported in France and in 2014 USA reported 675
Active Surveillance Network in the USA, Listeriosis is a
sporadic illness (Angel et al. 2019).
hospitals among the patients over 55 of age with underlying
health issues (Gaul et al. 2013). Five patients died with
listeriosis and all the ten patients with immuno-compromise
conditions were under the treatment of corticosteroid
which might have increased their susceptibility to invasive
listeriosis. The outbreak strain could be isolated from the
processing facility and from many bags of diced celery
recovered from the manufacturing facility.
A multi-state outbreak was reported in USA in 2011 that
occurred through eating cantaloupe. A total of 147 persons
were infected causing 33 deaths and one miscarriage. As
many as 99% of the patients were hospitalized mostly people
above 60 years of age, and seven cases were associated
with pregnancy. Five subtypes of L. monocytogenes were
1/2a and 1/2b were acquired from the environment and
food products (Robert et al. 2017). An outbreak occurred in
2014 in Illinois and Michigan by eating mung bean sprouts,
L. monocytogenes
presence in sprouts and irrigation water samples which
were collected during a routine inspection. WGS revealed
that all the isolates collected from food, environment and
the patients were highly related. Listeria monocytogenes was
still found in the subsequent inspection in that production
environment (Robert et al. 2017).
In July 2014 in California, a packing company recalled various
stone fruits such as whole peaches, nectarines, plums and
pluots due to detection of Listeria. Four exact Pulsotypes
were detected from patients by PFGE typing from the stone
fruit samples (Robert et al. 2017). An outbreak occurred
during 2014-2015 in California from Caramel apples
affecting 35 people, of which 34 were hospitalized, 11 were
pregnancy related, one resulting in fetal loss and seven
died. Three invasive illnesses (meningitis) were reported.
FDA isolated L. monocytogenes from the apple packing
facility and also from the caramel apples. WGS revealed
that the isolates were highly similar to those isolated from
the patients (Robert et al. 2017). An outbreak of listeriosis
was reported in March 2016 which was traceable to
consumption of frozen vegetables produced by CRF Frozen
Foods of Pasco, Washington. It was found that organic and
frozen vegetables were the source of infection. The Listeria
isolate from the frozen peas was found to be closely related
with one isolate from an ill person based on WGS.
Multistate outbreak of listeriosis associated with raw milk
produced by miller’s organic farm in Pennsylvania was
reported in 2016.
in 2016 only when the U.S. Food and Drug Administration
related with Listeria isolates from the patients using
whole genome sequencing (Anon 2016a). CDC and FDA
investigated a multistate outbreak of listeriosis in 2016
Dole Processing Facility. Nineteen people were infected in
the outbreak from nine states; all were hospitalized, one
person died and one illness was related with pregnancy.
Whole genome sequencing (WGS) showed that the isolates
were closely related and the ill people in Canada were also
infected with the same strain (Anon 2016b).
In 2017, eight people were infected in an outbreak reported
from four states of US linked with soft raw milk cheese
made by Vulto Creamery of Walton, New York. All were
hospitalized, one illness was found in a newborn and
two people died (Anon 2017). CDC reported a multistate
outbreak of listeriosis linked with pork products in 2018
produced by Long Phung Food Products. The outbreak
was over as of January 29, 2019 with four hospitalization
cases (Anon 2018a). Eight people were infected with a
Listeria
which occurred due to eating of Hard-boiled Eggs from
Almark Foods of Gainesville and Georgia. Five people were
hospitalized and one death was reported (Anon 2019a).
Another Listeriosis outbreak was reported from 13 states in
2019 (n = 24). In all, 22 patients were hospitalized and two
of them died. Ill people ranged in age from 35 to 92 years
and 63 % percent of them were female. There was evidence
of infections in several Canadian provinces connected
with cooked diced chicken. WGS revealed that the type of
strain which made people sick in Canada was similar with
the strain in the United States, although the source of the
In a recent report dated April 2020, FDA and CDC investigated
a multistate outbreak of L. monocytogenes contamination
Malakar, Borah, Das and Kumar
40
through enoki mushrooms imported from Korea. The total
cases of illness were 36 with 4 deaths (Anon 2020). The
overview of Listeriosis outbreaks recorded by CDC is shown
in Table 2.
Table 2: Overview of Listeriosis Outbreaks by
Source of Samples Year of
Outbreak
References
Cantaloupes 2011 Robert et al. (2017)
Ricotta Salata Cheese 2012 Lomonaco et al.
(2015)
Cheese 2013 Anon (2013)
Dairy Products 2014 Anon (2014a)
Cheese 2014 Anon (2014b)
Bean Sprouts 2014 Robert et al. (2017)
Caramel Apples 2014 Robert et al. (2017)
Ice Cream 2015 Anon (2015a)
Soft Cheeses 2015 Anon (2015b)
Raw Milk 2016 Anon (2016a)
Packaged Salads 2016 Anon (2016b)
Frozen Vegetables 2016 Anon (2016c)
Vulto Creamery Soft Raw Milk
Cheese
2017 Anon (2017)
Prok Products 2018 Anon (2018a)
Deli Ham 2018 Anon (2018b)
Hard- Boiled Eggs 2019 Anon (2019a)
Unknown 2019 Anon (2019b)
Deli- Sliced Meats and Cheeses 2019 Anon (2019c)
Enoki Mushrooms 2020 Anon (2020)
LISTERIOSIS IN ANIMALS
Listeria monocytogenes can infect animal species such
listeriosis cases occur mostly clinically, while birds may
suffer from sub-clinical infections and pigs rarely develop
listeriosis. Most infections are sub-clinical, but sporadic.
Ruminants may transmit the infection to humans through
contaminated animal products. Direct transmission may
occur rarely, especially during calving or lambing from
infected animals (Wesley 2007). L. monocytogenes was
epidemic disease of rabbits and guinea-pigs (Murray et al.
1926). It was recorded that more than 40 species of wild
and domesticated animals may be infected from this disease
(Seeliger 1961). In the United Kingdom, listeriosis is most
common in sheep and has a major veterinary importance
in cattle, sheep and goats (Anon 1992). A number of
conditions like uterine infections are associated with
Listeric encephalitis (Wilesmith and Gitter 1986). Listeric
encephalitis is a neurological disease of sheep described
disease’ (Gill 1931). The organism, L. monocytogenes, was
isolated from the lesions (Gill 1933) and listeric encephalitis
of sheep, cattle and goats (Vandegraff et al. 1981). The
clinical symptoms of the infection are due to the effect of
the lesions in the brain stem (Rebhun and deLahunta 1982)
with some common symptoms such as dullness, walking in
circle and turning of the head to one side, drooping of the
eyelid and ear due to unilateral paralysis of facial nerve.
The animals also salivate because of partial pharyngeal
paralysis. In case of sheep and goats, leaning and then death
within 2 or 3 days, but it may take longer in case of cattles.
Rare cases of listeric myelitis resulting in limb paralysis
have been reported in sheep (Seaman et al. 1990). Listeric
abortion occurs frequently in ruminants and many other
species of domesticated animals caused by L. monocytogenes
(Kennedy and Miller 1992). In the United Kingdom, listeric
abortion was most commonly found in sheep (Anon 1992)
which is usually sporadic (Kennedy and Miller 1992).
from India was reported from the uterine pus of ewe,
from which L. monocytogenes was isolated (Dhanda et al.
1959). In Jammu and Kashmir, 111 abortion cases were
recorded in sheep out of 800 lambings during 1977–78
and a serotype 4b strain of L. monocytogenes was isolated
from one of 22 samples of stomach content of aborted
foetuses (Vishwanathan and Uppal 1981). Investigation
L. monocytogenes and L.
ivanovii could be isolated from vaginal swabs of sheep and
goats with a history of abortions (Sharma et al. 1996). In
Himachal Pradesh also, isolation of L. monocytogenes and L.
ivanovii was reported from ewes (Nigam et al. 1999). Listeria
monocytogenes was also isolated from aborted foetuses of a
cow and buffalo (Dutta and Malik 1981). Massive occasional
outbreaks of septicaemia were recorded in pregnant ewes
(Low and Renton 1985). Septicaemia may also occur in the
neonate as an extension of intrauterine infection which is
relatively uncommon. Stored fodder and the environment
are the main source of contamination of animal listeriosis.
Listeria monocytogenes lives in soil, water and decaying
vegetables and can contaminate animal feed mainly by
silage (Wiedmann et al. 1997b). After oral ingestion, the
bacteria enter into intestinal mucosa in case of septicaemic
A Comprehensive Review on Molecular Characteristics and Food-Borne
41
of hind brain (brainstem and cerebellum), L. monocytogenes
invades the brain stem through cranial nerves usually
with an incubation period of 2–3 weeks (Roberts and
Wiedemann 2003).
Listeric mastitis results in culling of the infected animals
from a herd. Recovery of the organism was reported from
milk and milk products (Bhilegaonkar et al. 1997). Listeria
monocytogenes and L. ivanovii were isolated from milk and
faecal samples of mastitic cattle and buffaloes and also
from endometritis cases in buffaloes (Shah and Dholakia
1983). Serotypes 1/2c and 4b of L. monocytogenes were
isolated from the endometrium of infertile cows (Srivastava
et al. 1985). Listeria ivanovii and L. monocytogenes were
isolated from sheep and goats with endometritis from
Himachal Pradesh (Mahajan and Katoch 1997). Incidence of
L. monocytogenes was reported from 4.4% buffaloes with a
previous record of reproductive disorders (Shakuntala et al.
chickens with neurological signs with a mortality rate of
40% (Vijayakrishna et al. 2000). Listeria monocytogenes
has been recognised as one of the agents associated with
subclinical mastitis; therefore, to reduce disease incidence
and to avoid L. monocytogenes contamination in animals,
creation of awareness among livestock farmers is important
for its effective control. Animal production units via faecal
contamination of food products are the main reservoir for
L. monocytogenes and these are also the source for human
infection (Esteban et al. 2009).
CONCLUSION
Listeria monocytogenes
animals and foods worldwide. Genital listeriosis is the most
common clinical form in humans reported so far. In animals,
spontaneous abortions, subclinical mastitis, meningo-
encephalitis and endometritis are commonly reported.
There is no reference laboratory for listeriosis in India,
though the occurrence of listeriosis in man and animals has
been reported from time to time.
To minimize the risk of foodborne Listeriosis in vulnerable
populations, the types of food materials involved in the
each stage of food handling, effective strategies may be
implemented by integrating principles such as hazard
analysis and critical control points (HACCP). Recent studies
have indicated that improving control measures in some
regions can decrease the prevalence of L. monocytogenes
in food products. However, the percentage of illness
particularly of invasive disease has remained stable, in some
cases or has increased over a period. The possible reasons
for increased reports of cases may be due to consumption of
non-traditional food.
Listeria monocytogenes
in the past due to limitations in laboratory detection and
also its long incubation period, in addition to its ability
its resistance to disinfectants. Diagnostic advances used
for early detection may be a factor correlated with the
changing epidemiology in the recent years. Though PFGE is
often used as a primary screening tool, the development of
of outbreaks more rapidly. In many countries, listeriosis is
not a reportable disease, which makes it unrecorded during
routine data collection (Desai et al. 2019).
Listeria monocytogenes strains can persist for years or
decades in food processing plants is the primary source
of post-processing contamination during manufacturing
of food products and in retail or food service settings. The
persistence may be due to the constancy and growth of some
strains in niches within the food environment such as cracks
to clean. Research on strain persistence has found the role of
or processing obstacles. Resistance to disinfectants which is
used to sanitize food environments, equipment and utensils
has been viewed as a possible mechanism for persistence.
to permanently eradicate L. monocytogenes from food
environments because of its ubiquitous nature. Therefore,
eradication and suppression of the organism must be
actively managed by adequate hygienic practices of a food
premise and equipment, by effective cleaning, sanitation
procedures and personnel practices (Robert et al. 2017).
Over the last decade many outbreaks have been linked with
foods as an agent for Listeria transmission. To keep track
of the food borne infections including listerosis, a national
food safety code should be applied covering all particulars
of Indian food safety under a united system. Creation of
awareness among consumers is required to keep morbidity
and mortality as low as possible. The epidemiological
studies of listeriosis would help to understand the sources
of infection, path of transmission and better management of
the infection.
Malakar, Borah, Das and Kumar
42
ACKNOWLEDGMENTS
This work was supported by Advanced Level State Biotech
Hubs, of Mizoram University, Aizawl and College of Veterinary
Science, Khanapara, Assam funded by the Department of
Biotechnology, New Delhi Government of India.
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46
Potential of Iron Pillared Clay as Active
Nanocatalyst for Rapid Decolorization
of Methylene Blue
Lalhmunsiama1 and Seung-Mok Lee2*
1Department of Industrial Chemistry,
School of Physical Sciences, Mizoram University, Aizawl–796004
2Department of Environmental Engineering, Catholic Kwandong University,
Gangneung, Republic of Korea
E-mail: *leesm@cku.ac.kr
Abstract—In this study, the iron-pillared clay nanocatalyst (ICN) was employed as a nanocatalyst for decolorization of
methylene blue in aqueous solutions without hydrogen peroxide. The changes in clay structure after the incorporation of
iron-oxide particles was studied with the help of XRD analytical data. The SEM micrographs showed higher heterogeneous
concentrations. The degradation is very fast and more than 90% is removed within 30 mins. A small amount of ICN is
effective for degradation of MB and the reusability test showed that ICN can be reuse for several times for the degradation
responsible in the degradation of MB. This study indicates that ICN must be low cost and environmentally friendly active
nanocatalyst for degradation of MB present in aquatic environment.
Keywords: Nanocatalyst, dye, clay, degradation, kinetic,
INTRODUCTION
Wastewaters produce from dye manufacturer and
application industries pose a serious concern in the
environmental points of view due to their negative impact
environmental water bodies is becoming a growing concern
to environmentalists and civilians. A long term sustainable
established to eliminate this issue. Dye wastewater should
impacts towards the environment and living things.
this paper reviews existing research papers on various
biological, chemical and physical dye removal methods
Although there are numerous existing tried and tested
methods to accomplish dye removal, most of them have a
common disadvantage which is the generation of secondary
pollution to the environment. This paper highlights enzyme
degradation (biological. Methylene blue is one of the most
been reported that an acute exposure to methylene blue
caused various disorder in human body [3]. Therefore,
various techniques have been developed to remove dyes
Among the common methods employed for dye removal,
attention of environmentalists due to their potential ability
to degrade a wide range of organic pollutants [4,5]. Reactive
oxygen species (ROS) such as singlet oxygen, superoxide,
hydrogen peroxide, and hydroxyl radicals are the important
Science and Technology Journal Vol. 7 Issue: 2 July 2019 ISSN: 2321-3388
Potential of Iron Pillared Clay as Active Nanocatalyst for Rapid
47
species in AOP and they have been proved to be very
effective in oxidation of wide range of organic pollutants in
water. Fenton process has been widely used for removal of
various types of organic contaminants. In Fenton process,
the reaction between hydrogen peroxide as an oxidant and
iron ions as a catalyst produce highly active species, mainly
non-selective ·OH radicals with oxidation potential of 2.8
V [6]. Usually, a quantity of hydrogen peroxide is required
in Fenton reaction to generate active hydroxyl radicals for
the degradation of pollutants [7]. Nonetheless, for real
application in wastewater treatment plant, use of excess
hydrogen peroxide is not suitable. Therefore, it is an interest
certain organic pollutants in absence of hydrogen peroxide.
Recent study has shown the production of reactive oxygen
species by natural materials such as clay minerals and
sediments in absence of hydrogen peroxide and it was
observed that the hydroxyl radicals (•OH) produced
from oxygenation of structural Fe(II) in clay minerals
under circumneutral pH and dark condition. This study
has shown that the •OH production was linearly correlated
with the Fe(II) amount and the 1,4-dioxane degradation
another study, the ferrocenated iron oxide nanocatalysts
was successfully used for the decolorization of methylene
blue in the absence of light and hydrogen peroxide within
two hours and the material could be reuse for several times
using aqueous sodium chloride as a reactivator [9]. Similarly,
the ferrocenated compounds were successfully synthesized
without co-precipitation with other iron species and the
catalyst showed a good performance in the decolorization of
methylene blue and oxidation of phenylboronic acid without
light and excess addition of hydrogen peroxide [10]. In the
light of these reports, the development of a simple, low cost
and environmentally friendly method for generation of ROS
without radical initiator has attracted the attention of our
research groups.
To implement a solid catalyst in real water treatment
leaching of active cations, stability over a wide range of pH,
temperature, and also must be available at a reasonable
cost [11]. In this study, the iron oxide nanoparticles were
prepared and immobilized into natural bentonite clay
to obtain the iron iron-pillared clay nanocatalyst. Use of
iron oxide in water treatment technology has advantages
since iron-oxide is abundant in nature and is relatively
inexpensive [12]. Moreover, naturally abundant clay is an
interesting option due to low cost and high mechanical
stability [13]via nitrate-contaminated potable water, and
contribute to eutrophication. The elimination of nitrate
from water systems has been thoroughly examined;
however, unconventional and low-cost technologies are
greatly needed. Clays and clay minerals are widely-used for
environmental applications, mostly due to their non-toxicity,
worldwide abundance, low cost and physicochemical
properties (high surface area, ion exchange capacity, high
sorption and catalytic properties. The synthesized catalyst
was characterized and further assesses for its capability to
degrade methylene blue dye in aqueous solutions in absence
of hydrogen peroxide under batch experimental systems.
MATERIAL AND METHODS
Methylene blue powder was procured from Acros organics,
USA. FeCl3 was purchased from Junsei chemicals Co.
Ltd., Korea and Duksan pure chemicals Co. Ltd., Korea,
respectively. NaHCO3, NaOH and HCl were purchased from
Duksan pharmaceuticals Co. Ltd., Korea. Bentonite clay was
obtained from Daejung Chemicals and metals Co. Ltd., Korea
The appropriate amount (5.84 g) of FeCl3.6H2O was dissolved
in 500 ml of water and then 2.0 g of sodium carbonate was
slowly added into the ferric chloride solution with continous
stirring. The solution was kept at room temperature for 24
h to form the polycations of iron. The solution was taken in
a beaker and then 20 g of bentonite was added slowly and
magnetically stirred for 15 h at 60 oC. It was then centrifuge
and the slid particles were collected, washed with distilled
water and completely dried at 120 oC. Furthermore, the
solid material was calcined at 400 o
furnace to obtain iron-pillared clay nanocatalyst (ICN).
the samples were evaluated by obtaining the nitrogen
adsorption-desorption isotherm at 77 K using a Protech
Korea BET surface area analyzer (Model ASAP 2020). Prior to
the measurements, the samples were degassed at 423 K for 4
h to remove any residual moisture. The surface morphology
Lalhmunsiama and Lee
48
scanning electron microscope (SEM; FE-SEM-Model: SU-70,
Hitachi, Japan) equipped with an energy dispersive X-ray
spectroscopy (EDX) system and the elemental composition
of the materials were also obtained. The X-ray diffraction
recorded with an X-ray diffraction instrument (PANalytical,
Netherland; Model X’Pert PRO MPD) using Cu Ka radiation at
a wavelength of 1.5418 Å. Moreover, the functional groups
transform-infrared spectrometry (FT-IR) with the KBR disk
method (Bruker, Tensor 27, USA). Moreover, the iron content
in the materials was evaluated by US EPA 3050B method.
