8 food biochemistry and food phần 173

P1: SFK/UKS
BLBS102-c44

P2: SFK
BLBS102-Simpson

856

March 21, 2012

14:34

Trim: 276mm X 219mm

Printer Name: Yet to Come

Part 8: Food Safety and Food Allergens

Threlfall, E.J. 2000. Epidemic Salmonella typhimurium DT104 – a
truly international multiresistant clone. J Antimicrob Chemother
46: 7–10.
Townsend S et al. 2007. The presence of endotoxin in powdered
infant formula milk and the influence of endotoxin and Enterobacter sakazakii on bacterial translocation in the infant rat. Food
Microbiol 24: 67–74.
Travers K, Barza M. 2002. Morbidity of infections caused by
antimicrobial-resistant bacteria. Clin Infect Dis 34: S131–S134.
Tsai GJ, Chen TC. 1996. Incidence and toxigenicity of Aeromonas
hydrophila in seafood. Int J Food Microbiol 31: 121–131.
Tu SI et al. 2000. Detection of Escherichia coli O157:H7 using
imunomagentic capture and luciferin-luciferase ATP measurement. Food Res Int 33: 375–380.
Van Acker J et al. 2001. Outbreak of necrotizing enterocolitis associated with Enterobacter sakazakii in powdered milk formula. J

Clin Microbiol 39: 293–297.
Van Driessche E et al. 2003. Isolation of Arcobacter species from
animal feces. FEMS Microbiol Lett 229: 243–248.
Van Driessche, E et al. 2004. Occurrence and strain diversity of
Arcobacter species isolated from healthy Belgian pigs. Res Microbiol 155: 662–666.
Vandamme P. 2000. Taxnomy of the family Campylobacteriaceae.
In: I Nachamkin, MJ Blaser (eds.) Campylobacter, 2nd edn. ASM
Press, Washington, DC, pp. 3–26.
Vandamme P, De Ley, J. 1991. Proposal for a new family Campylobacteraceae. Int J Syst Bacteriol. 41: 451–455.
Vandamme P et al. 1991. Revision of Campylobacter, Helicobacter
and Wolinella taxonomy:emendation of generic descriptions and
proposal of Arcobacter gen. nov. Int J Syst Bacteriol 41: 88–
103.
Vandamme P et al. 1992a. Outbreak of recurrent abdominal cramps
associated with Arcobacter butzleri in an Italian school. J Clin
Microbiol 30: 2355–2337.
Vandamme P et al. 1992b. Polyphasic taxonomic study of the
emended genus Arcobacter with Arcobacter butzleri comb. Nov.
and Arcobacter skirrowii sp. nov. an aertolerant bacterium isolated from veterinary specimens. Int J Syst Bacteriol 42: 344–
356.
Vandenberg O et al. 2004. Arcobacter species in humans. Emerg
Infect Dis 10: 1863–1867.
Vaughn RD et al. 2001. Development of a quartz crystal microbalance (QCM) immunosensor for the detection of Listeria monocytogenes. Enzyme Microb Technol 29: 635–638.
Velusamy V et al. 2010. An overview of foodborne pathogen detection: In the perspective of biosensors. Biotechnol Adv 28:
232–254.
Vidal M et al. 2005. Single multiplex PCR assay to identify simultaneously the six categories of diarrheagenic Escherichia coli associated with enteric infections. J Clin Microbiol 43: 5362–5365.
Volokhov D et al. 2002. Identification of Listeria species by
microarray-based assay. J Clin Microbiol 40: 4720–4728.
Volokhov D et al. 2003. Microarray-based identification of thermophilic Campyloabcter jejuni, C. coli, C. lari, and C. upsaliensis. J Clin Microbiol 41: 4071–4080.
Von Abercron SM et al. 2009. Low occurrence of Clostridium difficile in retail ground meat in Sweden. J Food Prot 72: 1732–1734.

Von Grawvenitz A. 2007. The role of Aeromonas in diarrhea: a
review. Infection 2: 59–64.

