Biosensor-Based Methods for the Determination of Foodborne Pathogens

CHAPTE R 12

Biosensor-Based Methods for the Determination of Foodborne Pathogens Burcin Bozal-Palabiyik, Aysen Gumustas, Sibel A. Ozkan, Bengi Uslu Ankara University, Ankara, Ankara, Turkey

1 Introduction Although not as fatal as diseases like heart attacks, respiratory problems, or cancer, diseases caused by foodborne pathogens are some of the most widely seen health problems not only in the underdeveloped world, but also in the developed. Even the United States, one of the most developed countries in the world in terms of health conditions, is not immune from foodborne diseases. According to the Center for Disease Control and Prevention (CDC), each year roughly one in six Americans (or 48 million people) get sick, 128,000 are hospitalized, and 3,000 die due to these types of illnesses (CDC, 2014). Thirty-one different pathogens (bacteria, virus, or parasites) infected millions of people in the US between 2000 and 2008, claiming thousands of lives (CDC, 2012). In the absence of proper sanitation conditions, foodborne diseases are more widespread and fatal. According to the World Health Organization (WHO), children under five years old are more sensitive to foodborne pathogens and children account for one third of deaths from these pathogens (WHO, 2015a). The worst conditions are in Africa, which represents one third of the global death toll for foodborne diseases. Every year, 91 million people in Africa fall ill and 137,000 of them die as a result of these diseases (WHO, 2015b). The severity of foodborne diseases requires an efficient struggle strategy, not only including proper sanitation, effective treatment, and educational policies, but also, and more importantly, the detection of pathogens in food. There are several traditional methods (such as culture-based methods, immunological-based reactions, and polymerase chain reaction) used for this purpose. Since these conventional methods are time consuming, exhaustive, and expensive, more effective methods have been investigated. Among these effective methods, biosensor-based detection is very promising because it does not require any preenrichment, it is cost effective and simple. This chapter deals with detection of foodborne pathogens via biosensors. There are different methods of detection based on electrochemical, optical, thermal, and mass-based transducers; Foodborne Diseases http://dx.doi.org/10.1016/B978-0-12-811444-5.00012-9

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380  Chapter 12 however, in this chapter only electrochemical and optical biosensors will be discussed because they are most widely preferred. In order to discuss that, first, foodborne pathogens must be described. Secondly, conventional methods used for the detection of these pathogens are investigated in order to compare and contrast their advantages and disadvantages vis-à-vis biosensor-based methods. Third, biosensors and biosensor-based methods are analyzed in detail. Within this context, the definition of biosensor as well as its classifications is given and then immobilization techniques for designing bioreceptors are covered. After that, electrochemical and optical biosensors are examined in line with their applications shown in recently published literature.

2  Causes of Foodborne Diseases Foodborne disease has been defined by the World Health Organization (WHO) as “any disease of an infectious or toxic nature caused by, or thought to be caused by, the consumption of food or water.” According to this definition, foodborne diseases include all food and waterborne illnesses associated with the gastrointestinal tract and exhibiting symptoms such as diarrhea and/or vomiting or other gastrointestinal manifestations, such as paralytic shellfish poisoning, botulism, and listeriosis. Illnesses caused by toxic organic and inorganic chemicals, but exclude illness due to allergies and food intolerances are also included (Lund, 2008; Motarjemi et al., 2014; WHO, 2016). A wide range of agents cause foodborne diseases. These agents stem from chemical, physical, and biological sources and when they present in food at hazardous levels to humans, refer to food hazards as defined by the Food Safety Management Systems. Food hazards are divided into three main categories: 1. Chemical hazards 2. Physical hazards 3. Biological hazards Chemical hazards may come from variety of sources such as the environment (air, soil, and water), intentional use of chemicals (pesticides and veterinary drugs), food processing, and the addition of food additives, and can be classified into three categories: 1. Naturally occurring chemicals 2. Intentionally added chemicals 3. Unintentionally or incidentally added chemicals (Safefood HACCP: Online Training Course, 2011; Safefood 360 Whitepaper, 2013; Shaw, 2013) Naturally occurring chemicals originate from a variety of animals, plants, or microorganisms such as animal toxins, plant toxins, mycotoxins, bacterial toxins, and phytoestrogens. Animal toxins include fish toxins, such as ciguatoxin, maitotoxin, and tetrodotoxin, and also shellfish

Biosensor-Based Methods for the Determination of Foodborne Pathogens  381 toxins such as diarrheic-causing saxitoxin, and neurotoxic brevetoxin and gimbretoxin. Plant toxins are the most commonly encountered natural toxins in our food. They have a wide variety of molecular structures and mechanisms of toxicity. Cucurbitacins (produced by members of the cucumber family), glycoalkaloids (found in potatoes), and lectins (present in most plants, especially seeds and tubers such as beans, cereals, and potatoes) are some examples of the more important plant toxins found in food. Mycotoxins are produced by toxigenic mold strains as secondary metabolites and involve a large number of toxins. Some of the most important of those toxins include aflatoxins (produced by the fungus (mold) Aspergillus flavus, which grows on stored nuts and grains), zearalenone (produced by several Fusarium spp.), ochratoxin A (produced by several species of Penicillium and Aspergillus), and patulin (produced by certain species of Penicillium, Aspergillus, and Byssochylamys). Bacterial toxins can be classified as either exotoxins or endotoxins. Exotoxins are soluble proteins excreted by bacteria and released after cell lysis. Shiga toxin (produced by Shigella spp.), botulinum neurotoxin (produced by Clostridium botulinum), enterotoxins (produced by Staphylococcus aureus), hemolysin (produced by Escherichia coli), and streptolysin O (Streptococcus pyogenes) are examples of exotoxins. Endotoxins are lipopolysaccharides derived from the cell membrane of Gram-negative bacteria and released mainly when bacteria are lysed. E. coli heat-labile toxin and E. coli O111:B4 are examples of endotoxins (Bhunia, 2008; Henkel et al., 2010; Motarjemi et al., 2014; Ray and Bhunia, 2013; Shaw, 2013). Some chemicals are intentionally added to food for a technological purpose during its manufacture and processing. Most additives are restricted to certain foods at maximum specified levels. These additives are safe when used at established safe levels but can be dangerous when those levels are exceeded. Preservatives (nitrite and sulfating agents), nutritional additives (niacin), and color additives are some intentional food additives (Safefood HACCP: Online Training Course, 2011; Safefood 360 Whitepaper, 2013; Shaw, 2013). Unintentionally or incidentally added chemicals are not deliberately added to foods. Most incidental additives have no effect on food safety even if they are present at high levels. Incidentally chemicals include agricultural chemicals (pesticides and herbicides), prohibited substances (insecticides and toxic elements), prohibited compounds (arsenic, cadmium, lead, zinc, mercury, and cyanide, polychlorinated biphenyls), plant chemicals (sanitizers, cleaning compounds, and lubricants), and paints (Safefood HACCP: Online Training Course, 2011; Safefood 360 Whitepaper, 2013; Shaw, 2013). Physical hazards include any potentially harmful extraneous materials found in a food that are foreign to that particular food. These materials are usually non-toxic but they are associated with unsanitary conditions during the production, processing, handling, storage, and distribution of food. Physical contaminants include insects, hair, nails, nail polish flakes, metal fragments, pieces of plastic, wood chips, broken glass, staples, plastic fragments, bones,

382  Chapter 12 or bits of packaging. Biological hazards pose the biggest threat to food safety and include bacteria, viruses, and parasites. These microorganisms cause most foodborne diseases by contaminating foods and they are referred to as foodborne pathogens (Motarjemi et al., 2014; Safefood HACCP: Online Training Course, 2011). Biological hazards include bacteria, viruses, and parasites, referred to as foodborne pathogens, which cause foodborne diseases (Table 12.1). They are also the most important and frequently encountered foodborne agents and among these hazards. Bacterial pathogens comprise the majority of confirmed foodborne disease outbreaks and cases. The hazards that are caused by bacteria can be categorized as spore-formers and non-spore-formers. When the bacteria exist as a spore (spore-formers) it is resistant to heating, freezing, chemicals, and other treatments. The spore allows the bacterium to remain dormant for long periods of time and spores are not hazardous during this period. Some significant examples of foodborne pathogens that cause diseases along with their infective dose, time of onset, symptoms, and food sources are summarized in Table 12.1 (Adams and Moss, 2008; Bhunia, 2008; Motarjemi et al., 2014; Riemann and Cliver, 2006; Safefood HACCP: Online Training Course, 2011).

3  Detection of Foodborne Pathogens As previously mentioned, foodborne diseases caused by pathogens or related toxins may result in severe health problems, which may have lethal consequences. This makes the detection of foodborne pathogens in foods extremely important for guaranteeing food safety. There are several methods for detecting pathogens: • • •

•

•

•

Culture methods ATP bioluminescence Microscopic methods • Direct epifluorescent filter technique • Flow cytometry Immunological methods • Immunochromatographic lateral flow assays • Enzyme-linked immunosorbent and enzyme-linked immunofluorescent assays • Agglutination techniques Molecular methods • The polymerase chain reaction • Microarray methods Biosensors • Electrochemical biosensors • Optical biosensors • Mass biosensors • Thermal biosensors

Table 12.1: Foodborne pathogens and cause diseases. Pathogen

Disease

Bacteria Aeromonas hydrophila

Gastroenteritis

Bacillus cereus

Campylobacter jejuni

>107 CFU gm–1 24 h (diarrhea) Mild diarrhea, blood and mucus in the stool, and symptoms of septicemia Brucellosis 3 weeks Headache, intermittent fever, <500 cells sweating, weakness, chills, malaise, joint, muscle pain, endocarditis or myocarditis, and spondylitis Ba. cereus food Diarrheal type: Diarrheal type: abdominal cramps, >106 poisoning 6 to 15 hours; watery diarrhea; emetic type: vomiting organisms g–1 and nausea emetic type: 0.5 to 6 h Campylobacteriosis <500 cells 2 to 5 days Diarrhea (bloody), cramps, fever, and vomiting

Clostridium botulinum

Botulism

Cl. perfringens

Perfringens food poisoning

Enterotoxigenic Escherichia coli (ETEC)

