Methodologies for the characterization of microbes in industrial environments: a review

  • Johanna Maukonen
  • Jaana Mättö
  • Gun Wirtanen
  • Laura Raaska
  • Tiina Mattila-Sandholm
  • Maria Saarela
Review Paper


There is growing interest in research and development to develop novel tools to study, detect, and characterize microbes and their communities in industrial environments. However, knowledge about their validity in practical industrial use is still scarce. This review describes the advantages and limitations of traditional and molecular methods used for biofilm and/or planktonic cell studies, especially those performed with Listeria monocytogenes, Bacillus cereus, and/or Clostridium perfringens. In addition, the review addresses the importance of isolating the microorganisms from the industrial environment and the possibilities and future prospects for exploiting the described methods in the industrial environment.


Biofilm Culture Molecular techniques Fingerprinting 


Microorganisms inhabiting the food and processing industries are mostly benign, but some can be harmful to the processing and safety of the product. Therefore, the control of harmful microorganisms is essential. Industrial processes that deal with any biological material provide nutrients and conditions for microorganisms to grow, either in the shelter of sessile biofilms on surfaces or as planktonic cells in the circulating process waters. Moreover, in most natural and industrial systems where the supply of nutrients is sufficient, microorganisms grow as spatially organized, matrix-enclosed, multispecies communities in biofilms. Besides a solid surface, the microbes need only water to initiate biofilm formation [72, 73, 270]. Microbial biofilms and biofouling of surfaces and interfaces within the industrial environment are major problems. In industry, the first step is identifying the problem of biofilms and biofouling in a particular process or site. Subsequently, it is important to determine the best possible methods for detection of biofilms in situ, so that they can be characterized and possibly further studied in the laboratory. Finally, this information can be used to define strategies for controlling biofilm formation in that specific environment [208].

Microorganisms in food and in industrial environments are distributed unevenly; and there is a great variation in the cell density and composition of microbial population over space and time. Typically, the microbial cells are located in the surfaces of the food matrix and process equipment; and the cell density and species distribution may vary in different parts of a food product [77, 156, 158]. Changes in the ecosystem cause continuous qualitative and quantitative variation in the composition of the microbial community over time [152]. Both intrinsic (e.g. chemical composition, natural microbiota) and extrinsic factors (e.g. processing, storage conditions) affect microbial growth [272] and consequently the composition of the microbial community. All these factors affect the actual sampling of the industrial environment, making it a demanding task to perform.

Published data on microbial detection and characterization from industrial environments is abundant and therefore the following review is restricted to the most relevant and widely applied techniques and to just a few specific bacterial species important in the food and process industrial environments, namely Listeria monocytogenes, Bacillus cereus, and Clostridium perfringens. Both traditional and molecular identification and characterization methods for bacteria and their communities are discussed, in addition to typing methods for bacterial isolates obtained from the industrial environment and/or foodstuff. Future prospects for the exploitation of the described methods in the industrial environment and industrial samples are also addressed.

Hygiene and safety problems in the industrial environment

Problems caused by bacterial biofilms

Biofilms protects microbes from hostile environments and act as a trap for nutrient acquisition [270]. In multispecies biofilms, mixed-species microcolonies are formed by a part of the sessile population when cells of metabolically cooperative species are juxtaposed and are thus in a position to benefit from interspecies substrate-exchange and/or mutual end-product removal. This level of structural organization and metabolic specialization explains the remarkable metabolic efficiency of microbial biofilms and their universal and inherent resistance to antimicrobial agents [72]. In practice, a biofilm on improperly cleaned surfaces is a barrier between microbes and disinfectants, antibiotics, or biocides [54, 228, 298, 458]. Biofilm components can also protect microbes from the effects of steam: e.g. the bacterial slime of Bacillus sp. improves the heat resistance of the bacterium, extending the autoclaving time required for efficient sterilization to several hours [270]. Besides causing problems in cleaning and hygiene [184], biofilms can cause energy losses and blockages in condenser tubes, cooling fill materials, water and wastewater circuits, and heat exchangers [64]. Biofilms can occasionally cause health risks by releasing pathogens into drinking-water distribution systems [44, 453]. In food processing water-supply systems, biofilms cause problems in granular activated carbon columns, reverse osmosis membranes, ion exchange systems, degasifiers, water storage tanks, and microporous membrane filters [132, 287]. Commonly found microbes in the food industry and on food contact surfaces are enterobacteria, lactic acid bacteria, micrococci, streptococci, pseudomonads, and bacilli [458]. The formation of resistant spores that can contaminate process equipment and food products is a special concern for the food processing industry and for the consumer [17].

The level of hygiene in the paper and board industry is important, since the end-products are often in contact with foodstuffs. The microbial isolates from the paper and board industry are mainly bacilli, enterobacteria, pseudomonads, or actinomycetes, but moulds, yeasts, anaerobic sulfate-reducing bacteria, and clostridia may also be detected [168, 333, 392]. The growth of B. cereus, clostridia, coliforms, and staphylococci in the paper-making process is detrimental to product hygiene [323, 379]. Aerobic and anaerobic spore-forming bacteria, such as bacilli and clostridia, which are not killed during the drying stage of paper-making, are the most important microbes from the safety point of view [194, 324, 341, 407]. Slime build-up in paper-processing machines, caused by microbial biofilms, may cause significant economic losses, mainly due to machinery-running problems in addition to spots, holes, and quality problems in the end-product. The machinery slime can also contain polymers of microbial origin, fibers, and inorganic precipitates. Common bacteria detected and identified from paper-processing machinery slimes include enterobacteria, bacilli, pseudomonads, and Clavibacter spp. The total number of microbes in the slime can reach 1012 colony-forming units (cfu)/ml. Pathogens, such as B. cereus, can also be found in these machinery slimes. Anaerobic bacteria, such as sulfate-reducing bacteria, can be involved in the initiation and progress of corrosion [36, 168, 408]. Also, heat-stable microbial metabolites, mainly enzymes and toxins, can cause problems if migration occurs from a packaging material into a foodstuff. Volatile metabolites, such as the fatty acids produced by many Clostridium spp. and the hydrogen sulfide produced by sulfate-reducing bacteria, can cause organoleptic problems in end-products [107, 168, 341].

Prevention of hygiene and safety problems

The elimination of biofilms is a very difficult and demanding task, because many factors affect the detachment, such as temperature, time, mechanical forces, and chemical forces [453]. Harmful microorganisms may enter the manufacturing process and reach the end-product in several ways, e.g. through raw materials, air in the manufacturing area, chemicals employed, process surfaces, or factory personnel [193, 323].

The target of microbial control in a process line is two-fold: to reduce or limit the number of microbes and their activity and to prevent and control the formation of deposits on process equipment. The present most efficient means for limiting the growth of microbes are good production hygiene, a rational running of the process line, and a well designed use of biocides and disinfectants. Novel means to control slime formation are constantly sought, e.g. through the control of environmental factors on the process line and the use of surface-active agents, (bio)-dispersants, enzymes, and new biocidal chemicals, in addition to non- or minimally toxic chemicals [36, 108, 215]. The cleanliness of surfaces, the training of personnel and good manufacturing and design practices are important in combating hygiene problems in the food industry [179]. Disinfection after the removal of biofilms, using suitable cleaning procedures, is also required in food plants where wet surfaces provide favorable conditions for microbial growth [116, 286].

In the food industry, equipment design and choice of surface materials are important in fighting microbial biofilm formation [179, 454]. The most practical material in processing equipment is steel, which can be treated with mechanical grinding, brushing, lapping, and electrolytic or mechanical polishing. Dead ends, corners, cracks, crevices, gaskets, valves, and joints are vulnerable points for biofilm accumulation [67, 115, 322]. Poorly designed sampling valves can destroy an entire process or give rise to incorrect information, due to biofilm formation at measuring points. Valves are vulnerable to microbial growth and thus constitute a hygiene risk [67]. Also, hoses, tubes, filters, etc. containing polyvinylchloride increase the risk of contamination, due to this material: it is more easily contaminated and it deteriorates more easily than steel [331]. Problems with the accumulation of particulates and cells occur whenever cleaning is inappropriate for any reason [278]. Inadequate cleaning and sanitation of surfaces coated with biofilms presents a source of contamination within the process [458].

Achieving a clean food plant must be the aim of the plant managers, who have to invest the necessary time and money to accomplish it. Monitoring methods and cleaning procedures, including the program, cleaning agents, disinfectants, and cleaning equipment, must be carefully planned [454]. Cleaning in the process industry should be based on systematic planning. The knowledge that microbes grow differently on surfaces, compared with suspensions, is the first step in developing advanced regimes in process hygiene [178]. Biofilm formation in industrial systems reflects a disturbance in the process [270]. Biofilms are less likely to accumulate in well designed systems, which are effectively cleaned. Results indicate that low-pressure cleaning in itself is not effective enough to remove biofilms unless the cleaning agent is effective [457]. The efficiency of cleaning agents is assessed by their ability to remove biofilms from process surfaces together with their ability to kill the bacteria present in the biofilm [457]. The cleaning effect in open systems can be enhanced using double-foaming or through scrubbing. In closed processes, the removal of biofilms from surfaces can be performed using efficient flow conditions in combination with effective cleaning agents [457]. Strong oxidizing and/or disinfective agents are used to combat microbial deposits on equipment surfaces in problem areas. Satisfactory elimination of biofilms using only disinfectant treatment cannot be achieved, even if the agent is very effective against freely suspended cells [286]. Sources, problems, and control of microbial contaminants in industrial processes are presented in Fig. 1.
Fig. 1.

Sources, problems, and control of microbial contaminants in industrial processes

Monitoring hygiene and safety problems

Monitoring practices based on sampling of the liquid phase do not reflect the location or extent of microbes growing in biofilms on surfaces [69]. The methods used for monitoring process hygiene are often based on conventional cultivation, using various types of agar plates or adenosine triphosphate (ATP) measurement. Conventional cultivation requires several days before the result can be obtained and it enumerates cells able to form colonies on the given agar [457]. The measurement of ATP is an often used method for detecting biological growth [4, 147], e.g. for the measurement of total hygiene [457]. The detection limit of ATP measurement for bacteria is 103–104 cfu/ml[39].

The detection of deposit build-up on equipment surfaces at an early stage enables effective countermeasures and thus results in an improvement in the process hygiene. Successful on-line monitoring of microbiological deterioration in the process industry has great beneficial impact, of both economic and environmental value. On-line monitoring saves both expense and the environment when gentle cleaning methods can be used and unnecessary procedures avoided.

A reliable identification of industrial microbial isolates is often difficult to obtain. Over the past decade, many improvements have been seen in both conventional and modern methods for the detection and identification of microorganisms from the industrial environment. Phenotypic analyses (e.g. the fatty acid methyl ester test, or sodium dodecyl sulfate–polyacrylamide gel electrophoresis) have traditionally played an important role in microbial identification and classification. Genotypic analyses (e.g. partial 16S rDNA sequencing, ribotyping) have proved very useful and accurate in the identification and classification of industrial microbial isolates, since the physiological properties of the industrial microbes may be different from those of the reference strains. Industrial strains are usually well adapted to their specific environments and do not often possess the typical characteristics of any species hitherto described. Thus, the effective use of molecular methods requires the development of extensive identification libraries. Furthermore, in many cases, the results of phenotypic and genotypic tests are not in good agreement, which further hampers identification [333, 413].

L. monocytogenes, B. cereus, and C. perfringens in the industrial environment

L. monocytogenes, B. cereus, and C. perfringens are important pathogens in industrial environments, especially due to their ability to endure adverse/harmful process conditions.

L. monocytogenes is a significant food-borne pathogen and may cause epidemic and sporadic outbreaks. The infective dose of L. monocytogenes is related both to the level of contamination of the food product and to the host susceptibility. L. monocytogenes is able to grow across a wide range of temperatures (including very low temperatures) and pHs; and it is extremely salt-tolerant. Due to this good tolerance to environmental stress-factors, L. monocytogenes is difficult to remove from the factory environment once it has become a part of the house microbiota. The typical food vehicles for L. monocytogenes are dairy, meat, and fish products [for reviews, see 119, 251].

