Nano-Optical Sensors for Food Safety and Security

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491
19
Nano-Optical Sensors for Food Safety and Security
Euiwon Bae and Arun K. Bhunia
19.1 Introduction
19.1.1 Overview
Recent incidence of foodborne outbreaks due to pathogen contamination demands a microbial surveil-
lance strategy that could routinely monitor food systems for the presence of harmful microorganisms
administered naturally or intentionally. Continued outbreaks from food commodities remind us the
vulnerability of the food supply chain to microbial contamination that may occur at any step of the food
production chain: during harvesting of raw materials, processing, packaging, transport, or retail distribu-
tion. Foodborne infections may be of bacterial, viral, mold, or parasitic origin. Centralized food manu-
facturing practices and globalized raw material sourcing and processed food distribution made food
safety a global issue that requires concerted efforts to improve food safety and food defense, and manage-
ment practices. Globally food-related outbreaks are widespread; however, foodborne infection statistics
AQ1
CONTENTS
19.1 Introduction ...................................................................................................................................491
19.1.1 Overview ..........................................................................................................................491
19.1.2 Optical Methods for Nanometer Scale Detection ........................................................... 492
19.2 Elastic Light Scattering ................................................................................................................ 492
19.2.1 Introduction ..................................................................................................................... 492
19.2.2 Measurement Principle and Instrument .......................................................................... 493
19.2.3 Application of ELS .......................................................................................................... 494
19.3 Fourier Transform Infrared Spectroscopy ................................................................................... 494
19.3.1 Introduction ..................................................................................................................... 494
19.3.2 Measurement Principle and Instrument .......................................................................... 495
19.3.3 Application in Food Safety .............................................................................................. 495
19.4 Hyperspectral Imaging ................................................................................................................ 496
19.4.1 Introduction ..................................................................................................................... 496
19.4.2 Measurement Principle and Instrument .......................................................................... 496
19.4.3 Application in Food Safety .............................................................................................. 497
19.5 Fiber-Optic Biosensor .................................................................................................................. 498
19.5.1 Introduction ..................................................................................................................... 498
19.5.2 Measurement Principle and Instrument .......................................................................... 499
19.5.3 Application in Food Safety and Food Defense ............................................................... 499
19.6 Surface Plasmon Resonance ........................................................................................................ 500
19.6.1 Introduction .....................................................................................................................500
19.6.2 Measurement Principle and Instrument .......................................................................... 500
19.6.3 Application in Food Safety and Food Defense ............................................................... 501
19.7 Future Trends ............................................................................................................................... 501
Acknowledgments .................................................................................................................................. 502
References .............................................................................................................................................. 502
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492 Optochemical Nanosensors
are available only for a few developed countries [1]. The Centers for Disease Control and Prevention
(CDC) in the United States estimates 48 million cases of foodborne illness occur each year, among
which 3000 are fatal. The major foodborne pathogens include Listeria monocytogenes, Salmonella
(non-typhoidal serotypes), Escherichia coli O157:H7, E. coli non-O157 STEC (Shiga-toxin–producing
E. coli), Campylobacter, Norovirus, and Toxoplasma gondii and are responsible for a majority of out-
breaks [2]. The economic losses to the food industry account in billions of dollars ($152 billion/year) [3].
Traditional technique for detecting pathogenic bacteria from food involves culturing and plating. Even
though this method is regarded as a gold standard [4], it involves many labor-intensive steps; sample
preparation, rinsing or homogenization, growing, plating, and subculturing, and takes up to 3–10 days
to provide results. To expedite detection, many researchers have used antibody or nucleic acid–based
methods [5,6]. The actual assay time is reasonably fast, requiring a few minutes to hours, but a prolonged
sample preparation and enrichment step (18–48 h) made many of these tests rather lengthy [7]. In recent
years, nucleic acid–based technologies such as multiplex-PCR [8], PCR coupled with mass spectrometry
[9], or high-density genome sequencing or microarray [10,11] are shown to be promising novel tech-
nologies for food safety and food defense applications. Alternatively, biosensor-based methods includ-
ing electrical, electrochemical, mass-based, and optical sensors have been developed to facilitate rapid
detection and diagnosis of infective agents in food or clinical samples [12–19].
