Current State of Development of Biosensors and Their Application
in Foodborne Pathogen Detection
AND ARUN K. BHUNIA https://orcid.org/0000-0003-3640-1554
Molecular Food Microbiology Laboratory, Department of Food Science,
Department of Comparative Pathobiology, and
Purdue Institute of
Inﬂammation, Immunology and Infectious Disease, Purdue University, West Lafayette, Indiana 47907, USA
MS 20-464: Received 25 November 2020/Accepted 11 March 2021/Published Online 12 March 2021
Foodborne disease outbreaks continue to be a major public health and food safety concern. Testing products promptly can
protect consumers from foodborne diseases by ensuring the safety of food before retail distribution. Fast, sensitive, and accurate
detection tools are in great demand. Therefore, various approaches have been explored recently to ﬁnd a more effective way to
incorporate antibodies, oligonucleotides, phages, and mammalian cells as signal transducers and analyte recognition probes on
biosensor platforms. The ultimate goal is to achieve high speciﬁcity and low detection limits (1 to 100 bacterial cells or
piconanogram concentrations of toxins). Advancements in mammalian cell–based and bacteriophage-based sensors have
produced sensors that detect low levels of pathogens and differentiate live from dead cells. Combinations of biotechnology
platforms have increased the practical utility and application of biosensors for detection of foodborne pathogens. However,
further rigorous testing of biosensors with complex food matrices is needed to ensure the utility of these sensors for point-of-
care needs and outbreak investigations.
Biosensors can be used for pathogen detection in various food matrices.
Speciﬁcity and sensitivity of biosensors rely on biorecognition molecules and signal output.
Working principles of different biosensors can be combined for improved sensitivity.
Bacteriophage-based sensors are highly speciﬁc and becoming more commonly used.
Cell-based sensors can determine the virulence of viable and active pathogens and toxins.
Key words: Biosensor; Escherichia coli; Foodborne pathogens; Food safety; Listeria;Salmonella
In 2010, the World Health Organization estimated the
global burden of foodborne illness as 600 million cases with
420,000 deaths each year caused by 31 foodborne
pathogens (51). In the United States, 48 million foodborne
disease cases result in 128,000 hospitalizations and 3,000
deaths annually. Among identiﬁed foodborne illnesses, only
9.4 million per year are caused by 24 known pathogens, but
most cases (38.6 million) are caused by unknown agents or
unknown transmission vehicles (77). The economic burden
associated with these foodborne illness cases is estimated as
$78 billion per year, including the cost for loss of work
hours, medical bills, product recalls, bankruptcy, and
Among all known bacterial foodborne pathogens,
Salmonella, Clostridium perfringens, Campylobacter spp.,
Staphylococcus aureus, Shigella spp., and Shiga toxin–
producing Escherichia coli (STEC) are the top six
pathogens that cause the most foodborne illness cases in
the United States (77). Other commonly found bacterial
foodborne pathogens are Bacillus cereus, Brucella spp.,
Clostridium botulinum, enterotoxigenic E. coli, Listeria
monocytogenes, Vibrio spp., and Yersinia enterocolitica (77,
83), which are responsible for millions of infections in the
United States annually (77). Microbial toxins in food also
can cause illnesses. Problematic exotoxins include botuli-
num toxin from C. botulinum, staphylococcal enterotoxin
from S. aureus, diarrheagenic and emetic enterotoxins from
B. cereus, mycotoxins from toxigenic molds, and seafood
toxins primarily from microalgae. The production of these
toxins in nanogram quantities in food can have severe health
Control of foodborne pathogens is vital for protecting
public health. Various interventions have been used to
reduce the risk of pathogen exposure through food,
including but not limited to the implementation of new
standards and updated testing plans. For example, to verify
that the preventive approaches are adequate, the U.S.
Department of Agriculture (USDA) Food Safety and
Inspection Service (FSIS) (98) has published a new
* Author for correspondence. Tel: 765-494-5443; Fax: 765-494-7953;
Journal of Food Protection, Vol. 84, No. 7, 2021, Pages 1213–1227
Copyright Ó, International Association for Food Protection
pathogen reduction performance standard, outlining sam-
pling, testing, and control methods for Salmonella and
Campylobacter in poultry products. The FSIS has identiﬁed
Salmonella as an adulterant in ready-to-eat (RTE) products
and adopted a zero-tolerance policy. For an easier
implementation of this policy, the FSIS has frequently
updated testing recommendations for Salmonella in RTE
Industries commonly use product recalls as voluntary
corrective actions to remove products containing adulterants
from commerce (102). However, voluntary recalls usually
result in food wasting, use of transportation resources, and
labor costs. In 2019, 118,830 lb (53,949 kg) of food were
discarded because of three Salmonella-related recalls (96,
97). The FSIS-approved Salmonella detection method takes
48 h to provide screening results, 120 h to provide a
presumptive result, and up to 192 h to provide the ﬁnal
result (99, 101). The lengthy and troublesome recall process
puts consumers at great risk of exposure to foodborne
pathogens. Therefore, rapid pathogen detection is needed
for the safety of the food industry and consumers.
The FSIS has used several rapid pathogen screening
methods, such as the 3M molecular detection system (100)
and PCR assays (99). However, both methods depend on the
detection of nucleic acids and may generate false-positive
screening results. To obtain ﬁnal results, the FSIS still
requires the use of culture-based methods, which include
nonselective and selective sample enrichment, followed by
biochemical or serological conﬁrmatory tests, genotyping
by whole genome sequencing, and evaluation of antimicro-
bial resistance (101). The culture-based method is labor-
intensive and time-consuming but is still considered the
“gold standard”for the food industry because it is the only
currently approved test for identifying viable pathogens in
Researchers continue to develop various alternative
detection tools, including biosensor-based methods, in
recognition of the disadvantages associated with the
existing methods. Biosensor-based tools have continued to
gain the attention of researchers because of their sensitivity
and potential portability for onsite deployment. The
development of a biosensor usually requires incorporation
of a biological recognition probe onto a surface that can
transduce an ampliﬁed signal when the analytes bind to that
surface (15, 16). The interaction between the analyte and the
recognition molecule can be categorized into four types:
immunological interaction (Fig. 1a), aptamer recognition
(Fig. 1b) and DNA hybridization, bacteriophage recognition
(Fig. 2a), and pathogen–eukaryotic cell interaction (Fig. 2b
and Table 1) (16, 24). Upon binding, the analyte can be
detected through either a label-dependent or a label-free
method. With a label-dependent biosensor, captured ana-
lytes are labeled with a marker (reporter). The signal is
usually a colorimetric or ﬂuorescent change mediated by the
marker. With a label-free biosensor, the binding of the
analyte and the molecule could yield changes in the
FIGURE 1. Schematic illustration of bio-
sensor working principles. (a) On-chip
culture of bacteria. Viable cells divide on
the microarray, and the speciﬁc binding of
bacteria to dedicated antibodies is moni-
tored in a label-free manner (61). (b)
Quartz crystal microbalance (QCM) apta-
sensor for the rapid detection of E. coli
(117). MHDA, 16-mercaptohexadecanoic
acid; EDC, N-3-dimethylaminopropyl-N0-
ethylcarbodimide hydrochloride; NHS, N-
hydroxysuccinimide; PEG-thiol, polyethyl-
ene glycol methyl ether thiol.
