Biosensors for detection of pathogenic bacteria - Semantic Scholar

Biosensors & Bioelectronics 14 (1999) 599 – 624 www.elsevier.com/locate/bios

Review

Biosensors for detection of pathogenic bacteria Dmitri Ivnitski, Ihab Abdel-Hamid, Plamen Atanasov, Ebtisam Wilkins * Department of Chemical and Nuclear Engineering, Uni6ersity of New Mexico, Albuquerque, NM 87131, USA Received 23 November 1998; received in revised form 1 June 1999; accepted 19 July 1999

Abstract This paper presents an overview of different physicochemical instrumental techniques for direct and indirect identification of bacteria such as: infrared and fluorescence spectroscopy, flow cytometry, chromatography and chemiluminescence techniques as a basis for biosensor construction. A discussion of publications dealing with emerging biosensors for bacterial detection is presented. The review presents recent advances in the development of alternative enzyme- and immunosensors for detection of pathogenic bacteria in a variety of fields (e.g. clinical diagnostics, food analysis and environmental monitoring). Depending on the biological element employed: enzyme; nucleic acid and antibody based biosensors are discussed. Depending on the basic transducer principles, recent advances in biosensing technologies that use electrochemical, piezoelectric, optical, acoustic and thermal biosensors for detection of pathogenic bacteria are overviewed. Special attention is paid to methods for improving the analytical parameters of biosensors including sensitivity and analysis time as well as automation of assay procedures. Recent developments in immunofiltration, flow-injection and flow-through biosensors for bacterial detection are overviewed from the system’s engineering point of view. Future directions for biosensor development and problems related to the commercialization of bacterial biosensors are discussed in the final part of this review. © 1999 Elsevier Science S.A. All rights reserved. Keywords: Bacteria; Biosensor; Optical; Electrochemical; Piezoelectric; Genosensors; Artificial nose

1. Introduction

1.1. Bacteria and microbial diseases Bacteria, viruses and other microorganisms are found widely throughout nature and the environment. Bacterial pathogens are distributed in soil, marine and estuarine waters, the intestinal tract of animals, or water contaminated with fecal matter. An average person carries more than 150 kinds of bacteria which exist both inside and outside the body (Madigan et al., 1997). The majority of microorganisms carry out essential activities in nature, and many are closely associated with plants or animals in beneficial relations. However, certain potentially harmful microorganisms can have profound effects on animals and humans and may be * Corresponding author. Tel.: +1-505-2772928; fax: + 1-5052775433. E-mail address: [email protected] (E. Wilkins)

the cause of different infectious diseases (Table 1). Bacteria can spread easily and rapidly requiring food, moisture and a favorable temperature. Worldwide, infectious diseases account for nearly 40% of the total 50 million annual estimated deaths. Microbial diseases constitute the major cause of death in many developing countries of the world. From a military point of view, there are a number of pathogenic bacteria which can be considered possible biological warfare agents, some of which are listed in Table 1 (Compton, 1987; Malcolm Dando, 1994). These microorganisms are resistant to environmental conditions, most of the human population is completely susceptible, and the diseases they cause are severe with a high fatality rate. A large quantity of these fatal organisms could easily be grown and preserved for several years. A growing number of bacterial pathogens have been identified as important food- and waterborne pathogens (Swaminathan and Feng, 1994; McNamara, 1998; Slutsker et al., 1998). Estimates of the yearly incidence

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D. I6nitski et al. / Biosensors & Bioelectronics 14 (1999) 599–624

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of foodborne illness vary widely from several million cases to 81 million cases in the USA, with bacterial foodborne outbreaks accounting for 91% of the total outbreaks (Beran et al., 1991; Potter et al., 1997). In fact, the incidence of human diseases caused by foodborne pathogens, such as Salmonella sp., Escherichia coli, Staphylococcus aureus, Campylobacter jejuni, Campylobacter coli and Bacillus cereus has not decreased (Swaminathan and Feng, 1994). For example, E. coli is a typical inhabitant of the human intestinal tract and can also be a causative agent of intestinal and extra-intestinal infections. E. coli O157:H7 is a rare strain of E. coli that is considered to be one of the most dangerous foodborne pathogens (Griffin and Tauxe, 1991; Buchanan and Doly, 1997). This O157:H7 strain produces large quantities of a potent toxin, in the lining of the intestine, and causes severe damage resulting in hemorrhagic colitis or hemolytic uremic syndrome which may lead to death, especially in children (Rowe et al., 1991). E. coli can easily contaminate ground beef, raw milk and chicken, therefore, careful control of this pathogen is extremely important especially in the fields of food production. Salmonella is another example of a dangerous foodborne pathogen as all species and strains of Salmonella may be presumed pathogenic for man (Jay, 1992). Salmonellosis is an infectious disease that continues to plague human populations in both developed and developing countries. Outbreak investigations have shown that between 1973 and 1987, 59%

of salmonellosis outbreaks could be traced to a specific food vehicle (Tietjen and Fung, 1995). Current practices for preventing microbial diseases rely upon careful control of various kinds of pathogenic bacteria in clinical medicine, food safety and environmental monitoring. Approximately 5 million analytical tests — for Salmonella only — are performed annually in the United States (Feng, 1992; Meng and Doyle, 1998). The effective testing of bacteria requires methods of analysis that meet a number of challenging criteria. Time and sensitivity of analysis are the most important limitations related to the usefulness of microbiological testing. Bacterial detection methods have to be rapid and very sensitive since the presence of even a single pathogenic organism in the body or food may be an infectious dose. Extremely selective detection methodology is required because low numbers of pathogenic bacteria are often present in a complex biological environment along with many other non-pathogenic organisms. For example, the infectious dosage of a pathogen such as E. coli O157:H7 or Salmonella is as low as 10 cells and the existing coliform standard for E. coli in water is 4 cells/100 ml (Federal Register, 1990, 1991; Greenberg et al., 1992).

1.2. Con6entional methods for detection of bacteria Conventional bacterial identification methods usually include a morphological evaluation of the microorgan-

Table 1 Pathogenic bacteria, diseases they cause, toxins they secrete, infection sources and mortality rates for humans infected by microorganisms used as biological warfare agent (BWA)a Bacteria

Disease

Toxin

Infection sources

Mortality when used as BWA

Bacillus anthracis Brucella melitensis Campylobacter jejuni Clostridium botulinum Coxiella burnetti Corynebacterium diphtheriae Escherichia coli Francisella (Pasteurella) tularensis Mycobacterium tuberculosis Rickettsia rickettsi

Anthrax Brucellosis Diarrhea dysentry Botulism Pneumonia Diphtheria Gastroenteritis Tularemia

Edema factor – – Neurotoxin – Diphtheria toxin Enterotoxin –

Milk or meat, BWA Milk or meat, BWA Dairy products, meats, mushrooms Food BWA BWA Meats, fish, milk, rice, vegetables BWA

Fatal Low – – Low Low – Low

Tuberculosis Rocky Mountain-spotted fever Paratyphoid

– –

BWA BWA

High High

–

–

Typhoid fever Bacillary dysentry Pneumonia Pneumococcal pneumoni Syphilis Cholera Bubonic plague

– Neurotoxin Enterotoxin Erythrogenic toxin – Enterotoxin Plague toxin

Fecal contamination, eggs, milk, meats BWA Fecal contamination Human carriers Human carriers

High – – –

Infected exudate or blood Fecal contamination BWA

– High Fatal

Salmonella paratyphi Salmonella typhi Shigella dysenteriae Staphylococcus aureus Streptococcus pneumoniae Treponema pallidum Vibro cholerae Yersinia pestis a

BWA, biological warfare agent.


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1.3. Bacterial identification using instrumental methods

Fig. 1. Flow cytometry: automated single cell detection using optics and fluorescent markers. Adapted with permission from Salzman et al. (1990).

ism as well as tests for the organism’s ability to grow in various media under a variety of conditions. Although standard microbiological techniques allow the detection of single bacteria, amplification of the signal is required through growth of a single cell into a colony. This process is relatively time-consuming. Traditional methods for enumerating coliform bacteria (colony counts) are often slow (up to 72 h are required to obtain confirmed results) and may vary in time since the development of a colony containing 106 organisms will take between 18 and 24 h. Generally, no single test provides a definitive identification of an unknown bacterium. Traditional methods for the detection of bacteria involve following basic steps: preenrichment, selective enrichment, biochemical screening and serological confirmation (Helrich, 1990; Kaspar and Tartera, 1990; Tietjen and Fung, 1995; Hobson et al., 1996). Hence, a complex series of tests is often required before any identification can be confirmed. The results of such tests are often difficult to interpret and not available on the time scale desired in the clinical laboratory. Some new technologies are very sensitive but analysis time is lengthy. For example, the polymerase chain reaction (PCR) can be used to amplify small quantities of genetic material to determine the presence of bacteria. The PCR method is extremely sensitive, but requires pure samples and hours of processing and expertise in molecular biology (Meng et al., 1996; Sperveslage et al., 1996). In response to this problem, considerable effort is now directed towards the development of methods that can rapidly detect low concentrations of pathogens in water, food and clinical samples. For this purpose, a number of instruments have been developed using various principles of detection, e.g. chromatography, infrared or fluorescence spectroscopy, bioluminescence, flow cytometry, impedimetry and many others (Nelson, 1985; Bird et al., 1989; Lloyd, 1993; Fenselau, 1994; Van Emon et al., 1995; Wyatt, 1995; Hobson, et al., 1996; Basile et al., 1998; Perez et al., 1998).