The degradation of MB using ICN was studied in batch
taken in borosilicate glass beaker and 0.1 g of ICN was added
in the solution with continuous stirring with magnetic
stirrer. The effects of various physico-chemical parameters
were studied in the degradation of MB using ICN, including
(10 to 50 mg/L), contact time (2 to 60 min), and dose of
the adsorbents (1.0 to 5.0 g/L). After several interval of
time, the suspended ICN samples were separated using
a centrifugation instrument (Hanil science industrial co.
ltd, South Korea) and the absorbance of MB solutions was
max 665 nm using uv-visible spectrophotometer
((Model: Humas HS 3300). Further, in order to study the
mechanistic involved in the degradation of MB by ICN, the
effect of scavengers such as NaN3, 2-propanol and NaHCO3
were employed to scavenge the hydroxyl radicals or
superoxide anion.
In addition, reusability of ICN was evaluated in batch studies.
Once after the degradation was completed as described in
section 2.4, the spent ICN was separated by centrifugation
and then wash with pure water for two times. It was
completely dried and again the same material was used for
the next degradation experiment.
RESULTS AND DISCUSSION
The X-ray diffraction data of pristine bentonite and ICN
were collected and graphically shown in Fig. 1. The pristine
bentonite has shown the diffraction peak corresponding
of iron and heat treatment, this peak was shifted to the
This observation indicates that iron oxide particles were
successfully intercalated between the interlayer’s of
bentonite clay [14]. Moreover, the sharp peaks obtained
at 2θ values of 33.3o
characteristic peaks obtained at 35.6o and 51.8o are
attributed to Fe2O3 [16]. The prominent peak shown at 20o
and 27o corresponds to SiO2 [17], whereas a distinguish
peak obtained at 61.9o
which indicate the clay phase is dioctahedral [18].
010 20 30 40 50 60 70 80
d
d
d
d
d
Intensity (a.u.)
2
θ
Bentonite
ICN
d
Fig. 1: The X-Ray Diffraction Pattern Obtained for
Bentonite and ICN.
The SEM micrograph were obtained for bentonite and ICN
and shown in Fig. 2 (a & b). The SEM image showed the ICN
possessed more heterogeneous structure and iron oxide
particles were aggregated on the surface as well as pore of
the clay. The particles are very small in size and randomly
distributed on the surface of the bentonite clay. Moreover,
the EDX analytical graph are shown in Fig. 2 (c & d) have
shown that a distinct peak corresponding to iron were
nanoparticles into bentonite. Therefore, SEM-EDX analysis
clearly proved that iron oxide nanoparticles are successfully
incorporated on the bentonite clay. Further, the surface area
of bentonite found to be increased after the incorporation of
iron oxide and heat treatment; the surface area of bentonite
and ICN were 63.41 and 82.55 m2/g, respectively. The large
surface area of ICN provides an additional advantage that
the large number of MB dye molecules can be adsorb and
then degrades simultaneously.
Potential of Iron Pillared Clay as Active Nanocatalyst for Rapid
49
Fig. 2: SEM Micrograph Obtained for (a) Bentonite and (b) ICN
and ICN
Batch experiments were conducted to study the removal
behaviour of MB by pristine bentonite and ICN under the
same experimental conditions. The initial concentration of
MB was maintained at 20 mg/L and the material dose is 2
g/L. The results obtained are graphically shown in Fig. 3. It is
observed that the removals were fast in both cases; however,
high compared to the pristine bentonite. Almost 100 %
removal was achieved within 20 mins using ICN whereas
the bentonite has shown the maximum removal about 75%
only. It was observed that some MB colour remained in the
solution even after the equilibrium adsorption achieved by
pristine bentonite as displayed in Fig. 4. On the other hand,
the ICN degrade the MB molecules and a clear solution was
obtained within a short period of time. Therefore, these
results indicate the suitability of using ICN for degradation
of MB in aqueous solutions.
10
only. It was observed that some MB colour remained in the solution even after the
equilibrium adsorption achieved by pristine bentonite as displayed in Fig. 4. On the
other hand, the ICN degrade the MB molecules and a clear solution was obtained within
a short period of time. Therefore, these results indicate the suitability of using ICN for
degradation of MB in aqueous solutions.
Fig. 3. Removal of methylene blue (MB) using pristine bentonite and ICN
(Concentration: 20 ppm; dose: 2 g/L).
Fig. 4. Picture showing the removal efficiency of methylene blue (MB) using pristine
0
0.2
0.4
0.6
0.8
1
1.2
010 20 30 40 50 60 70
Ce/Co
Time (min)
Bentonite
ICN
Fig. 3: Removal of Methylene Blue (MB) using Pristine
Bentonite and ICN (Concentration: 20 ppm; Dose: 2 g/L).
Lalhmunsiama and Lee
50
Fig. 4:
Methylene Blue (MB) using Pristine Bentonite and ICN
(Concentration: 20 ppm; Dose: 2 g/L).
Effect of pH
The effect of initial solution pH on the degradation of MB by
ICN was studied within the pH range of 3.0 to 11.0 and the
initial MB concentration were taken was 20 mg/L. As shown
in Fig. 5, rapid degradation of MB was observed within a
wide range of pH and the extent of MB removal was similar
in all the studied various pH. The extent of degradation was
slightly lower at pH 3 and 5 within the reaction time of 10
to 30 min. This difference can be explained with the help of
point of zero charge of the ICN material and the speciation
of MB in aqueous solutions. The pHpzc of the ICN was found
to be pH 6.6. This indicate that the ICN carries net positive
charge at lower pH whereas it carries net negative charge
at pH higher than the pHpzc due to dissociation of surface
functional groups [19,20]respectively. Further, the AMS was
characterized by the XRD (X-ray Diffraction. On the other
hand, the speciation studies showed that MB exists as the
cationic species at higher pH and undissociated/uncharged
species at acidic media. At pH 3, MB is present as uncharged
species with a small fraction of MB+ species. The uncharged
species is equivalent to cationic species at pH ~4 and then
MB+ species is the dominant beyond this pH [21]. The cationic
MB molecules were strongly attracted towards the surface
of ICN at higher pH which resulted in rapid degradation of
MB. On the other hand, the ICN carries net positive charge at
lower pH and the uncharged MB/cationic species were less
attracted towards the surface of ICN which cause a slight
retardation in degradation of MB. However, this difference
diminished as the reaction time was increased up to 60 min.
The pH dependence study clearly showed that the ICN can
directly used in the removal of MB from wastewater without
prior adjusting of solution pH.
12
Fig. 5. pH dependent degradation of methylene blue using ICN (Concentration: ~20
ppm; dose: 2 g/L)
3.2.3. Effect of initial concentrations
The degradation of MB using ICN were performed at various initial concentrations (i.e.,
10, 20, 30, 40 and 50 mg/L) and the results are shown in Fig. 6. The time required for
decolorization of MB was found to be increased as the initial concentration of MB was
increases from 10 to 50 mg/L. As seen in the figure, the kinetic of degradation is
relatively faster while the initial concentration were maintained at 10 and 20 mg/L, and
more than 90 % of MB were removed within 10 min and then degradation reaction was
slowed down beyond 10 min. The degradation rate was significantly slowed down as
the initial concentration of MB was increased to 30 mg/L. Further, this study showed
that atleast 60 min is required for complete degradation of 30 mg/L of MB using ICN.
0
0.2
0.4
0.6
0.8
1
010 20 30 40 50 60 70
Co/Ce
Time (min)
pH 3 pH 5
pH 7 pH 9
pH 11
Fig. 5: pH Dependent Degradation of Methylene Blue
using ICN (Concentration: ~20 ppm; dose: 2 g/L)
Effect of Initial Concentrations
The degradation of MB using ICN were performed at various
initial concentrations (i.e., 10, 20, 30, 40 and 50 mg/L)
and the results are shown in Fig. 6. The time required for
decolorization of MB was found to be increased as the
initial concentration of MB was increases from 10 to 50
is relatively faster while the initial concentration were
Potential of Iron Pillared Clay as Active Nanocatalyst for Rapid
51
maintained at 10 and 20 mg/L, and more than 90 % of MB
were removed within 10 min and then degradation reaction
was slowed down beyond 10 min. The degradation rate was
was increased to 30 mg/L. Further, this study showed that
atleast 60 min is required for complete degradation of 30
mg/L of MB using ICN.
13
Fig. 6. Degradation of MB using ICN at various concentrations (pH: ~7, dose: 2 g/L)
Further, the kinetics of MB degradation was studied by utilizing the known
pseudo first order kinetic equation [22]. The graphs are plotted between the ln C
t
/C
o
and time as shown in Fig. 7. The rate constant (k) of pseudo-first order kinetic are
obtained to be 37.0 x 10
-2
, 10.0 x 10
-2
, 6.90 x 10
-2
, 6.00 x 10
-2
, 5.40 x 10
-2
min
-1
for 10, 20,
30, 40 and 50 mg/L, respectively. The rate constant is decreasing as the concentration
increases and this result indicate an efficient degradation at lower concentrations of the
MB dye [23]. Previous studies also shown that the degradation of MB using SnO-
decorated TiO
2
nanoparticle [24], PbBi
2
Nb
2
O
9
(Bulk)/TiO
2
(Nano) hetero structured
composites [25], NiO–ZnO–Ag nanocomposites [26] and biosynthesized zinc oxide
nanoparticles [27] were well d efined by pseudo first order kinetic model.
0
0.2
0.4
0.6
0.8
1
010 20 30 40 50 60 70
C/Co
Time (min)
50 ppm
40 ppm
30 ppm
20 ppm
10 ppm
Fig. 6: Degradation of MB using ICN at Various
Concentrations (pH: ~7, Dose: 2 g/L)
Further, the kinetics of MB degradation was studied by
The graphs are plotted between the ln Ct/Co and time as shown
in Fig. 7. The rate constant (k
are obtained to be 37.0 x 10-2, 10.0 x 10-2, 6.90 x 10-2, 6.00 x 10-2,
5.40 x 10-2 min-1 for 10, 20, 30, 40 and 50 mg/L, respectively.
The rate constant is decreasing as the concentration
lower concentrations of the MB dye [23]. Previous studies
also shown that the degradation of MB using SnO-decorated
TiO2 nanoparticle [24], PbBi2Nb2O9(Bulk)/TiO2 (Nano) hetero
structured composites [25], NiO–ZnO–Ag nanocomposites
[26] and biosynthesized zinc oxide nanoparticles [27] were
14
Fig. 7. Plot of ln C
t
/C
o
versus time (min) for the removal of MB using ICN.
3.2.4. Effect of dose of ICN
In order to optimize the effect of dose of ICN in the degradation of MB, the
experiments was performed by increasing the dose of the ICN from 0.5 g/L to 3.0 g/L
with the initial MB concentration of 20 mg/L. The result were plotted as the percentage
removal versus dose of the ICN and shown in Fig. 8. The percentage removal is almost
same for the dose of 0.5, 1.0, 2.0 and 3.0 g/L; therefore, these findings indicate that even
small amount of ICN is efficient in degradation of MB in aqueous solutions.
-4.5
-3.5
-2.5
-1.5
-0.5
010 20 30 40
ln Ct/Co
Time (min)
10 mg/L 20 mg/L 30 mg /L
40 mg/L 50 mg/L
0
20
40
60
80
100
0.5 g/L 1 g/L 2 g /L 3 g/L
% Removal
Dose of ICN
20 min 30 min
Fig. 7: Plot of ln Ct/Co Versus Time (min) for the Removal
of MB using ICN.
Effect of Dose of ICN
In order to optimize the effect of dose of ICN in the
degradation of MB, the experiments was performed by
increasing the dose of the ICN from 0.5 g/L to 3.0 g/L with
the initial MB concentration of 20 mg/L. The result were
plotted as the percentage removal versus dose of the ICN
and shown in Fig. 8. The percentage removal is almost same
for the dose of 0.5, 1.0, 2.0 and 3.0 g/L; therefore, these
in degradation of MB in aqueous solutions.
14
Fig. 7. Plot of ln C
t
/C
o
versus time (min) for the removal of MB using ICN.
3.2.4. Effect of dose of ICN
In order to optimize the effect of dose of ICN in the degradation of MB, the
experiments was performed by increasing the dose of the ICN from 0.5 g/L to 3.0 g/L
with the initial MB concentration of 20 mg/L. The result were plotted as the percentage
removal versus dose of the ICN and shown in Fig. 8. The percentage removal is almost
same for the dose of 0.5, 1.0, 2.0 and 3.0 g/L; therefore, these findings indicate that even
small amount of ICN is efficient in degradation of MB in aqueous solutions.
-4.5
-3.5
-2.5
-1.5
-0.5
010 20 30 40
ln Ct/Co
Time (min)
10 mg/L 20 mg/L 30 mg/L
40 mg/L 50 mg/L
0
20
40
60
80
100
0.5 g/L 1 g/L 2 g/L 3 g/L
% Removal
Dose of ICN
20 min 30 min
Fig. 8: Percentage Removal of Methylene Blue using
Various Doses of ICN (Concentration: 20 mg/L).
15
Fig. 8. Percentage removal of methylene blue using various doses of ICN
(Concentration: 20 mg/L).
3.3. Reusability of ICN
Reusability of a catalyst is an important parameter for practical implication of the
study in real waste water treatment technologies. Moreover, the reusability of materials
could render the cost-effectiveness of materials for real treatment technology.
Therefore, reusability test was conducted at initial MB concentration of 20 mg/L with
ICN dose of 2 g/L. A detail procedure is given in section 2.3. It was observed that the
percentage removal of MB remain almost same even after the ICN was reused for 9
times (Fig. 9). Therefore, this study strongly suggests that ICN must be a useful material
for the complete degradation of MB in water.
Fig. 9. Reusability of ICN for degradation of methylene blue (Conc. 20 mg/L, pH: ~7,
dose: 2 g/L)
3.2.5. Effect of Scavengers
To study the catalytic action of the synthesized materials in the degradation of
methylene blue, the scavengers such as NaN
3
, 2-Propanol and NaHCO
3
were utilized to
scavenge the active radicals. In this study, the degradation of MB was conducted in
presence of 1000 mg/L of the scavengers (i.e., NaN
3
, 2-Propanol and NaHCO
3
) and the
0
20
40
60
80
100
123456789
% Removal
Number o f reusabil ity
Fig. 9: Reusability of ICN for Degradation of Methylene
Blue (Conc. 20 mg/L, pH: ~7, Dose: 2 g/L)
Reusability of a catalyst is an important parameter for
practical implication of the study in real waste water treatment
technologies. Moreover, the reusability of materials could
render the cost-effectiveness of materials for real treatment
technology. Therefore, reusability test was conducted at
initial MB concentration of 20 mg/L with ICN dose of 2 g/L. A
Lalhmunsiama and Lee
52
detail procedure is given in section 2.3. It was observed that
the percentage removal of MB remain almost same even after
the ICN was reused for 9 times (Fig. 9). Therefore, this study
strongly suggests that ICN must be a useful material for the
complete degradation of MB in water.
Effect of Scavengers
To study the catalytic action of the synthesized materials
in the degradation of methylene blue, the scavengers such
as NaN3, 2-Propanol and NaHCO3 were utilized to scavenge
the active radicals. In this study, the degradation of MB was
conducted in presence of 1000 mg/L of the scavengers (i.e.,
NaN3, 2-Propanol and NaHCO3) and the results obtained
are given in Fig. 10. The 2-Propanol and NaHCO3 are good
scavengers of OH radicals whereas the NaN3 is a scavenger
for [28,29]. It is interesting to observe that the presence of
2-propanol and NaHCO3 cause to decrease the percentage
degradation of MB, whereas NaN3
affect. The results suggest that the double bond in MB
molecules are attacked by hydroxyl radical species which
results in the degradation of the dye molecules [30,31].
the ICN are mainly responsible for the degradation of MB in
aqueous media.
16
results obtained are given in Fig. 10. The 2-Propanol and NaHCO
3
are good scavengers
of OH radicals whereas the NaN
3
is a scavenger for O
[28,29]. It is interesting to
observe that the presence of 2-propanol and NaHCO
3
cause to decrease the percentage
degradation of MB, whereas NaN
3
did not show significant affect. The results suggest
that the double bond in MB molecules are attacked by hydroxyl radical species which
results in the degradation of the dye molecules [30,31]. Thus, according to this study,
∙OH radicals generated from the ICN are mainly responsible for the degradation of MB in
aqueous media.
Fig. 10. Effect of scavengers on the degradation of MB using ICN (Conc. 20 mg/L, dose: 2
g/L).
4. Conclusions
The iron oxide particles were successfully immobilized into bentonite clay. The
changes in clay structure after the incorporation of iron-oxide particles evidently
observed with the help of XRD analytical data. The SEM micrographs showed higher
heterogeneous structure of the modified clay, i.e., ICN compared to pristine clay and the
0
20
40
60
80
100
No scavenge r NaN3 2- propanol NaHCO3
% Removal
Fig. 10: Effect of Scavengers on the Degradation of MB
using ICN (Conc. 20 mg/L, Dose: 2 g/L).
CONCLUSIONS
The iron oxide particles were successfully immobilized
into bentonite clay. The changes in clay structure after the
incorporation of iron-oxide particles evidently observed
with the help of XRD analytical data. The SEM micrographs
surface area of ICN (82.54 m2/g) is considerably higher
2/g). Further, the
EDX analytical data indicate the successful incorporation of
iron-oxide into bentonite clay. Batch experiments showed
that ICN could degrade MB within a wide range of solution
concentrations. The degradation is very fast and more than
90% is removed within 30 mins. A small amount of ICN is
effective for degradation of MB in aqueous media and the
reusability test showed that ICN can be reuse for several
times for the degradation of MB in aqueous solutions. The
generated from the ICN are responsible degradation of
MB. This study indicates that ICN must be low cost and
environmentally friendly catalyst for degradation of MB dye
present in aquatic environment.
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54
Mathematical Series
R. Raja Aswathi1*, K. Pazhani Kumar2 and B. Ramakrishnan3
1,2,3Department of Computer Science, S.T. Hindu College, Nagercoil, India
E-mail: 1*rajaaswathi@outlook.com, 2skpk73@gmail.com, 3ramsthc@gmail.com
Abstract
of attributes. The EC4.5 (Exponential C4.5) is an extension of C4.5 algorithm which uses exponential of split value to predict
value derived from the Madhava series. By using this technique an optimized gain value is obtained that reduces uncertainty.
From the obtained result the TMC4.5 has far better results than the C4.5 and EC4.5 algorithms.
Keywords: Data mining, Decision Tree, ID3, C4.5, TMC4.5.
INTRODUCTION
Data mining is the analysis of useful patterns from a large
existing database. The data mining model is of two types as
predictive and descriptive. The predictive model predicts
the data using known results that are gathered from
historical data. The tasks of the data mining predictive
the classes using the similarity of patterns or relationship
in data. Clustering, sequence discovery, summarization and
association rules are descriptive in nature (Dunham, 2003).