1214 XU ET AL. J. Food Prot., Vol. 84, No. 7
microsystem, such as an impendence ﬂuctuation. With the
label-free antibody-based surface plasmon resonance (SPR)
sensor for detecting L. monocytogenes (Fig.1a),the
antibody is coated on a gold surface to capture bacteria,
and SPR imaging is used to monitor bacterial growth. As an
alternative to use of an antibody, an aptamer can be used to
bind to the target. A quartz crystal microbalance (QCM)
sensor (Fig. 1b) detects a small change in mass upon analyte
binding. Bacterial phages can be used to capture E. coli
from drinking water (Fig. 2a), and transfected HEK 293
cells have been used to bind lipopolysaccharide (LPS) to
produce ﬂuorescent signals upon interaction (Fig. 2b).
Although each biosensor relies on different binding
mechanisms between a recognition molecule and the
analyte, they all follow the basic idea of a biosensor and
report the interaction as an indicator of the presence or
absence of the analyte.
Other types of label-free biosensors, such as a light-
scattering sensor, do not require recognition molecules and
are not considered prototypical biosensors. With a light-
scattering sensor such as BARDOT (bacterial rapid
detection using optical scattering technology), the speciﬁc-
ity of the assay can be improved by using immunomagnetic
beads to capture target bacteria or by using selective agar
media to facilitate the growth of the target bacterial colony
for laser identiﬁcation (11). The properties of illuminated
scatterers, such as the refractive index, size, and shape,
dictate the light scatter pattern. Because bacterial morphol-
ogy and composition are expected to be reproducible when
bacteria are grown under similar conditions, the character-
istics of the scattered light pattern could be used to identify
foodborne pathogens at the genus, species, or serovar levels
by comparison of the bacterial scatter images with those in a
library (17, 86).
FIGURE 2. (a) Schematic representation of detection of Escherichia coli in drinking water using a T7 bacteriophage–conjugated
magnetic probe (20). Three steps were involved: (i) separation of E. coli from drinking water with the magnetic probe; (ii) T7
bacteriophage infection of E. coli cells and consequent release of β-gal; (iii) β-gal catalyzed CPRG hydrolysis to produce colorimetric
readout. (b) Schematic illustration of the working principle of a 293/hTLR4A-MD2-CD14 cell-based sensor (90).
J. Food Prot., Vol. 84, No. 7 BIOSENSORS FOR FOODBORNE PATHOGENS 1215
TABLE 1. Biosensors with different principles and signal generation mechanisms
Sensor type Signal type
organism Sample preparation Validation matrix Reference
Immunosensor Surface plasmon
1hþenrichment ~10 CFU/mL Listeria 25 g of food in 225 mL of FB; 24 h preenrichment. Smoked salmon, raw
milk, duck leg
,80 min ,100 CFU; Escherichia coli Food sample homogenized and centrifuged;
Impedance change ,1hþsample
,300 CFU/mL Salmonella 325 g of turkey breast þ2,925 mL of BPW shaken
for 1 min; supernatant filtered through 100- and 20-
μm-pore-size cell strainer
RTE turkey breast 54
Colorimetric ~2 h 10 CFU/mL Salmonella,
Meat surface swabbed with a lactoferrin-bound cotton
Fluorescence 5 min þenrichment 1 CFU/mL E. coli Food samples þmodified E. coli broth containing
FITC enriched for 8–10 h; heat killed for 5 min
Bread, milk, jelly 89
Fiber-optic sensors 20 min þenrichment 247 CFU/mL Salmonella
12-h incubation then centrifugation and resuspension
~24 h 10
CFU/mL L. monocytogenes,
E. coli O157:H7
18-h enrichment then 2-h bacterial coating on fiber-
optic sensor and 2-h reporter antibody immersion
RTE beef, chicken,
12–24 h 10
CFU/mL Salmonella enterica 25 g of sample þ225 mL of BPW, incubated for 4 h
at 35628C; 1–10 mL of RV broth added, incubated
for 4 h at 42 60.58C; XLT4 plates used for light
Chicken carcass 1
2hþ18 h enrichment 3 310
CFU/mL L. monocytogenes,
10 g of sample þ90 mL of FB or LEB, enriched for
18 h; 10 mL of enriched sample concentrated by
IMS for 30 min
Soft cheese, hotdog 59
Chemiluminescence 1 h 4.5 310
CFU/mL E. coli E. coli bound with aptamer and free aptamer removed
GO nanoparticles; aptamer-bound E.
coli mixed with TPA and detected with
Fluorescence 135 s þaptamer
CFU/mL E. coli Aptamer incubated with sample for 10–25 min then
Milk, water 123
Colorimetric ,9h ,10 CFU/mL Salmonella Enriched with BPW for 6 h and concentrated with
IMS; DNA then extracted
NR 2.1 and 0.15 pM Salmonella Different concentrations of target DNA used directly NR 91
NR 5.3 pM Vibrio Different concentrations of target DNA used directly NR 62
Phage Chromatography 8 h 1 CFU/mL E. coli Food sample spiked with phages that were then
captured with antibody-coated nanoparticles
Colorimetric 6 h 10 CFU/mL E. coli Phage-MBs with water samples incubated for 15 min
and separated with magnetic beads; bound bacterial
complex washed and resuspended in CPRG for 2 h
Chemiluminescence 7 h 5 CFU E. coli Sample homogenized in mTSB with novobiocin and
spiked with phage lysate
Ground beef 119
1.5 h 10
CFU/mL E. coli Spiked sample with phages in M9 medium for 1.5 h Water 30
1216 XU ET AL. J. Food Prot., Vol. 84, No. 7
Two important characteristics make biosensors excep-
tional foodborne pathogen detection tools. First, the limit of
detection (LOD) of the most recently developed biosensors
is exceptionally low: 5 to 100 CFU/mL for bacterial
pathogens or piconanogram concentrations for toxins (16,
32, 35, 59). The low LOD allows the user to reduce the
preenrichment time, thus reducing the total detection time.