B. cereus is widely distributed in nature (soil contains 105–106 spores/g) and is extremely tolerant to different environmental stresses. B. cereus is a non-competitive bacterium. However, food processes can select for it, since pasteurization is insufficient to kill the spores and many of the strains are psychrotrophic [18]. Spores of B. cereus are very hydrophobic [196] and adhere tightly to surfaces. Vegetative cells, especially cells in the late stationary growth phase, are also hydrophobic [318]. B. cereus strains may produce emetics and/or enterotoxins, which leads to food poisoning when a toxin-producing strain is present at levels >105 cells/g [200, 417]. But strains causing food poisoning at lower cell levels (103–104 cells/g) have also been found [18].

C. perfringens is mainly restricted to meat products, since this bacterium is unable to synthesize several amino acids. C. perfringens spores survive insufficient heating of the food product, vegetative cells reproduce at temperatures between 10 °C and 47 °C , and the generation time in optimal growth conditions is short [18]. Wild strains of C. perfringens are mainly enterotoxin-negative. However, enterotoxin-positive C. perfringens strains may cause food poisoning, especially through cooked food [283]. Aerotolerant vegetative cells survive for some time under aerobic conditions, but do not multiply.

Characteristics of L. monocytogenes, B. cereus, and C. perfringens are presented in Table 1.
Table 1.

Typical characteristics of Listeria monocytogenes, Bacillus cereus, and Clostridium perfringens. cfu Colony-forming units, ND not detected


L. monocytogenes

B. cereus

C. perfringens

Minimum water activity (aw; for growth)




Growth temperature

−1.5 °C to 50 °C

4–55 °C

12–50 °C

pH (for growth)

pH 4.1–9.4

pH 4.3–9.3

pH 5.5–9.0

Heat resistance of spores (at 100 °C)

3–8 min

0.3–13.0 min

Number of bacteria per gram in food reported in infective cases

<10–104 cfu/g (high risk groups)

105-109 cfu/g (normal population)

105–107 cfu/g (diarrheal type)

105–108 cfu/g (emetic type)

106–107 cfu/g

Incubation period

18–20 h (diarrheal type), in other types even 2 months

8–16 h (diarrheal type)

0.5–5.0 h (emetic type)

8–24 h


Diarrhea, fever (healthy adults)

Sepsis, meningitis, fecal infection (adult high risk groups)

Meningitis, sepsis, pneumonia, fecal infections (new-borns)

Fever, preterm delivery, stillbirth (during pregnancy)

Abdominal pain, nausea, vomiting (emetic type)

Abdominal pain, diarrhea, nausea (diarrheal type)

Abdominal pain, nausea, acute diarrhea

Food vehicles

Ready-to-eat foodstuffs with long shelf-life (fish, meat products, soft cheeses) and vacuum-packed products

Meat products, soups, vegetables, pudding, milk, and milk products (diarrheal type)

Rice, pasta and noodles (emetic type)

Incompletely cooked or slowly cooled food products, meat, poultry, shellfish, fish, and dairy products

Occurrence in food biofilms

Yes (dairy, fish, meat)

ND (dairy)


Occurrence in environmental site biofilms

Yes (plentiful)

Yes (non-food)


Isolation of microorganisms from the industrial environment

Sample collection and processing

Due to the spatial and temporal heterogeneity and technical problems related to sampling in the industrial environment, obtaining a representative sample from certain foods and food-related industry is a demanding task. Microbes are often tightly attached to surfaces and the process equipment may contain parts that are hard to access, such as dead-ends or bends in pipework. Different methods, such as swabbing, rinsing, agar-flooding, and contact agar methods, have been employed for sampling in the industrial environment [141, 333, 352, 456, 457]. The conditions during sample transportation have a great impact on sample quality; and the time between sampling and processing should be limited to the minimum.

Recovery of microbial cells from the sample matrix is a critical step and may lead to biases in the qualitative and quantitative estimation of the microbial community [for a review, see 131]. In most cases, samples need to be macerated and homogenized to liberate the microbial cells from the sample matrix. The cells in natural samples, e.g. naturally contaminated food, can be tightly attached to the matrix [62] and may need vigorous processing. Maceration of a food matrix may change the chemical environment of the sample and release substances that are toxic or inhibitory to some microbes. Dilution is also a potential bias-causing step and needs standardization [131]. Several methods have been developed to concentrate the sample, either to decrease the detection limit of the target microbes or to overcome the inhibitory effect of the matrix on the detection method, e.g. polymerase chain reaction (PCR). Selective or non-selective enrichment cultures are widely used for amplifying populations of food pathogens before detection by cultivation or molecular techniques [62]. However, the enrichment step is not always optimal for the recovery of the target species and may lead to false-negative results, especially when a too-selective enrichment medium is used for a sample containing injured cells [131]. Moreover, enrichment precludes attempts to quantitate results and, due to differences in growth rate between different populations, enrichment may lead to a bias in the recovery of different species [105]. Immunomagnetic separation (IMS) is a technique in which magnetic particles coated with specific antibodies are used to capture the target cells [312]. IMS has been used to concentrate food pathogens from sample homogenates or enrichment cultures, followed by detection with cultivation, immunological, or molecular methods [62, 102, 188, 312]. In addition, centrifugation and filtration techniques are commonly used in concentrating cells from certain types of sample.

Microbial cells are exposed to several environmental stresses during food processing and storage, which may change the physiological status of the cells. In addition to culturable and metabolically active cells and autolysing dead cells, microbial cells in many other physiological states can be found in samples [222]. In several food matrices, the dominant cells are those in a stationary growth phase, which are still metabolically active [131]. Adverse conditions, such as nutrient depletion and low temperature, can lead to viable but non-culturable cells (VBNC), which do not produce colonies on media that normally support their growth. However, VBNC cells remain metabolically active and infective [222, 273]. Two different types of cells contribute to the silent but active majority: (1) known species for which the applied cultivation conditions are just not suitable or which have entered a non-culturable state and (2) unknown species that have never been cultured before, due to a lack of suitable methods [16]. Sub-lethally injured cells, which do not grow on selective media but grow on non-selective media, may be present in processed samples and processed foods [152, 202]. In addition, cells in certain microbial groups have the ability to form spores, which are extremely resistant dormant states [222]. VBNC and injured cells may resuscitate or recover under appropriate conditions [58, 391]. The recovery of injured cells is highly dependent on the chemical composition of the enrichment medium and on the degree of the injury and the presence of accompanying microbes [244, 374, 391]. Besides culture, VBNC and injured cells can be more easily detected by fluorescent staining or molecular techniques [for reviews, see 152, 223, 274], which do not rely on the viability of the target cells.

The sample-processing method is dependent on the properties of the target microbial groups and on the method used in the subsequent detection step. If detection is performed by culture-based methods, the target strains must retain viability and culturability during sample processing, whereas the inhibitory components of the sample matrix play an important role in PCR-based detection. The sample matrix studied plays an important role when deciding which method to use for microbial detection and identification.


An effective cultivation procedure for the detection of food pathogens should suppress competitive microorganisms to the extent that the diagnostic system allows easy and reliable detection of the target genera/species. Several selective culture-based techniques are available for the detection and enumeration of L. monocytogenes, B. cereus, and C. perfringens in environmental and food samples; and the international standard methods for the detection and enumeration of these food pathogens are based on cultivation [77, 417]. The cultivation and subsequent identification of isolates using conventional techniques are time-consuming. It may take more than one week to obtain the complete results [77, 338]. During the past decade, much effort has been put into the development of more rapid culture techniques, many of which are based on the use of fluorogenic and/or chromogenic culture media.

Microbial community analysis by cultivation

An ideal method for studying microbial communities would detect and enumerate all microbial species present in the samples with equal efficiency. It was speculated that many microbial communities are too diverse to be counted exhaustively, which led to the application of statistical approaches for the estimation of diversity [192]. In food microbiology, especially in food hygiene surveys, cultivation has usually been aimed at the detection of selected groups/species of microorganisms, rather than the assessment of the complexity and dynamics of the microbial community. Culture-independent, DNA-based methods have also had limited applications in the investigation of microbial communities in foods and food-related industries; and the diversity of industrial microbial populations is therefore poorly known [152].

Microbial community analysis by cultivation is extremely laborious, especially when complex samples with high diversity are studied. When cultivation is used for microbial community analysis, several non-selective and selective culture media should be included, in addition to different growth conditions (different temperatures, atmospheres), followed by accurate identification of a large number of isolates from each medium, to get an overview of the diversity of the microbial population in the sample. The dominant cultivable population is recovered from non-selective media, whereas selective media allow the detection of groups or species that are present at lower numbers. Traditional methods for the identification of isolates are based on the assessment of several phenotypic features, which is often inaccurate and may lead to underestimation of the species diversity. It is generally known that conventional cultivation methods recover less than 1% of the total species of microbes present in environmental samples [16, 150, 440], partly due to the poor ability of the routinely used culture media and growth conditions to recover a large fraction of the microbial population [315]. In industrial environments and in food matrices, the processing parameters are likely to select for certain types of microbes; and the composition of the dominant microbial groups or species may be predicted more easily than in complex natural ecosystems. The development of culture media and conditions on the basis of the chemical and physical parameters of the environment investigated could enhance community analysis by cultivation. Combining an optimal cultivation technique with accurate identification of isolates with molecular microbiological methods would yield useful information on the diversity and the culturability of the microbes present in food and industrial environments. Knowledge of the function of the microorganisms in the ecosystem is also of utmost importance [315], which necessitates assessment of the properties of isolates.

Detection of L. monocytogenes by cultivation

In most countries, there is a requirement for the absence of L. monocytogenes in most food products. However, since several countries have established quantitative guidelines for L. monocytogenes in certain types of foods, such as raw meat and some ready-to-eat products, convenient enumeration methods are also needed. Several standard methods are available for the detection of listeria in foods [77, 171, 172, 438]. The detection of L. monocytogenes in food and environmental samples by cultivation typically includes enrichment step(s) for resuscitation of injured cells and concentration of the cells, followed by plating on selective media and confirmation of the tentative identifications of suspected colonies by biochemical tests [for a review, see 77]. Current conventional culture techniques take approximately one week to complete [77, 338]. The recent methodology development has focused on the optimization of enrichment steps and the development of new differential culture media to obtain faster and more reliable detection of L. monocytogenes in food and environmental samples [338].

An ideal enrichment medium facilitates the recovery of injured cells and the enrichment of L. monocytogenes over the competing microbiota [177]. The selective agents in commonly used Listeria spp.-selective agars are lithium chloride (LiCl), polymyxin B or colistin, acriflavine, and cephalosporins [for a review, see 77]. Most conventional selective enrichment broths rely on nalidixic acid and acriflavine as selective agents; and cycloheximide and LiCl have also been used in the enrichment step [77, 139, 416, 439]. In a more selective enrichment broth, L-PALCAMY, nalidixic acid is substituted by ceftazidime and polymyxin [416]. The selective enrichment step used in conventional procedures can be inadequate in facilitating the recovery of injured cells. The delayed recovery of injured cells in selective media [202, 435] and the inhibitory effect of selective agents, e.g. LiCl on some L. monocytogenes strains [74, 202], have been reported. Optimization of the composition of enrichment media and the use of a two-stage enrichment procedure, where selective agents are added after non-selective pre-enrichment, facilitate the recovery of injured cells [202, 338, 391]. Enrichment broths may include an indicator system, e.g. aesculin–ferric iron, which can be used for presumptive indication of the presence of Listeria spp. in the sample. Most agars have an aesculin–ferric iron indicator system and, additionally, a second indicator system based on mannitol fermentation can be added to the media [77]. Besides L. monocytogenes, all other Listeria spp. and some interfering microbes produce aesculinase, which complicates the use of aesculin hydrolysis as a differential characteristic [177].