19.1.2 Optical Methods for Nanometer Scale Detection
Application of optical diagnostics tools in pathogen detection offers many advantages: methods are
generally non-destructive, i.e., they maintain original sample integrity, deliver results quickly, and the
system can be engineered to make portable. Depending on the technology platform and the probes or
reagents used, they can be made highly sensitive or specic. Some optical sensor platform works inde-
pendent of any probes or labeling reagents and the inherent natural biomolecular differences provide
specicity for a given pathogen. The signals generated from a nano-scale object are roughly proportional
to their volumes. Researchers have also found a number of ways to amplify sensor signals by (i) increas-
ing the detector sensitivity, (ii) increasing the transducer signal by ltering the unwanted signal or noise,
and (iii) amplifying the signal generated from the sample itself. Detector sensitivity can be improved by
selecting ultra-sensitive devices such as photomultiplier tube (PMT), where a single photon is amplied
about 1000 times, or cooled charge coupled device (CCD), where thermal and electronic noise levels are
very low. Selection of appropriate target-specic labels can also increase the sensitivity. Signal-to-noise
ratio (SNR) can be enhanced by spectrally blocking unwanted signals and by accepting only desired
ones. Finally, sensitivity can be enhanced by increasing the amount of the target analyte deposited on
the sensor platform. For example, PCR allows amplication of the target DNA from a pathogen, which
can be readily detected by an optical sensor. Furthermore, amplication can be achieved by allowing
microbial cells to grow to a level that are within the threshold detection limit for the sensor. Here, we
reviewed the ve major optical sensors that are currently being developed as nano-optical sensors for
food safety and biosecurity applications.
19.2 Elastic Light Scattering
19.2.1 Introduction
Elastic light scattering (ELS) is dened as an optical measurement technique that utilizes the character-
istics of the spatial distribution of the scattered light. ELS signal strength is very high compared to other
spectroscopic and inelastic scattering techniques. By analyzing the ELS signal, it is possible to solve an
inverse-scattering problem without any specic labeling reagent such as nucleic acid (DNA or RNA) or
antibody probes, uorophore molecules, or enzymes. Due to its unparallel performance, ELS has been
used in diverse science and engineering elds such as astronomy, semiconductor industry, and biology.
Furthermore, ELS method is non-destructive, i.e., it maintains the sample integrity during interrogation
and the signal measurement is instantaneous.
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19.2.2 Measurement Principle and Instrument
In ELS, the scattered light emanating from a sample is captured by a photo detector, PMT or CCD. PMT
works well as a point detector since it has high gain amplication capacity while CCD is better suited for
detecting 2-D spatial distribution of the scattered light.
As shown in Figure 19.1, when an incident beam with wavelength (λ) impinges on a mixture of par-
ticles with diameter (D), the elastically scattered light (Is) spreads out to the 4π spherical direction with
different polar and azimuthal angles (θ, Φ). The scattered light can be measured and analyzed in three
different ways: (i) integration of the amount of all the scattered light called total scattering cross-section
(TSC). This is proportional to the size of the individual scatterer, i.e., sample, and it can estimate the
diameter of the sample. (ii) Measurement of the point intensity using PMT at a scattering angle. This
determines the differential scattering cross-section (DSC) for the designated scattering angle to provide
angle-resolved scattering (ARS) patterns. Typically, this type of measurement is performed in a gonio-
metric setup to rotate the point detector in both azimuthal and polar directions. (iii) Finally, generation
of a TSC versus wavelength, which is dened as an elastic scattering spectroscopy (ESS). To achieve
this, a broadband light source and series of lters are required to provide a band-limited incident beam
that sweeps across the different wavelengths. Typical ELS measurements have strong signals and are
label-free in nature but they suffer from high background noise when coherent light is used. This worsens
as the size of the scatterer (sample size) becomes smaller since their scattering intensity decrease with
the sixth power of the diameter. Therefore, to improve detection at the nano-scale, researchers proposed
using high refractive index materials such as gold or silver nano-particle as a scattering contrast agent
since metals have larger scattering cross-section per volume [20,21].