This paper concentrates on the predictive model based
algorithms. Decision tree, as the name implies, is a
predictive model that can be viewed as a tree structure,
question and the leaves of the tree are partitions of the
et al. 2006; Hussina
et al. 2014). The two important steps in the decision tree
technique that are most common in practice are to built
the tree and then apply the tree to the existing database.
Regression Tree (CART), Chi-Squared Automatic Interaction
Detection (CHAID), C4.5 or J48, Scalable Parallelizable
Induction of Decision Trees (SPRINT). TheEC4.5 algorithm
has many advantages over the ID3 algorithm as it is an
extension of C4.5, despite of that it gives almost equivalent
results when the attributes are same in number. However, in
this paper the performance of the predictive EC4.5 is further
enhanced by incorporating exponential and sin technique of
the Taylor-Madhava Series with the gain ratio. As a result an
RELATED WORK
ID3 algorithm was introduced by Quinlan Ross .It is based on
Hunt’s algorithm and the algorithm is serially implemented
(Idriss et al. 2019). The ID3 algorithm is uses a general
it has many advantages, such as understandable decision
rules and the intuitive model (Gaganjot et al. 2014). It also
has some of its own disadvantages, for example: (1) the
existence of a problem of multi-value bias on the process of
attribute selection (Fayyad et al. 1992); (2) it is not easy to
calculate information entropy (Liang et al. 2008; Exarchos et
al. 2007) by using logarithmic algorithms, which increases
control (Quinlan, 1987), and the ID3 algorithm cannot
handle large datasets with categorical attributes which
Science and Technology Journal Vol. 7 Issue: 2 July 2019 ISSN: 2321-3388
An Extended C4.5 Classification Algorithm using Mathematical Series
55
results in a bigger tree generation. The ID3 approach uses
entropy values of the attributes to predict the information
gain of the attributes.
ID3 calculates the entropy value using the given by the
below equation,
(P) =
The information gain is calculated from the following
formula,
METHODOLOGY
To overcome the uncertainty in EC4.5 the Taylor-Madhava
series is calculated along with the split information value.
DATA COLLECTION
The dataset contains the results of heart disease dataset
which is used to compare the uncertainty level of the
algorithms. This dataset uses 9 attributes with 303 instances
which are represented in numerical formats.
Table 1: Represents the Dataset Variables Format and
Type (UCI Machine Learning Repository, 1988)
S. No Attribute Name Format
1. Chest Pain type (cp) 1: Typical angina
2: Atypical angina
3: Non-anginal pain
4: Asymptomatic
2. Cholesterol(chol) 168, 178, . . . . .
3. Fasting Blood Sugar >
120 mg/dl (fbs)
(1 = true; 0 = false)
4. Resting
Electrocardiographic
Results (restecg)
0: normal
1: having ST-T wave abnormality
2: showing probable or definite
left ventricular hypertrophy by
Estes’ criteria
5. Exercise Induced
Angina (exang)
(1 = yes; 0 = no)
6. Slope of the peak
exercise ST segment
(slope)
1: up sloping
2: flat
3: down sloping
7. Number of major
vessels coloured by
fluoroscopy (ca)
(0-3)
8. thal 3 = normal; 6 = fixed defect;
7 = reversable defect
9. Diagnosis of Heart
Disease (Target)
0: < 50% diameter narrowing
1: > 50% diameter narrowing
EXISTING METHODOLOGIES
C4.5
The C4.5 decision tree algorithm is an improved version of
the ID3 algorithm. It uses the gain ratio value to predict the
splitting attribute, where as the ID3 uses information gain
value to determine the splitting attribute.
Consider the probability distribution (P = p1, p2, p3, . . . pi)
and D denotes the dataset.
The entropy value of P is,
(1)
The information gain is calculated as,
G (2)
The split information of the dataset D is,
SplitInfo (D) = (3)
The split information value is calculated for all attributes in
the dataset D. The Gain (P, A) is divided by the equation (3)
to get the GainRatio (D, A),
GainRatio (D, A)
= (4)
C4.5 Algorithm
Input: Database D
1. Tree = Null
2. If D is empty OR has no more attributes to split then
3. Terminate
4. End if
5. for all attributes where a∈D do
6. Calculate the GainRatio for ‘a’
7. End for
8. ahigh = Attribute with highest gain value
Aswathi, Kumar and Ramakrishnan
56
9. Tree= Create a decision node with ahigh in the root
10. Ds = Reduced Sub-Dataset from D based on the ahigh
attribute
11. for all Ds do
12. Trees = C4.5 (Ds)
13. Attach the Trees to the Tree based on the attribute
values
14. End for
15. Return Tree
EC4.5 (Exponential C4.5)
In the exponential C4.5, the SplitInfo (D) is replaced by b as
GainRatio (D, A) = (5)
From the above equation (5), b is represented as,
=
(6)
From the above equation if b = 1 then the gain value of ID3=
C4.5. It is overcome by the Taylor series (Idriss et al. 2019).
Consider the Taylor series for the exponential function ex,
e = (7)
For x = 1 and n taking the limit, n ® ¥
+ + + . . . . (8)
Which implies, n ® ¥ = eb
𝐸𝑠𝑝𝑙𝑖𝑡 = eSplitInfo(D) (9)
That is equivalent to the format,
( ) (10)
By dividing the equation (2) by (10) the EC4.5 is analyzed
(Idriss et al. 2019),
(11)
EC4.5 Algorithm
Input: Database D
1. Tree = Null
2. If D is empty OR has no more attributes to split then
3. Terminate
4. End if
5. for all attributes where a∈D do
6. Calculate the EC4.5 using exponential value for ‘a’
7. End for
8. ahigh = Attribute with highest exponential split value
9. Tree= Create a decision node with ahigh in the root
10. Ds = Reduced Sub-Dataset from D based on the ahigh
attribute
11. for all Ds do
12. Trees = EC4.5 (Ds)
13. Attach the Trees to the Tree based on the attribute
values
14. End for
15. Return Tree
In the Taylor-Madhava C4.5, the SplitInfo (D) is replaced by
l as,
GainRatio (D, A) =
(12)
As derived from the above equation l is represented as,
l = (13)
(Idriss et al. 2019) From the above equation, if we consider
the value of l = 1. Then the value of Gain Ratio in C4.5 will
be equal to the gain value in ID3. The limitation of ID3 is
the Taylor Madhava Series is used.
Consider the Taylor series for the exponential function
ex at a = 0 is,
e =
The Madhava Sin Series is represented as,
+ - (14)
For x = 1 and n taking the limit, n ® ¥
+ + + . . . . (15)
And
An Extended C4.5 Classification Algorithm using Mathematical Series
57
1- + + (16)
If n ® ¥ the value of x®l, which implies, el and sin l. From
equation (13), the value of l is equivalent to SplitInfo(D).
Now the split value is found by summing the exponential
and sin split information as,
Split = eSplitInfo(D) + Sin (SplitInfo(D)) (17)
By dividing the gain value and information value the TMC4.5
is analyzed,
TMC4.5 = (18)
Input: Database D
1. Tree = Null
2. If D is empty OR has no more attributes to split then
3. Terminate
4. End if
5. for all attributes where a
∈
D do
6. Run TMC4.5 (a)
7. Set ahigh = Attribute with highest split value
8. Calculate the split information using sin and
exponential value for all ‘a’
9. End for
10. Tree= Create a decision node with ahigh in the root
11. Ds = Sorted Dataset D based on the ahigh attribute from
TMC4.5 (a) function
12. Creating Tree from Ds dataset
13. for all a
∈
Ds do
14. Trees = TMC4.5 (a)
15. Attach the Trees to the Tree based on the attribute
values
16. End for
17. Return Tree
IMPLEMENTATION OF PROPOSED
Before using the entire dataset to predict the gain values,
out the performance of algorithms. The algorithms that are
subjected to this test are ID3, C4.5, EC4.5 and TMC4.5. The
below datasets presented are used for the comparison of
algorithm with best gain value.
The data provided in the about table are tested with the C4.5,
EC4.5 and TMC4.5 algorithms to predict the gain values of
the splitting attributes. And the outcome is represented in
the below Table 3. The reduction in gain value of algorithm
has the high rate of producing correct outcome and here the
TMC4.5 has the reduced gain value and it can handle data
Table 2: Shows the Dataset with 10 Records and 9 Attributes
Dataset 1
ID cp Chol Fbs Restecg Exang Slope ca Thal Target
132331000011
222500100021
312040002021
412360102021
532331000011
602561012230
704070001330
802170110030
932821001120
10 0 288 1 0 1 0 2 3 0
Aswathi, Kumar and Ramakrishnan
58
Table 3: Represents the Uncertainty Level of C4.5, EC4.5
and TMC4.5 with 10 Records
Selected Attributes C4.5 EC4.5 TMC4.5
Exang 0.4491 0.1639 0.1242
Thal 0.4438 0.14745 0.12105
Cp 0.3923 0.11432 0.0992
Ca 0.3882 0.1267 0.10496
Chol 0.3203 0.04407 0.04403
Slope 0.1609 0.05411 0.04415
Restecg 0.0395 0.01443 0.0109
Fbs 0.0290 0.0106 0.0081
The comparison of C4.5, EC4.5 and TMC4.5 are represented
in the bellow Fig. 1
Fig. 1: Represents the Uncertainty Level of the
Algorithms
RESULTS AND DISCUSSION
From the implementation process of 10 records the TMC4.5
algorithm produces better results and now the 303 records
Gain Ratio value of C4.5, EC4.5 and TMC4.5 are compared to
below table represents the comparison of gain ratio values.
Table 4: Represents the Uncertainty Level of C4.5, EC4.5
and TMC4.5
Selected Attributes C4.5 EC4.5 TMC4.5
Thal 0.1646 0.0583 0.0464
Exang 0.1560 0.0571 0.0433
Cp 0.1176 0.0359 0.0306
Ca 0.1116 0.0351 0.0295
Slope 0.0902 0.0320 0.0253
Chol 0.0794 0.00486 0.00483
Restecg 0.0221 0.0081 0.00624
Fbs 0.0093 0.0030 0.0023
EC4.5 and TMC4.5 algorithms. Here the TMC4.5 results
in very less uncertainty as compared to the other two
algorithms with 303 records.
Fig. 2: Represents the Uncertainty Level of the Algorithms
and TMC4.5 has Very Less Level of Uncertainty
CONCLUSION
This paper proposes an improved version of the algorithm
C4.5. The TMC4.5 uses the information theory of entropy
attribute using the sin and exponential split value. The best
the predicted results the TMC4.5 algorithm produces an
optimized results and very low possibility of uncertainty in
predicted data.
REFERENCES
Clayson D, Sheffet MJ (2006) Personality and the Student Evaluation
of Teaching, Journal of Marketing Education, vol. 2, no. 28,
pp. 149-160.
Dunham MH (2003) Data mining: Introductory and advanced
topics, Pearson Education, 4-5.
Exarchos TP, Tsipouras MG, Exarchos CP, Papaloukas C, Fotiadis DI,
Michalis LK (2007) A methodology for the automated creation
of fuzzy expert systems for ischaemic and arrhythmic beat
tree. Artif. Intell. Med, 40, 187–200.
Fayyad UM, Irani KB (1992) The Attribute Selection Problem in
Decision Tree Generation. In Proceedings of the National
12–16 July 1992, pp. 104–110.
the Prediction of Diabetes, International Journal of Computer
Application, vol. 98, no. 5, pp. 13-17.
An Extended C4.5 Classification Algorithm using Mathematical Series
59
Hussina B, Merbouha A, Ezzikouri H (2014) A Comparative Study
of Decision Tree ID3 and C4.5, International Journal of
Advanced Computer Science, vol. 3, no. 1, pp. 13-19.
Algorithm Based on Taylor’s Series, JJCIT; (0):1.
Liang J, Shi Z (2008) The Information Entropy, Rough Entropy and
Knowledge Granulation in Rough Set Theory, Int. J. Uncertain.
Fuzziness Knowl.-Based Syst. 2008, 12, 37–46.
Quinlan JR (1987) Generating Production Rules from Decision
Trees. In Proceedings of the International Joint Conference
1987; pp. 304–307.
UCI Machine Learning Repository (1988) Heart Disease Dataset,
http://archive.ics.uci.edu/ml/datasets/Heart+Disease
60
A Pressure-based Compressible-Liquid Flow
Model for Computation of Instantaneous
Valve Closure in Pipes
R. Jishnu Chandran1* and A. Salih2
1,2Department of Aerospace Engineering,
Indian Institute of Space Science and Technology,
Thiruvananthapuram–695547, Kerala, India
E-mail: *jishnuchnadranr@gmail.com
Abstract—
interrelations are studied in detail. The proposed model could successfully capture the entire physics of the problem,
including the compressible modeling of the liquid involved and could produce high accuracy numerical results. The results
Keywords:
Pressure Surge, Pipe Flow
INTRODUCTION
incompressible even for the pressure ranges well above the
ambient conditions, where we treat the liquid density as a
constant. The Mach numbers associated with such cases
remain very small, and hence the compressible solvers
(density-based) are not employed for their modeling.
The incompressible pressure-based solvers are widely
empl
pressure-based solvers are computationally less expensive
than the density-based solvers due to the non-enforcement
of the acoustic t
be constant.
encounters pressure magnitudes much higher than the
ambient pressure where one can expect variations in the
liquid’s density with local pressure. The conventional
physics, cannot capture the variations in density. In the
present work, a simple and generalized ‘pressure-based
the compressibility effects in a liquid using a pressure-
density varies only with pressure. Various equations of state
for liquids are available in literature for relating the liquid
density to its temperature and pressure.
The physical problem considered in this study is the
instantaneous valve closure in a large diameter irrigation
liquid encountering substantial pressure variations. The
employing a dedicated equation of state for water along
with a segregated pressure-based solver of a commercial
Science and Technology Journal Vol. 7 Issue: 2 July 2019 ISSN: 2321-3388
A Pressure-based Compressible-Liquid Flow Model for Computation
61
problem, involving compressible effects in liquids. The
performance and accuracy of this model is evaluated by
comparing the results with those obtained using a well-
established density-based method for handling such class
equations (Students 1903, Guinot 2003).
LIQUID FLOW MODEL
Pressure-based methods are primarily developed for low-
advancements in the formulation of this method enabled
conditions. A comprehensive review of various pressure-
based methods is available in (Acharya et al. 2007). Advanced
pressure-based methods are reported in the works (Chen
1989, Rincon and Elder 1997, Moukalled and Darwish
2001, Shterev and Stefanov 2010, Chen and Przekwas
2010, Lourier et al. 2012, Miller et al. 2013, Kambrath and
Cary 2014, Miettinen and Siikonen 2015, Xiao et al. 2017,
Kraposhin et al. 2018, Denner 2018), most of which are
The present work proposes a generalized ‘pressure-based
the compressible effects in liquid under isothermal high-
pressure applications. The proposed model achieves this
by using dedicated equations of state (EOS) for liquids in
conjunction with any suitable pressure-based algorithms.
pressure-based algorithms to simulate compressible liquid
hence an isothermal EOS is preferred, which directly relates
could be used by assigning the temperature a constant value
throughout the simulation process. The methodology used
described in detail below.
The proposed model could be used for the computation of
equation of state into any generic pressure-based algorithm.
The initial value of the density of the liquid is estimated
for a system of coupled non-linear equations solves the
system sequentially in a loop until convergence criteria are
algorithm is shown in Fig. 1.
Fig. 1: Flow Chart of the Pressure-based Compressible-
Liquid Flow Model using a Typical Segregated Pressure-
Based Algorithm
W
equations are solved one after the other sequentially to
values applied to the pressure correction equation provides
the correction for pressure. Using this, the pressure is
fed into the equation of state of the liquid to generate the
which is yet to be computed, could be estimated using the
The solution eventually is checked for convergence. On
reaching convergence, the solver loop stops for a steady-
state problem, else the loop repeats. For transie
Chandran and Salih
62
value for the next time step, and then the time marching
process continues. It may be noted that whenever there is a
correction in pressure, the density is immediately updated
using the EOS.
OUS VALVE CLOSURE
The test case chosen for performance analysis of the
proposed pressure-based compressible-liquid model is
the instantaneous closure of a valve in a constant diameter
circular pipe. Instantaneous valve closures are among the
intensity in pressure in a short duration. The variations
less unidirectional as stated in (Ghidaoui et al. 2005), ``rapid
and energy are far greater than their radial counterparts”.
A study on laminar water-hammer (Mitra and Rouleau
2005) and turbulent water-hammer (Vardy and Hwang
2005) also support the unidirectional approach in the
problem formulation. Detailed literature on valve induced
transients are available in the books (Wylie 2003, Tijsseling
and Bergant 2007) and related works are also reported in
(Bazargan-Lari et al. 2013, Choi et al. 2015, Kodura 2016 ).
As a practically relevant problem, an irrigation pipe is
chosen for the present study. For the pipe dimension and the
the report on the Pipe Distribution System for Irrigation
(Indian National Committee on Irrigation and Drainage
1998) constituted by the Ministry of Water Resources, Govt.
of India, is referred. The schematic of the physical domain
chosen is shown in Fig. 2.
Fig. 2: Schematic of the Physical Domain
The report (Indian National Committee on Irrigation and
Drainage 1998) suggests a maximum allowable velocity
close to 1.5 m/s and the diameter of pipes ranging from 0.3 m
m/s are selected for this study. An L=3.0 m long pipe with its
domain is initialized with a uniform value of 1.5 m/s. This
instantaneous closure of the valve. The analysis is carried
out for three different operating pressure ranges within the
maximum range allowable as per the report viz., 1 bar, 2.5
millisecond from the instantaneous valve closure are chosen
for the comparative study.
NUMERICAL MODEL AND EVALUATION
STRATEGY
the segregated-pressure-based algorithm explained in
Section 2, is used to numerically simulate the transient
test problem. The viscous terms are excluded from the
rectangular two-dimensional axisymmetric computational
domain of length 3.0 m and width 0.45 m is used, as shown
in Fig. 3.
Fig. 3: 2D Axisymmetric Computational Domain and
Boundary Types
where the bottom boundary is the axis of symmetry, and the
top face is a wall with no penetration boundary condition. A
(left) boundary. On the right boundary, a no penetration
boundary condition is imposed. This is tantamount to an
instantaneous full closure of the valve at the beginning of
is initialized to a uniform value of 1.5 m/s. The domain
is discretized by taking 800 uniform control volumes
lengthwise and 50 control volumes in the radial direction.
Grid independent results are obtained for all the pressure
ranges considered in the study.
Liquid water is modeled to be compressible through the
use of the Tait EOS. The Tait EOS for compressible liquids
Scottish mathematician P. G.
A Pressure-based Compressible-Liquid Flow Model for Computation
63
Tait in (Tait 1888), and its historical evolution is presented
in (Dymond and Malhotra 1988). This state equation
establishes a nonlinear relationship between density and
pressure under isothermal conditions for water as in Eq.1
(Saurel et al. 1999).
01 (1)
0
K
PP
o
θ
ρ
θρ
= −+
(1)
Here
0
P
and
0
ρ
are respectively the reference values of
liquid pressure and density. Parameter
0
K
is the reference
bulk modulus of water, which is taken as
9
2.2 10×
Pa.