Second, a biosensor can be easily delivered with a
microﬂuidic device, making the system portable for point-
In this review, we cover the most recent developments
for the ﬁve types of biosensors, especially those based on
biorecognition molecules and a label-free light-scattering
method. We also analyze the feasibility of each method for
its near real-time detection capability from a practical
perspective. We hope this review is an inspiration for future
researchers to develop advanced rapid foodborne pathogen
detection tools to ameliorate the public health threat of
BIOSENSORS BASED ON ANTIBODY
Optical and mass-based immunosensor platforms.
The antibody is a critical reagent for establishing the
speciﬁcity of a biosensor. The biosensor that relies on an
antibody is an immunosensor. The ﬁnal use for an
immunosensor could be for immunoassays, such as the
latex agglutination assay, lateral ﬂow assay, and enzyme-
linked immunosorbent assay (ELISA) (9, 80). Antibodies
can be immobilized on a sensor surface to capture the target
analyte. Upon binding, changes in the microenvironment
can be measured or visualized. For example, antibody-
coated QCM can be used as a mass-sensitive biosensor (Fig.
1). Bacteria bind to antibodies, reducing the oscillation
frequency as the crystal mass increases (79). Fulgione et al.
(32) used this method to detect Salmonella enterica serovar
Typhimurium in chicken meat, and the LOD was estimated
as ,1 CFU/mL for a sample that was enriched for 2 h at
Immunosensors represent a major subgroup within the
broad category of optical biosensors (48, 58, 110). Optical
sensors measure the reaction between a receptor and an
analyte as an optical signal (83). The SPR response is one of
the parameters commonly measured with an optical sensor
(105). The receptor-analyte interaction causes changes in
the refractive index, resulting in a resonance angle shift
(83). Morlay et al. (61) used an immunochip SPR imaging
system to detect L. monocytogenes in food samples. The
antibodies were coated on the gold surface of the biochip
(Fig. 1), and the SPR signal was monitored in real-time after
the addition of enriched lettuce samples. With this system,
L. monocytogenes was detected in 30 min after a 24-h
enrichment of samples. The advantage of this setup is real-
time monitoring of bacterial growth by using SPR imaging.
waveguide (ﬁber). An evanescent wave is generated with
a laser diode along the optical ﬁber to activate the analyte-
bound ﬂuorescence-conjugated secondary antibody as an
indication of the presence of the analyte. With these ﬁber-
TABLE 1. Continued
Sensor type Signal type
organism Sample preparation Validation matrix Reference
Cell based Electrochemical 24 h 3.5 310
ng/mL LPS Juice samples centrifuged and supernatants collected;
samples analyzed after autoclave sterilization
Peach juice, orange
Colorimetric 6 h þenrichment 10
STEC/Stx Samples enriched in mTSB at 428C for 15 h and
resuspended in LB before detection
Ground beef 95
Fluorescence ~2 h detection þ6h
0.075 μg/mL LPS Juice samples centrifuged and supernatants analyzed
after autoclave sterilization
Fruit juice (apple,
FB, Fraser broth; BPW, buffered peptone water; FITC, ﬂuorescein isothiocyanate; PBS, phosphate-buffered saline; NR, not reported; RV, Rappaport-Vassiliadis; XLT4, xylose lysine tergitol 4
agar; LEB, Listeria enrichment broth; IMS, immunomagnetic separation; TPA, tetra-n-propylammonium hydroxide; MCE, microchip electrophoresis with chemiluminescence; MB, magnetic
beads; CPRG, chlorophenol red-β-D-galactosidase; mTSB, modiﬁed tryptone soy broth; LPS, lipopolysaccharides; STEC, Shiga toxin–producing E. coli; Stx, Shiga toxin; LB, Luria-Bertani
J. Food Prot., Vol. 84, No. 7 BIOSENSORS FOR FOODBORNE PATHOGENS 1217
optic sensors, the signal is an electric current proportional to
the ﬂuorescence light intensity measured by a photodiode
(104). A traditional ﬁber-optic sensor was developed for
detection of S. enterica (1). Recent progress in development
of ﬁber-optic sensors involved the improvement of sensor
fabrication and assay sensitivity. Kaushik et al. (47)
designed a single mode–multimode–single mode biosensing
device for a simpler and more cost-effective sensor
fabrication process. These researchers removed the cladding
region of the multimode ﬁber, which increases the
interaction between the propagating modes of guided light
and the ambient medium. After immobilizing recognizing
antibodies on the sensing platform, the ﬁber was incubated
with bacteria suspended in buffer solution. This fabrication
improved assay sensitivity for Salmonella Typhimurium
detection, and an LOD of 247 CFU/mL was obtained for the
presence of E. coli and S. aureus. This research group (48)
also developed a ﬁber-optic SPR immunosensor interfaced
with an MoS
nanosheet, simplifying and improving the
antibody immobilization. The updated version of the ﬁber-
optic sensor has been tested for E. coli in spiked drinking
water and orange juice, with an LOD of 94 CFU/mL.
Another typical immunosensor, the lateral ﬂow strip
assay (LFSA), has been evolving for years (19, 38, 80, 121)
and has gained considerable attention because of it is
disposable, mass producible, and easy to fabricate and can
have point-of-care applications (38, 116). With the LFSA,
chromatic or ﬂuorescent antibodies are conjugated on a pad.
After the sample solution is applied, the analytes labeled
with the antibody keep moving forward by capillary action
toward the pad’s detection zone and are identiﬁed by the
colored or ﬂuorescent signal. The recent trend of incorpo-
rating an LFSA in foodborne pathogen detection focuses on
increasing the device’s sensitivity (55, 71, 89), which may
require concentrating the analytes in the sample solution
before analysis. One of the typical methods for concentra-
tion is antibody-conjugated nanoparticles.