Figure 19.2 shows typical ELS instruments for both single cell and bacterial colony detection.
Bacterial growth on solid surface results in the formation of colonies. Even though optical sensors are
also developed to detect and identify bacterial colonies at micrometer range, approaches to detect and
identify colonies at the submicron level are often difcult since individual colonies of different species
or serovars may have biomaterial signatures that may not be adequate to provide distinguishing features.
Prolonged growth, resulting in higher cell mass, may provide differential signals that can be detected and
identied rapidly. Figure 19.2a shows the principles of uorescence activated cell sorter (FACS), which
is also called ow cytometer. Even though uorescence tag is generally used, the system still measures
the forward and right angle scatter to differentiate the cell size. Figure 19.2b displays the BActerial Rapid
Total
intensity
Vs. size
Point
intensity
Vs. angle
D
D
λ
λ
Is
∑I
s
∑Is
Is
θ
θ, φ
φ
Total
intensity
Vs. spectrum
FIGURE 19.1 Schematic diagram depicting principles of elastic light scattering sensor (ELS). When the incident light of
wavelength (λ) impinges samples consists of particle of diameter (D), scattered light (Is) will spread out in hemispherical
direction (θ and Φ). Scattered light intensity can be measured and plotted against D, or θ and Φ, or λ to retrieve sample
information.
AQ2
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494 Optochemical Nanosensors
Detection using Optical scattering Technology (BARDOT) instrument which consists of a diode laser
of 635 nm and a CCD detector to capture 2D scattering patterns from bacterial colonies [22,23]. Once
the scattering patterns are captured, it is stored in the database to be used as a ngerprint library for
future detection and identication of pathogens or non-pathogenic bacteria using advanced classication
algorithm [22,24].
19.2.3 Application of ELS
The rst reported attempt to differentiate bacterial culture by using light scattering was by Wyatt
[25,26], who used ARS to differentiate different microbial species suspended in liquid. Since then, many
researchers [27–30] manipulated the incident light and used polarized light to determine the size and
diameter distribution [27–31], sporulation and differentiation [31], structure and changes in the morphol-
ogy [29,32], and metal toxicity on bacterial cells [32]. Furthermore, the light scattering pattern of E. coli
cells was investigated using a scanning ow cytometer (SFC) where the scattering polar angle was set to
5–100 [33]. Various cell types suspended in liquid were differentiated by measuring the hologram from
a lens-less holographic imaging device [34–36]. To identify bacterial colony growth on solid agar plate
using 2-D spatial scattering pattern, BARDOT device was introduced [22,23,37]. Later, this system was
used to measure bacterial colony growth and differentiation [23,38,39]. A quantitative image processing
algorithm was developed using Zernike polynomial and a support vector machine [24]. The BARDOT
system was automated to include a secondary camera to map colony locations in the Petri dish, and
colony centering and traveling salesman algorithm to generate scatter images of colonies of interest [39].
This system was successful in detecting and identifying pathogenic bacteria such as L. monocytogenes,
Staphylococcus, Salmonella, Vibrio, and E. coli O157:H7 from inoculated meat, vegetable, and seafood
samples [37,40].
19.3 Fourier Transform Infrared Spectroscopy
19.3.1 Introduction
Fourier transform infrared spectroscopy (FTIR) is one of the non-destructive chemical imaging tech-
niques which measures the wavelength dependent light absorption characteristics to identify samples
based on the chemical composition and unique molecular vibration [41,42]. Typical wavelength range is
from near-IR (714 nm–2.5 μm), mid-IR (2.5–25 μm), to far-IR (25–1000 μm). Since molecules and their
structures respond differently to the incoming IR light, it is possible to provide a unique chemical nger-
print signature of molecules by understanding their fundamental and rotational vibrations and recording
their spectral signatures (Figure 19.3).