Highly selective enrichment media are useful for the detection of Listeria spp. from samples that are heavily contaminated with interfering organisms [77]. The detection of L. monocytogenes after enrichment is complicated by the fact that other faster-growing species of Listeria, such as L. innocua, may overgrow during enrichment, which may lead to an underestimation of the presence of L. monocytogenes [40, 257, 319]. In food samples, non-pathogenic Listeria spp. typically outnumber L. monocytogenes; and it is probable that L. monocytogenes may not be detected on media that do not allow differentiation by colony appearance [218]. A selective agar medium containing sheep blood was developed to differentiate L. monocytogenes from non-pathogenic Listeria spp. on the basis of hemolysis [40, 75, 214, 319]. Pathogenic and non-pathogenic Listeria spp. can be distinguished on the basis of phosphatidylinositol-specific phospholipase C (PIPLC) activity [305]. Chromogenic culture media based on PIPLC activity were recently developed and applied for the detection of L. monocytogenes in food and environmental samples [177, 218, 338].

The success of enumerating L. monocytogenes by direct plating is dependent on having a sufficient number of L. monocytogenes cells in the samples, compared to the number of interfering organisms [158]. For the direct enumeration of Listeria spp. from food and environmental samples, spread-plates on selective agar media [90, 143, 156, 172, 249] or most probable number (MPN) counts on standard enrichment media [118, 171] can be used. In addition, a hydrophobic grid membrane filter, a technique that allows fast presumptive enumeration of L. monocytogenes from environmental and food samples, has been developed [113].

Restaino et al. [338] described a L. monocytogenes-selective detection system (LMDS) containing optimized steps of pre-enrichment, enrichment, and selective differential plating. LMDS allows the specific detection of L. monocytogenes in food and environmental samples [177, 338]. Presumptive results for the presence of pathogenic Listeria spp. in the sample can be obtained by the detection of fluorescence in the enrichment broth (containing a fluorogenic substrate based on PIPLC activity) within 1 day; and the complete detection and identification takes just 4–5 days [177, 338]. Restaino et al. [338] reported higher specificity and sensitivity for isolation of L. monocytogenes from naturally contaminated sites by LMDS than by a USDA standard method. The ability of different conventional standard procedures to detect L. monocytogenes has proved comparable [439]. However, the recovery of L. monocytogenes can be enhanced by using several parallel procedures in the analysis of samples [77, 171].

Detection of B. cereus by cultivation

Several selective and non-selective culture media have been developed for the detection of B. cereus from foods [for a review, see 417]. The enumeration of B. cereus in food and industrial samples is commonly based on a plate-counting culture technique, except for samples with low cell numbers (<10 cfu/g) or dehydrated starchy foods, for which the MPN method is preferred. Direct-plating on a non-selective medium such as blood agar is suitable for the detection of B. cereus in samples with a high number of target cells, e.g. in foods implicated in outbreaks, whereas selective media are needed for the enumeration of B. cereus from food and industrial samples, which typically contain higher numbers of interfering organisms. The selective agents employed in B. cereus media are polymyxin B or colistin, LiCl, and actidione. Color indicators, such as phenol red, bromocresol purple, or bromothymol blue, are added to help in the assessment of colony appearance [417]. Lecithinase production, negative mannitol fermentation, and nitrate reduction are the key properties used in the identification of B. cereus in standard procedures [325]. However, lecithinase-negative isolates or isolates showing weak lecithinase activity or other aberrant biotypes, such as negative nitrate reductase, have been detected from food and industrial samples and may lead to false-negative results [182, 325, 417]. Since the selective agents in various B. cereus selective isolation media are similar, the performance of different media in the enumeration of B. cereus from food samples is comparable [182, 200, 417]. A selective and differential chromogenic medium based on PIPLC activity has been developed for the enumeration of B. cereus and B. thuringiensis [260].

Detection of C. perfringens by cultivation

Detection and enumeration of C. perfringens from food and environmental samples is usually based on cultivation on agar plates, but a MPN technique can also be applied. Several selective culture media for the detection and enumeration of C. perfringens are based on sulfite reduction as a differential characteristic and cycloserine as a selective agent [21, 161, 276]. Selectivity of cultivation can be increased by using an elevated incubation temperature, since C. perfringens is able to grow rapidly at 45 °C [2, 355]. The shortcoming of the selective culture and growth conditions is the poor recovery of injured vegetative cells of C. perfringens [161, 183, 355]. Fluorogenic and chromogenic substrates have been applied to culture media to enable more reliable presumptive identification of C. perfringens [21, 260, 355]. Routine methods described for confirming the identification of C. perfringens isolates are based on testing the gas production from lactose and sulfite reduction on lactose–sulfite medium, or alternatively testing motility and nitrate reduction on motility–nitrate medium in combination with lactose fermentation and gelatin liquefaction on lactose–gelatin medium [110]. In a study by Eisgruber et al. [110], the lactose–sulfite approach enabled the identification of less than 50% of pure cultures of C. perfringens; and motility–nitrate combined with lactose–gelatin procedures also failed to identify some C. perfringens strains. The reverse adenosine 3′,5′-cyclic phosphoric acid test and acid phosphatase reaction proved to be easy to perform and confirmation reaction tests were reliable [110]. Also, Adcock and Saint [5] reported acid phosphatase in combination with beta-galactosidase activity as a reliable and extremely fast test for the confirmation of C. perfringens.

Fluorescence-based, non-specific detection of microorganisms

Fluorescence microscopy is widely used in microbial ecology. There are several advantages in its use. It is fast and rather easy to use, it allows the visualization of spatial distribution of cells in the sample and, with a suitable combination of fluorescent stains, differentiation between viable and dead cells is possible. However, direct identification of microbes is not possible with conventional fluorescent stains. Distinguishing cells on the basis of morphology is therefore important, because fluorochromes are not specific for bacterial species or genera [223].


There are five major attributes of fluorescence as a tool in microscopy: specificity, sensitivity, spectroscopy, temporal resolution, and spatial resolution [398]. Important phenomena in fluorescence microscopy are fading, photo-bleaching of the fluorochrome, and fluorescence-quenching, the loss of fluorescence due to the interaction of the fluorochromes with other molecules in the environment. The use of antifading agents can at least partly solve fading problems [398, 449]. Epifluorescence microscopy [327] and confocal scanning laser microscopy (CSLM) [50] are used for studying specimens that fluoresce. The excitation spectrum in epifluorescence microscopy is the product of the emission spectrum of the light source, the bandpass of the filter, and the reflectance spectrum of the dichroic mirror [449]. Excitation filters are band-pass filters chosen to pass light at the absorption spectrum of fluorochrome, while blocking the longer wavelength light of the fluorescence spectrum. In contrast, emission filters are chosen to pass the light in the emission spectrum, while blocking the light of the excitation spectrum [398]. In epifluorescence microscopy, multilayered samples, e.g. biofilms, can only be analyzed two-dimensionally [459]. In CSLM, a specimen is scanned with a focused laser beam and fluorescent signals are detected by a photomultiplier. A confocal pinhole allows only the signals arising from a focused plane to be detected. CSLM allows detailed, non-destructive examination of thick microbial samples, e.g. biofilms. Thus, the impact of various biocides (for example) can be studied at different optical sections in more detail than with epifluorescence microscope [24, 60, 72, 88, 234; for a review, see 235]. In addition, determination of the three-dimensional relationship of cells and three-dimensional computer reconstruction from optical thin sections becomes possible [60, 234].

The analysis of fluorescence from samples under the microscope can be evaluated either manually or with computer-aided image analysis programs. Manual evaluation is usually chosen when evaluating whether microbes are present in the sample or not. When more information is needed, image analysis is normally used. Image analysis includes image acquisition, processing, and segmentation, object recognition and measurement, and data output [60]. The image analysis systems allow rapid quantification of many parameters, which could hitherto only be described qualitatively, e.g. fluorescence intensity, quantification of different sizes of microorganisms, and percentage of area covered by biofilm [45, 221, 284, 450]. However, the analysis of particles with different brightness is still problematic and thresholds have to be established for deciding what is a bacterium and what is background [45].

Flow cytometry

Flow cytometry (FCM) combines the advantages of microscopy and biochemical analysis for the measurement of the physical and chemical characteristics of individual cells as they move in a fluid stream past optical or electronic sensors [76, 98, 99, 100]. Emitted light is detected by photodetectors and data are analyzed by computer-aided means. FCM permits simultaneous measurement of multiple cellular parameters, both structural and functional, such as cell size and DNA content [211, 402, 433], and allows rapid characterization of individual cells (more than 103 cells/s) in homogeneous and heterogeneous populations [52, 99, 211, 402, 433]. FCM has a higher throughput and can more readily be automated than microscopic quantification of microbial populations [433]. Furthermore, FCM requires only small sample sizes [29].

Fluorescent stains

Fluorochromes are stains or probes that are added to cells to obtain a fluorescent signal [398]. Detection of labeled molecules depends on both the intensity of fluorescence and the ability to resolve specific fluorescence from background fluorescence [109]. The use of fluorescent stains, with the ability to distinguish between living and dead bacteria, is becoming increasingly important [for a review, see 274]. The use of fluorogenic indicators of metabolic activities in microscopy provides information on the physiological status of individual cells [271, 464]. Moreover, the fluorescent nature of the compounds greatly facilitates their use in studying bacteria associated with optically nontransparent surfaces [271, 342, 362, 368, 457, 464].

Enumeration of bacteria

Numerous fluorescent stains are used for the detection of both biofilm and suspension samples, to study the viability and/or the total number of microorganisms. The most commonly used stains for the detection of total number of bacteria are acridine orange (AO) and 4′,6-diamidino-2-phenylindole (DAPI) [223].

AO binds to DNA and RNA [390]. The distribution of dead, metabolically inactive but living, and living cells cannot be determined by the standard technique of AO staining, because DNA retains its staining properties in nonviable cells [223, 455]. The emission spectra of AO upon binding to nucleic acids are highly dependent on substrate structure, i.e. AO complexed with single- and double-stranded nucleic acid emits red and green fluorescence, respectively [284]. Since AO is known to stain all organic material—e.g. food residues—it has been used for studying biofilms and environmental samples [87, 180, 181, 455] and to enumerate the total number of bacteria, while using a culture technique to determine the number of viable and culturable bacteria [42, 68, 106]. AO has also been used for enumeration of L. monocytogenes cells, both in biofilms [185, 254, 457] and in suspension [37].

DAPI is a nonintercalating, DNA-specific stain [223, 328, 402], which fluoresces blue or bluish-white when bound to DNA. When unbound or bound to non-DNA material, e.g. polyphosphates, it may fluoresce in various shades of yellow. As with AO, DAPI cannot be used for viability-staining [223]. DAPI is rapidly replacing AO as the most commonly employed bacterial stain for a wide range of sample-types. With both DAPI and AO, bacteria are identified on the basis not only of color but also of size and shape. DAPI has been used for the enumeration of L. monocytogenes [37, 63] and B. cereus [31, 431] cells. DAPI has also been used as a counterstain with Evans blue [26], erythrosine B (ERB) [219], 5-cyano-2,3-ditolyl tetrazolium chloride (CTC) [49, 51, 190, 271, 342, 362, 388, 457, 463], and iodonitrophenyltetrazolium (INT) [103].

Viability stains

There are several stains that target either viable bacteria [e.g. CTC, INT, rhodamine 123, fluorescein diacetate (FDA), carboxy-FDA, ChemCrome B] or nonviable bacteria [e.g. rhodamine B, calcofluor white, Evans blue, bis-1,3-dibutylbarbituric acid trimethine oxonol (oxonol dye), propidium iodide (PI), ERB]. The two most commonly used viability-staining systems for industrial samples are CTC-DAPI and the LIVE/DEAD BacLight viability kit (Molecular Probes, Eugene, Ore.).

