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Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
Digital Object Identifier 10.1109/ACCESS.2020.Doi Number
Nutritional quality and safety traceability
system for China’s leafy vegetable supply chain
based on fault tree analysis and QR code
Yuhong Dong1, Zetian Fu1*, Stevan Stankovski2, Siyu Wang1, Xinxing Li3*
1Beijing Laboratory of Food Quality and Safety, College of Engineering, China Agricultural University, Beijing 100083, PR China
2University of Novi Sad, Novi Sad 21000, Serbia
3College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, PR China
Corresponding author: Xinxing Li (lxxcau@cau.edu.cn), Zetian Fu (fzt@cau.edu.cn)
This work was supported by the National Science Foundation for Young Scientists of China (61802411).
ABSTRACT Leafy vegetables are consumed in most daily diets worldwide. As living standards improve,
food quality, safety requirements, and nutrition are becoming increasingly important to consumers when
purchasing leafy vegetables. This study proposes an evaluation and traceability method that can be used to
track the nutritional quality of leafy vegetables. Employing the principles of the Hazard Analysis and
Critical Control Point (HACCP) system combined with fault tree analysis (FTA), a traceability model for
the entire production and sale process of leafy vegetables is constructed. Four common leafy vegetables,
spinach, rape, lettuce, and celery are examined in this research to establish a nutritional quality index
system using fuzzy mathematics subordinate function method to evaluate nutritional quality. A nutritional
quality and safety traceability system based on browser/server architecture and quick response (QR) code is
then designed and developed for full traceability of leafy vegetable quality. This method can ensure food
safety and hygiene through the control of key factors affecting food safety throughout the entire supply
chain process.
INDEX TERMS Leafy vegetables, traceability, nutritional quality, quick response (QR) code
I. INTRODUCTION
Consumption of vegetables provides the majority of vitamin
A and C required by the human body [1]. According to a
China Industry Information report, 692.71 million tons of
vegetables were consumed in China in 2018, with per capita
consumption at approximately 475 kg annually. This amount
exceeds the annual per capita vegetable consumption of other
countries worldwide. As the country with the world's highest
vegetable production [2], the status of vegetables in the daily
diet of Chinese people is particularly important. From a
perspective of health, leafy vegetables are an important
component of the daily diet, providing the body with a
variety of nutrients including minerals, vitamins, and dietary
fiber [3]. The various minerals and vitamins contained in
leafy vegetables can stimulate appetite and regulate the acid-
base balance in the body, while dietary fiber works to
regulate the human digestive system [4]. High rates of
agricultural product safety issues have occurred in China in
recent years, including many cases of vegetable
contamination affecting the health of consumers. As such,
the quality and safety of leafy vegetables is receiving
increasing attention from consumers. The rapid development
of information technology has facilitated the use of
traceability systems to effectively communicate food quality
and safety [5]. Traceability technology can intuitively
provide consumers with information on the quality of the
agricultural products, increasing consumer trust in the
product.
There are five main supply chain processes before
vegetables are consumed: planting, harvesting, processing,
storage and transportation, and sales (Figure 1). Problems at
any point along the production to sales process will have a
dramatic influence on the quality of leafy vegetables,
meaning that research into a system for full quality
traceability is essential [6]. According to research on whole-
process traceability, full traceability information can be
recorded in the system to achieve quality and safety
monitoring of leafy vegetables. The newly revised "Food
Safety Law of the People's Republic of China" (2018 Edition)
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.3019593, IEEE Access
Y. Dong et al: Preparation of Papers for IEEE Access (February 2020)
4 VOLUME XX, 2020
states that food producers and operators should collect and
record production and operation information to establish a
food traceability system. The establishment of a full
traceability system of agricultural products from farmland to
the dining table can promote the development of transparent
management of the entire supply chain. This process also
provides an easy method of detecting quality and safety
issues in products and locating the process in which it occurs,
meaning the product can be recalled with adequate time. As
such, it is an effective means to ensure national food quality
and safety.
FIGURE 1. Flow chart of leaf vegetable production and distribution
Vegetal or animal food traceability from the perspective of
the relationship between soil-plant-animal is an increasingly
important global concern.
Food quality and safety are important to human health.
The focus of current research is on the use of traceability
technology to obtain information on food quality and safety;
however, few studies have examined the nutritional quality
of leafy vegetables. Several studies have established
traceability systems for the production of leafy vegetables,
and these technologies primarily involve tracing information
relating to quality and safety. However, few studies have
examined the traceability of leafy vegetable nutritional
quality information, which has often resulted in an inability
to meet consumer demand for vegetable nutritional quality
information. The nutritional quality of leafy vegetables
primarily refers to the nutrient content of leafy vegetables.
However, because leafy vegetables are diverse, the
nutritional characteristics of leafy vegetables differ—often
even between species. Simply comparing the contents of
various nutrients does not accurately capture the nutritional
quality. Therefore, there is a demand for the scientific
method to be used to comprehensively evaluate the
nutritional quality of leafy vegetables, which not only
transmits information on the nutritional quality of leafy
vegetables to consumers through the traceability system but
also helps improve the commercial value of the products of
vegetable production and operation enterprises.
The main contributions of this paper are as follows.
First, to develop an optimal means by which consumers
could obtain information on leafy vegetable quality and
safety online, we studied the entire process from the
production to the sale of leafy vegetables. Specifically, we
traced several different types of information across the entire
supply chain and proposed a quality and safety traceability
system for China’s leafy vegetables. Second, as the existing
traceability system lacks assessments of the nutritional
quality of leafy vegetables, this study explores a method for
the comprehensive evaluation of the nutritional quality of
leafy vegetables, analyzes the indicators reflecting quality,
proposes nutritional quality grading standards, and uses
nutritional index content and the results of evaluations to
characterize the nutritional quality of the products. This
information is then added to the traceability system. Finally,
the platform ASP.NET was used as the development
language to establish a high-quality and safe traceability
system based on a browser/server (B/S) architecture.
