1:2; ,,=.§,...§E§a§ ,4 :3. L m”. _ 3,, ,m imam. «$.51... .. '0 7‘ Eu... $33.1 to . 9415 I hrwqw A m In". t u v ; : . 35:9...“ vitrmufi: :1 .1. 9 .5» . ‘ :1 :H....€1¢1:le. A ‘5 9‘1i3i4at ”Mia... mm. pa .1), .1 Lu“ 3. .3 3.1.3.03”: mug A...” .4: «1-3». . tit... . & l...’ www- 1003 54:9 0027; This is to certify that the thesis entitled Fabrication and Evaluation of an Electrical Immunoassay Biosensor for the Rapid Detection of Foodborne Pathogens in Food and Water presented by Zarini Muhammad-Tahir has been accepted towards fulfillment of the requirements for the MS. degree in Agricultural Engineering I Major Professor’dSignature 12/12/02 Date MS U IS an Affirmative Action/Equal Opportunity Institution _ LIBRARY Michigan State University PLACE IN RETURN BOX to remove this checkout from your record. To AVOID FINE return on or before date due. MAY BE RECALLED with earlier due date if requested. DATE DUE DAEEPQED g DATE DUE my 1 412005 6/01 c~JClRCIDatoDuap659JS FABRICATION AND EVALUATION OF AN ELECTRICAL IMMUNOASSAY BIOSENSOR FOR THE RAPID DETECTION OF FOODBORNE PATHOGENS IN FOOD AND WATER By Zarini Muhammad-Tahir A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Department of Agricultural Engineering 2002 ABSTRACT FABRICATION AND EVALUATION OF AN ELECTRICAL IMMUNOASSAY BIOSENSOR FOR THE RAPID DETECTION OF FOODBORNE PATHOGENS IN FOOD AND WATER By Zarini Muhammad-Tahir The thesis describes the development of an electrical immunoassay (El) biosensor for detecting foodborne pathogens. The biosensor consists of two components: an immunosensor that is based on an electrical sandwich immunoassay and a reader for signal measurement. The architecture of the immunosensor utilizes the lateral flow format, which allows the movement of fluid from one region to another by capillary action. The El biosensor performs better than the previous biosensor developed in this laboratory. The biosensor provides a specific, sensitive, low volume, and real-time detection strategy. Initial results show that the biosensor can detect approximately 8 X101 colony-forming units (CFU) per ml in 6 minutes. Results are presented to highlight the performance of the biosensor for detecting enterohemorrhagic Escherichia coli and Salmonella species in food and water samples. The ability to change the specificity of the antibodies will enable the biosensor to be used as a semi-quantitative detection device for other types of foodborne pathogens. Such a device can be used to enhance food safety and biosecurity of the food supply and can also be adapted for use in medical diagnostics and bio-defense. Copyright by Zarini Muhammad Tahir 2002 This thesis is dedicated to my mother and father, Zahirin and Zalina Muhammad- Tahir, for their unconditional love, support and trust. iv ACKNOWLEDGMENTS Special thanks to.... Dr. Evangelyn Alocilja for the research opportunity, enthusiastic encouragement and support. Dr. Elliot Ryser, Dr. Ewen Todd, and Dr. John Gerrish for serving as committee members. Dr. Wes Osbum for giving the opportunity of using the Biosafety Level II lab. Tracy Kamikawa for lab assistance. Alocilja Research Group for technical and emotional support. TABLES OF CONTENTS List of Tables ...................................................................................................... viii List of Figures ....................................................................................................... ix Introduction ........................................................................................................... 1 Chapter 1 Literature Review .................................................................................................. 5 Escherichia coli ..................................................................................................... 5 Salmonellae .......................................................................................................... 9 Factors in the Emergence of Foodborne Disease ............................................... 12 Contamination Control in Food Processing ......................................................... 15 Current Control Methods and Detection .............................................................. 17 Biosensors .......................................................................................................... 20 Electrical Immunoassay Biosensor ..................................................................... 22 Conductive Polymers .......................................................................................... 24 Chapter 2 Materials and Methods ........................................................................................ 30 Materials and Methods (Chapter 3) ..................................................................... 30 Materials and Methods (Chapter 4) ................................ 35 Materials and Methods (Chapter 5) ............. . ....................................................... 40 Chapter 3 Fabrication of the Electrical Immunoassay Biosensor ......................................... 42 Results ................................................................................................................ 42 Discussion ........................................................................................................... 47 Chapter 4 Sensitivity and Specificity Analysis of the Electrical Immunoassay Biosensor ....53 Results ................................................................................................................ 53 Discussion ........................................................................................................... 59 Chapter 5 Performance of the Electrical Immunoassay Biosensor in Fresh Produce samples ............................................................................................................................ 62 Results ................................................................................................................ 62 Discussion ........................................................................................................... 69 Conclusion .......................................................................................................... 72 vi Appendices ........................................................................................ 76 Appendix A Research Procedures and Protocols .......................................................... 77 Appendix B Data for Chapter 3 ................................................................................ 89 Appendix C Data for Chapter 4 ................................................................................ 97 Appendix D Data for Chapter 5 .............................................................................. 129 Appendix E Water testing ....................................................................................... 190 Appendix F ......................................................................................... 194 Bibliography ...................................................................................... 1 96 vii LIST OF TABLES Table 1.1 Examples of conducting polymer-based biosensors ........................... 26 Table 2.1. Combinations of antibody concentrations (pg/ml) used for preparing the conjugate and capture pads. .................................................................. 33 Table 2.2: Interpretation of results on differential media ..................................... 39 Table 3.1. Bacterial counts on the capture pad with varying antibody concentration (pg/ml) on the conjugate and capture pads. .......................... 46 Table 4.1. Resistance drop of the biosensors tested with various bacterial samples ........................................................................................................ 56 Table 5.1. Average signal (resistance drop) of biosensors used in lettuce samples inoculated with varying inocula. ..................................................... 63 Table 5.2. Average signal (resistance drop) of biosensors used in alfalfa sprout samples inoculated with varying inocula. ..................................................... 64 Table 5.3. Average signal (resistance drop) of biosensors used in strawberry samples inoculated with varying inocula. ..................................................... 65 viii LIST OF FIGURES Figure 3.1 lmmunosensor ................................................................................... 43 Figure 3.2. Electrical immunoassay biosensor .................................................... 43 Figure 3.3. Schematic diagram of the immunosensor. Conjugate pad for polyaniline-labeled antibody adsorption (A). Capture pad with two silver electrodes (B). Gap between electrodes is the site for antibody immobilization. ............................................................................................. 44 Figure 3.4. Resistance drop of the various polyaniline concentrations ............... 46 Figure 3.5. Resistance drop of varying distances between electrodes .............. 47 Figure 3.7 Result of the antibody experiments. ................................................... 49 Figure 3.8. Scanning electron microscope image of polyaniline ......................... 50 Figure 4.1. Resistance drop of the Sal biosensor tested with S. Typhimurium. ..54 Figure 4.2. Resistance drop of the EHEC biosensor tested with E. coli O157:H7 ‘ (ATCC 35150). ............................................................................................. 55 Figure 4.3 Resistance drop of the EC biosensor tested with non-pathogenic E. coli ............................................................................................................... 55 Figure 4.4. Resistance drop of the three biosensors tested with a mixture of E. coli O157:H7, Salmonella species, and non-pathogenic E. coli. .................. 58 Figure 4.5. Cross-section of a capture pad before (A) and after (C) analyte application. ................................................................................................... 59 Figure 4.6. SEM images of the capture pads. (A) Before sample application. (B) After applying with 101 CFU/ml (C) 105 CFU/ml, and (D) 107 CFU/ml of bacterial cultures. ......................................................................................... 61 Figure 5.1. Resistance drop of the biosensors used in lettuce samples inoculated independently with E. coli 0157: H7, non-pathogenic E. coli and S. Typhimurium ................................................................................................ 66 Figure 5.2. Resistance drop of the biosensors used in sprout samples inoculated independently with E. coli 0157: H7, non-pathogenic E. coli and S. Typhimurium ................................................................................................ 67 Figure 5.3. Resistance drop of the biosensors used in strawberry samples inoculated independently with E. coli 0157: H7, non-pathogenic E. coli and S. Typhimurium. ........................................................................................... 67 Figure 5.4. Resistance drop of the biosensors used in strawberry samples inoculated with a mixture of E. coliO157: H7, non-pathogenic E. coli and S. Typhimurium. Figures for the rest of the samples are available in Appendix E. ................................................................................................................. 68 INTRODUCTION Bacterial contamination in food and water supplies is a major food safety concern. Foodborne illnesses cause billions of dollars in medical costs, lost productivity, and product recalls (Hui et al. 1994). In the United States, the incidence of foodbome illness is estimated to be 76 million cases with 5,000 deaths (Mead et al. 1999), and an annual economic burden of 22 billion US dollars (NFSI 1997). Fresh produce and drinking water are some of the food and water sources, which are highly vulnerable to microbial contamination. In recent years, per capita consumption of fresh produce has increased significantly. During the year 2000, the total cost for fresh produce consumption in the US were 78.5 billion (USDA 1996). Additionally, in the last decade, the amount of imported fresh produce has doubled from countries all over the world (USDA 1996). The increase in fresh fruit and vegetable consumption, unfortunately, has been paralleled with an increase in the number of foodborne illnesses. The Centers for Disease Control and Prevention (CDC) has reported a significant increase in the number of produce-associated outbreaks in the US- 103 outbreaks affecting 6,082 people (CDC 2002). The rate of outbreaks went from 4.6 from 1973 to 1987, to 9.8 from 1988 to 1992 (T umer 1998; Jay 2000). A 1997 Escherichia coli O157:H7 outbreak involving unpasteurized apple juice in several Western States and an outbreak involving cider in the Northeast are two examples of the hazardous effect of foodborne pathogens in these food products (CDC 2002). Other produce-related outbreaks have involved cantaloupe, waterrnelons, tomatoes, fresh basil, alfalfa sprouts, melon and iceberg lettuce (Beuchat and Jee-Hoon 1996; Thayer and Raykowski 1999). E. coli O157:H7 and Salmonella species have emerged as the most important bacterial foodborne pathogens, especially in fresh fruits and vegetables. These pathogens not only cause serious illnesses but also are also lethal to infants, elderly, and immune-compromised individuals. E. coli 0157:H7 is a pathogen of particular concern due to its combination of severe consequences of infection, its low infectious dose, and its association with commonly consumed foods (Jay 2000). Salmonella species also have been associated with produce and water-related outbreaks and have been identified as the most prevalent foodborne pathogens (Davies 1997). Previous studies have shown that these pathogens are the main cause of outbreaks in most developed countries (Buzby and Roberts 1996) including the United States, Canada and the United Kingdom. There is no definitive way to tell simply by looking at a food whether or not it is contaminated. To date, the presence of bacteria in food is determined through microbiological testing. These approaches, however, are time consuming and laborious. Therefore, there is a growing need in the food industry for microbial detection systems that are rapid, specific, sensitive, and reliable. In this study, a new microbial detection device, known as a biosensor has been developed to enhance the detection of foodborne pathogens in food and water supplies. The said biosensor is an electrical immunoassay (El) biosensor, which comprised of two types of proteins: a capture protein and a reporter protein. The capture protein is immobilized on a platform between two electrodes, while the reporter protein is attached to a conductive polyaniline. After adding the sample, the target protein (analyte) binds to the reporter protein and forms a sandwich complex with the capture protein. The conductive polymer that is attached to the reporter protein acts like a messenger to report the amount of target protein captured. The message is translated into an electrical signal in the form of resistance. By replacing the two types of proteins with antibodies specific to the target bacteria of interest, it is possible to utilize this technology to detect the presence of pathogenic bacteria in a sample. The overall goal of the research is to design and fabricate an electrical immunoassay (El) biosensor for foodborne pathogen detection in food and water supplies. The objectives of the study are as follows: 1. To design and fabricate an El biosensor to detect pathogens in real- time with an electronic data collection system (Chapter 3). 