CTC is a monotetrazolium redox stain that produces a red-fluorescent formazan when it is chemically or biologically reduced [342, 463]. CTC can be chemically reduced in a low-redox environment and hence its use is restricted to aerobic or microaerophilic systems. Usually, CTC is used in connection with DAPI [342]. With computerized image analysis, it is possible to scan a colonized surface and rapidly quantify the respiratory activity of CTC-stained cells [362]. The utilization of CTC allows the clear resolution of individual cells by epifluorescence microscopy [464]. CTC can also be used for non-destructive analysis of the architecture and distribution of physiological activity within a biofilm [362]. The CTC method provides a convenient and rapid approach for e.g. quantification of the effect of biocides [51, 190, 362, 388, 457, 460, 464]. CTC-DAPI has been used for viability analysis of L. monocytogenes cells in biofilms [271, 457] and in suspension [271].

Molecular Probes′ LIVE/DEAD BacLight viability kit provides a two-color fluorescence assay of bacterial viability. It has been proven useful for a diverse array of bacterial genera, including both Gram-negative and Gram-positive species [20]. The stains in the LIVE/DEAD kit are a membrane-permeating green fluorescent nucleic acid dye, SYTO 9, which stains viable cells, and a red fluorescent nucleic acid dye, PI, which does not permeate membranes, but stains dead cells. The background remains virtually nonfluorescent. Due to the interference of biofilm matrix polysaccharides and slime with the stain, LIVE/DEAD staining is not usually suitable for biofilms attached to a surface [271]. LIVE/DEAD samples should be analyzed immediately, whereas samples stained with CTC-DAPI and thereafter filtered on a microscopic membrane can be stored for several weeks. Therefore, CTC-DAPI offers a more convenient tool for viability investigation [271]. The LIVE/DEAD kit has been used for viability analysis of L. monocytogenes cells in suspension [203, 271, 457].

Molecular techniques in bacterial detection and identification

A molecular technique used for the detection of pathogens must be capable of detecting low numbers of target bacteria in samples which may contain a considerable background of interfering microorganisms and several matrix-derived compounds that may hamper the detection. In microbial community analysis, the method should allow the detection of different groups or species present in the ecosystem with similar efficacy, to avoid biases in the evaluation of species distribution and the complexity of the microbiota. Biases may be introduced by initial sample-handling [294] and during the extraction of nucleic acids from microbes in the sample.

Molecular techniques can be utilized in the detection and identification of microbes in two ways: (a) identification is performed directly from sample material, or (b) identification is based on combined culture and molecular detection. The sample matrix studied plays an important role when the decision between the two choices is made. If the matrix is known to contain factors that can inhibit a PCR reaction (for example) and are difficult to remove, it is often best to use the combination of a culture technique and a suitable molecular technique. There are two major techniques applied in the molecular detection and identification of bacteria: PCR and hybridization. When molecular tools were first introduced for the detection and identification of microbes, hybridization methods were widely applied. The rapid evolution of PCR techniques led to the present situation, where hybridization is mainly used in combination with PCR. However, a technique called in situ hybridization, in which bacteria are detected in their natural microhabitat, proves useful in applications where enumeration of the target organisms is warranted. Through its automation of the procedure, the recent development of DNA microarrays allows the simultaneous identification of a huge number of specific sequences by hybridization [165].

Release of nucleic acids from sample material

The reliable and reproducible lysis of microbial cells and the extraction of intact nucleic acids from environmental and industrial habitats is a demanding task [423, 446]. In addition, the removal of substances which may interfere with hybridization or PCR amplification, such as food components or process additives, may be difficult [446]. The procedures for cell lysis can be enzymatic (e.g. lysozyme, lyticase, proteinase), chemical (e.g. detergents, guanidium isothiocyanate), or mechanical (e.g. freeze–thaw/freeze–boil cycles, bead-beating, microwave heating) [for reviews, see 62, 347]. In many cases, e.g. in the identification or fingerprinting of isolates obtained by culture, the crude cell lysate can be used directly in a subsequent molecular analysis. However, since food and environmental samples may contain inhibitory compounds [233, 350, 364], further processing of the cell lysate is often necessary when direct molecular detection methods are applied. Processing steps include the removal of proteins, which is commonly done by phenol–chloroform extraction [432] or salt saturation [169], followed by precipitation of nucleic acids by ethanol, isopropanol, or polyethylenglycol precipitation, and purification of the nucleic acids [for a review, see 347]. When the metabolically active fraction of the community is of interest, the analysis should be performed with RNA rather than DNA. However, while extracting RNA from industrial and environmental samples, special attention should be paid to avoiding the degradation of RNAs with RNAses during the extraction procedure [for a review, see 423]. There are several articles describing different RNA extraction procedures [e.g. 123, 195, 285]. Several commercial kits are also available for DNA and RNA extractions.

Each step included in the sample preparation reduces the nucleic acid yield and decreases the sensitivity of detection. In food microbiology, a large effort is put into optimizing sample manipulation prior to cell lysis, to concentrate the target cells and to remove inhibitory substances from the sample matrix. A short enrichment culture and harvesting the bacterial cells from the sample by centrifugation, filtration, and immunomagnetic beads are applied to sample-processing in the detection of food-borne pathogens [177, 232, 307, 312, 437; for a review, see 62]. The sensitivity of the molecular method can be improved by an enrichment culture [6, 437], but this also precludes attempts to quantitate the number of target organisms in the sample. It should also be noted that some enrichment media might contain substances inhibitory to PCR [437].

Target sequences for molecular detection

Environmental microbiological studies are often based on ribosomal RNA (rRNA) or rDNA sequences. rDNA and rRNA are ideal targets for nucleic acid probes and primers for several reasons: (1) they are functionally conserved and present in all organisms, (2) 16S and 23S rDNA are composed of sequence regions with higher and lower evolutionary conservation, (3) 16S rDNA sequences have already been determined for a large fraction of the validly described bacterial species, and (4) the natural amplification of rRNA with high-copy numbers per cell (usually more than 10,000) greatly increases the sensitivity of rRNA-targeted techniques [384]. 16S rDNA sequences can be used to infer phylogenetic relationships and to identify unknown microbes by database comparisons [310]. Due to the patchy evolutionary conservation of rDNA sequences, the specificity of rDNA- or rRNA-targeted detection or identification can be tailored to the needs of the investigator, reaching from the subspecies to the kingdom level [10, 386]. It has also been proposed that rRNA content is appropriate for assessing changes in metabolically active bacterial populations, since rRNA content depends on bacterial activity [430]. In contrast to 16S rDNA, the intergenic spacer region (ISR) between 16S and 23S rDNA is highly variable in length and often shows species-specific sequence traits useful for designing molecular markers. Hence, in many cases, the ISR sequences are more applicable targets for diagnostic PCR-amplification than 16S rDNA [35, 210; for a review, see 163]. In addition, ISR amplicons can be separated into fingerprints by conventional electrophoresis [122].

Besides rDNA, other target genes can be used for the molecular detection of selected microbial groups/species from food and industrial samples. Genes associated with virulence factors, such as the toxin-producing listeriolysin O (hlyA) gene in L. monocytogenes [38], are commonly used for the detection of food-borne pathogens. In addition, genes coding for physiological properties, e.g. the cold-shock protein genes present in psychrotrophic B. cereus -group strains [137], can be used as target molecules for detection. In addition to known genes, species-specific sequences selected on the basis of random amplified polymorphic DNA (RAPD) analysis can be used [114]. However, this approach is hampered by the fact that relatively little sequence data is available from genomes, which results in difficulties in both creating the specific primer pairs and evaluating their specificity.


PCR amplification

In PCR, a thermostable DNA polymerase enzyme is used to exponentially amplify a target DNA sequence defined by two oligonucleotide primers [288, 289, 290, 351]. The amplified DNA fragment can be visualized either by agarose gel electrophoresis, which allows size-determination of the PCR product, or by hybridizing the PCR product with a labeled probe. Combining PCR with a hybridization step improves the sensitivity and specificity of the assay. PCR is very sensitive and small amounts of contaminating DNA carried from one run to the next (for example) can give false-positive results.

Many types of sample matrix (e.g. foods) contain factors which can either totally inhibit the PCR reaction or cause partial inhibition, leading to non-exponential amplification of the target DNA [233, 350, 364]. Inhibition may be avoided or reduced by pre-PCR sample manipulations, such as dilution of the sample material, short enrichment culture, extraction of the DNA from the sample, or harvesting the bacterial cells from the sample by centrifugation, filtration, or immunomagnetic beads coated with monoclonal antibodies specific to the target organism. However, even partial inhibition of the PCR reaction inevitably leads to reduced sensitivity and excludes the possibility of performing quantitative PCR. To minimize the risk of obtaining false-negative amplification results, suitable external standards should be used, which are coamplified together with the target DNA in the PCR reaction [337]. The sensitivity of the PCR assay can be improved by enrichment culture prior to PCR [6, 437], but this also precludes attempts to quantitate the number of target organisms in the sample. Thus, amplification of target DNA sequences from sample materials containing inhibitory factors for PCR can provide information on the presence, but not on the numbers and usually not on the viability of target organisms (except in the case where an enrichment step is included). It should also be remembered that PCR detects nonviable cells, as long as intact target nucleic acid sequences are available as templates [216].

When PCR is applied to environmental or industrial samples, several problems arise, including inhibition of PCR amplification by co-extracted contaminants, differential PCR amplification, formation of PCR artifacts, e.g. chimeric molecules (leading to the description of non-existing species), and DNA contamination. It should also be noted that 16S rDNA sequence variations due to rrn operon heterogeneity can interfere with the analysis [for a review, see 423]. When PCR is used in direct bacterial detection from sample materials containing other microbes, validation of the protocol applied is of utmost importance. The chosen method has to be tested on a large panel of strains representing the target species, closely related species, and other microbes commonly present in the sample material. This, together with the fact that different methods have to be applied to overcome the inhibitory effects of different sample matrixes, necessitates the use of tailor-made approaches for each microbe–sample matrix pair. A positive control for each analysis is important for confirming that inhibitory substances do not interfere with the detection and cause false-negative results [177].

Quantification of the initial amount of target is not possible in traditional end-point PCR, because the amount of PCR product is determined when the reaction has already reached the plateau phase. In real-time PCR, the amount of PCR product is measured at each cycle and also during the exponential phase, which enables the quantification of the initial template amount. The real-time measurement is based on fluorescent dyes that either bind to double-strand DNA or hybridize to a specific sequence. Since real-time PCR is especially vulnerable to inhibitory compounds, internal standards should always be used when complex sample matrixes are studied [337].

There are numerous articles reporting the identification of L. monocytogenes by PCR amplification. Most of the reported studies used PCR primers specific for fragments of the listeriolysin O (hlyA) gene [6, 34, 38, 41, 43, 47, 71, 96, 130, 133, 140, 175, 176, 191, 207, 241, 256, 299, 300, 302, 303, 316, 349, 401, 429, 437] and/or PCR primers specific for the invasion-associated protein (iap) gene [6, 55, 56, 164, 264, 265, 266, 267, 299, 300, 426]. A short enrichment period before PCR amplification greatly improves the sensitivity of the assay [6, 130]. Other PCR protocols using inlA [9, 207] and genes encoding flagellin (flaA) [389], fibrinectin-binding protein (fbp) [149], aminopeptidase [452], transcription activation protein (prfA) [71, 101, 353, 376, 443], and 16S rRNA [233, 434] as targets for specific detection of L. monocytogenes have also been introduced. Another approach is the use of 16S–23S rDNA spacer regions for Listeria genus-specific and L. monocytogenes species-specific PCR assays [155]. Multiplex-PCR targeting different sequences of iap [56], hlyA and 23S rDNA [191], or hlyA and 16S rRNA [445] have been developed for the rapid identification of L. monocytogenes. PCR protocols have been used to identify Listeria spp. from water, skimmed and raw milk, ice-cream, cheese, soft cheese, mozzarella cheese, cooked sausage products, fermented sausage, ham, pork, ground beef, minced beef, chicken skin, turkey, raw and cooked poultry products, seafood, raw fish, cold smoked fish, coleslaw, cabbage, lettuce leaves, and vegetables [6, 41, 47, 71, 101, 130, 133, 140, 164, 175, 176, 186, 191, 233, 236, 265, 267, 299, 300, 302, 303, 316, 353, 376, 401, 434, 437, 445].