Traceability information for consumers is then stored in the
QR code on product packaging.
The remainder of the paper is organized as follows.
Section II introduces the related works and discusses the
most relevant previous findings. In Section III, the
architecture of the proposed nutritional quality and safety
traceability system is described in detail. Section IV presents
the results of several analyses of the traceability system,
including Hazard Analysis, Critical Control Point (HACCP)
analysis, fault tree analysis (FTA), and the membership
function method. In Section V, the implementation of the
entire process of the traceability system of leafy vegetables is
described, and the QR code error correction effect is verified.
Finally, Section VI concludes the paper and provides
possible optimization directions for further system
development.
II. RELATED WORK
Vegetal or animal food traceability from the perspective of
the relationship between soil-plant-animal has become an
increasingly important global concern. With the advancement
of science and technology and changes in market demand,
understanding and improving research and developmental
trends of the traceability system have played an important
role in studies of the traceability system. Researchers both in
China and abroad have discussed the possibility of
combining traceability systems to improve the current
management of food quality and safety.
Dulf et al. proposed a methodology for a vegetable food
traceability system in which the area of vegetables harvested
and global yield are taken into consideration [7]. Mainetti et
al. proposed a web-based, low-cost traceability system for
vegetable quality and safety supervision for ready-to-eat
fresh vegetables using a system database to manage the
vegetable production process [8]. Significant progress has
also been made in research on traceability systems in China.
For example, Jiang et al. [9] designed a leafy vegetable
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.3019593, IEEE Access
Y. Dong et al: Preparation of Papers for IEEE Access (February 2020)
VOLUME XX, 2020 5
tracing system based on quick-response (QR) code, Radio
Frequency Identification (RFID), wireless network sensing,
and other technology. This method facilitates the full
tracking of the production line from seed to sales, ensuring
leafy vegetable quality and safety supervision. Jin et al. [10]
proposed a food safety traceability system based on the
Internet of Things, in which three kinds of heterogeneous
multi-source information are processed to track food safety.
Lin et al. [11] proposed a food safety traceability system
based on the blockchain and EPC Information Services. They
also developed a prototype system that showed superior
performance in tamper-proof ability, privacy protection, the
degree of decentralization, and the amount of on-chain data.
Numerous researchers have established traceability systems
for the vegetable production process, and most have focused
on the traceability of quality and safety issues. However, few
studies have been conducted on the traceability of vegetable
nutritional quality information; consequently, the consumer
demand for such nutritional information remains unfulfilled.
The nutritional contents of each vegetable are varied, and
there are also often differences between vegetable species.
Comparing the contents of various nutrients separately does
not fully reflect nutritional quality; thus, a scientific and
comprehensive method for evaluating the nutritional quality
of vegetables is required. This information can then be
provided to consumers through the traceability system. This
process is also helpful for vegetable producers as it enhances
the commercial value of their products.
To develop a feasible approach for the nutritional quality
and safety traceability system for China’s leafy vegetable
supply chain, HACCP analysis, FTA, the membership
function method, and QR code are needed to obtain
foundational knowledge.
A. HACCP ANALYSIS
The HACCP system is a food safety assurance system that is
recognized and accepted internationally. It is a scientific,
reasonable, and systematic method for hazard identification,
evaluation, and control. The HAACP system ensures quality
in production, processing, manufacture, and preparation of
food in consumption, as well as safety during consumption
[12-13].
Using the HACCP system can ensure the whole
monitoring process is safe and scientific. The analysis of key
control points provides the basis for establishing the entire
process traceability system of leafy vegetables. The HACCP
system is a quality and safety control methodology that
comprehensively analyzes the biological, chemical, and
physical hazards that may occur in all processes, and
establishes control measures to minimize hazards. The
system generally consists of seven steps: hazard analysis
(HA), determine for critical control points (CCP), determine
for critical limits (CL), establishment of monitoring
procedures, establishment of corrective measures,
establishment of verification, and establishment of an
effective record-keeping program [14]. A comprehensive
HACCP analysis can be completed using the above seven
steps. The HA and CCP are used to analyze the whole
process of production, storage, and transportation of leafy
vegetables, which can identify potential hazards in the entire
process and obtain the critical control points that require
monitoring. This method provides a basis for constructing a
traceability system of leaf quality and safety.
B. FAULT TREE ANALYSIS
Fault tree analysis is a graph-based logic deduction method
which connects various events through logical symbols to
form a tree diagram composed of logical relationships [15].
This method is used to evaluate the security of a system and
is based on deductive reasoning, using the tree to represent
possible breeches of the system and various causes of the
incident. Qualitative and quantitative methods are employed
in analysis of the accident tree to identify the main cause of
the accident [15-16]. The fault tree is made up of events
which include the top event (the system's least desirable
event), basic or bottom event (the smallest unit event that
caused the top event to occur), non-basic events (negligible,
low probability events), and intermediate event (event
between top event and basic event).
In FTA, a circle is commonly used to represent a basic
event, a rectangle represents a top event or an intermediate
event, and a diamond represents a non-basic event. The logic
symbols used to connect events mainly include AND gates
and OR gates. An AND gate represents multiple events
failing at the same time, causing the output event to fail. An
OR gate means that a single event failed, causing the output
event to fail.
The FTA process can typically be divided into the
following steps: determining the analysis target, construction
of the fault tree, simplifying and normalizing the complex
fault tree, qualitative analysis, quantitative analysis, and
summarizing FTA results.