2. To deterrnlne the sensitivity and specificity of the biosensor in pure cultures of E. coli, E. coIiO157zH7, and Salmonella (Chapter 4). 3. To test the ability of the biosensor to detect E. coli, E. coli O157:H7, and Salmonella in artificially contaminated fresh produce samples (Chapter 5). The long-term goal of this research is to develop an automated, rapid, inexpensive, and sensitive diagnostic tool to detect any type of foodborne pathogen or spoilage organisms in food and water supplies. CHAPTER 1 LITERATURE REVIEW ESCHERICHIA COLI Escherichia coli is a normal microflora of the intestinal tract of humans and warm-blooded animals. E. coli strains are classified based on serology that identifies the O (somatic), H (flagella) and K (capsular) antigens (Doyle 1997). All together there are more than 160 serotypes of E. coli (Salyers and Whitt 1994; Neill 2001). Initially, E. coli was not considered pathogenic. Over the past decades however, it has become evident that some E. coli strains can cause a variety of diseases, such diarrhea, dysentery, septicemia, and hemolytic uremic syndrome (Riemann and Cliver 1998). The pathogenic strains are categorized based on the virulence genes which encode for factors such as the ability to invade and attach to host cells, and the ability to produce toxins (Riemann and Cliver 1998). These genes are often located on plasmids and can be transferred from one E. coli strain to another. Categorizing E. coli strains based on virulence genes has led to the classification of E. coli based on virotypes (Salyers and Whitt 1994; Neill 2001). At present, six virotypes have been identified: enteropathogenic (EPEC), enterotoxigenic (ETEC), enteroinvasive (EIEC), enteroaggregative (EAEC), enterohemorrhagic (EHEC) (Riemann and Cliver 1998), and diarrheagenic adherent E. coli (DAEC) (Neill 2001). ESCHERICHIA COLI O157:H7 Escherichia coIiO157zH7 has emerged in the past 20 years as a pathogen of major public health importance. This pathogenic serotype produces toxins that cause bloody diarrhea in humans. Shiga toxin-producing strains of E. coli O157:H7 (STEC) first gained broad notoriety in the US in 1982 following two outbreaks of bloody diarrhea involving patrons of a fast food restaurant chain (Acheson 1996). This organism has been identified as one of the most dangerous foodborne pathogens based on its virulence and pathogenicity (Buchanan 1997). E. coli O157.1-I7 is not in the ETEC group since heat labile and heat stable toxins are not produced (Neill 2001). This organism is non-invasive and thus cannot be classified as EIEC (Riley 1983). E. coli 0157:H7 strains produce verotoxin, which is shown to have a close structural and antigenic similarity to the Shiga toxin of Shiga/Ia dysenteriae. Hence, this toxin is also known as Shiga-like toxin (SLT), or Shiga toxin (Stx) (O' Brien et al. 1983; Neill 2001). EPIDEMIOLOGY E. coli 0157:H7 strains have been reported to cause severe illnesses in healthy individuals as well as those with chronic disease. The current understanding of human infection with E. coli0157zH7 illnesses ranges from mild and non-bloody diarrhea, to a severe and life-threatening illness with such complication as hemolytic uremic syndrome (HUS) in children and thrombotic thrombocytopenic purpura (TT P) in adults (Doyle 1997). Illness typically begins with a short, mild, bloody diarrhea, which progresses within 24 hours to voluminous bloody diarrhea that is often associated with severe abdominal pain and cramp (Ostroff et al. 1989). Hospitalization rates for illness caused by E. coli 0157:H7 are high, accounting for 50% of all reported cases. Although bloody diarrhea is a common symptomsof E. coli 0157:H7 and other EHEC infections, some strains can also cause non-bloody diarrhea (Slutsker et al. 1997). A rising incidence of HUS has been noted (Martin et al. 1990) with 6%- 90% of all E. coli 0157.117 patients developing HUS (Rowe et al. 1991). The increased incidence of HUS following E. coli 0157.117 infections may be related to age, antibiotic use, anti-motility agents, and toxin type of the infecting strain (Neill 2001). Symptoms of HUS include intravascular destruction of red blood cells (microangiopathic hemolytic anemia), thrombocytopenia, lack of urine formation (oligo-anuria), edema, and acute renal failure (Buchanan 1997). HUS has a fatality rate of 3-5% and a considerable risk of long-term morbidity, such as hypertension, chronic renal failure and disability (Neill 2001 ). Cases of 'l'l'P also have been associated with E. coli 0157.1-I7 infections (Ramsey and Neill 1986). TTP resembles HUS histologically. Patients who developed TTP after E. coli 0157:H7 infections are reported to have bloody diarrhea with the overall features of illness resembling those of HUS cases (hemolytic anemia, thrombocytopenia, and renal failure) accompanied with fever and altered mental status (Neill 2001). Few cases of TTP are found in children as this disease is generally restricted to adults (Buchanan and Doyle 1997). Thrombotic thrombocytopenic purpura, however, is a rare syndrome for E. coli 0157:H7 infection (Doyle 1997). A seasonal pattern has been observed for E. coli O157:H7 outbreaks with most cases occurring in summer (Ostroff et al. 1989; Griffin and Tauxe 1991). Interestingly, a similar pattern also has been observed for HUS cases even prior to the discovery of E. coli O157:H7. The increased incidence of HUS during summer suggests an increase in exposure to E. coli 0157:H7 probably through such risk factors as barbecuing and swimming (Neill 2001). For example, this pathogen was the causative agent of two outbreaks of severe bloody diarrhea in Oregon and Michigan in which transmission occurred via contaminated hamburgers (Riley et al. 1983). The Centers for Disease Control and Prevention estimates that there are 20,000 illnesses a year due to E. coli O157:H7 infections (Jay 2000). Human infections with E. coli 0157:H7 are usually linked to consumption of contaminated and improperly cooked beef, unpasteurized milk, fresh fruits and vegetables (Griffin and Tauxe 1991; Beesley 1993). Some E. coli 0157:H7 cases, however, have involved drinking and swimming water. In 1999, hundreds of people became sick after drinking contaminated water in Washington County, New York and swimming in contaminated water in Clark County, Washington (USEPA 2002). Human and animal wastes are the main vectors for E. coli transmission (USEPA 2002). During rainfall, snowmelts, or other types of precipitation, produce-surface runoff may wash E. coli into sources of drinking water, such as creeks, rivers, streams, lakes, or groundwater. Additionally, during flood seasons, these pathogenic bacteria also have been found in wells. When these water sources are not treated or are inadequately treated, E. coli may end up in drinking water. The USEPA has outlined that to be considered safe for swimming and drinking, average E. coli counts in water must be below 130 cells per 100 ml (USEPA 1 986). SALMONELLAE Salmonellae are members of a large and varied group of Gram negative, non-spore forming rods, which is commonly known as the Family Enferobacteriaceae (Weil and Saphra 1953). Salmonella Enterica is recognized as the main cause of foodborne illnesses in most developed countries (Buzby and Roberts and Roberts 1996). In the US, an estimated 8-18 thousand hospitalizations, 2,400 cases of septicemia, and 500 deaths are associated with Salmonella infections each year (USDA-FSIS 1998). The United State Department of Agriculture (USDA) also has identified Salmonella enterica as the most prevalent and costly of the known foodborne pathogens (Davies 1997). Salmonella was first found by Gaffky in 1884 (Weil and Saphra1953). As eariy as 1886, Gaffky showed that this organism was involved in cases of foodborne illnesses (Weil and Saphra 1953). There are now more than 2,000 serotypes or serovars of the genus, of which 50 -150 serotypes have been linked to disease outbreaks (Ziprin 1994). All Salmonella serotypes are classified based on their closeness of features as determined by DNA hybridization (Le Monor and Popoff 1987). Most of the Salmonella are classified in two species, namely: Salmonella enterica and Salmonella bogori (Jay 2000). For example, the serotype Typhimurium is classified among S. enterica species, and is referred to as Salmonella enterica subsp. enterica serovar Typhimurium, or S. Typhimurium (Buzby and Roberts 1996; Jay 2000). Some Salmonella serotypes might produce enterotoxins that are biochemically and immunologically related to Cholera toxin, heat-labile toxin and Shiga toxin (Inoue et al. 1996), with some isolates also producing low levels of enterotoxin (Ziprin 1994). SALMONELLA TYPHIMURIUM STRAIN DEFINITIVE PHAGE TYPE 104 (DT 104) Multidrug-resistant Salmonella enterica subsp. enterica serovar Typhimurium DT 104 has emerged during the last decade as a global health problem because of its association with animal and human diseases (CDC 1996). The DT 104 epidemic has now spread worldwide, with several outbreaks since 1996 in the US and Canada (Davis 1999; Besser 2000). Multidrug-resistant S. Typhimurium DT 104 strains are commonly resistant to ampicillin, chlorampheniconlorfenicol, spectinomycin/streptomycin, sulfonamides, and tetracyclines (WHO 1997). This organism initially emerged in cattle in 1988 in England and Wales (WHO 1997). Studies show that increased use of antibiotics in animals is one of the factors involved in the dramatic increase in drug-resistant microorganisms. Ciprofloxacin resistance in DT104 has increased from 1% in 1994 to 6% in 1995, coincident with the licensing of this drug for veterinary use in the UK in 1994 10 (Killalea 1996). Surveys of S. Typhimurium isolates from cattle and humans in Australia, France, Hong Kong, and Spain all reveal an increased incidence in resistance to multiple antibiotics in this organism (WHO 1997). In addition to acquiring infections from contaminated food, human cases also have occurred where individuals came in contact with infected cattle. Data shows that 34 of 83 DT104 cases studied in a 1993 epidemiological study required hospitalization and 10 died (Glynn et al. 1998). EPIDEMIOLOGY Typically, S. enterica serotypes cause acute gastroenteritis (Buzby and Roberts 1996). Susceptibility to Salmonella depends on several factors; the virulence of the serotype, the individual‘s immune system, and the quantity of Salmonella ingested (Buzby and Roberts 1996; Jay 2000). Individuals at greatest risk of contracting salmonellosis include children, the elderly, and individuals with immune deficiencies (Buzby and Roberts 1996). Many salmonellosis cases however, have been reported among healthy individuals (Ziprin 1994). While a cell concentration of 107 - 109 CFU/g is generally required to induce salmonellosis (Jay 2000), some Salmonella serotypes can cause illness at levels as low as one cell (Buzby and Roberts 1996). Initial symptoms of salmonellosis include diarrhea, along with abdominal pain, nausea, vomiting, headaches, chills, myalgia, and low-grade fever (Nisbet and Ziprin 2001). Diarrhea is due to invasion of the intestinal muscosa and a concomitant inflammatory response that leads to secretory diarrhea mediated by 11 activation of cyclic adenosine monophosphate (Moss 1980). These symptoms develop in 12-36 hours after ingesting the organism (Buzby and Roberts 1996; Jay 2000). Salmonella infections can also cause a secondary-disease syndrome, or sometimes a chronic disease (Buzby and Roberts 1996). Many Salmonella serotypes penetrate the intestinal lining in humans, with this organism also frequently invading the bloodstreams to cause septicemia (Buzby and Roberts 1996). In a study of 74 consecutive cases of salmonellosis, S. Typhimurium was the most common strain to cause septicemia. Complications of septicemia include meningitis, pneumonia and endocarditis (Jay 2000). FACTORS IN THE EMERGENCE OF FOODBORNE DISEASE Foodborne diseases caused by bacterial pathogens have a major public health impact. Several factors have been associated with the emergence of foodborne disease, including human demographics and behavior, technology and industry, international travel, and microbial adaptation (Altekruse 1998). 1. Changes in human demographics and behavior Demographic changes occurring in industrialized nations have resulted in increased numbers of people becoming susceptible to foodborne infections. In the US, there is a growing population experiencing immune deficiency as a consequence of aging, HIV and chronic infections (Altekruse 1994). In 1995, an estimated one million people in the US were infected with HIV (Altekruse 1994). Salmonella and other foodborne pathogens are more likely to produce 12 more severe, recurrent, or persistent illnesses in this population. Reported cases of salmonellosis are higher in HIV-infected individuals than in the general population (Altekruse 1994). Aging of population also contributes significantly in the increase of foodborne illnesses. During the 20"1 century, the median age of the US population has increased steadily, (USBC 1990) as the “baby boom” generation ages (USBC 1993). Increased susceptibility of the elderly to foodborne infections is reflected by the increased rate in hospitalizations and deaths due to Salmonella enteriditis outbreaks in nursing homes (Mishu 1994). Changes in food-related behavior of consumers also have been recognized as a factor in the increasing rate of foodborne illness. Many individuals have increased their intake of fresh fruits and vegetables in response to health promotion efforts. For example, the National Cancer Institute recommends consuming five servings of fresh fruits and vegetables per day as part of a healthy lifestyle (Bowersox 1992). Because feces or soil may contaminate the surface of produce during growth, harvest and distribution, consumption of fresh produce can cause foodborne infections. Additionally, most fresh produce is likely to be consumed raw without undergoing processing that can inactivate pathogenic bacteria. Since 1990, foodborne outbreaks have been associated with a wide variety of produce including melons, lettuce, alfalfa sprouts, and tomatoes as well as fresh apple 13 cider and orange juice (Buchanan and Doyle 1997; Thayer and Raykowski 1999) The shift toward food consumption outside the home is an important factor in the emergence of foodborne disease. In the US, the percentage of income spent on food eaten away from home has increased steadily (Manchester and Clauson 1995). Fast-food restaurants and salad bars were rare 40 years ago but are now primary locations for food consumption in today's fast-paced society (Manchester and Clauson 1995). Outbreaks occurring outside the household account for 80% of the total reported foodborne outbreaks (Bean et al. 1996). E. coli O157:H7 infection, for example, was first recognized in 1982 when two outbreaks in the US were associated with undercooked hamburger in a fast-food restaurant chain (Acheson and Gerald 1996). 