Common methods used for identification of bacteria are: counting the cells by microscope or by flow cytometry; measuring physical parameters by piezocrystals, impedimetry, redox reactions, optical methods, calorimetry, ultrasound techniques and detecting cellular compounds such as ATP (by bioluminescence), DNA, protein and lipid derivatives (by biochemical methods), radioactive isotopes (by radiometry) (Nelson, 1985; Ramsay and Turner, 1988; Ding et al., 1993; Rodrigues and Kroll, 1990; Lloyd, 1993; Sharpe, 1994; Swaminathan and Feng, 1994; Hobson et al., 1996; Wang et al., 1997a,b,c; Zhai et al., 1997; Zhu and Wang, 1997; Frat Amico et al., 1998). Among these the primary physico-chemical methods of bacterial identification are those which involve the detection of some naturally occurring component of the bacterium. For example, the Microbial Identification System (Newark, DE) uses gas chromatography to produce a fatty acid profile for detection and identification of microorganisms (Swaminathan and Feng, 1994). Another method for bacterial identification is based on the use of infrared (IR) spectroscopy (Rossi and Warner, 1985). Bacteria are smeared onto an IR cell and an IR absorbence spectra is acquired using conventional instrumentation. However, the main limitation of IR spectroscopy is that it involves an evaluation of the chemical composition of bacteria which is especially similar at the molecular level. Because of the inherent limitations of this technique, reports of its application to bacterial detection became less frequent from 1960 onwards. In contrast to the IR identification methods, flow cytometry does not generate data from all the individual molecular components of the microorganism. Flow cytometry may be considered as a form of automated fluorescence microscopy in which, instead of a sample being fixed to a slide, it is injected into a fluid which passes through a sensing region of flow cell (Fig. 1). In the flow cytometer, cells are carried by laminar flow of water through a focus of light, the wavelength of which matches (as closely as possible) the absorption spectrum of the dye with which the cells have been stained. On passing through the focus, each cell emits a pulse of fluorescence and the scattered light is collected by lenses and directed onto sensitive detectors (photomultiplier tubes). These detectors transform the light pulses into an equivalent electrical signal. The light scattering of the cells gives information on their size, shape and structure, cell mass and bacterial growth (Salzman et al., 1990; Pinder et al., 1990; Lloyd, 1993). Flow cytometry is a highly effective means for rapid analysis of individual cells at rates of up to 1000 cells per second (Melamed et al., 1990; Lloyd, 1993; McClelland and Pinder, 1994). By labeling the cells with specific

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fluorochromes or fluorescent conjugates that bind with high specificity to one particular cellular constituent, it is possible to measure a wide variety of cell constituents, such as proteins, carbohydrates, DNA, RNA and enzymes. Flow cytometry is conveniently used as a bacterial counter in clinical, environmental, and industrial microbiology (Boye and Lobner-Olesen, 1991; Steen et al., 1990). The advantage of flow cytometry lies in its ability to make rapid, quantitative measurements of multiple parameters of each cell within a large number of cells; this makes it possible to define the properties of the overall population and of component subpopulations. However, flow cytometry has been used almost exclusively for measurements of mammalian, or at least eukaryotic cells, while they have remained a rarity in microbiology, including bacteriology. The lack of progress in microbiological applications of flow cytometry was the result of several problems encountered when analyzing bacteria. The small size of bacteria and the low number of DNA molecules present to be stained (typically three orders of magnitude less than a mammalian cell) required instruments with high sensitivity and sophistication. The main experimental difficulty in analyzing bacteria using flow cytometry is that many of their biological characteristics (including size, shape and DNA content) vary depending upon the growth conditions used, or the source from which the organism were obtained (Allman et al., 1993). Therefore, strict reproducibility of conditions is required in order to produce consistent data. Finally, the capital cost involved in flow cytometry analyses is high which further restricts its use.

2. Biosensors for microorganisms Currently most microbiological tests are centralized in large stationary laboratories because complex instrumentation and highly qualified technical staff are required. In recent years, however, intensive research has been undertaken to decentralize such tests so that they can be performed virtually anywhere and under field conditions (Griffiths and Hall, 1993; Owen, 1994). Hence the development of portable, rapid and sensitive biosensor technology with immediate ‘on-the-spot’ interpretation of results are well suited for this purpose. Areas for which biosensors show particular promise are clinical diagnostics, food analysis, bioprocess and environmental monitoring. The importance of biosensors results from their high specificity and sensitivity, which allow the detection of a broad spectrum of analytes in complex sample matrices (blood, serum, urine or food) with minimum sample pretreatment (Turner et al., 1986; Schmid and Scheller, 1989; Hall, 1990; Luong et al., 1991; Edelman and Wang, 1992; Feng, 1992; Alvarez-Icaza and Bilitewski, 1993; Deshpande and

Rocco, 1994; Rogers et al., 1995; Morgan et al., 1996; Blum, 1997; Kress-Rogers, 1997). Biosensors for bacterial detection generally involve a biological recognition component such as receptors, nucleic acids, or antibodies in intimate contact with an appropriate transducer. Depending on the method of signal transduction, biosensors may be divided into four basic groups: optical, mass, electrochemical, and thermal sensors (Goepel, 1991; Sethi, 1994; Goepel and Heiduschka, 1995). In addition, biosensors can be classified into two broad categories: sensors for direct detection of the target analyte and sensors with indirect (labeled) detection. Direct detection biosensors are designed in such a way that the biospecific reaction is directly determined in real time by measuring the physical changes induced by the complex formation. Indirect detection biosensors are those in which a preliminary biochemical reaction takes place and the products of that reaction are then detected by a sensor. Finally, biosensors for bacteria can also be divided into sensors operating in batch (intermittent) and continuous (monitoring) mode. This part of the review has been divided into six sections: biosensors based on direct (label-free) detection of bacteria, biosensors based on monitoring bacterial metabolism, biosensors based on detection of enzyme labels, flow-injection biosensors, genosensors and the emerging artifical nose. A brief summary indicating some of the biosensors covered in this review is presented in Table 2.

2.1. Direct (label-free) detection of bacteria Several techniques have been described that allow direct, label-free monitoring of cells at solid-liquid interfaces (Ebato et al., 1994; Morgan et al., 1996; Piehler et al., 1996; Medina, et al., 1997; Ghindilis et al., 1998; Frat Amico et al., 1998). These techniques are based on direct measurement of a physical phenomena occurring during the biochemical reactions on a transducer surface. Signal parameters such as changes in pH, oxygen consumption, ion concentrations, potential difference, current, resistance, or optical properties can be measured by electrochemical or optical transducers.

2.1.1. Optical biosensors Optical transducers are particularly attractive for application to direct (label-free) detection of bacteria. These sensors are able to detect minute changes in the refractive index or thickness which occur when cells bind to receptors immobilized on the transducer surface. Several optical techniques have been reported for detection of bacterial pathogens including: monomode dielectric waveguides (Sloper et al., 1990; Lukosz et al., 1991), surface plasmon resonance (Pollard-Knight et al., 1990; Karlsson et al., 1991; Bringham-Burke et al.,


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Table 2 Features of bacterial sensorsa Bacteria type

Biosensor type

Assay format

LOD (cells/ml)

Reference

Optical biosensors Staphylococcus aureus Salmonella typhmurium Escherichia coli O157:H7 Salmonella typhmurium

RM EWI FA IMAS

Direct Direct Indirect Indirect

8×106 5×108 cfu/ml 105 103

Watts et al., 1994 Schneider et al., 1997 Pyle et al., 1995 Yu and Bruno, 1996

Piezoelectric biosensors Candida albicans Escherichia coli Salmonella Phylococcus epidermidis

QCM QCM QCM QCM

Direct Direct Direct Direct

106 106 106 102

Muramatsu et al., 1986 Plomer et al., 1992 Koenig and Gratzel, 1993a,b Bao et al., 1996

Electrical impedance biosensors Staphylococcus aureus Proteus 6ulgaris

BAWI

Direct Direct

106 3×102

Silley and Forsythe, 1996 Deng et al., 1996

Potentiometric immunosensors Neisseria meningitidis Brucella militensis Francisella tularensis

LAPS LAPS LAPS

Indirect

103 6×103 3×103

Libby and Wada, 1989 Lee et al., 1993a,b Thompson and Lee, 1992

Indirect

5×102 1–5 cfu/g 8×103 50

Nakamura et al., 1991 Brooks et al., 1992 Brewster et al., 1996 Abdel-Hamid et al., 1999a,b

Amperometric immunosensors Escherichia coli Salmonella Salmonella Escherichia coli O157:H7

a RM, resonant mirror; EWI, evanescent wave interferometer; FA, flourescent labeled antibody method; IMAS, immuno-magnetic assay system; LAPS, light-addressable potentiometric sensor array; LOD, limit of detection; QCM, quartz crystal microbalance; BAWI, bulk acoustic wave impedance sensor.

1992; Medina et al., 1997; Frat Amico et al., 1998), ellipsometry (Nakamura et al., 1991; Swenson, 1993), the resonant mirror (Watts et al., 1994) and the interferometer (Schneider et al., 1997). Swenson (1993) utilized an ellipsometric technique for the development of a label-free instrument (BDS-240) for detection of bacteria. The main component of the BDS-240 system is an optical unit that consists of an LED/filter excitation source and a photodiode detection system. Metabolizing bacteria would result in an increased CO2 concentration which in turn affects an emulsion of an aqueous colorimetric pH indicator, thus modulating the fluorescence detected at the photodiode. Selectivity of this sensor depends on the selectivity of the culture medium being used to grow the bacteria. This system was used for positive/negative non-quantitative tests of both aerobic and anaerobic bacteria. The resonant mirror is another technique that maybe used for direct detection of bacteria (Cush et al., 1993). It is based on the use of a thin layer ( 100 nm) of a high refractive index dielectric material and a thicker layer ( 1 mm) of low refractive index material. At certain angles of incidence, light maybe coupled into the high refractive index layer where it undergoes multiple total internal reflections at the top interface, allowing an element of light, the evanescent wave, to penetrate to the sample overlayer. On reflection, the

light undergoes a phase change, and by monitoring the angle at which this occurs, it is possible to detect changes within the evanescent field. Watts et al. (1994) used a resonant mirror biosensor for detecting S. aureus in the range of 8 × 106 –8× 107 cells/ml and a detection time of 5 min. Schneider et al. (1997) described an evanescent wave interferometer that uses a single planar wave of polarized light (Fig. 2). Light from a diode laser source is coupled into the waveguiding film as a single broad beam. The light then passes through multiple sensing

Fig. 2. Schematic view of the Hartman interferometer, showing top view (left) and side view (right). Adapted with permission from Schneider et al. (1997).

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regions on the surface of the chip. An array of integrated optical elements is used to combine light passing through adjacent regions which have been functionalised with specific or non-specific receptors. Using this technique it was possible to directly detect Salmonella typhmurium in the range of 5 ×108 to 5 ×1010 CFU/ml with a detection time of 5 min. The main advantage of the above techniques is their short detection time, however this is compromised by their severe lack of sensitivity. Direct fluorescence techniques can also be used for bacterial identification. Direct methods are those in which the natural fluorescent components of the bacterium are examined. All bacteria examined by direct methods must produce or contain some suitable fluorophore. An example of a direct fluorescence method is the identification of Bacteroides species by the fluorescence of cells held under an ultraviolet lamp (Slots and Reynolds, 1982). Some species of Bacteroides were found not to fluoresce, whereas others emitted fluorescence of characteristic colors. Generally a mixture of fluorescent metabolic products is detected. In many schemes used in the clinical environment, fluorescence is detected visually while the sample is held under a UV lamp. This approach has the advantages of simplicity, low cost, and rapidity. However, there is at least one major limitation to the utility of direct methods. That is, only those bacteria which contain or produce some fluorescent pigment may be examined. Therefore, the utility of this approach is very limited (Rossi and Warner, 1985). Glazier and Weetall (1994) described a method for direct detection of E. coli using silver membrane filters as the bacteria collecting filters. Using this method they were able to detect 1.7×105 cells/ml with an overall analysis time of 15 min.