C.MEMBERSHIP FUNCTION METHOD
The main methods of agricultural product quality evaluation
include sensory quality evaluation [17-18], mean nutritional
value assessment [19-20], principal component analysis [21],
and the subordinates of fuzzy mathematics function method.
Sensory quality evaluation is often influenced by subjective
factors. For this method, the average nutritional assessment
technique is applied to a small number of evaluation
parameters, then the average amount of the detection values
of nutrient composition indicators are calculated. The
principal component analysis method is suitable for a large
number of evaluation parameters, and in order to simplify the
data structure, a new comprehensive index is sought. The
linear relationship between the original indicator, principal
component contribution rate, and the correlation of the index
is first established to discern the index that has the greatest
impact on the product. The average membership function
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.3019593, IEEE Access
Y. Dong et al: Preparation of Papers for IEEE Access (February 2020)
4 VOLUME XX, 2020
value of fuzzy mathematics is then employed to indicate the
relative quality of the comprehensive index in the evaluation.
This method been used successfully in previous nutritional
quality analysis of agricultural products [22-23]. Zhang et al.
used the membership function method of fuzzy mathematics
to analyze and evaluate the nutritional quality of different
varieties of radish, finding varieties with superior nutritional
quality among 18 kinds of radishes [24]. Bai et al. also used
the average membership function method to achieve an
effective comprehensive evaluation of nutritional quality
when investigating the correlation analysis of potato nutrients
[25].
This study utilizes the fuzzy mathematics membership
function method to incorporate nutritional quality into the
system. According to the average membership function value
of good and poor quality indicators, the higher the average
membership function value of the good quality, the higher
the good index content, and the better the nutritional quality.
Conversely, the larger the average membership function
value of poor quality, the worse the quality. The difference
between the two can be used for comprehensive evaluation,
which is proportional to the nutritional quality [26].
The basic steps of the membership function method of
fuzzy mathematics are described as follows:
1) After determining the various nutritional quality
indicators of the leafy vegetables, membership function
values of the various nutritional quality indicators are
calculated according to (1):
min
max min
() XX
XXX
−
=−
(1)
where X is a detected value of an index, Xmax is the
maximum values, and Xmin is the minimum values.
2) According to the membership function value of the
reference value of the nutritional quality evaluation index and
the membership function value of the measured value, the
average membership function values of each type are
individually calculated according to (2):
ij
i
X
Xn
=
(2)
where i is the average membership function value, j is the
type of quality indicator, Xij is the jth quality indicator of the
ith average membership function value, and n is the number
of quality indicators when calculating the ith average
membership function value.
3) The difference between the average membership
function values of the two types of indicators of the reference
value and the detected value is then respectively calculated as
a reference difference value and a detection difference value.
D. QR CODE
The QR code is often used in the traceability evaluation
process due to its text information storage capacity. By
scanning the QR code with a smart phone, the consumer can
easily obtain traceability information about the product. Two-
dimensional code technology is also increasingly employed
in the study of traceability systems. Peng et al. used two-
dimensional code to carry information on planting,
processing, and sales in the vegetable supply chain, making it
convenient for consumers to obtain traceability information
from intelligent terminals [27].
In this paper, the code rule for QR code is used with
longitudinal combining of the nutritional quality information
of leafy vegetables obtained during the process of quality
evaluation. A different code model is utilized for different
kinds of information during coding, and the resulting QR
code for mobile phones can be used to communicate the
nutritional quality of leafy vegetables [28]. A QR code has
the following advantages: a larger amount of data can be
stored in the QR code; mixed content such as numbers,
characters, and Chinese text can be utilized; certain fault
tolerance is provided (meaning that it can be read normally
after partial damage); it contains high space utilization.
During the processing of leafy vegetables, the complexity
of the environment may cause QR code malfunction, so the
error-correction ability of the QR code is important. The
error-correcting code of the QR code is generated after the
data sequence code is produced according to the error-
correction algorithm. This error-correction code can
guarantee that the symbols maintain readability after
suffering staining or a certain degree of damage, to ensure
the QR code retains the information.
The coding used for QR code error-correction is based on
Reed-Solomon (RS) cycle error control code generation. The
RS code can correct random errors and burst errors, and can
be used to construct alternative code. Using RS code
provides a strong error correction capability with high coding
efficiency, convenient construction, a relatively simple
algorithm, and is easy to implement in a digital system. It is
the most effective and widely used method of error-control
coding and provides a two-dimensional bar code.
III. DESIGN OF NUTRITIONAL QUALITY AND SAFETY
TRACEABILITY SYSTEM FOR LEAFY VEGETABLES
A. STRUCTURE OF THE SYSTEM
The overall framework of the traceability system structure
established in this paper is provided in Figure 2. The system
is designed from the two perspectives of production
enterprises and consumers. As can be seen from Figure 2, the
entire traceability system is divided into the information
collection layer, information processing layer, service layer,
and user layer.
The information collection layer is mainly used to collect
information about planting, harvesting and processing,
storage and transportation, sales, origin detection, and market
or supermarket entry detection recorded by enterprises that
produce and circulate leafy vegetables. A product file will
then be created using this information and uploaded to the
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.3019593, IEEE Access
Y. Dong et al: Preparation of Papers for IEEE Access (February 2020)
VOLUME XX, 2020 5
FIGURE 2. Overall structure of the traceability system
traceability center database by the information processing
layer to save or create traceability information. The service
layer refers to the terminals that can provide traceability
information, including smart phones, computers, and query
terminal machines provided by the supermarket. The user
layer is an interactive system that allows producers, operators,
and consumers to carry out the transmission of information.