2. Changes in industry and technology The consolidation of food industry has a major impact on the emergence of foodborne disease. Large volumes of food products are normally distributed to customers from centralized suppliers. When mass-distributed products are contaminated, the cases of illnesses are scattered rather than localized (MacDonald 1985; Killalea 1996). In 1985, one particular outbreak of salmonellosis from a large Midwest milk processor was estimated to have resulted in approximately 250,000 illnesses (MacDonald 1985) 3. Changes in travel and commerce International travel has increased dramatically during the 21“t century. While 5 million international tourists travelled internationally in 1950, 937 14 million international travelers are projected in 2010 (WTO 2000). Travelers may become infected with foodbome pathogens that are uncommon in their nation of residence. Acquisition of such infections abroad may complicate diagnosis and treatment of the disease when they return home. 4. Microbial adaptation In the US, 8. Typhimurium DT 104 is a growing concern. Ninety percent of S. Typhimurium DT 104 isolates are resistant to ampicillin, amphenicol, streptomycin, sulphonamides, and tetracycline. Additional strains resistant to trimethoprim and Ciprofloxacin have emerged in the United Kingdom during the 19903 (Altekruse 1998). Surveillance for human S. Typhimurium DT 104 infections in the UK indicates higher hospitalization and fatality rates with this organism compared to other Salmonella serotypes (Lee 1994; Altekrus 1998). CONTAMINATION CONTROL IN FOOD PROCESSING In an effort to reduce the incidence of foodborne illnesses, new regulations require all food processing plants to conduct regular microbial testing. A program known as Hazard Analysis and Critical Control Point, or HACCP, has been adopted by both the Food and Dmg Administration (FDA) and the United States Department of Agriculture (USDA) to identify and prevent biological, chemical and physical hazards in foods (Bernard 1997). HACCP programs are now required for processed food, seafood, and juice industries, as well as meat and poultry processing plants. FDA now is considering developing regulations that would establish HACCP as the food safety standard throughout other areas of 15 the food industry, including both domestic and imported food products. The system is not only practiced by numerous US food companies but also in most other developed countries. HACCP involves seven principles (Bernard 1997): 1. Analyze hazards. Potential hazards associated with food are identified. The hazard could be biological, such as a microbe; chemical, such as a toxin or allergen; or physical, such as ground glass or metal fragments. 2. Identify critical control points. These are points in a food's production at which the potential hazard can be controlled, reduced or eliminated. Examples are cooking, cooling, packaging, and metal detection. 3. Establish preventive measures with critical limits for each control point. For a cooked food, for example, this might include setting the minimum cooking temperature and time required to ensure the elimination of any harmful microbe. 4. Establish procedures to monitor the critical control points. Such procedures would include determining how and by whom cooking time and temperature should be monitored. 5. Establish corrective actions to be taken when monitoring shows that a critical limit has not been met. For example, reprocessing or disposing of food if the minimum cooking temperature is not met. 16 6. Establish procedures to verify that the system is working properly. For example, testing time-and-temperature recording devices to verify that a cooking unit is working properly. 7. Establish effective record keeping to document the HACCP system. This would include records of hazards and their control methods, the monitoring of safety requirements and action taken to correct potential problems. New challenges to the US food supply have prompted FDA to consider adopting a HACCP-based food safety system on a wider basis. One of the most important challenges is the increasing number of new food pathogens. For example, between 1973 and 1988, bacteria not previously recognized as important causes of foodborne illness, such E. coli O157:H7, have become more widespread (Jay 2000). CURRENT CONTROL METHODS AND DETECTION This section examines current methods for microbial detection especially in the food industry. Microbial detection includes conventional plating methods, modern molecular and immunologic detections, and the application of biosensors. The parameters most often used to evaluate a method are sensitivity, specificity (Barbour 1997), detection time, and user-friendliness of the method. Sensitivity characterizes the number or concentration of target organisms required for a positive result (Barbour 1997). Increasing the assay sensitivity will 17 increase the accuracy of the test. Specificity is generally measured by testing a group of bacterial strains within the target genus or species against a group of related non-target strains (Barbour 1997). Following enrichment of a food sample, 108 — 109 bacteria per ml may be present in the enrichment broth with only a portion of this population comprising the target pathogen. Therefore, the challenge is to achieve a very low sensitivity without sacrificing the specificity of the detection method. Detection time is critical, especially in the food processing facilities. If the detection time is long, the contaminated food products may have already reached the consumers, which could in turn result in a very expensive product recall. Finally, it is advantageous for microbial detection methods to be relatively simple and easy to use. This feature will definitely expand the application of the detection method not only in research laboratories, but also in a small food industry that cannot afford to have an on-site laboratory. CONVENTIONAL METHOD Conventional plating is the most widely used method for determining the number of viable bacteria in a food product (Jay 2000). This method requires cultivation, isolation, morphological examination, and biochemical identification (Deshaonde 1996). This conventional method usually requires hands-on preparation and 24-48 hours of incubation time before suspected pathogens can be identified (Jay 2000).The advantages of this method in identifying viable cells are the accuracy and reproducibility of the results. These method however is laborious and time-consuming. 18 MOLECULAR AND GENETIC-BASED METHODS Most genetic and molecular methods for pathogen detection are based on the formation of a nucleic acid hybrid. The nucleic acid hybridization is typically between DNA or RNA molecules present in the target organism and a diagnostic probe with DNA having a sequence complementary to the target pathogen (Barbour 1997). Typically, the molecular and genetic detection methods begin with cell lysis followed by DNA or RNA preparation for hybridization (Jay 2000). Some of the current molecular methods are nucleic acid probes and polymerase chain reaction (PCR) (Jay 2000). The advantages of using these methods are their high sensitivity. The drawbacks are the time and expertise required to perform the test (Barbour 1997; Jay 2000). IMMUNOLOGIC DETECTION METHODS Immunologic detection methods are analytical tests that use antibodies. These methods take advantage of the unique binding characteristics of the immunoglobulin molecule to specific antigens (Barbour 1997). A variety of techniques have been used to detect antigen-antibody reactions. The principal techniques presently in use are enzyme immunoassays, immunofluorescence, and radioimmunoassays (D'Souza 2001). In a typical enzyme immunoassay procedure, the antigen-antibody reaction is detected by the formation of a colored product from a colorless substrate. In an immunofluorescence technique, a reaction is indicated by emission of small quantities of light, which must generally be observed microscopically (Cahn 1993; D'Souza 2001). 19 Radioimmunoassays use radioactive labeled substances that detect antigen- antibody binding based on measured amounts of radioactivity (Cahn 1993). These immunologic methods are reliable but are slow and tedious microscopically (Cahn 1993; D'Souza 2001). Recently, several types of electrical immunoassays have been developed. Such methods use electrical signals to measure the immune reaction. One strategy involves the use of antibody-labeled colloidal gold particles. Gold particles are used in immunodiagnostic tests where the results are determined optically by observing small amounts of light reflected as a result of the antigen- antibody reaction (Kim et al. 2000). Silver also has been used with or without gold in developing electrical immunoassay techniques (Brunelle 2001), BIOSENSORS Biosensors have emerged recently to provide rapid detection methods in addition to the current classical methods. A biosensor is an analytical device that integrate a biological sensing element with an electronic transducer (Turner 1998; Ivnitski et al. 1999). The general function of a biosensor is to convert biological events into quantifying electrical responses (Cahn 1993). Biosensors make use of a variety of transducers, such as electrical, electrochemical, optical, piezoelectric crystal, and acoustic wave. The sensing elements may be enzymes, antibodies, DNA, receptors, organelles, and microorganisms, as well as animal and plant cells. 20 Some of the major attributes of a biosensor technology are its specificity, sensitivity, reliability, portability, real time analysis, and simplicity of operation (D'Souza 2001). Two types of biosensors will be described in detail as a comparison to the device investigated in the study. First to be described is the electronic nose (E- nose) developed by Alocilja (2000) at Michigan State University. The E-nose basically consists of several commercialized gas sensors coupled to an artificial neural network. The E-nose technology is cheap, requires less than 24 hours to complete detection, and is shown capable to differentiate E. coli O157:H7 from non-0157: H7 E. coli by generating a distinctive gas signature (Younts 1999). This technology is easy to use as no sample preparation or pre-enrichment is required. There are several limitations for this technology, however. One of the issues is related to the biosensor specificity. In mixed cultures, it is hard to distinguish the gas signatures between E. coli O157:H7 and other non-target bacteria (Younts 1999). The gas signature of other foodborne pathogens such as S. Typhimurium, also has not been established. The second biosensor is the surface plasmon resonance (SPR) biosensor, which has gained popularity in the last couple of years. Meeusen (2000) has converted an off-the-shelf SpreetaT“ sensor to a SPR biosensor for S. Typhimurium and E. coli 0157:H7 detection in the pork production chain. The specificity of the SPR biosensor is higher than the E-nose as it utilizes a sandwich immunoassay technique. The detection limit of the biosensor for both 21 pathogens in pure culture is 107 CFU/ml. The SPR biosensor requires 30 minutes for detection after 5 1/2 hours of enrichment (Meeusen 2000). ELECTRICAL IMMUNOASSAY BIOSENSOR The focus of this study is to evaluate an electrical immunoassay-based biosensor (El) as a microbial detection device. The El biosensor uses antibodies as biological elements that are attached to an electrical transducer. Materials used in El biosensors are: materials for electrodes and support, materials for immobilizing the biological sensing element, materials for outer membranes, the biological sensing element, and electrically active materials (Zhang 2000). ELECTRODES AND SUPPORTING SUBSTRATE Metal is commonly used to fabricate the electrodes and the supporting system. Metals such as platinum, silver, gold, and stainless steel have long been used for making the electrodes because of their excellent electrical and mechanical properties. These chemically inert materials possess low electrical resistivity and have very pure crystal structures that provide low residual currents and high signal-to—noise ratios (Ivnitski et al. 1999). MATERIALS FOR OUTER MEMBRANES Electrical immunoassay-based biosensors are usually covered with a thin membrane to control diffusion, reduce interference and to protect the sensing elements from the outer environment. Materials used for outer membranes 22 include glass (Kim et al. 2000), copper wafers (Sergeyeva et al. 1994), plastics, and semi-conductive platforms (Hatch et al. 1999). BIOLOGICAL SENSING ELEMENTS Improvements in biosensor design have been frequently directed at the incorporation of active biological molecules, including enzymes and mediators such as ferrocene and its derivatives, cofactors based on nicotinamide adenine dinucleotide (NADH+ and NADP"), catalysts, antibodies, and DNA (Ivnitski et al. 1999). MATERIALS FOR IMMOBILIZING BIOLOGICAL ELEMENTS Most materials used for immobilizing biological elements are multifunctional agents, such as glutaraldehyde and hexamethyl diisocyanate, which form crosslinks between the bio-catalytic species or proteins. The process called coreticulation creates complex matrices, which make multi-enzyme immobilization possible (Zhang 2000). ELECTRICALLY ACTIVE MATERIALS Materials used as electrical transducers in El biosensors include silver (Brunelle 2001), gold, and organic conductive polymers (Kim et al. 2000). Organic conductive polymers, such as polyaniline, polypyrrole, polyacetylene, and polythiophene (Hoa 1992), offer several advantages in the development of El biosensors. Organic conductive polymers provide an appropriate interaction 23 between the biological element and the electrodes. Therefore, the electrical connection between the sensing region and the electrodes is more efficient. CONDUCTIVE POLYMERS Plastic, under certain circumstances, can be made to behave very much like a metal. This is a discovery for which Alan J. Heeger, Alan G. Macdiarrnid, and Hideki Shirakawa received the 2000 Nobel Prize in Chemistry with polyacetylene identified as the first conducting polymer (Skotheim 1987). Plastics are long chain molecules, repeating themselves. In becoming electrically conductive, a polymer must imitate a metal, that is, its electrons need to be free to move rather than bind to