Time of Onset Symptoms

<3 ng

4 h to 8 days

>106 vegetative 16 h cells g–1 >106 spores g–1 Gastroenteritis 107 to 1010 8 to 44 h (travelers’ diarrhea) billion cells

107 to 1010 Enteropathogenic Infantile diarrhea E. coli (EPEC) billion cells Enterohemorrhagic Hemorrhagic colitis 10 to 100 cells E.a coli (EHEC)

4 hours 1 to 9 days

Vomiting, diarrhea, blurred vision, double vision, muscle weakness, difficulty in swallowing, may result in respiratory failure and death Watery diarrhea and intense abdominal cramps Watery diarrhea (without blood or mucus), abdominal cramps, rarely accompanied by high fever or vomiting, low-grade fever, nausea, malaise Profuse, watery diarrhea; vomiting; low-grade fever Watery diarrhea (grossly bloody and occurring every 15 to 30 minutes), abdominal cramps, nausea or vomiting, low-grade or absent fever

Food Sources Fish, shellfish, beef, pork, lamb, and poultry Cattle (Br. abortus); pigs, hares, reindeer, and wild rodents (Br. suis) Diarrheal type: Meats, vegetables, milk; emetic type: rice products, soups, and puddings Raw and undercooked poultry, unpasteurized milk, and contaminated water Improperly canned foods, especially home-canned vegetables, fermented fish, baked potatoes in aluminum foil Meats, poultry, gravy, dried or precooked foods, time and/or temperature-abused foods Brie cheese, curried turkey, mayonnaise, crabmeat, deli food, and salads

Raw beef, chicken, mayonnaise, lettuce, and pickles Yogurt, mayonnaise, fermented sausages, cheeses, unpasteurized fruit juices, lettuce, spinach (Continued)

Biosensor-Based Methods for the Determination of Foodborne Pathogens  383

Brucella abortus; Br. suis

Infective Dose

Pathogen

Disease

Infective Dose

Time of Onset Symptoms

Food Sources

Enteroinvasive E. coli (EIEC)

Bacillary dysentery

200 to 5,000 cells

12 to 72 h

Listeria monocytogenes

Listeriosis

<1,000 cells

3 days to 3 months

Salmonella paratyphi S. typhi S. typhimurium S. enteriditis

Typhoid fever

<1,000 cells

1 week to 2 months

1 cell

6 to 72 h

10 to 200 cells

8 to 50 hours

Staphylococcus aureus

Nontyphoidal salmonellosis Shigellosis (bacillary dysentery) Staphylococcal food poisoning

Any food contaminated with human feces from an ill individual, either directly or via contaminated water Unpasteurized milk, chocolate milk, ice cream, soft cheeses made with unpasteurized milk, raw vegetables, raw poultry, meats, fermented raw-meat sausages, hot dogs, deli meats, raw, and smoked fish Eggs, spices, cocoa, meats, poultry, milk and dairy products, chocolate, coconut, fish, shrimp, yeast, sauces, peanut butter, fruits and vegetables

105 organisms/g–1

1 to 7 h

Streptococcus pyogenes

Streptococcus Group A infections

<1,000 organisms

1 to 3 days

Vibrio cholerae

Cholera

>106

1 to 3 days

Shigella dysenteriae

Abdominal cramps, diarrhea, vomiting, fever, chills, generalized malaise, blood and mucus in the stools Fever, nausea, muscle aches, headache, vomiting, diarrhea, stiff neck, confusion, loss of balance, and convulsions

Abdominal pains, diarrhea, constipation, high fever, headache, achiness, loss of appetite, and rosecolored spots Headache, abdominal cramps, nausea, vomiting, diarrhea, fever Abdominal cramps, diarrhea, fever, vomiting, blood, and mucus in the stools Nausea, vomiting, abdominal cramps, diarrhea, dehydration, headache, muscle cramp, transient changes in blood pressure and pulse rate High fever, headache, nausea, inflamed throat, vomiting, pain on swallowing, malaise, and rhinorrhea

Raw produce, poultry, milk and dairy products Meats and meat products, bakery products, milk and dairy products, and salads

Pasteurized and unpasteurized milk, eggs, ice cream, cream, seafood, and salads (potato, egg, and shrimp) Abdominal cramp, vomiting, mild and Seafood watery diarrhea, rice-water stools

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Table 12.1: Foodborne pathogens and cause diseases. (cont.)

Disease Infective Dose 108 organisms V. parahaemolyticus infection V. vulnificus infection 1,000 organisms

Yersinia enterocolitica

Yersiniosis

Viruses Hepatitis A virus Hepatitis

Hepatitis E virus Hepatitis

104 to 106 organisms

Time of Onset Symptoms Food Sources 4 to 90 h Diarrhea, abdominal cramps, vomiting nausea, and fever 12 h to 21 Abdominal cramp, vomiting, diarrhea, days fever, bloodborne infection, bleeding within the skin 1 to 11 days Abdominal pain, diarrhea, vomiting, Oysters, pork, beef, lamb, fish, fever crabs, and raw milk

10 to 100 viral particles

15 to 50 days

Jaundice, diarrhea, vomiting, abdominal pain, myalgia, fever, headache, nausea, and anorexia

NK

3 to 8 weeks

Jaundice, abdominal pain, vomiting, fever, anorexia, arthralgia, and hepatomegaly Acute-onset vomiting, abdominal cramps, nausea, vomiting, diarrhea, low-grade fever, headache, muscle aches, and chills Mild, watery diarrhea, vomiting, dehydration, hypovolemic shock, and death

Noroviruses

Viral gastroenteritis 1 to 10 viral (Winter vomiting particles disease)

24 and 48 h

Rotavirus

Viral gastroenteritis 10 to 100 viral (Winter diarrhea) particles

<48 h

Anisakiasis (Anisakidosis)

24 h to 2 weeks

Diarrhea, abdominal pain, vomiting, and nausea

18 days to several weeks

Abdominal pain, nausea, loss of appetite, vomiting, and self-limiting pneumonia Profuse, nausea, watery diarrhea, vomiting, stomach cramps, and fever Watery diarrhea, abdominal cramps, nausea, loss of appetite, weight loss, fatigue, vomiting, headache, body aches, and fever

Parasites Anisakis simplex

Ascaris lumbricoides

Ascariasis

Cryptosporidium parvum Cyclospora cayetanensis

Cryptosporidiosis Cyclosporiasis

1 worm

10 to 100 oocysts NK

7 to 10 days 7 to 10 days

Vegetables, salads, fruits, fruit juices, shellfish, contaminated drinking water, shellfish, milk and milk products Sausage, wild boar meat, old pork livers, tomatoes, and strawberries Contaminated drinking water, salads, fruit, and oysters

Salads and fruits

Cephalopods, squid, cod, haddock, fluke, herring, flounder, and monkfish Vegetables and fruits

Contaminated water supplies, fresh produce, milk, and juices Raspberries, basil, and lettuce

(Continued)

Biosensor-Based Methods for the Determination of Foodborne Pathogens  385

Pathogen V. parahaemolyticus V. vulnificus

Pathogen

Disease

Infective Dose

Time of Onset Symptoms

Food Sources

Diphyllobothrium latum Entamoeba histolytica

Diphyllobothriasis

One or more larval worms <10 cysts

15 days

Raw or undercooked fish such as sushi, sashimi, and ceviche Raw foods, fecal contamination in drinking water and foods

Giardia lamblia

Giardiasis

≥1 cysts

1 to 2 weeks

Taenia solium; T. saginata

TaeniasisCysticercosis

1 cysticercus larva

Trichinella spiralis

Trichinellosis (Trichinosis)

2 larvae

Taeniasis: 2 to 4 months Cysticercosis: 2 months to several years 1 to 4 weeks

Trichuris trichiura

Trichuriasis

Toxoplasma gondii

Toxoplasmosis

Amebiasis (Amoebiasis)

NK

2 to 4 weeks

5 to 23 days

Mild diarrhea, abdominal discomfort, loss of appetite, and anemia (rarely) Mild diarrhea (along with mucus and blood), nausea, fever, abdominal distention, weight loss, and liver tenderness Malodorous diarrhea, abdominal cramps, flatulence, weight loss; malaise Taeniasis; diarrhea, abdominal pain, nausea, malaise, change in appetite Cysticercosis: headache, altered mental status, seizures, and increased intracranial pressure Abdominal discomfort, vomiting, diarrhea, nausea, fever, muscle pain, and weakness Abdominal pain, nausea, diarrhea, mucus and blood in the stool, vomiting, rectal prolapse, and anemia Blurred, vision, tearing, redness, and sensitivity to light in the eye, fever, swollen lymph glands, muscle aches, and headache

Raw vegetables and fruits (leafy greens and berries) Taeniasis; pork (T. solium), beef (T. saginata) Cysticercosis; pork, raw vegetables Undercooked meats such as reptiles, bear, and birds Raw foods

Undercooked or raw meats (lamb, pork, and wild game)

NK: not known. Source: from Barros-Velázquez, J., 2016. Antimicrobial Food Packaging. Academic Press, Elsevier, San Diego, USA; Lampel, K.A., Al-Khaldi, S., Cahill, S.M., 2012. Food and Drug Administration. Bad Bug Book, Foodborne Pathogenic Microorganisms and Natural Toxins. 2nd Ed. Center for Food Safety and Applied Nutrition (CFSAN) of the Food and Drug Administration (FDA), U.S. Department of Health and Human Services, Maryland, USA; Centers for Disease Control and Prevention (CDC) 2013, Parasites. Available from: http://www.cdc.gov/parasites/az/index.html. [25 April 2016].

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Table 12.1: Foodborne pathogens and cause diseases. (cont.)

Biosensor-Based Methods for the Determination of Foodborne Pathogens  387 In this chapter, all of these techniques are described briefly with a focus on electrochemical and optical biosensor systems for the detection of foodborne pathogens.

3.1  Culture Methods Culture methods are standard microbiological techniques based on growing microorganisms on selective solid or liquid enrichment culture media, isolation of pure cultures, and identification of microorganisms. A single bacterial species can be detected in a complex food matrix with these techniques that give both qualitative and quantitative information about target organisms, thus they are frequently used sensitive, selective, and accurate methods. In spite of these advantages, they are labor-intensive, time-consuming methods due to their preparation, inoculation, isolation, and identification steps (Adams and Moss, 2008; López-Campos et al., 2012).