Sheath flow
Laser
(a) (b)
PMT
Unsorted cells Laser diode Mirror
XY
stage
Bacteria
sample
CCD
image sensor
Fluorescence
signal
Right angle
scattered light
Forward
scattered light
FIGURE 19.2 Examples of elastic light scattering sensor (ELS): (a) uorescence activated cell sorter (FACS) and
(b)BActerial Rapid Detection using Optical scattering Technology (BARDOT) instrument, which consists of a diode laser
of 635 nm and a CCD detector to capture 2D scattering patterns from bacterial colonies.
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495Nano-Optical Sensors for Food Safety and Security
19.3.2 Measurement Principle and Instrument
There are two major variations in the instrument structure of FTIR: dispersive and Fourier-transform.
The dispersive-type spectrometer uses typical diffraction gratings to spread the incoming broadband IR
source and lter narrow bandwidths to record the spectrum by scanning the dispersed light. In contrast,
the Fourier-transform-type measures the time-domain data and converts them into frequency domain
yielding a spectrum via Fourier transform technique. Since none of the existing detectors respond to
the optical frequencies (∼1014 Hz), interferometric setup is used to convert this time-domain signal into
measurable and visible fringe patterns. As shown in Figure 19.4, infrared photons from light sources
enter the Michelson interferometer unit which is split and directed towards xed and moving mirror
and recombined to the sample chamber (in sample holder unit). The outgoing spectrum is then detected
by infrared detectors (either thermal or pyroelectric types). This process is repeated at different moving
mirror positions (labeled dx) and the resulting raw interferograms were transformed into spatial (dx) and
intensity relationship. Finally, once the data are transformed using Fourier transform, the absorption
spectra are obtained.
19.3.3 Application in Food Safety
Naumann and his colleagues [41] were the rst to apply FTIR for the identication of different bacte-
rial species. Since then, it has been extensively studied because of its unique capability of correlating
the chemical signature of molecules unique to bacterial genera or species. FTIR coupled with vari-
ous chemo-metrics such as HCA (hierarchical cluster analysis), PCA (principle component analysis),
and ANN (articial neural network) was used to identify and differentiate bacteria, yeast, and other
microorganisms [43,44]. Specically, FTIR was successfully applied in the identication of E. coli,
Bacillus, Pseudomonas, Listeria, and Staphylococcus [44]. A variation of FTIR called photoacoustic
spectroscopy (PAS) that utilizes the photoacoustic effect (modulated incident photon energy is con-
verted to pressure oscillation and measured by microphone or piezotransducers) was able to demonstrate
bacterial contamination on the surface of produce [45,46]. In food safety and food defense applications,
FTIR coupled with ANN was used to analyze the presence of foodborne pathogens or bio-threat agents
[12,47,48]. Analytical procedure for direct application of FTIR for food testing was also reported [49–51].
Since FTIR does not require any labeling reagent for discrimination, it is highly attractive for rapid and
label-free detection of pathogenic bacteria: Listeria monocytogenes [52], Staphylococcus aureus [53],
IR source
Sample
IR detector
Wavelength
Wavelength
Rotation
IR photon
Vibration
Intensity
Intensity
FIGURE 19.3 Schematic diagram depicting principles of Fourier transform infrared spectroscopy (FTIR).
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496 Optochemical Nanosensors
Salmonella [54], and E. coli O157:H7 [50]. Even though the reported detection limit for FTIR to be in the
range of 103 CFU/mL [41,50], most experiments were conducted using bacterial concentrations ranging
from 108 to 109 CFU/mL to ensure good signal-to-noise ratio [44,47,50,51].
19.4 Hyperspectral Imaging
19.4.1 Introduction
Hyperspectral imaging technology is often referred to as imaging spectrometry, and it was originally
developed to perform satellite imaging for geophysics and remote sensing applications. Hyperspectral
imaging system collects the spatial intensity information across many electromagnetic spectra and gen-
erates complete spatio-spectral map of the terrain or the object. Multispectral measurement has also
been used similarly but the measurement technique typically uses spectral band consisting of 10–20
discrete wavelengths. In contrast, in hyperspectral measurement, the spectrum can be a sweep from vis-
ible to infrared ranges with very small bandwidth (several nm). Hyperspectral imaging technique has
been used widely in crop production and disease monitoring in agriculture, mineralogy, physics, and
land surveillance. Recently, it has drawn signicant interests in biological sensor development because it
has the capability to generate unique ngerprints in both spatial and spectral domain without the use of
any labeling reagents or biological probes [55–57]. Spatial information is typically recorded as intensity
images while the spectral information consists of reection intensity and generates three-dimensional
data set called hyperspectral data cube (Figure 19.5).