atoms. Although, the polymer must contain alternating single and double bonds, called conjugated double bonds (Figure 1.1), to become electrically conductive, electrons must be either removed (oxidation), or inserted (reduction) into the polymer structure. This process is known as doping. In the case of polyaniline, the electrical conductivity of the polymer can be changed either by an acid/base treatment or electrochemical technique (Salaneck 1987). There is a considerable number of biosensors based on conducting polymers utilizing different biological entities, such as antibodies, DNA, whole cells, enzymes and polyanions. The combination of the selectivity and sensitivity of the bio-molecule with the novel electrochemical properties of conducting polymers provides such a powerful bio-sensing device (Sadik 1995). A summary of conducting polymer-based biosensors is shown in Table 1.2. These 24 biosensors were shown to exhibit improved sensitivities and faster response times compared to the conventional methods (Sadik 1995). However, due to the complex processes involved in polymer formation, difficulties in obtaining reproducible polymer films have been reported (Barsch et al 1994; West et al 1993; POLYANILINE Polyaniline is a family of interesting materials due to its unusual transport, magnetic and optical properties (Sergeyeva et al. 1996). It is the first conducting polymer to be commercialized and now has applications ranging from batteries (Osama et al. 1992) to biosensors (Kim et al. 2000; Killard et al. 2000). In the biosensor development, polyaniline has been used as a label for an enzyme- based method and immunoassay system. The latter method is more preferred as the enzyme-based method requires labeling procedure and several washing steps to separate bound and unbound molecules (Killard et al. 2000). The immunoassay system, with its characteristic of high affinity and excellent selectivity, is ideal for a rapid detection device (Paek et al. 1999). Killard et al. (2000) have focused on an electrochemical immunosensor based on polyaniline in analyzing the pesticide atrazine for environmental monitoring. The detection limit claimed is as low as 1 part per billion. Sergeyeva et al. (1996) have examined the potential of using polyaniline in immunosensor analysis by using interdigitated electrodes as the platform. Kim et al. (2000), on the other hand, has integrated polyaniline with 25 Table 1.1 Examples of conducting polymer-based biosensors Ilmmobilized Biosensor Analyte Ibiomoleculei Reference PPy/AHSA HSA AHSA (John et al. 1991) PPy/ATHAU THAU ATHAU (Sadik et al. 1994) PPy/P-Cres Phenol Phenol (Barnett et al. 1994) PANI/GOx Glucose Glucose (T amiya et al. 1989) PANI/cell Whole cell lgG (Kim et al. 2000) PANI/ Attrazine lgG (Killard et al. 2000) PPy/Urease Urea Urease (Adeloju et al. 1993) PPy/Dopa Dopamine Whole cell (Deshpande and Hall 1990) lgG=lmmunoglobulin, HSA=Human serum albumin, AHSA: anti- human semm albumin, THAU=Thaumatin, ATHAU=anti-Thaumatin, P-Cres=P-Cresol Bovine Serum albumin conjugate, GOx =Glucose oxidase, Dopa= Dopamine, Ppy=Polypyrole, PANI= polyaniline 26 gold colloids to provide a more sensitive colorimetric immunosensor for microbial detection. Different forms of polyaniline exist based on the oxidation levels. Polyaniline is believed to be composed of the basic chemical units shown in base form (Figure 1.1) (Macdiarrnid et al. 1987; Yen Wei 1989). H H N N N: N Reduced unit ’ Y Oxidized unit 1-3, . Figure 1.1 Polyaniline structure When 0 < y <1, they are called poly (paraphenyleneamineimines) in which the oxidation state of the polymer increases continuously with decreasing value of y. The fully reduced form, also known as leucoemeraldine, has a y value equal to 1. The most oxidized form (y equal to 0) is called pemigraniline. Finally, the intermediate form, with y equal to 0.5, is called emeraldine. The terms leucoemeraldine, emeraldine, and pemigraniline refer to the different oxidation states of polyaniline (Macdiarrnid et al. 1987). Between the three types of polyaniline, emeraldine is the most stable polyaniline in air (Sergeyeva et al. 1994) The emeraldine base has an equal number of reduced [-(C5H4)-(N(H)- (C6H4)-(N(H)-] and oxidized [-(C5H4)-N=(C6H4)=N-] repeat units (Figure 1.2). The electrical properties of the emeraldine base can be interchanged from insulating 27 (conductance (o) <10"° ohm'1cm") to conducting (o a 5-10 ohm'1cm") through protonation (Epstein 1987; Macdiarrnid et al. 1987). Upon treatment with acid of varying pH levels, protons (H*) are added to a fraction of the unprotonated nitrogen sites (Macdiarrnid et al. 1987). In this case, the number of electrons on the polymer backbone is held constant while varying the number of protons (Epstein 1987). For example, treatment of emeraldine base with aqueous 1M HCI resulted in nearly complete protonation of a conducting emeraldine salt (Macdiarrnid et al. 1987) as shown in Figure 1.3. Protonation of the emeraldine base with aqueous acid brings about an increased in conductivity 10 ohm'1cm'1 (Macdiarrnid et al. 1987). (01.00 Figure 1.2 Emeraldine base ‘ Cl- CI- ,/ H N N H+ Figure 1.3 Emeraldine base treated with HCI 28 Synthesized polyaniline is green in its hydrocholoride salt form and has an electrical conductivity of 10 ohm'1cm'1 (Yen Wei 1989). The polymer in the base form is blue, and is insoluble in water and other common organic solvents such as benzene. Polyaniline, however, at room temperature, has a solubility rate of 20-50% in N, N-dimethylforrnamide (DMF), depending on the reaction time or aniline polymerization (Yen Wei 1989). Polyaniline was chosen as the electrical transducer in the proposed El biosensor due to its excellent stability in liquid form, promising electronic properties (Syed and Dinesan 1991), and strong bio-molecular interactions (Imisides 1996). 29 CHAPTER 2 MATERIALS AND METHODS MATERIALS AND METHODS FOR THE FABRICATION OF THE El BIOSENSOR (CHAPTER 3) 2.1 Construction materials for the biosensor Experiments were done to identify the most appropriate membranes for each of the following sections of the biosensor: sample application, conjugate, detection and absorption membranes. The membranes were chosen based on different criteria. The sample application membrane must provide easy and quick flow of the sample with no or minimal interference. The conjugate membrane must be able to adsorb the polyaniline-conjugated antibody and yet allow easy flow when dissolved. The capture membrane must have pores of sufficient size to allow non-target antigens to flow through and provide good adsorption properties for the immobilized antibody. The absorption membrane must be able to hold the liquid sample. Two types of electrodes were tested: copper and silver. Selection was based on the electrical properties and ease of handling during fabrication. Two kinds of platform materials were evaluated: glass and etched copper wafers. These materials were chosen based on previous studies (Sergeyeva et al. 1996; Kim et al. 2000). 30 2.2 Bacteria isolates and culturing A characterized strain of E. coli O157:H7 (ATCC #43895) was obtained from the Biosystems Engineering collection, Michigan State University. A 10-ul loop of the frozen culture was transferred in a 10-ml of nutrient broth (Difco Laboratories, Detroit, MI) and incubated for 24 hours at 37°. Two additional transfers were made with the last sample used as the stock culture. All experiments were performed in a certified Biological Safety Level II laboratory. Cultures were serially diluted in 0.1% of peptone water to generate a varying concentration of the bacteria. 2.3 Antibody preparation A lyophilized affinity purified polyclonal antibody for E. coli O157:H7 was purchased from Kirkegaard & Perry Laboratories Inc. (Gaithersburg, MD) and stored at 4°C until rehydrated. Antibodies were rehydrated with phosphate buffer (Sigma-Aldrich, MO) according to the manufacturer’s instruction. 2.4 Conjugate membrane preparation A water-soluble polyaniline was synthesized by following a standard procedure of oxidative polymerization of aniline monomer (Sigma-Aldrich, MO) in the presence of ammonium persulfate (Sigma-Aldrich, MO) (Sergeyeva et al. 1994). Details of the polymerization process can be found in Appendix A. A mixture of the rehydrated antibody and the synthesized polyaniline was allowed to react for 30 minutes. The antibody-polyaniline conjugate was centrifuged at 13,000 rotation per minute (rpm) for three minutes using 0.1 M tris buffer (Sigma- Aldrich, MO) as the blocking reagent. The conjugated antibody was diluted in 31 0.1M LiCl (Sigma-Aldrich, MO) and stored at 4°C. When ready, 10ul of the conjugated antibody was applied to the conjugate pad and allowed to dry. 2.5 Absorption and application pads preparation To prepare the absorption and application pads, cellulose membranes were washed three times with distilled water to remove dirt and surface residues. The membranes were allowed to dry and stored inside a petri dish to maintain a clean surface. 2.6 Capture pad preparation Affinity purified antibodies were immobilized on the nitrocellulose (NC) membrane as follows. First, the NC membrane was saturated with 10% (v/v) methanol in water for 45 min and allowed to dry. The membrane was then saturated with 0.5% (v/v) glutaraldehyde for one hour. After drying, 0.5mg/ml of antibodies was pipetted onto the membrane and incubated at 37°C for one hour. Inactivation of residual functional groups and blocking were done simultaneously by incubating the membrane with 0.1M tris buffer, pH 7.6, containing 0.1% Tween-20 for 45 min. Finally, the membrane was allowed to air-dry. 2.7 Varying antibody concentrations The optimum concentrations of antibody used on the capture and conjugate pads were determined in this study. The following concentrations of E. coli 0157: H7 antibodies were prepared in sterile phosphate buffer (pH 7.0): 50ug/ml, 150pg/ml, and 500ug/ml. Permutation of the various concentrations was applied to the conjugate and capture pads. An E. coli O157:H7 concentration of about 101 CFU/ml was prepared by serial dilution process and confirmed by 32 plating on CHROM Agar (Microbiology, France). Biosensors prepared with various antibody concentrations (Table 2.1) were tested with 0.1 ml of the E. coli 0157:H7 solution. After the solution reached the absorption pad, the capture pad was removed from the biosensor and washed with 0.1 ml of distilled water. To identify the optimum antibody concentration, the washed solution was surface plated on the CHROM agar. A total of nine sets of biosensors were prepared and tested as mentioned above. Results were assessed for differences in the number of organisms captured using varying concentrations of antibodies on the conjugate and capture pads. Table 2.1. Combinations of antibody concentrations (pg/ml) used for preparing the conjugate and capture pads. Conjugate pad Capture pad rug/mil MW“ 500 500 500 150 500 50 1 50 1 50 150 500 150 50 50 50 50 500 50 1 50 2.8 Varying polyaniline concentrations A total of five sets of biosensors were prepared containing five different concentrations of polyaniline were used on the conjugate area: 0.0019/ml, 0.01g/ml, 0.1g/ml, 1g/ml, and 109/ml. The polyaniline was conjugated with the optimum antibody concentration found in the previous experiments. The distance 33 between electrodes for all biosensors was fixed at 0.5 mm. These biosensors were tested with approximately 104 CFU/ml of E. coli 0157:H7. Data were compared to find the correlation between polyaniline concentration and the signal generated. 2.9 Varying the distance between the electrodes The next experiment determined the optimum distance between the electrodes. The optimum antibody and polyaniline concentrations found in the previous experiments were used. Various electrode gap-sizes (0.5 mm, 1mm, 2mm, 3mm, 4mm, and 5mm) were fabricated on the capture pads. Results were compared to see which distance would yield the strongest signal using approximately 104 CFU/ml of E. coli0157zH7. 2.10 Signal measurement The prepared immunosensor was connected to a multimeter and computer for signal measurement. To begin detection, 0.1 ml of the bacteria serial dilution was pipetted onto the application pad. Measurements were taken at 2-minute interval over a period of 6 minutes. The generated signal was captured using a multimeter (BK multimeter, MA) in the form of resistance. The biosensor was calibrated using a sterile diluted nutrient broth (Difco, Mi). 2.11 Confirmation CHROM agar (Microbiology, France) was used to plate 0.1 ml of the last dilution of the bacterial culture. After 24 hours of incubation at 37°C, suspected colonies were identified and counted according to the manufacturer's instruction. The estimated population of the original culture was determined by back—counting the 8-folds dilution. 2.12 Signal processing A drop in resistance was determined from the difference between the resistance output of the blank and the inoculated samples. The difference in measured resistance (resistance drop) was the reduction in resistance due to the electron transfer facilitated by the polyaniline-labeled antibody between the electrodes. 2.13 Statistical analysis Statistical analysis was conducted using a single factor analysis of variance (ANOVA). All biosensors were assumed to have the same physical properties. For the purpose of this study, the biosensor responses were considered statistically different when the P-value was less than 0.05 (95% confidence interval). MATERIALS AND METHODS FOR THE SENSITIVITY AND SPECIFICITY ANALYSIS (CHAPTER 4) The biosensor consisted of two parts: the immunosensor and the electronic data collection system. The immunosensor contained four different regions: application, conjugate, detection, and absorption. The system was constructed as shown in Figure 2.1: The cellulose membrane was used for sample application and absorption pads, the fiber glass membrane was for the conjugate pad and the nitrocellulose (NC) membrane for the capture pad. Silver 35 electrodes were fabricated on the NC membrane to electrically connect the immunosensor with the electronic data acquisition system consisting of a copper wafer and an ohmmeter linked to a computer. The four regions were prepared as previously described (Sections 2.3, 2.4, 2.5, and 2.6) Analyte Absorptlon (A) membrane Conjugate Mem brane Electrode Application I Electrode Wafer membrane J Capture membrane Figure 2.1. Schematic diagram of the immunosensor. Conjugate pad for polyaniline-labeled antibody adsorption (A). Capture pad with two silver electrodes (B). Gap between electrodes is the site for antibody immobilization. 