Nanobead-based immunosensor platforms. The in-
corporation of gold nanoparticles (AuNPs) in the LFSA is a
common recent practice (29, 66, 69, 108). AuNPs were
widely used for antibody conjugation because of their high
surface-to-volume ratios, increasing the amount of immo-
bilized antibody. The morphology of the AuNPs has a
signiﬁcant effect on the sensitivity of the assay. Zhang et al.
(120) compared three types of hierarchical ﬂower-like
AuNPs and found that the tipped ﬂower-like AuNPs were
more sensitive for detection of E. coli O157:H7 than were
large ﬂower-like and popcorn-like AuNPs. Because of their
hierarchical structure, the tipped ﬂower-like AuNP probes
can detect E. coli O157:H7 levels as low as 10
CFU/mL for popcorn-like probes and 10
mL for large ﬂower-like probes. The size of the AuNPs also
impacts sensitivity. Cui et al. (25) found that AuNPs of 35
nm were more sensitive for detection of E. coli O157:H7
CFU/mL) than were AuNPs of other sizes (sensitivities
CFU/mL). The AuNP-antibody–E. coli
complex separated from unbound bacteria had an LOD of
CFU/mL, which is much lower than that of a
conventional AuNP-based LFSA (22). Modiﬁcation of
AuNPs has also been investigated to obtain better
performance. Ríos-Corripio et al. (73) obtained a stable
antibody-AuNP colloidal solution by incorporating protein
A into the AuNPs. Their results indicate the feasibility of
using protein A–AuNP bioconjugate colloidal solutions as
probes to detect Salmonella in contaminated samples.
Other commonly used small particles are magnetic
beads. Antibody-coated magnetic (immunomagnetic) beads
are generally used to separate and concentrate bacteria from
sample matrices after the enrichment process (8), thus
avoiding background cross-reactions. Papadakis et al. (67)
used immunomagnetic beads in an acoustic biosensor to
capture Salmonella Typhimurium from milk before addition
of the sample. The use of magnetic beads reduced the
preenrichment time to only 3 h. Magnetic nanoparticles of
0.8 μm had the highest capture rate (73%) after 3.5 h of
incubation compared with of 1.0- and 3-μm nanoparticles.
Another research group obtained similar results with
chicken rinsate and liquid egg white matrices (21). They
found that immunomagnetic beads of 100 nm had a higher
recovery rate (88 to 96%) for Salmonella Enteritidis than
did beads of 500 and 1,000 nm after 30 min of incubation.
Electrochemical immunosensors. Antibody-conjugat-
ed small particles have tremendous potential for capturing
analytes but could also be used in other types of biosensors.
The excellent conductivity of AuNPs enhances signal
transduction by allowing enhanced electron transfer (44).
Recent applications of AuNP-conjugated antibodies in
biosensors have included measuring impedance changes
after antibody-antigen binding. The binding between
recognizing antibodies and antigens can be used to estimate
the activity of an enzyme, such as horseradish peroxidase,
that is conjugated to the antibody. The amount of enzyme
present is further quantiﬁed by changes in impedance after
addition of speciﬁc substrates, such as the redox probe
thionine and hydrogen peroxide, that generate electrons
(106). Biosensors based on this principle are electrochem-
ical impedance immunosensors. Fei et al. (28) developed a
simple, rapid, and economical immunosensor by coating the
antibody-conjugated AuNPs on a screen-printed carbon
electrode to monitor the impedance change after binding of
two Salmonella serovars. To improve the sensitivity and
conductivity of the electrochemical immunosensor with
AuNPs, Xiang et al. (111) developed an electrochemical
immunosensor for Salmonella detection by coating an
electrode with a chitosan-AuNP composite ﬁlm. Anti-
Salmonella capture antibodies were immobilized on the ﬁlm
through anodic oxidation. After incubation with Salmonella,
a secondary anti-Salmonella horseradish peroxidase–conju-
gated antibody and 2-hydroxy-1,4-naphthoquinone were
applied to generate an electronic signal, which was detected
by the sensor. The reason proposed for the ultralow LOD (5
CFU/mL) of the sensor was that well-dispersed AuNPs
enhanced the electrochemical signal and the performance of
the chitosan ﬁlm. Liu et al. (54) coated antibody onto a gold
electrode and placed it in microchannels. The impedance
change was measured by subtracting the measured imped-
1218 XU ET AL. J. Food Prot., Vol. 84, No. 7
ance after antibody coating from the measured impedance
after antigen-antibody binding. This biosensor detected
Salmonella in RTE turkey samples after 1 h with an LOD of
Surface-enhanced Raman scattering and quantum
dot immunosensors. The microﬂuidics setup was also used
in combination with antibody-coated surface-enhanced
Raman scattering (SERS)–tagged gold nanostars. The
incorporation of antibodies in the SERS platform improves
the speciﬁcity of the sensor. Rodríguez-Lorenzo et al. (74)
coated the SERS-tagged gold nanostars with a thin silica
mesoporous layer for functionalization with an antibody to
L. monocytogenes. Because of the higher distribution of
monoclonal antibody C11E9–speciﬁc antigens on the
surface of L. monocytogenes cells than on the surface of
Listeria innocua cells, more gold nanostars were distributed
on L. monocytogenes, allowing this sensor to differentiate
between the pathogenic L. monocytogenes and the non-
pathogenic L. innocua in real time.
Measurement of ﬂuorescent emissions by the imped-
ance-based immunosensor is another approach for detec-
tion. Quantum dots (QDs) are commonly used markers for
antibody conjugation because of their wide excitation range
and narrow emission wavelengths (23, 60, 114). Hu et al.
(39) incorporated QDs in a lateral ﬂow setup by using
polymer nanobeads as a carrier to assemble QDs layer by
layer. The sensor’s result could be read by the naked eye
after the QDs were excited with UV light. Although
generation of detection results visible with naked eye could
also be achieved with enzyme-labeled antibodies and
chromogens, such as horseradish peroxidase and 3,30-
diaminobenzidine as substrate, the merit of QDs is that no
substrate is needed, and the results are visible immediately
after exposure to UV light. In recent studies, QDs have been
used with immunomagnetic beads for Salmonella detection.