3.2  ATP Bioluminescence Methods Bioluminescence is the emission of light produced by organisms through a chemical reaction. This reaction relies on the enzyme-substrate interaction, enhanced in the presence of adenosine triphosphate (ATP) and requires two chemicals: luciferase (an enzyme) and (a substrate). The ATP bioluminescence technique is based on measuring the emission of light that emerged as a result of a luciferase-luciferin reaction shown here: Luciferin + Mg2+ + ATP  Oxyluciferin + CO 2 + Light Luciferase

The quantity of light emitted can be measured by lumenometers, expressed as relative light units, and is proportional to the concentration of ATP that directly relates to the number of microorganisms present in a sample. The levels of ATP vary depending on the organisms, and correlation between ATP levels and organisms are associated with this variety. This method is used to estimate the total microbial load, but it is also applicable to a high number of bacteria. The ATP bioluminescence technique is mostly used to detect organic residue and microbial contaminants on food contact surfaces within a short time (Adams and Moss, 2008; LópezCampos et al., 2012).

3.3  Microscopic Methods 3.3.1  Direct epifluorescent filter technique Direct epifluorescent filter technique (DEFT) is a microscopic technique based on counting the number of viable cells in a sample and it was first used for bacterial enumeration in raw milk in the 1980s. This technique involves pretreatment (varied by food), filtration of the food samples through a polycarbonate membrane for concentrating, collecting the microbial cells on the filter, staining the filtered cells using fluorochrome such as acridine orange (mostly

388  Chapter 12 used) or 4’, 6-diamidino-2-phenylindole, and detecting the bacteria by using epifluorescence microscopy. It is a very labor-intensive technique and the detection limit of this technique is in the order of 103 cell mL–1, but automated and semi-automated systems have been developed to speed up the process and modifications of this method have been explored to improve the sensitivity. This method is mostly used in the dairy industry, but it has also been used for other beverages and foods (Bari and Ukuku, 2016; Betts et al., 1988; López-Campos et al., 2012). 3.3.2  Flow cytometry Flow cytometry is a laser-based technique that counts microorganisms and evaluates physical and chemical characteristics such as the shape, size, and DNA content of the cells. In this technique, fluorescently labeled samples are injected into a fluid that is passed through a flow cell. The samples are carried by a laminar flow of water through a beam of light and the light is emitted from fluorescent stains. The scattered light is collected by means of lenses and directed on a number of sensitive detectors. The data received from the detectors can be correlated with physical or chemical characteristics of bacteria. Using this method it is possible to detect 105–107 cells g–1 in food samples. A flow cytometer consists of a light source such as a laser or a mercury lamp, a flow cell, optical filters for different wavelength detection, light detectors such as photodiodes or photomultiplier tubes for signal detection and amplification, and a data processing unit (Fig. 12.1) (Bari and Ukuku, 2016; Comas-Riu and Rius, 2009; López-Campos et al., 2012).

Figure 12.1: Schematic Diagram of the Imaging Flow Cytometry. Source: reprinted with permission from Jahan-Tigh, R.R., Ryan, C., Obermoser, G., Schwarzenberger, K., 2012. Flow Cytometry. J. Invest. Dermatol. 132, 1–6.

Biosensor-Based Methods for the Determination of Foodborne Pathogens  389

3.4  Immunological Methods Immunological methods are based on a reaction and are specifically binding between artificially labeled antibodies and antigens. Two types of antibodies are used in these techniques as follows: polyclonal antibodies, which are directed against different epitopes on the same antigen, and monoclonal antibodies, which bind to the same epitope on a target antigen. The antibodies can be labeled with fluorescent reagents, enzymes, or biotin, thus the antigen–antibody interaction may be visualized more easily. The detection limit of this technique is in the order of 104–105 CFU mL–1, depending upon the type of antibody and its affinity. There are several types of immunological methods used to detect foodborne pathogens, such as immunochromatographic methods, enzyme-linked immunosorbent assay (ELISA), enzyme-linked immunofluorescent assays, and agglutination techniques (Bari and Ukuku, 2016; Bhunia, 2008; McMeekin, 2003). 3.4.1  Immunochromatographic lateral flow assays Immunochromatographic lateral flow assays are membrane-based techniques that are widely used, simple, suitable, and fast. The method is comprised of varied porous membranes as follows: glass fiber membrane, used as both a conjugate pad and a sample pad, a nitrocellulose membrane occurred reaction, and a cellulose nitrate membrane used as an absorption pad. A typical configuration for an immunochromatographic lateral flow assay is shown in Fig. 12.2. In the assay format, the captured antibodies are immobilized on the nitrocellulose membrane as two lines: a test line specific for the target antigen and a control line specific for unbound antibodies. Detection antibodies are labeled by color reactant (gold nanoparticles, fluorescent, or paramagnetic monodisperse latex particles) and immobilized on the conjugate pad. A sample containing the target antigen applied to the sample pad and is migrated through to the conjugate pad. The target antigen in the sample binds to the labeled antibody and flows through to nitrocellulose membrane contained test and control lines by capillary movement. After passing these reaction zones the sample flows toward the

Figure 12.2: A Typical Configuration for an Immunochromatographic Lateral Flow Assay. Source: reprinted with permission from Gessler, F., Pagel-Wieder, S., Avondet, M.-A., Böhnel, H., 2007. Evaluation of lateral flow assays for the detection of botulinum neurotoxin type A and their application in laboratory diagnosis of botulism. Diagn. Micr. Infec. Dis. 57, 243–249.

390  Chapter 12 absorbent pad. Positive results are indicated by the presence or the absence of the test line (Bhunia, 2008; McMeekin, 2003; Wild et al., 2013). 3.4.2  Enzyme-linked immunosorbent and enzyme-linked fluorescent assays Enzyme-linked immunosorbent assay (ELISA) is a biochemical technique that is used to detect the presence of antigen in a sample. There are four variations of this technique: direct, indirect, competitive, and sandwich. In the direct ELISA technique, the antigen is immobilized on a polystyrene plate in order to capture the antibody conjugated to an enzyme and the substrate is added for detecting color. In the indirect technique, the antigen is immobilized on the surface of microtiter plate and a specific primary antibody is applied over the surface. Then, the secondary antibody that is conjugated to an enzyme is added onto the plate to bind to the primary antibody. After that, substrate solution is added for the development of color. In competitive ELISA, the primary antibody is mixed with the antigen and this complex is added to the microtiter plates, preimmobilized with the antigen. After washing and removing the unbound antibodies, the secondary antibody that is conjugated with an enzyme and substrate are added for the development of color. Finally, in sandwich ELISA, an antibody is immobilized on the surface of the plates and antigen is added. The secondary antibody labeled with an enzyme and substrate is added for detecting color. Enzyme-linked fluorescent assay (ELFA) is the same principle as ELISA. In this method, the secondary antibody is labeled with enzyme-produced fluorescence and is detected by a spectrofluorometer or a fluorescence microscope (Bhunia, 2008; López-Campos et al., 2012; McMeekin, 2003; Wild et al., 2013).

3.5  Molecular Methods Molecular methods are highly sensitive and rapid methods for differentiating, identifying, and defining pathogens. These methods can detect specific parts of DNA or RNA molecules in the target organisms by hybridizing them. However the molecular techniques require a lot of expertise and high costs, which most laboratories cannot supply (Simjee, 2007; Simpson et al., 2012). 3.5.1  The polymerase chain reaction Polymerase chain reaction (PCR) is the most used molecular method used to amplify a few copies of a specific region of DNA. In this technique, specific oligonucleotide primers that are usually about 20 base pair long are used. PCR is performed in repeated cycles, including denaturation of double stranded DNA by heating, primer annealing by cooling the mixture, extension of DNA using Taq polymerase, and amplification. The results of PCR are detected by agarose gel electrophoresis for visual identification (Bhunia, 2008; McMeekin, 2003; Simjee, 2007).

Biosensor-Based Methods for the Determination of Foodborne Pathogens  391 3.5.2  Microarray methods Microarray methods are miniaturized techniques for simultaneous analysis of the entire genome of an organism in a single experiment. The method is based on the interaction between an oligonucleotide probe immobilized on the surface, which is a carrier material such as glass or a synthetic membrane, and the fluorescent-labeled target. The microarray includes isolation of the nucleic acids, labeling of the nucleic acids with fluorescent dyes (Cy3 and Cy5 are mostly used dyes), hybridization, detection by laser scanner, extraction of the signal intensities from images, and bioinformatics analysis steps. Microarrays are powerful tools in genomic analysis of foodborne pathogens (López-Campos et al., 2012; Rasooly and Herold, 2008; Simjee, 2007).

3.6 Biosensors The word sensor, which is derived from the Latin word sentire meaning “to perceive,” can simply be defined as the devices used for transforming the changes in the external world to perceivable signals. In other words, sensors are devices that transform a mechanical, thermal, acoustic, magnetic, electrical, or radiant effect into a measurable and recordable electrical signal (Khanna, 2011). Sensors can be distinguished into three categories: (1) physical sensors, used for sensing physical factors such as distance, mass, temperature, or pressure; (2) chemical sensors, used for measuring chemical substances in accordance with the chemical or physical responses they give; and (3) biosensors, used for measuring chemical substances via a biological sensing element (Eggins, 2002). A clear-cut definition of the concept of biosensor is hard to achieve, not only because there are numerous definitions in literature, but also because of interdisciplinary attitudes in describing and utilizing these devices. However, the most simple and widely accepted definition of the biosensor is as follows: A biosensor is a device that combines a transducer and a biologically sensing element (biochemical receptor) (Coulet, 1991). Since it is a combination of a transducer and a biologically sensing element, these two concepts should be defined as well. As such, a biochemical receptor is a device used for detecting or measuring (in some cases, recording, indicating, or responding to) a physical property; while a transducer is a device that converts an observed physical or chemical change to a measurable signal (Eggins, 2002). The foundations of the biosensor were first laid down by Leland Clark, who designed the oxygen electrode to detect dissolved oxygen in blood in 1956; at that time, the very concept of a biosensor had not been coined yet. This was followed by the composition of the first enzyme biosensor for the detection of glucose in 1962, which was composed of a Clark amperometric oxygen electrode as a transducer and glucose-oxidase enzyme as the biological sensing element. The commercial production of glucose biosensors was not realized until 1975 (Coulet, 1991; Kaya et al., 2011).