2.14 Construction of the immunosensor All prepared membranes were arranged in the order mentioned in Figure 2.1 and attached onto an etched copper plate (Figure 2.2) using double-sided tape. Salmonella, E. coli O157:H7 and E. coli biosensors (to be referred to as Sal, EHEC, EC biosensors, respectively) were prepared with antibodies specific to each organism and stored at 4°C. 2.15 Construction of the electronic data collection system The etched copper wafer served as the platform for connecting the immunosensor to the ohmmeter. The copper wafer was designed as in Figure 2.2 and etched using ferric chloride according to the manufacturer's instruction 36 (Radio Shack, MI). The ohmmeter, which was linked to a computer, was used for measuring and recording the resistance generated by the biosensor. 25mm <—— —> 75mm Figure 2.2 An etched copper wafer. The white and black parts indicate the non- conductive and conductive regions, respectively. Region A has a distance of 5 mm. 2.16 Bacteria isolates and culturing Characterized strains of E. coli O157:H7 (ATCC #35150 and ATCC #43895), non-pathogenic E. coli (ATCC #25922), Salmonella Typhimurium (ATCC #14028) and Salmomella Thompson (ATCC # 8391 ) were obtained from the Biosystems Engineering collection, Michigan State University. All strains were cultured as previously described in section 2.2. 2.17 Mixed culture preparation A mixed culture was used to determine the specificity and sensitivity of the biosensor in the presence of non-target bacteria. A mixture of five organisms, E. coli O157:H7 isolates (ATCC #35150 and ATCC #43895), non-pathogenic E. 37 coli, 8. Typhimurium and S. Thompson, was prepared by culturing each organism in 10 ml of nutrient broth at 37°C for 24 hours. Cells from 1 ml of each microbial culture were harvested by centrifugation at 2000 rpm for 15 minutes. The resulting pellets were suspended in 2 ml of peptone water. All five suspended mixtures were combined to make a 10 ml solution. The bacterial suspension was serially diluted in 0.1% peptone water. A duplicate bacterial suspension was plated on Chrom, McConkey, and Brilliant Green agars and incubated at 37°C to determine the number of organism present. 2.1 8 Sensitivity testing Sensitivity testing was conducted to determine the detection limit of the biosensors. A series of 10-fold serial dilutions was prepared from the stock culture using 0.1% peptone water. The E. coli O157:H7 dilution was tested with the EHEC biosensor, the non-pathogenic E. coli dilution with the EC biosensor, and the Salmonella dilution with the Sal biosensor. The mixed culture dilutions were used to test all biosensors. Serial dilutions were also plated on Chrom, McConkey, and Brilliant Green agars and incubated at 37°C to determine the number of organism present. 2.19 Specificity testing Specificity testing was performed to determine the ability of the biosensors to select for the target organisms. The Sal, EHEC, and EC biosensors were tested with sample diluents accordingly: 1. Non-inoculated samples (negative control) 2. Serial dilution of the target organism that the antibody was not specific for: 38 . E. coli 0157:H7 dilutions using Sal and EC biosensors . S. Typhimurium dilutions using EC and EHEC biosensors . S. Thompson dilutions using EC and EHEC biosensors . Non- pathogenic E. coli dilutions using Sal and EHEC biosensors 3. Serial dilution of the target organism for which the biosensor was prepared for: . E. coli O157:H7 dilutions using EHEC biosensors . S. Typhimurium dilutions using Sal biosensors - S. Thompson dilutions using Sal biosensors . Non-pathogenic E. coli dilutions using EC biosensors 2.20 Plate count Numbers of the target organisms were determine by surface plating 0.1 ml of E. coli O157:H7, Salmonella species, and the non-pathogenic E. coli dilutions on CHROM (Microbiology, France), Brilliant Green, and McConkey agar (Difco Laboratories, Detroit, MI) respectively. After incubating at 37°C for 24 hours, suspected organisms were confirmed following a standard protocol in the Bacteriological Analytical Manual of the Food and Drug Administration (BAM) and manufacturer's instructions. Table 2.2: Interpretation of results on differential media except for S. Typhi Differential Medium Tarfit manism Colony color McConkey Agar E. coli Dark purple CHROM Agar E. coli O157 Mauve Brilliant Green Agar Salmonella species Pinkish white or red 39 MATERIALS AND METHODS FOR THE PERFORMANCE OF THE BIOSENSOR IN EVALUATING FRESH PRODUCE SAMPLES (CHAPTER 5) In this chapter, the biosensor was prepared and analyzed according to the previous section (Materials and Methods for Chapter 4). 2.21 Mixed culture preparation Bacterial cultures containing a mixture of three organisms were prepared and analyzed to determine the specificity of the biosensors in the presence of non-target bacteria. E. coli O157:H7, non-pathogenic E. coli, and S. Typhimurium were cultured and mixed as previously described in section 2.17. 2.22 Preparation of produce sample Alfalfa sprouts, iceberg lettuce and strawberries were purchased from a local supermarket. Produce visibly free from mechanical damage and decay were used. Twenty-five grams of each sample were weighed and placed in a petri dish under a biosafety hood. For inoculation, 1ml of fully-grown bacteria or the mixed culture was applied to the surface of each produce sample. The samples were left for 45 minutes under the biosafety hood to allow for cell attachment. The samples were then washed to discard any unattached cells. The samples were processed to recover the cells. For alfalfa sprout and strawberries, the cell recovery process was performed by hand-rubbing the sprouts in 100 ml of 0.1% peptone water for 2 minutes. For iceberg lettuce, a stomacher was used. 40 After the cell recovery process, the solution was filtered using a bio-filter membrane (BlOPath, FL) to concentrate the cells. Then, the membrane filter was aseptically transferred to a test tube containing 10 ml of 0.1% peptone water. The test tube was vortexed thoroughly for 4 minutes' to release the trapped cells from the membrane. The concentrated cells were serially diluted with 0.1% peptone water, hereinafter referred to as bacterial dilution. For example, an E. coli 0157:H7 dilution would mean the serial dilution of a produce sample inoculated with the E. coliO17zH7 bacteria. 2.23 Sensitivity testing Sensitivity testing using the inoculated produce samples was done following similar procedure as previously described for pure culture (section 2.18). 2.24 Specificity testing Specificity testing using the inoculated produce samples was also done as previously described for pure culture (section 2.19). ' Experiment leading to this finding is available in Appendix F 41 CHAPTER 3 FABRICATION OF THE ELECTRICAL IMMUNOASSAY BIOSENSOR This chapter reports the results of the fabrication of the electrical immunoassay (El) biosensor. The objective of this chapter was to identify the initial architecture of the biosensor by evaluating the construction materials, the best concentrations of polyaniline and antibodies, and the distance between electrodes. These factors were then used to assess the performance of the biosensor in detecting a model pathogen. RESULTS 3.1 Biosensor construction The biosensor consisted of two parts: The immunosensor (Figure 3.1) and the electronic data acquisition system (Figure 3.2). The immunosensor was comprised of four different pads (Figure 3.3): sample application, conjugate, capture, and absorption. A cellulose membrane was chosen for both the sample application and absorption pads. A fiber glass membrane grade G6 was for the conjugate pad, and a nitrocellulose membrane (NC), with a flow rate of 160 sec per 4 cm, was determined to be the best pad for the capture section. The electrodes were fabricated on the NC membrane to electrically connect the immunosensor to the electronic data acquisition system. Silver paste was the easiest to handle and thus was used in subsequent development of the biosensor. 42 When attaching the immunosensor on the platform, an etched copper wafer generated more stable Signal and was used as the platform for the later experiments. Figure 3.1 lmmunosensor Figure 3.2. Electrical immunoassay biosensor 43 Analyte Absorption (A) membrane Conjugate Mem bra ne Electrode Application Wafer membrane II Electrode Capture membrane Figure 3.3. Schematic diagram of the immunosensor. Conjugate pad for polyaniline-labeled antibody adsorption (A). Capture pad with two silver electrodes (B). Gap between electrodes is the site for antibody immobilization. Detection concept To begin the detection process, 0.1 ml of solution containing the antigen was added onto the sample application pad. By capillary action, the solution flowed to the conjugate pad and dissolved the polyaniline-labeled antibody. The antibody-antigen reaction occurred and formed a complex. This complex moved to the capture pad containing the immobilized antibody. A second antibody- antigen reaction occurred and formed a sandwich immune complex. The unbound materials were subsequently transported by capillary flow to the absorption pad. Signal Generation The resistance across the electrodes was infinite prior to the sample application. After sample application, the generated signal fluctuated while the sample flowed by capillary action to the absorption pad. Dispersion time from the sample pad to the absorption pad was less than one minute. In the presence of sufficient antigens, the signal stabilized and was recorded. The presence of microorganisms was confirmed by the standard plate count method according to the protocol outlined by the Bacteriological Analytical Manual of the Food and Drug Administration. Optimum antibody concentration Table 3.1 shows the result of the antibody concentration experiment. Cell concentrations ranging from 8.8 X 101 to 9.3 X 101 CFU/ml of E. coli 0157:H7 were used to test nine different sets of biosensors. After plating the capture pad along with the washed solution, the maximum number of bacterial cells ranging from 1.0 X101 to 7.3 X 101 CFU/ml were found. The antibody concentration of 5001ng on the capture pad and 150pg/ml on the conjugate pad were resulted in the highest ratio between the number of captured cells and the actual cell concentration tested (Table 3.1). Optimum polyaniline concentration An average of 8.7 X 10‘ CFU/ml of cells were added onto the application pad of five sets of biosensors as detailed in Chapter 2. Figure 3.4 shows that the biosensor generated the most signal when ig/ml of the soluble polyaniline was used. An excess amount of polyaniline (i.e. 109/ml in Figure 3.4) however, had an adverse effect, generating a lower signal than the 1g/ml concentration. 45 Table 3.1. Bacterial counts on the capture pad with varying antibody concentration (jig/ml) on the conjugate and capture pads. Antibody Antibody Plate IconcentratlonSIconcentratlonsr counts used on the used on the of Bacterial count conjugate pad capture pad sample on the capture Ratio AverageI (Irelml) (091ml) KCFU’m') ad (CFU/ml) I(CapturedIActual Ratio Trial Trial Trial Trial Trial Trial 1 2 3 1 2 3 500 500 88 30 30 40 0.34 0.34 0.45 0.38 500 150 90 0 10 20 0.00 0.11 0.22 0.11 500 50 89 0 0 0 0.00 0.00 0.00 0.00 150 150 88 40 30 20 0.45 0.34 0.23 0.34 150 50 93 0 10 10 0.00 0.11 0.11 0.07 50 50 89 0 0 0 0.00 0.00 0.00 0.00 50 500 91 20 20 30 0.22 0.22 0.33 0.26 50 150 91 10 10 0 0.11 0.11 0.00 0.07 80.0 70.0 -- g 60.0 3 v 50.0 G 2 '0 40.0 .. § 30.0 5 g 20.0 -- 10.0 ~- 0.0 - 0.001 0.01 0.1 1 10 Polyaniline concentrations (glml) Figure 3.4. Resistance drop of the various polyaniline concentrations. 46 Optimum distance between electrodes A silver pen (Chemtronics, GA) was used to fabricate the varying distances between electrodes on the capture pad. An average cell concentration of 7.6 X104 CFU/ml was used. Overall, the 0.5-mm distance yielded the most signal (drop in resistance) (Figure 3.5). 70.0 60.0 .. 50.0 I‘ ’ 40.0 30.0 20.0 Resistance drop (Kohm) 10.0 0.0 T T l T 0 1 2 3 4 5 Distance between electrodes (mm) Figure 3.5. Resistance drop of varying distances between electrodes DISCUSSION The sensitivity of an immunoassay system is usually defined as the minimum concentration of analytes that can produce a signal distinguishable from the background (Chard 1987). Therefore, the difference between the noise and signal is used as a representation of the biosensor sensitivity. To attain a sensitive biosensor, the best architecture of the biosensor and the optimal assay conditions were evaluated through literature review and experimentations. 47 The biosensor allowed the liquid sample to laterally flow from one pad to another, through capillary action. The biosensor performance depended on the type of membrane chosen. For application and absorption pads, cellulose membranes were chosen for their excellent thickness and strength properties when exposed to the sample volume. The nitrocellulose membrane was chosen for the capture pad based on several factors: NC membranes bind with protein by electrostatic force, which is stronger than other types of membranes (Millipore 1999). Furthermore, the NC membrane is completely neutral and its binding property is independent from the pH of the immobilization solution. This allows high antibody binding capacity on the membrane irrespective of the pH of the sample applied. The fiber glass membrane was used for the conjugate pad because of its low non-specific binding and consistent flow characteristics (Millipore 1999). Silver was chosen as the material for fabricating the electrode because of its high chemical inertness and low resistivity. This material also has a pure crystal structure that provides low residual currents and a high signal to noise ratio (Ivnitski et al. 1999). An experiment was performed to determine the optimum antibody concentrations to be used on the conjugate and capture pads. Antibody concentrations ranging from 50-500 )1ng were typically used in other immunoassay techniques (Watts 1994; Elkind 1998; Kim et al. 2000). It was hypothesized that the highest antibody concentration used in the experiment (i.e. SOOug/ml) for both pads would result in the highest capture ratio (the ratio between the number of captured cells and the actual cell concentration tested). 48 Figure 3.7 graphically shows the capture ratios for varying antibody concentrations on the conjugate and capture pads. Capture pads with an antibody concentration of 500pg/ml resulted in the highest capture ratio regardless of the antibody concentration used on the conjugate pads. Of all antibody concentrations used on the conjugate pads, a concentration of 150ug/ml resulted in the highest capture ratio. Therefore, the optimum concentration of antibodies was found to be 500ug/ml and 150ug/ml for the capture and conjugate pads, respectively. A water-soluble polyaniline was prepared by oxidative polymerization of aniline monomer (Sergeyeva et al. 1994). The synthesized polyaniline was green in the acidic medium and turned dark blue after treatment with ammonia. Capture ratio 0.9 - 0.8 - 0.7 - 9.0.09.0 NOD-5010) 11111 .0 —L I O , Conjugate pad Conjugate pad (150ug/ml) (500ug/ml) \ ¢ K Conjugate pad (50ughn0 50 150 500 Antibody concentrations on capture pads (uglml) Figure 3.7 Result of the antibody experiments. 