Wall PG et al. 1995. Transmission of multi-resistant strains of
Salmonella typhimurium from cattle. Vet Rec 136: 591–592.
Walsh C et al. 2008. Transfer of ampicillin resistance from
Salmonella Typhimurium DT104 to Escherichia coli K12 in food.
Lett Appl Microbiol 46: 210–215.
Wang L et al. 2007. Rapid and simultaneous quantitation of Escherichia coli O157:H7, Salmonella, and Shigella in ground beef
by multiplex real-time PCR and immunomagnetic separation. J
Food Prot 70: 1366–1372.
Warren BR et al. 2006. Shigella as a foodborne pathogen and current methods for detection in food. Crit Rev Food Sci Nutr 46:
551–567.
Waswa J, et al. 2007. Direct detection of E. coli O157:H7 in selected
food systems by a surface Plasmon resonance biosensor. LWT –
Food Sci Technol 40: 187–192.
Weese JS. 2010. Clostridium difficile in food – innocent bystander
or serious threat? Clin Microbiol Infect 16: 3–10.
Weese JS. 2009a. Clostridium difficile in food-innocent bystander
or serious threat? Clin Microbiol Infect 16: 3–10.
Weese JS et al. 2009b. Detection and enumeration of Clostridium
difficile spores in retail beef and pork. Appl Environ Microbiol
75: 5009–5011.
Weese JS et al. 2010. Detection and Characterization of Clostridium
difficile in retail chicken. Lett Appl Microbiol 50: 362–365.
Werber D et al. 2005. International outbreak of Salmonella Oranienberg due to German chocolate. BMC Infect Dis 3: 7.
Wesley IV et al. 2000. Fecal shedding of Campylobacter and
Arcobacter spp. In dairy cattle. Appl Environ Microbiol 66:
1994–2000.
Westrell T et al. 2009. Zoonotic infections in Europe in 2007: a

summary of the EFSA-ECDC annual report. Eurosurveillance
14: 1–3.
Woteki CE, Kineman BD. 2003. Challenge and approaches to reducing foodborne illness. Annu Rev Nutr 23: 315–344.
Wu FM et al. 2001. Factors influencing the detection and enumeration of Escherichia coli O157:H7 on alfalfa seeds. Int J Food
Microbiol 4: 93–99.
Yadav AS, Verma SS. 1998. Occurrence of enterotoxigenic
Aeromonas in poultry eggs and meat. J Food Sci Technol India
35: 169–170.
Yan J-J et al. 2005. Cephalosporin and ciprofloxacin resistance in
Salmonella, Taiwan. Emerg Infect Dis 11: 947–950.
Yoder S et al. 1999. PCR comparison of Mycobacterium avium
isolates obtained from patients and foods. Appl Environ Microbiol
65: 2650–2653.
Yoshida C et al. 2007. Methodologies towards the development of
an oligonucleotide microarray for determination of Salmonella
serotypes. J Microbiol Methods 70: 261–271.
Younts S et al. 2002. Differentiation of Escherichia coli O157:H7
from non O157:H7 E. coli serotypes using a gas sensor-based,
computer-controlled detection system. Transactions of the ASAE
45: 1681–1685.
Younts S et al. 2003. Experimental use of a gas sensor-based instrument for differentiation of Escherichia coli O157:H7 from
non O157:H7 Escherichia coli field isolates. J Food Prot 66:
1455–1458.
Yu CX et al. 2004. Spectroscopic differentiation and quantification of microorganisms in apple juice. J Food Sci 69: S268–
S272.


P1: SFK/UKS
BLBS102-c44

P2: SFK
BLBS102-Simpson

March 21, 2012

14:34

Trim: 276mm X 219mm

Printer Name: Yet to Come

44 Emerging Bacterial Food-Borne Pathogens and Methods of Detection

Yuan Y et al. 2009. Universal primer-multiplex PCR approach
for simultaneous detection of Escherichia coli, Listeria monocytogenes and Salmonella spp. in food samples. J Food Sci 74:
M446–M452.
Zhang Q, Plummer P. 2008. Mechanisms of antibiotic resistance in
Campylobacter. In: I Nachamkin, et al. (eds.) Campylobacter,
3rd edn. ASM Press, Washington, DC, pp. 263–276.