392  Chapter 12

Figure 12.3: Schematic Diagram of a Biosensor.

As it can be seen from Fig. 12.3, a biosensor is composed of a bioactive sensing layer and a proper transducer producing a measurable signal. The sensing layer might be a natural receptor, detecting the analyte, such as metabolites, drug residues, toxins, small molecules, ions, DNAs, RNAs, pathogens, and so on that have been investigated, or an artificial receptor that mimics the natural receptor (Coulet, 1991). Detecting the analyte as a measurable output signal proportional to its quantity or concentration depends on the consequential order of certain events. To start, the first chemical or physical signal observed by the bioactive layer through molecular detection (this process is called the sensing step) is transformed into the second signal by the transducer (this process is called the transformative step). In other words, in the sensing step, the biological element of the biosensor recognized the analyte in the solution or atmosphere; the linking of an analyte with the receptor forms the first measurable signal. On the other hand, in the transformative step the transducer transforms this first signal into a quantitative optical or electrical signal. In order to form the second signal, electrochemical, optical, thermal, and mass-sensitive methods are preferred. In addition to these four sensing mechanisms, thermal, mechanical, chromatographic, magnetic, liquid, or ultrasonic sensing are also possible. The biological component, which senses the targeted molecule selectively, could be various affinity systems, such as an enzyme for substrates, antibodies for antigens, lectin for sugar, or nucleic acids for complementary strands (Coulet, 1991; Sadana, 2003). There are two forms of sensing in biosensors. The first is called bio-affinity sensing, while the second one is called bio-metabolic sensing. Both processes require binding of the analyte with a complementary receptor. In bio-affinity sensing, the transducer senses the presence of analyte-receptor couples, such as antigen-antibody binding. In bio-metabolic sensing, analytereceptor interaction results in the emergence of a product that is sensed by a bio-recognition element (Sadana, 2003).

Biosensor-Based Methods for the Determination of Foodborne Pathogens  393 3.6.1  Properties of an ideal biosensor There are several properties of an ideal biosensor: 1. Drift and Long-Term Stability: The lifetime of a biosensor can be described as the length of time that biosensors continue to be sensitive under normal working conditions. In other words, an ideal biosensor should have a longer lifetime during which it should endure the internal environment of the human body and also function normally (Buerk, 1993; Wang and Liu, 2011). The factors that might lead to the reduction of drift and long-term stability are oxide formation on the electrode and fouling and poisoning by direct absorption of proteins or other chemical substrates on the membrane (Buerk, 1993). 2. Selectivity: Selectivity can be defined as the ability of a biosensor to measure a single component (or the components under investigation) in the presence of others. In other words, an ideal biosensor should be immune from interference as much as possible and should not be influenced by the presence of other chemical species (Buerk, 1993; Wang and Liu, 2011). 3. Sensitivity: Sensitivity is defined as the ratio of incremental change in sensor output to incremental change in the measured material in the input. There are several factors affecting the sensitivity of the designed biosensor, such as the physical size of the sensor, the thickness of the membrane, mass transfer from the chemical species from the sample to the sensing region or processes disturbing its proper functioning in time. An ideal biosensor should allow convenient measurement of the output signal (Buerk, 1993; Wang and Liu, 2011). 4. Calibration: A standard solution or gases containing the targeted analyte in different concentrations could calibrate an ideal biosensor. In practice, performing regular and periodic calibrations is important for following changes in sensitivity over time (Buerk, 1993). 5. Linearity: The more the calibration curve of a biosensor is close to a specified straight line, the more linear that particular biosensor is. A perfectly linear biosensor has a constant sensitivity over the concentration range from zero to the maximum substrate concentration, which is physically dissolved in the measurement environment. If the calibration curve can be obtained with enough accuracy for evaluating the signal of the biosensor, then it is not a requirement for a biosensor to be perfectly linear (Buerk, 1993). 6. Limit of Detection: For an ideal biosensor, the lowest detected substrate concentration should be limited only by the resolution of the electronic instrumentation for measurement. 7. Background signal: If the measured noise is large vis-à-vis the input, it is very difficult to determine a clear signal and since the human body is very complex, various signals might interfere and make detection difficult (Buerk, 1993; Wang and Liu, 2011). Current leakage, small potential differences in electronical devices, or metal-metal contacts in the

394  Chapter 12 wire leads of the biosensor might lead to a background signal impairing a lower limit of detection (Buerk, 1993). 8. Hysteresis: If the output of a biosensor has different paths when the input is increasing and subsequently decreasing then it is assumed that there is hysteresis. Energy adsorption or changes in the chemical media upon measurement may result in this problem. However, an ideal biosensor should not be affected by its measurement history; in other words, it should have zero hysteresis (Buerk, 1993). 9. Dynamic Response: How quickly a biosensor responds to a change in the concentration of the target analyte it measures is a very important factor and it is very much related with the physical properties and relative size of the biosensor probes. In other words, the mass flux of the target analyte is dependent on the concentration differences, effective diffusion coefficients and thickness of each element of the sensor (Buerk, 1993). 10. Biocompatibility: If the biosensors have medical applications, biocompatibility is a significant property. An ideal biosensor could be inserted directly into the bloodstream without much blood clotting and platelet interaction. Moreover, inflammatory responses and undesired scar growth might have negative impacts on biosensor performance. Sterilization is a crucial factor for enhancing the biocompatibility of biosensors (Buerk, 1993). 11. Temperature: Biosensors are temperature-dependent as well. The performance of a biosensor is closely related with the temperature, since enzyme reactions might change with a change in temperature (Buerk, 1993). 3.6.2  Immobilization of the biochemical receptor Making the biochemical element of the biosensor insoluble and fixing it on a proper solid interface or transducer surface immobilizes it. This process is required for several reasons: (1) through immobilization, expensive biochemical reagents can be reused and this lowers the cost of biosensor production; (2) immobilization also increases the stability of the biochemical components and makes the three-dimensional structure more rigid, which, at the end, increases the functionality of the biosensor; (3) immobilization decreases cross-reactivity of the biochemical component to the structures relative to an analyte and thereby maintains better conditions for signal transduction; and (4) enzyme-carrier complexes can be easily separated from the sample (Evtugyn, 2014; Scheller and Schubert, 1992). There are different methods for immobilization and none of them can be considered the best method for the detection of all kind of molecules. In other words, the method of immobilization chosen for designing a biosensor depends on the nature, character, type of biological material, type of transducer, as well as the physical properties of the analyte investigated (Narsaiah et al., 2012). These methods could also be classified as physical and chemical immobilization. The most-widely preferred methods of immobilization are as follows: (1) adsorption, (2) covalent binding, (3) entrapment and microencapsulation, and

Biosensor-Based Methods for the Determination of Foodborne Pathogens  395

Figure 12.4: Illustration of Adsorption Method.

Figure 12.5: Illustration of Covalent Binding on the Surface.

(4) cross-linking. Among these methods, adsorption and entrapment are among the physical methods, while covalent binding and cross-linking are among the chemical methods (Scheller and Schubert, 1992; Shantilatha et al., 2003). Adsorption: Adsorption (Fig. 12.4) is the simplest immobilization method for a biomolecule over an insoluble supportive element. The procedure for adsorption is as follows: First, the biomolecule and supportive matrix are mixed under appropriate conditions and after a certain incubation period, the material that is not dissolved in the solution is separated from the mixture through centrifuge or filtration (Scheller and Schubert, 1992; Shantilatha et al., 2003). The active materials used in this method are anionic and cationic ion-change resins, active coal, silica gel, clay, aluminum oxide, porous glass, and ceramics. The carrier should have high affinity and capacity and after adsorption it should remain active. The carrier should not adsorb the reaction products and biocatalyst inhibitors. The advantages of this method are its simplicity and that it does not require chemicals or non-physiological coupling conditions disturbing enzyme or cell functioning; as a result of this, loss of activity is barely visible (Scheller and Schubert, 1992). The most significant disadvantage of this method is that the proteins could be easily separated from the supportive matrix as a result of changes in experimental conditions such as pH, ionic strength, temperature, solvent type, and so on (Scheller and Schubert, 1992; Shantilatha et al., 2003). Covalent binding: In covalent binding (Fig. 12.5), different functional chemical groups are used for immobilization through peptide bond, diazo and isourea linkages, or alkylation reactions. The problem with this method is the possibility of inactivation of the active sites of the bioreceptor. To avoid this problem, functional groups or spacers, such as polysaccharides

396  Chapter 12 (cellulose, dextran, and agarose derivatives), molecules with high molecular weights (collagens, gelatin, and albumin), synthetic polymers (polyvinyl chloride and ion-change resins), and inorganic materials (porous glass) are used to reactivate the surface (Scheller and Schubert, 1992; Shantilatha et al., 2003; Trojanowicz, 2009). It is very important to be careful of inactivation of active spaces on the surface as well as reactions of the immobilized element with the binding surface; if these happen, biosensing activity would decrease. Another problem is overloading the matrix with enzymes; this would also decrease the activity of the biosensor. The choice of the matrix is very important because the macromolecules might be bound to the insoluble matrix. Biological elements such as enzymes, cells, antibodies, antigens, or nucleic acids can be immobilized over a solid surface, a matrix such as a porous gel, or a semipermeable membrane (Shantilatha et al., 2003). Covalent binding has three consecutive processes: (1) activation of the carrier, (2) binding the biomolecule, and (3) separation of the adsorbed biomolecules (Scheller and Schubert, 1992). Entrapment: Entrapment methods (Fig. 12.6) are also widely preferred in biosensor design. The bioreceptors are immobilized through entrapment behind the membrane, within selfassembled monolayers, or within polymeric matrix membranes. The most important factor for successful immobilization in these methods is the preservation of sufficient mobility of the substrate or products of biochemical reaction (Trojanowicz, 2009). Entrapment within polymeric gels protects large molecules from diffusion in the reaction mixture; on the other hand it allows small substrates and effector molecules to diffuse. It is a mild procedure that is similar to adsorption, since biomolecules are not bound to matrices, membranes, or each other covalently. Therefore this method is widely used. The most significant matrices chosen in this method are alginate, carrageenan, collagen, cellulose triacetate, polyacrylamide, gelatin, agar, silicon rubber, polyvinyl alcohol, and prepolymers cross-linked by the addition of water (such as polyurethans) or light (Scheller and Schubert, 1992). A similar method is microencapsulation, in which the bioreceptors are enveloped within different forms of semipermeable membranes. In this method, all enzymes present in the

Figure 12.6: Illustration of Entrapment Methods.