49 These changes correspond to the transformation of emeraldine salt into the base form (Sergeyeva et al. 1994). An increase in signal (resistance drop) with increasing concentration of polyaniline (Figure 3.4) appears to be reasonable, as more polyaniline can form more contact with neighboring polyaniline chains. A decreased signal at the concentration of 109/ml (Figure 3.4) may be explained by the over-packing of polyaniline molecules which cause some steric hindrance in the interaction between the polyaniline and the antibody molecules (Sergeyeva et al. 1994). A polyaniline concentration of 1g/ml yielded the most reduction in resistance. A scanning electron microscope (SEM) image of polyaniline is shown in Figure 3.8. I'____—"'—'I 500nm Figure 3.8. Scanning electron microscope image of polyaniline After the antigen reached the capture pad and formed a sandwich complex, the polyaniline acted as a bridge between the electrodes. The polymer 50 structure extended out to bridge adjacent cells for signal generation (Kim et al. 2000). This electrical event initially caused a fluctuation in the generated signal which ranged from a few seconds to one minute depending on the level of antigen attached to the antibody. The resistance signal produced was stable for approximately 10 minutes, but started to increase Significantly thereafter. Chiang (1986) suggested that drying of the membrane decreased the ineffectiveness of polyaniline, resulting in the increased resistance. In order to improve the sensitivity of the biosensor, the optimum distance between the electrodes was investigated. The shortest distance of 0.5 mm resulted in the greatest reduction in resistance. Shortening the average distance between the electrodes enhanced the electron transfer by hopping (Heller 1990; Ryder 1997). Half a millimeter was the shortest distance that could be fabricated using the silver pen. The chemical composition and temperature of the assay were optimized to reduce background noise and increase the performance of the biosensor. The antibody-antigen reaction is optimal at pH 7.0 (Paek et al. 1999). Hence, tris buffer was used to maintain a neutral pH. Numerous immunoassays have used tris buffer not only to sustain the neutral environment for antibody-antigen binding and but also to reduce the background noise (Sergeyeva et al. 1996; Paek et al. 1999; Kim et al. 2000). A previous study shows that an inorganic salt, lithium chloride not only makes polyaniline soluble, but also dopes the polymer to make it more conductive (Angelopoulos 1997). The conductivity however, is highly 51 affected by temperature (Quadrat 1998). In this study, the assays were maintained at room temperature (25°C). 52 CHAPTER 4 SENSITIVITY AND SPECIFICITY ANALYSIS OF THE ELECTRICAL IMMUNOASSAY BIOSENSOR RESULTS Sensitivity Figures 4.1-4.4 show the signal responses (resistance drop) of the El biosensor to varying analyte concentrations. The biosensor generated a parabolic curve with two parts. The resistance drop was proportional to the analyte concentration between 101 and 104 or 105 or 106 CFU/ml, and inversely proportional to the higher analyte concentrations. Table 4.1 shows the responses of Sal, EHEC, and EC biosensors to various bacterial samples. ANOVA revealed that the detection limits for the Sal, EHEC, and EC biosensors were 8.3 1 0.6 X 10', 7.9 :l: 0.3 X 10‘, and 7.5 :l: 0.3 X 101 CFU/ml, respectively. The data also showed that signals generated from one concentration level were not significantly different than the next level of cell concentration. The signal (resistance drop) from samples inoculated with 105 CFU/ml of S. Thompson was not statistically different than that of 106 CFU/ml (Table 4.1). Specificity Table 4.1 also shows the biosensor specificity to target organisms. The Sal biosensors responded only to Salmonella cultures, and not to the E. coli cultures. The EHEC biosensors responded only to the E. coli 0157:H7 culture and not to others. The EC biosensors were found responding to both E. coli 53 0157:H7 and non-pathogenic E. coli, but not to Salmonella cultures. All biosensors also were shown responding to the mixed sample inoculated with all target organisms. The signal generated by the biosensor to the non-target bacteria was not significantly different from the blank (Table 4.1). I I I E . 70.00 ' . l E 60.00 * -m T "" ' I ,3 50.00 h; I ~___~ I Q a i.“ It ‘mm ' 2 40.00 ELEM- . I. I c H m ,, all. . f 3 3000 j . I ‘r ' g f i f 8 3 I ' . J ‘v .5! ' m 10.00 i? . I » T" '1 __. ‘ 0.00 . ‘4' we: T w; . WA .7.-. " T » .. » .5 1 0‘1 1 0‘2 10‘3 1 0‘4 1 0‘5 10‘6 1 0‘7 Cell concentrations (CFUImI) Figure 4.1. Resistance drop of the Sal biosensor tested with S. Typhimurium. 54 m. w m. m. m. m. m o o 0 o 0 5 4 3 2 1 AEgov: :05 3:80:81 1012 1003 10A4 10A5 10‘6 10A7 Cell concentrations (CFUIml) 10‘1 Figure 4.2. Resistance drop of the EH EC biosensor tested with E. coli O157:H7 (ATCC 35150). 60.00 1 40.00 a 10.00 ‘1 w. m 0 0 3 2 Eco! no.5 3530.3”. 50.00 10‘3 10‘4 10‘5 10‘6 10‘7 10‘8 2 0‘ 1 10‘1 Cell concentrations (CFUIml) Figure 4.3 Resistance drop of the EC biosensor tested with non-pathogenic E. coli 55 .28.... 38.3. o .8..- .2 38.8 .2 38.3. o 8.0 o .5. x8... x...- .2x..m. 88 o .8... .2 1.8 .28... .88 o 2.. o .8..- x8... .28.... .88 o .2..- .2 .83. .2 .88 o 8.8- o .8.“ x8... x2. .28.... .28 o .8..- .2 .88 .2 .68 o 8.. o .88 x8... x... .288. :82 o .8..- N2 38.: N2 38.: o 28- o .82 .8838 x8... x2. .28... .82 o .3? .28.. .8... .2x2 .52 o 5...,- o .28 8.8 .28. .82 o .8..- o .2..- o .8...- .2xm... LE. .28. .8... .28. 38.8 o .8..- o .2..- o .8... .28... 38.8 .28. .88 .28. .88 o .3..- o .88- o .88 .28... 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E05 9:58.83 .3 2.0.6.3000 3:203 3.0.5.3 .. .o 0. ago mm; .53 3.20:8 05 .o. $ch 0.....- .Amodvav .92 853:8 83 .m 282...“. 2.3053... 2m 20:08.38 2.88% 5.2. 32.. 8:28.82. 3.68 mum-63$ .2 x N... .8. .N .2 .8. .N .2 3:8 .2 52.8 o 8.? o . 2N x E. x m... x m... .2 x N... . 8.8 .2 . 8.8 .2 9.2.8 .2 .88 o 8.? o .8? x :- x m... x a... .2 x N... 3 .08 .2 3 6.8 .2 3 «N? .2 38...... o m..- o .2... x E. x o... x a... .2 x N... :88 .2 .88.. .2 22.8 .2 :88 o 8.0 o .2... x E. x o... x a... .2 x N... .88 .2 .88 .2 .88 .2 38...... o 8...- o .8.N x E. x o... x m... .2 x N... 2.82 .2 ... 2.2 .2 .88 .2 ... .08 o 8.8- o . 8.. .8588 x E. x o... x m... .2 x N... :22 .2 3:2 .2 :88 .2 .8... o 8..- o . .vN 0m x E. x m... x a... .2 x N. .82 .2 x E .82 .2 x 8.. .8... .2 x 8.. . .o... o 8..- o .2... .2 x 3. .88 o . .N.N .2 .88 .2 .28 o 8..- o .8... x m... x E. .2 x m... 2. 2.8 o . 8. .- .2 2. 88 .2 2. 8.8 o 3.. .- o .8.o. x m... x E. 57 Performance in the mixed culture The data in Table 4.1 also show the results of the biosensors in samples inoculated with a mixture of five organisms. The responses of Sal, EHEC, and EC biosensors tested with the mixed culture were statistically different than the blank. In this experiment, the response pattern was found similar to Figures 4.1- 4.3 (Figure 4.4). 1 [D Sal biosensor IEHEC biosensor IEC biosensor} 70.00 E 60.00 50.00 40.00 30.00 20.00 , Resistance drop( Kohm) 10.00 1‘ 0.00 - 10*1 10"2 1043 10M 10"5 10‘s 10"? 10‘s Cell concentrations (CFU) Figure 4.4. Resistance drop of the three biosensors tested with a mixture of E. coli O157:H7, Salmonella species, and non-pathogenic E. coli. 58 DISCUSSION Immediately after sample application, the solution carrying the antigen move to the conjugate pad and dissolved the polyaniline-labeled antibody (Ab-P) to form an antibody-antigen complex (Figure 4.53). This complex moved to the capture pad containing the immobilized antibody. A second antibody-antigen reaction occurred and formed a sandwich (Figure 4.5C). The polyaniline in the sandwich formed a molecular wire and bridges the two silver electrodes by polymerization. Unbound materials, including the non-target organism, were subsequently separated by capillary flow to the absorption pad. PMifine-antiwdy (Ab- P) Sandwich complex (Ab--Ag--Ab-p) No signal Signal generated A c Figure 4.5. Cross-section of a capture pad before (A) and after (C) analyte application. Antibodies used in the Sal biosensor were specific to a wide range of Salmonella species as listed in the manufacturer’s manual. Similarly, the antibodies used in the EC biosensor were specific to all E. coli serotypes. The antibodies used in the EHEC biosensor, however, were only specific to E. coli 59 O157:H7. Data showed the El biosensor was highly specific to select target antigen even in samples with multiple organisms (Table 4.1). The biosensor response however, shows a parabolic curve instead of a linear relationship (Figures 4.1-4.4). At concentrations above 10‘, 105 or 106 CFU/ml, signal was shown to decrease. This phenomenon may be explained by the nature of sandwich immunoassays which rely on the interaction between the labeled antibodies attached to the antigen and the limited number of antibodies fixed on the capture site (Hatch et al. 1999). At high concentrations (i.e. above 10‘, 105 or 106 CFU/ml), the binding site may be over occupied with the antigen, thus obstructing the charge transfer within the conductive polymer structure. Cavalier (1995) also indicated that protein molecules served as a barrier against conduction by interfering with the electron hopping between electrodes. This phenomenon is referred to here as an over-crowding effect. The over-crowding effect may be responsible for the low signal (resistance drop) generated at high concentrations. A scanning electron microscope (SEM) image (Figure 4.6) of the capture pad shows that the density of bound cells varied between 101 (Figure 4.63), 105 (Figure 4.6C), and 107CFU/ml (Figure 4.60). The detection limit of the Sal, EHEC, and EC biosensors was 8.3 :l: 0.6 X 10‘, 7.9 :l: 0.3 X 10‘, and 7.5 :l: 0.3 X 101 CFU/ml, respectively. Statistical results, however show that the biosensor is not yet suitable for quantitative analysis as the signal generated from one level of bacterial concentration to another was not statistically different. 60 20 Im 20pm (A) (B) ‘20).tm (C) A (D) Figure 4.6. SEM images of the capture pads. (A) Before sample application. (B) After applying with 101 CFU/ml (C) 105 CFU/ml, and (D) 107CFU/ml of bacterial cultures. 61 CHAPTER 5 PERFORMANCE OF THE ELECTRICAL IMMUNOASSAY BIOSENSOR IN FRESH PRODUCE SAMPLES The purpose of this study was to assess the feasibility of the El biosensor to detect E. coli O157:H7, non-pathogenic E. coli, and Salmonella Typhimurium in the selected produce samples. RESULTS Biosensor sensitivity assays Tables 5.1-5.3 show the results of the biosensor sensitivity experiments. The resistance drop for each test was calculated. An average from the three replicates and a standard deviation were also determined. For most of the samples tested, the drop in resistance was proportional to the analyte concentration from 101 to 104 or 105 CFU/ml (Figures 5.1-5.3). Tables 5.1-5.3 show the responses of the Sal, EHEC, and EC biosensors to varying inocula. Table 5.1 shows the detection limit of the Sal, EHEC, and EC biosensors in lettuce samples inoculated with the target bacteria were 8.4 :t 0.2 X101, 7.3 :l: 0.06 X 10‘, and 8.9 :l: 0.1 X 101 CFU/ml, respectively. Signals generated from the EC and EHEC biosensors to bacteria cell concentration ranging from 5 to 6.3 X 101 CFU/ml were not statistically different than the blank (highlighted in Table 5.1). The detection limits of the Sal, EHEC, and EC biosensors for the inoculated sprout samples were 7.8 :l: 0.2 X 10‘, 7.8 :l: 0.1 X 10‘, and 8.1 :i: 0.4 X 101 CFU/ml, 62 .modva. .o>o. coco—258 exeda .a 590:6 2.5055.» 96 22.85%» .canU 5.; .532... in. e... 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As for the strawberry samples, the detection limits for the Sal, EHEC, and EC biosensors were 7.8 :I: 0.4 X 10‘, 8.2 :l: 0.07 X 10‘, and 8.3 :l: 0.4 x 101 CFU/ml, respectively (Table 5.3). Biosensor specificity assays Tables 5.1, 5.2, and 5.3 also show the biosensor specificity to the target organisms. The Sal biosensors responded only to S. Typhimurium and not to E. coli. The EHEC biosensors responded only to E. coli 0157:H7 and not to other organisms. The EC biosensor responded to both E. coli and E. coli O157:H7 but not to S. Typhimurium. All biosensors also were shown to respond to the mixed sample inoculated with all three-target organisms. The resistance drop from the non-target bacteria was not significantly different from the blank. [DSaI biosensors IEHEC biosensors IEC biosensorsj Resistance drop (Kohm) 10‘3 10‘4 10‘5 Cell concentrations (CFUImI) Figure 5.1. Resistance drop of the biosensors used in lettuce samples inoculated independently with E. coli 0157: H7, non-pathogenic E. coli and S. Typhimurium 66 70.0 60.0 50.0 40.0 30.0 20.0 10.0 Resistance drop (Kohm) l 10‘1 DSal biosensors IEHEC biosensors IEC biosensors] 10‘2 10‘3 10M 105 106 Cell concentrationqCFUIml) Figure 5.2. Resistance drop of the biosensors used in sprout samples inoculated independently with E. coli 0157: H7, non-pathogenic E. coli and S. Typhimurium lci Sal biosensors I EHEC biosensors I EC biosensors l 70.0 8.88.8 0000 20.0 i 10.0 Resistance drop (Kohm) 0.0 , 10‘2 10m 1044 105 106 Cell concentrations (CFUIml) Figure 5.3. Resistance drop of the biosensors used in strawberry samples inoculated independently with E. coli 0157: H7, non-pathogenic E. coli and S. Typhimurium. 67 PERFORMANCE IN PRODUCE SAMPLES INOCULATED WITH A MIXED CULTURE The data in Tables 5.1-5.3 also show the results of the biosensors in produce samples inoculated with a mixture of three organisms. The responses of Sal, EHEC, and EC biosensors tested with samples inoculated with the mixed culture were statistically different than the blank. In this experiment, the response pattern was found similar to Figures 5.1-5.3 (Figure 5.4). ll: Sal biosensors I EHEC biosensors I EC biosensors! Resistance drop (Kohm) N .0' O 99' oo ! 