According to the overall design of the system, the seven
functional modules of the quality and safety traceability
system are illustrated in Figure 3. The planting information
management module includes information on the production
site environment, seed type, planter, and field management;
the harvesting and processing information management
module includes the records of harvesting information,
processing information, packaging information, and operator
information; the storage and transportation information
management module includes information on storage and
transportation methods and conditions, as well as
management and transportation; the producer/pre-marketing
detection information management module covers the
detection information of quality and safety indicators of the
leafy vegetables at the production site and before entering the
market, as well as the nutritional quality evaluation results at
the production site; the traceability information query module
includes producer information, with quality safety and
nutritional quality information stored in a two-dimensional
code for consumers to access using smart phones; the login
module predominantly refers to the verification of the user's
identity, distinguishing the consumer user from management
personnel and setting access rights.
FIGURE 3. Functional modules of the traceability system
B.
SYSTEM DATABASE
The trace information and data of the leafy vegetables is the
basis for constructing the entire traceability system. A system
data flow chart is then created according to the structure and
function design results of the quality and safety of leafy
vegetables. Figure 4 illustrates how each process is generated
in the system. Corresponding information must then be
entered into the traceability system. According to the results
of HACCP and FTA, it is necessary to collect the business
information data of key points including field management,
production place, quality and safety detection, and storage
and transportation before the market, to ensure quality and
safety traceability.
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.3019593, IEEE Access
Y. Dong et al: Preparation of Papers for IEEE Access (February 2020)
6 VOLUME XX, 2020
FIGURE 4. System data flow chart
IV. RESULTS AND DISCUSSION
A. HACCP ANALYSIS RESULTS
According to the HACCP principle, this study analyzes the
potential hazards of leafy vegetable planting, post-harvest
processing, storage, transportation, and sales. The following
Table I summarizes the potential hazards for leafy vegetables
in circulation. Utilizing the principle of HACCP, the
potential hazard is judged on whether it is a critical control
point or not.
TABLE I
ANALYSIS OF WHOLE PROCESS HAZARDS OF LEAFY VEGETABLES AND KEY CONTROL POINTS
Process
Judgment basis
Potential hazard
Significant
hazard
CCP
Prevention
Biological
Physical
Chemistry
Place of
production
Production environment
█
█
█
█
Selecting a standard production place
Species
selection,
breeding,
planting
Species and quality of the seed
█
Species suitable for the production
environment, standard seeds, proper
sowing date, and method
Field
management
The quality and usage amount
of pesticides and fertilizers,
pathogenic microorganisms in
irrigation water
█
█
█
█
Quality-assured pesticides and
fertilizers, detecting pathogenic
microorganisms on schedule, and
choosing fully decomposed organic
fertilizers
Product harvest
Pesticide interval, sundries
mixed during harvesting
█
█
Harvest when reaches the pesticide
safety interval
Post-harvest
treatment
Mechanical damage or
polluted by unclean water
█
█
Ensure clean water and safe
preservative, qualified packing
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
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Y. Dong et al: Preparation of Papers for IEEE Access (February 2020)
VOLUME XX, 2020 7
material
Origin
inspection
Hazardous substances
inspection
█
█
█
Strengthen the inspection and
supervision
Storage
Improper storage environment
and placement
█
█
█
█
Regularly monitor the temperature and
humidity of the storage environment
and check the quality of the vegetables
in time
Transport
Temperature, humidity and
cleanliness of the transport
environment
█
█
█
█
Install a monitoring sensor on the
transport vehicle
Market
inspection
Detection of toxic and
hazardous substances
█
█
█
█
Strengthen the market supervision
Sales and shelf
life
management
Exceeds shelf life, unhygienic
sales shelves, and purchasing
damage
█
█
Timely organize the products in the
sales area and correctly guide
consumers
Note: █ represent affirmation
Results of the hazard analysis confirm that the key control
points in the process of leaf vegetable production and
distribution are origin selection, field management,
production place inspection, storage, transportation, and
supermarket inspection.
B. FTA results
1) RESULTS OF FAULT TREE CONSTRUCTION
In this study, the peak event is identified as the problem
affecting the quality and safety of leafy vegetables. The
intermediate events and the basic events are confirmed by the
analysis of the event, layer by layer. Qualitative analysis of
the tracing process of leafy vegetables, including planting,
post-harvest processing, quality and safety inspection,
storage, transportation, and sales is then undertaken to
determine intermediate events. Basic and non-basic events
are connected to top events through logical events [19], as
shown in Figure 5.
FIGURE 5. Logical events tree
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Before qualitative analysis of the fault tree can be
undertaken, simplified and normalized processing is first
performed. Pretreatment can reduce computational
complexity and improve the analysis speed of the fault tree.
Simplification mainly refers to the removal of redundant
events and logical gates. The events are represented by code
for further normalization such that the fault tree contains only
top events, intermediate events, and basic events, as well as
AND gates and OR gates [29]. The normalized fault tree is
shown in Figure 6.
FIGURE 6. Normalized fault tree
2) QUALITATIVE ANALYSIS RESULTS OF FTA
The main purpose of qualitative analysis of the fault tree is to
identify all the minimum cutsets. The higher the number, the
less reliable the system is. In this paper, the descending
method (Fussell-Vesely) and Boolean algebra method are
used to determine the minimum cutset [30-31].
The steps to determine the minimum cutset based on
descending method are shown in Table 5. The top event T is
the OR gate, and the top events M1~M5 are written in a
column. The above five intermediate events are then
searched individually. For example, the M1 is OR gate, and
its three input events M6, M13, and M14 are written in one
column. All M6, M13, and M14 are AND gates, and these
input events are written in a line, respectively. Under M2 is
the OR gate, and the input event is written into a column.
Under M9 is the AND gate, and the input event is written as
a line. Under M3 is the OR gate, and the basic events X2~X5
are written into one column. Under M4 is the OR gate, M10,
M11 are written as a column, M10 and M11 are all AND
gates, and the input events are put into one row, respectively.