857

Zhang W et al. 2008. A new immuno-PCR assay for the detection
of low concentration of shiga toxin 2 and its variants. J Clin
Microbiol 46: 1292–1297.
Zhang Y et al. 2007. Genome evolution in major Escherichia coli
O157:H7 lineages. BMC Genomics 16: 121.


P1: SFK/UKS

BLBS102-c45

P2: SFK
BLBS102-Simpson

March 21, 2012

14:38

Trim: 276mm X 219mm

Printer Name: Yet to Come

45
Biosensors for Sensitive Detection of
Agricultural Contaminants, Pathogens
and Food-Borne Toxins
Barry Byrne, Edwina Stack, and Richard O’Kennedy

Introduction
Contaminant Monitoring
Traditional Means of Assessment
Instrumentation-Based Analysis
Biosensors
Biacore
Sensor Surfaces
Assay Configuration
Antibodies
Antibody Production Strategies
Optical Immunosensors for Quality Determination

Electrochemical Sensors
Alternative Biosensor Formats
Pathogens
Bacterial Pathogens
Fungal Pathogens
Toxins
Mycotoxins
Water and Marine Toxins
Legislation
Conclusion
Acknowledgements
Websites of Interest
References

Abstract: Immunosensors permit the rapid and sensitive analysis
of a range of analytes. Here, we provide a critical assessment of how
such formats can be implemented, with emphasis on the detection
of bacterial and fungal pathogens, agricultural contaminants (e.g.
pesticides and herbicides) and toxins.

INTRODUCTION
The monitoring of quality of food destined for human consumption is a key consideration for farmers, the food industry, legislators and, most importantly, for consumers (Karlsson 2004).
Hence, it is an absolute necessity to ensure that any contaminants

that may have a deleterious effect on human health are monitored qualitatively and quantitatively in a sensitive and reliable
manner. For example, herbicides and pesticides are extremely
effective at suppressing the growth of plant and insect populations on agricultural produce, such as tomatoes and strawberries.
However, the extensive use of such potentially toxic compounds
may compromise the quality of the product, and prolonged exposure may manifest itself as chronic toxicity in human hosts
(Keay and McNeil 1998). Furthermore, bacterial strains such

as Salmonella typhimurium and Listeria monocytogenes, which
are causative agents of salmonellosis and listeriosis, respectively,
can act as opportunistic pathogens and cause death through the
ingestion of contaminated produce. Consequently, the development of suitable methods for their rapid detection is an absolute
necessity. Finally, there is also an urgent need to accurately
monitor the distribution of toxins, including fungal (e.g. mycotoxins) and water-borne toxins (e.g. phycotoxins), which cause
severe illness through the consumption of contaminated food
(e.g. nuts, shellfish meat). In summary, rapid, sensitive and accurate methodologies are essential for the evaluation of product
quality and for satisfying legislative requirements.

CONTAMINANT MONITORING
Traditional Means of Assessment
There are several standard methods that are currently used to
monitor the quality of food. As an example, fruit and vegetable
produce may be inspected by monitoring the colour, gloss, firmness, shape and size of the product, as well as noting the presence
or absence of visible defects. This visual inspection may be performed alongside more invasive methods, including the analysis
of the soluble solid content of the product and determining the
acidity, and is particularly useful for produce such as apples,
pears and berries. The main advantage of such tests relates to
the fact that they may be carried out immediately post-harvest

Food Biochemistry and Food Processing, Second Edition. Edited by Benjamin K. Simpson, Leo M.L. Nollet, Fidel Toldr´a, Soottawat Benjakul, Gopinadhan Paliyath and Y.H. Hui.
C 2012 John Wiley & Sons, Inc. Published 2012 by John Wiley & Sons, Inc.