19.4.2 Measurement Principle and Instrument
Typical hyperspectral measurement system used in food industry consists of three core components:
broadband light source, sample stages, and collection optics equipped with a detector. Broadband light
source, typically tungsten light, provides the wide spectrum of electromagnetic energy to impinge the
Interferometer
unit
Sample holder
unit
Detection unit
Intensity
Intensity
dx
dx
Fourier transform
Wavelength
IR detector
Sample chamber
Beam splitter
Fixed mirror
Moving mirror
IR source
FIGURE 19.4 Interoferometric setup of FTIR. When infrared photons from light sources enter the Michelson interfer-
ometer unit, which is then split and directed toward xed and moving mirror, they are recombined to the sample chamber
in the sample holder unit.
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497Nano-Optical Sensors for Food Safety and Security
sample under investigation. For 2-D spatial measurement (Figure 19.6), sample stages require a 1-D
translation movement when the image from the reection or transmission is collected in line-scan mode.
The reected or transmitted light from this narrow 1-D strip is collected via relaying optics and travels
through dispersive component such as prism or grating in such a way that different electromagnetic
energy is decomposed and projected onto different spatial location. Continuing scanning of the sample
thus generates a three-dimensional data cube as shown in Figure 19.5.
19.4.3 Application in Food Safety
One of the earliest reports on application of hyperspectral imaging was by Park et al. [57], who exam-
ined microbial contamination on poultry carcasses. They applied multispectral measurement coupled
Spectral
analysis for
each spatial
pixel location
Spatial analysis for
each wavelength
location
Spectrum (λ)
Spatial (XY)
λ
Intensity
FIGURE 19.5 (See color insert.) Schematic diagram showing principles of hyperspectral or multispectral imaging setup.
Spatial information is typically recorded as intensity images while the spectral information consists of reection intensity
and generates three-dimensional data set called hyperspectral data cube.
Relaying
optics
Diffraction
grating
2D detector
Spectrally
dispersed
Record spatial intensity
Broad band
light source
Sample on
movable
stages
FIGURE 19.6 Schematic diagram showing typical setup for hyperspectral imaging setup used in food industry. It consists
of three core components: broadband light source, sample stages, and collection optics equipped with a detector.
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498 Optochemical Nanosensors
with linear discriminate model to predict contamination with 83%–97% accuracy. Since then most
application in food industry was directed toward online application to monitor quality of products
such as tomatoes [58], apples [59,60], cucumbers [61], and poultry [56,62]. One of the most interest-
ing applications of hyperspectral measurement was proposed by Kim and his colleagues [63,64] to
inspect apples for possible contamination with fecal matters online in real time during sorting and
quality assessment. They used both visible and near-infrared spectra. Using uorescence signal emit-
ted from the fecal matter as indicator, the system was able to accurately (100%) detect the contamina-
tion but the accuracy was slightly compromised (99.5%) when two band classication methods were
used. Recently, Yoon et al. [65,66] used hyperspectral imaging system to classify Campylobacter
colonies from non-Campylobacter organisms based on spectral absorption characteristics using
400–900 nm wavelengths. The results were highly reproducible with very high accuracy (97%–99%)
and data were obtained in 24 h after plating. In another study, multispectral imaging using three nar-
row bands of 743, 458, and 541 nm was employed to detect and differentiate toxigenic fungi with 97%
of accuracy [67].
19.5 Fiber-Optic Biosensor
19.5.1 Introduction
Fundamental properties of optical ber are its ability to deliver the incoming light signal and to act as
a transducer of outgoing light [68]. Due to the mass production and lower cost of optical bers, ber-
optic detection platform has been a popular detection system that has found wide usage in various
elds. It had been used to measure pressure [69], displacement [70], temperature [71], acceleration [72],
and liquid levels [73]. In addition, it is one of the earliest biosensing platforms developed for the
detection of biological and chemical agents primarily in the U.S. Naval Research Laboratory [74–76].