Under M5 is the AND gate, and the input events are put into
a row. TABLE II
DOWNSTREAM METHOD FOR MINIMUM CUT SET PROCESS
Step
1
2
3
Process
M1
M6
M13
M14
X9, X24, X25
X26, X27
X28, X29, X30
M2
X12
M9
X12
X14, X16, X17
M3
X2
X3
X4
X5
X2
X3
X4
X5
M4
M10
M11
X19, X21
X22, X23
M5
M12
X6, X8
According to Table II, it can be concluded that all cutsets
of the fault tree are:
{X9X24X25} {X26X27} {X28X29X30} {X12} {X14X16X17} {X2} {X3} {X4} {X5},
{X19X21},{X22X23},{X6X8}
， ， ， ， ， ， ， ，
The minimum cutset obtained by Boolean algebra method
is as follows:
T M1 M2 M3 M4 M5
(M6 M13 M14) (X12 M9) X2 X3 X4 X5 (M10 M11) M5
(X9X24X25 X26X27 X28X29X30) (X12 X14X16 17) 2 3
X4 X5 (X19X21 X22X23) X6X8
X X X
= + + + +
= + + + + + + + + + + +
= + + + + + +
+ + + + +
It can be seen that the minimum cutset obtained by
Boolean algebra method is identical to the the cutset obtained
by the descending method. Therefore, the 12 minimum
cutsets of the fault tree in this paper are:
{X9X24X25} {X26X27} {X28X29X30} {X12} {X14X16X17} {X2} {X3} {X4} {X5},
{X19X21},{X22X23},{X6X8}
， ， ， ， ， ， ， ，
The number of minimum cutsets can reflect the degree of
danger to the system and the leafy vegetable quality. The
safety fault tree includes 12 minimum cutsets, indicating that
the system is more dangerous.
The minimum cutset is of great significance for reducing
the possibility of problems in the traceability process of leafy
vegetables. According to the definition of the minimum
cutset, it is known that quality problems of leafy vegetables
will not occur when at least one basic event in the minimum
cutset does not occur, or the occurrence probability is
significantly reduced.
A single point of failure can be eliminated by removing
the first-order minimum cutset [32]. The 12 minimum cutsets
of the single-leaf vegetables quality and safety fault tree are
the first-order minimum cutsets, indicating that the basic
events {X12}, {X2}, {X3}, {X4}, and {X5} are most likely
to cause the top event to occur. The first-order minimum
cutset has a great influence on the reliability of the system, so
the primary objective is to eliminate it as must as possible.
3) QUANTITATIVE ANALYSIS RESULTS OF FTA
① Results of structural importance analysis
The important coefficient of each basic event is calculated
according to the above formula, then all basic events are
sorted according to the calculation result. This process
determines which basic events need to be monitored in the
fault tree, and proposes control measures to improve system
reliability. The importance of the quality and safety of leafy
vegetables constructed in this paper are analyzed during this
process, and the ranking results are provided as follows:
(X5) (X4) (X3) (X2) (X12)
(X8) (X6) (X23) (X22) (X21) (X19) (X27) (X26)
(X17) (X16) (X14) (X30) (X29) (X28) (X25) (X24) (X9)
I I I I I
I I I I I I I I
I I I I I I I I I
= = = =
= = = = = = =
= = = = = = = =
The above results show that the basic events X2~X5
(detection process) and X12 (harvesting process) are the
events which have the highest structural importance for all
basic events. Additionally, these events all belong to the first-
order minimum cutset in the fault tree, which can easily
affect the reliability of the system and require the most
monitoring attention. The structural importance of the basic
events in the planting process is generally low, and the
structural importance of the basic events in the post-harvest
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
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Y. Dong et al: Preparation of Papers for IEEE Access (February 2020)
VOLUME XX, 2020 9
processing, storage, transportation, and sales process is at an
intermediate level.
②Results of probability importance analysis
According to the previously outlined structural importance
and probability importance analysis methods, the importance
analysis of the quality and safety fault tree of leafy
vegetables is carried out. Table III lists the specific contents
of each basic event in the fault tree and calculates the
probability of each basic event degree. This paper lists the
probability of occurrence of each basic event by
summarizing the data of the predecessors and the opinions of
experts. According to the data, the probability of occurrence
of a fault caused by human factors in the basic event is set to
0.003 (3.00 10-3), and the probability of occurrence of an
event caused by other factors is set as the relative probability.
Thus, the analysis of probability importance can be
performed and the basic event with a greater degree of
influence on the top event can be determined.
TABLE III
BASIC EVENT PROBABILITY IMPORTANCE RANKING TABLE.
Code
Content
Probability
Probability
importance
Code
Content
Probability
Probability
importance
X12
Substandard pesticide interval
3.00 10-3
9.88 10-1
X2
Excessive pesticide residues
3.00 10-3
9.88 10-1
X3
Excessive heavy metal content
3.00 10-3
9.88 10-1
X4
Excessive nitrate content
3.00 10-3
9.88 10-1
X5
Excessive pathogen
3.00 10-3
9.88 10-1
X26
Unqualified fertilizer
3.00 10-3
2.96 10-3
X21
Unsuitable storage conditions
3.00 10-3
2.96 10-3
X22
Unclean transport
3.00 10-3
2.96 10-3
X23
Unsuitable transportation conditions
3.00 10-3
2.96 10-3
X6
Exceeds shelf life
3.00 10-3
2.96 10-3
X8
Purchasing damage
3.00 10-3
2.96 10-3
X28
Unqualified pesticide
3.00 10-3
2.96 10-6
X29
Banned pesticides
3.00 10-3
2.96 10-6
X30
Unqualified amount of pesticide
3.00 10-3
2.96 10-6
X14
Unclean water
3.00 10-3
2.96 10-6
X16
Improper preservative
3.00 10-3
2.96 10-6
X17
Unqualified packaging material
3.00 10-3
2.96 10-6
X24
Excessive pathogen
1.00 10-3
2.96 10-6
X25
Toxic and harmful residues
3.00 10-3
2.96 10-6
X9
Substandard air quality
1.00 10-3
2.96 10-6
Based on results presented in Table III, the following
conclusions can be drawn:
a. The leafy vegetable quality and safety failure tree top
event occurrence probability is P(T)=0.01495.