Biosensor-Based Methods for the Determination of Foodborne Pathogens  397

Figure 12.7: Illustration of Cross-Linking Method.

solution are immobilized instead of selective immobilization of a particular enzyme molecule. Microencapsulation creates artificial cells that have membranes similar to those of natural cells. Therefore the size of molecules moving in and out of the cell can be controlled. Large molecules such as enzymes or proteins are reserved within the encapsulated space while smaller molecules such as the substrate and product molecules can easily be diffused from the synthetic membrane. One of the most significant advantages of this method is the high surface area emerged per unit of immobilized enzyme, which increases sensing ability (Shantilatha et al., 2003). Cross-linking: Finally, in cross-linking immobilization (Fig. 12.7) a supportive material is not required; this method is based on the chemical binding of receptor species to each other to form a large, three-dimensional structure (Trojanowicz, 2009). The bioreceptor species is covalently bound by means of bi- or multifunctional reagents. For example, protein molecules can be cross-linked within themselves, with the other molecules, and functionally with inert proteins such as albumin or gelatin. Bio-macromolecules can be adsorbed on an insoluble carrier or encapsulated within a gel and then cross-linked as well. The advantage of this method is its simplicity and strong chemical bonds established among the biomolecules. Moreover, the cross-linking degree is closely related with the physical properties and particle size to be influenced. Major disadvantages, on the other hand, include decreasing activity because of the chemical degradation of the main regions of the protein (Scheller and Schubert, 1992; Trojanowicz, 2009).

398  Chapter 12 3.6.3  Classification of biosensors Biosensors can be classified either in terms of the transducer or in terms of the biorecognition element. The classification based on the type of transducer is as follows (Shantilatha et al., 2003; Suleiman and Guilbault, 1994): •

•

•

•

Biosensors based on electrochemical transducers: This class of biosensors are made of (1) potentiometric transducers in which the analytical signal is the potential drop between the working and reference electrodes or between two reference electrodes separated by a semipermeable membrane; (2) voltammetric/amperometric transducers in which the oxidation or reduction current of the electroactive species is measured; (3) conductometric transducers in which the electrical conductivity of the solution in the course of a biochemical reaction is measured; and (4) impedimetric transducers that measure the impedance of an electrochemical cell and the impedance variation with AC frequency (Korotkaya, 2014). Biosensors based on optical transducers: Optical biosensors make use of optical principles for the transduction of a biochemical interaction into a suitable output signal. The biosensing event can be detected by the change in diverse optical properties such as absorbance, reflectance, luminescence, refractive index, and surface plasmon resonance (Lechuga, 2005). Biosensors based on mass transducers: Biosensors based on mass transducers can be classified as piezoelectric, crystalline, and as a surface acoustic wave (Suleiman and Guilbault, 1994). In designing piezoelectric biosensors, crystals become victims of elastic deformation when an electric potential is applied. This potential produces a wave in the crystal with a certain frequency that makes the cyrstal surface adsorb the analyte. The surface is covered by a biological recognition element and it asters the resonance frequency that shows that the binding has occurred (Korotkaya, 2014). Biosensors based on thermometric transducers: In these types of sensors the analyte concentration is determined by a thermistor measuring the amount of heat (Korotkaya, 2014).

Classification based on the type of biological element is as follows (Shantilatha et al., 2003): •

•

Enzymatic sensors: An enzymatic biosensor is composed of an enzyme as a bioreceptor integrated with a transducer to produce a discrete and continuous signal proportional to the analyte concentration (Karunakaran et al., 2015a). Enzymatic sensors can be classified as substrate biosensors which are used to determine specific substrates of enzymatic reaction, such as glucose oxidase-based enzymatic sensors, while inhibiting biosensors are used for determining substances reducing the activity of an enzyme (Korotkaya, 2014). Immunosensors: Immunosensors are biosensors in which the bioreceptor is an antibody and the immunological reaction occurs in direct proximity to the transducer. They are

Biosensor-Based Methods for the Determination of Foodborne Pathogens  399

•

•

•

based on molecular recognition of a specific antigen through the sensing antibody. An ideal immunosensor should be portable, simple to handle and operate, cheap and disposable or easily renewable, it should have the ability to work without reagents, and have a decreased assay time (Yakovleva and Emnéus, 2008). DNA sensors: Nucleic acids are the bioreceptor of the DNA sensors; these nucleic acids are not isolated from a living organism, rather their fragments known as DNA probes or primers are employed as the biological element. These probes, also called aptamers, are synthesized through the amplification of DNA via a ploymerase chain reaction. In order to increase their stability, these probes could be modified as well. They are used to detect proteins and non-macromolecular compounds interacting with certain DNA fragments such as regulatory proteins, DNA-damaging tumor markers, and anti-cancer drugs (Korotkaya, 2014). Microbial sensors: Different from other classes of biosensor, microbial biosensors have a biological component separate from the recording device because the response of the microorganism to variations in the chemical composition of the medium is quite slow. Therefore, a higher concentration of living cells is required. These biosensors are used in the determination of oxidizable organics in wastewater or of antimicrobial agents suppressing microbial respiration (Korotkaya, 2014). Cell-based sensors: Cell-based sensors can be considered as having an intermediate position between enzyme and DNA sensors, since they involve intracellular entities that have a complex hierarchical structure. They are used for detecting biochemical processes such as the toxic action of pollutants (Korotkaya, 2014).

Although there are many types of biosensors that have been mentioned, in this chapter only electrochemical and optical biosensors will be analyzed in detail. 3.6.3.1  Electrochemical biosensors

Electrochemistry deals with the reactions that occur on an electrode’s surface and have significant advantages in terms of biosensing. It is not influenced from reaction volume and very small amounts of samples can be used in assays. Particularly in immunoassays, very low limit of detection is attainable and particles affecting chromophore, fluorophore, and spectrophotometric detection do not influence electrochemical detection. Electrochemical measurements are also possible in colored and blurry samples (Ronkainen et al., 2010). Electrochemical sensors are a component of electrochemical cells composed of two or three electrodes. A typical three-electrode system consists of an indicator/working electrode, a reference electrode, and an auxiliary/counter electrode. Indicator electrodes are generally made of chemically stable solid and conductive materials such as platinum, gold, or carbon (i.e., graphite). The most widely used reference electrode is the Ag/AgCl electrode, which is silver coated with a silver chloride solution. A platinum wire is preferred for a counter electrode. In this system, the reference electrode is set remotely from the site where oxidation

400  Chapter 12 or reduction occurs to maintain a known and stable reference potential. One of the advantages of using such a system is that the transfer of the electrolytic charge is over the counter electrode instead of the reference electrode, so the reference electrode is be protected from changes in the half-cell potential (Ronkainen et al., 2010). The two-electrode system, on the other hand, is composed of working and reference electrodes. If the current intensity is small enough, the reference electrode could carry the charge without any negative effect (Ronkainen et al., 2010). The advantages of electrochemical biosensors are manifold. First of all, generally, these biosensors are of miniature size, enhancing their range of utilization. Secondly, they are compatible with modern microfabrication technologies; in other words, they are extensively used for sensing in micro and nano scales. Third, they are not very expensive and they do not consume a significant amount of power resources to be operable. Finally, their high sensitivity makes their limit of detection very low (Liu et al., 2008). There are different types of electrochemical biosensors. One of the most widely preferred electrochemical biosensors is the voltammetric/amperometric biosensor. Voltammetric biosensors are based on the process of electrolysis via electrochemical oxidation or reduction on the working electrode, which is generally made of platinum, gold, or carbonbased materials (Thevenot et al., 2001). The electrolytic current is limited to the mass transport rate of the molecules toward the electrode surface. Major voltammetric methods are linear sweep, cyclic, hydrodynamic, differential pulse, square wave voltammetry, AC voltammetry, polarography, and stripping voltammetry (Ronkainen et al., 2010). Hong et al. (2015) designed an electrochemical biosensor for sensitive, selective, and rapid detection of norovirus, which causes sporadic or epidemic gastrointestinal diseases. In this biosensor, concanavalin A, a lectin extracted from Canavalia ensiformis, was used as the biological sensing element, while a nanostructured gold electrode was preferred as a transducer (Fig. 12.8). Fig. 12.8 shows the schematic representation of this biosensor. In the figure, mercaptohexanole was used as a blocking agent. After linking concanavalin A with the norovirus, first the detection antibody and then the secondary antibody, to which alkaline phosphatase (ALP) was attached, were immobilized on the surface. The substrate of the ALP enzyme was 4-amino phosphate (APP) and the enzyme transformed APP into 4-aminophenol. 4-aminophenol was oxidized electrochemically and a current response emerged. Linearity obtained with cyclic voltammetry within the current-novovirus concentration range was found 10–2–10–6 copies mL–1 (R2 = 0.998) and LOD was found 35 copies mL–1 while assay time was 1 h. When it was applied to lettuce extract, LOD was found 60 copies mL–1. Amperometry differs from voltammetry by the lack of scan potential. While in amperometric methods, the current is measured at a constant potential, in voltammetric methods the current is measured at controllable, changing potentials. The current value is measured at a linear potential range and is directly proportional to the analyte concentration (Grieshaber et al., 2008). Amperometric methods measure the current either by stepping the potential