The density exponent,
θ
for water is 7.15. The Tait EOS
pressure as it very accurately predicts the density for a
temperature around 300 K. At this temperature range, the
density estimates show an average error below 0.25 %
and maximum error below 0.70 % over the wide pressure
range extending from 1 bar to 10,000 bar (Chandran and
Salih 2019).
problem. Property gradients are evaluated by the least-
squares cell-based method. The transient simulation is run
for a duration of 1 millisecond with a uniform time step size
7
5 10t−
∆= ×
s.
EVALUATION STRATEGY
The capability of the proposed model for handling the
compressibility effects of the liquid, and its performance
moving compression front, needs a thorough investigation.
A well-established density-based method to handle such
in liquids is the classical water-hammer formulation. The
equations of the water hammer formulation are derived
from the conservation of mass and momentum principles.
in (Joukowsky 1898); however, the theory of water hammer
phenomena is generally associated with the name of Allievi
(Students 1903). (Guinot 2003) gives a detailed description
of the hyperbolic conservation equations for the one-
dimensional water hammer. The one-dimensional density-
based approach detailed by (Guinot 2003) is adopted for
solving the instantaneous valve closure problem. This result
is chosen as the benchmark for evaluating the performance
model. For the one-dimensional water hammer formulation,
pressure conditions. In this formulation speed of sound is
computational domain is discretized uniformly into 800
control volumes in the axial direction.
RESULTS AND DISCUSSION
closure in a constant diameter pipe is numerically solved for
three different operating pressure conditions viz., 1 bar, 2.5
bar, and 25 bar (gauge). The pressure-based compressible-
compressible liquid and a segregated pressure-based
For the purpose of illustration, we have used the pressure-
based algorithm available in the commercial ANSYS Fluent
pressures is also solved on a one-dimensional domain using
classical water hammer formulation (Guinot 2003). The
result obtained using the proposed compressible-liquid
model has been compared with that of the density-based
classical water-hammer formulation. Figures 4(a) and 4(b)
at a time of one millisecond after the valve’s closure.
liquid model are plotted using solid symbols, and continuous
lines show those computed with the classical water-hammer
method. It can be seen that the results of the proposed
model are in excellent agreement with the results of the
benchmark solution of the water hammer problem. It can
also be observed that this agreement with the benchmark
results is consistent over all the three operating pressure
ranges considered.
Chandran and Salih
64
(a)
(b)
Operating Pressures from the Proposed Compressibility
Model and the 1D Water Hammer Model
deceleration from the instantaneous valve closure is
illustrated in Fig. 4(a). Due to the instantaneous closure of
the valve, a compression pressure wave is generated at the
valve (right boundary), which then propagates to the left
at the sound speed. This compression front is visible in the
presented in Fig. 4(b). From this plot, it can be seen that the
compressibility of the liquid increases with an increase in the
operating pressure range. The density predicted by the Tait
EOS incorporated into the pressure-based incompressible
algorithm is well-matched with the estimates using the
compressible water-hammer formulation. An important
discontinuity representing the compression front captured
with the proposed model exhibits slight dissipation
compared to the results of water hammer formulation.
This can be attributed to the fact that the pressure-based
solver introduces some amount of diffusion associated
with the solver. The pressure-based solver does not enforce
the acoustic time scale; instead, its time scale is based on
drastically reduced. Whereas, this leads to a situation where
the compression front, is not as sharp as those observed
with a density-based solver which strictly enforces the
acoustic time scale.
(a)
(b)
Fig. 5: Surge in Pressure and Density for Different
Operating Pressures and Initial Flow Velocities
A Pressure-based Compressible-Liquid Flow Model for Computation
65
The magnitude of the surge in the pressure and the
associated density change due to the instantaneous valve
operating pressures are shown in Fig. 5(a). The magnitude
of the surge in the pressure is observed to increase with the
rise in operating pressure, which is attributed to the high
The change in density is, however, observed to drop with the
rise in operating pressure. This is due to decrease in the ratio
of the maximum surge pressure to the operating pressure
with the increase in operating pressure. The estimated
values of this ratio are 11.45, 7.23, and 1.82, respectively,
for the operating pressures of 1 bar, 2.5 bar, and 25 bar. The
importance of these ratios cannot be overemphasized as it is
directly related to the safety of the pipe structure. For lower
operating pressure, the pressure surge magnitudes are many
times greater than the operating range of pressure, and if
this information is not taken into account while deciding the
design’s safety factor, it could lead to catastrophic failure of
the pipe structure.
The surge in pressure and the associated change in density
displayed in Fig. 5(b). As expected, the magnitude of the
surges in pressure and the density change is found to be
compression in the vicinity of the closed valve is higher with
The instantaneous valve closure in an irrigation pipe is
successfully simulated using the proposed pressure-based
solver using the Tait EOS. The accuracy and reliability of
the proposed model are clearly revealed in comparison
with the results of the classical water-hammer formulation.
Compared to the computationally expensive density-based
solvers, the proposed pressure-based model can be an
alternate choice for the numerical modeling of low-speed
EOS is available, the model can be extended to solve a large
CONCLUSIONS
is proposed for the simulation of isothermal low-
incorporates a liquid equation of state into a pressure-
based incompressible solver algorithm. Instantaneous valve
closure in an irrigation pipe is the test problem chosen and is
numerically simulated using the proposed model. The model
employs the Tait EOS for simulating compressibility effects
in water, and a segregated pressure-based algorithm is used
to simulate the problem on a 2D axisymmetric domain.
The results are compared against those generated with the
density-based one-dimensional water hammer formulation,
which is used as the benchmark. The comparative evaluation
liquid compressibility with high accuracy. The surge in the
accurately. Variations in the magnitude of the surges in the
pressure and the density changes over different operating
pressure ranges revealed the importance of the ratio of
maximum pressure to operating pressure in determining the
safety of the structure. The model combines the advantages
of an incompressible pressure-based solver and that of a
density-based compressible solver. Thus, we conclude that
model is a very accurate and inexpensive numerical tool
for modeling isothermal low-speed compressible liquid
the availability of the relevant equation of state.
ACKNOWLEDGMENT
The authors are thankful to the Department of Aerospace
Engineering, Indian Institute of Space Science and
Technology for extending the support and encouragement
in completing this study.
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67
Electrodes for Estimation of Nimesulide Drug using
Voltammetry Technique: A Revisit
Abhik Chatterjee
Dept. of Chemistry, Raiganj University, Raiganj–733134, India
E-mail: abhikchemistry@gmail.com
Abstract—
different types of pain. Nimesulide is a safe drug but uncontrolled use of this medicine creates many problems. So detection
and estimation of nimesulide is very important. Electro analytical methods are good choice for detection and estimation.
for voltammetric study have been presented with detection limit.
Keywords: Nimesulide, electrode, cyclic voltammetry, scan rate, peak potential, nanoparticle.
Science and Technology Journal Vol. 7 Issue: 2 July 2019 ISSN: 2321-3388
INTRODUCTION
Nimesulide is a medicine which lowers the different types
of pain, and this drug has wonderful application in the case
of osteoarthritis and rheumatoid arthritis. Antipyretic along
with analgesic properties make this drug very attractive
(Bernareggi 2001). The molecular structure and formula
of Nimesulide, N-(4-nitro-2-phenoxyphenyl) methane
inhibits the enzyme cyclo-oxygenase (COX), thereby
blocking the generation of prostaglandins. Prostaglandins
is the key enzyme in the biosynthesis of prostaglandins.
Nimesulide is selctive for COX-2 (cyclo-oxygenase-2). Due to
free radical scavenger property of the drug, it helps to protect
Normally nimesulide is safe, but it has also side effects due
to over dosage or excess use. Some of them are:
● Nausea, vomiting, diarrhea,
abdominal discomfort, heartburn, abdominal cramps.
● Dizziness and drowsiness,
Headache.
● Blood in urine, decrease in urination
and kidney failure.
Thus, sensitive determination of nimesulide at trace level is
highly recommended (Bukkitgar et al. 2016; Maltese et al.
2004; Pereira et al. 2013).
NH
N
O O
O
S
OO
Fig. 1: Nimesulide
It is very exciting that nimesulide was never sold in the
USA but has been marketed in many countries without any
restriction. In India, it is banned in children below twelve
years of age.
TECHNIQUE FOR DETECTION OF
NIMESULIDE
Techniques for detection and estimating of nimesulide
include chromatography (Maltese et al. 2004; Zacharis et
al. 2009), electrochemical methods (Catarino et al. 2003;
Chatterjee
68
Furlanetto et al. 2000; Wang et al. 2006; Zhang et al. 2010) and
spectrometry (Constantinescu et al. 2009; Hemmateenejad
et al. 2008). However, chromatography and spectrometry
require time-consuming steps and expensive instruments.
Electrochemical methods have been widely applied, and
the tremendous progress in electrochemical methods in
cost-effective, accurate and relatively short analysis time
when compared with the other methods (Chatterjee 2017;
Farghaly et al. 2014). Apart from these, other advantages
consist of large temperature window, simultaneous analysis
of multiple substances, it offers kinetic and mechanistic
properties and it needs very little amount sample volumes,
often in the microliter range, various solvents along with
electrolytes may be used (Xu et al. 2009). Electrochemical
techniques serve the pharmaceutical and drug analysis
centres since 1960s. Among them voltammetric analysis is
et al. 1987).
VOLTAMMETRIC METHOD
This method is based on potential given to an electrode
and measurement of the resulting current. Potential is the
key parameter which controls the electrochemical process
occurs in the solution (reduction or oxidation) at the
electrode. Above current is called diffusion current and is
used for the quantitative estimation of different analytes
ranging from organic to inorganic materials, biomaterials
(Barker 1958; Lawrence et al. 2002). Voltammetry
provides information about oxidation–reduction behaviour,
adsorption process, kinetics of electron transfer processes;
thermodynamic properties of solvated species (Hefnawey
et al. 2004). There are different types of voltammetry
techniques. Each technique has some merits over others.
d.
PULSE VOLTAMMETRY
Polarography has been extensively applied for the estimation
of many drugs since nineteenth century (Gilpin 1979).
Barker and co-workers at Harwell developed the Pulse
voltammetry technique (Barker et al.1952). It is also called
pulse polarography and was originally given for the DME
(dropping mercury electrode). The pulse method provides
a series of pulses of increasing amplitude. The Square-
wave voltammetry (SWV) is a large-amplitude differential
technique (Clough 1992; Hamm 1958).
STRIPPING VOLTAMMETRY
Stripping voltammetry method is an extremely sensitive
electrochemical technique (Kissinger et al. 1996; Wang 1985).
There are different categories of stripping voltammetry
like anodic stripping voltammetry, cathodic stripping
voltammetry, and adsorptive stripping voltammetry etc.
Each technique has some advantages and disadvantages.
Among different electrochemical techniques, voltammetry
methods have been very popular and have made valuable
contributions in drug industry. Recent voltammetric methods
are sophisticated and easy to handle due to advancement
in instrumentation, computer accessories. In voltammetric
method, we need an electrode (working electrode) where
electrode reaction occurs. Solid or mercury based electrodes
act as working electrodes. Solid electrodes have advantages
over mercury based electrodes. It is easy to handle,
mechanically stable and has large anodic potential window
(Uslu et al. 2007; Uslu et al. 2007; Wang et al. 1999).
From Table 1, we see that different kinds of electrodes
have been used for nimesulide determination, and Table 2,
represents the detection limit and linear range of different
electrodes.
Table 1: Electrochemical Techniques and Different Electrodes
Type of Electrodes Technique Reference
TiO2 nanoparticles/GCE DPV (differential pulse voltammetric ) (Bukkitgar et al. 2016)
Cysteic acid/MWCNTs DPV (Wang et al. 2006)
MWCNTs/GCE Cyclic voltammetry and linear sweep voltammetry (Zhang et al. 2010)
Fe3O4 magnetic nanoparticles/GCE DPV (Jin lei et al. 2011)
ER-GONRs/SPCE Voltammetry,amperometry (Govindasamy et al.2017)
MWCNTs/CPE Voltammetry et al. 2016)
Gold electrode Cyclic and differential pulse Voltammetry (Malode et al. 2013)
Glassy Carbon Electrode Voltammtric method and linear sweep voltammtry (ElSayed et al.2009)
Carbon paste electrode DPV (Malode et al. Z. Phys. Chem 2013)
Silicon carbide nanoparticles/GCE Voltammetry and Chronoamperometry (Ghavami et al. 2012)
Electrodes for Estimation of Nimesulide Drug using Voltammetry
69
Table 2: Different Electrodes and Detection Limit and Linear Range
Type of Electrode Limit of Detection Linear Range/M Reference
TiO2 nanoparticles/GCE 3.37 nM 1.0*10-7 to 4.0*10-5 (Bukkitgar et al. 2016)
Cysteic acid/MWCNTs 50 nM 1.0 * 10-7 to 1.0 *10-5 (Wang et al. 2006)
MWCNTs/GCE 160 nM 3.2*10-7 to 6.5*10-5 (Zhang et al. 2010)
Fe3O4 magnetic nanoparticles/GCE 130 nM 2.6*10-6 to 1.0*10-4 (Jin lei et al. 2011)
ER-GONRs/SPCE 3.5 (+-1.57) nM 1.0*10-8 to 1.5*10-3 (Govindasamy et al. 2017)
MWCNTs/CPE 1.07 nM 6*10-8 to 1*10-5 et al. 2016)
Gold electrode 1.1 nM 2.0*10-7 to 1.2*10-6 (Malode et al. 2013)
Glassy Carbon Electrode 32 nM 4.0*10-7 to 5.0*10-5 (ElSayed et al.2009)
Carbon paste electrode 8.6 nM 0.5*10-6 to 10*10-6 (Malode et al. Z. Phys. Chem 2013)
Siliconcarbide nanoparticles/GCE 30 nM 0.09 * 10-6 to 8.7*10-6 (Ghavami et al. 2012)
MECHANISM OF NIMESULIDE REACTION
ON ELECTODES
Redox properties of drugs can be described from
electrochemical study. Redox characteristics of drug
molecules can provide metabolic destiny of the drug (or
their in vivo redox processes or pharmaceutical activity).
Nimesulide follows nitrite reduction mechanism (scheme1)
during electrochemical process (Govindasamy et al.2017).
Cyclovoltammetric experiments corroborate the following
type of mechanism.
NH
N
O O
O
S
O O
NH
NH
O
S
O O
NH
NO
O
S
O O
HO
HH
H
e--e-,
e-,
Fig. 2: Nimesulide, Hydroxy Nimesulide,
Nitrosonimesulide
Scheme 1. Pathway for the electroreduction of nimesulide
onto ER-GONRs/SPCE electrode (Govindasamy et al.2017).
Govindasamy et al. (Govindasamy et al.2017) found that
cathodic peak appears in the forward sweep (- 0.60V vs Ag/
AgCl) which was due to irreversible reduction of nitro group
to hydroxyl amine. During the reverse sweep and second
cycle, reversible redox peaks were found, which indicates
redox reactions of hydroxyl amine to nitroso. Thus, the
electrocatalytic reduction mechanism of nimesulide follows
nitrite reduction pathway as given in Scheme 1.
OXIDATION MECHANISM
According to Malode et al. (Malode et al. 2013), nimesulide
offers one sharp anodic peak in all pH range in their
investigation. As per their explanation and reaction
mechanism, this is probably due to methyl sulfonamide
group oxidation.
Cyclic Voltammetry (CV) study on different electrodes–
effect of scan rate, and pH variation:
CV is the most powerful tool for the study of the
electrocatalytic activity of drug molecules.
et al. (Lueje
et al. 1997). They used droping mercury electrode as the
working electrode and nimesulide in solution showed
cathodic response in a range of pH (2-12). This peak
corresponds to the nitro group reduction in position
4. Patil et al. optimised the experimental parameters
using the differential pulse polarography (DPP) for
the characterization of Nimesulide (0.1 M NaOH as the
supporting electrolyte, scan rate 6mV/sec). The calibration
curves for Nimesulide were linear with the limit of detection
(LOD) 5.02X 10-6 M obtained by the DPP method (Patil et
al
and tremendously applied in pharmaceutical companies.
Govindasamy et al. (Govindasamy et al. 2017) investigated
the electrocatalytic behavior of ER-GONRs/SPCE electrode
towards reduction of nimesulide by cyclic voltammetry in
the potential window, +0.40 V to -0.80V. Their study indicates
that the crest current (Ipc or peak current) increases as
the rate of scan increases and the corresponding potential
(peak potential) is negatively shifted. The electrochemical
process is diffusion controlled as the square root of scan
rate has linear dependence with peak current (Ipc). Their
Chatterjee
70
study also reveals that electrocatalytic reduction process
of nimesulide is chemically irreversible {Ep vs log (scan
(Govindasamy et al
pH on the electroreduction process of nimesulide. With
increasing pH of the solution, reduction peak current
increases and reaches maximum at pH 7.0, so reaction
occurs smoothly at pH 7. Afterwards, the decreasing trend
of current is observed. Thus, the electrode is not a good
choice at alkaline pH. But on gold electrode surface, the
electrochemical responses of nimesulide in 0.2 M phosphate
buffer solution with different pH values and at a scan rate of
0.05 Vs-1 were studied (Malode et al. 2013) and the highest
peak current was found at pH 6.5. After proper selection of
pH, they varied the scan rates ranging between 10 and 200
mVs-1 and peak current versus square root of the scan rate
gave a linear relationship. Their observation shows that the
electrode reaction is diffusion controlled. The peak potential
shifted to positive values with increasing scan rates. Further,
calculated value of the number of electron (n) in the electro
oxidation of nimesulide was 2. ElSayed et al. studied the
nimesulide reduction process at GCE with the help of cyclic
voltammetry in B-R buffer (Britton-Robinson buffer) of
different pH values (ElSayed et al.2009) and it was observed
that reduction peak at all pH related to the reduction of the
nitro group, along with a small anodic peak in the anodic
direction appeared. The difference between Epc and Epa
was about 640 mV and Ipa/Ipc was about 0.18. Thus, the
nimesulide reduction onto this electrode is quasi reversible
in nature. On increasing the pH of the solution, the cathodic
peak potential shifted to more negative potentials indicating
the involvement of hydrogen ions in the reduction process
and anodic part was nearly vanished on increasing pH. The
reduction peak is due to the four-electron reduction of nitro
group to the corresponding hydroxylamine (ElSayed et
al.2009). Again reduction on GCE is adsorption controlled.
Cyclic voltammograms of nimesulide at bare GCE and Fe3O4/
GCE were compared by Jin lei research group (Jin lei et al.
2011). They found a reduction peak at about –0.683 V on
the bare GCE and at –0.625 V on Fe3O4/GCE electrode at
pH 5.0 (acetic acid-sodium acetate). On the backward scan,
there was no oxidation peak either on bare GCE or on Fe3O4/
a totally irreversible process. These magnetic nanoparticles
increase the sensitivity towards nimesulide estimation
around three times compared to normal electrode. These are
due to the high surface area and the electrocatalytic effect
of Fe3O4 nanoparticles. According to their study nimesulide
were observed when nimesulide reduction happened on
carbon electrode (MWCNTs/GCE) in PBS buffer solution
of pH 6.6 and the MWCNTs/GCE showed a good catalytic
response to reduction of the nimesulide (Zhang et al. 2010).