2.1.2. Bioluminescence sensors Recent advances in bioanalytical sensors have led to the utilization of the ability of certain enzymes to emit photons as a byproduct of their reactions. This phenomenon is known as bioluminescence and maybe used to detect the presence and physiological condition of cells. The potential applications of bioluminescence for bacterial detection were initiated by the development of luciferase reporter phages by (Ulitzur and Kuhn, 1987). In their system (Ulitzur and Kuhn, 1987) introduced the genes encoding luciferase into the genome of a bacterial virus (bacteriophage). If this virus infects a host bacteria, a bioluminescent phenotype can be conferred to a previously non-bioluminescent bacteria. Bioluminescence systems have been used for detection of a wide range of microorganisms (Prosser, 1994; Prosser et al., 1996; Ramanathan et al., 1998). Folley-Thomas et al. (1995) used the TM4 bacteriophage to detect Mycobacterium a6uim and Mycobacterium paratuberculosis, however, a concentration of 104 cells was required to

produce a detectable luciferase signal and the response declined after 2 h. Sarkis et al. (1995) used the L5 bacteriophage to detect Mycobacterium segmantis. Using this bacteriophage it was possible to detect one hundred cells of M. segmantis in a few hours and 10 cells in two days. Using the same approach, Salmonella spp. and Listeria were also detected (Turpin et al., 1993; Chen and Griffiths, 1996). Recently, the use of the A511 bacteriophage led to the construction of a polyvalent system for the detection of a wide range of Listeria strains (Loessner et al., 1996). Using this bacteriophage, it was possible to detect one viable cell/gram of Listeria monocytogenes within 24 h. A sensitive and specific method has been developed for the specific detection of Salmonella newport and E. coli (Blasco et al., 1998). In this method, bacteriophages were used to provide specific lysis of the bacteria and cell content release was measured by ATP bioluminescence. Increased sensitivity was obtained by focusing on the bacteria’s adenylate kinase as the cell marker instead of ATP. Light emission was proportional to cell numbers over three orders of magnitude, and 103 cells were readily detectable in a 0.1 ml sample. The bioluminescence approach is a new attractive approach due to its extremely high specificity and the inherent ability to distinguish viable from non viable cells. However, the main disadvantages is the relatively long assay time as well as its lack of sensitivity that becomes apparent when low numbers of bacteria are to be detected.

2.1.3. Piezoelectric biosensors Piezoelectric (PZ) biosensor systems are very attractive systems which, in principle, may be used for direct label-free detection of bacteria (He et al., 1994; Harteveld et al., 1997; Schmitt et al., 1997; Bunde et al., 1998). This technology offers a real-time output, simplicity of use and cost effectiveness. In the last decade many reports have been published using piezoelectric sensors for a wide range of applications in the food industry, environmental monitoring, clinical diagnostics and biotechnology (Suleiman and Guilbault, 1994; Marco and Barcelo, 1996). The theoretical basis of piezoelectricity and the practical application of a PZ sensors for the determination of various kinds of microorganisms were illustrated in a series of reports (Plomer et al., 1992; Koenig and Gratzel, 1993a,b; Suleiman and Guilbault, 1994; Le et al., 1995; Bao et al., 1996; Hobson et al., 1996). The general idea is based on coating the surface of the PZ sensor with a selectively binding substance, for example, antibodies to bacteria, and then placing it in a solution containing bacteria. The bacteria will bind to the antibodies and the mass of the crystal will increase while the resonance frequency of oscillation will decrease proportionally. PZ immunosensors were developed for Vibrio cholerae


(Carter et al., 1995), Candida albicans (Muramatsu et al., 1986), Salmonella typhimurium (PrusakSochaczewski et al., 1990), L. monocytogenes (Jacobs et al., 1995) and members of the Enterobacteriaceae family (Plomer et al., 1992). In the immunogravimetric microbial assay (Muramatsu et al., 1986), a PZ crystal coated with anti-C. albicans antibody was used for the detection of C. albicans concentrations in the range of 106 – 108 cells/ml. The sensor showed no response to other yeast species, and frequency shifts due to nonspecific adsorption were not significant. A technique using a quartz crystal microbalance (QCM) sensor coated with a thin culture medium film was also developed and applied to determine Staphylococcus epidermidis in the range of 102 – 107 cells/ml (Bao et al., 1996). However, the PZ membrane was not strong enough to hold out against several autoclavings. (Prusak-Sochaczewski et al., 1990) have developed a biosensor based on a QCM for the detection of S. typhimurium. The antibody was selective for the common structural antigen present in a large number of Salmonella species. An identical crystal without Ab was used as a reference to correct for temperature fluctuations and other interferences. The sensor response to S. typhimurium in a microbial suspension was linear in the concentration range 105 – 109 cells/ml. The time required to obtain a constant response depended on the cell concentration. An assay time of 5 h was required for detecting a concentration of 105 cells/ml of S. typhimurium. The coated crystal was stable for six to seven assays. For repeated use, the bound bacteria needed was removed from the crystal by washing with 8 M urea. (Koenig and Gratzel, 1993a,b) have used a similar device to detect Salmonella, E. coli, Yersinia pestis and Shigella dysenteriae. The frequency shifts were measured after incubating the sensor surface with the bacterial sample for up to 45 min, washing with PBS and then air drying. The response of the sensor to bacteria was linear within the range of 106 – 108 cells. According to the authors, the sensor could be reused at least 12 times. The best method for regenerating the antibody surface of the sensor is performed using competition with antigen-specific synthetic peptides. Recently, antigen monolayers assembled onto Au surfaces associated with a quartz crystal were used for the microgravimetric QCM detection of Chlamydia trachomatis in urine samples (Ben-Dov et al., 1997). The sensing interfaces consist of a primary cystamine monolayer assembled onto Au electrodes associated with the quartz crystal. The monolayer is further modified with goat IgG-antimouse IgG Fc-specific Ab that act as sublayers for the association of the sensor-active anti-C. trachomatis. A QCM (EG and G Model QCA 917) interfaced to a computer was used in the studies. A PZ crystal immunosensor has been developed for the detection of enterobacteria in drinking water using

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antibodies against the enterobacterial common antigen (ECA) (Plomer, et al., 1992). Anti-ECA antibody coated crystals were dipped for 40 min into a 25-ml solution of the bacterial suspensions to be measured. Similarly, a reference crystal coated with antiatrazine antibody was incubated in the same solution. The crystals were then washed and dried and the resonant frequency was measured and plotted vs the concentration of E.coli K12. A response is observed for 106 –109 cells/ml of E. coli K12. The reproducibility of the measurements was 30% at a concentration of 106 cells/ ml. Repeated usage of the coated crystal by removing the bound bacteria with urea or glycine-HCl buffer was not possible. Not only was the bacteria removed, but part of the antibody was removed, making reuse impossible. A flow-injection system, based on a PZ biosensor was also developed for detection of S. typhimurium (Ye et al., 1997). The anti-Salmonella sp. antibody was immobilized onto a gold coated quartz crystal surface through a polyethylenimine-glutaraldehyde (PEG) technique and dighiobis-succinimidylprpionate (DSP) coupling. The biosensor had responses of 23–47 Hz in 25 min when the PEG immobilization technique was employed, with R\ 0.94 for S. typhimurium concentrations of 5.3×105 to 1.2×109 CFU/ml. A disadvantage of PZ sensors is the relatively long incubation time of the bacteria, the numerous washing and drying steps, and the problem of regeneration of the crystal surface. This last problem may not be important if small crystals can be manufactured at low cost so that disposable transducers are economically feasible. Possible limitations of this technology include also the lack of specificity, sensitivity and interferences from the liquid media where the analysis takes place.

2.1.4. Electrical impedance biosensors Microbial metabolism usually results in an increase in both conductance and capacitance, causing a decrease in impedance. Therefore, the concepts of impedance, conductance, capacitance and resistance are only different ways of monitoring the test system and are all inter-related (Silley and Forsythe, 1996; Milner et al., 1998). The relationship between impedance (Z), resistance (R), capacitance (C), and frequency ( f ) for a resistor and a capacitor in series is expressed as follows (Hadley and Yajko, 1985; Bataillard et al., 1988): Z 2 = R 2 + 1/(2pfC)2. Impedance usually is measured by a bridge circuit. Often a reference module is included to measure and exclude non specific changes in the test module. The reference module serves as a control for temperature changes, evaporation, changes in amounts of dissolved gases, and degradation of culture medium during incubation. The impedance method was accepted by the Association of Official Analytical Chemists, Intl. (AOAC) as a

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first action method (Gibson et al., 1992). This method is well suited for detection of bacteria in clinical specimens, to monitor quality and detect specific food pathogens, also for industrial microbial process control, and for sanitation microbiology (Swaminathan and Feng, 1994; Silley and Forsythe, 1996). This technique has been used for estimating microbial biomass (Harris and Kell, 1985), for detecting microbial metabolism (Dezenclos et al., 1994; Palmqvist et al., 1994) and for detecting the concentration and physiological state of bacteria (DeSilva et al., 1995; Dupont et al., 1996; Ehret et al., 1997). Current instruments usually detect active metabolizing bacteria when 106 – 107 bacteria per milliliter are present in the culture media. Applied to bacterial detection in urine, concentrations of 105 cells/ ml can be detected using the impedance technique with a detection time of 2.5 h. A very important parameter in cell culture is the number of viable cells. Viable cells are commonly measured microscopically after suspending the cells in a dye such as Trypan Blue. A new biosensor for real-time monitoring of concentration, growth and physiological state of cells in culture media was proposed by (Ehret et al., 1997). This biosensor is based on impedance measurement of adherently growing cells on interdigitated electrode structures. Cell density, growth and long-term behavior of cells on the electrodes change the impedance of the biosensor. The main effect of cells on the sensor signal is due to the insulating property of the cell membrane. The presence of intact cell membranes on the electrodes and their distance to the electrodes determine the current flow and thus the sensor signal. The biosensor provides information about spreading, attachment and morphology of cultured cells. Several analytical devices based on the use of impedance technology have been proposed for detection of bacteria, such as Bactometer and the Malthus M1000s (Swaminathan and Feng, 1994; Silley and Forsythe, 1996). Most impedance analysis is completed in 20–25 h. Pless et al. (1994) investigated the detection of Salmonella using the impedance method in 250 food samples. Food samples were pre-enriched 14 – 16 h at 37°C in peptone water. A reusable Bulk Acoustic Wave (BAW)–Impedance Sensor has been developed for continuous detection of growth and numbers of Proteus 6ulgaris on the surface of a solid medium under ordinary conditions (Deng et al., 1996, 1997). The proposed sensor relies on the fact that bacteria can transform uncharged or weakly charged substrates into highly charged end products causing an alteration in the conductance of the medium. The sensor is simple and rapid and bacteria can be detected using the proposed method in the range of 3.4°102 – 6.7 × 106 cells/ ml.