10"1 10A2 10A3 10A4 10A5 10"6 i Cell concentrations (CFUImI) Figure 5.4. Resistance drop of the biosensors used in strawbeny samples inoculated with a mixture of E. coli 0157: H7, non-pathogenic E. coli and S. Typhimurium. Figures for the rest of the samples are available in Appendix E. 68 DISCUSSION The biosensor used the method of lateral flow technique to enable the liquid sample to move from one membrane to another through capillary action. The biosensor also utilized the immunoassay technique, in which its specificity depends on the unique binding characteristics of the immunoglobulin molecule (Barbour 1997). Each sensitivity test displays a similar pattern of signal response to varying bacterial concentration when plotted on a semi-log scale. Instead of being linear, a parabolic curve was observed with two distinct regions. A linear increasing response was observed at low cell concentrations (i.e. below 104 or 105 CFU/ml). Above this point, the opposite signal response was observed. Similar patterns had been noted in previous studies. In a study using gold to label the antibody in a sandwich-immunoassay, a similar parabolic curve was observed (Kim et al. 2000). The cause for this is not yet fully understood, but steric hindrances that can occur as binding sites are saturated at high concentrations may be partly responsible (Sergeyeva et al. 1994; Kim et al. 2000). The El biosensor is still in the developmental stage and several obstacles remain to be overcome. Figures 5.1- 5.4 reveal the inconsistency in the signal pattern between biosensors, such as in the case of the biosensor response to inoculated strawberry samples. The signal from the EHEC biosensor was proportional to cell concentrations of 101 to 104 CFU/ml and inversely proportional thereafter. The signal from the Sal biosensor, however, was proportional to the 69 target organism at concentrations up to 105 CFU/ml before decreasing gradually. This inconsistency is not understood yet. Factors such as biosensor-to-biosensor variability and variation in the sample matrix may have contributed to the variation of the results. Particles that detached from the produce samples during homogenizing may also contribute to the inconsistency of the signal pattern. These detached particles may interfere with the conduction of polyaniline or even clog the biosensor. The inoculum levels used to inoculate the produce samples were high (~108 CFU/ml), however, only about 106 CFU/ml of cells were attached to the samples. This significant loss of bacteria in the sample mainly due to the sample preparation method used this study. Therefore, a new efficient way of preparing the sample needs to be investigated in order to further increase the reliability of the biosensor in testing a food matrix. The issue of sample size was also addressed in the study. To run a single biosensor, only 0.1 ml of sample was needed. If a very small number of organisms were present in the original sample, the probability of capturing those organisms in a 0.1-ml sample size was low. Therefore, a method utilizing bio- filtration was chosen to concentrate a 100-ml sample to 10-ml. This technique is commonly used in water filtration studies as a way to increase the sample size without having to sacrifice the biosensor sensitivity. In this experiment, no pre- enrichment was needed. Therefore, detection was completed in less than 20 minutes, including the time required to homogenize and filter the sample. 70 Finally, this study shows that the performance of the biosensor is equal to or even better than other analytical systems which normally have sensitivities and specificities ranging from 103 to 105 CFU/ml (Ivnitski et al. 1999; D'Souza 2001). The detection limits for the Sal, EHEC, and EC biosensors for inoculated produce samples were approximately 8 :l: 0.3 X 10‘, 7.8 :i: 0.4 X 10‘, and 8.3 :I: 0.4 X 101 CFU/ml, respectively. The high sensitivity of the biosensor is an excellent characteristic that should be researched further in the development of a rapid detection tool for food and water samples. 71 CONCLUSION The electrical immunoassay (El) biosensor described in this research represents a novel technique for microbial detection. The combination of lateral flow system and an electrical immunoassay method produced a versatile, rapid, highly sensitive and specific biosensor to detect foodborne pathogens. The major findings and obstacles found in the study are summarized below. This study contained four distinct objectives. The first objective was to determine the best architecture for the biosensor and the optimum parameters associated with the type of assay to be used. Experimental results showed that the cellulose membrane was superior for both the sample application and absorption pads. The fiberglass membrane was evaluated for the conjugate pad, and the nitrocellulose membrane was chosen for the capture area. Between the glass and copper wafer, the copper wafer provided the best platform to connect the immunosensor to a multimeter. Silver paste was used to construct the electrodes on the etched copper wafer. An antibody concentration of 500ug/ml on the capture pad, 150ug/ml on the conjugate pad, a polyaniline concentration of ig/ml, and a 0.5-millimeter distance between electrodes were optimal for the biosensor. The second objective was to further evaluate the performance of the biosensor in multiple types of microbial cultures. Biosensor specificity and sensitivity were assessed. The response curve had a parabolic shape. ANOVA revealed that the detection limits for the Sal, EHEC, and EC biosensors to target organisms were 8.3 :l: 0.6 X 10‘, 7.9 :t: 0.3 X 10‘, and 7.5 a: 0.3 X 101 CFU/ml, 72 respectively. The biosensor was specific only to the target organism even in samples with multiple inocula. Using ANOVA, the response signals (resistance drop) generated in samples inoculated with target bacteria were significantly different than those of the blank. These response signals however, were not statistically different between various inoculum levels of concentration. The third objective of the study was to test the biosensor using produce samples inoculated with different target organisms. Inoculated produce samples were first filtered through a bio-membrane filter to concentrate the cells. This method was used to increase the probability of capturing the target organism in a small (0.1ml) volume of sample. The specificity and sensitivity testing in this experiment were consistent with the result found in the second objective. The detection limits for the Sal, EHEC, and EC biosensors for inoculated produce samples were 8.0 :I: 0.3 x 10‘, 7.8 e 0.4 x 10‘, and 8.3 :I: 0.4 x 101 CFU/ml, respectively. Sample preparation and detection were completed in approximately 20 minutes. The El biosensor faces several issues that need to be addressed before it can be adopted as a rapid microbial detection device. The first issue concerns the over-crowding effect. As previously discussed, the effect causes a decrease in the generated signal at high contamination levels (i.e. above 105 CFU/ml). This is a common finding especially when dealing with immunoassay techniques. The explanation for the parabolic curve, however, is not yet fully understood. In this study, it was hypothesized that cell crowding might have hindered the charge transfer within the polyaniline structure, leading to the decreased signal. 73 The second issue concerns the effect of variability between biosensors. Biosensor-to-biosensor variability does occur at the manufacturing stage, especially when preparing the biosensor without the use of machines. As shown from the data, variability may have contributed to inconsistencies in the signal response. Evidently, there is a need for precision manufacturing of the El biosensor. This will not only eliminate biosensor-to-biosensor variability effect, but will also increase the reliability of the biosensor as a diagnostic tool. The third issue concerns the sample preparation method used in testing food matrices, such as fresh fruits and vegetables. The method used in the study was not sufficient to recover very low levels of organisms in the samples tested. Although the biosensor exhibits high specificity and sensitivity, these characteristics may be less efficient if the method used to recover microbial contaminants from the sample is ineffective. Lastly, the biosensor gives no information about the viability of organisms captured. The antibodies used in this study detected only surface antigens. Therefore, it is impossible to investigate whether the biosensor captured dead or viable cells. The ability to give information on cell viability is critical especially when testing samples in food processing plants, in which the product is subjected to multiple bacteria-reduction processes. Outcomes from this study are promising for the development of a rapid, sensitive, and specific device for microbial detection in food and water supplies. A preliminary testing of water samples obtained from the Red Cedar river and MSU dairy farm are demonstrated in Appendix E. The biosensor can be 74 optimized to be an affordable, portable, user friendly and field-based device as an alternative to bulky and more expensive detection methods. With further research and refinement of the biosensor architecture and fabrication, the El biosensor may be adaptable as a rapid diagnostic tool in food safety. 75 APPENDICES 76 APPENDIX A RESEARCH PROCEDURES AND PROTOCOLS Appendix A.1: Media/reagents preparation ......................................................... 78 Appendix A.3: Polyaniline polymerization ........................................................... 80 Appendix A.4: Antibody preparation ................................................................... 81 Appendix A.5: Conjugating polyaniline to antibodies .......................................... 82 Appendix A.6: lmmunosensor preparation .......................................................... 83 Appendix A.7: Detection ..................................................................................... 85 Appendix A.8: Mixed culture preparation ............................................................ 86 Appendix A.9: Produce sample preparation (For chapter 5) ............................... 87 Appendix A.10: Water sample preparation ......................................................... 88 77 Appendix A.1: Media/reagents preparation Brilliant Green Agar (Difco Laboratories, NJ) 1. Add 58 g of powder in 1 L of distilled water. 2. Autoclave 15 min at 121°C. McConkey Agar (Difco Laboratories, NJ) 1. Add 50 g of powder in 1 L of distilled water. 2. Autoclave 15 min at 121 °C. QHROM Agar (Microbiology, France) 1. Add 29 g of powder in 1 L of distilled water. 2. Bring to boil (100°C). 3. Cool in water bath to 48°C. Nutrient broth (Difco Laboratories, NJ) 1. Add 8 g of powder in 1 L of distilled water. 2. Distribute 10 ml per test tube 3. Autoclave 15 min at 121°C. 9.1%(w/v) ngtone water (Difco Laboratories, NJ) 1. Add 8 g of powder in 1 L of distilled water. 2. Distribute 10 ml per test tube 3. Autoclave 15 min at 121°C. 10% (v/v) methanol solution 1. Mix 10ml methanol (Sigma-Aldrich, MO) with 100 ml of distilled water. 2. Vortex. 0.5% (v/v) glutaraldehyde solution 1. Mix 0.5ml glutaraldehyde (Sigma-Aldrich, MD) with 100ml-distilled water 2. Vortex. 100mM tris buffer containing 0.1% tween-20 (or 0.1% casein) 1. Add 12.1 g of tris in 1 L distilled water. 2. Adjust the pH to 7.6 using 1 N sodium hydroxide (NaOH) or hydrochloride acid (HCI). 3. Autoclave for 45 min at 121°C. 4. Add 1ml tween-20 (0.19 of casein). 78 Appendix A.2: Creating pure cell cultures and plate counting To create stock cultures of E. coli0157zH7, the non-pathogenic E. coli, and Salmonella species 1. Aseptically transfer the isolate to 10ml of nutrient broth (NB) (Difco, laboratories) in a sterile 14 ml tube culture. 2. Incubate the inoculated tube culture for 24 hours at 37° C. Plate counting 1. PNP’P‘PPN 9. 10. 11. Prepare eight 15-ml test tubes with 9 ml 0.1% peptone water (Sigma- Aldrich, MO). Label the test tubes C1, 02, ...... CB. Aseptically transfer 1 ml of the grown bacterial culture into CI. Vortex. Transfer 1 ml of C1 into C2. Vortex. Repeat steps 4 and 5 until reaching C8. Transfer 0.1ml of CB on a differential medium. Spread the bacterial solution with a sterile hockey stick on the surface of the medium. Incubate the plate at 37°C for 24 hours. Count the plate. Back count 8-fold dilutions for every step, to determine the original culture concentration. 79 Appendix A3: Polyaniline polymerization 1. Mix 5 ml of 0.4 M of Aniline (Sigma-Aldrich) and 40 ml of 0.2 mM ammonium persulfate (APS), with 80ml of 1M HCI. . Leave to react for 30 min or until polymerization is complete (Le. the clear solution turns blue) 2 3. Filter the product using a Whatman filter paper. 4. Collect the resulting product from the filter paper using a scraper. 5. Dissolve the product in a 5-ml of N, N Dimethylforrnamide (DMF). 6. 7 8 9. 1 Filter the above solution using a Whatman filter paper. . Discard the product trapped on the filter paper. . Add 2 ml of 2M HCI in the resulting filtrate. Filter the above solution again. 0. Dissolved the final product M phosphate buffer containing 10% (v/v) DMF. 80 Appendix A.4: Antibody preparation 1. Antibodies were hydrated with phosphate buffer pH 7.0 (PB) to make the desired concentrations For example: Making 500ug/ml of E. coli 0157:H7 antibody (Kirkegaard & Peny Laboratories, Inc) 1. Mix 1g/ml of the antibody with 2ml of PB in a vial. 2. Vortex thoroughly. 3. Store at 4°C. 81 Appendix A.5: Conjugating polyaniline to antibodies Add 800m of the antibody in 8 ml of polyaniline. Leave to react for 30 min. Add 1 ml of tris buffer containing 0.1% of casein. Leave to react for another 30 min. Centrifuge at 13000 rpm for 3 min in the 1.5 ml of centrifuge vial. Discard the liquid. Add 1 ml of tris buffer with 0.1% casein into each vial. Centrifuge again. Repeat the above procedure twice. 10. Dilute the resulting pellets in 1 ml of 0.1M lithium chloride (LiCI). 11. Store at 4°C before use PQNQPPPN.‘ 82 Appendix A.6: lmmunosensor preparation Analyte Absorptlon Conjugate membrane Membrane Electrode Application Wafer membrane l J Electrode Capture membrane Figure A.5: Schematic diagram of the immunosensor. Table A.5: Dimension of the immunosensor mm All the membranes were purchased from Millipore (Massachusetts) Capture pad preparation —L . Cut the nitrocellulose membranes (NC) into smaller pieces (6-7 cm) to fit into a petri dish. . Wash the membranes with distilled water three times . Immerse the membranes in 10% methanol. The membranes are left to air- dry for 30 min. . Proceed with washing the membranes with 0.5% (v/v) glutaraldehyde solution. The membranes are left to dry for 60 min. 5. Apply 0.5mg/ml of antibodies on the membranes. Seal the petri dish with a parafilm, and incubate for 60 min. 6. Wash the membrane with 100mM tris buffer, containing 0.1% (v/v) tween- 20. Seal the petri dish with parafilm again, and incubate for 45 min. 7. Store the prepared NC membranes at 4°C. «ROOM 83 Application and absorption pad preparation 1. Cut the cellulose membranes into smaller pieces (6-7 cm) to fit into a petri dish. 2. Wash the membranes with distilled water three times. 3. Allow the membrane to air dry. 4. Store at membranes at 4°C. Conjugate pad preparation 1. Soak the conjugate pad with the antibody conjugated with polyaniline and left to air dry. 2. The pads must be completely dry before the membranes are assembled. Assembling the immunosensor 1. All the membranes are arranged and cut according to the dimension in Table A5. 2. Use silver pen to make two electrodes, 0.5mm apart on the capture pad. 3. Attach the immunosensor on the etched copper water using a double sided tape. 84 Appendix A.7: Detection 1. Attach aligator clips to both electrodes. 2. Connect the immunosensor to an ohmmeter. 3. Transfer 0.1 ml of the sample onto the application pad. 4. Record reading at 2 min, 4 min, and 6 min with the multimeter. 85 Appendix A8: Mixed culture preparation For Chapter 4 1. ‘99.