In this case, the sensitivity of the electrode increased around
seven times compared to GCE (Zhang et al. 2010) whereas
sensitivity of the nanoparticle based electrode increased
around three times compared to GCE (Jin lei et al. 2011). The
reduction process on to MWCNTs/GCE electrode at different
because a linear relation exist between the peak current
and the scan rate(scan rate varies between 0.02 to 0.2Vs).
were used for the determination of nimesulide by Ghavami
et al. (Ghavami et al. 2012). Electro reduction was happened
at a potential -526 mV (at the bare GCE) and at -387 mV
But surface fouling on
is a major drawback, and Santos da Silva etal adapted new
(Santos da Silva et al. 2013) and overcome the problem.
CONCLUSION
From the above review, it is seen that nano based electrode
materials are good choice for nimesulide detection. Scan
rate study showed that with increasing scan rate, peak
current increases. pH study reveals that pH of the medium is
a vital for detection of the said drug and alkaline regions are
not suitable for any kind of electrodes. To detect the trace
amount of nimesulide, new electrode to be prepared and it
is a big challenge for the scientists.
ABBREVIATIONS
CV-cyclic voltammetry, DPV-differential pulse voltammetry,
MWCNTs-Multiwalled carbon nanotubes, ER-GONRS-
Electrochemically reduced grapheme oxide nanoribbons,
SPCE-Screen printed carbon electrode; GCE-Glassy carbon
electrode; CPE-Carbon paste electrode.
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72
Synergistic Effect of Thiourea-Zn2+ and
L-Phenylalanine on the Inhibition of Corrosion of
Mild Steel in Acid Medium
M.B. Geetha1*, J. Sathish2 and S. Rajendran3
1Department of Chemistry,
St. Michael College of Engineering & Technology, Kalayarkoil–630551, India
2Department of Chemical Engineering,
St. Michael College of Engineering & Technology, Kalayarkoil–630551, India
3Department of Chemistry, St. Antony’s College of Art and Sciences for Women,
Dindigul–624 005, Tamil Nadu, India
E-mail: *geethamanomenon@gmail.com
Abstract—The formulation consisting of 100 ppm Thiourea, 25 ppm Zn2+ and 250 ppm of L-Phenylalanine has 95%
2+ ions. Polarization study
on the metal surface. FTIR spectra exposed the presence of Fe2+-Thiourea, Fe2+-L-Phenylalanine complex and Zn(OH)2 in
Keywords: Corrosion, Mild Steel, Thiourea, Polarisation, Atomic force Microscopy
INTRODUCTION
Corrosion inhibition of Mild Steel (MS) is a matter of
theoretical as well as practical importance. It has been widely
used in industries such as pickling, cleaning, descaling,
etc., and because of their aggressiveness, inhibitors are
used to reduce the dissolution of metals. Many organic
compounds containing N, S, O, & P have been reported as
inhibitors (Bentiss et al .1999). Also the corrosion inhibition
organic compounds, Thiourea (TU) and its derivatives
in corrosive media. The corrosion inhibition of Thiourea
and its derivatives have been extensively investigated in
various aqueous corrosive media. .As TU molecule contains
one sulphur and two nitrogen atoms; hence Thiourea and its
derivatives can function as very good corrosion inhibitors.
MATERIALS AND METHODS
MS specimen (0.026% S, 0.06% P, 0.4% Mn and 0.1% C
and rest iron) of the dimensions 1.0 X 4.0 X 0.2 cm were
trichloroethylene and used for the weight-loss method
and surface examination studies.
Elements S P Mn C Fe
Composition 0.026 0.06 0.4 0.1 Rest
1 g of L-Phenylalanine was dissolved and made up to 100
of this solution was diluted to give 100 ml of 100 ppm of
Science and Technology Journal Vol. 7 Issue: 2 July 2019 ISSN: 2321-3388
Synergistic Effect of Thiourea-Zn2+ and L-Phenylalanine
73
L-Phenylalanine. 1 g of Thiourea was dissolved in doubled
ml of this solution was diluted to give 100 ml of 100 ppm of
TU. 25 ppm of Zinc Sulphate solution is also prepared.
MS specimens in duplicate were immersed in 100 ml of
the sulphuric acid solution at pH-4 containing various
concentrations of inhibitor in the presence and absence of
Zn2+ for one day. The corrosion product cleaned with Clark’s
solution. The weights of the specimens before and after
immersion were determined using a balance, Shimadzu
AY62 model.
equation (1)
( )
21
100 1 / %IE W W= −
(1)
where W1 and W2 are Corrosion rate in the absence and
presence of inhibitor respectively.
The corrosion rate (CR) was calculated using the formula
87.6 W/DAT mm/y (2)
Where W = weight loss in mg, D = 7.87 g/cm3, A = surface
area of the specimen (10 cm2), T = 24 hrs
Polarization Study
Polarization study was conducted in Electrochemical
Impedance Analyzer model CHI 660A provided with
iR compensation option. The system operates with three
electrodes, one is the working, another is a counter electrode
and the third is a reference electrode. The working electrode
consists of a rectangular MS specimen with one electrode
face of 1 cm2 constant area exposed. The counter electrodes
are a rectangular platinum foil. Saturated calomel electrode
was used as the reference electrode. These three electrodes
were immersed in the pH-4 H2SO4 solution in the absence
and presence of inhibitor. The system was given a 5-10 min
time gap to reach the steady state open circuit potential.
The determinations were carried out at a scan rate of 0.005
Vs. The Tafel slopes, corrosion potential (ECorr) and current
(ICorr) values were determined.
MS specimens immersed for one day in various test solutions
surface was carefully removed and mixed scrupulously with
KBr, so as to make it homogeneous. The FTIR spectra were
carried out in a Perkin–Elmer–1600 spectrophotometer.
AFM is the most versatile and powerful microscopy whereby
surface morphology and properties to generate a 3D surface
image. Besides imaging surfaces with nanometer resolution,
the AFM was capable of determining surface roughness,
probing local changes in friction, calculating surface forces
and assessing local elasticity changes over a sample surface.
AFM uses a cantilever with a very sharp tip which interacts
with the sample surface. To acquire the image resolution,
AFMs can generally measure the vertical and lateral
photo detector. The differences between the segments of
photo-detector signals specify the laser spot position on the
ever
RESULTS AND DISCUSSION
The experimental results obtained from weight loss studies
for different inhibitor concentrations for the MS corrosion
in H2SO4 for one-day immersion at pH-4 are given in table
1. TU inhibits the corrosion of MS. As the concentration of
TU increases, the IE increases. 250 ppm of Thiourea alone
gives 55 % IE Table I (a). 250 ppm of L-Phenylalanine alone
gives 38 % IE only Table 1 (b). In order to increase the IE,
TU is combined with 25 ppm of Zn2+, the IE increases with
increase in the TU concentration, 25 ppm Zn2+ has 10 % IE
and 250 ppm TU have 55 % IE. The combination of 250 ppm
TU and 25 ppm Zn2+ shows 82%.
To increase the IE, TU- Zn2+ is combined with different
concentrations of L-Phenylalanine. It is found that when
L-Phenylalanine is added, the IE of TU-Zn2+ increases.
The increase in IE is more pronounced at 250 ppm of L-
Phenylalanine . The combination of 100 ppm TU, 25 ppm
Zn2+ and 250 ppm L-Phenylalanine shows 95% (Table 1
c). Therefore, this ternary combination better IE than the
individual inhibitors (Abdel-Fatah 2012; Ridhwan et al. 2012;
Geetha, Sathish and Rajendran
74
Umamathi et al. 2008). This suggests that the synergistic
effect exists between TU, Zn2+ and L-Phenylalanine (Gowri
et al. 2013; Manimaran et al 2012; Zhao and Mu 1999). Due
rate decreases (Thiraviyam et al. 2012). That is, the system
passes from passive region to active region (Rao et al. 2011).
(IE) Different Inhibitors System in Controlling the Corrosion
of MS Immersed in H2SO4 Solution at pH-4 Obtained by
Weight Loss Method.
Table 1: (a) TU
TU
ppm
Zn 2+ 0 ppm Zn 2+- 25ppm
IE % CR mm/y IE % CR mm/y
0
50
100
150
200
250
---
38
45
48
50
55
0.1947
0.1205
0.1113
0.1020
0.0974
0.0881
10
55
65
74
80
82
0.1762
0.0881
0.0672
0.0510
0.0394
0.0348
Table 1: (b) L-Phenylanine
L-Phenylalanine ppm IE %
50 30
100 32
150 34
200 36
250 38
Table 1 (c): TU and L-Phenylanine
TU
ppm
Zn2+
ppm
L-Pheny-
lalanine
ppm
Corrosion
Rate
mm/y
I.E %
0
0
100
100
100
100
100
0
25
25
25
25
25
25
0
0
50
100
150
200
250
0.1947
0.1762
0.02087
0.01855
0.01623
0.01391
0.00927
---
10
89
91
92
93
95
Potentiodynamic polarization study is used to validate the
corrosion behavior and also used to study the kinetics of
the cathodic and anodic reactions (Helal and Badawy 2011;
Mary et al. 2015).
Figure (1) depicts the potentiodynamic polarization curves
of MS immersed in H2SO4 solution at pH-4 containing 100
ppm of TU, 25 ppm of Zn2+ and 250 ppm of L-Phenylalanine.
The corrosion parameters are presented in Table 2. In the
presence of inhibitors, the corrosion potential shifted to
cathodic side (from -598 mV to -601 mV vs SCE). But the
shift is not very much. The largest shift evidenced by this
inhibitor system is 3mV. Therefore, it is ensured that this
system functions as a mixed type inhibitor (Hebbar et al.
2014; Ashassi-Sorkhabi et al. 2011). Simultaneously, in
Simultaneously, in the presence of the inhibitor system, the
corrosion current decreases from 2.394x10-6 A/cm2 to 5.205
x10-7A/cm2 and LPR value increases from 16724.9 ohm cm2
to 723439.8 ohm cm2. LPR value increased with the decrease
in corrosion current density indicates the adsorption of the
inhibitor on the metal surface to block the active sites and
inhibit corrosion and diminishes the corrosion rate with the
et
al. 2004; Kavitha and Manjula 2014).
a) pH-4 H2S04; b) pH-4 H2S04+TU (100 ppm)+Zn2+ (25
ppm)+L-Phenylalanine (250 ppm)
Fig. 1: Polarization Curves of MS Immersed in Various
Test Solutions
Table 2: Corrosion Parameters of MS Immersed in
H2SO4 Solution at pH-4 in the Absence and Presence of
Inhibitor System TU (100 ppm) and Zn2+(25 ppm)–L-
Phenylalanine(250 ppm) Obtained by Polarization
Method
TU ppm
Zn2+ ppm
L-Phenylalanine
ppm
Ecorr
mV vs. SCE
bc
mV/ decade
ba
mV/ decade
LPR ohm cm2
Icorr
A/cm2
0 0 0 -598 205 167 16724.9 2.394x10-6
100 25 250 -601 196 155 723439.8 5.205x10-7
Synergistic Effect of Thiourea-Zn2+ and L-Phenylalanine
75
FTIR spectrum is used to resolve the bonding type and
the nature of inhibitors adsorbed on the metal surface
(Sangeetha et al. 2012; Ruba Helen Florence et al. 2005;
Karthik et al. 2015). Fig. (3a) shows the FTIR spectrum
C=O and C–N stretching frequency emerge respectively
at 1594.55 cm-1 and 1175.01 cm-1. The N–H stretching
and bending frequencies emerge at 3415.60 cm-1 and
1474.47 cm-1 respectively. The C=O, C-N and N-H stretching
frequency emerge respectively at 1732.92 cm-1, 1198.30
cm-1 and 3410.43 cm-1 in the FTIR spectrum (KBr) of pure
L- Phenylalanine(Fig. 3 b).
metal surface following immersion in H2SO4 solution at pH-4
containing TU (100 ppm) and Zn2+ (25 ppm) and 250 ppm
C=S stretching frequency has shifted to 1593.55 cm-1. The
NH stretching frequency shifted to 3401.51 cm-1. The C-N
stretching frequency shifted to 1383.45 cm-1. The peak at
765.07 cm-1 is due to Zn–O stretching. This implies that TU
and L-Phenylalanine coordinated with Fe2+, through their
polar groups resulting in the formation of Thiourea-Fe2+
complex and L- Phenylalanine- Fe2+ complex and Zn(OH)2
formed on the metal surface( Epshiba et al 2017 ; Srimathi
et al. 2014 ). This complex inhibits the corrosion.
4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.0
0.0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100.0
cm-1
%T
3927.13
3415.60
2936.35 2745.73
2674.20
2488.68
2242.29
1594.55
1474.47
1436.73
1391.12
1355.44
1175.01
1035.95
846.05
803.70
720.39
569.87
465.95
4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.0
0.0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100.0
cm-1
%T
3754.21
3410.43
3203.94
2613.87
2530.15
2263.81
2089.55
1947.62
1732.92
1598.39
1480.43
1198.30
1046.35
790.22
737.76
702.03
642.50
604.73
547.86
(a) Pure TU (b) Pure L-Phenylalanine
4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400. 0
0.0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100.0
cm-1
%T
3776.15
3401.51
2809.92
2724.71
2181.93
1593.55
1501.99
1441.46
1425.91
1383.45
1350.05
765.07
572.21
(c) Film Formed on the Metal Surface after Immersion in Sulphuric Acid Solution at pH-4 Containing TU (100
ppm) and Zn2+(25 ppm)+L-Phenylalanine(250 ppm)
Fig. (3) FTIR Spectra
Geetha, Sathish and Rajendran
76
Atomic Force Microscopy imaging is a very informative
research method and measures the roughness of a sample
surface at a high resolution in the order of fractions of
a nanometer (Fouda et al. 2014; Singh & Quraishi 2011; Wang
et al. 2011; Satapathy et al. 2009). The two dimensional AFM
images of the polished metal corroded metal surface and in
the presence of inhibitors TU, Zn2+ and L-Phenylalanine are
shown in Fig. 4 respectively.
The average roughness (Ra), root mean square roughness
(Rq) and maximum peak-to-valley height (P-V) value for MS
surface engrossed in the different environment is displayed
in Table 4. It is inferred from the table that the polished
MS surface Rq, Ra and P-V height values that shows a more
homogeneous surface are respectively 26.351 nm, 21.327
nm and 129.754 nm. The slight roughness noted on the
polished MS surface is due to the atmospheric corrosion.
The Rq, Ra and P-V height values for the MS surface immersed
in H2S04 solution at pH-4 are respectively 152 nm, 132 nm
and 584 nm suggesting that MS surface in H2SO4 solution
at pH-4 is severely corroded. But in the presence of TU,
Zn2+ and L-Phenylalanine smoother surface was obtained
and the Rq, Ra and P-V height values are decreased to
27.572 nm, 21.058 nm and 133.520 nm respectively. The
reduction of these parameters established that MS surface
becomes smoothened due to the deposition of inhibitors
on the metal surface. The surface smoothness is caused
2+,
L-Phenylalanine-Fe2+ complex and Zn(OH)2 on the metal
surface thus retarding the mild steel corrosion[(Sahayaraja
& Rajendran 2012).
Before immersion in pH-4 H2S04; b) After one day immersion in pH-4 H2S04 c) After one day immersion in pH-4 H2S04+TU (100
ppm) + Zn2+ (25 ppm)+ L-Phenylalanine (250 ppm)
Fig. (4): 2D AFM Images and Topography of the MS Surface
Table 4: AFM Data for MS Surface Immersed in Inhibited and Uninhibited Environment
Sample RMS (Rq)
Roughness (nm)
Average
Roughness (Ra) (nm)
Maximum peak-to-valley
Height (P-V) (nm)
Polished MS 26.351 21.327 129.754
MS immersed in H2SO4solution at pH-4 152 132 584
MS immersed in H2SO4 solution at pH-4 containing 100
ppm of Thiourea and 25 ppm of Zn2+ + 250 ppm of L-
Phenylalanine
27.572 21.058 133.520
Synergistic Effect of Thiourea-Zn2+ and L-Phenylalanine
77
CONCLUSIONS
The inhibitor formulation containing 25 ppm Zn2+, 100 ppm
TU and 250 ppm L-Phenylalanine showed 95 % inhibition
mixed inhibitor while AC impedance spectra recognized the
exposed the presence of Fe2+-TU, Fe2+-L-Phenylalanine
complex and Zn(OH)2
out the protective layer formation on the metal surface and
its surface methodology.
ACKNOWLEDGEMENTS
The author is thankful to the management for the support
of research work.
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78
Neuro-Statistic Model
Satyendra Nath Mandal1, Sanket Dan1, Pritam Ghosh11, Kunal Roy1,
Kaushik Mukherjee1 , Dilip Kumar Hajra2 and Santanu Banik3
1Kalyani Government Engineering College, Kalyani, Nadia (W.B)–741235
2Department of Agronomy, Faculty of Agriculture, UBKV, Pundibari, Cooch Behar, WB–736165
3ICAR-National Research Centre on Pig, Rani, Guwahati, Assam–781131
E-mail: 1satyen_kgec@rediffmail.com
Abstract—
The model consists of four sub modules which work together as a layered structure. We captured multiple individual
pig images using hue based segmentation algorithm and then calculated the statistical properties like entropy, standard
deviation, variance, mean, median, mode and color properties like H.S.V from the content of the individual segmented
images. We fed all the extracted properties into Neural Network for Pig Breed (NNPB) to perform pig breed prediction with
predict the breed of 50 pig images and achieved the prediction accuracy of 90%.
Keywords:
INTRODUCTION
and there is no unique method that provides a robust and
sets. The ANN uses many neurons which are computational
units, connected with weighted links, inputs and biases.
The neurons are producing output through activation
functions and are transmitting it to the neurons at the
into the network as input. The pattern will be processed
through the network and produce some output. The error
produced output. The training is to be performed to reduce
error by adjusting weights of links associated layers and
input patters and their assigned classes for tuning the
hyperparameters. Finally, the model will be applied on
computer as 2D matrix of pixels and each pixel is fed into
is equal to the product of rows and column of 2D matrix.
The same numbers of weights are also associated for input
connections from input to a single neuron in the hidden
layer. Generally, the fully connected neural network is used
neurons and the number of neurons in the problem space is
dependent on number of input parameters. The number of
weights in fully connected layer with 1000 neurons for image
is something like 150M for one layer. That is why the image
Science and Technology Journal Vol. 7 Issue: 2 July 2019 ISSN: 2321-3388
Pig Breeds Classification using Neuro-Statistic Model
79
have used pre-trained model for classifying their images
in transfer learning fashion where the features extraction
part remains unchanged. They do not modify the feature
extractions part and features of existing models to classify
ways to classify the images based on the features extracted
from its content. The aim of this paper is to develop pig
of the content of breed images and their color components.
Mali and Duroc were captured from three organized pig
farms. An arrangement was created at the time of capturing
pigs to make the uniform background for all the pig images.
captured by mobile phones with 10MP cameras. The 50 pigs
were captured from each pig breed. We have segmented the
pig images using hue based segmentation algorithms by
us. The captured images are stored in computer memory
as 2D matrix of pixels. We have extracted the statistical
properties of the content of images like mean, median,
mode, standard deviation, variance and color components
like H, S and V values, from each segmented breed images.