Biosensor-Based Methods for the Determination of Foodborne Pathogens  401

Figure 12.8: (A) Schematic representation of developed biosensor. (B) Cyclic voltammetry and (C) Nyquist plots after the assembly of the nanostructured gold electrode in the presence of 2.5 mM Fe(CN)64–/3– + 0.1 M KCl, pH = 7.4 (scan rate of 50 mV s–1). (a) The nanostructured gold electrode; (b) after immobilization of Concanavallin A; (c) blocking by mercaptohexanol; (d) after immobilization of Norovirus; (e) after immobilization of detection antibody; (f) after immobilization of secondary antibody with alkaline phosphatase. Source: Reprinted with permission from Hong, S.A., Kwon, J., Kim, D., Yang, S., 2015. A rapid, sensitive and selective electrochemical biosensor with concanavalin A for the preemptive detection of norovirus. Biosens. Bioelectron. 64, 338–344.

directly to the desired value or by retaining the current at the desired potential. The advantages of amperometric biosensors include easy configuration, low-cost, non-laborious operation, easy adaptability to other target molecules or organisms, portability, and capability of integration with other biosensor technologies (Boyacı and Mutlu, 2011). Since the design of the Clark oxygen electrode, amperometric biosensors have been used extensively in the detection of the pathogens such as E. coli O157:H7, Salmonella, L. monocytogenes, and Campylobacter jejuni and they are generally more sensitive than the potentiometric biosensors. Amperometric biosensors detect pathogens when the sensor potential is set at a value where the analyte produces current (Velusamy et al., 2010). Each analyte has a specific oxidation/reduction potential and therefore amperometric biosensors have additional selectivity. The problem with the amperometric method is the emergence of a charging current at the constant potential during detection; this minimizes the background signal and negatively affects the limit of detection (Ronkainen et al., 2010). Potentiometry measures the potential at the working electrode when there is either zero or insignificant current between the working and reference electrodes. In other words, potentiometry informs about the ionic activity at an electrochemical reaction. Potentiometry generates a logarithmic concentration response, which results in the detection of very small changes in concentration (Velusamy et al., 2010). The relationship between the concentration

402  Chapter 12 and potential in potentiometric measurements is shown by the Nernst equation (Grieshaber et al., 2008): E = E0 +

RT lnQ nF

In this equation, E stands for cell potential, E0 for the constant potential contribution to the cell, R for the universal gas constant, T for the absolute temperature, n for the charge number of the electrode reaction, F for the Faraday constant and lnQ for the ratio of ionic concentration at the anode to the ionic concentration at the cathode. The most widely used potentiometric sensors are the glass pH electrodes and ion-selective electrodes (such as those for K+, Ca+2, Na+, Cl–). These chemical sensors can be associated with biological elements, such as enzymes, to form a biosensor (Ronkainen et al., 2010). Although potentiometric biosensors are not used much for the detection of pathogens, light-addressable potentiometric sensor (LAPS) is an exception. A transient photocurrent is coupled to a thin silicon layer and induced by a transient illumination via several light sources such as light emitting diodes (LEDs). In this method, the magnitude of the photocurrent is influenced by the potential applied to the silicon layer. LAPS is used for detecting pathogens like E. coli (Velusamy et al., 2010). Conductometry measures the ability of an analyte (i.e., electrolyte solutions) or a medium (i.e., nanowires) to conduct electrical currents between electrodes or reference nodes (Grieshaber et al., 2008). When conductometric transducers are associated with biological elements, such as enzymes, they turn out to be biosensors. The charged products of these enzymes change the ionic strength and therefore conductivity can be enhanced. Conductometric biosensors are preferred in clinical and environmental analysis. Their most important advantages include insensitivity to light, miniature size, easily integrated structure, and the unnecessity for a reference electrode, while the basic disadvantage is the requirement for a significant amount of change in the total ionic strength for reliable measurement (LecaBouvier and Blum, 2010). Conductometric immunosensors are utilized for the detection of enterohemoragic foodborne pathogens such as E. coli O157:H7 and Salmonella spp. (Ronkainen et al., 2010). Electrochemical impedance spectroscopy was defined by Lorenz and Schulze in 1975 and measures the resistive and capacitive properties of a particular material through a sinusoidal AC actuating signal with small amplitude. In order to obtain an impedance spectrum, the frequency is changed along a wide range. The in-phase and out-of-phase current responses are determined to obtain resistive and capacitive components respectively. Major advantages of impedance methods include the capability to sample electron transfer at high frequencies while sampling mass transfer at low frequencies (Ronkainen et al., 2010). It also allows label-free detection; however the limit of detection is generally higher compared to other

Biosensor-Based Methods for the Determination of Foodborne Pathogens  403

Figure 12.9: (A) The bacteria sensing mechanism of nanoporous membrane via impedance spectrum; (B) the principle of simultaneous detection for two types of bacteria using the microfluidic device integrated with nanoporous membranes. Source: Reprinted with permission from Tian, F., Lyu, J., Shi, J., Tan, F., Yang, M., 2016. A polymeric microfluidic device integrated with nanoporous alumina membranes for simultaneous detection of multiple foodborne pathogens. Sensor. Actuat. B-Chem. 225, 312–318.

electrochemical methods (Velusamy et al., 2010). Impedance biosensors are used, for example, for the determination of E. coli O157:H7 and St. aureus. Tian et al. (2016) performed a simultaneous analysis of E. coli O157:H7 and St. aureus from mixed bacteria samples using an electrochemical biosensor. In this study, they used a polymeric microfluid chip integrated with functionalized nanoporous alumina. As a biological sensing element, an antibody was used and spectroscopy was preferred for the detection of bacteria impedance. Fig. 12.9 schematizes this biosensor system. The linear detection range was found between 102–105 CFU mL–1 and LOD was found around 102 CFU mL–1. Other electrochemical biosensors used for foodborne pathogen detection are summarized in Table 12.2. The table includes some of the biosensors that were developed from 2005 to present. 3.6.3.2  Optical biosensors

Although the first optical biosensors had been designed by the early 20th century, it was not until the late 1980s that optical biosensors became commercialized. From then on, however, the use of this class of biosensors in research and development, particularly in pharmaceutical and diagnostic industries, has been immense (Cooper, 2002). Simply speaking, an optical biosensor measures the changes in amplitude, phase, frequency, or polarization of light (Narsaiah et al., 2012). It is mainly composed of a light source, an optical transmission

Detection Method

Biological Element

Immobilization Procedure

Impedance

Antibody

Entrapment

Impedance

Antibody

Covalent binding

Chronocoulometry

Antibody

Foodborne Pathogen (analyte)

Assay/ Detection time LOD/LOQ

Escherichia coli O157:H7 — and Staphylococcus aureus (simultaneous detection) 1 min E. coli K-12 and Sta. aureus

Application

Reference

∼102 CFU mL−1 Mixed bacteria Tian et al. (2016) samples

<102 CFU mL−1 Microbial cocktail samples of E. coli and St. aureus Agricultural 6 × 102 water CFU mL−1

Yamada et al. (2016)

Salmonella

3h

E. coli O157:H7

<1 h

102 CFU mL−1

Novovirus Aeromonas

1h —

Listeria monocytogenes

—

Voltammetry

Commercially available antiSalmonella magnetic particles Antibody Covalent binding Concanavalin A Adsorption DNA Covalent binding Antibody Covalent binding Antibody Entrapment

E. coli O157:H7

45 min

Potentiometry

Aptamer

NS

L. monocytogenes

0.67 h

Voltammetry

DNA

Aeromonas hydrophila

∼7 h <4 h

Voltammetry

Antibody

E. coli K-12

<5 min

102 CFU mL−1

—

Impedance

Antibody

Entrapment Covalent binding Covalent binding Covalent binding

35 copies mL−1 Lettuce extract Hong et al. (2015) Fernandes et al. <102 CFU mL−1 Tap water (2015) Chen et al. (2015) 300 CFU mL−1 Lettuce samples — Wang and Alocilja 10 CFU mL−1 (2015) Ding et al. (2014) <10 CFU mL−1 Coastal seawater Ligaj et al. (2014) ∼1.6 × 10−13 M Fishes and ∼1.6 × 10−13 M vegetables

E. coli

—

—

—

Impedance Voltammetry Voltammetry Impedance

—

Wang et al. (2016)

Li et al. (2015)

Yamada et al. (2014) Lu et al. (2013)

404  Chapter 12

Table 12.2: Selected electrochemical biosensors for foodborne detection from 2005 to present.

Detection Method Voltammetry and impedance Impedance

Biological Element DNA

Voltammetry

DNA

Voltammetry

DNA

Assay/ Detection time LOD/LOQ

Application

Reference

Covalent binding Adsorption

L. monocytogenes

—

267 pM

—

Urkut et al. (2011)

E. coli

104 CFU mL−1

Water sources

Mejri et al. (2010)

Covalent binding Covalent binding

A. hydrophila

20–25 min 15 min

—

—

Tichoniuk et al. (2010) Zhang et al. (2010)

Covalent binding Impedance Antibody Covalent binding Impedance Antibody Covalent binding Voltammetry and Single-stranded Covalent impedance DNA binding Amperometry Antibody Commercially available antiSalmonella magnetic particles Amperometry DNA Covalent binding Chronoamperometry Antibody Adsorption

Amperometry

Antibody

Impedance

Antibody

Covalent binding Covalent binding Cross-linking

Impedance

Antibody

Cross-linking

— Ba. anthracis and Sa. enteritidis (simultaneous determination) — E. coli

50 pg mL−1 and — 0.5 ng mL−1 10–6 pM

— — —

Solanki et al. (2009) Barreiros dos Santos et al. (2009) Das et al. (2009)

E. coli O157:H7

—

Sa. typhimurium

—

10–100 CFU mL−1 103 CFU mL−1

E. coli O157:H7

—

0.5 nM

—

Wang et al. (2009)

Salmonella

50 min

7.5 × 103 CFU mL−1

Milk

Liébana et al. (2009)

E. coli

1 day

∼5 × 102 CFU

Meat juice

Sa. typhimurium

—

5 × 103 cells mL−1 ∼20 cells mL−1

Chicken meat samples

Pöhlmann et al. (2009) Salam and Tothill (2009)