The immunomagnetic beads ﬁrst capture the bacteria then
release the attached QDs, to provide a measure of
ﬂuorescence intensity. Xue et al. (114) fabricated an
immunosensor with antibody-conjugated QDs to capture
magnetic beads bound to bacteria, forming an immuno-
magnetic bead–target-QD sandwich. This immunosensor
detected E. coli and Salmonella in 2 h, with LODs of 15 and
40 CFU/mL, respectively.
Although antibodies are the basis of the speciﬁcity of
immunosensors, one limitation of these antibody-based
methods is they recognize only a speciﬁc part of the analyte.
For commercial applications, antibodies could be costly for
the detection of the pathogen. When relying on antibodies
alone, immunosensors cannot differentiate between live and
dead pathogen cells, so that they do not have an advantage
over a more economical method, such as PCR. However,
one major advantage of immunosensors is their ability to
integrate with the lateral ﬂow assay, making this assay
portable for point-of-care deployment and fast results (,20
min). Future research on immunosensors could focus on the
development of the detection platform as a combination of
an immunosensor and other types of biorecognition
elements to enhance speciﬁcity, reliability, and portability.
NUCLEIC ACID–BASED BIOSENSORS
A nucleic acid–based biosensor utilizes a known
sequence of oligonucleotides as the sensing element. This
type of biosensor either is based on DNA hybridization of
complementary strands or relies on the interaction of DNA
molecules and the analytes. Unlike antibodies, nucleic acid
strands are easier and cheaper to synthesize. With the
combination of DNA ampliﬁcation, the nucleic acid–based
biosensor can be more sensitive and speciﬁc(31, 107, 113).
For the basic nucleic acid–based biosensor, single-stranded
DNA is immobilized on an electrode surface. An electrical
signal is generated when the target DNA strand binds to the
immobilized sequence and/or undergoes hybridization (7,
49). The newly developed nucleic acid–based sensors also
take advantage of nanomaterials to expand the sensors’
functions for different food materials (56, 113).We
evaluated the application of sensors based on aptamers
and DNA hybridization.
Aptasensors. Aptamers, which are short single-strand-
ed oligonucleotides with a high binding afﬁnity for speciﬁc
proteins and bacteria, are commonly used to capture the
target analyte. Nucleic acid–based biosensors that utilize
aptamers are designated aptasensors. Although aptamers
play a role similar to that of antibodies in immunosensors,
aptamers can be cost-effectively generated in vitro via
systemic evolution of ligands by exponential enrichment
(117) (Fig. 1). This method relies on the exposure of the
target protein or bacteria to a DNA or RNA library. The one
with the highest binding afﬁnity is selected for further
ampliﬁcation and sequencing (53).
When used as a recognition probe on a biosensor,
aptamers are also very effective when compared with
antibodies. Aptamers are smaller, easier to synthesize, and
more stable for storage than antibodies, and antibodies can
compromise the signal by interfering with the nanomaterial
by forming large insulating layers on electrochemical
sensors (46). However, antibodies have higher afﬁnity to
their target than do aptamers (68). Future studies on
aptasensors could focus on improving aptamer-analyte
Comparable to the application of antibodies in
immunosensors, aptamers are used in combination with
nanoparticles such as AuNPs (118) or chemiluminescence
(50), directly on a surface within a microﬂuidic device
(123), or on a ﬁber-optic) probe (64) to enhance the
detection outcome. Researchers also have used various
strategies to collect or remove the bacteria-bound aptamer
complex or free aptamer. Zhang et al. (118) fabricated a dual
recognition system with vancomycin, which interacts with
the bacterial cell wall, and aptamers to detect S. aureus and
E. coli. Pathogen-speciﬁcaptamersweremodiﬁed on
AuNPs as SERS tags, and vancomycin was modiﬁed with
@Au magnetic nanoparticles (Fe
bacteria were captured by the Fe
@Au-Van and collected
with a magnet. With the addition of the aptamer-conjugated
AuNP SERS tag, signals were acquired after laser
excitation. This setup had an LOD of 50 and 20 cells per
mL for E. coli and S. aureus, respectively. The researchers
J. Food Prot., Vol. 84, No. 7 BIOSENSORS FOR FOODBORNE PATHOGENS 1219
also conﬁrmed the speciﬁcity of the assay by demonstrating
reduced or low signal from other bacterial pathogens,
including Klebsiella pneumoniae, Staphylococcus epider-
midis, Pseudomonas aeruginosa, Acinetobacter baumannii,
L. monocytogenes, and Streptococcus pneumonia.
Khang et al. (50) explored the conjugation of an E. coli
O157:H7–speciﬁc aptamer with 6-carboxyﬂuorescein,
which can emit intense light upon the addition of a guanine
chemiluminescent reagent. The free aptamer was removed
through the π-πstacking interaction between the free
aptamer and graphene oxide–iron nanocomposites. The
strength of the light was proportional to the increase in
target concentration, and the LOD of this biosensor was 4.5
Zhang et al. (123) investigated the combination of
aptamer and a microﬂuidic device. They successfully
developed an aptasensor for E. coli detection with a
bacteria-speciﬁc aptamer in conjugation with microchip
capillary electrophoresis (MCE)-coupled laser-induced
ﬂuorescence. In this case, the separation of free aptamers
and complex peaks by MCE is identiﬁed and achieved
based on the differences between their electrophoretic
mobilities, which are inﬂuenced by the charge-to-mass
ratio difference of the free aptamer and the complex. The
LOD of this device was 3.7 310
Biosensors based on DNA hybridization. Similar to
the setup of aptasensors, DNA hybridization sensors also
have single-stranded DNA (ssDNA) as the recognized
probe, which binds to the complementary ssDNA from the
targeted bacteria. A common strategy of such a sensor is to
immobilize an ssDNA probe on a transducer’s surface,
which emits a signal upon binding to a cDNA target (72).
The critical aspect of this type of sensor is a transducer
surface that has the optimal characteristic for the detection
purpose. Hybridization events can be converted into a
quantiﬁed signal by the transducer in the electrochemical
Carbon is commonly used in electrochemical biosen-
sors because of its large surface area, low cost, ease of
fabrication, good conductivity, biocompatibility, and robust
mechanical strength (40, 72, 122). Tabrizi and Shamsipur
(91) developed an electrochemical DNA biosensor with a
nanoporous glassy carbon electrode. They covalently linked
the amino-modiﬁed Salmonella ssDNA probe sequence
with the carboxylic group on the carbon electrode’s surface.