All computed statistical properties are fed into neural
network and trained using supervised learning for mapping
statistical properties with breed classes. We have developed
which takes statistical parameters and color components as
inputs and produces a single numeric value as output which
module. Out of 50 images for each breed, we have used 30
images for training the network, 10 for validating it and
Matlab 2016 for extracting the values of the statistical
parameters, color components and neural network. Finally,
we used the trained model to predict the breed of 50 pig
images and achieved the prediction accuracy of 90%.
This type of work has not been done before in pig breeds
The rest of the paper is organized into 7 sections excluding
Section 1 which is Introduction. Section 2 is about the
related works relevant to this paper. Section 3 describes the
theoretical background needed for this paper. Section 4 & 5
explains the methods, tools and dataset used in this paper.
Section 6 showcases the details of the results obtained and
its implication. Section 7 consists of the concluding remarks
and lastly the acknowledgements are given in Section 8
followed by the references.
RELATED WORK
Extraction and analysis of statistical features from images
have been studied for a long time. Measuring statistical
properties of pixel pairs at several distances in the image
has been used for texture analysis (Wu & Chen, 1992). Image
calculated from digital images (Kahou & Sulema, 2010) and
Malhotra, & Singh, 2018) have been attempted. It is very
important to measure the effect of such statistical measures
on digital image processing (Kumar & Gupta, 2012)
because it can be used to classify normal and abnormal
mammograms (Youssef, Rabeh, Zbitou, & Belaguid, 2014).
features include content based image retrieval (Thilagam
& Arunesh, 2019) characterize images based on brightness
distribution (Al-Ani & Alheeti, 2017) which describes the
lighting condition in any image.
Various statistical parameters have been used to differentiate
Naz, 2015).
Surface texture analysis using statistical-texture-features
like surface morphology has been used to differentiate
between defective and non-defective drug tablets based on
external factors like temperature, humidity and moisture
(Tahir & Fahiem, 2014). Thus it is seen that external
factors can affect the statistical features of an image. With
occupational stress analysis and prediction (Pandey, Saxena,
& Bhatt, 2015) and prediction of inside air temperature of
pillar coolers based on parameters like outside temperature,
watering and airing (Islam & Morimoto, 2017). Also animal
networks has also been established (Trnovszký, Kamencay,
establishes that CNNs are better than SVMs with respect to
Mandal, Dan, Ghosh, Mustafi, Roy, Mukherjee, Hajra and Banik
80
ARTIFICIAL NEURAL NETWORK ,
STATISTICAL PARAMETERS AND IMAGE
CLASSIFICATION
processing system which resembles in characteristics with a
biological neural network. ANN’s possess large number of
highly interconnected processing elements called neurons.
Each neuron is connected with each other by a connection
link. Each connection link is associated with a weight
which contains information about the input signal. This
information is used by the neural net to solve a particular
problem. The neuron computes the weighted sum of all
inputs along with their connecting weights and biases. The
result is fed into activation function which is fed into next
neurons of the next layer. Finally, the output is produced
from output layers. The supervised learning is used in
target and actual output produced by network based on
inputs and weights. The error is reduced by updating the
weights of connections among the layers known as training
the network. The training is an iterative process and the
iteration is stopped either when the maximum value is
reached or the error reaches a steady state for successive
iterations. The basic structure of a neural network model is
depicted in Figure 1.
Network
In every iteration the weights of the connections associated
with inputs and layers are updated until stopping conditions
are reached. The connection weights are updated by the
guidance of training functions. In this paper, Levenberg-
Marquardt algorithm (trainlm) training algorithm is used to
update weights of connections. The Levenberg-Marquardt
algorithm uses the approximation to the Hessian matrix.
The Hessian matrix can be approximated as:
And the gradient can be computed as:
Where
of the network errors with respect to the weights and biases,
and is a vector of network errors.
When the scalar is zero, this is just Newton’s method,
using the approximate Hessian matrix. When is large, this
becomes gradient descent with a small step size.
The various statistical measurements on content and color
gray scale images are stored as 2D matrix in memory. The
details of parameters are furnished in this paper.
In RGB color model, red, green and blue lights are mixed
together in various ratios to reproduce a broad array of colors
(Program to Change RGB color model to HSV color model).
The artists are used HSV (hue, saturation, value) color model
for thinking about a color in terms of hue and saturation and
values. The transformation from RGB to HSV as follows:
Pig Breeds Classification using Neuro-Statistic Model
81
Algorithm RGB to HSV color Model
Input RGB color Model
Output H,S and V values computed from RGB
Method
Begin
Step 1
Divide r, g, b by 255:
Step 2
Compute
:
Step 3
Hue calculation:
if and
equal 0, then
if equal r, then compute
if equal g, then compute
if equal b, then compute
Step 4
Saturation computation:
if equal 0, then
if does not equal 0, then compute
Step 5
Value computation :
Step 6
Return h, s and v
End
Entropy of Image
Entropy is the statistical measurement of randomness and
it may be used for characterization the texture of the input
(1)
where is the number of gray levels (256 for 8-bit images),
is the probability of a pixel having gray level , and is the
base of the logarithm function.
Standard Deviation
The unbiased estimate of the standard deviation, of
the brightness’s within a region (
called the sample standard deviation (Image Processing
Fundamentals) and is given by:
(2)
the sample mean of the pixel intensities or brightness of
image (Garg, M., Malhotra, M., & Singh, H. 2018) ). The mean,
(3)
(4)
(5)
(6)
Where g is the gray value or intensity of pixel at location (x,
y), r and c are row and column of image size W.
Mandal, Dan, Ghosh, Mustafi, Roy, Mukherjee, Hajra and Banik
82
Variance of Image
numbers, X1 – XN, would be written like this:
(7)
distances of each term in the distribution from the mean
(Variance & Standard Deviation).
If the pixels are arranged in order either in ascending or
descending of their intensities values, the middle value is
call median. The image is containing n pixels, the median is
written as:
Or
(8)
The mode is the intensity value of pixels of an image which
occurs with largest frequencies.
PROPOSED MODEL AND MODULES
model comprising of a number of sub models in layered
from organized pig farms using some restrictions. We
have segmented the individual pig images and extracted
statistical parameter values and H, S, V color components
from the segmented pig images. We have developed the
values of statistical parameters and color components with
is used to categorize the output of NNBP module and
the breed of pig is declared based on values of statistical
parameters and color components of the input pig image
as shown in Figure 2.
We converted the captured images into equal size. Every
pixel of an image was converted to HSV color model.
The Hue was determined by keeping the Saturation and
Value constant for each breed of pig taken in the study by
excluding background of a photo. The HSV image was then
converted to binary image with the help of calculated Hue.
In the binary image, the visible parts of the pig in original
picture turned to white while the background turned to
black. The black and white binary image thus obtained
might contain some white blobs. The areas of white blobs
were measured. Except largest blob, all others blobs were
inverted to black to grow a mask image. The obtained mask
image was overlapped with original RGB image to get the
segmented image of the pig as shown in Figure 3.
Fig. 3: Hue Calculation and Segmentation of Pig
To classify breed from pig images, feed forward neural
relationship. Ten (10) parameter values like hue, saturation,
value, entropy, standard deviation, mean, median, mode,
sum and variance are calculated from the content of each
parameter values are then fed into the network’s input layer.
One hidden layer with ten neurons and the output layer with
one neuron are used in this network. The tangent sigmoid
transfer function (tansig) is used at hidden layer and linear
transfer function is used at output layer. The Levenberg-
Marquardt algorithm (trainlm) training algorithm is used to
update weights of connections and maximum iteration is set
as 1000 as shown in Figure 4.
Pig Breeds Classification using Neuro-Statistic Model
83
The supervised neural network is used in this paper. The pig
breeds and their class assignments are given in Table 1.
Table 1: Pig Breed and Assigned Class
Sl. No Pig Breed Assigned Class
1 Yorkshire 1
2 Duroc 2
3 Ghungroo 3
4 Hampshire 4
5 Mali 5
Algorithm Class assignment
Input Output of NNPB module
Output Class of pig Image
Method Begin
If
Output of NNPB module
Then Pig Image is in Class 1
Else If
Output of NNPB module && Output
of NNPB module
Then Pig Image is in Class 2
Else If
Output of NNPB module && Output
of NNPB module
Then Pig Image is in Class 3
Else If
Output of NNPB module
& Output of NNPB module
Then Pig Image is in Class 4
Else
Pig Image is in Class 5
End
The output of network produces numeric values. The
follows.
CAPTURING PIG IMAGES AND CREATING
PIG BREED IMAGE DATABASE
The images of pigs were captured in such a way that right or
left side of their body was clearly visible is shown in Figure 5.
Fig. 5: Capturing the Pig Images
The steps were imposed for capturing the pigs as follows:
1. One green color curtain was placed over standing
place and three sides of the pigs to have uniform
background.
2. The camera was placed in one side parallel to pigs
and keeping about 2 meters distance from it.
3. Lens of camera was hold in a position which was
approximately in the middle of the length of the
animal perpendicular to the median sagittal plane.
4.
animal to snapshoot the entire visible parts of a pig.
5. The photos were captured using both smart phones’
camera and DSLR camera under natural light in site
Ghungroo, Mali, Hampshire, Duroc, and Yorkshire (Figure 6)
were taken for study. To make use of breeds which are pure,
Mandal, Dan, Ghosh, Mustafi, Roy, Mukherjee, Hajra and Banik
84
images of individual animals from the said breeds were
collected from organized farms, maintained by the premier
research institutes of India, namely; 1) ICAR National
Research Centre on pig, Rani, Assam, 2) ICAR Research
Complex for NEH Region, Umiam, Meghalaya and 3) ICAR
Research Complex for NEH Region, Tripura Centre, Tripura.
Fig. 6: Images of Five Pig Breeds with Uniform
Background
RESULT AND DISCUSSION
assigns class for input images. The result of each module is
given separately as follows.
named Yorkshire, Hampshire, Duroc, Ghungroo and Mali are
used in this paper. All images are fed into the segmentation
module and segmented image are produced. The segmented
images are used in next layer as inputs. One sample
segmented image from each pig breed is shown in Figure 7.
Fig. 7: Segmented Pig Images of Five Pig Breeds
In Figure 7, the background of all images is same and the site
not create any problem at the time of extracting statistical
and color components from segmented images.
The segmented images from segmentation module are
fed into Statistical components and color components
retrieval module. The H,S and V color component values
are computed using RGB to HSV conversion algorithm.
The Entropy, Standard deviation Mean, Median, Mode and
numerically as shown in Table 1. The total 250 pigs from
5 pig breeds used in this experiment and ten parameters
values from each segmented pig image are extracted. The
outputs of this module are furnished as demo tabular form
in Table 2. In table 2, extracted data from two segmented pig
images from each breed are given and their corresponding
breed and class values are furnished.
The extracted data from statistical Parameters and Color
Component Retrieval modules as furnished in Table 2 is
fed into “Neural Network Pig Breed” neural network. The
ten (10) parameter values named hue, saturation, value,
entropy, standard deviation, mean, median, mode, sum
and variance from each segmented image are used as
input and the network produces its class value which is
shown in Table 3.
The NNPB network is simulated using Matlab 2016. The
NNPB module produced accuracy as 99.89%, 99.66% and
97.54% in training, validation and test respectively. The best
performance, regression error plot, Error histogram and
training state of NNPB are shown from Figure 8 to Figure
11. The performance of network is shown in Figure 8. It is
observed that the MSE error is decreased due to decrease
in weights during training by Levenberg-Marquardt
backpropagation training algorithm. The blue, green and red
lines are shown the MSE for the test, validation and training
set, respectively. The best performance of validation is
shown at epoch 10 where error is 0.093015. The training of
network is stopped at 16 epochs when the validation error
reached a steady-state. The regression error plot is given at
Figure 9. The data points are represented by circles and the
during training, validation and testing. The average R value
for training, validation and testing is 0.98783 ( )
Pig Breeds Classification using Neuro-Statistic Model
85
demonstrates the distribution of errors with the training,
test and validation dataset. It is found that the maximum
instances of MSE (around 93) are distributed close to zero
line, which is shown by the orange line. Figure 11 depicts the
training state of the network up to the moment of stopping.
The gradient and µ values are 0.13285 and 0.00001,
respectively. It is shown that validation check at epoch 16
has few validation failures.
0246 8 10 12 14 16
16 Epochs
10
-3
10
-2
10
-1
10
0
10
1
Mean Squared Error (mse)
Best Validation Performance is 0.093015 at epoch 10
Train
Validation
Test
Best
Fig. 8: Performance of Neural Network
12345
Target
1
2
3
4
5
Output ~= 1*Target + 0.002
Training: R=0.99896
Data
Fit
Y = T
12345
Target
1
2
3
4
5
Output ~= 0.94*Target + 0.17
Validation: R=0.97664
Data
Fit
Y = T
12345
Target
1
2
3
4
5
Output ~= 0.83*Target + 0.63
Test: R=0.97546
Data
Fit
Y = T
12345
Target
1
2
3
4
5
Output ~= 0.95*Target + 0.16
All: R=0.98783
Data
Fit
Y = T
Fig. 9: Training, Test and validation for NNPB
0
20
40
60
80
100
Instances
Error Histogram with 20 Bins
-1.182
-1.094
-1.006
-0.918
-0.8302
-0.7424
-0.6545
-0.5667
-0.4789
-0.391
-0.3032
-0.2154
-0.1275
-0.03969
0.04815
0.136
0.2238
0.3116
0.3995
0.4873
Errors = Targets - Outputs
Training
Validation
Test
Zero Error
Fig. 10: Network Error Histogram
10
-2
10
0
10
2
gradient
Gradient = 0.13285, at epoch 16
10
-5
10
-4
10
-3
mu
Mu = 1e-05, at epoch 16
0246 8 10 12 14 16
16 Epochs
0
5
10
val fail
Validation Checks = 6, at epoch 16
Fig. 11: Network Training State
at section 4.5. 50 images are used to test the trained NNPB
test the network. The overall accuracy of proposed model
is 90% is furnished in Table 4 and graphically represented
in Figure 12. The breed wise accuracy graph is given in
Figure 13.
Mandal, Dan, Ghosh, Mustafi, Roy, Mukherjee, Hajra and Banik
86
Sl. No Pig Breed No. of Test
Images
No. of
Classified
Successfully
Accuracy
(%)
01 Yorkshire 10 07 70
02 Duroc 10 08 80
03 Ghungroo 10 10 100
04 Hampshire 10 10 100
05 Mali 10 10 100
Overall 90
CONCLUSION AND FUTURE WORK
of statistical parameters extracted from the content of its
at organized pig farms using mobile phones. The Levenberg-
Marquardt back propagation training algorithm with a
minimum mean squared error and maximum correlation
model is used to predict the breed of 50 pig images and the
Table 3: Statistical Parameter Values
Sl.No Image ID H S V Entropy Std Mean Sum Median Mode Variance Breed Class Value
1 G1 0.152 0.107 0.074 2.374 24.748 16.342 1863.030 0.000 1.308 6519.171 Ghungroo 3
2 G2 0.145 0.105 0.074 2.290 24.628 16.313 1859.722 0.000 1.278 6635.538 Ghungroo 3
3 Y1 0.336 0.253 0.255 4.132 56.091 53.229 6068.157 0.096 31.222 9582.595 Yorkshire 1
4 Y2 0.278 0.236 0.203 3.516 55.982 41.956 4782.939 0.025 16.606 8683.902 Yorkshire 1
5 M1 0.142 0.062 0.141 3.599 45.877 35.085 3999.646 0.000 10.606 4572.518 Mali 5
6 M2 0.104 0.047 0.112 3.035 35.341 28.077 3200.798 0.000 5.409 4694.001 Mali 5
7 H1 0.224 0.230 0.159 4.112 36.803 33.704 3842.253 0.040 19.449 10009.343 Hampshire 4
8 H2 0.191 0.196 0.153 4.120 36.020 32.382 3691.535 0.010 18.854 9453.684 Hampshire 4
9 D1 0.073 0.259 0.259 4.092 56.073 52.393 5972.768 0.010 33.924 9889.457 Duroc 2
: : : : : : : : : : : : :
: : : : : : : : : : : : : :
250 D2 0.078 0.305 0.265 4.226 58.881 52.341 5966.879 0.040 27.641 8910.063 Duroc 2
Pig Breeds Classification using Neuro-Statistic Model
87
Table 4: Input, Target and Actual Output of NNPB Network
Sl.No Image ID H S V Entropy Std Mean Sum Median Mode Variance Target
output
Actual
Output
1 G1 0.152 0.107 0.074 2.374 24.748 16.342 1863.030 0.000 1.308 6519.171 3 3.0019
2 G2 0.145 0.105 0.074 2.290 24.628 16.313 1859.722 0.000 1.278 6635.538 3 2.9784
3 Y1 0.336 0.253 0.255 4.132 56.091 53.229 6068.157 0.096 31.222 9582.595 1 0.9932
4 Y2 0.278 0.236 0.203 3.516 55.982 41.956 4782.939 0.025 16.606 8683.902 1 0.9918
5 M1 0.142 0.062 0.141 3.599 45.877 35.085 3999.646 0.000 10.606 4572.518 5 4.9857
6 M2 0.104 0.047 0.112 3.035 35.341 28.077 3200.798 0.000 5.409 4694.001 5 5.0225
7 H1 0.224 0.230 0.159 4.112 36.803 33.704 3842.253 0.040 19.449 10009.343 4 4.0037
8 H2 0.191 0.196 0.153 4.120 36.020 32.382 3691.535 0.010 18.854 9453.684 4 3.9346
9 D1 0.073 0.259 0.259 4.092 56.073 52.393 5972.768 0.010 33.924 9889.457 2 1.9852
: : : : : : : : : : : ; : :
: : : : : : : : : : : :
250 D2 0.078 0.305 0.265 4.226 58.881 52.341 5966.879 0.040 27.641 8910.063 2 1.8660
prediction accuracy is 90%. Therefore, proposed model can
individual pig images. The images from more pig breeds will
will be used in future.
ACKNOWLEDGEMENTS
The authors would like to thank ITRA (Digital India
Corporation, formerly Medialab Asia), MeitY, Govt. of
India, for funding; Dr. Amitabha Bandyopadhyay, Senior
Consultant, ITRA Ag & Food for valuable suggestions;
ICAR National Research Centre on pig, Rani, Assam, ICAR
Research Complex for NEH Region, Umiam, Meghalaya and
ICAR Research Complex for NEH Region, Tripura Centre,
Tripura for permitting us to access their organized pig farms
and Dr. Sourabh Kumar Das, Principal, Kalyani Government
Engineering College for his continuous support.
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89
Fuzzy Logic: An Easiest Technique to
Predict Celiac Disease
Sunny Thukral1* and Jatinder Singh Bal2
1,2Sant Baba Bhag Singh University, Jalandhar–144030, India
E-mail *sunnythukral@davcollegeasr.org
Abstract—The need for the proposed system mounts due to expensive clinical cost, the prolonged period of Genetic testing
and especially painful for an individual to perform all certain clinical tests to diagnose celiac disease. With this proposed
method, an individual can foretell celiac disease by just input crisp values of varied symptoms using fuzzy logic. A case study
was conducted using a questionnaire procedure to obtain out the requisite symptoms in Amritsar, Punjab on 700 individuals;
having 303 females and 393 males. So, the proposed system will be implemented using Mamdani Model and forms the
proposed system will have a disease prediction of 96.11% accuracy according to the input values given by an individual to
authenticate the celiac disease. The proposed system will provide a fruitful outcome for individuals and physicians for celiac
disease disclosure in few seconds without any painful testing strategy.