b. Probability importance analysis results can reflect which
basic event probability reduction can reduce the top event
probability. The results show that for the basic events X2, X3,
X4, X5, and X12, probability changes have a greater impact
on top event probability change. Larger risk factors require
more monitoring and specific actions taken to prevent top
events from occurring. The influence of X9, X24, and X25 is
relatively small, and the remaining events are moderately
affected.
c. With the development of agriculturally related
technologies, the probability of basic events involved in the
whole process of leafy vegetable traceability may alter,
resulting in changes to the analysis of probability importance.
Based on the analysis of structural importance and
probability importance of the fault tree of leafy vegetables, it
can be determined that, among the various factors that cause
leafy vegetables to be unable to circulate due to either quality
or safety issues, the key control point is pre-marketing
detection which is the last process before entering the market,
and must be tested in strict accordance with the given
standards. The use of fertilizers during the planting process is
also a key monitoring point. Although the impact of different
basic events on the top event varies, to ensure the reliability
of the system, it is still necessary to consider all influencing
factors and propose preventive and control measures to trace
the quality and safety of leafy vegetables.
C. RESULTS OF NUTRITIONAL QUALITY
CLASSIFICATION OF LEAFY VEGETABLES
Using the fuzzy function subordinate function method to
process the reference values of the four leafy vegetables
nutritional quality indicators, the membership function value
of each index is calculated, and the results are provided in
Table IV. TABLE IV
MEMBERSHIP FUNCTION VALUE OF THE NUTRITIONAL
QUALITY INDEX REFERENCE VALUE OF LEAFY VEGETABLES
Index\Name
Rape
Spinach
Celery
Lettuce
Moisture (g)
0.750
0.333
0.333
0.333
Protein (g)
0.667
0.333
0.250
0.364
Dietary fiber (g)
0.500
0.600
0.250
0.571
Vitamin A (μg)
0.250
0.500
0.667
0.462
Thiamine (mg)
＼
0.231
0.600
0.303
Riboflavin (mg)
0.333
0.200
0.500
＼
VC (mg)
0.667
0.750
0.333
0.600
VE (mg)
＼
＼
＼
0.333
Ca (mg)
0.333
0.714
0.333
0.600
P (mg)
＼
0.750
0.429
0.500
Mg (mg)
＼
＼
＼
0.565
Fe (mg)
0.667
0.600
0.500
＼
Nitrate (mg/kg)
0.300
0.300
0.300
0.300
Oxalate (mg/100 g)
0.333
0.267
0.255
0.324
The average membership function value is also calculated
as a grading standard for comprehensive evaluation, and the
grading results of leafy vegetables nutritional quality are
shown in Table V.
TABLE V
GRADING OF LEAFY VEGETABLE NUTRITIONAL QUALITY BASED ON AVERAGE MEMBERSHIP FUNCTION VALUES
Index\Name
Rape
Spinach
Celery
Lettuce
Average membership
function value of good
quality index
High
≥0.450
≥0.529
≥0.447
≥0.491
Medium
0.300~0.450
0.300~0.529
0.300~0.447
0.300~0.491
Poor
0.100~0.300
0.100~0.300
0.100~0.300
0.100~0.300
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.3019593, IEEE Access
Y. Dong et al: Preparation of Papers for IEEE Access (February 2020)
10 VOLUME XX, 2020
Average membership
function value of poor
quality index
High
≤0.317
≤0.284
≤0.278
≤0.312
Medium
0.300~0.500
0.300~0.500
0.300~0.500
0.300~0.500
Poor
≥0.500
≥0.500
≥0.500
≥0.500
According to the proposed evaluation method, the data is
then substituted for verification. In this investigation, leaf
lettuce from Beijing Xiaotangshan, purchased from Beijing
Century Hualian Supermarket, was used as a sample. The
chemical quality index was tested according to the detection
method described above. The test results are presented in
Table VI, from which the membership function values of the
respective nutritional quality indicators were calculated.