858


P1: SFK/UKS
BLBS102-c45

P2: SFK
BLBS102-Simpson

March 21, 2012

14:38

Trim: 276mm X 219mm

Printer Name: Yet to Come

45 Biosensors for Sensitive Detection of Agricultural Contaminants, Pathogens and Food-Borne Toxins

by the farmer, and at a minimal cost (Mitchum et al. 1996).
As further examples, grain and nuts may be inspected for the
presence of fungal contamination, while bacterial spoilage may
be indicated by the presence of an uncharacteristically strong
odour, such as in coleslaw and milk. However, these tests are
not sufficient for providing confirmation to the consumer that
the product satisfies regulations with respect to acceptable maximum residue limits (MRLs). Furthermore, it is not possible to
provide accurate quantitative or qualitative analysis of individual
contaminants through these methodologies, including those that
cannot be seen by visual inspection (e.g. mycotoxins). Hence,
it is common practice for food samples to be removed and sent
to an external laboratory where comprehensive in situ analysis
may be performed (Giraudi and Baggiani 1994).

Instrumentation-Based Analysis
There is a selection of different methodologies available for
quality determination, and some of the more relevant examples are discussed in this section. Product firmness can be determined through the implementation of the Magness-Taylor

test, namely a destructive method that assesses the maximum
force required to perforate the product in a specific way (Abbott
2004). This has been applied for the analysis of fruit, including
pears (G´omez et al. 2005). The non-destructive determination
of elasticity may also be permitted through the measurement
of acoustic responses, with the signal being interrogated using
fast Fourier transform-based analysis. Shmulevich et al. (2003)
demonstrated the efficacy of this approach for evaluating the
firmness of apples, and monitored product softening over time
in a controlled atmosphere environment. While these methods
are suitable for monitoring the structural properties of the product in question, they do not permit rigorous quality evaluation.
More suitable analytical methods include near-infrared (IR)
spectroscopy, implemented by Berardo et al. (2005) for the detection of mycotoxigenic fungi and associated toxic metabolites,
and scanning electron microscopy, applied for the inspection
of ultrastructural changes of the epicuticular layer of oranges

treated with fludioxonil, a pesticide used to control the growth of
two Penicillium species (Penicillium digitatum and Penicillium
italicum) (Schirra et al. 2005). Furthermore, gas chromatography (GS) or mass spectrometry (MS) and high-performance
liquid chromatography (HPLC) are accurate and highly sensitive methods for the detection of an array of contaminants,
including pesticide, herbicide and toxins residues. The latter
method may also be used to detect the presence of indicator
molecules that are representative of product freshness, including
flavonoids (MacLean et al. 2006), while liquid chromatography
coupled with mass spectrometry (LC-MS) can accurately monitor product bitterness. This was demonstrated by Dourtoglou
et al. (2006) for the analysis of olives (Olea europaea). In spite
of the efficacy of these analytical platforms, the instrumentation
needed to perform this analysis is expensive and bulky and may
require extensive operator training. In addition, analysis times
may also be extensive, as many contaminants require lengthy

sample pre-treatment prior to assessment.
Here, we focus on the application of biosensor-based platforms that are rapid, sensitive and reliable and are frequently
used for the detection of herbicide and pesticide residues, bacterial and fungal pathogens and toxins (fungal and water-borne).

BIOSENSORS
A biosensor can be defined as an analytical device that incorporates a biological element for promoting biorecognition of
an analyte of interest (e.g. herbicide, bacterial cell or toxin).
A schematic representation of a biosensor, illustrating the three
main components of the system, namely a bioligand, a transducer
and a readout device, is shown in Figure 45.1. Biosensor-based
platforms can use a wide selection of different recognition elements, including nucleic acid probes, lectins and antibodies
(Table 45.1). The focus in this chapter will be placed on the
use of antibodies for detection of contaminants that are of interest to the food industry with examples of enzyme- and nucleic
acid-based detection also provided.

Analytical matrix

Antigen
Antibody
Sensor surface
Transducer

859

Computational data
analysis

Figure 45.1. General format of a biosensor. The biorecognition element is in contact with the transducer, which converts the signal to an
output shown on the computer. For illustrative purposes, an antibody-based platform is shown, with non-specific antigens represented by
squares and triangles, respectively.

P1: SFK/UKS
BLBS102-c45

P2: SFK
BLBS102-Simpson

March 21, 2012

14:38

Trim: 276mm X 219mm

860

Printer Name: Yet to Come

Part 8: Food Safety and Food Allergens

Table 45.1. The Recognition Elements Commonly
Used in Sensor Systems
1.