For the PCR detection of B. cereus, various sequences are used as targets, including genes encoding 16S rRNA, hemolysin BL, cereolysin AB, non-hemolytic enterotoxin, enterotoxin T, gyrase B, IS231, and 16S-23S rDNA spacer region [166, 167, 173, 187, 226, 261, 404, 422, 461]. Recently, Bach et al. [31] developed a neutral metallopeptidase gene-based real-time quantitative PCR assay for quantification of B. cereus. It was noted that PCR analysis of the 16S–23S rDNA spacer region reveals identical patterns for B. cereus and B. thuringiensis [166] and that discrimination between B. cereus and B. thuringiensis is difficult when gyrB gene-based primers are used [66]. PCR is used to discriminate psychrotolerant and mesophilic strains of the B. cereus group [422], to investigate the growth, sporulation, and germination of B. cereus strains isolated from dairy and meat products [17], and to detect B. cereus from milk [226, 367]. Tsen et al. [404] developed a multiplex-PCR assay targeting simultaneously both the enterotoxin and 16S rRNA genes of B. cereus.

There are PCR assays targeting 16S rDNA and genes encoding alpha-, beta-, epsilon-, tau-, and enterotoxins for the rapid identification of C. perfringens strains from food, animal, and clinical specimens [22, 23, 57, 217, 220, 230, 253, 268, 326, 395, 406, 436]. There is a duplex PCR assay targeting alpha-toxin or the phospholicase C and enterotoxin (cpe) genes for the rapid detection and identification of enterotoxigenic C. perfringens strains in food and fecal samples [25, 117, 395, 396]; and there is a multiplex PCR assay targeting simultaneously five toxin genes for the analysis of clinical C. perfringens isolates [381].

Also, broad-range PCR primers, targeting many bacterial species of interest, have been developed for the detection of pathogenic bacteria, including L. monocytogenes and Bacillus spp. The essential part of this assay is the confirmation of the target species/genera with specific probes [159]. There are also PCR assays for determining the total bacterial load, using real-time PCR with a universal probe and primer set [297]. Another new approach to quantitate environmental DNA sequences involves a multiplexed, bead-based method with flow cytometry [382].


Hybridization techniques can be used in bacterial identification either alone or combined with a preceding PCR step. In hybridization, a labeled probe (a denatured DNA fragment varying in size between tens of basepairs to kilobasepairs) anneals to a denatured target DNA (genomic DNA or PCR amplification product) with sequence homology [10, 380]. Target DNA can be directly blotted onto a membrane, or if size information of the hybridization target is warranted, the target DNA is first run through agarose gel and then transferred to a membrane. Detection of hybrids is based on a radioactive signal, fluorescence, or color reaction, depending on the type of the label. By determining the intensity of the hybridization signal, the number of target organisms can be estimated [123, 122, 336, 364, 366, 386; for a review, see 311]. With dot-blot hybridization, nucleic acids can be fairly rapidly analyzed for the presence of specific sequences [for reviews, see 357, 365]. This technique is commonly used to confirm the identity of PCR products [38, 43, 79, 245, 367, 437]. A miniaturized and automated form of dot-blot hybridization is called a microarray (see DNA microarray, below).

Hybridization probes targeting 16S rDNA, the listeriolysin or enterotoxin genes, iap, inlA, prfA, the 16S–23S rRNA spacer region, or genomic sequences related to the expression of surface antigens have been developed for the detection and identification of L. monocytogenes [47, 83, 84, 154, 164, 242, 320, 339, 426, 434, 443]. Detection of B. cereus with hybridization is mainly performed to confirm the results obtained with specific PCR [79, 367, 437]. 16S rDNA, phospholipase and cereolysin AB genes, and 16S–23S rDNA spacer region sequences are used as probes in B. cereus detection [79, 334, 367, 399]. Hybridization is used e.g. to detect B. cereus from traditional Indian foods [334] and for enterotoxic B. cereus detection [261]. DNA hybridization has also been used to identify enterotoxic C. perfringens strains [85, 411] and to detect enterotoxic C. perfringens from Mexican spices and herbs [343] and from the feces of Mexican subjects [418].

Fluorescent in situ hybridization

The detection of whole-bacterial cells via labeling of specific nucleic acids with fluorescence-labeled oligonucleotide probes is called fluorescent in situ hybridization (FISH). FISH requires no cultivation and cells can be fixed before analysis, enabling the storing of samples prior to analysis [11, 12, 13, 14, 15, 94, 262, 383]. The whole-cell or in situ hybridization technique is now a much-used molecular tool in environmental microbiology, since organisms or groups of organisms can be identified with minimal disturbance of their environment and spatial distribution. Due to the fact that environmental conditions influence the cellular rRNA content, the amount of rRNA is considered to correlate with the growth rate [329]; and in situ hybridization using rRNA-targeted oligonucleotides can therefore be a powerful tool for the assessment of bacterial activities.

FISH in combination with epifluorescence microscopy is a widely applied method to analyze microbial communities [16]. The sensitivity and objectivity can be greatly enhanced by digital image analysis [335]. The application of FISH combined with conventional fluorescence microscopy for the analysis of complex microbial biofilms can be impaired by biofilm thickness, background fluorescence caused by humic substances or detritus, and the inherent autofluorescence of phototrophs. These problems can be circumvented by using FISH with CSLM [425; for a review, see 427]. The advantage of CSLM for the study of complex environments is that undisturbed samples can be analyzed without removal or homogenization of biofilm or other material [234]. Sample thickness is not limiting, since light from out-of-focus planes is excluded [263].

There are only a few published studies in which FISH has been used to identify Clostridium spp., Listeria spp., or Bacillus spp. This is most likely caused by the fact that FISH is much more difficult to perform with Gram-positive bacteria than with Gram-negative bacteria, due to the permeability problems associated with Gram-positive bacteria [138, 258]. Furthermore, FISH cannot be used to quantify bacterial spores [377]. FISH has been used to study the growth of B. cereus inoculated on tomato seeds [377] and to detect an uncultured Bacillus sp. from Dutch grassland soil [126]. FISH has also been used to identify the Clostridium histolycum -group, including C. perfringens [138] and Clostridium spp. [205] from human fecal samples. In addition, FISH has been used to detect Clostridium spp. from rice straw in anoxic paddy soil [441].

Recently, a microscopic method combining FISH and microautoradiography was developed [240, 314]. With this combination, it is possible to simultaneously determine the identities, activities, and specific substrate-uptake profiles of individual microbial cells within complex microbial communities under different environmental conditions [240].

DNA microarray

DNA microarrays facilitate the study of large numbers of genes simultaneously by hybridization of DNA or mRNA to a high-density array of immobilized probes [134, 248, 363]. The DNA microarray is basically a miniaturized form of dot-blot hybridization in a high-throughput format. There are two major types of DNA microarrays: an oligonucleotide-based array and a PCR product-based array. Microarrays allow the production of a gene expression profile or signature for particular organisms under certain environmental conditions. These can be used to study variability between the same or related species and between ancestor and descendants. As a result, microarrays provide information on the molecular basis of microbial diversity, evolution, and epidemiology [for reviews, see 32, 95, 145, 252, 462]. To our knowledge, there is a single published study indicating the successful use of an oligonucleotide microarray for the differentiation of closely related Bacillus spp. [247] and there is one study in which microarray was used for the identification of C. perfringens [451]. However, the genome projects of L. monocytogenes [153] and C. perfringens [375] are now complete, which will help the build-up of specific microarrays for these species.

Genetic fingerprinting techniques

Genetic fingerprinting techniques can be used to characterize bacterial communities or single bacterial isolates. The genetic fingerprinting of microbial communities provides a pattern or profile of the community diversity, based upon the physical separation of unique nucleic acid sequences [385]. Community analysis techniques are relatively easy and rapid to perform and they allow simultaneous analysis of multiple samples, enabling the comparison of the genetic diversity of microbial communities from different habitats, or the study of the behavior of individual communities over time. Community analysis can be performed with techniques such as denaturing-gradient gel electrophoresis (DGGE), temperature-gradient gel electrophoresis (TGGE), and single-stranded conformational polymorphism (SSCP). There is also a new approach based on heteroduplex mobility analysis of 16S rDNA fragments for targeted detection of sub-populations of bacteria within diverse microbial communities [405].

Fingerprinting of bacterial isolates can be performed by a variety of techniques, including e.g. ribotyping, amplified ribosomal DNA restriction analysis (ARDRA), pulsed-field gel electrophoresis (PFGE), RAPD, repetitive element sequence-based PCR (rep-PCR), and amplified fragment length polymorphism (AFLP). All these techniques aim at differentiating bacterial isolates at the subspecies level, preferably even at the strain-level.

An overview of the genetic fingerprinting techniques described in this review, with their advantages and limitations, is presented in Table 2.
Table 2.

Overview of genetic fingerprinting techniques described in this review. AFLP Amplified fragment length polymorphism, ARDRA amplified ribosomal DNA restriction analysis, DGGE denaturing-gradient gel electrophoresis, PFGE pulsed-field gel electrophoresis, RAPD randomly amplified polymorphic DNA, rep repetitive element sequence, SSCP single-strand conformational polymorphism, TGGE temperature-gradient gel electrophoresis




Community level


Community structure and dynamics can be studied, Identification of community members possible

Only those populations making up over 1% of the total community can be detected

Strain level


Can be automated, Good discriminatory power, Can be used for bacterial identification,

Expensive, laborious, and manually slow to perform


Fairly simple and fast

Limited discriminatory power


Very high discriminatory power

Expensive, Slow to perform


Fast, simple, and cost-effective

Reproducibility problems possible


Fast, simple, and cost-effective

Reproducibility problems possible


Good discriminative power

Expensive, Laborious

Community analysis

Denaturing/thermal gradient gel electrophoresis

In DGGE [129] and TGGE [348], PCR-amplified DNA fragments of the same length but with different DNA sequences can be differentiated [70, 129, 296, 348]. Separation in DNA fragments is based on the electrophoretic mobility of a partially melted double-stranded DNA molecule in polyacrylamide gels containing either a linear gradient of DNA denaturants (a mixture of urea and formamide in DGGE) or a linear temperature gradient (TGGE). Partially melted DNA fragments are held together with a G+C-rich oligonucleotide, a GC-clamp. Therefore, each denaturing fragment generates only a single band in the gel [for a review, see 294]. DGGE/TGGE performed after PCR gives an insight into the predominant microbial populations; and DGGE/TGGE performed after reverse transcriptase (RT)-PCR helps identify the predominant active microbial populations [104, 123, 124, 400, 466]. DGGE/TGGE can also be used in combination with quantitative RT-PCR to quantify rRNA sequences in complex bacterial communities [125].

DGGE/TGGE analysis combines a direct visualization of bacterial diversity and the opportunity to subsequently identify community members by DNA fragment sequence analysis or hybridization with specific probes [292, 293]. Sequence analysis or hybridization performed after DGGE/TGGE has detected Bacillus-like sequences and Clostridium spp. in various environmental and clinical samples [104, 121, 127, 128, 198, 201, 231, 243, 317, 345, 358, 378, 397, 441, 465].

DGGE/TGGE has some specific limitations. DGGE/TGGE can be used to separate only relatively small fragments [295] and it displays only the rDNA amplicons obtained from the predominant (over 1% of the population) species present in the community [291, 292, 294]. The presence of heterogeneous 16S rRNA genes (16S rRNA genes that exhibit small sequence variations in the genome of a given strain) can result in several bands in a DGGE/TGGE profile [125, 306, 356, 466]. Furthermore, a single band may represent more than one strain [124, 127, 356, 371, 409]. The construction of 16S rDNA clone libraries and the screening for different clones by DGGE may overcome these deficiencies [127, 128, 148, 294, 358].