2.2. Indirect detection of bacteria 2.2.1. Fluorescence labeled biosensors Microorganisms are immunogenic due to the presence of proteins and polysaccharides in their outer coats. This permits the development of immunoassay techniques for bacterial detection. In fluorescent immunoassays (FIA), fluorochrome molecules are used to label immunoglobulins. The fluorochrome absorbs short-wavelength light and then emits light at a higher wavelength which can be detected using fluorescent microscopy. Fluorescein isothiocyanate and rhodamine isothiocyanate-bovin serum albumin are the most common fluorochromes used to tag antibodies. Direct and indirect detection methods are used to test bacteria-containing samples (Colwell et al., 1985; Donnelly and Baigent, 1986; Brayton et al., 1987; Kaspar and Tartera, 1990). Food samples tested by FIA are typically from enrichment cultures because the number of bacteria in the original sample is insufficient to be directly detected and due to the interference caused by food particulates producing background fluorescence. Water samples have been analyzed directly by concentrating bacteria using membrane filtration. Polycarbonate filters are commonly used in this procedure (Hobbie et al., 1977; Kaspar and Tartera, 1990). Identification of bacteria by fluorescent immunoassays (FIA) takes advantage of the high degree of specificity inherent in the immunological reaction. Recently (Cao et al., 1995) described a fluorescent immunoassay to detect a specific protein-polysaccharide surface antigen (F1) of Y. pestis. The capture antibody was either a protein G-purified rabbit anti-plague Ab or a monoclonal Ab to the F1 antigen. Samples containing 5 ng/ml F1 could be assayed within 30 min. Using an antibody to the protective protein co-expressed with the anthrax toxins (Wijesuriya et al., 1994) have shown that a device based on this approach could be used as a clinical diagnostic tool for the presence of anthrax. In the same report, an approach simpler than the fluorescent-labeled antibody sandwich format was demonstrated. Cells in a sample were first treated with a dye (Nile Red) which fluoresces only when incorporated into a lipid membrane. A specific monoclonal antibody to the cell surface antigens was used as the capture antibody and 3 × 103 cells/ml of B. anthrax was detected. Pyle et al. (1995) utilized the fluorescent antibody technique followed by incubation with cyanoditolyl tetrazolium chloride (CTC) to detect respiratory activity. After capture of the bacteria and incubation with CTC the fluorescein conjugate was added and bacteria enumerated. Using this technique it was possible to detect E. coli O157:H7 in the range of 105 –109 CFU/ml with an assay time of 4 h. This technique was also used for detecting S. typhimurium and Klebsiella pneumoniae.


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assay time was one hour. The sensitivity of the IMAS for Bacillus anthrax spores, E. coli O157:H7 and S. typhimurium detection is approximatly 100 cells/ml in PBS and 1000 cells/ml in biological samples. Decreased sensitivity of the IMAS detection in biological samples was due to sample interference.

Fig. 3. A diagram of the IMAS. Samples are initially processed by the IMAS, then analyzed by an ECL, a fluorimeter and a flow cytometer. Adapted with permission from Yu and Stopa (1995).

Fig. 4. Schematic representation of bioelectrocatalysis based on an oxidoreductase in a cytoplasmic membrane of a microorganism. S, substrate; P, product; Mox and Mred oxidized and reduced forms of a mediator respectively; DH, oxidoreductase. Adapted with permission from Takayama et al. (1993).

Chowdhury et al. (1995) used a similar technique for detecting Vibrio cholera O1 and O139. V. cholera cells were incubated with a yeast extract in the presence of nalidixic acid. This caused the substrate responsive, viable cells to elongate and enlarge and were readily detectable using a fluorescent antibody. Using this technique it was possible to detect 109 cells/ml of V. cholera with an assay time of at least 6 h. An immunomagnetic assay system (IMAS) has been developed for detection of virulent bacteria in biological samples (Yu and Stopa, 1995; Yu and Bruno, 1996; Vernozy-Rozand et al., 1997). The IMAS includes a magnetic separator for capturing the antigen and an electro-chemiluminescent detector. In addition, IMAS was coupled to a flow cytometer and to a continuous fluorimeter (Fig. 3). This approach, like other chemiluminescence techniques, offers high signal-to-background ratios and is comparable in sensitivity to radioisotopic methods but has the advantage over other chemiluminescence techniques of being initiated by a voltage potential and thus providing better-controlled luminescence (Yu and Bruno, 1996). In the fluorescence sandwich immunoassay, biotinylated antibody plus streptavidin-coated magnetic beads and fluorescein-conjugated antibody were used for measurement. The total

2.2.2. Microbial metabolism based biosensors Microorganisms are able to transduce their metabolic redox reactions into quantifiable electrical signals by oxidoreductase reactions and an appropriate mediator as illustrated in Fig. 4 (Takayama et al., 1993). Many possible mediators have been used for characterization of microbial respiratory chain components (Kalab and Skladal, 1994). Thus, the microbial content of a sample can be determined by monitoring microbial metabolism. To date, various combination of biosensors based on the monitoring of microbial metabolism have been reported (Wilkins, 1978; Matsunaga et al., 1979; Holland et al., 1980; Turner et al., 1986; Libby and Wada, 1989; Nakamura et al., 1991; Jouenne et al., 1991; Ding et al., 1993; Hitchens et al., 1993; Takayama et al., 1993; Gehring et al., 1996; Perez et al., 1998). The transducer can either detect consumption of oxygen or the appearance/disappearance of an electrochemically active metabolite. Wilkins (1978) and Wilkins et al. (1978) described a method for monitoring the redox potential generated at a platinum electrode as a result of the microbial oxidation of electroactive substances in the supporting medium. This direction was continued in the works of (Holland et al., 1980) and (Junter et al., 1980). They showed that direct potentiometric detection may be applicable to a wide range of bacteria. However, the response times for the detection of 106 cells/ml varied from 2 to 4 h and this simple system suffered from low sensitivity due to the high background noise. Since the detection time relies upon microbial growth, this approach cannot provide a real-time analysis and pre-enrichment steps are required. Takayama et al. (1993) demonstrated mediated electrocatalysis based on the bacterium Gluconobacter industrius. Bacteria was immobilized on the surface of carbon paste electrode containing p-benzoquinone (BQ) which worked as a catalyst to oxidize D-glucose in the presence of BQ according to the scheme in Fig. 4. It was shown that the bioelectrocatalytic behavior of the G. industrius–BQ– electrode system is very similar to that observed with a glucose oxidase–BQ–electrode system. Glucose dehydrogenase (GDH), an enzyme present in the bacterial cell membrane, was the catalyst for producing the BQ-mediated electrocatalytic current. A current magnitude as high as 200 mA/cm2 was obtained with 2.4×106 cells in the presence of BQ and 10 mM glucose. A steady-state current was attained in about 30 s. Takayama et al. (1993) demonstrated that mediators such as K3Fe(CN)6 and dichlorophenol indophenol

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were capable of accepting electrons from GDH or from some other part of the respiratory chain in the cell membrane. Recently, bioelectrochemical instrumentation has been developed (Ding et al., 1993) for rapid determination of E. coli using a flow-injection system. Electrochemical measurement of a mediator, K3Fe(CN)6, reduced by microbial metabolism allowed the determination of fungi and bacteria in 20 min. E. coli was determined in the range of 4.7× 106 – 2.4 ×109 CFU/ml when the microorganisms were not separated from the cultivation medium. As the mechanism of microbial mediator reduction is not yet fully understood, the application of this approach has to be investigated further (Ding et al., 1993). Hitchens et al. (1993) describe a method for enumerating microorganisms which combines electrochemical detection with flow injection analysis. This method is based on the measurement of electrical currents generated by active microbes in the presence of redox mediators (i.e. low molecular weight electron acceptors) that can diffuse through the bacterial cell membrane. Analysis of samples could be performed in 10 min at a lower detection limit of 105 cells/ml. The results also provided new insights into factors that limit the lower limits of sensitivity of the mediated amperometric detection system. Electrochemical biosensors based on the Clark-type oxygen electrode have been introduced for the rapid determination of E. coli, S. aureus and Enterococcus serolicida in a biological sample (Endo et al., 1996). The cell suspension was filtered through a cellulose nitrate membrane (pore size, 0.45 mm). The membrane along with the captured cells was set on the platinum working cathode of a Clark oxygen electrode and covered with a dialysis membrane. The microbial electrode was immersed in 0.05 M phosphate buffer until the output current became stable. The electrode was then taken out and placed in a solution containing sodium azide, which suppressed the growth of most microorganisms except E. serolicida. A linear relationship was obtained in the range of 1.4 × 107 –7.2×107 cells/ml with an assay time of 2 h. However, the linear range of the oxygen electrode is limited because of low oxygen concentrations. Also, the response fluctuated as a result of variations in circulating oxygen concentrations. The main drawbacks of biosensors based on the monitoring of microbial metabolism are related to their poor selectivity and slow response times. This type of biosensor can only be used for well-defined samples because of the possible presence of enzymes from sources other than the bacteria of interest.

2.2.3. Electrochemical immunodetection of bacteria Electrochemical sensors have some advantages over optical-based systems in that they can operate in turbid media, offer comparable instrumental sensitivity, and

are more amenable to miniaturization. Modern electroanalytical techniques have very low detection limits (typically 10 − 9 M) that can be achieved using small volumes (1–20 ml) of samples (Jenkins et al., 1988). Furthermore, the continuous response of an electrode system allows for on-line control and the equipment required for electrochemical analysis is simple and cheap compared to most other analytical techniques. The recently developed light addressable potentiometric sensor (LAPS) based on field effect transistor (FET) technology has proved to be highly successful for immunoassay of bacteria (Libby and Wada, 1989; Thompson and Lee, 1992; Lee et al., 1993a,b; Menking and Goode, 1993; Gehring et al., 1998). A LAPS consists of n-type silicon doped with phosphorous and an insulating layer in contact with an aqueous solution where the immunoreaction takes place. The difference between the charge distribution at the surface of the insulating layer and a FET is used to detect changes in the potential at the silicon-insulator interface. A LAPS measures an alternating photocurrent generated when a light source, such as a light emitting diode (LED) flashes rapidly (Fig. 5). The photocurrent can only be measured on these discrete zones where the sensor is illuminated. Thus LAPS may measure local changes by multiplexing the LED and consequently measuring different analytes simultaneously using a single sensor (Owicki et al., 1994). Based on this principle a device called Threshold (TM) from Molecular Devices is on the market. (Libby and Wada, 1989) used a LAPS in an immunofiltration procedure for the detection of the pathogenic bacteria, Neisseria meningitidis and Y. pestis. The bacteria were captured by filtration on either polycarbonate or nitrocellulose membranes, through which solutions containing the respective monoclonal antibodies labeled with peroxidase were then filtered. The enzyme activity was monitored in the LAPS reading chamber in the presence of peroxidase substrates. In the case of N. meningitidis 103 cells could detected in 20 min, whereas a 2.5 h ELISA with the same reagents detected 6× 104 cells. The LAPS has also been used to detect Brucella militensis (Lee et al., 1993a,b), Francisella tularensis (Thompson and Lee, 1992), Coxiella burnetti (Menking and Goode, 1993) and E. Coli (Gehring et al., 1998). For B. militensis the lower detection limit was 6 × 103 cells and 3.4 × 103 cells for F. tularensis during an incubation time of 1 h. Although FET-based devices offer improvements to potentiometric monitoring of bacteria, there are several problems associated with these devices such as light sensitivity of the materials used in their construction, poor reproducibility and selectivity. Almost all microorganisms can now be sensed amperometrically by their enzyme-catalyzed electrooxidation/electroreduction or their involvement in a bioaffinity reaction. Because amperometric detection is