As shown in the top part of Figure 19.7, overall measurement is performed by comparing the net
change from interrogating light source to the detected light through the ber. As the incident light
travels through the ber, the biological recognition element, i.e., the analyte and uorophore-labeled
receptor interaction, generates a disturbance in the ber signal, which is then measured by an
output detector.
n2
θ
Antibody
Lower mode
Higher mode
Lp
n1
nn
Cladding
Core
Step
index
fiber
Graded
index
fiber
Cross section
Cross section
Output light
Optical
transducer
Biochemical
reaction
Input light
FIGURE 19.7 Schematic diagram showing light propagation and detection of analyte on ber-optic sensor.
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499Nano-Optical Sensors for Food Safety and Security
19.5.2 Measurement Principle and Instrument
Fundamental measurement principle of ber-optic sensor relies on coupling between biochemical reac-
tion and optical transducer, which is achieved in four different ways (Figure 19.8). Figure 19.8a shows the
intensity-based ber-optic sensor, which uses a section that is devoid of cladding to immobilize biorecog-
nition molecule to capture target analyte. Since refractive indices of cladding (n2) are different from the
effective refractive index (ne) of recognition and target analyte complex, the reection coefcient of each
beam interacting in this area will be affected, which will result in intensity variation at the measurement
end. Figure 19.8b displays the similar concept but it uses the broadband light. Since refractive indices are
function of the wavelengths used, broadband source will provide spectroscopic means of differentiating
the molecule–molecule interactions rather than pure intensity measurement. Figure 19.8c displays the
evanescent wave ber-optic sensor with sandwich conguration where the immobilized biorecognition
molecule rst binds to the analyte, which is then detected by uorophore-labeled antibody or biomol-
ecule. In this setup, cladding is removed from the wave-guide. The launched incident light is generally
larger than the critical angle. The evanescent wave penetrating 100–200 nm into the surrounding core
acts as the probe beam and uorescence tag serves as a signal transducer. Figure 19.8d shows the layout
for SPR-ber-optic sensor, which combines the principle of SPR and the ber-optic signal transduction
setup. Metal layer with negative permittivity is deposited outside the dielectric ber. When the incident
beam inside the core is larger than the critical angle and matches the SPR condition, energy is transferred
from the evanescent wave to the surface plasmon on the metal layer to disturb the reected light from the
recognition molecule and target analyte complex.
19.5.3 Application in Food Safety and Food Defense
The rst report on ber-optic sensor for food safety application was made by Lim and his colleagues[77],
who used this sensor for detection of E. coli O157:H7 from a complex food matrix. Later, Ferreira etal.[78]
reported intensity-based measurement of E. coli O157:H7 from a set number of bacterial cells to calculate
Biorecognition
molecule
n2
ne
Core
Cladding
(a)
Fluorescence tag
Evanescent
wave
(c)
n2
ne
Broad band light
(b)
(d)
Metal
layer
Surface
plasmon
FIGURE 19.8 Fundamental measurement principle of ber-optic sensor. Signal intensity depends on coupling between
biochemical reaction and optical transducer that is achieved in four different ways: (a) intensity-based ber-optic sensor
uses a section that is devoid of cladding to immobilize biorecognition molecule that captures target analyte; (b) displays
the similar concept but it uses the broadband light; (c) displays the evanescent wave ber-optic sensor with sandwich
conguration where the immobilized biorecognition molecule rst binds to the analyte, which is then detected by uoro-
phore-labeled antibody or biomolecule; (d) shows the layout for surface plasmon resonance (SPR)-ber-optic sensor, which
combines the principle of SPR and the ber-optic signal transduction setup.