Single-stranded conformation polymorphism

SSCP analysis detects sequence variations between different DNA fragments, which are usually PCR-amplified from variable regions of the 16S rRNA gene. The technique is based on the fact that a single base modification can change the conformation of a single-strand DNA molecule, altering the migration speed of the molecules in a non-denaturing gel [313, 344]. DNA fragments of the same size but with different base composition can thus be separated [170].

The limitations of the SSCP method are similar to those of DGGE/TGGE. The discriminatory power and reproducibility of SSCP analysis is usually most effective for fragments up to 400 bp in size, depending on the length of the fragment studied, the position of the sequence variations in the gene studied, and the test conditions [413]. In addition, PCR-SSCP detects bacterial populations that make up 1% or more of a bacterial community [239]. A major limitation of SSCP for community analysis is the high rate of DNA strand-annealing after the initial denaturation during electrophoresis [372].

Besides community studies, PCR-SSCP analysis can be adapted for the rapid identification of Gram-negative and Gram-positive bacteria at the genus and species levels [48, 79, 91, 239, 241, 260, 369, 415, 429, 447], to discriminate between B. cereus and B. subtilis [448], and for detecting Listeria spp. [241, 260, 415, 429, 447, 448], Clostridium-related bacteria [91], and Clostridium spp. [92, 447].

Typing of microorganisms

Prior to molecular techniques, phenotypic methods such as biotyping and serotyping were used for bacterial strain differentiation. These techniques are still used today, but more reliable and often less laborious fingerprinting can be achieved with molecular techniques. Regardless of whether phenotypic or genotypic techniques are applied, fingerprinting is preceded by culture and single-colony subculture steps. Thus, even though PCR and hybridization can be used both in bacterial detection and fingerprinting, the techniques applied differ in a profound way. While detection methods are able to find the target organisms in a sample containing hundreds of other bacteria, fingerprinting methods are not genus- or species-specific and can therefore only be applied to pure bacterial cultures. When molecular techniques were first applied for bacterial fingerprinting, both conventional restriction endonuclease analysis (REA) of genomic DNA and plasmid profiling were used. Both techniques have their limitations. With conventional REA, complicated patterns with hundreds of restriction fragments are obtained, which makes the profile comparison difficult. With plasmid profiling, far simpler profiles are obtained, but this technique is suitable only for bacteria carrying (several) plasmids [for a review, see 413].


When conventional REA is combined with a hybridization step, a far simpler and thus more easily comparable fingerprint is obtained. This technique, where genomic restriction fragments are separated by gel electrophoresis, transferred to a Nylon membrane, and hybridized to a probe, is called restriction fragment length polymorphism (RFLP). By far the most widely applied RFLP technique is (classic) ribotyping, in which rRNA genes (usually both 16S and 23S rRNA genes, or a whole rRNA operon containing 16S, 23S, and 5S rRNA genes and their spacer regions) are used as a probe. Since a rRNA operon contains both conserved and hypervariable regions, the same probe (e.g. originating from Escherichia coli) can be used in ribotyping different bacterial species [160]. The strain differentiation in ribotyping is thus based on the unique hybridization pattern (fingerprint) obtained and not on the specificity of the probe. Differences in the hybridization patterns originate from restriction endonuclease recognition site-variation within variable regions of rRNA genes and their spacer regions. The discriminatory ability of ribotyping is greatly influenced by both the probe (whole rRNA operon vs a single gene) and the restriction endonuclease applied. Obviously, the best discrimination is obtained when the whole operon is used as a probe and an optimal restriction endonuclease for each bacterial genus is selected from a panel of restriction endonucleases tested. However, when ribotyping is used as a taxonomic tool, riboprints of isolates representing different genera and species are compared; and thus the same restriction endonuclease has to be applied for all bacteria. The invention of an automated ribotyping system (Riboprinter; Dupont Qualicon, Wilmington, Del.) greatly facilitated bacterial fingerprinting, thus allowing larger numbers of bacterial isolates to be characterized and compared than when ribotyping is performed manually.

Classic ribotyping has been used to characterize C. perfringens isolates associated with food-borne cases and outbreaks, e.g. ground meat [225, 359, 360]. Ribotyping has also been used for B. cereus typing [19, 325, 332, 361]. There are several publications on L. monocytogenes ribotyping, including the characterization of isolates from the smoked fish, meat, poultry, and seafood industries, from different foods, and from human and animal listeriosis cases [8, 33, 89, 142, 157, 204, 207, 209, 277, 304, 308, 393, 394].

Amplified rDNA restriction analysis

In addition to classic ribotyping, rDNA-based fingerprints can be obtained by a technique called ARDRA. In ARDRA, bacterial rRNA gene(s) are first amplified by PCR, using conserved sequences of rDNA as primers. The PCR amplification product is then digested with restriction endonuclease and restriction fragments are resolved electrophoretically to obtain a fingerprint [414]. Although ARDRA fingerprinting is faster to perform than classic ribotyping, its discriminatory power is often inferior to that of ribotyping. This is due to the fact that smaller areas of the rRNA operon (and none of the sequences surrounding the rRNA genes) are targeted in ARDRA than in ribotyping. The few references on applying the ARDRA technique for Bacillus, Clostridium, or Listeria fingerprinting describe RFLP analysis of PCR-amplified 16S rDNA (16S rDNA-RFLP) for the characterization of psychrophilic and psychrotrophic clostridial strains associated with spoilage of vacuum-packed meats, typical and atypical Listeria isolates, and enterotoxic B. cereus [53, 261, 415].

Pulsed-field gel electrophoresis

Due to the problems encountered with conventional REA of bacterial genomes, a technique was developed for bacterial fingerprinting, using profiles consisting of fewer numbers of larger-sized genomic restriction fragments [111]. In this technique, bacterial genomic DNA is restricted in situ (in a gel block) with a rare cutting restriction endonuclease, such as SmaI, SfiI, NotI, or BssHII, and the restriction fragments are separated by PFGE, which is a special technique capable of the resolution of large DNA fragments. With PFGE, highly discriminative fingerprinting of bacterial isolates can be performed. Of the different molecular fingerprinting methods, PFGE has in most cases proved to be the most discriminatory. However, PFGE is a laborious technique and it is not usually applied in studies where large numbers of isolates are characterized.

PFGE has been used in the fingerprinting of C. perfringens clinical isolates, but has only infrequently been applied in the fingerprinting of B. cereus. However, there are many more data on the applicability of PFGE for L. monocytogenes typing. PFGE has been used to characterize L. monocytogenes isolates from food items, the environment, and human listeriosis cases, to trace a contamination in an ice cream plant, in fish-, seafood-, and meat-processing plants, and in pig slaughterhouses [27, 28, 65, 86, 97, 120, 151, 204, 255, 280, 281, 282, 308, 309, 373]. PFGE analysis of clostridia proves challenging, due to their endogenous DNAse activity [229]. However, PFGE was successfully applied in the characterization of C. perfringens strains associated with food-borne-disease or antibiotic-diarrhea, especially when cell pre-treatment steps that interfere with the DNAse activity were included [253, 269, 326, 381]. In B. cereus typing, PFGE was applied to investigate a bacillus pseudo-outbreak in a pediatric unit [246].

Random amplified polymorphic DNA

In RAPD fingerprinting, one or two primers (usually 10–12 bp long) are arbitrarily selected and allowed to anneal to the bacterial genomic DNA template at a low stringency. In RAPD, several amplification products of varying sizes are obtained. These products are resolved electrophoretically to yield a RAPD-fingerprint [330, 442]. RAPD typing is fast to perform, especially in cases where fingerprinting can be performed directly on single-colonies growing on an agar plate. Due to the low stringency of the PCR amplification, RAPD-fingerprints can show some variation (especially in band strengths) and therefore the fingerprint comparisons have to be done visually by an experienced person. However, when strictly identical conditions (same thermocycler, reagents, etc.) are used, the method usually works well [279]. RAPD banding-pattern reproducibility can be improved by using a procedure where the same strains are exposed to three different annealing temperatures (with increasing stringency) and by identifying the stable amplicons [78]. This triplicate procedure naturally makes the RAPD fingerprinting technique more laborious.

RAPD is best suited for studies where a specific bacterial strain (e.g. a certain food-borne pathogen) is sought among large number of isolates. Bacterial isolates with fingerprints clearly different from the specific bacterial strain can quickly be identified with RAPD and rejected. Thereafter, the remaining (fewer) strains may be further characterized with another, more laborious technique (e.g. ribotyping, PFGE, AFLP). RAPD is not well suited for interlaboratory or taxonomic studies, or studies where the aim is to develop a fingerprint database.

RAPD can be used for the fingerprinting of B. cereus isolates in a versatile manner [19, 80, 81, 301, 387]. For example, it proved useful in the differentiation of psychrotolerant B. cereus strains [238] and B. cereus isolates from spontaneously fermented food [354] and in tracing the source of B. cereus contamination in pasteurized milk, ethyl alcohol, and food-poisoning outbreaks [112, 144, 189]. There are several reports on RAPD fingerprinting of L. monocytogenes, including studies where RAPD was used in typing isolates from different foods (cheese, poultry products, (cold-)smoked salmon, meat products, imitation crab meat), animals, human listeriosis cases, and to trace contamination in pork slaughtering and cutting plants, in fish, seafood, and meat processing plants, in a poultry processing environment, and in a dairy environment [7, 30, 46, 59, 93, 120, 135, 136, 151, 199, 224, 237, 250, 259, 277, 428, 444]. To our knowledge, RAPD-typing has not been reported for C. perfringens, although another food-borne Clostridium, C. botulinum, has been fingerprinted with RAPD [197].

Repetitive element sequence-based PCR

Repetitive chromosomal elements, which are found randomly distributed in bacterial genomes, are the targets of rep-PCR amplification. In rep-PCR, primers anneal to repetitive parts of the chromosome and amplification occurs when the distance between primer binding sites is short enough to enable this [419]. The repetitive DNAs can be classified either as short sequence repeats (SSRs) or variable number of tandem repeats (VNTRs). Variations of rep-PCR include enterobacterial repetitive intergenic consensus PCR (ERIC-PCR), BOX-PCR, repetitive extragenic palindromic unit sequence PCR, and VNTR-PCR [410].

rep-PCR techniques are fairly infrequently applied for the characterization of Clostridium, Bacillus, or Listeria strains. To our knowledge, there is only one report of Clostridium rep-PCR, where C. botulinum strains were characterized [197]. For B. cereus typing, ERIC-PCR, BOX-PCR, and VNTR-PCR have been applied [3, 162, 227, 321, 361] and rep-PCR has been used for the typing of other Bacillus spp. [82, 174]. For Listeria spp., fingerprinting rep-PCR and ERIC-PCR have been used [212, 213, 370].

Amplified fragment length polymorphism

AFLP involves restriction of total bacterial DNA with two restriction enzymes of differing cutting frequencies (e.g. HindIII, TaqI), followed by ligation of the fragments to oligonucleotide adapters complementary to the sequences of the restriction sites (restriction-half-site-specific adapters). Selective PCR amplification of subset of fragments is achieved using primers corresponding to the contiguous sequences in the adapter and restriction site, plus a few nucleotides in the original target DNA. When only one of the primers is labeled, only a subset of amplified fragments is detected during visualization [206, 424]. A variation of this technique has been developed, where only a single restriction enzyme is used [146].

AFLP is a fairly new technique and therefore only scarce data are available on its application in B. cereus, C. perfringens, and L. monocytogenes fingerprinting. In the few papers published, AFLP proves a sensitive and reproducible technique for the typing of C. perfringens and L. monocytogenes [1, 275, 340]. AFLP was used to trace an outbreak of B. cereus infections in a neonatal intensive care unit to the balloons used in manual ventilation and to study B. cereus soil isolates [403, 412].