TABLE VI
DETECTION VALUE AND MEMBERSHIP FUNCTION VALUE OF NUTRITIONAL QUALITY INDEX OF LETTUCE IN SUPERMARKET
Nutritional
quality index
Test method
Formula
Data 1
Data 2
Data 3
MFV
Moisture
Vacuum drying method
20
110
100%
mm
Xmm
−
=
−
96.3
96.1
96.2
0.500
Protein
Kjeldahl determination
12
2( ) 0.014 100%
V V N
XF
m
−
=
1.26
1.16
1.19
0.300
Vitamin A
High-efficiency liquid chromatography
1 3.00
c100
AA
EV
=
4.03
4.16
4.08
0.380
VC
2,6-dichloro-indigo
34
() 100
cV V T
Vm
−
=
9.20
9.30
9.40
0.500
VE
High-efficiency liquid chromatography
1 3.00
c100
EA
EV
=
96.8
95.1
95.6
0.290
VB
2,6-dichloro-indigo
34
() 100
bV V T
Vm
−
=
0.013
0.014
0.012
0.500
Ca
Atomic absorption spectrometry
00
3( ) 1000
1000
C C V
Xm
−
=
41.9
40.5
41.2
0.500
1.03
1.00
1.05
0.600
P
31.3
29.5
30.7
0.670
Dietary fiber
Enzyme weight method
12
12
( ) 2 + + 100%
()
R R P A B
SS
m m m m m
DF mm
+−
=
+
（ ）
1.03
1.08
1.04
0.200
Tannin
Spectrophotometry
5g
T
V
m
=
339
333
335
0.330
Nitrate (NO3)
Ultraviolet spectrophotometry
68
7
n
NVV
mV
=
742
752
749
0.700
Note: MFV represents the membership function value; X1 represents the content of dry matter in the sample, %; m0 represents the weight of the weighing vessel,
g; m1 represents the weight of the weighing vessel and the sample, g; m2 represents the weight of the weighing vessel and the dried sample; X2 represents the
content of crude protein in the sample, %; V1 represents the volume of hydrochloric acid standard solution consumed by the sample, ml; V2 represents the volume
of hydrochloric acid standard solution consumed by the blank reagent, ml; N represents the equivalent concentration of hydrochloric acid standard solution; m
represents the weight of the sample, g; F represents protein conversion coefficient, 6.25; cA represents the concentration of vitamin A, g/mL; cE represents the
concentration of vitamin E, g/mL; A represents the average UV absorbance value of vitamins; V represents the volume of standard solution added, μL; E
represents the 1% specific absorption coefficient of a certain vitamin; Vc represents the content of vitamin C, mg/100g; V3 represents the volume of dye solution
consumed when titrating the sample solution, mL; V4 represents the volume of dye solution consumed when titrating blank, mL; T represents the titer of 2,6-
dichloro-indigo, mg/mL ; λ represents the dilution factor; Vb represents the content of vitamin B, mg/100g; X3 represents the content of mineral elements in the
sample, mg/kg; C represents the concentration of elements in the sample solution for determination, μg/mL; C0 represents the concentration of elements in the
blank solution, μg/mL; V0 represents the constant volume of the sample, mL; DF represents the content of dietary fiber in the sample, %; mR1 and mR2 represent
the mass of the residue of the double sample, mg; mP represents the mass of protein in the sample residue, mg; mA represents the mass of ash in the sample
residue, mg; mB represents the mass of the blank, mg; mS1 and mS2 represent the mass of the samples; ωT represents the tannin content in the sample, mg/kg; ρg
represents the concentration of gallic acid in the sample solution, mg/L; V5 represents the constant volume of the sample solution, mL; ωN represents the nitrate
content in the sample, mg/kg; ρn represents the concentration of nitrate in the sample solution found from the standard curve, mg/L; V6 represents the constant
volume of the extract, mL; V7 represents the volume of the suction filtrate, mL; V8 represents the constant volume of the sample solution, mL.
The average membership function values and differences
of the two types of indicators were further calculated and
compared with the reference value of the lettuce nutritional
quality index (Table VII). The nutritional quality of the
sample was then comprehensively evaluated according to the
classification results of the nutritional quality of the leafy
vegetables.
According to the comparison results in Table 6, the
nutritional quality of the leaf lettuce purchased at the
supermarket was comprehensively evaluated. Vitamin
content of the lettuce was evaluated according to a
comparison of the average membership function value of the
good quality index with the reference value. Nutrient quality
indicators were also assessed, e.g., it was found that mineral
elements present were in the middle-upper level. The average
TABLE VII
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
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Y. Dong et al: Preparation of Papers for IEEE Access (February 2020)
VOLUME XX, 2020 11
AVERAGE MEMBERSHIP VALUE OF LEAF LETTUCE IN
SUPERMARKET
Index\Name
Lettuce
(reference value)
Sample
Good quality
0.491
0.445
Adverse quality
0.312
0.517
Difference
0.179
0.0700
membership function value of the adverse quality index was
greater than 0.5, indicating that the content of nitrates in the
evaluated lettuce was too high, affecting the nutritional
quality. It can also be observed that the difference between
the two is small, at less than 0.1, so the nutritional quality
evaluation result of the lettuce sample was judged to be poor.
V. SYSTEM IMPLEMENTATION AND ANALYSIS
A. IMPLEMENTATION OF THE WHOLE PROCESS
TRACEABILITY SYSTEM OF LEAFY VEGETABLES
This study combines the quality and safety traceability model
of leafy vegetables using ASP.NET as the development
language, and develops a full-track traceability system for
leafy vegetables based on B/S architecture for production
managers and consumers. The system can provide key
information on important monitoring processes in the
production, processing, storage, transportation, distribution,
and sale of leafy vegetables. The main interface of the system
includes homepage, traceability code query, standards for
vegetable safety, news, and technical services, as shown in
Figure 7. The user can log in through the homepage, then the
code query is used to access traceability information by
inputting the traceability code (Figure 8).
FIGURE 7. Main system interface
Trace information of the leafy vegetable provided to the
customer, including producer, quality, safety, and nutritional
information, is stored in two-dimensional code which has the
capacity to store the most text information (Figure 8).
The consumer only needs to scan the QR code with a
smartphone to enter the traceability platform and access a
large amount of traceability information. Figure 9 provides
the information (seeding, prevention, irrigation, fertilization,
nutrition) obtained by scanning the traceability QR code with
WeChat.
FIGURE 8. Tracing code query results
FIGURE 9. Information obtained by scanning QR code with WeChat
B.QR CODE ERROR CORRECTION TEST RESULTS
The QR code on the package of vegetables may be damaged
during the packaging, transportation, and marketing of
products. The error correction ability of QR code can
guarantee some degree of recognition, but it will be hard to
recognize the code if there is significant damage. The
recognition rate of QR code was tested using different error
FIGURE 10. Test pictures generated by different error correction levels
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Y. Dong et al: Preparation of Papers for IEEE Access (February 2020)
12 VOLUME XX, 2020
correction levels in combination with different damage
conditions to determine the suitable error correction level (L,
M, Q, H). The test picture is shown in Figure 10.