Differential pulse voltammetry and electrochemical imped-
ance spectroscopy have been used to monitor hybridization
events, with LODs of 2.1 and 0.15 pM, respectively (91).
Similar strategies were used in a biosensor for the detection
of Vibrio parahaemolyticus. Nordin et al. (62) used a
screen-printed carbon electrode modiﬁed with polylactide-
stabilized AuNPs as the surface to bind with and immobilize
ssDNA. Differential pulse voltammetry used to assess this
hybridization events had an LOD of 5.3 pM.
DNA hybridization–based biosensor strategies also
include the use of ssDNA with a ﬂuorescence tag as the
probe for ﬂow cytometry. Generally, the bacteria are
permeabilized and then incubated with a complementary
ﬂuorescing ssDNA probe and analyzed by ﬂow cytometry.
Because of the abundance of rRNA in bacteria, 16S and 23S
rRNA are usually used as the target for the ssDNA probe
(76). Bisha and Brehm-Stecher (18) developed a ﬂow-
through imaging cytometry system for characterization of
Salmonella subpopulations in alfalfa sprouts with a cocktail
of two 23 rRNA–targeted ssDNA probes.
Handheld DNA sequencing device. Another exciting
and emerging technology that has recently been applied in
foodborne pathogen detection is DNA sequencing with a
handheld device. Yang et al. (115) applied direct metatran-
scriptome RNA-seq and multiplex reverse transcription
(RT) PCR amplicon sequencing with the MinION sequencer
(Oxford Nanopore Technologies, Oxford, UK) to detect
three pathogens (Listeria, E. coli, and Salmonella) in lettuce
juice extract or brain heart infusion simultaneously. After
extracting RNA from the bacteria cocktail incubated in
lettuce juice extract or brain heart infusion, whole
metatranscriptome or mRNA of bacteria-speciﬁc genes
were converted to DNA libraries and sequenced with the
MinION sequencer. Both metatranscriptome RNA-seq and
RT-PCR amplicon sequencing detected all three pathogens;
however, the samples tested by sequencing the RT-PCR
amplicon needed a shorter incubation time (4 h) than did the
RNA-seq (24 h). The metatranscriptome RNA-seq mis-
identiﬁed some reads as Bacillus, Lactobacillus, or
Staphylococcus, probably because of their genetic similarity
to Listeria. This study revealed the excellent portability and
efﬁciency of the MinION sequencer as a promising
technology for detection of foodborne pathogens.
Phage-based biosensors use a bacteriophage as the
recognition element (5, 34, 65, 87). The advantage of this
approach is associated with the unique characteristics of
phages. They are easily produced and less sensitive to the
effects of pH and temperature (45). Because of the diversity
of bacteriophages and selection through phage display,
phage-based recognition also is highly speciﬁcand
accurate. Similar to the immunosensor and aptasensor, the
binding of the target to the immobilized phages can generate
signals that can be detected via QCM, a magnetoelastic
platform, SPR, and electrochemical methods (27, 87).
Horikawa et al. (37) combined wireless phage-coated
magnetoelastic biosensors with a surface-scanning detector,
allowing real-time monitoring of bacterial growth.
Phage display technology has been an effective method
for developing probe phages that can bind to various
bacteria. In 1985, Smith (88) discovered that a foreign
peptide can be inserted into the viral chromosome and
expressed on the surface of the recombinant ﬁlamentous
phages without affecting their general ﬁtness. This ﬁnding
became the foundation of phage display technology, in
numerous recombinant phages expressing a library of
peptides are screened and the phages with strong and
speciﬁc binding to the target are chosen (92). De Plano et al.
(26) constructed a library of M13 phages expressing a
recombinant major coat protein (pVIII) containing random
1220 XU ET AL. J. Food Prot., Vol. 84, No. 7
9-mer peptides, and viral clones that speciﬁcally bound to S.
aureus, P. aeruginosa, or E. coli were selected. The
speciﬁcity of the phages was conﬁrmed with an ELISA.
The selected clones were covalently conjugated to magnetic
beads as recognizing probes. After incubating the beads
with a blood sample containing the targeted pathogens, the
targets were captured and detected with micro-Raman
spectroscopy. This method was used to detect 10 CFU of
pathogen in 7 mL of blood. Generally, phage display
technology is a powerful tool for rapid development of
probes similar to antibodies and can be incorporated into
The second strategy for phage-based biosensors takes
advantage of the infection mechanism of host-speciﬁc
phages. Various forms of signals are generated from either
the increasing number of progeny phages and the bacterial
cell contents after lysis or the transferred plasmid harboring
the signaling gene (65).
To detect the increasing number of progeny phages,
Martelet et al. (57) used speciﬁc immunomagnetic separa-
tion beads to capture the progeny phages and detected them
by liquid chromatography coupled with targeted mass
spectrometry. In their platform, phage T4 was used for E.
coli infection. This method allowed detection of viable E.
coli cells in food matrices. To detect cell contents released
after lysis, Chen et al. (20) fabricated a bacteriophage-based
sensor for E. coli (Fig. 2). Bacteriophage-conjugated
magnetic beads were used to capture E. coli. After phage-
mediated lysis, the endogenous β-galactosidase was detect-
ed with chlorophenol red-β-D-galactopyranoside (CPRG).
Zhang et al. (119) developed a phage-based sensor for
detection of signals from the phage-infected bacterial
plasmid. They modiﬁed the E. coli–speciﬁc bacteriophage
ΦV10 to allow the expression of NanoLuc luciferase. After
the phage infects the E. coli cell, the cell harbors the
plasmid containing the luciferase-coding gene and produces
a robust bioluminescent signal after addition of luciferin.
Franche et al. (30) developed a substrate-independent
phage-based sensor by integrating the full luxCDABE
operon and tested it with different bacterial promoters.
They found that the PrplU bacterial promoter was the most
efﬁcient for signal emission and thus detection of E. coli.