2.

3.
4.
5.
6.

7.
8.

Antibodies and antibody fragments: Derived by enzyme
digestion or genetic engineering (Fab fragment, scFv
and diabody).
Lectins, namely carbohydrate-binding proteins. Only
suitable for the detection of glycosylated entities, such
as glycoproteins.
Enzymes: e.g. those specific for one particular substrate
(i.e. horseradish peroxidase for hydrogen peroxide).
Cell membrane receptors.
A living cell: eukaryotic or prokaryotic.
Nucleic acid-based probes: DNA and RNA or peptide
nucleic acid.
Aptamers.
Chemically generated recognition surfaces: Including
plastibodies (artificial antibodies or molecularly
imprinted polymers).

Fab, fragment antigen binding; scFv, single-chain variable fragment.

A transducer is a device, such as a piezoelectric crystal or
photoelectric cell that converts input energy of one form into
output energy of another, with the output signal generated proportional to the concentration of the target analyte (Luong et al.
1995). There are many different types of transducers that can
be used in biosensor-based platforms, and a selection of these is
shown in Table 45.2. Finally, a readout device typically consists
of a computer-linked monitor that presents the data in a form
that can easily be interpreted by the end-user.

Many different biosensor platforms have dedicated software
packages that can facilitate in the interpretation of biomolecular interactions. For example, Biacore, a frequently used com-

mercial optical biosensor based on surface plasmon resonance
(SPR), which is discussed in more detail later, uses Biaevaluation software that presents data in the form of a sensorgram (Fig.
45.2). Here, interactions between immobilised and free entities
can be easily visualised by monitoring changes in refractive index (RI). This correlates to a change in mass imparted by the
interaction between the two binding elements (e.g. antibody and
cognate antigen), with units of measurement referred to as response units (RU). In Biacore analysis, a response of 1000 RU
is representative of a change in resonance angle of 0.1◦ , which
corresponds to an alteration in the surface coverage of the sensor
surface of approximately 1 ng/mm2 .
While Biacore is an excellent example of a biosensor that
is commonly used for the evaluation of quality of food (which
is discussed in more detail later) other biosensor platforms are
also applicable that are based on electrochemical, piezoelectric,
magnetic and thermal detection (Byrne et al. 2009). Many of
these platforms are developed ‘in-house’ and have their own
dedicated software packages for the interpretation of data, and
key examples of these novel sensors are also described later.
Finally, for a biosensor to be applicable in any field, including in
the monitoring of quality of food produce, it must have certain
characteristics. These are listed in Table 45.3.

Biacore
Biacore (GE Healthcare) uses an optical-based transducer system for the measurement of analytes based on the principle of
SPR. SPR works on the principle of total internal reflection
(TIR), a phenomenon that occurs at the interface between two
non-absorbing materials, such as water and a solid. When a
source of light is directed at such an interface from a medium

with a higher RI to a medium of lower RI (such as light travelling
through glass and water), the light is refracted to the interface

Table 45.2. Examples of Transducers Commonly Used in Biosensor Systems
Type
Electrochemical

Example
Conductimetric
Potentiometric
Voltammetric

Field effect
transistor-based

Field effect transistor

Optical

Surface plasmon resonance

Thermal

Calorimetry

Surface acoustic
wave
Piezoelectric

Rayleigh surface wave

Electrochemical quartz
crystal microbalance

Principle of Use
Solutions containing ions conduct electricity. Depending on the reaction,
the change in conductance is measured.
Measurement of the potential of a cell when there is no current flowing to
determine the concentration of an analyte.
A changing potential is applied to a system and the resulting change in
current is measured.
A current flows along a semi-conductor from a source gate to a drain. A
small change in gate voltage can cause a large variation in the current
from the source to the drain.
Surface plasmon resonance (a detailed explanation is given in this
chapter).
Heat exchange is detected by thermistors and related to the rate of a
reaction.
An immobilised sample on the surface of a crystal affects the
transmission of a wave to a detector.
A vibrating crystal generates current that is affected by a material
adsorbed onto its surface.