Discriminative power of different techniques

A number of studies have been performed where the discriminative power of the above mentioned techniques have been compared. For B. cereus fingerprinting, RAPD proved a somewhat more discriminative method than ribotyping, whereas the discriminative abilities of ribotyping and ERIC-PCR were equal [19, 361]. For C. perfringens typing, ribotyping and PFGE proved equally discriminative [360]. In L. monocytogenes fingerprinting, RAPD, PFGE, and AFLP were equally discriminative [97, 151, 224, 420, 421]. Ribotyping has proved either equally discriminative or a bit less discriminative than PFGE and RAPD in L. monocytogenes typing [224, 250, 308]. These results indicate that RAPD, PFGE, ribotyping, rep-PCR, and AFLP are all suitable methods for subspecies-level fingerprinting of B. cereus, L. monocytogenes, and C. perfringens. The discriminative capabilities of the techniques are about equal and the results obtained with different techniques are generally in very good agreement with each other. The choice of restriction endonuclease in PFGE and ribotyping and the choice of primers in RAPD have a great impact on the discriminative ability of these techniques. Therefore, the applied technique has to be tailor-made for each bacterial species, to obtain the best possible discriminative ability.

Future prospects for the exploitation of the described methods in the industrial environment

Biofilms cannot be eliminated from industrial systems by any of the current methods available. Thus, the primary challenge is to control rather than eradicate biofilms from the industrial environment [61]. Knowledge about the microbiota present in the industrial environment will help to control the formation and build-up of biofilms, since specific characteristics of each microbiota can be considered when preventative and/or control measures are applied. Applications of the microbiological methods described in this review are presented in Table 3. It should be remembered that sampling is a crucial step in the characterization/identification procedure. If it is performed inadequately, the characterization of microbiota will inevitably be biased.
Table 3.

Applications of microbiological methods described in this review. FISH Fluorescence in situ hybridization


Available methods

Detection of selected species/groups


Specific PCR

Specific hybridization (including microarray)


Strain-level identification







Quantification of specific microorganisms


Quantitative PCR


Culture hybridization

Activity of the microorganisms

Metabolic stains in combination with microscopy

RT-PCR alone or combined with other techniques

RNA hybridization

Community analysis

Epifluorescence/confocal laser scanning microscopy





Specific hybridization (including microarray)

The emergence of new detection and real-time methods is linked to the need for a better assessment of the microbiological quality of products. This objective can be reached through an increase in detection specificity and a reduction in analysis time [346]. In particular, in situ techniques should enable progress in understanding the ecology of complex microbial communities in minimally disturbed samples. The most important weakness of culture-independent methods is that the taxonomic interpretation of data appears problematic [152]. Although various new detection methods are applied to detect microorganisms from the industrial environment, the use of culture techniques will persist, since the international standard methods for the detection and enumeration of pathogens are based on cultivation. In addition, in many industrial quality control laboratories, resources for the use of new molecular methods are inadequate.

In routine food control, PCR assays may shorten the time needed to identify e.g. L. monocytogenes, although enrichment may be necessary prior to the detection. By using virulence-associated genes as primers or probes, the presence of pathogenic species can be rapidly determined. However, dead bacterial cells may constitute a problem in basic PCR detection in hygiene control. For example, heat-treated samples may contain dead or damaged cells with no relevance to product safety, although the dead bacteria may still create positive signals due to the stability of their DNA molecules [311]. In some circumstances (when the RNAse activity of the bacterial population is not destroyed in the sample prior to analysis), RT-PCR can be applicable in assessing the viable and active populations in samples. DNA-based detection methods, especially PCR, may gradually replace traditional methods for assaying microorganisms in food. When applicable (e.g. when no enrichment step is required), real-time PCR (which enables the quantification of target sequences) can prove highly useful for the rapid analysis of food pathogens. However, PCR detection of pathogens in food samples is still time-consuming, particularly in the case of large-scale testing [364]. High-throughput methods, such as dot-blot hybridization using microarrays, have promising future potential for routine diagnostic and quality control procedures in industrial settings [245].

One of the challenges for microbial ecology is to gain more information below the bacterial community, genera, and even species level. Subspecies-level identification is especially important when a source of contamination is traced in an industrial environment. DNA fingerprinting techniques provide effective molecular tools to identify and type microorganisms to subspecies level [152]. When typing of the microbial isolates is performed, e.g. to trace a contamination source, the importance of including sufficient numbers of isolates from each sample site should be remembered. Once efficiently integrated, the typing techniques provide precise information on the heterogeneity of the target bacterial population at a given time/space combination. However, fingerprinting methods are laborious and time-consuming, since isolation and cultivation of a large number of bacterial isolates cannot be avoided. Another limitation is that the unculturable strains present in natural ecosystems cannot be reached with typing methods.

In conclusion, bacterial detection, identification, and typing from industrial samples remains a laborious task, mainly due to the fact that frequently large numbers of samples need to be analyzed. The development of automated techniques that allow high-throughput analysis of large numbers of samples will greatly facilitate studies on industrial microbial ecology in the future.