This system had an LOD of 10
CFU/mL, and the detection
time was 1.5 h without enrichment or a sample concentra-
The major advantage of phage-based sensors is their
ability to differentiate between live and dead bacteria
because bacteriophages can proliferate in only live bacterial
cells. In contrast to antibodies, phages can be produced in
bulk, making phage-based sensors a more economical
choice (27). An obvious challenge is to isolate a
bacteriophage that has a broad host range so that false-
negative results can be avoided. Bacteriophages usually
recognize a particular receptor on the bacterial surface;
therefore, phage-based sensors must be tested with a
relatively large group of isolates of both the target and
nontarget bacteria to reduce the possibility of false-negative
MAMMALIAN CELL–BASED SENSOR
The limitation of the sensors listed above is that none
can conﬁrm the functionality of the targeted toxin and
pathogen; antibodies pick up a speciﬁc region on the
surface, and aptamers recognize only a partial sequence of
the analyte. Phage-based sensors may detect live bacteria
but still cannot conﬁrm their pathogenic attributes. Cell-
based sensors (CBSs) overcome this problem by using live
cells as recognition elements (94). The targeted analytes can
interact with the cells in the same way they interact with
human intestine or other tissues. Signals can be generated
through various types of cellular responses after addition of
the analytes. However, a CBS can generate electrochemical
(33, 43), colorimetric (95), or ﬂuorescent (90) signals
similar to those generated by other types of biosensors.
Three-dimensional CBSs are in high demand because cells
grow in a three-dimensional matrix that mimics the actual
tissue conﬁguration of the mammalian host; therefore, these
CBSs can be highly sensitive and accurate (13, 14, 43, 95).
The commonly used approach to monitoring cellular
responses is to measure cytotoxicity (12–14). To and
Bhunia (95) proposed a three-dimensional Vero cell–based
sensor to detect STEC cells or Shiga toxin. When exposed
to STEC or Shiga toxin, the cytotoxicity values increased
and were used as an indicator of the presence of STEC in
the food sample. The LOD was estimated as 10
for bacteria and about 32 ng/mL for Shiga toxin in 6 h.
Other cellular activities, such as viability, apoptosis, and
intracellular calcium, could also be monitored as an
alternative approach to direct pathogen or toxin detection
because exposure to various triggers inﬂuences those
activities. Jiang et al. (42) reported a mast cell–based
electrochemical sensor for detecting N-acyl-homoserine-
lactone, a quorum signaling molecule produced by patho-
genic bacteria. They utilized rat basophilic leukemia (RBL-
2H3) mast cell line to detect N-acyl-homoserine-lactone and
effectively converted the biological recognition into a
quantiﬁable signal with a β-hexosaminidase assay, ﬂow
cytometry, and calcium measurement (42).
To produce a signal simultaneously as the cells respond
to the analytes, a plasmid carrying a reporter gene could be
transfected into the cell or a reporter gene could be inserted
after an inducible gene upon analyte exposure. The reporter
gene system could be used to predict toxicity by measuring
the chemical-induced response. This system also could be
used to predict the presence of pathogens by detecting the
signaling pathway induced by the bacterial component.
Abu-Bakar et al. (3) used a luciferase reporter plasmid to
reﬂect in vivo responses to toxins. Under chemical-induced
stress, transcription factors such as aryl hydrocarbon
receptor and nuclear factor (erythroid-derived 2)–like
wildtype are activated, resulting in elevated murine
cytochrome P450 2a5 gene (Cyp2a5) transcription. By
constructing the regulatory regions of Cyp2a5 in a
luciferase reporter plasmid, the toxin concentration in a
sample could be predict. Sun et al. (90) took advantage of
the ability of the TLR4 transmembrane protein to recognize
lipopolysaccharides (LPS), an outer membrane component
of gram-negative bacteria, and developed a CBS to detect
J. Food Prot., Vol. 84, No. 7 BIOSENSORS FOR FOODBORNE PATHOGENS 1221
LPS in food products as a biomarker for gram-negative
bacterial contamination (Fig. 2). Cells were transfected with
a recombinant plasmid that contained the key target gene
promoter MCPIP1 of the LPS toxicity pathway and a green
ﬂuorescent protein. After exposure to LPS, the intensity of
ﬂuorescence was analyzed to reﬂect the LPS concentration
One problem that keeps CBSs from being used
commercially is their expense, due to high maintenance
costs and short shelf life. Extending the shelf life and
reducing the maintenance costs of CBSs are essential for
realizing the full potential of these sensors. Three
approaches have been deﬁned to address these problems:
utilizing lyophilization or encapsulation, using hardy cells
that can naturally survive, and incorporating an automated
machine to maintain a constant pool of actively growing
cells (75). The ﬁrst approach, lyophilization and encapsu-
lation, requires speciﬁc storage conditions, such as 20 or
808C, to maintain viability (36). To address the second
approach, Widder et al. (109) explored seeding rainbow
trout gill epithelial cells onto a microﬂuidic biochip to
monitor drinking water toxicity by measuring the imped-
ance. This CBS could be stored at 68C to maintain the
biological integrity of the cell line. For the third approach,
an automated system could keep cells alive or active for a
longer time but probably would not be portable, limiting its
Xu et al. (112) reported another method to extend the
shelf life of CBS to 14 weeks. They used formalin ﬁxation
to preserve the biological activity of the cells. Live enteric
pathogen cells can adhere to the host cell surface and be
detected with speciﬁc antibodies. This type of sensor, a
mammalian cell–based immunoassay (MaCIA), takes
advantage of the target pathogen’s adhesion to the host
cells (mammalian cells) and speciﬁc detection of the
captured pathogen by designated antibodies. The MaCIA
can be used to detect Salmonella Enteritidis from inoculated
ground chicken, cake mix, milk, and egg samples at 10
CFU/25 g within 16 to 21 h with the traditional enrichment
method, but the detection time could be shortened to 10 to
12 h with an on-cell enrichment method. The MaCIA also
Salmonella cells because live Salmonella cells have much
higher adhesion efﬁciency than do dead cells. MaCIAs need
further optimization and rigorous testing with various food
matrices before routine use can be recommended. The
utility of MaCIAs could be broadened for the detection of
other foodborne pathogens by using pathogen-speciﬁc
antibodies. Combinations of biosensors, such as an
immunosensor and a CBS, are worth exploring because
such approaches could enhance the accuracy of testing
results by utilizing the positive attributes of each platform.
LIGHT SCATTERING SENSOR
Light scattering sensors have been evolving for years,
from detection of bacterial cells in suspension to direct on-
plate colony detection (11). This phenotypic screening tool
has been used to identify bacteria at the genus, species, and
serovar level by comparing the light scattering pattern to the
scatter signature in a classiﬁcation library. Unlike the other
biosensors, light scattering sensors do not need a bio-
recognition molecule, making them easier to fabricate.