Keywords: Abdominal Pain, Anemia, Celiac Disease, Fuzzy Logic, PyCharm
Science and Technology Journal Vol. 7 Issue: 2 July 2019 ISSN: 2321-3388
INTRODUCTION
Several diseases have already been foretold with the fuzzy
approach. Fuzzy logic deals with the multi-value approach
which consists of complex crisp values according to input
and output parameters. With the aid of a fuzzy inference
system, the intact database that is in the form of fuzzy
depends upon the kind of application.
Fig. 1: Fuzzy System Architecture
Fuzzy logic is a type of technique to tackle the problem of
uncertainty and ambiguity of the data. The input given to
the system is based on the crisp values which should be
formulated to fuzzy values depends upon the membership
of fuzzy values to convert them into fuzzy if-then rules
using fuzzy database refers to fuzzy inference system.
The evaluation process needs to de-fuzzify, fuzzy values
to crisp outputs to generate single output value for the
system. Celiac disease can be prophesied using fuzzy rules
that should be accessible for individuals and physicians for
recognizing the disease. Celiac disease is a class of chronic
disease that vitiates the small intestine when the celiac
patient engrosses gluten in the body. Gluten stays in wheat,
barley, rye, etc. From the viewpoint of (Bascunun 2017),
(Gee 1888) a few mm of gluten can be taken but more
than that will harm small intestine ending in gut damage.
(Dowd 1974) stated that the celiac disease can transpire
at any age i.e. to a newborn baby or an old person. Marsh
IgA, Biopsy, Genetic testing, Gliadin clinical tests, etc. There
are very rare peoples who aware of celiac disease due to
similar symptoms with other diseases given by (Murry
2004). In additional words, the prescription of celiac
disease does not come straight-forward because diarrhea,
vomiting and stomach pain is very normal in other regular
chronic diseases as these three are the basic symptoms of
celiac disease as recommended by (Husby 2012) based on
ESPGHAN guidelines. So, celiac patients require gluten-free
nutrition to handle any type of intestine predicament from
the disease. The role of the primary consumption of gluten
of genetic disease that comes from the parental background.
Thukral and Bal
90
LITERATURE REVIEW ON FUZZY LOGIC
AND CELIAC DISEASE
Fuzzy logic is consistently used in recognizing chronic
has been accomplished with different fuzzy parameters
using the Mamdani model by (Moein 2009) and team
members using the Netbox toolbox with 100% accuracy
of the system. Chronic diseases having a genetic approach
have been detected using the pattern recognition routine
with fuzzy logic and neural network in 2011 by (Adeli 2011)
with fetched samples from the UCI repository. The role of
hybrid fuzzy logic originates with dental disease detection
applying all major parameters of dental disease by (Parewe
2016) with accuracy achieved 82% . Cholera diagnosis using
fuzzy logic has been offered by Uduak and Mfon (2013)
with MATLAB tool to prophesy the disease.Liver diagnosis
has been reported using fuzzy by Hashmi and Khan (2015)
with three diverse types of liver diseases been realized by
the fuzzy system. (Kaur 2016) proposed viral infection
recognition using fuzzy logic with six different parameters
to automate the system. The symptomatic study has been
characterized by (Manikandan 2017) with the prediction
of lung diseases using neural network and fuzzy logic
with 95% accuracy of the fuzzy system. Recently (Zarandi
2018) aimed a fuzzy expert system for diagnosing kidney
disease. The study was conveyed in Iran on 400 specimens
to diagnose chronic kidney or non-chronic disease patients
having 80% accuracy. Colorectal cancer detection was
presented by (Chowdhury 2018) using a fuzzy logic
approach. The implementation has been executed on Matlab
with three input parameters to detect bowel cancer. On the
additional side, Celiac disease has been wholly addressed
by (Sood 2006) to unearth out the prevalence ratio of celiac
disease on school children in Punjab. 1:310 prevalence
consequences were proclaimed with an entirety conducted
case study on 4347 children. Another related authenticity
was proffered by (Makharia 2011) to commemorate the
prevalence value in the northern region of India. More than
10,000 individuals were randomly selected for the diagnosis
process of celiac disease. 1:96 prevalence outcomes were
produced with 31 celiac patients.
STUDY CONDUCTED IN PUNJAB
The erudition of celiac or non-celiac selves has been
assembled via Celiac Disease Awareness Camp organized
in Amritsar, Punjab where 700 persons involved in the
given camp. The data was collected through questionnaire
technique with all possible symptoms. Persons that visited
in the camp came from diverse sectors of Punjab including
Ajnala, Amritsar, Baba-Bakala, Beas, Gurdaspur, Taran-Taran
and their surroundings with distinct ages. The graphical
depiction was executed in SPSS software for presenting
frequency distribution with age-group as shown in Figure
2. The survey consists of data set with every gastro-
intestinal symptom present in almost all celiac patients. The
questionnaire also consists of height, weight, age, gender,
Body Mass Index (BMI) with necessary family background
necessary symptoms for the proposed system.
Fig. 2: Age-Group Description
Out of 700 persons, 303 and 393 persons proclaimed
Females and Males respectively. Most of the individuals in
Punjab are still unaware of Celiac disease because of the
association of symptoms among other well-known diseases.
The consequence of the camp was 134 celiac patients out
of which 75 Females and 59 Males were chronicled and the
recommendation was not to use any gluten products in the
diet shown in the given Figure 3. The outcome from the
study conducted in Punjab, indicates that the female celiac
ratio is too much in comparison with male correspondents.
The symptoms found in almost all celiac patients matched
with equivocal symptoms with every celiac patient. Due to
this common behavior, it is easy to think about to develop
fuzzy system which can predict the fuzzy system based on
similar symptoms among them.
Fig. 3: Gender Wise Report
So, in other words, 56% of female representation appears
real while screening celiac records. The overall evaluation
Fuzzy Logic: An Easiest Technique to Predict Celiac Disease
91
concerning celiac disease principally comes to females
through the world endures truly from the survey. It
is manifest from the results that the replies of female
participants (75) carrying celiac disease outnumbered male
respondents (59).
PROPOSED SYSTEM IMPLEMENTATION
The proposed system implemented with numerous
parameters among them Body Mass Index (BMI) considered
to be an essential parameter with abdominal pain in
Python. Mostly strengthened celiac patients have Body
Mass Index (BMI) either Underweight or Overweight as
contrasted to healthy individuals. Figure 4 represents all
the basic six input parametrs given to the proposed fuzzy
system with appropriate range of symptoms retrieved form
the survey. The selection of symptoms for the fuzzy sytem
parameters. Every antecedent refers to fuzzy-if response
Fig. 4: Celiac Input Parameters
The representation in fuzzy graph with one of the
membership function with range according to weekly
memebership value of the variable as fuzzy input.
Fig. 5: Vomiting Fuzzy Graph
The system implemented with python using multiple
crisp values to every input parameter whereas the disease
prediction output parameter consists of eleven crisp values
for the apprehension of celiac disease. The triangular
membership function was applied to develop fuzzy system
according to its range which differs with every symptom. The
values vary according to symptom due to varied level in the
format of daily, weekly or hourly report. The representation
values as once in a week, thrice in a week, and daily input
with unique membership range. Similarly, every fuzzy input
with distinct techniques.
( )
( )
=≤≤
x<0,x>4
00x4
4-x / where a = 0, b = 4 and m = 0
4
ow
( ) ( )
( )
=
x<1, x>9
µ x x-1 /4 1<x<5 where a=1, b=9 and m=5
thw 5<x <9
9 - x / 4
0
( )
( )
x<6, x>10
µ x where a = 6, b = 10 and m = 10
daily x-6 /4 6
=<x<10
0
( )
( )
x<6, x>10
µ x where a = 6, b = 10 and m = 10
daily x-6 /4 6
=<x<10
0
using three different range as underweight, normal and
overweight in the view of fuzzy graph. The representation of
the crisp input parameter Body Mass Index (BMI) described
in Figure 6.
Fig. 6: Body Mass Index (BMI) Fuzzy Graph
The proposed system will have 486 fuzzy database rules
that have been formulated based on the symptoms given in
Thukral and Bal
92
ESPHAGN guidelines and existing studies in the world.
( ) ( )
x < 0, x > 22
µ x = whe re a = 0, b = 22 and m = 0
uw 22 - x /22 0 < x < 22
0
( ) ( )
( )
x<1, x>9
µ x = x-1 /4 1< x<5 where a =1, b=9 and m=5
thw 5<x <9
9 - x / 4
0
( ) ( )
0x < 6, x > 10
µ x where a = 6, b = 10 andm = 10
daily x - 6 / 4 6 < x < 10
=
Other parameters used in the proposed system as
abdominal pain with mild, moderate and severe values.
Abdominal pain generates in the celiac patients whenever
any celiac patient consumes gluten in the diet. The range
of abdominal pain depends upon domain which should be
required to feed the data in the fuzzy system as crisp inputs.
lack of blood in the body as another vital symptom for the
predictive system. The range is as similar to the abdominal
pain intensity with fuzzy crisp inputs. Diarrhea parameter
is associated as intensity with never, once in a week, thrice
in a week and on daily basis data. The range of the diarrhea
parameter is 0-10 for the fuzzy system. Weight loss act as
crucial parameter for the fuzzy system when any individual
loss their weight abruptly in few months with values are
underweight and static or underweight and losing.
RESULTS AND DISCUSSIONS
The rule viewer of the prediction of celiac disease produced
output by differentiating 486 rules from the fuzzy engine
using Mamdani Model given by (Mamdani 1975). From the
given input, 8 different kinds of rules matched and comprise
crisp input, when compared with its value, produces two
indices that lie in different precincts to estimate a single
any individual submit the symptomatic value into the
proposed fuzzy system. The inputs given by the individual
(BMI) evaluator has already been evaluated using PyCharm
software based on Python Language. The system also
display every single symptom plot chart based on the input
given by any user. The bar chart slider toolbar also provided
Fig. 7: Celiac Disease Input Symptoms with Crisp Inputs
The celiac disease system produces an output of 96.11%
with graphical layout. It also recommends the clinical
the symptomatic data given by one of the celiac patient.
In traditional approach, the similar process was evaluated
rules that lie among the given input matched with all set
same fuzzy input given to the fuzzy system. The evaluation
the membership function either trapezoidal or sigmoid
function. Every given matched rules from the entire fuzzy
database is required to be recorded to compare with the
fuzzy input. The fuzzy inference system check every stored
fuzzy rule in the database crosscheck with the fuzzy input an
compute single value that should be accumulated together
Fuzzy Logic: An Easiest Technique to Predict Celiac Disease
93
whereas the upshot of the proposed system will have a
disease prediction of 96.11% accuracy according to the
input values given by an individual to authenticate the celiac
disease.
# If the membership function is a singleton fuzzy set:
if len(x) == 1:
return
# else return the sum of moment*area/sum of area
for i in range(1, len(x)):
x1 = x[i - 1]
x2 = x[i]
y1 = mfx[i - 1]
y2 = mfx[i]
if not(y1 == y2 == 0.0 or x1 == x2):
if y1 == y2: # rectangle
moment = 0.5 * (x1 + x2)
area = (x2 - x1) * y1
elif y1 == 0.0 and y2 != 0.0: # triangle, height y2
moment = 2.0 / 3.0 * (x2-x1) + x1
area = 0.5 * (x2 - x1) * y2
elif y2 == 0.0 and y1 != 0.0: # triangle, height y1
moment = 1.0 / 3.0 * (x2 - x1) + x1
area = 0.5 * (x2 - x1) * y1
else:
moment = (2.0 / 3.0 * (x2-x1) * (y2 + 0.5*y1)) / (y1+y2) + x1
area = 0.5 * (x2 - x1) * (y1 + y2)
sum_moment_area += moment * area
sum_area += area
return sum_moment_area / np.fmax(sum_area,
The code from the python is described with one module
describes sum of area method used in the proposed fuzzy
system to compute its probable value. Similar types of
different modules can be fetched from the fuzzy system
including import external libraries as numpy, tkinter and
skfuzzy. It indicates that the system when compared with
outcome in terms of probability with lesser time to evaluate.
Fig. 9: Fuzzy Celiac Output with Re-commendation
The input given is in the framework of fuzzy system based on
symptoms with the given scenario comes out to be 96.11%
whereas the traditional method of producing similar
shows the accuracy of the proposed system in a better way
as depicted in Figure 9. So, the proposed system produces
better outcome in terms of accuracy in comparison with
(Thukral 2019).
CONCLUSION
The proposed system yielded excellent output accuracy of
procedure of 95%. The collected set of data from the camp
illuminates that female ratio carrying celiac disease is
much more as correlated to males as depicted in ESPHAGN
guidelines. The awareness level of celiac disease in the
world is very miniature as received in the collected data.
The evaluation of the system without python by practicing
inputs supplied in the form of symptoms. Few parameters
like abdominal pain and varied Body Mass Index (BMI)
having vital inputs for the system as it seems optimum
matched with all existing celiac patients. The projected
outline is to increase some parameters by reconstructing
fuzzy rules or by using distinct tools to appraise the accuracy
celiac disease.
CONFLICTS OF INTEREST
ACKNOWLEDGMENT
Author S. Thukral is grateful to Principal, DAV College,
Amritsar for his kind support and for providing necessary
facilities to carry out this work.
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Publishing 648: 323-327. doi:10.1007/978-3-319-67137-
6-37
95
Hydrophobicity Character -lactalbumin
Nanoparticles: An Ultrasonic Study
K. Kavitha1 and L. Palaniappan2*
1,2Department of Physics,
Annamalai University, Chidambaram–608 002, TN, India
E-mail: *lp_dde_au@yahoo.com
Abstract—Effect of pH and cosolvent on the stabilization of protein structure is a well established study in protein or
food science. Among the various applications of proteins, the use of protein nanoparticle as drug or bioactive compound
carriers is the one which is of most interest to many diverse researchers. The synthesis of such protein nanoparticles and
their characterization is of prior requirement for the realization of these drug or bioactive carriers. On this basis, the
present work deals with the ultrasonic analysis of hydrophobic interactions exhibited by the α-lactalbumin nanoparticle
synthesized by heat treatment using acetone as desolvating agent. In order to enrich the variations in hydrophobicity, heat
or temperature and cosolvent (glucose) are included in the study. The results are interpreted in terms of the interactions
existing among the components and the evolved discussions reveal the bulk nature of the medium is controlled by the
existing hydrophobicity interactions. The obtained results indicate that the dependency of protein denaturation on heat
and the strengthening of non- covalent interactions by the cosolvent and/or the steric exclusion effect can be attributed to
Keywords: α-lactalbumin nanoparticle, glucose, Ultrasonic velocity, Viscosity, heat, hydrophobic interactions
INTRODUCTION
Proteins are the most abundant organic molecules of the
living system and are, among others, the macromolecules
that perform almost all important tasks in an organism,
called as the machinery of life. Proteins are formed by
joining amino acids by amide bonds into a stretched
chain. They differ in length (from 30 to over 30,000
amino acids), and in the arrangement of the amino acids
(Branden and Tooze 1991). It is a well known fact that the
protein three-dimensional structure determines protein
function. But the structure, otherwise called as ‘the fold’
variety of biological functions and are dynamic rather than
in sequence and solubility.
Solubility is an important criterion especially in food
route of administration. Over the last 10 years, nanoparticle
engineering processes have been developed and reported
for enhancement of solubility of poorly aqueous soluble
drugs and bioactive compounds. In this approach, poorly
water soluble compounds are formulated as nanometre
sized compound or drug particles (Kocbek et al. 2006).
According to Muller et al (2001), nanoparticles are solid
colloidal particles ranging in size from 1 to 1000 nm (1 µm).
Arroyo-Maya et al (2012) have analyzed, adopted various
techniques for the preparation of bovine α-lactalbumin
(α-LA) nanoparticles, successfully synthesized by various
methods and critically commented the relative merits and
demerits along with the particle size of the synthesized
nanoparticles. They have also made an extensive study on
the synthesized particles such as transmission electron
microscopy, size and distribution, molecular weight and
zeta potential, surface hydrophobicity, in-vitro digestibility,
antioxidant capacity, etc and concluded that the controlling
hydrophobic interactions are a means to control the size
of α-LA nanoparticles. Further they commented on the
two different pre-treatment processes, the heat and high
hydrostatic pressure on the size of the nanoparticles.
Science and Technology Journal Vol. 7 Issue: 2 July 2019 ISSN: 2321-3388
Kavitha and Palaniappan
96
The objective of the present work is to study the effect
of heat pre-treatment and cosolvent on the hydrophobic
interaction existing in the said system by using ultrasonic
the bulk nature of the system and is a well-established
approach for the study of molecular interactions (Swain
and Priyadarshini 2010; Taulier et al 2005; Palaniappan and
Nithyanandham 2019; Bahadur 2017). Further the effects of
trend of observed ultrasonic parameters.
MATERIALS AND METHODS
All the chemicals used are AR grade. 0.2 M aqueous
solutions of both monobasic and dibasic sodium phosphates
were mixed in different proportions to prepare phosphate
buffer of pH 9, marked as system B whereas the system B
+ G indicates the 1M solution of glucose prepared in same
phosphate buffer of pH 9 (Velusamy and Palaniappan 2016)
and both are used as solvent (re-dispersion agent) for the
synthesized α-LA nanoparticle.
Powdered α-LA from bovine milk purchased from Sigma
Aldrich is used for nanoparticle preparation. Nanoparticles
of bovine α-lactalbumin (α-LA) were prepared by
desolvation process using acetone as desolvating agent,
heat as pre-treatment, cross- linkage by glutaraldehyde
solution, followed by 5 cycles of centrifugation and then
Though the procedure (Arroyo-size of nanoparticle,
the present work has been done for three different
temperatures, viz., 333 K and 333±5 K in order to understand
the importance at/near that temperature. The obtained
pellets were re-dispersed in the above said solvents B and
B+G and are respectively called as B+L and B+G+L.
The DLS technique basically measures the apparent
hydrodynamic radius (or diameter) of the particles. This
technique is employed here as it can able to determine
as well as to sense the changes in size due to processing
(Alexander and Dalgleish 2006). The size measurements
were carried out using Zetasizer Nano ZS90 (Malvern
Instruments Ltd., Malvern, UK) at a scattering angle of 90o
at 298 K after dilution of the nanoparticles with deionised
water in the ratio of 1:400.
The pH of these solutions was measured by the digital pH
meter. After preparation, the stock solution was kept stored
at 293 K overnight. These solutions were then degassed and
each measurement was made after 20 minutes of thermal
equilibration (303±0.01 K).
The choice of glucose quantity to be 1 M is a safe
concentration that ensures that it will not rupture any of
the amino acid residues in the chain (Morrison and Boyd
1992)
parameters. Same way, protein concentration taken in the
present work (5 mg/ml), though extremely quite high in
terms of physiological values, it is worth to note that this is
needed to have good reliable ultrasonic values and the same
can be extended for biosensor application studies (Malhotra
et al 2017). Thus the present study is well planned to
analyze the situation in all possible dimensions with the
available ultrasonic data.