a heterogeneous process of electron transfer, electrochemical measurements at electrode interfaces are easier to execute in very small volumes than optical measurements. Suitable electrode materials for amperometry are noble metals, graphite, modified forms of carbon (carbon paste, glassy carbon, pyrolytic graphite), and conducting polymers. Ag/AgCl is the most common reference electrode. Interest in miniaturized electrochemical biosensors and the development of inexpensive and disposable sensors has led to the application of thick- and thin-film technology in the manufacturing of biosensors. Amperometric biosensors have the advantage of being highly sensitive, rapid, and inexpensive (Ghindilis et al., 1998). Amperometric systems have a linear concentration dependence compared to a logarithmic relationship in potentiometric systems. This makes amperometric immunosensors well suited for bacterial assay. Amperometric immunosensors aimed at microbial analysis have recently been reported (Mirhabibollahi et al., 1990; Nakamura et al., 1991; Brooks et al., 1992; Kim et al., 1995; Brewster et al., 1996; Rishpon and Ivnitski, 1997). In the work of Nakamura et al. (1991), an electrode system consisting of a basal-plane pyrolytic graphite (BPG) electrode and a porous nitrocellulose membrane filter to trap bacteria was used for the detection of bacteria in urine. The peak current of a cyclic voltammogram increased with increasing initial cell concentration of E. coli in urine. Urine containing 5× 102 –5×105 cells/ml was measured with this system. The susceptibility of bacteria to various antibiotics was also determined from the peak current. Mirhabibollahi et al. (1990), Brooks et al. (1990) and Brooks et al. (1992) utilized an enzyme-linked amperometric im-

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munosensor for the detection of S. aureus and Salmonella in pure cultures and in foods. This immunosensor could detect 104 –105 CFU/ml of S. aureus. However, the electrochemical detection step was awkward to perform, and there were variations in the signals produced by different strains of bacteria. This approach was modified in a later work by the same authors (Brooks et al., 1992) utilizing alkaline phosphatase as the enzyme-marker and phenyl phosphate as the substrate followed by the amperometric detection of phenol. They also proposed another system which incorporated an enzyme amplification step and relied on the amperometric detection of reduced mediator (ferrocyanide). Both systems were able to detect low numbers (1–5 CFU/g or per ml) of Salmonella in food after non-selective (18 h) and selective (22 h) enrichment steps. Kim et al. (1995) described a novel liposome-based amperometric biosensor for the detection of haemolytic microorganisms. The potential of this approach was illustrated for detection of various strains of L. monocytogenes, Listeria welshimeri and E. coli. Bacterial concentrations were determined in the range of 4.7× 106 –2.4×109 CFU/ml. Recently immunomagnetic beads have been applied in immunoelectrochemical assays for the detection of S. typhimurium (Brewster et al., 1996; Gehring et al., 1996). This technique combines the selectivity of antibody-coated superparamagnetic beads with the rapidity and sensitivity of electrochemical detection of bacteria in a format termed enzyme-linked immunomagnetic electrochemistry. In this case, heat-killed S. typhimurium were sandwiched between antibody-coated magnetic beads and an enzyme-conjugated antibody (Fig. 6). With the aid of a magnet, the beads were localized onto the surface of

Fig. 5. Basic schematic of silicon field effect potentiometric sensors. (a) Configuration of a capacitor. The capacitance changes as a function of the potential applied. (b) Schematic representation of a light-addressable potentiometric sensor (LAPS). An alternating photocurrent is generated when light emitting diodes flash rapidly. Adapted with permission from Marco and Barcelo (1996).

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Fig. 6. Schematic representations of enzyme-linked immunomagnetic colorimetric (ELIMC) and electrochemical (ELIME) assays. AP, alkaline phosphatase; pNPP, p-nitrophenyl phosphate; pNP, p-nitrophenol; pAPP, p-aminophenyl phosphate; pAP, p-aminophenol; and pQI, p-quinone imine. Adapted with permission from Gehring et al. (1996).

disposable graphite ink electrodes in a multi-well plate format. After magnetic separation, the liquid was removed by aspiration and 200 ml of p-aminophenylphosphate (2.7 mM in 0.2 M Tris, pH 9.6) was added to the electrochemical cell and p-aminophenol, the product of the enzymatic reaction, was measured using square wave voltammetry. Using this technique, a minimum of 8× 103 cells/ml of S. typhimurium in buffer was detected in :80 min. However, these immunoassays are both labor intensive and time-consuming due to the many washing steps. Also, complex instrumentation is required for their automation.

The biosensors presented so far are characterized by a lengthy analysis time and are greatly lacking sensitivity. It has been demonstrated that diffusion to the interface of the electrode surface is a rate-limiting step during heterogeneous electrochemical immunoassays (Gorovits, et al., 1993). Due to this diffusion control, the time required for achieving reaction equilibrium between the immobilized antibody and the antigen in solution is usually on the order of several tens of minutes. The general approach to achieve significantly short immunoassay times is to reduce transport limitations, across the unstirred layer of solvent, to the solid surface. The acceleration of the diffusion-controlled rate of immunological and enzymatic reactions on the solid-solution interface has been accomplished by: intensive mixing of the solution (liquid phase); the utilization of highly dispersed carbon-based immunosorbents as electrode materials (Krishnan et al., 1996); or by using cascade schemes, where the enzyme label is linked catalytically to other enzymes (Litman et al., 1980; DiGleria et al. 1989; Duan and Meyerhoff, 1994; McNeil et al., 1995; Ivnitski and Rishpon, 1996; Ivnitski et al., 1998). A ‘pseudo-homogeneous’ amperometrical immunoassay (without a washing step) was also developed for S. aureus (Rishpon and Ivnitski, 1996, 1997) and its configuration is presented in Fig. 7. The amplification of the analytical signal was achieved by combining enzyme-channeling reactions, optimizing hydrodynamic conditions, and electrochemical regeneration of mediators within the membrane layer of an anion-exchange polyethylenimine –glucose oxidase–antibody modified electrode. The immunosensor enables preferential measurement of surface-bound conjugate

Fig. 7. Separation-free enzyme channeling immunoassay for Staphylococcus aureus. Reaction scheme at the electrode surface includes (i) capturing of the target microorganism by an anti-protein A antibody immobilized on the electrode; (ii) sandwich formation by the peroxidase (HRP) labeled anti-protein A antibody; (iii) in situ generation of hydrogen peroxide by glucose oxidase (GOD) co-immobilised on the electrode to react with amino-salicyclic acid (catalysed by the peroxidase label) and to give a quinone-imine which is reduced at the electrode generating cathodic current response.


relative to the excess enzyme-labeled reagent in the bulk sample solution. S. aureus cells were detected in pure culture at concentrations as low as 1000 cells/ml in a relatively short assay time of 30 min. A novel approach based on using partially immersed immunoelectrodes has been demonstrated for fast and sensitive immunoassay of E. coli O157:H7 (AbdelHamid et al., 1998a). Immunoelectrodes were operated while being partly immersed in the solution resulting in the formation of a supermeniscus on the electrode surface. This supermeniscus is characterized by a thickness of : 0.4–0.8 mm and plays an important role in providing optimal hydrodynamic conditions for the current generation process. The sensitivity of the partially immersed electrode was six folds greater than that of the fully immersed one. The partially immersed immunoelectrode allows determination of E. coli cell concentrations in the range of 150 – 7000 cells/ml. Since the magnitude of the diffusionally limited current is inversely proportional to the thickness of the diffusion layer, the enhanced sensitivity effects observed with the partially immersed electrodes may be explained by facilitated diffusion of analyte, conjugate and substrate molecules to the electrode surface in the upper part of the meniscus and in the supermeniscus where the electrode, electrolyte and gas phase meet. This new immunoassay approach can be easily extended to the detection of other bacterial cells and may be a basis for creating new, highly sensitive and rapid immunosensors.

2.3. Flow immunosensors Most microbial assays are currently based on solidphase enzyme-linked immunoassays (ELISA) using microtitration plates. This is a powerful analysis tool used in biomedical research due to its high reproducibility and possibility to simultaneously conduct a large number of assays (Hock, 1996). However, disadvantages of heterogeneous ELISA methods include the small sample volume (200 ml) that the microtitration plate holds and the long incubation time required for each ELISA step. Also, the sensitivity of ELISA methods is insufficient for direct measurement of bacteria and other microorganisms in the original samples. Because low numbers of pathogenic bacteria are often present in a biological sample, an analytical standard often used for pathogenic bacteria is to detect cells in 25 g of food (Wyatt, 1995). Obviously, it is not possible to put a 25 g sample directly in a microtitration plate. Therefore, in many situations and in order to increase assay sensitivity, it would be desirable to concentrate the bacteria into a smaller volume prior to the assay or by growing a single cell into a colony. Several possible formats for concentrating cells in analytical systems were described by Wyatt (1995). The most attractive technique for the