Although biorecognition molecules are not essential for
light scattering sensors, biorecognition systems such as
immunomagnetic beads can improve assay speciﬁcity by
isolating target bacteria from the food matrix before the
bacteria are subjected to the sensor. After selective
enrichment and plating on selective agar, the unique light
scatter pattern of each colony is projected with a 635-nm
laser diode (81). This unique scatter pattern is then
compared with those in a classiﬁcation library for
identiﬁcation. During this process, colony integrity and
cells viability are maintained; thus, viable cells are available
for further molecular, biochemical, or pathogenetic conﬁr-
matory tests. Therefore, light scattering sensors are real-
time, nondestructive detection and identiﬁcation tools in
contrast to traditional identiﬁcation methods, such as
biochemical tests or PCR assays.
The recent progress in research on light scattering
sensors has focused on establishing a comprehensive
signature library. BARDOT is the leading technology for
light scattering sensors (Purdue University, West Lafayette,
IN), and its application has been investigated by expanding
the library. BARDOT has been used for rapid screening of
Bacillus (86), Salmonella (1, 81), Listeria (2, 10), Vibrio
(41), STEC (2, 93), and Staphylococcus spp. (6, 52), and the
accuracy rate is often .90%. BARDOT also has been used
for the identiﬁcation and differentiation of members of the
Enterobacteriaceae (82) and the study of bacterial pheno-
types resulting from mutations (85) or exposure to
antibiotics (84, 124).
What sets BARDOT apart from other biosensors is its
ability to identify bacteria in real time. However, unlike
other biosensors that incorporate a recognition molecule to
target virulence properties, such as a pathogenic gene
(nucleic acid–based sensors), a pathogenic factor (immu-
nosensors), or a pathogenic interaction (CBSs), BARDOT
relies solely on the light scatter signature, which makes it
difﬁcult to distinguish pathogenic and nonpathogenic strains
with similar patterns. Bacterial culture conditions, such as
incubation time and growth media, may also affect the light
pattern, limiting the application of this tool to speciﬁc time
frames and enrichment conditions. The commercial success
of BARDOT depends on the availability of a reliable library
for each pathogenic strain, which may take a considerable
amount of time for optimization. BARDOT may ﬁnd its
broad utility as a screening tool before use of other
conﬁrmatory tests (e.g., PCR assay, whole genome
sequencing, or mass spectroscopy), which are often
expensive and lengthy.
CONCLUSIONS AND FUTURE PERSPECTIVES
Researchers are always seeking new detection tools
that are faster and more accurate. Biosensors are promising
tools for testing samples outside the laboratory at the point
of need, thereby facilitating speedy outbreak investigation
and source tracking. The progress of biosensor development
in the last 5 years, as covered in this review, has been
enormous. Although various approaches have been used to
build the sensors, the ultimate goals of enhancing
1222 XU ET AL. J. Food Prot., Vol. 84, No. 7
sensitivity, reducing detection time, and improving porta-
bility remain coherent.
Traditional immunoassays such as the ELISA and
lateral ﬂow assay have been used for detection of foodborne
pathogens. Immunosensors have leveraged the basic
principles of the traditional methods while boosting assay
sensitivity. The recent trend is to utilize nanoparticles, such
as AuNPs, for antibody coating to increase the amount of
coated antibody and the conductivity for excellent electro-
chemical signal transduction. Other trends for improving
sensitivity include incorporating QCM and SPR into the
detection process to construct a faster and more sensitive
tool. The limitation of these sensors is that the sample
amount is relatively small compared with that stipulated by
the FSIS or the U.S. Food and Drug Administration (25 g of
food sample homogenized in 225 mL of enrichment broth).
Better approaches that allow concentration of bacteria from
25-g food samples into small volumes will be necessary for
further practical application of these ultrasensitive immu-
Similar to the immunosensors, nucleic acid–based
biosensors are attractive because instead of using an
antibody as the recognition molecule, ssDNA is used to
capture the analytes. However, this assay requires an extra
step to release nucleic acid from cells. One advantage of this
type of sensor is that oligonucleotides are cheaper to
produce than are antibodies, which makes nucleic acid–
based biosensors more economical. Although both immu-
nosensors and nucleic acid–based biosensors have provided
accurate analyte identiﬁcation in a shorter time, they cannot
differentiate functional from nonfunctional analytes (dead
cells) and thus are more likely to generate false-positive
Phage-based and mammalian cell–based biosensors can
overcome some limitations of other biosensors. Researchers
can take advantage of the biological properties of phages or
mammalian cells to design biosensors that can be used in
functionality tests. A serious concern with these biosensors
is their dependence on biological activity. These sensors
usually require strict maintenance and storage conditions to
preserve their biological activity. Therefore, new approach-
es for extending the shelf life of these biosensors will
become the essential goal for future investigations.
Light scattering sensors allow identiﬁcation of bacterial
phenotypes with a laser diode and computer interface and
can identify bacteria at the genus, species, and serovar
levels. The actual detection time is much faster than that of
other biosensors, but these sensors also have limitations.
Because accuracy is dependent on the light scatter patterns,
growing time and conditions for these colonies are
extremely critical. Determination of the proper combination
of enrichment media and incubation time and development
of an exhaustive library for each pathogenic strain could be
time-consuming but worthwhile due to the highly accurate
real time detection capabilities of light scattering sensors.
In general, biosensors have received much attention in
the past 5 years. Researchers have tried numerous
approaches to close gaps in biosensor design and assay
performance. Because a majority of food products are
expected to be free of pathogens and toxins (i.e., low or no
risk category), a biosensor platform that can quickly assess
the safety risk of such products would be extremely useful
and would ﬁnd practical application. Therefore, more
studies should be focused on advancing the practical utility
of each type of sensor as a screening tool or as a ﬁnal
decision-making tool to ascertain the safety of the food
products. These faster and more sensitive sensors could be
used commercially to improve food safety and security
while reducing food waste.
The research in the authors’laboratory is supported in part by the
USDA, Agricultural Research Service (agreement 59-8072-6-001), the
USDA National Institute of Food and Agriculture (Hatch accession
1016249), the National Academy of Sciences (NAS) and U.S. Agency for
International Development (USAID) (AID-263-A-15-00002), and the
Purdue University Center for Food Safety Engineering. Any opinions,
ﬁndings, conclusions, or recommendations expressed in this publication
are those of the author(s) and do not necessarily reﬂect the view of the
USDA, USAID, or NAS.
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