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500 Optochemical Nanosensors
sensitivity of 0.016 dB/h per bacterium. Analyte 2000 is the core ber-optic instrument developed by
Research International (Monroe, WA, USA). It is widely used for the detection of various pathogens includ-
ing E. coli O157:H7 [77,79], L. monocytogenes [80–83], Salmonella enteritidis [84,85], staphylococcal
enterotoxin B [86], and Vaccinia virus [87] in a sandwich conguration using uorophore-labeled anti-
body. Later, a portable and semi-automated system was developed called RAPTOR (Research International,
Monroe, WA) to report the detection of foodborne pathogens or biothreat agents [88] such as S. typhimurium
[89], Bacillus anthracis and Francisella tularensis [90], and staphylococcal enterotoxins [91]. Using the eva-
nescent wave principle, planner waveguide technology (NRL Array biosensor) was developed by Ligler and
her colleagues [92] at the Naval Research Laboratory (USA) to detect multiple biothreat agents including
bacterial pathogens, mycotoxins, and microbial and non-microbial toxins in an array format using antibodies.
19.6 Surface Plasmon Resonance
19.6.1 Introduction
Surface plasmon resonance (SPR) sensor utilizes the coherent electron oscillation that is generated at the
metal–dielectric interface during coupling of excited light of specic wavelength. This sensor generates
surface wave that travels parallel to the metal–dielectric interface and the eld strength shows an evanes-
cent characteristics, which diminishes quickly from the surface, typically up to several hundred nano-
meters. When the biorecognition molecule, i.e., antibody, enzymes, receptors, or nucleotides, captures
target analyte, it results in a change in the reection intensity, which is translated into the refractive index
variation. In general, SPR is considered a label-free detection system since it does not require any differ-
entiating reagents or uorophores at the sample preparation stage. However, it requires antibody or bio-
recognition molecules to be pre-deposited onto the SPR surface to provide specicity for target analyte.
19.6.2 Measurement Principle and Instrument
The measurement principle of SPR is illustrated in Figure 19.9. Depending on SPR conguration, the
analyte containing samples are either deposited or own through uidic channels to the SPR surface,
which is pre-coated with biorecognition molecules. An incident p-polarized light beam striking the
prism surface at a particular angle greater than the total internal reection (TIR) angle generates sur-
face plasmons at the surface of metal layer (gold or silver). Surface plasmons are special modes of the
electromagnetic eld comprised of transverse magnetic (TM) polarized waves, i.e., light polarization
occurs parallel to the plane of incidence where the height of the evanescent wave is approximately one-
fourth of the wavelength of the incoming light that strikes the prism surface. As binding events unfold
at the sensor surface, the refractive index of the medium near the dielectric–metal interface changes and
as a result there is a shift in the reected light, which is detected via photodiode array (PDA) or CCD.
For surface plasmons to occur at the interface of metal–dielectric, the real part of the permittivity of
the two media should be of opposite charges [93]. Metals such as gold and silver fulll such conditions
when interrogated by light in the IR-visible spectrum. Gold is widely used in most of the commercially
available SPR systems as it provides the added advantage of enabling well-established surface chemistry
for immobilization of bioreceptors. As shown in Figure 19.9, the target molecules are captured by the
biorecognition molecule that lies within the penetration depth of the evanescent waves. The detection of
target molecules by SPR is typically achieved by measuring the shift in the reection intensity prole
or recording the time-resolved resonance signal. The rst case provides the thickness measurement by
observing the reectivity variation and matching with the Fresnel multilayer reection as
I R I
R=0 (19.1)
where
IR is the reected intensity measured by the detector
R is the total reection coefcient
I0 is the incident intensity
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501Nano-Optical Sensors for Food Safety and Security
If we dene r12 as reection coefcient between prism and metal layer, r23 as between metal and air, the
total reection coefcient and a phase delay, δ, from the multilayer is expressed as
Rr r i
r r i
n D
=+
+=
12 23
12 23
2 2
2
1 2
2exp
exp
cos( )
( )
( )δ
δδπ θ
λ
(19.2)
where
n2 is the refractive index of the medium
D is the thickness
θ2 is the refracted angle
Secondly, SPR can also provide real-time measurement of resonance signal, which is directly related to the
binding kinetics of the molecule. This type of time-dependent dynamic measurement monitors the real-time
changes in the reectivity close to the resonance point and reports association and dissociation of molecules.