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concentration of bacteria is membrane filtration in conjunction with flow systems (Duverlie et al., 1992; Brakstad and Maeland, 1993; Clark et al., 1993; Valcarcel and DeCastro, 1993; Paffard et al., 1996; Abdel-Hamid et al., 1999a). This procedure, called the flow immunofiltration assay, can be an excellent alternative for detection of bacterial pathogens because it not only overrides the effects of diffusional limitations, but also allows the concentration of bacteria on the membrane by filtering a large volume of the sample. Heterogeneous flow immunofiltration assays offer extremely accelerated binding kinetics (Ijsselmuiden et al., 1989; Morais et al., 1997). First of all, there is a high surface area to volume ratio in the immunosorbent. Second, the flowing stream actively brings the sample in contact with the solid-phase antibody. This factor results in a greatly enhanced antigen-antibody encounter rate and in nearly quantitative immunobinding during the short time of the immunoreaction. This approach has also dramatically increased the potential for automation of immunoassays. Clark et al. (1993) described an apparatus for use in an enzyme linked immunofiltration assay (ELIFA) which incorporated a peristaltic pumping system allowing continuous filtration of reagents through a nitrocellulose membrane clamped between two 96-well plates. Upon completion of the immuno- and enzymatic reactions, the chromogenic reaction product from each well was collected in a microtiter plate and quantitatively determined using an ELISA reader. This method was further developed for the detection of E. coli (Paffard et al., 1996). Quantitative bacterial detection was based on precipitated chromogen determined by scanning densitometry and the ELIFA method was capable of detecting 103 bacterial cells within 40 min. A very interesting flow injection immunosensor has been developed for the determination of E. coli in artificially contaminated food samples (Bouvrette and Luong, 1995). This approach was based on direct noncompetitive heterogeneous immunoassay of E. coli cells with an antibody column and a fluorescence detector. The advantage of this method is that bacterial concentrations can be determined without using any labeled compounds. The technique is based on the direct detection of the cell’s b-D-glucuronidase activity (GUD). Owing to the specificity of the antibody towards E. coli, the immunosensor was selective for detection of E. coli in the presence of Shigella boydii and another GUDpositive bacterium. However, the detection limit for E. coli was on the order of 5× 107 CFU/ml which is less than the detection limit of the standard ELISA procedure for microbial cells (typically 106 CFU/ml). Another promising format of immunoassay is based on the use of flow injection systems and antibodycoated magnetic particles. This technique can be easily automated, the analyses performed quickly and contin-

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uously and the renewal of the sensing surface of immunosensor was easily accomplished. In order to regenerate the immunoreagent, the magnet was removed and the magnetic particles were washed down. At that moment, the immunosensor was ready for injection of new antibody modified magnetic particles for another analytical cycle. The system has been applied to detect B. anthrax spores, E. coli O157 and S. typhimurium (Yu and Stopa, 1995; Brewster et al., 1996; Gehring et al., 1996; Yu and Bruno, 1996; Vernozy-Rozand et al., 1997; Brewster and Mazenko, 1998; Perez et al., 1998). This format offers several advantages for automatic immunoassays: the many required washing steps are inherent and the immunoassays can be carried out rapidly and can be automated easier than formats using tubes, microtiter plates or other similar reaction vessels. A continuous configuration of immunoassay used in conjunction with a biosensor has such important properties as (Valcarcel and DeCastro, 1993): transferring the injected or aspirated sample to the sensor; conditioning the sample (pH adjustment, mixing with other reagents, masking) for optimal development of the reaction and detection that is to take place at the sensor surface; regeneration of the sensor between samples; facilitating reliable calibration and increasing the sensor selectivity and sensitivity via a continuous separation module; boosting precision through reduced human participation in biosensor-related operations. The typical output of a flow-injection system is a peak that results from the dispersion of the injected sample. Flow immunosensors have been applied in a different fields of medicine, biotechnology, environmental and bioprocess monitoring (Schmid, 1991; Ding et al., 1993; Valcarcel and DeCastro, 1993; Bouvrette and Luong, 1995; Puchades and Maquieira, 1996; Lu et al., 1997; AbdelHamid et al., 1998b; Ghindilis et al., 1998). Several semi-automated systems have been developed for microorganism identification. A flow-injection immunoanalysis system has recently been used to detect E. coli in artificially contaminated food samples (Bouvrette and Luong, 1995). The flow-injection system equipped with a 5 ml flow cell consisted of a peristaltic pump, antibody column and fluorescence detector. Anti-E. coli antibodies were covalently immobilized onto porous aminopropyl glass beads. The system successfully detected E. coli in 30 min and was reusable for at least 300 repeated assays and the detection limit was on the order of 5×107 CFU/ml. A disadvantage of the generally used regeneration methods is that they use relatively aggressive chemicals such as 8 M urea, 0.2 M glycine-HCl (pH 2.8) or 0.2 M ethanolamine (pH 8). Treatment of the antibody modified surface of the biosensor with harsh chemicals did not completely remove the bound bacteria and partial desorption of the immobilized antibody occurred during each regeneration cycle, resulting in a decrease in sensitivity. Perez et

al. (1998) proposed amperometric flow-injection system for measuring of viable E. coli O157. This system is based on the selective immunological separation of a bacterial strain and the generation of a signal by bacterial cells. For the immunological step, immunomagnetic beads were selected as the immunocapture reagent. Electrochemical detection was carried out using redox mediators [potassium hexacyanoferrate (III) and 2,6dichlorophenolondophenol]. The detection limit was 105 CFU/ml, and the complete assay was performed in 2 h. The basic advantages of this system and the system presented by (Bouvrette and Luong, 1995) are the need for only unlabeled antibodies and the ability of detecting viable cells. Recently, a flow-injection amperometric immunofiltration assay system has been developed for rapid detection of E. coli O157:H7 (Abdel-Hamid et al., 1999a). A schematic of the flow-through immunosensor is presented in Fig. 8. The immunosensor consists of a disposable antibody-modified filter Nylon membrane resting on top of a working carbon electrode. The buffer flows through the filter membrane and then through the hollow channel in the working, counter and reference electrodes respectively. By combining the flow-injection amperometric system with an immunofiltration technique, a high immunoassay sensitivity and a substantial reduction in the time required to detect E. coli O157:H7 was achieved. The amperometric immunosensor allows the detection of E. coli cells with a lower detection limit of 50 cells/ml and an overall analysis time of 40 min. The immunosensor can be easily adapted for assay of other microorganisms and was further developed for detection of total E. coli and total Salmonella. A detection limit of 50 cells/ml with an analysis time of 35 min was achieved (Abdel-Hamid et al., 1999b). This approach offers a number of advantages over the ELISA, including greater antibody binding capacity, higher sensitivity, easier discrimination between specific and non-specific signals, reduction of assay time and simpler operation.

2.4. Genosensors Gene probes are certain to play an increasingly important role in health care, agriculture and environmental monitoring (Mikkelsen, 1996; Wang et al., 1997a,b,c; Zhai et al., 1997; Zhu and Wang, 1997). Military applications of gene probes are associated with ultrasensitive determination of microorganisms, viruses (biological warfare) and trace amounts of special chemicals in various environments. Gene probes are already finding applications in detection of disease-causing microorganisms in water supplies, food, or in plant, animal or human tissues (Tenover, 1988; Highfield and Dougan, 1992; Kapperud et al., 1993; Sharpe, 1994; Tietjen and Fung, 1995; Feng, 1996; Zhai et al., 1997).


Fig. 8. Scheme of the processes in a flow-through immunosensor for rapid detection of bacteria: (a) the immunofiltration membrane with antibodies and the flowing of the sample to be analysed; (b) immobilized antibodies capture bacterial cells and flowing of the conjugate solution; (c) formation of the immuno-complex, flowing of the peroxidase substrates and amperometric signal generation.

Clinical applications of gene probes are another prime area of intensive development (Zhai et al., 1997). The term nucleic acid (gene) probe describes a segment of nucleic acid which specifically recognizes, and binds to, a nucleic acid target. The recognition is dependent upon the formation of stable hydrogen bonds between the two nucleic acid strands. This contrasts with interactions of antibody-antigen complex formation where hydrophobic, ionic and hydrogen bonds play a role. The bonding between nucleic acids takes place at regular (nucleotide) intervals along the length of the nucleic acid duplex, whereas antibody-protein bonds occur only at a few specific sites (epitopes). The frequency of bonding is reflected in the higher association constant

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for a nucleic acid duplex in comparison with an antibody-protein complex and indicates that highly specific and sensitive detection systems can be developed using nucleic acid probes (McGown et al., 1995; Skuridin et al., 1996). The specificity of nucleic acid probes relies on the ability of different nucleotides to form bonds only with an appropriate counterpart. Since the nucleic acid recognition layers are very stable, an important advantage of nucleic acid ligands as immobilized sensors is that they can easily be denatured to reverse binding and then regenerated simply by controlling buffer-ion concentrations (Graham et al., 1992). The detection of specific DNA sequences provides the basis for detecting a wide variety of bacterial pathogens. The original DNA hybridization test for bacteria in foods used a radioactively labeled probe (Feng, 1992). The main disadvatages of radiolabelled probes are the short-shelf life of 32 P-labelled probes, high cost, hazards, and disposal problems associated with radioactive waste. The limitation of nucleic acid probes is also a problem associated with cultivating bacteria to a detectable level. Hybridization requires the presence of at least 105 –106 bacteria in the sample to obtain a positive signal. Therefore, without pre-enrichment of the target organism, DNA hybridization approach does not provide the required sensitivity to detect bacteria at required level (Tietjen and Fung, 1995). However, progress of a gene amplification method (the polymerase chain reaction) extremely enhances the sensitivity of DNA probes, at least three orders of magnitude (Sailki et al., 1985). This technique uses the heat-stable DNA polymerase of Thermus aquaticus, and allows short lengths of a double-stranded target DNA (template) to be copied in vitro thousands or millions of times, very quickly. According to Jones et al. (1993) a PCR-gene probe based assay has high potential for improving monitoring of foodborn bacteria. To date only methods involving the PCR have been employed to detect foodborne pathogens. The PCR method is an extremly specific and sensitive method. Bacteria can be detected directly, without cultivation, by extraction and isolation of nucleic acids from real samples, followed by hybridization with specific probes. Without any enrichment steps, the PCR method detects less than 40 cells/gram of a given food sample (Tietjen and Fung, 1995).Since sensitivity is not a limiting factor, a promising alternative way to conduct nucleic acid based assays is by using non-radioactive labelled probes, which is associated with the development of biosensor technologies (Wang et al., 1997a,b,c; Zhai et al., 1997; Zhu and Wang, 1997). Two aims of biosensor assay development should be emphasized: (i) an improvement over conventional nucleic acid assay (gene probe) performance and (ii) the design of special gene probe techniques for special applications under special conditions. Based on the nature of the physical detection principle used in

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the transducer, genosensing systems can be classified as optical, gravimetric and electrochemical. The principles and applications of the electrochemical DNA biosensors were described and discussed in a number of reviews (Mikkelsen, 1996; Wang et al., 1997a,b,c). Pathogens responsible for disease states, bacteria and viruses, are detectable via their unique nucleic acid sequences. Recently DNA hybridization electrochemical biosensor for the detection of DNA fragments to the waterborne pathogen Cryptosporidium have been developed (Wang et al., 1997a,b,c). The sensor relies on the immobilization of an oligonucleotide unique to the Cryptosporidium DNA onto the carbon-paste transducer, and employs a highly sensitive chronopotentiometric transduction mode for monitoring the hybridization event. Very short (3 min) hybridization periods give rise to well defined hibridization signals at mg/ml concentrations of the Cryptosporidium target, while longer (20–30 min) ones permit ng/ml detection limit. Similar hybridization/chronopotentiometric schemes are currently being developed for other pathogens, such as E. coli, Giardia and Mycobacterium tuberculosis (Wang et al., 1997a,b,c). The ultimate goal of this research is to design an array of microelectrodes on a chip for the simultaneous field monitoring of multiple pathogens in water supplies. Evanescent wave methods of total internal reflection fluorescence (TIRF) (Graham et al., 1992) and LAPS (Hafeman et al., 1988) were described as labeling methods for DNA assay. Direct (labelless) monitoring of hybridization reactions has been demonstrated with surface plasmon resonance (SPR) (Pollard-Knight et al., 1990; Schwarz et al., 1991) and PZ acoustic wave devices (Wu et al., 1990; Andle et al., 1992). Two commercially available optical sensors, both based on evanescent wave technology, have been used for detection of DNA –DNA interactions. The Biacore system (Pharmacia, Sweden) uses SPR which arises in thin metal films under conditions of total internal reflection. In the sensing element of this instrument, a gold transducer surface is modified with a dextran matrix on which the biological probe is immobilized (often via avidin – biotin links). Oligonucleotides are introduced within a fluid flow system. Hybridization is carried out at room temperature and positive signals are obtained within several minutes. A similar optical sensor, the IAsys system (Affinity Sensor, UK), has been introduced where the gold film is replaced by titania or hafnia, acting as a dielectric resonant layer of high refractive index (resonant mirror). The sensing surface is modified with either a derivatized dextrin matrix or an aminosilane. The resonant mirror method has been used to detect DNA hybridization with an estimated limit of detection in the femtomolar range. Regeneration of the surface-immobilized probe was possible, allowing reuse without a significant loss of hybridization activity (Titball and Squirrell, 1997).