St. aureus

1.5 h

Milk

Sa. typhimurium

∼6 min

Sa. typhimurium

—

3.7 × 102 cells mL−1 5 × 102 CFU mL−1 10 CFU mL−1

— Milk

Escamilla-Gómez et al. (2008) Nandakumar et al. (2008) Pournaras et al. (2008) (Continued)

Biosensor-Based Methods for the Determination of Foodborne Pathogens  405

Voltammetry

Antibody and phage DNA

Foodborne Pathogen (analyte)

Immobilization Procedure

Covalent binding NS

E. coli O157:H7

Assay/ Detection time LOD/LOQ 6 min 62.7 CFU mL−1 40.7 CFU mL−1 35.3 CFU mL−1 88.4 CFU mL−1 72.6 CFU mL−1 58.0 CFU mL−1 — —

E. coli O157:H7

∼1 h

Covalent binding Antibody Covalent binding Phage Covalent binding Antibody Covalent binding Antibody Covalent binding Single-stranded Covalent DNA binding Phage NS

L. monocytogenes

—

5 × 103 CFU mL−1 4.1 pg mL−1

E. coli O157:H7

35 min

1.2 × 103 cells

Ground beef

E. coli

—

—

—

V. parahaemolyticus

—

—

E. coli O157:H7

35 min

E. coli O157:H7

—

7.37 × 104 CFU mL−1 8.0 × 105 CFU mL−1 —

—

Ba. cereus and <8 h Mycobacterium smegmatis Sa. spp. L. monocytogenes 1 h

10 cells mL−1

—

—

—

E. coli O157:H7

104 CFU mL−1

Romaine lettuce

Detection Method Conductometry

Biological Element Antibody

Voltammetry

Antibody

Amperometry

Antibody

Impedance

Antibody

Impedance Impedance Amperometry Impedance Voltammetry Amperometry Voltammetric Impedance

Immobilization Procedure Cross-linking

Oligonucleotide Covalent binding Antibody Cross-linking

Foodborne Pathogen (analyte) Ba. cereus

CFU: colony-forming units; DNA: deoxyribonucleic acid; NS: not stated

∼1 min

Application Reference Alfalfa sprouts Pal et al. (2008) Strawberries Lettuce Tomatoes Fried rice Cooked corn — Cho et al. (2008) Milk

Lin et al. (2008)

—

Tully et al. (2008) Varshney et al. (2007) Gervais et al. (2007) Zhao et al. (2007)

Ground beef

Varshney and Li (2007) Berganza et al. (2007) Yemini et al. (2007) Farabullini et al. (2007) Radke and Alocilja (2005)

406  Chapter 12

Table 12.2: Selected electrochemical biosensors for foodborne detection from 2005 to present. (cont.)

Biosensor-Based Methods for the Determination of Foodborne Pathogens  407 medium, a bioreceptor immobilized on the surface such as antibodies, enzymes, or microorganisms, and finally an optical detection system (Patel et al., 2010). The advantage of optical biosensors over other types of biosensors include their non-invasive and extremely stable nature, compact, and minimally invasive design, possibility of remote monitoring in case of hazardous or inaccessible places, lack of the risk of electrical shocks, lack of electromagnetic interferences, high sensitivity, and possibility of multiplexing by carrying signals of different wavelengths for multi-parameter detection (Duval and Lechuga, 2015; Narsaiah et al., 2012). Optical biosensors can be classified in terms of transduction mechanism or the geometry of the biosensor. Major transduction mechanisms used in optical biosensors are absorption, fluorescence, and luminescence. The biosensors based on absorption operate by sending light via an optical fiber to the sample and the amount of light absorbed by the analyte is measured. Different analytes and concentration levels can be determined with this method as a result of different wavelengths with which different analytes are associated. Absorptionbased sensors are simple devices that can be used easily without higher costs of production (Patel et al., 2010). Optical biosensors based on fluorescence operate on the principle that each fluorescent analyte has its own unique fluorescence spectrum. By using biooptrodes, light is absorbed at one wavelength and then emitted at a longer wavelength at distinct energy levels (Patel et al., 2010). In this method, a particular biomolecule is selectively labeled and then discretely excited and observed apart from the sample background. These biosensors are composed of a fluorescent bioreceptor and an optical transducer and a bioactive layer on the surface of the sensor is created via immobilization of whole cells. Generally, optical fibers are used in these sensors which send the excitation radiation to the bioreceptor (Karunakaran et al., 2015b). This class of optical biosensors is widely preferred because of high sensitivity, availability of a wide variety of commercial fluorophores, improvements in fluorescent proteins, and lowcost instrumentation (Medintz and Delehanty, 2008). However, this technique has a significant disadvantage, which is its laborious and even problematic operation due to contamination, human error, or mechanical damage (Duval and Lechuga, 2015). Duan et al. (2015) developed an optic biosensor based on fluorescence resonance energy transfer from dual quantum dots to carbon nanotubes for simultaneous detection of Vibrio parahaemolyticus and Salmonella typhimurium. They used aptamer as the biological recognition element. The fluorescence resonance energy transfer was realized from green-emitting quantum dots (gQDs) and redemitting quantum dots (rQDs) toward carbon nanoparticles (CNPs). (Fig. 12.10). gQDs and rQDs were modified with aptamers 1 and 2 (Apt.1 and Apt.2) recognizing V. parahaemolyticus and Sa. typhimurium respectively. QDs fluorescence was strongly quenched in the presence of carbon nanoparticles. When target analytes were added, QDs-aptamer target complex was formed and quenching caused by carbon nanoparticles was suppressed. QDs fluorescence was found to be directly proportional for both pathogens at the concentration range of 50–106

408  Chapter 12

Figure 12.10: Schematic Illustration of the Dual Fluorescence Resonance Energy Transfer From QDs-apts to CNPs for the Simultaneous Detection of Pathogenic Bacteria. Source: Reprinted with permission from Duan, N., Wu, S., Dai, S., Miao, T., Chen, J., Wang, Z., 2015. Simultaneous detection of pathogenic bacteria using an aptamer based biosensor and dual fluorescence resonance energy transfer from quantum dots to carbon nanoparticles. Microchim. Acta. 182, 917–923.

CFU mL–1 (Fig. 12.11). The detection limits were found as 25 CFU mL–1 and 35 CFU mL–1 for V. parahaemolyticus and Sa. typhimurium respectively. The biosensor was applied on chicken and shrimp samples. Optical biosensors based on chemi- or bioluminescence operate at the absence of an external light source. In the case of chemiluminescence, sensing is made through excitation of molecules with a chemical reaction, while in the case of bioluminescence, such a chemical reaction is naturally experienced by some living organisms (Patel et al., 2010). In chemiluminescence-based optical biosensors, luminol is generally used as a light source, since, as a result of the chemical reaction between luminol and H2O2, a detectable luminescence signal emerges. In order to catalyze this reaction, some enzymes such as horseradish peroxidase, alkaline phosphatase, and β-galactosidase are used. Enzymebased sensing is possible with this method (Biran et al., 2008). This method is preferred in medical diagnostics for simple instrumentation, fast dynamic response, and wide calibration range. Less quantitative accuracy because of short lifetime, expensive instrumentation, and unsuitability for real-time monitoring are among the disadvantages of this method (Karunakaran et al., 2015b). Bioluminescence, on the other hand, is based on natural reactions

Biosensor-Based Methods for the Determination of Foodborne Pathogens  409

Figure 12.11: (A) Typical recording output for the simultaneous detection of different concentrations of bacteria by the developed method; (B) standard curve of the related fluorescence intensity (F-F) versus the concentrations of bacteria. Source: Reprinted with permission from Duan, N., Wu, S., Dai, S., Miao, T., Chen, J., Wang, Z., 2015. Simultaneous detection of pathogenic bacteria using an aptamer based biosensor and dual fluorescence resonance energy transfer from quantum dots to carbon nanoparticles. Microchim. Acta. 182, 917–923.

of living organisms during processes like cell-to-cell signaling, self-protection, mating, attracting prey, or finding food. Here, the luminescent activity is catalyzed by the luciferase enzyme in the presence of oxygen. Through bioluminescent sensing, intra-cellular events can be detected (Biran et al., 2008). In terms of the geometry of biosensor, optical biosensors can be classified as optrodes, surface plasmon resonance-based sensors, and fiber grating based sensors. Optrodes (an abbreviation for “optical electrodes”) are based on a fiber optic device used for the measurement of the concentration of specific or a group of chemicals in a sample. An optrode is composed of

410  Chapter 12 a light source, an optical fiber for transmitting light, a sensing material immobilized on the surface of the fiber, and a detecting device. Here, light is launched from the source through the fiber to its end on which the sensing element is immobilized, and then the optical signal is recorded through a detector that can be either the same or a different fiber. If biological sensing elements are used, then this system is called “biooptrodes.” The most significant advantages of using optical fibers are their small size, flexibility, durability, and non-electrical nature, which allows for better in vivo monitoring inside a patient’s body (Biran et al., 2008). Surface plasmon resonance (SPR) based optical biosensors are based on the surface plasmon waves to detect changes in the interaction of the target analyte with the bioreceptor. Since its first use in 1983, it has turned out to be a very significant label-free sensing device. In these biosensors, the transducer is composed of a thin metal film (generally of gold because of its chemical stability) supporting an electromagnetic field known as surface plasmon polariton. This transducer can recognize a change in the refractive index at the sensor surface when the target analyte interacts with the bioreceptor. As a result of this change the propagation constant of the surface plasmon polariton changes as well, and a signal readable by a spectrophotometer comes out. Major advantages of SPR biosensors include high sensitivity, real time detection, anti-interference capability, and rapid detection. Moreover, lesser amounts of reagents and samples are needed and pretreatment of samples is not required (Karunakaran et al., 2015b). Vaisocherová-Lísalová et al. (2016) developed an SPR biosensor for the detection of E. coli O157:H7 and Salmonella spp. from hamburger and cucumber samples. Accordingly, they first coated the SPR chips with poly (3-acryloylamino-propyl)-(2-carboxy-ethyl)-dimethylammonium) polymer brushes. They functionalized this polymer surface at three consecutive steps: (1) the activation of carboxyl groups, (2) covalent binding of antibodies to the activated groups, and (3) deactivation of residual active groups. Fig. 12.12 schematically shows the use of a prepared SPR biosensor in pathogen analysis. This figure demonstrates a three-step assay for detection: After the interaction of the antibody at the polymer surface with the bacteria, the biotinylated secondary antibody was non-specifically bound with the bacteria. At the final step, gold nanoparticles functionalized with streptavidin were bound with the secondary antibody. The biosensor could detect the bacteria in less than 80 minutes, meaning that it is quite efficient in terms of time-consumption. A third class of biosensors based on sensor geometry is fiber grating based biosensors. Fiber grating increases sensitivity and selectivity and enables multi-parameter and multifunctional sensing capability (Patel et al., 2010). In this type of sensor, grating requires physical perturbation on the sensor surface and light associates into the grating at a specific angle and wavelength. These sensors can be easily and cheaply produced, since they are comprised of a thin-film waveguide deposited on a glass support. On this film, a grating can be employed by photolithography or imprinting. These sensors have been used for sensing herbicides or