19.6.3 Application in Food Safety and Food Defense
Among the optical sensors, SPR sensors marketed by several manufacturers had been tested with many
pathogens and toxins from wide varieties of food matrices. SPR has been used to detect heat-killed or live
cells of L. monocytogenes at 106 cell/mL using mouse or rabbit antibodies or phage-displayed single chain
fragment variable (scFv) antibodies that were covalently immobilized on sensor platform [94–97]. Oh et al.
[98] detected S. enteric serovar Typhimurium at a concentration of 102 CFU/mL using protein G to immo-
bilize anti-Salmonella antibody on gold surface. SPR biosensors were also used effectively for the detec-
tion of Salmonella in milk at 1.25 × 105 CFU/mL [99], E. coli O157:H7 [100–103], short DNA sequences of
Brucella, E. coli and S. aureus [104], enterotoxins from Staphylococcus [105,106], and mycotoxins [107].
19.7 Future Trends
Currently developed detection technologies are capable of providing results within 12–24 h and are proven
useful to address food safety and biosecurity needs. With the introduction of novel nano- optical sen-
sors,the detection time, however, can be substantially shortened to 4–8 h. Most of the nano-opticalsensors,
Flow-in
Gold film
substrate
Prism
Light
source
I
Peak angle Time
Detector
Antigen
Target sample Evanescent wave
~100–200
nm
Surface
plasmon
Resonance signal
Flow-out
Reaction chamber
FIGURE 19.9 Schematic diagram depicting analyte detection using surface plasmon resonance sensor (SPR). The
biorecognition molecule that lies within the penetration depth of the evanescent waves captures the target analyte.
Thedetection is typically achieved by measuring the shift in the reection intensity prole or recording the time-resolved
resonancesignal.
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502 Optochemical Nanosensors
such as SPR and ber-optic, however, require specic biorecognition molecules such as antibodies, bac-
teriophage proteins, bioreceptors, nucleic acids, or other capture molecules for specicity. The future
scope of these types of biosensors lies on their ability to detect multiple analyte or wide varieties of
biothreat agents. Strategies to achieve such goals would be to use biorecognition molecule(s) that can
interact with wide varieties of pathogens/toxins. In contrast, label-free methods such as ELS and FTIR
do not require any labeling reagents or probes and can provide identity of the agent instantly provided
the database contain the spectral information. The future challenge is to develop algorithm that would
allow detection/identication of previously unknown or unclassied organisms. Rajwa et al. [40] in a
recent study reported the use of Bayesian approach to learning with nonexhaustive training dataset for the
classication/identication of previously unknown bacterial classes. Another bottleneck for these label-
free methods is the time required for signal amplication since the minute changes in nanometer scales
have to be amplied to be macroscopically measurable. For example, BARDOT requires the incubation
time of 12–24 h for a colony of most pathogens to reach to a certain size for detection. To reduce detec-
tion time to less than 8 h, a generally practiced food industry work shift, a new concept of micro-colony
detection method was introduced [108,109]. The rst experimental results [110] show both promise and
limitation on differentiating bacterial colonies in the size regime. Pathogens belonging to three different
genera (Salmonella enterica serovar Montevideo, Listeria monocytogenes F4244, and E. coli DH5α)
were tested when the colony size (diameter) was around 100–200 μm range after about 7–11 h of growth.
Even though the scattering pattern displayed differences in the number of rings and circularity, the differ-
ential characteristics among genera were lowered compared to the fully-grown 1-mm-diameter colonies.
Further investigation on detecting micron-sized colonies with a more sensitive optical method such as
Mueller Matrix formalism [111] may aid in early detection. Since each measurement modality has its own
strengths and weaknesses, there is a great interest in using hybrid methodology such as combining SPR
and ber-optics sensors or enhancing ELS sensor signals by using high refractive index materials such as
gold or silver nano-particle as a scattering contrast agent have been proposed [20,21].
Acknowledgments
The USDA (1935-42000-035) and NIH (1R56AI089511-01) provided the funds to conduct biosensor-
related research in the author’s laboratories.
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Author Queries
[AQ1] Please check the identied heading levels for correctness.
[AQ2] Please check the sentence starting “When the incident light of wavelength …” in the Figure
caption 19.1.
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