Two types of hybridization are currently used: (i) pseudo-homogeneous hybridization, which can be achieved in systems with high surface-to-volume ratio, such as porous membranes and highly dispersed immobilization carriers; (ii) solid-phase hybridization, which is preceded by transfer to a membrane. The main disadvantage of solid-phase relative to pseudo-homogeneous hybridization is the longer time required and the need for several manipulations (Aubert et al., 1997; Yang et al., 1997). In this case engineering approaches for conducting the hybridization process in pseudo-homogeneous conditions can be applied. Conducting the hybridization reaction in a constant flow mode (in flow-through sensor systems as a variant of flow-injection assay) facilitates elimination of transport restrictions. The development of enzyme-linked immunofiltration assay technique for rapid detection of Toxoplasma gondii DNA in real samples was described (Aubert et al., 1997). Hybridization technqiues are being currently formatted for a relatively fast (ca. 24–48 h) identification of bacteria (Avanissaghajani et al., 1996; Goh et al., 1997; Guschin et al., 1997). However, the DNA hybridization method has a number of problems. This method is not straightforward, complicated (multistep assay) and time-consuming. There is also a problem of false amplifications.

2.5. The electronic nose ‘Electronic nose’ systems have advanced rapidly during the past 10 years, the majority of applications being within the food and drink industry (Gardner and Bartlett, 1992; DiNatale et al., 1997; Kress-Rogers, 1997; Haugen and Kvaal, 1998; Schaller et al., 1998). Electronic nose systems comprise sophisticated hardware, with sensors, electronics, pumps, flow controllers, software, data pre-processing, statistical analysis (Schaller et al., 1998). The sensor array of an electronic nose has a very large information potential and responds to both odorous and odourless volatile compounds. In the electronic nose the signal pattern from a sensor array, comprised usually of individual sensing elements with limited specificity, is collected by a computer, where a first pre-treatment of the data is carried out. These data are then further processed by suitable software based on artificial neural networks approach for training and learning (Winquist et al., 1993). A major effort is required to define the mathematical relationship that describes the application, and to validate the model obtained (Blixt and Borch, 1999). A series of electronic noses have been produced commercially during the last few years (Gardner and Bartlett, 1994; Haugen and Kvaal, 1998). The theoretical basis and the practical application of the electronic noses have been illustrated in a number of reports (Gardner and Bartlett, 1992, 1994; Gibson et al., 1997; Kress-


Rogers, 1997; Haugen and Kvaal, 1998; Wit and Busscher, 1998). At present, different detection principles (heat generation, conductivity, electrochemical, optical, dielectric and magnetic properties) are used in the basic sensing elements of the electronic. The ideal sensors to be integrated in an electronic nose should fulfil the following criteria (Schaller et al., 1998): high sensitivity (down to 10 − 12 g/ml), they must respond to different compounds present in the headspace of the sample; high stability and reproducibility; short recovery time; easy calibration; they must also be robust and portable. Artificial electronic noses have been used for measurement of beer quality (Pearce et al., 1993), meattaint (Bourrounet et al., 1995), ground meat (Winquist et al., 1993; Haugen and Kvaal, 1998) and the freshness of fish (Schweizer-Berberich et al., 1994). One of the first attempts to identify microorganisms with an electronic nose was made by Craven et al. (1994). An array of four different commercial metal oxide gas sensors was used to sample the head space of six pathogenic bacteria (Clostridia perfringens, Proteus, Haemophilus influenzae, Bacteroides fragilis, Oxford staphylococcus and Pseudomonas aeruginosa) grown in blood agar. In this test the four-element electronic nose was able to classify correctly 62% of the pathogens. Gibson et al. (1997) reported on the use of an array of 16 conducting-polymer resistive gas sensors to detect 12 different bacteria from cultures grown on agar plates. More recently, an investigation into the use of an electronic nose to predict the class and growth phase of two pathogenic bacteria, E. coli and S. aureus, has been performed by Gardner et al. (1998). The sample from the head space was passed into a sensor chamber which contained six commercial metal oxide odour sensors. The sensors were chosen based on the knowledge that secondary metabolites of growing microorganisms are hydrocarbons, alcohols, aldehydes, acids, ammonia and so on. The performance of 36 different pre-processing algorithms has been studied in the basis of nine different sensor parameters and four different normalization techniques. Authors demonstrated that the type of bacterium can be correctly predicted for 96% of all samples taken during a 12 h incubation period. The growth phase of the bacteria was correctly predicted for 81% of all unknown samples. In other work, Rossi et al. (1995) were able to discriminate between seven species of the bacteria Micrococaoceae that can be found in fermented meat products. The species investigated in culture were respectively four aromatic (Micrococcus and Staphylococcus) and three pathogenic bacteria strains. One hundred percent of the bacteria samples were classified correctly into their respective groups based on factorial discriminant analysis. Recently (Gibson et al., 1997; Haugen and Kvaal, 1998; Holmberg et al., 1998; Namdev et al., 1998) described the feasibility of using

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electronic noses for the following applications: monitoring lot-to-lot variation in bioprocess medium ingredients, detection and simultaneous identification of microorganisms, bacteria classification; and evaluating bioprocess performance during cultivation of microorganisms at inoculum and production stages. Data evaluation and classification have been made on measurements by an electronic nose on the headspace of samples of different types of bacteria growing on petri dishes (Holmberg et al., 1998). E. coli, Enterococcus sp., Proteus mirabilis, P. aeruginosa, and Staphylococcus saprophytica were selected for this study. An approximation of the response curve by time was made and the parameters in the curve fit were taken as important features of the data set. A classification tree was used to extract the most important features. These features were then used in an artificial neural network for classification. Using the ‘leave-oneout’ method for validating the model, a classification rate of 76% was obtained. The detection and simultaneous identification of a range of microorganisms by measuring the volatile compounds produced from plate cultures has been carried out using a neural network classifier (Gibson et al., 1997). Headspace samples were taken from static atmospheres formed from inoculated agar plates after a suitable growth period at 37°C and analysed using a standard 16 sensor array. The sensor array was made up of 16 different electroconductive polymer materials and was operated in a transient flow mode. Electropolymerisation of a variety of monomeric substrates selected from different heterocyclic compounds, such as substituted pyrroles, thiophenes and anilines was the basic method of sensor production. The response curves produced were processed using standard back propagation neural network techniques to provide identification. The overall classification rate for 12 different bacteria and one pathogenic yeast was 93.4%. Data for a sub-set of seven bacteria gave 100% classification using the same methods. Thus, an array of ‘conductive polymer’ sensors with different chemical sensitivities produces a set of different responses to the same odor. The responses are analyzed mathematically, using pattern recognition techniques, to differentiate between different odors with a high level of sensitivity. In microbiology the smell of a culture of bacteria often provides a clue to the identification of the organism present and it is usual for trained microbiologists to be able to identify microorganisms by smell alone (Gibson et al., 1997). A deterioration in food freshness is often associated with microbial spoilage. A large amount of different gaseous components are released from substrates contaminated with spoilage organisms. The traditional approach to characterize volatile compounds has been sample extraction followed by GC-MS analysis. This approach is very tedious and requires some knowledge of the molecules involved. Frequently,


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several variables such as pH value, acidity, carbohydrate, temperature, protein may all need to be kept within narrow target bands (Kress-Rogers, 1997). In this plane, an electronic nose can be applied either in monitoring factors influencing spoilage or in monitoring factors indicating spoilage. Blixt and Borch (1999) described the use of an electronic nose in the quantitative determination of the degree of spoilage of vacuumpackaged beef. Beef from four different slaughterhouses was sliced, vacuum-packaged and stored at 4°C for 8 weeks. Samples were withdrawn for bacterial analyses (aerobic bacteria, lactic acid bacteria, Brochothrix thermosphacta, Pseudomonas and Enterobacteriaceae) and analysis of the volatile compounds during the storage period. A trained panel was used for the sensorial evaluations. The volatile compounds were analysed using an electronic nose containing a sensory array composed of 10 metal oxide semiconductor field-effect transistors, four Taguchi type sensors and one CO2-sensitive sensor. Partial least-squares regression was used to define the mathematical relationships between the degree of spoilage of vacuum-packaged beef, as determined by the sensory panel, and the signal magnitudes of the sensors of the electronic nose. The mathematical models were validated after 6 months using a new set of samples. The stability of the sensors during this period was examined and it was shown that the sensitivity of five of the 11 sensors used had changed. Using the six remaining sensors, the signal patterns obtained from the meat from the different slaughterhouses did not change over a period of 6 months. It was shown that the degree of spoilage as calculated using a model based on two Tagushi sensors, correlated well with the degree of spoilage determined by the sensory panel. The spoilage of raw meat caused by microbiological pro-

cesses taking place during storage represents a great problem in the meat industry (Haugen and Kvaal, 1998). The method currently used for determining the status of meat, with respect to spoilage, is analysis of the total bacterial count. A drawback with the bacteriological method is the incubation period of 1–2 days that is required for colony formation. Instead, the growth of specific spoilage bacteria can be analysed. Chemical compounds such as acetate, ethanol, lactic acid, CO2 may be used as spoilage indicators in meat products (Dainty and Mackey, 1992; Dalgaard, 1995; Borch et al., 1996). Winquist et al. (1993) used an array of 10 MOSFET, five Taguchi (MOS) and one IR-based CO2-sensor, respectively, for measuring ground pork and ground beef during storage up to 8 days at 4°C. The storage time could be well predicted even with a reduced number of sensors by using artificial neural network.