“??? S" I“ Grow five organisms (E.coli0157:H7 ATCC#43895 and 35150, non- pathogenic E. coli, S. Typhimurium, and S. Thompson) in 10 ml nutrient broth. Transfer cultures by loop inocula at 3 consecutive 24-hr intervals to 10 ml NB. Centrifuge 1 ml of each microorganism at 2000rmp X 15 min using a 1.5- ml centrifuge vial. Discard the liquid. Add 1 ml of 0.1 % peptone water into the vial. Centrifuge. Repeat the above procedure for 2 more times. Suspend each pellet in 2 ml of 0.1 % peptone water. Combine all five suspended mixtures to make a 10-ml solution. 0. Serially dilute the suspended solution. For Chapter 5 Awesome 9 N Grow three organisms (Eco/i 0157:H7, non-pathogenic E. coli, and S. Typhimurium) in 10 ml nutrient broth. Transfer cultures by loop inocula at 3 consecutive 24-hr intervals to 10 ml NB. Centrifuge 1 ml of each microorganism at 2000rmp X 15 min using the 1.5- ml centrifuge vial. Discard the liquid. Add 1 ml of 0.1 % peptone water into the vial. Centrifuge. Repeat the above procedure for 2 more times. Suspend each pellet in 3. 33 ml of 0.1 % peptone water. Combine all five suspended mixtures to make a 10-ml solution. 0. Serially dilute the suspended solution. 86 Appendix A.9: Produce sample preparation (For chapter 5) Produce preparations 1. 2. 3. Weigh 25 g of lettuce, strawberries, and alfalfa sprouts samples. Prepare two batches of each sample. Mix the first batch of each sample with 225ml of nutrient broth. Incubate for 24 hours at 37° C. Inoculation 1. 2. 3. Pipette 1 ml of inoculum onto the surface of the sample (second batch). Leave for 45 min Wash three times with 100 ml distilled water. Sample processing 1. Mix the inoculated sample with 100ml of 0.1% peptone water. 2. 3. Repeat the above method with hand-rubbing technique for alfalfa sprouts Use stomacher for 2 min to homogenize the lettuce samples. and strawberries samples. Filtering N9!” PPN.‘ Attach the Biopath SPTM Analytical Filter Funnel to a pump. Transfer 100ml of processed produce samples into the funnel. Filter. Aseptically transfer the membrane to a test tube with a 10-ml of 0.1% peptone water Vortex for 4 min. Aseptically transfer the membrane from the test tube to a plating agar. Serially dilute the suspended solution. 87 Appendix A.10: Water sample preparation To inoculate distilled water 1. 99959!“ Prepare eight 15-ml test tubes with 9 ml 0.1% peptone water. Label the test tubes w1, w2, ...... w8. Aseptically transfer 1 ml of grown bacterial culture in WI. Vortex. Transfer 1 ml of M into w2. Vortex. Repeat the above procedure until reaching w8. To test field samples N95" PPNT‘ Attach the Biopath SP7” Analytical Filter Funnel to a pump. Transfer 100ml of processed produce samples into the funnel. Filter. Aseptically transfer the membrane to a test tube with a 10-ml of 0.1% peptone water. Vortex for 4 min. Aseptically transfer the membrane from the test tube to a plating agar. Serially dilute the suspended solution. 88 APPENDIX B RESULT FOR CHAPTER 3 FABRICATION OF THE ELECTRICAL IMMUNOASSAY BIOSENSOR Figure B.1.a: Biosensor results with varying polyaniline concentration in ~10‘ CFU/ml of E. coli 0157:H7 in pure culture. ................................................. 91 Figure B.2.a: Biosensor results with varying distance between electrodes. ........ 92 Figure B.3.a: E. coli O157:H7 biosensor responses with varying concentration of E. coli O157:H7 (ATCC #43895) ............................................... 95 89 Abbreviations 1. 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W20? 30.. 9.0.. «53.0. «8......c8c8 =8 00.00 00.0V (mqox) dmp oouamsau 00.0w F 00.00 96 APPENDIX C RESULT FOR CHAPTER 4 SENSITIVITY AND SPECIFICITY ANALYSIS OF THE ELECTICAL IMMUNOASSAY BIOSENSOR Figure C.1.a: EHEC biosensor responses with varying concentration of E. coli 0157:H7 (ATCC #35150) .................................................................................. 100 Figure C.2.a: Sal biosensor responses with varying concentration of E. coli O157:H7 (ATCC #35150) .................................................................................. 102 Figure C.3.a: EC biosensor responses with varying concentration of E. coli O157:H7 (ATCC #35150) .................................................................................. 103 Figure C.4.a: EHEC biosensor responses with varying concentration of E. coli O157:H7 (ATCC #43895) ................................................................................. 105 Figure C.5.a. Sal biosensor responses with varying concentration of E. coli O157:H7 (ATCC #43895) in pure culture. ......................................................... 107 Figure C.6.a: EC biosensor responses with varying concentration of E. coli O157:H7 (ATCC #43895) in pure culture. ......................................................... 108 Figure C.7.a: Sal biosensor responses with varying concentration of S. Typhimurium ..................................................................................................... 1 10 Figure C.8.a: EC biosensor responses with varying concentration of S. Typhimurium ..................................................................................................... 1 12 Figure C.9.a: EHEC biosensor responses with varying concentration of S. Typhimurium ..................................................................................................... 113 Figure C.10.a: Sal biosensor responses with varying concentration of S. Thompson ......................................................................................................... 114 Figure C.11.a: EC biosensor responses with varying concentration of S. Thompson ......................................................................................................... 116 97 Figure C.12.a: EHEC biosensor responses with varying concentration of S. Thompson ......................................................................................................... 117 Figure C.13.a: EC biosensor responses with varying concentration of non- pathogenic E. coli. ............................................................................................. 118 Figure C.14.a: Sal biosensor responses with varying concentration of non- pathogenic E. coli .............................................................................................. 120 Figure C.15.a: EHEC biosensor responses with varying concentration of non- pathogenic E. coli .............................................................................................. 121 Figure C.16.a: EHEC biosensor responses with the mixture of E. coli 0157:H7 (ATCC # 35150), E. coli O157:H7 (ATCC # 43895), S. Thompson, S. Typhimurium, and non- pathogenic E. coli ........................................................ 122 Figure C.17.a: EC biosensor responses with the mixture of E. coli O157:H7 (ATCC # 35150), E. coli 0157:H7 (ATCC # 43895), S. Thompson, S. Typhimurium, and non- pathogenic E. coli. ....................................................... 124 Figure C. 18. a: Sal biosensor responses with the mixture of E. coli O157:H7 (ATCC #35150), E. coli_ 0157: H7 (ATCC # 43895), S. Thompson, S. Typhimurium, and non- pathogenic E. coli. ...................................................... 126 98 Abbreviations 1. Normalization was calculated by taking the difference between the outputs of the blank and the sample. 2. Plate counts “ = Standard plating on differential media according to the type of biosensors used. Plating on Brilliant Green Agar for Sat biosensor, CHROM Agar for EHEC biosensor, and McConkey Agar for EC biosensor. 3. 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(wqox) dOJp counting 128 APPENDIX D RESULT FOR CHAPTER 5 PERFORMANCE OF THE ELECTRICAL IMMUNOASSAY BIOSENSOR IN FRESH PRODUCE Lettuce samples Figure D.1.a: EHEC biosensor responses on the lettuce samples inoculated with varying concentration of E. coli O157:H7. ......................................................... 133 Figure B.2.a: Sal biosensor responses on the lettuce samples inoculated with varying concentration of E. coli O157:H7. ......................................................... 135 Figure D.4.a: Sal biosensor responses on the lettuce samples inoculated with varying concentration of S. Typhimurium. ......................................................... 138 Figure D.5.a: EC biosensor responses on the lettuce samples inoculated with varying concentration of S. Typhimurium. ......................................................... 140 Figure D.6.a: EHEC biosensor responses on the lettuce samples inoculated with varying concentration of S. Typhimurium. ............................ . ........................... .141 Figure D.7.a: EC biosensor responses on the lettuce samples inoculated with varying concentration of non-pathogenic E. coli. ............................................... 142 Figure D.8.a: Sal biosensor responses on the lettuce samples inoculated with varying concentration of non-pathogenic E. coli. ............................................... 144 Figure D.9.a: EHEC biosensor responses on the lettuce samples inoculated with varying concentration of non-pathogenic E. coli ................................................ 145 Figure D.10.a: Sal biosensor responses on the lettuce samples inoculated with a mixture of S. Typhimurium, E. coli O157:H7, and non-pathogenic E. coli ......... 146 Figure D.11.a: EC biosensor responses on the lettuce samples inoculated with a mixture of S. Typhimurium, E. coli O157:H7, and non-pathogenic E. coli ......... 148 Figure D.12.a: EHEC biosensor responses on the lettuce samples inoculated with a mixture of S. Typhimurium, E. coli O157:H7, and non-pathogenic E. coli. .......................................................................................................................... 150 129 wen—- 4.1.1 Alfalfa sprout samples Figure D.13.a: Sal biosensor responses on the alfalfa sprout samples inoculated with S. Typhimurium .......................................................................................... 152 Figure D.15.a: EHEC biosensor responses on the alfalfa sprout samples inoculated with S. Typhimurium. ....................................................................... 155 Figure D.16.a: EHEC biosensor responses on the alfalfa sprout samples inoculated with E. coIiO157zH7. ....................................................................... 156 Figure D.17.a: Sal biosensor responses on the alfalfa sprout samples inoculated with E. coli 0157:H7 .......................................................................................... 158 Figure D.18.a: EC biosensor responses on the alfalfa sprout samples inoculated with E. coli O157:H7 .......................................................................................... 159 Figure D.19.a: EC biosensor responses on the alfalfa sprout samples inoculated with non-pathogenic E. coli. .............................................................................. 161 Figure D.20.a: EHEC biosensor responses on the alfalfa sprout samples inoculated with non-pathogenic E. coli. ............................................................. 163 Figure D.21.a: Sal biosensor responses on the alfalfa sprout samples inoculated with non-pathogenic E. coli. .............................................................................. 164 Figure D.22.a: Sal biosensor responses on the alfalfa sprout samples inoculated with a mixture of S. Typhimurium, E. coli O157:H7, and non-pathogenic E. coli. .......................................................................................................................... 165 Figure D.23.a: Result of EC biosensor responses on the alfalfa sprout samples inoculated with a mixture of S. Typhimurium, E. coli O157:H7, and non- pathogenic E. coli. ............................................................................................. 167 Figure D.24.a: EHEC biosensor responses on the alfalfa sprout samples inoculated with a mixture of S. Typhimurium, E. coli O157:H7, and non- pathogenic E. coli. ............................................................................................. 169 Strawberg samples Figure D.25.a: EC biosensor responses on the strawberry samples inoculated with non-pathogenic E. coli. ...................................... 171 Figure D.26.a: EHEC biosensor responses on the strawberry samples inoculated with non-pathogenic E. coli. .............................................................................. 173 130 L. ‘J‘J. Figure D.27.a: Sal biosensor responses on the strawberry samples inoculated with non-pathogenic E. coli ............................................................................... 174 Figure D.28.a: EHEC biosensor responses on the strawberry samples inoculated with E. coli O157:H7 .......................................................................................... 175 Figure D.29.a: Sal biosensor responses on the strawberry samples inoculated with E. coli O157:H7 .......................................................................................... 177 Figure D.30.a: EC biosensor responses on the strawberry samples inoculated with E. coli O157:H7 .......................................................................................... 178 Figure D.31.a: Sal biosensor responses on the strawberry samples inoculated with S. Typhimurium .......................................................................................... 180 Figure D.32.a: EC biosensor responses on the strawberry samples inoculated with S. Typhimurium .......................................................................................... 182 Figure D.34.a: Sal biosensor responses on the strawberry samples inoculated with a mixture of S. Typhimurium, E. coli O157:H7, and non- pathogenic E. coli. .......................................................................................................................... 184 Figure D.35.a: EHEC biosensor responses on the strawberry samples inoculated with a mixture of S. Typhimurium, E. coli O157:H7, and non- pathogenic E. coli. .......................................................................................................................... 186 Figure D.36.a: EC biosensor responses on the strawberry samples inoculated with a mixture of S. Typhimurium, E. coli O157:H7, and non- pathogenic E. coli. .......................................................................................................................... 188 131 Abbreviations 1. Normalization was calculated by taking the difference between the outputs of the blank and the sample. 2. Plate counts °‘ = Standard plating on differential media according to the type of biosensors used. Plating on Brilliant Green Agar for Sal biosensor, CHROM Agar for EHEC biosensor, and McConkey Agar for EC biosensor. 3. Plate counts “1 = Standard plating on differential media according to samples tested. Plating on Brilliant Green Agar for S. Typhimurium and S. Thompson, CHROM Agar for E. coli O157:H7, and McConkey Agar for non-pathogenic E. coli. 4. STD=Standard deviation 5. *-Estimated bacterial populations by back-counting every step from the last dilution 132 0. ..o 059.... c. 0.0.. 5. 003.00, 80:08.02 “0.. ..n. 059". mod? 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