Biosensor-Based Methods for the Determination of Foodborne Pathogens  411

Figure 12.12: Illustration of functionalized polymer surface in three steps: (1) the activation of carboxyl groups; (2) covalent binding of antibodies to the activated groups; and (3) deactivation of residual active groups. Source: reprinted with permission from Vaisocherová-Lísalová, H., Víšová, I., Ermini, M.L., Špringer, T., Song, X.C., Mrázek, J., Lamacˇová, J., Lynn Jr., N.S., Šedivák, P., Homola, J., 2016. Lowfouling surface plasmon resonance biosensor for multi-step detection of foodborne bacterial pathogens in complex food samples. Biosens. Bioelectron. 80, 84–90.

mycotoxins (Chamberlain and Ratner, 2012). Selected optical biosensors in literature, from 2005 to present, are summarized in Table 12.3.

4 Conclusion Diseases caused by foodborne pathogens are quite widespread in the world, even causing serious epidemics not only in the underdeveloped, but also in the developed world. Though in countries where sanitary conditions are better, these diseases can be contained. In underdeveloped countries, the results of these diseases might be severe, claiming thousands of lives annually. In order to prevent these diseases effectively, the detection of foodborne pathogens is extremely important. There are various conventional methods of prevention, such as culture or immunological-based ones, however, they are generally expensive and time-consuming. Therefore, more efficient methods are required and biosensor-based detection is among the most widely preferred new detection methods. Biosensors can be classified in terms of the transducer or the bio-recognition element and this chapter particularly examines transducer-based classification by focusing on two types of biosensors, electrochemical and optical biosensors. Electrochemical biosensors are generally preferred for their compatibility with modern microfabrication technologies, their cost-effectiveness, and easier application, as well as their low limit of detection. Optical biosensors, on the other hand, are preferred because of their extremely stable nature, compact and minimally invasive design, possibility of remote monitoring, and high sensitivity. There are different electrochemical or optical techniques used in biosensing including amperometric, voltammetric, potentiometric, conductometric, and impedance techniques

Immobilization Procedure

Detection Method

Biological Element

Surface plasmon rezonance

Antibody

Fiber optic

Antibody

Dual fluorescence resonance energy transfer Surface plasmon resonance Surface plasmon resonance

Aptamer

Surface plasmon resonance

Bacteriophage Covalent binding

Fiber optic

Antibody

Covalent binding

Colorimetric

Antibody

Adsorption

Surface plasmon resonance

Antibody

Covalent binding

Covalent binding

Foodborne Pathogen (Analyte)

Assay/ Detection Time LOD/LOQ

Escherichia coli O157:H7

< 80 min

Salmonella sp. Covalent binding Covalent binding

Application

Reference

57 CFU mL−1 and 17 CFU mL−1 7.4 × 103 CFU mL−1 and 11.7 × 103 CFU mL−1 103 CFU mL−1

Cucumber and hamburger extracts Poultry

VaisocherováLísalová et al. (2016)

Salmonella enterica

2–3 h

Vibrio parahaemolyticus and Sa. typhimurium (simultaneous determination)

—

25 CFU mL−1 35 CFU mL−1

Frozen fresh shrimp and chicken breasts

Bacteriophage Covalent binding

Bacillus cereus

—

102 CFU mL−1

Cooked rice

Kong et al. (2015)

Antibody

E. coli O157:H7, Salmonella — Enteriditis, and Listeria monocytogenes (simultaneous determination) — Salmonella

0.6 × 106 CFU mL−1 1.8 × 106 CFU mL−1 0.7 × 107 CFU mL−1

—

Zhang et al. (2014)

—

Karoonuthaisiri et al. (2014)

Ohk and Bhunia (2013)

Covalent binding

L. monocytogenes, E. coli O157:H7, Sa. enterica (simultaneous determination) Staphylococcus aureus

—

8.0 × 107 CFU mL−1 (1 time immobilized) 1.3 × 107 CFU mL−1 (5 time immobilized) 103 CFU mL−1

40 min

1.5 × 105 CFU

Ready-to-eat beef, chicken, and turkey meats Milk

Norovirus

<15 min

104 TCID50 mL−1

Shellfish

Abdelhaseib et al. (2016) Duan et al. (2015)

Sung et al. (2013) Yakes et al. (2013)

412  Chapter 12

Table 12.3: Selected optical biosensors for foodborne detection from 2005 to present.

Biological Element

Fluorescence

Antibody

Fluorescence

Antibody

Fluorescence

DNA

Surface plasmon resonance Surface plasmon resonance Surface plasmon resonance Surface plasmon resonance Surface plasmon resonance Surface plasmon resonance Surface plasmon resonance Surface plasmon resonance

DNA

Immobilization Procedure

Foodborne Pathogen (Analyte)

Covalent binding

Single molecule of target DNA of L. monocytogenes, Sa. typhimurium, V. parahaemolyticus, and V. cholerae Entrapment E. coli K-12

Assay/ Detection Time LOD/LOQ

Application

Reference

—

—

—

Zhang et al. (2013)

15 min

103 CFU mL−1

—

1 ng mL−1

Baby spinach leaves —

<15 min

102 pM

—

Kim et al. (2011) Zhang et al. (2009) Piliarik et al. (2009)

Covalent binding Covalent binding

Sa. enterica serovar Enteritidis Brucella abortus, E. coli, St. aureus

Antibody

Covalent binding

Sa. typhimurium

25 min

1 × 106 CFU mL−1

Chicken carcass

Antibody

Covalent binding

Cryptosporidium parvum oocyst

—

1 × 102 oocysts mL−1

Natural water Kang et al. (2008)

Phagedisplayed antibodies Antibody

Adsorption

L. monocytogenes

-

2 × 106 CFU mL−1

—

Nanduri et al. (2007)

Covalent binding

E. coli

30 min

9 × 101 CFU mL−1

Water

Dudak and Boyacı (2007)

Antibody

Covalent binding

Campylobacter jejuni

45 min

103 CFU mL−1

Broiler meat

Wei et al. (2007)

Antibody

Covalent binding

E. coli O157:H7

∼30 min

102–103 CFU mL−1

Waswa et al. (2007)

Antibody

Covalent binding

— E. coli O157:H7, Sa. choleraesuis serotype typhimurium, L. monocytogenes, Campylobacter jejuni (simultaneous determination)

Milk, apple juice, and ground beef Apple juice

3.4 × 103 –1.2 × 105 CFU mL–1

Lan et al. (2008)

Taylor et al. (2006)

(Continued)

Biosensor-Based Methods for the Determination of Foodborne Pathogens  413

Detection Method

Foodborne Pathogen (Analyte)

Assay/ Detection Time LOD/LOQ

Covalent binding

Cr. parvum oocyst

∼30 min

Antibody

Covalent binding

V. cholerae O1

Antibody

Covalent binding

Antibody

Detection Method

Biological Element

Surface plasmon resonance Surface plasmon resonance Surface plasmon resonance Surface plasmon resonance Fiber optic (Fluorescence resonance energy transfer) Fluorescence

Antibody

Immobilization Procedure

Application

Reference

1 × 10 oocyst mL

—

Kang et al. (2006)

—

105 cells mL–1

—

Jyoung et al. (2006)

E. coli O157:H7

<1 h

103 CFU mL–1

—

Subramanian et al. (2006a)

Covalent binding

St. aureus

<2 h

105 CFU mL–1

—

Subramanian et al. (2006b)

Antibody

Covalent binding

Sa. typhimurium

5 min

105 CFU g–1

Pork samples

Ko and Grant (2006)

Antibody

Covalent binding Covalent binding

E. coli O157:H7

∼3 h

103 cells mL−1

—

Bacillus subtilis var. niger spores

—

104 spores mL−1

—

Zhu et al. (2005) Zourob et al. (2005)

Covalent binding

E. coli O157:H7

—

104 CFU mL−1

—

Taylor et al. (2005)

Adsorption

E. coli O157:H7

—

102 CFU mL−1

—

Lee et al. (2005)

Light Antibody scattering and fluorescence Surface Antibody plasmon resonance Fluorescence Antibody

2

NS: not stated; CFU: colony-forming units; DNA: deoxyribonucleic acid; TCID50: 50% tissue culture infective dose.

–1

414  Chapter 12

Table 12.3: Selected optical biosensors for foodborne detection from 2005 to present. (cont.)

Biosensor-Based Methods for the Determination of Foodborne Pathogens  415 used in electrochemical detection and absorption-based, fluorescence-based, chemi- or bioluminescence-based, and SPR-based techniques used in optical detection. In Tables 12.2 and 12.3, some of the studies performed on the detection of foodborne pathogens since 2005 are gathered. In these tables, the studies are listed based on the detection method, bio-recognition element, immobilization method for bio-recognition element, the foodborne pathogen detected, assay/detection time, and application of the biosensor. As it can be inferred from the tables, impedance among electrochemical methods and SPR among optical methods are the most widely preferred methods. Although conventional methods give results within days, as it can be seen from the tables, the assay time is quite shorter with these methods, taking only hours, or even minutes. Moreover, these methods demonstrate very low detection limits. These advantages prove that biosensor based detection is very promising for detecting foodborne pathogens.

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