Damage condition was tested at 5%, 10%, 15%, 20%, 25%,
and 30% in this research. Figure 11 shows the pretext process
results of the samples which were damaged (breakage and
defacement) at 15% condition under four error correction
levels, L, M, Q, and H.
(a) 15% breakage condition
(b) 15% defacement condition
FIGURE 11. QR code samples at 15% damage condition
Each damage condition of the samples was tested 25 times.
The recognition results of the samples with different
breakage condition for different error correction levels are
provided in Table VIII, and the recognition results of the
samples with different defacement condition for different
error correction levels are provided in Table IX.
TABLE VIII
RECOGNITION RESULTS OF THE SAMPLES WITH DIFFERENT
BREAKAGE CONDITION FOR DIFFERENT ERROR CORRECTION
LEVELS
Breakage
Condition
Error correction level
L
M
Q
H
5%
21
22
22
22
10%
5
22
22
22
15%
0
5
22
22
20%
0
0
14
22
25%
0
0
1
18
30%
0
0
0
2
TABLE IX
RECOGNITION RESULTS OF THE SAMPLES WITH DIFFERENT
DEFACEMENT CONDITION FOR DIFFERENT ERROR CORRECTION
LEVELS.
Defacement
Condition
Error correction level
L
M
Q
H
5%
22
22
22
22
10%
10
22
22
22
15%
0
11
22
22
20%
0
0
20
22
25%
0
0
6
18
30%
0
0
0
11
As shown in Figure 12, the recognition rate of the QR
code generated by different error correction levels can be
calculated according to the results of Tables 11 and 12.
510 15 20 25 30
0
10
20
30
40
50
60
70
80
90
100
510 15 20 25 30
0
10
20
30
40
50
60
70
80
90
100
Recognition rate (%)
(a) Breakage condition (%)
Level L
Level M
Level Q
Level H
Level L
Level M
Level Q
Level H
Recognition rate (%)
(b) Defacement condition (%)
FIGURE 12. Recognition rate of QR codes generated by different error
correction levels
To further explore the recognition ability of QR codes
generated by H-level, a damage location test was conducted.
In the test, 25 predicted break points were uniformly
searched on the generated QR code (Figure 13(a)) for 5%,
10%, 15%, 20%, 25%, and 30% breakage tests. The
recognition rate under different damage conditions is shown
in Figure 13(b) and Figure 13(c).
By comparing the test results above, the following
conclusions can be drawn:
a. Comparing the tested pictures illustrates that the higher
the error correction level, the more error correction code
characters are added to the encoded text in the QR code,
resulting in more complicated coding patterns.
(a) Test point
(b) Breakage condition
(c) Defacement condition
FIGURE 13. Analysis of the damage degree and recognition rate
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Y. Dong et al: Preparation of Papers for IEEE Access (February 2020)
VOLUME XX, 2020 13
b. The QR code generated with the error correction level H
still has certain recognition ability when there is about 30%
damage, and the recognition abilities of the QR code
generated by levels L, M, and Q are severely degraded at
10%, 15%, and 25%, respectively. Therefore, from a single
consideration of recognition performance, the error
correction level H is the best QR code error correction level.
c. Regardless of error correction level, the generated QR
code exhibits higher recognition ability when subjected to
5% breakage or defacement. For QR codes generated by the
same error correction level, the defaced QR code is more
capable of being identified than the broken QR code when
the damage area is higher than 5%.
d. If the points P1, P5, and P21, which represent the
position detection graphics, are damaged by more than 5%,
the QR code loses the ability to be recognized. Therefore, the
QR code should be placed in the position of the packaging
bag where the three points will not be easily broken or
defaced.
VI. CONCLUSION
According to the analysis and research carried out in this
paper, the following conclusions can be obtained:
a. A traceability model for quality and safety of leafy
vegetables was constructed based on the HACCP system and
FTA method. The key control points in the leaf-vegetable
production-transport-sale process were obtained using
HACCP analysis. Qualitative and quantitative analysis on
related events was then conducted using FTA. According to
the analysis results, it can be seen that in the process of field
management and storage and transportation, the safety
interval between the production environment, field
management, harvesting process, place of production, and
inspection by the supermarket are the key points that require
monitoring in the traceability process. By monitoring these
processes, problems can be discovered in time, thereby
improving the reliability of the entire traceability process.
b. An index system for nutritional quality evaluation was
constructed according to the nutritional quality characteristics
of leafy vegetables. Specific nutritional quality indicators
were determined according to the nutritional characteristics
of four common leafy vegetables. The Chinese national
standard was chosen as the detection method to determine
each nutritional quality index. The membership function
method of fuzzy mathematics was then used to evaluate the
nutritional quality of leafy vegetables, allowing the leafy
vegetables to be graded according to their nutritional quality.
The results of the nutritional quality evaluation can be used
as the nutritional quality information within the entire
traceability system, which improves the traceability
information of the system.
c. A traceability system for the quality and safety of leafy
vegetables was both designed and realized. The ASP.NET
framework was used as the development language to create a
quality and safety traceability system for leafy vegetables
based on B/S architecture which provides comprehensive
traceability information. Consumer information, including
records, inquiries, and nutritional quality information, was
then stored in a two-dimensional code. This method enables
the consumer to use a smart phone to scan the code of leafy
vegetables to quickly obtain traceability information of the
product.
ACKNOWLEDGMENT
This study was supported by the National Science
Foundation for Young Scientists of China (61802411).
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This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.3019593, IEEE Access
Y. Dong et al: Preparation of Papers for IEEE Access (February 2020)
14 VOLUME XX, 2020
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