MEASUREMENTS
Measured parameters in the present work include ultrasound
η) and surface tension
(Lf), acoustic impedance (z), relaxation time (t), relative
Experiment was carried out in four different temperatures,
viz., the room temperature 303 K, the nanoparticle forming
temperature 333 K, ± 5 degree of nanoparticle forming
temperature i.e., 328 K and 338 K. In the entire study,
the temperature was controlled to ± 0.01 K by water
thermostatic bath provided by Ragaa Industries, Chennai,
India. At least six repeated reliable observations were made
for the measurement of each property and the reported
values correspond to the average of these six independent
measurements. The standard deviation of all the trials for
each property was found to be satisfactory (not shown
here).
accuracy in the measurement was about ±0.0001 kgm-3.
The ultrasonic velocity (u) in all experimental solutions
was measured by a single frequency (2 MHz) ultrasonic
interferometer (Mittal’s model F-81). The accuracy of sound
velocity was ±0.1 ms-1. The viscosity measurements were
done by relative method using Ostwald’s viscometer of 10
ml capacity, accurate to ± 0.001mNsm–2.
Surface tension values are obtained at 303 K by drop weight
method, using platinum- iridium Du Nouy ring, accurate
to ±0.0001 kg. Details of measurements, instruments
Hydrophobicity Character of α-lactalbumin Nanoparticles
97
and the procedures adopted are available in our earlier
work (Palaniappan and Velusamy 2004; Velusamy and
Palaniappan 2013).
CALCULATED PARAMETERS
The chosen thermo acoustical parameters are calculated
using the following standard relations (Velusamy and
Palaniappan 2013; Velusamy et al 2007; Vanathi et al 2019;
Ravichandran and Ramanathan 2010; Kadi et al 2006).
[ ]
[ ]
[ ]
1
2
1/2
2
1/3
0
0
00
0
0
0
4
3u p
/
1/
1
22
T
A
vp
kv
pu
Lf K
z up
t
p
T uu
p
p p Cp
p
up
b
b
η
ϕ
ϕbϕ
−
=
=
=
=
=
=+−
= −−
where KT is the temperature-dependent constant having a
uo and u are the ultrasonic velocities of the solvent and
solution respectiv
the adiabatic compressibility of the solvent and [u] is the
.
[ ] [ ]
00
/
p
u u u uC= − (8)
RESULTS AND DISCUSSION
Some of the salient characters of the synthesized
nanoparticles obtained from Zetasizer Nano ZS90 using
DLS are presented in Table 1. The measured parameters
for all four systems, viz., buffer (B), buffer + glucose (B+G),
buffer + lactalbumin (B+L) and buffer + glucose + lactalbumin
(B+G+L) at various temperatures are summarized in
Table 2, whereas Table 3 shows the calculated values of
parameters against temperatures are depicted in Fig 1 A to
1 C respectively.
Inspection of Table 1 reveals that the particles obtained are
well in nano region and are well distributed. Nanoparticles
without pre-treatment are found to be almost of same
size. However, pre-treatment offers a narrow dispersion of
particles, as shown by polydispersity index.
Table 1: Characters of Nano Particles using DLS Study
Pre-treatment Hydrodynamic
Diameter in nm
Polydispersity
Index
None 184.7 0.172
333 K; 30 minutes 189.2 0.122
(333 + 5) K; 30 minutes 210.8 0.134
(333 – 5) K; 30 minutes 206.4 0.119
The perusal of Table 2 shows that in general, the effect of
temperature is found to lessen the magnitude of almost all
measured parameters. However there is a deviation of this
trend in viscosity values of B+G+L system at nanoparticle
forming temperature.
Density as well as sound velocity is the immediate measure
of the compactness of the substance. They basically
depend on the number of particles in the medium. Of the
four systems considered, phosphate buffer solution shows
minimum density whereas the B+G+L systems records
maximum values, that shows the availability of more
particles in the latter system. However, this is not true
for other two parameters. Apart from mass, the surface
morphology, surface area and shape are important (Waris
2003) for the other two measured parameters.
Hydrophobicity is an important factor of nanoparticles as
regards their applications, especially if designed for oral
delivery systems as revealed by Jun et al. (2011). Further
they suggested that these interactions are fundamental
to control the size of nanoparticles and are sensitive to
the changes in additive/cosolvent, concentration, pH, etc.
interesting in the sense it has no or negligible reversal of
trend in many physical parameters including density and
sound velocity (Velusamy and Palaniappan 2016). Thus
the observed variations reveal the peculiarity of pH 9 and
our.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Kavitha and Palaniappan
98
As regards viscosity, the system of B+L shows least magnitude
whereas the B+G has the higher compared with any other
systems, irrespective of temperature This shows that the
may be attributed to the aggregation of nanoparticles (Samuel
Ebinezer and Palaniappan 2007). It is peculiar to note that
In the case of surface tension also the B+L system at 333
K, has a higher surface energy and in B+G+L system the
same becomes the least. Energy minimum is an excellent
indication of stability (Burkhard Rost, 2010) and it is again
the viscosity and the surface tension suggests that the
addition of cosolvent supports the highly stable particle
suspension and as regards temperature, a value other than
333 K seems to be unfavourable.
Table 2: Measured Parameters at pH 9 for the systems of Buffer (B), Buffer + Glucose (B+G), Buffer + Lactalbumin
(B+L) and Buffer + Glucose + Lactalbumin (B+G+L)
Tem
pera
ture K
Density
Sound velocity
(u) m
Viscosity
Surface tension
System
B
System
B+G
System
B
System
B+G
System
B
System
B+G
System
B
System
B+G
303 1023.2 1075.4 1551.0 1593.8 0.8135 1.1178 0.2573 0.2684
328 1011.7 1060.4 1515.6 1588.6 0.5124 0.9925 0.1851 0.1842
333 1008.2 1058.2 1510.8 1575.5 0.4221 0.9851 0.1782 0.1775
338 1006.7 1056.4 1506.4 1565.2 0.3345 0.9652 0.1704 0.1707
System
B+L
System
B+G+L
System
B+L
System
B+G+L
System
B+L
System
B+G+L
System
B+L
System
B+G+L
303 1021.0 1079.3 1532.0 1585.2 0.6730 0.8538 0.2791 0.2592
328 1010.4 1063.2 1501.8 1580.4 0.4724 0.9314 0.1932 0.1762
333 1007.1 1059.0 1493.7 1571.2 0.4110 0.8992 0.1828 0.1721
338 1005.2 1057.2 1486.2 1561.3 0.3125 0.9522 0.1792 0.1684
Table 3: Calculated Parameters of Adiabatic compressibility termolecular free length (Lf), Acoustic
impedance (Z) and Relaxation time (t) at pH 9
Tempe
rature
K
System
B
System
B+G
System
B
System
B+G
System
B
System
B+G
System
B
System
B+G
303 4.0627 3.6606 4.1835 3.9711 1.5869 1.7139 4.4066 5.4558
328 4.3030 3.7368 4.4999 4.1930 1.5333 1.6845 2.9398 4.9487
333 4.3454 3.8071 4.5612 4.2693 1.5231 1.6671 2.4456 5.0005
338 4.3774 3.8639 4.6171 4.3379 1.5164 1.6934 1.9523 4.9726
System
B+L
System
B+G+L
System
B+L
System
B+G+L
System
B+L
System
B+G+L
System
B+L
System
B+G+L
303 4.1730 3.6871 4.2399 3.9854 1.5641 1.7109 3.7446 4.1974
328 4.3881 3.7657 4.5442 4.2096 1.5174 1.6802 2.7622 4.6765
333 4.4504 3.8250 4.6159 4.2793 1.5043 1.6639 2.4388 4.5860
338 4.5039 3.8803 4.6834 4.3471 1.4939 1.6506 1.8766 4.9264
Hydrophobicity Character of α-lactalbumin Nanoparticles
99
In Table 3 also, in general, the effect of temperature on the
2. Though the magnitudes are becoming opposite to Table
2, this Table 3 also supports the weakening of interactions
with rise in temperature. Compressibility and free length
are in increasing trend with temperature indicates that
the existing interactions are weakened (Mahendran and
Palaniappan 2011).
Acoustic impedance is a factor governed by the inertial and
elastic properties of the medium, or simply, the reluctance
of the medium for any change. This reluctance seems to be
least for B+L system and is appreciable for B+G+L system,
irrespective of temperature. This may be the indication of
the weak interactions. Presence of weak interactions with
more reasoning.
The magnitudes of interactions are found to be becoming
stronger with addition of cosolvent that ultimately
reduces the aqueous solvent. A similar trend as regards
relaxation time is noticed and it reveals that the addition
of glucose stabilizes the nanoparticles at all temperatures.
But in system B+G+L, in which glucose on hydrolysis
produces both types of charges and are available due to the
temporary or induced dipoles (Velusamy and Palaniappan
2013). The aqueous solutions of glucose have lower
dielectric constant than pure water. It indicates that the
electrostatic interactions are stronger in these solutions
than in pure water as reported in literature (Akerlof 1932)
and thereby restrict the uncoiling of α-LA or improves the
stability. Always the electrostatic interactions are not
weak in nature, but manifest as weak. Their inherited
dominancy is suppressed by the selective exclusion effect
of water molecules by the involved protein, termed as steric
exclusion effect (Miyawaki 2007) and hence the interactions
are said to be hydrophobic in nature.
Comparing the relaxation time (t) of the systems of B+L
and B+G+L, the protein with cosolvent takes larger duration
than without cosolvent. It is a general expectation that rise in
temperature will reduce the relaxation time as found in B+L
system. This is total contrary in B+G+L system, indicating
make the cosolvent to generate large number of ions in the
medium, which can attach with protein molecules, said
steric effect expels water, aids in further increase of ion
production, ultimately leads to higher t values.
It is to be noted that even in the presence of cosolvent,
at 333 K, t is not as high as in 333±5 K. This reveals the
importance of particular temperature as nanoparticle
forming temperature. It is only at 333 K, the system is in
minimum energy level and shows a very good re-dispersion
with a low relaxation time.
The other three calculated parameters, called as relative
parameters, viz., the relative association (RA) in Fig 1 A,
Fig. 1 C are the relative and realistic parameters that can
ascertain the effect of the extra component in the systems,
i.e., these parameters directly link system B+L with B+G+L.
It is a simple logic that, for the stability improvement by
glucose, the inclusion of glucose to the protein should make
the system to behave in such a way that it has to oppose
the trend of heat. In the system with glucose, the trend just
this trend. In the same way, the other two parameters are
also conveying that glucose largely supports for the stability
Fig. 1: A. Trend of Relative Association
Fig. 1: B. Trend of Partial Apparent Speciic Volume
Kavitha and Palaniappan
100
Fig. 1: C. Trend of Partial Apparent Speciic Adiabatic
Compressibility
that without glucose. A relatively less compressibility is the
indication of higher compactness and thus glucose provides
larger renaturation to the protein molecules. Further, the
magnitude of it is extremely low and almost same at all
temperatures indicating the role of cosolvent in the nullifying
CONCLUSIONS
Si
follows:
●The existing interactions in the chosen protein
nanoparticle systems are found to be hydrophobic
in nature and are sensitive to heat pre-treatment, pH
and cosolvent.
●
regards lactalbumin nanoparticles.
●
regards the distribution rather than the size of the
nanoparticles.
●Control of hydrophobic interactions by the alkaline
pH, especially pH 9, is very evident.
●The addition of cosolvent, glucose in this case, largely
aids in the reversal of denaturation due to heat pre-
treatment.
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Upadhyay, Thakur and Daimary
102
Nanoparticles
Mukesh Upadhyay1*, Ashok Kumar Thakur2 and Mohan Daimary3
1,2,3Department of Physics, North Eastern Regional Institute of Science & Technology,
Nirjuli, Arunachal Pradesh–791 109 (India)
E-mail: *mupadhyaya@rediffmail.com
Abstract—
studied their structural properties. The prepared nanoparticles were characterized by X-Ray diffraction (XRD) and Raman
approx. 12.72 nm. The Raman spectrum shows the frequency of the phonon in these nanoparticles and the Raman modes of
Keywords:
Science and Technology Journal Vol. 7 Issue: 2 July 2019 ISSN: 2321-3388
INTRODUCTION
Nanomaterial’s as well as nanotechnologies attracts attention
in researches. The physical properties and technologies in
sample preparation as well as device fabrication evoke on
accounts of the development of Nano science. Physical and
chemical properties of a given material are derived from
and lies IV-VI group layered semiconductor (An C et al.,
2003, Andres et al
by the German mineralogist Herzenberg. Single crystal of
mixture of Sn and S elements over the temperature range of
600 - 750 °C
et al., 2014, Esumi et al., 1998) and is stoichiometric under
Sn rich conditions and it will form Sn1-xSx in Sulfur rich
conditions. It has both indirect (1.09 eV) and direct (1.3 eV)
cm-1. It has both p-type and n-type conductivity depending
on the departure of Sn stoichiometry from ideal (Jiang et
al
by a variety of experimental techniques such as optical
spectroscopy etc. (Suresh 2014, Tanusevski et al., 2003).
In recent years, considerable efforts have been made in
the synthesis of SnS nanostructures. The authors (Xu et al.,
2009) have reported the synthesis of SnS quantum dots.
Where, SnBr2
glycol (EG) at room temperature in the presence of various
stabilizing ethanolamine as ligands. The ethanolamine is tri-
ethanolamine (TEA), N- methyl-di-ethanolamine (MDEA).
Among these ethanolamine’s small size and dispersed SnS
nanoparticles with average particles size 3.2 nm are found
in the presence of TEA. This could be attributed two regions:
(i) During nucleation, the strong binding of multiple
hydroxyl groups on the surface of SnS nanoparticles and
(ii) reaction of TEA with Sn2+ forms [Sn(TEA)n] 2+ complex.
Biswas et al
and nanosheets through thioglycolic acid (TGA) assisted
hydrothermal process (Biswas et al., 2007). The diameter
of the SnS nano-rods varied within 30-100 nm and the
crystal size and shape depends on the amount of TGA and
sulfur source.
Salavati-Niasari et al. (2010) reported the synthesis of
simple hydrothermal process in the presence of TGA. The
authors (Zhu et al., 2006) reported the synthesis of SnS
band gap transition at 1.53 eV and 1.43 eV respectively.
The Structural, optical and electrical characterization of
were studied by Rana Chandan and Saha Satyajit (2019).
103
SYNTHESIS OF TIN SULFIDE
NANOPARTICLES
Wet chemical process has been followed to synthesized Tin
Tin chloride was dissolved with 100 ml of distilled water
in a beaker of 250 ml under magnetic stirrer for 40 min.
completely added to the tin chloride solution within 90
min. under the continues vigorous stirring of tin chloride
solution. Afterwards the mixed solution was constantly
stirred for 2 hours. The reaction is carried out at room
temperature. At some moments during adding the sodium
chloride solution was turned into dark and brown color this
hrs. vigorous stirring of the mixed solution, the solution kept
and washed the prepared solution with deionized water or
nanoparticles were kept for 1 hr. at room temperature and
then 1 hr. in oven at 40 0C. Keeping the sample for 24 hrs.
at room temperature, the synthesized SnS nanoparticles |
were obtained.
CHARACTERIZATION
The crystallinity and phases of the precursor and SnS
nanoparticles were characterized by X-ray diffractometer,
scanning rate. Also the Raman imaging of the sample (SnS)
nanoparticles was done using Xplora one 785 nm Raman
microscope.
XRD ANALYSIS OF SNS NANOPARTICLES
Using X-ray diffractometer the crystallinity and phases of
the precursor and SnS nanoparticles were characterized.
plane (111). The other planes (221), (131), and (002) are
60.240 respectively. The full width half maxima value was
obtained from the dominant plane. The crystalline size of the
formation of the nanoparticle is depend on the value of full
width half maxima. It was clearly known from the Scherrer
equation, that the larger the value of FWHM smaller the
crystalline size of the nanoparticles. So crystalline size
directly depends on FWHM value. The crystalline size of the
nanoparticle depends on diffraction angle or the Bragg’s
angle also, but at a diffraction angle, a particular XRD peak
is observed. In the other words the dominant peak or the
peak having highest height occurs at a diffraction angle.
So, for same nanoparticle having same crystalline size, the
diffraction angle is same for dominant plane of the XRD
pattern. The crystalline size of the SnS nanoparticles were
calculated from the Scherrer equation.
D= cos
K
l
bθ
Where,
D = crystalline size
K= a constant and is equal to 0.9
Using FWHM and diffraction angle from the XRD pattern, the
crystalline size was found 12.72nm. It is a good agreement of
synthesis of SnS nanoparticles using wet chemical process.
with the Herzenbergite orthorhombic structure with lattice
parameters a = 4.3137 Å, b = 11.2853 Å and c = 3.9958
Å. These values of the lattice parameters are in good
agreement with JCPDS card no. (JCPDS: 39-0354) and the
result reported by Ariswan et al (2017).
RAMAN SPECTROSCOPY OF SNS
NANOPARICLES
Raman spectroscopy of SnS nanoparticles were performed
at the room temperature. Fig. 2 shows Raman spectroscopy
of SnS nanoparticle. The Raman modes for SnS nanoparticles
Upadhyay, Thakur and Daimary
104
were observed at 409.80 cm-1, 736.64 cm-1, and 1275.71cm-
1 with some weak intensity modes. Raman modes of SnS
which shifted towards lower wave number side, because
size of the nanoparticles compared to the bulk one is huge
band gap of nanoparticles.
Spectra having three distinguishable peaks at 409.80 cm-1,
736.64 cm-1 and 1275.71 cm-1. The 409.80cm-1and 736.64cm-
1
arises due to inter atomic vibration between metal (Sn)
and chalcogen (S). The 736.64 cm-1 peak is associated with
2S3, may be arising from the
intra-layer vibration of chalcogen–chalcogen.
1000
1050
1100
1150
1200
1250
1300
01000 2000 3000 4000
Intensity counts
Wave number (cm-1)
Fig. 2: Raman Spectrum of SnS Nanoparticles
CONCLUSION
wet chemical method. The characterization was performed
by X-ray diffractometer and Raman spectroscopy. XRD
plane (111) was found at the diffraction angle of 31.500 and
this result is good agreement with JCPDS curd no. (JCPDS:
Å, b = 11.2853 Å and c = 3.9958 Å. The Raman modes for
SnS nanoparticles were observed at 409.80 cm-1, 736.64
cm-1, and 1275.71cm-1 with some weak intensity modes.
nanoparticles were found to shifts towards lower wave
number side.
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105
Science and Technology Journal Vol. 7 Issue: 2 July 2019 ISSN: 2321-3388
Author Index
107
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Bhargava P, Mishra Y, Srivastava AK, Narayan OP, Rai LC (2008).
Excess copper induces anoxygenic photo-synthesis in Anabaena
doliolum: a proteomic assessment of its survival strategy.
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References to a
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Feyen, J, Shannon K, Neville M. Variability of urban water supply
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in fractured porous media. Dissertation, Stanford University.
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