2.6. Commercial instrumental systems A series of automated and semi-automated systems for microbiological analysis have been extensively described in a number of monographs, reviews and articles (Eden and Eden, 1984; Nelson, 1985; Turner et al., 1987; Nakata and Yoshikawa, 1990; Feng, 1992; Stager and Davis, 1992; Flint and Hartley, 1993; Fenselau, 1994; Sharpe, 1994; Tietjen and Fung, 1995; Hobson et al., 1996; Wang et al., 1997a,b,c; Zhai et al., 1997; Zhu and Wang, 1997). A list of commercial devices available for identifying bacteria is shown in Table 3. Cobra 2024 (Biocom, France) is one of the completely automated microscopic counting systems. It features three computers, attending to the sample preparation, staining, filtration, drying and image analysis. This system is capable

Table 3 Manufactures and/or developers of the commercial instruments for detection of bacteriaa Commercial instrument

Detection technique

Detection limit (cells/ml)

Analysis time (min, h)

Midas Pro (Biosensori SpA., Milan, Italy) The PZ 106 Immuno-biosensor System (Universal Sensors, New Orleans, USA) Bactometer (Bactomatic, Princeton, NJ, USA) Integrated Genetics, MA, USA Enzo Biochem, NY, USA

Amperometry Piezoelectric

106 106

20 min 40 min

Impedimetry DNA probe for Salmonella DNA probe for the bacterium Chlamydia DNA probe for bacteria Conductance Bioluminescence Bioluminescence Coulter counter Microcalorimetry Surface plasmon resonance Optical

105 1 cell/g –

3–8 h 2 days –

– 105 103 103 5×104 105 105 104

– 8–24 h 15 min 20 min 30 min 3h 1h 4h

Hybritech, CA, USA Malthus 2000 (Malthus Instruments, Stoke-on-Trent, England, UK) Unilite (Biotrace, Bridgend, UK) Lumac Biocounter (Lumac B.V., Schesberg, Netherlands) Coulter counter (Coulter Electronics, Canada) Thermal activity monitor (Thermometric, Northwich, Cheshire, UK) BIA-core (Pharmacia, Uppsala, Sweden) Vitek AutoMicrobic System (BioMerieux Vitek, Hazelwood, MO) a

Nelson, 1985; Feng, 1992; Tietjen and Fung, 1995; Hobson et al., 1996.


of detecting bacteria with a lower detection limit of 2× 104 CFU/ml with a throughput rate of 150 samples/ h (Sharpe, 1994). The AutoMicrobic System with the gram-negative identification card (GNI), the gram-positive identification card (GPI) and the Yeast Biochemical test kit (Vitek Systems, Hazelwood, MO) consists of a filling-sealer unit, a computer, an optical reader data terminal, and a multicopy printer. The GNI system correctly detected Salmonella, E. coli and other Enterobacteriaceae isolated from food samples (Knight et al., 1990; Stager and Davis, 1992) and confirmed identification by the GNI is reported in 4 – 18 h. In the review of Stager and Davis (1992), the analytical characteristics of the Vitek Systems were discussed in detail. Two commercial devices are currently available for preparing PZ immunosensors. The PZ 106 Immunobiosensor System (Universal Sensors, New Orleans, LA 70148, USA) contains a liquid flow cell and a computer program to make real time assays of biospecific interactions. The second is model QCA 917 (EG & G, Princeton Applied Research, Princeton, NJ, USA) designed for simultaneous electrochemical and weight measurements using a dip or a well holder. The impedance principle is applied to microorganism monitoring in commercial analytical systems such as the ‘Bactometer’ of BioMerieux Vitek, and ‘Bactobridge’ (Nelson, 1985; Swaminathan and Feng, 1994; Hobson et al., 1996). Bactometer and its modifications is the registered trademark for a series of automated systems for microbiological analysis that are made by Bactomatic (Princeton, NJ). These devices have been used for detection of microbial growth in blood, cerebrospinal fluid, and urine. The Bactometer incorporates an electronic analyzer-incubator, a microcomputer with specialized software, display and test card reports. The instrument can measure bacteria (after a preenrichment step) in 24–48 h when the bacterial concentration reaches 106 –107 cells/ml. The Bactobridge (TEM, Centronic Sales, King Henry’s Drive, New Addington, UK) uses a pair of special conductivity cells, which have well-matched capacitance and thermal properties. Each cell has a 100 ml volume containing gold-plated electrodes. The bridge is activated by a 10 KHz alternating current. Results are recorded on a computer and 103 CFU/ml can be detected within 3 h of incubation. Unlike the devices mentioned above, the Malthus Microbiological Analyzer (Malthus Instruments, Crawley, West Sussex, UK) and the Malthus M1000S from Radiometer America use conductance technology to estimate microbial populations including coliforms, lactic acid bacteria, fungi and yeasts (Feng, 1992; Gibson et al., 1992; Tietjen and Fung, 1995; Hobson et al., 1996). This analyzer detects changes in the electrical conductance of media caused by the growth and metabolism of microorganism. Salmonella positive samples can be detected in 24 h (which includes the preen-

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richment step) while negative tests require 40–46 h. The main problems of the impedimetric and conductimetric devices are their high cost of instrumentation and lengthy incubation times. The Lumac Biocounter, Netherlands and the ‘Unilite’, UK are developed for the estimation of microbial biomass based upon the bioluminescence principle. This approach is based on the fact that all microorganisms, except for viruses, contain ATP. The reaction is so specific for ATP that using a relatively crude preparation of luciferase, luciferin, and magnesium ion, a very specific and sensitive assay for microorganisms can be developed. Both analyzers can detect microorganisms in the range of 103 cells/ml in 10 min (Neufeld et al., 1985; Hobson et al., 1996). However, in the assay of milk and other biological samples, it is necessary to remove non-bacterial ATP present in somatic cells. More detailed information regarding automated system for microorganisms including comparison of features of automated identification systems can be found in reviews (Feng, 1992; Stager and Davis, 1992; Sharpe, 1994; Tietjen and Fung, 1995; Hobson et al., 1996).

3. Conclusions and future avenues Analysis of published literature has shown that despite the great R&D effort spent on developing biosensors in the last years, only a few biosensors for bacterial detection are commercially available or are approaching commercialization. The main reasons for this are both technology and market related. It is a challenge to create biosensors with the necessary properties for reliable and effective use in routine applications. The biosensor system must have the specificity to distinguish the target bacteria in a multi-organism matrix, the adaptability to detect different analytes, the sensitivity to detect bacteria directly, on-line without preenrichment and the rapidity to give real-time results. At the same time, the biosensor must have relatively simple and inexpensive configurations. Another obstacle is the tendency to focus only on the scientific basis of the technology while excluding the other equally important aspects. Research usually proceeds without a defined specification that is adhered to. There are a number of practical and technical issues which must be overcome in the development of bacterial biosensors for their commercialization. Table 4 illustrates the typical features of the ‘ideal’ biosensor. There is no biosensor system-to date-that has a bacterial specificty as that of the plate culture method, which is one of the crucial requirements of todays market. Obviously, enhancing the specificty of biosensor systems and incorporation of all the features in Table 4 within one bacterial biosensor device is a very complicated task. This is the main

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Table 4 A summary of the requirements for a bacterial biosensor Low detection limit

Ability to detect a single bacterial cell in a reasonably small sample volume (from 1 to 100 ml) Species selectivity Ability to distinguish an individual bacterial species in the presence of other microorganisms or cells Strain selectivity Ability to distinguish an individual bacterial strain from other strains of the same species Assay time 5–10 min for a single test Precision 1% Assay protocol No reagent addition needed Measurement Direct, without pre-enrichment Format Highly automated format (‘single button device’) Operator No skill needed to use the assay Viable cell count Should discriminate between live and dead cells Size Compact, portable, hand-held, design for field use

reason why the penetration of biosensors to the market is so slow. One of the problems facing the production of biosensors for direct detection of bacteria is the sensitivity of assay in real samples. The infectious dosages of pathogens such as Salmonella or E. coli O157:H7 is 10 cells and the existing coliform standard for E. coli in water is 4 cells/100 ml. The Environmental Protection Agency regulations specify the minimum frequency of water sampling and the maximum number if coliform organisms allowed; treated drinking water should contain no coliforms in 100 ml (Federal Register, 1991; Greenberg et al., 1992). Hence, a biosensor must be able to provide a detection limit as low as single coliform organism in 100 ml of potable water, with a rapid analysis time at a relatively low cost. Only in this case will the biosensor be convenient for on-line testing of bacterial pathogens in real samples. Thus, sensitivity is another issue that still requires improvement. There is also a problem of distinguishing between live and dead cells. Technical problems facing biosensor development include: the interaction of matrix compounds, methods of sensor calibration, the requirement for reliable and low maintenance functioning over extended periods of time, sterilization (particularly for clinical applications), reproducible fabrication of numerous sensors, the ability to manufacture the biosensor at a competitive cost, disposable format, and a clearly identified market. Bacterial biosensors should be easy to regenerate after each measurement while having reproducible results. Biosensors for bacteria should reduce human participation (to avoid contamination) and hence, automation must be an inherent attribute of the biosensor. Although biosensors available today are improvements over the original

formulations, they still represent the first generation of devices used to identify bacteria. It is our understanding that in the near future the second generation of biosensors will be fully automated analytical systems (Total Analytical Systems) based on combining of a multi-sensor technology with artificial neural network (as in the case of the electronic nose) or with other analytical and discriminative mathematical methods. The potential of the electronic nose approach within the food and drink industry, medical diagnostics and environmental control lies in the speed and simplicity of the method and also in the non-destructive determination of the sample. Artificial neural networks do not require any expert knowledge once programmed, and the only task of the operator is to indicate the objects to be recognized after which the network functions on its own. There are several markets which could support standalone biosensors. Highly sensitive and accurate biosensor systems could have great application in the medical diagnostics, food quality control, environmental monitoring, defense and other industries, particularly if biosensors could be designed such that multiple analytes could be detected simultaneously. The medical diagnostics field offers real opportunities for the exploitation of biosensors for bacterial detection. In fact, the opportunities for biosensors to enter into the clinical diagnostic market are wide open since few products have been commercialized. The most viable openings in the food industry will arise where a biosensor can rapidly detect total microbial contamination. The largest area of application for the environment lies in the development of biosensors for monitoring bacteria in drinking and waste water, rivers, reservoirs and supplies.

Acknowledgements The financial support of the DoE/Waste Management Education and Research Consortium of New Mexico is gratefully acknowledged. Dr. D. Ivnitski would like to thank the University of New Mexico School of Engineering for financial support.

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