  1. 1.
     Aarts HJ, Hakemulder LE, Hoef AM van (1999) Genomic typing of Listeria monocytogenes strains by automated laser fluorescence analysis of amplified fragment length polymorphism fingerprint patterns. Int J Food Microbiol 49:95–102PubMedGoogle Scholar
  2. 2.
     Abeyta C Jr, Wetherington JH (1994) Iron milk medium method for recovering Clostridium perfringens from shellfish: collaborative study. J AOAC Int 77:351–356PubMedGoogle Scholar
  3. 3.
     Abn-El-Haleem E, Moawad H, Zaki EA, Zaki S (2002) Molecular characterization of phenol-degrading bacteria isolated from different Egyptian ecosystems. Microbiol Ecol 43:217–224Google Scholar
  4. 4.
     Adams MR, Hope CFA (1986) Fast food techniques. Lab Pract 7:15–18Google Scholar
  5. 5.
     Adcock PW, Saint CP (2001) Rapid confirmation of Clostridium perfringens by using chromogenic and fluorogenic substrates. Appl Environ Microbiol 67:4382–4384CrossRefPubMedGoogle Scholar
  6. 6.
     Agersborg A, Dahl R, Martinez I (1997) Sample preparation and DNA extraction procedures for polymerase chain reaction identification of Listeria monocytogenes in seafoods. Int J Food Microbiol 35:275–280CrossRefPubMedGoogle Scholar
  7. 7.
     Aguado V, Vitas AI, Garcia-Jalon I (2001) Random amplified polymorphic DNA typing applied to the study of cross contamination by Listeria monocytogenes in processed food products. J Food Prot 64:716–720PubMedGoogle Scholar
  8. 8.
     Allerberger F, Fritschel SJ (1999) Use of automated ribotyping of Austrian Listeria monocytogenes isolates to support epidemiological typing. J Microbiol Methods 35:237–244CrossRefPubMedGoogle Scholar
  9. 9.
     Almeida PF, Almeida RCC (2000) A PCR protocol using inl gene as a target for specific detection of Listeria monocytogenes. Food Control 11:97–101CrossRefGoogle Scholar
  10. 10.
     Alwine JC, Kemp DJ, Stark GR (1977) Method for detection of specific RNAs in agarose gels by transfer to diazobenzyloxymethyl-paper and hybridization with DNA probes. Proc Natl Acad Sci USA 74:5350–5354PubMedGoogle Scholar
  11. 11.
     Amann RI, Binder BJ, Olson RJ, Chisholm SW, Devereux R, Stahl DA (1990a) Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl Environ Microbiol 56:1919–1925PubMedGoogle Scholar
  12. 12.
     Amann RI, Krumholz L, Stahl DA (1990b) Fluorescent-oligonucleotide probing of whole cells for determinative phylogenetic and environmental studies in microbiology. J Bacteriol 172:762–770PubMedGoogle Scholar
  13. 13.
     Amann RI, Ludwig W, Schleifer K-H (1992) Identification and in situ detection of individual bacterial cells. FEMS Microbiol Lett 100:45–50CrossRefGoogle Scholar
  14. 14.
     Amann RI (1995a) In situ identification of microorganisms by whole cell hybridization with rRNA-targeted nucleic acid probes. In: Akkermans ADL, Elsas DJ van, Bruijn FJ de (eds) Molecular microbial ecology manual. Kluwer, Dordrecht, pp 1–15Google Scholar
  15. 15.
     Amann RI (1995b) Fluorescently labelled, rRNA-targeted oligonucleotide probes in the study of microbial ecology. Mol Ecol 4:543–554Google Scholar
  16. 16.
     Amann RI, Ludwig W, Schleifer K-H (1995) Phylogenetic identification and in situ detection of individual microbial cells without cultivation. Microbiol Rev 59:143–169PubMedGoogle Scholar
  17. 17.
     Andersen Borge GI, Skeie M, Sørhaug T, Langsrud T, Granum PE (2001) Growth and toxin profiles of Bacillus cereus isolated from different food sources. Int J Food Microbiol 69:237–246CrossRefPubMedGoogle Scholar
  18. 18.
     Andersson A, Rönner U, Granum PE (1995) What problems does the food industry have with the spore-forming pathogens Bacillus cereus and Clostridium perfringens? Int J Food Microbiol 28:145–155Google Scholar
  19. 19.
     Andersson A, Svensson B, Christiansson A, Rönner U (1999) Comparison between automatic ribotyping and random amplified polymorphic DNA analysis of Bacillus cereus isolates from the dairy industry. Int J Food Microbiol 47:147–151CrossRefPubMedGoogle Scholar
  20. 20.
     Anonymous (1994) LIVE/DEAD BacLight viability kit (L-7007). Molecular Probes, Eugene, Ore.Google Scholar
  21. 21.
     Araujo M, Sueiro RA, Gomez MJ, Garrido MJ (2001) Evaluation of fluorogenic TSC agar for recovering Clostridium perfringens in groundwater samples. Water Sci Technol 43:201–204Google Scholar
  22. 22.
     Aschfalk A, Müller W (2002) Clostridium perfringens toxin types from wild-caught Atlantic cod (Gadus morhua L.) determined by PCR and ELISA. Can J Microbiol 48:365–368PubMedGoogle Scholar
  23. 23.
     Aschfalk A, Valentin-Weigand P, Müller W, Goethe R (2002) Toxin types of Clostridium perfringens isolated from free-ranging, semi-domesticated reindeer in Norway. Vet Rec 151:210–213PubMedGoogle Scholar
  24. 24.
     Assmus B, Hutzler P, Kirchhof G, Amann R, Lawrence JR, Hartmann A (1995) In situ localization of Azospirillum brasilense in the rhizosphere of wheat with fluorescent labeled, rRNA-targeted oligonucleotide probes and scanning confocal laser microscopy. Appl Environ Microbiol 61: 1013–1019Google Scholar
  25. 25.
     Augustynowicz E, Gzyl A, Slusarczyk J (2002) Detection of enterotoxigenic Clostridium perfringens with a duplex PCR. J Med Microbiol 51:169–172PubMedGoogle Scholar
  26. 26.
     Autio K, Mattila-Sandholm T (1992) Detection of active yeast cells (Saccharomyces cerevisiae) in frozen dough sections. Appl Environ Microbiol 58:2153–2157Google Scholar
  27. 27.
     Autio T, Säteri T, Fredrikson-Ahomaa M, Rahkio M, Lundén J, Korkeala H (2000) Listeria monocytogenes contamination pattern in pig slaughterhouses. J Food Prot 63:1438–1442PubMedGoogle Scholar
  28. 28.
     Autio T, Lundén J, Fredriksson-Ahomaa M, Björkroth J, Sjöberg A-M, Korkeala (2002) Similar Listeria monocytogenes pulsotypes detected in several foods originating from different sources. Int J Food Microbiol 77:83–90PubMedGoogle Scholar
  29. 29.
     Avery SV, Harwood JL, Lloyd D (1995) Quantification and characterization of phagocytosis in the soil amoebae Acanthamoeba castellanii by flow cytometry. Appl Environ Microbiol 61:1124–1132Google Scholar
  30. 30.
     Aznar R, Alarcon B (2002) On the specificity of PCR detection of Listeria monocytogenes in food: a comparison of published primers. Syst Appl Microbiol 25:109–119PubMedGoogle Scholar
  31. 31.
     Bach H-J, Tomanova J, Schloter M, Munch JC (2002) Enumeration of total bacteria and bacteria with genes for proteolytic activity in pure cultures and in environmental samples by quantitative PCR mediated amplification. J Microbiol Methods 49:235–245CrossRefPubMedGoogle Scholar
  32. 32.
     Ball KD, Trevors JT (2002) Bacterial genomics: the use of DNA microarrays and bacterial artificial chromosomes. J Microbiol Methods 49:275–284CrossRefPubMedGoogle Scholar
  33. 33.
     Baloga AO, Harlander SK (1991) Comparison of methods for discrimination between strains of Listeria monocytogenes from epidemiological surveys. Appl Environ Microbiol 57:2324–2331PubMedGoogle Scholar
  34. 34.
     Bansal NS (1996) Development of a polymerase chain reaction assay for the detection of Listeria monocytogenes in food. Lett Appl Microbiol 22:353–356PubMedGoogle Scholar
  35. 35.
     Barry T, Colleran G, Glennon M, Dunican LK, Gannon F (1991) The 16S/23S ribosomal spacer region as a target for DNA probes to identify eubacteria. PCR Methods Appl 1:51–56PubMedGoogle Scholar
  36. 36.
     Bennett C (1985) Control of microbial problems and corrosion in closed systems. Pap Technol Ind 11:331–335Google Scholar
  37. 37.
     Besnard V, Federighi M, Cappelier JM (2000) Development of a direct viable count procedure for the investigation of VBNC state in Listeria monocytogenes. Lett Appl Microbiol 31:77–81CrossRefPubMedGoogle Scholar
  38. 38.
     Bessessen MT, Luo QA, Rotbart HA, Blaser MJ, Ellison RT III (1990) Detection of Listeria monocytogenes by using the polymerase chain reaction. Appl Environ Microbiol 56:2930–2932PubMedGoogle Scholar
  39. 39.
     Betts RP (1992) An evaluation of the Bio-Orbit 1253 portable ATP luminometer. Campden food and drink research association, LondonGoogle Scholar
  40. 40.
     Beumer RR, Giffel MC te, Anthonie SVR, Cox LJ (1996) The effect of acriflavine and nalidixic acid on the growth of Listeria spp. in enrichment media. Int J Food Microbiol 13:137–148CrossRefGoogle Scholar
  41. 41.
     Bickley J, Short JK, McDowell DG, Parkes HC (1996) Polymerase chain reaction (PCR) detection of Listeria monocytogenes in diluted milk and reversal of PCR inhibition caused by calcium ions. Lett Appl Microbiol 22:153–158PubMedGoogle Scholar
  42. 42.
     Binnerup SJ, Jensen DF, Thordal-Chirtensen H, Sørensen J (1993) Detection of viable, but non-culturable Pseudomonas fluorescens DF57 in soil using a microcolony epifluorescence technique. FEMS Microbiol Ecol 12:97–105CrossRefGoogle Scholar
  43. 43.
     Blais BW, Phillippe LM (1993) A simple RNA probe system for analysis of Listeria monocytogenes polymerase chain reaction products. Appl Environ Microbiol 59:2795–2800PubMedGoogle Scholar
  44. 44.
     Block JC (1992) Biofilms in drinking water distribution systems. In: Melo LF, Bott TR, Fletcher M, Capdeville B (eds) Biofilms—science and technology. Kluwer, Dordrecht, pp 469–486Google Scholar
  45. 45.
     Bloem J, Veninga M, Sheperd J (1995) Fully automatic determination of soil bacterium numbers, cell volumes, and frequencies of dividing cells by confocal laser scanning microscopy and image analysis. Appl Environ Microbiol 61:926–936Google Scholar
  46. 46.
     Boerlin P, Bannerman E, Ischer F, Rocourt J, Bille J (1995) Typing of Listeria monocytogenes: a comparison of random amplification of polymorphic DNA with five other methods. Res Microbiol 146:35–49CrossRefPubMedGoogle Scholar
  47. 47.
     Bohnert M, Dilasser F, Dalet C, Mengaud J, Cossart P (1992) Use of specific oligonucleotides for direct enumeration of Listeria monocytogenes in food samples by colony hybridization and rapid detection by PCR. Res Microbiol 143:271–280CrossRefPubMedGoogle Scholar
  48. 48.
     Borin S, Daffonchio D, Sorlini C (1997) Single stranded conformation polymorphism analysis of PCR-tDNA fingerprinting to address the identification of Bacillus species. FEMS Microbiol Lett 157:87–93CrossRefPubMedGoogle Scholar
  49. 49.
     Bovill RA, Shallcross JA, Mackey BM (1994) Comparison of the fluorescent redox dye 5-cyano-2,3-ditolyltetrazolium chloride with p-iodonitrotetrazolium violet to detect metabolic activity in heat-stressed Listeria monocytogenes cells. J Appl Bacteriol 77:353–358PubMedGoogle Scholar
  50. 50.
     Brakenhoff GJ, Bloem P, Barends P (1979) Confocal scanning light microscopy with high aperture immersion lenses. J Microsc 117:219–232Google Scholar
  51. 51.
     Bredholt S, Maukonen J, Kujanpää K, Alanko T, Olofson U, Husmark U, Sjöberg A-M, Wirtanen G (1999) Microbial methods for assessment of cleaning and disinfection of food-processing surfaces cleaned in a low-pressure system. Eur Food Res Technol 209:145–152CrossRefGoogle Scholar
  52. 52.
     Breeuwer P, Drocourt J-L, Bunschoten N, Zwietering MH, Rombouts FM, Abee T (1995) Characterization of uptake and hydrolysis of fluorescein diacetate and carboxyfluorescein diacetate by intracellular esterases in Saccharomyces cerevisiae, which result in accumulation of fluorescent product. Appl Environ Microbiol 61:1614–1619PubMedGoogle Scholar
  53. 53.
     Broda DM, Musgrave DR, Bell RG (2000) Use of restriction fragment length polymorphism analysis to differentiate strains of psychrophilic and psychrotrophic clostridia associated with ′blown pack′ spoilage of vacuum packed meats. J Appl Microbiol 88:107–116CrossRefPubMedGoogle Scholar
  54. 54.
     Brown MRW, Gilbert P (1993) Sensitivity of biofilms to antimicrobial agents. J Appl Bacteriol Symp Suppl 74:87S–97SGoogle Scholar
  55. 55.
     Bubert A, Köhler S, Goebel W (1992) The homologous and heterologous regions within the iap gene allows genus- and species-specific identification of Listeria spp. by polymerase chain reaction. Appl Environ Microbiol 58:2625–2632PubMedGoogle Scholar
  56. 56.
     Bubert A, Hein I, Rauch M, Lehner A, Yoon B, Goebel W, Wagner M (1999) Detection and differentiation of Listeria spp. by a single reaction based on multiplex PCR. Appl Environ Microbiol 65:4688–4692PubMedGoogle Scholar
  57. 57.
     Buogo C, Capaul S, Häni H, Frey J, Nicolet J (1995) Diagnosis of Clostridium perfringens type C enteritis in pigs using a DNA amplification technique (PCR). Zentralbl Veterinarmed B 42:51–58PubMedGoogle Scholar
  58. 58.
     Busch SV, Donnelly CW (1992) Development of a repair-enrichment broth for resuscitation of heat-injured Listeria monocytogenes and Listeria innocua. Appl Environ Microbiol 58:14–20PubMedGoogle Scholar
  59. 59.
     Byun SK, Jung SC, Yoo HS (2001) Random amplification of polymorphic DNA typing of Listeria monocytogenes isolated from meat. Int J Food Microbiol 69:227–235CrossRefPubMedGoogle Scholar
  60. 60.
     Caldwell DE, Korber DR, Lawrence JR (1992) Confocal laser microscopy and digital image analysis in microbial ecology. In: Marshall KC (ed) Advances in microbial ecology vol 12. Plenum Press, New York, pp 1–67Google Scholar
  61. 61.
     Camper AK, McFeters GA (2000) Problems in biofouling in drinking water systems. In: Walker J, Surman S, Jass J (eds) Industrial biofouling; detection, prevention and control, vol 1. Wiley, Chichester, pp 13–38Google Scholar
  62. 62.
     Candrian U (1995) Polymerase chain reaction in food microbiology. J Microbiol Methods 23:89–103CrossRefGoogle Scholar
  63. 63.
     Carnio MC, Eppert I, Scherer S (1999) Analysis of the bacterial surface ripening flora of German and French smeared cheeses with respect to their anti-listerial potential. Int J Food Microbiol 47:89–97CrossRefPubMedGoogle Scholar
  64. 64.
     Characklis WG (1981) Fouling biofilm development: a process analysis. Biotechnol Bioeng 23:1923–1960Google Scholar
  65. 65.
     Chasseignaux E, Toquin MT, Ragimbeau C, Salvat G, Colin P, Ermel G (2001) Molecular epidemiology of Listeria monocytogenes isolates collected from the environment, raw meat and raw products in two poultry- and pork-processing plants. J Appl Microbiol 91:888–899CrossRefPubMedGoogle Scholar
  66. 66.
     Chen ML, Tsen HY (2002) Discrimination of Bacillus cereus and Bacillus thuringiensis with 16S rRNA and gyrB gene based PCR primers and sequencing of their annealing sites. J Appl Microbiol 92:912–919CrossRefPubMedGoogle Scholar
  67. 67.
     Chisti Y, Moo-Young M (1994) Cleaning-in-place systems for industrial bioreactors: design, validation and operation. J Ind Microbiol 13:201–207Google Scholar
  68. 68.
     Cloete TE, Steyn PL (1988) A combined membrane filter-immunofluorescent technique for the in situ identification and enumeration of Acinetobacter in activated sludge. Water Res 22:961–969Google Scholar
  69. 69.
     Cloete TE, Smith F, Steyn PL (1989) The use of planctonic bacterial populations in open and closed recirculating water cooling systems for the evaluation of biocides. Int Biodeterior 25:115–122CrossRefGoogle Scholar
  70. 70.
     Collins M, Myers RM (1987) Alterations in DNA helix stability due to base modifications can be evaluated using denaturing gradient gel electrophoresis. J Mol Biol 198:737–744PubMedGoogle Scholar
  71. 71.
     Cooray KJ, Nishibori T, Xiong H, Matsuyama T, Fujita M, Mitsuyama M (1994) Detection of multiple virulence-associated genes of Listeria monocytogenes by PCR in artificially contaminated milk samples. Appl Environ Microbiol 60:3023–3026PubMedGoogle Scholar
  72. 72.
     Costerton JW, Lewandowski Z, Beer D de, Caldwell D, Korber D, James G (1994) Biofilms, the customized microniche. J Bacteriol 176:2137–2142PubMedGoogle Scholar
  73. 73.
     Costerton JW, Lewandowski Z, Caldwell DE, Korber DR, Lappin-Scott HM (1995) Microbial biofilms. Annu Rev Microbiol 49:711–745PubMedGoogle Scholar
  74. 74.
     Cox JL, Dooley D, Beumer R (1990) Effect of lithium chloride and other inhibitors on the growth of Listeria spp. Food Microbiol 7:311–325Google Scholar
  75. 75.
     Cox LJ, Siebenga A, Pedrazzini C, Morton J (1991) Enhanced haemolysin agar (EHA)—an improved selective agar differential medium for isolation of Listeria monocytogenes. Food Microbiol 8:37–49Google Scholar
  76. 76.
     Crosland-Taylor PJ (1953) A device for counting small particles suspended in a fluid through a tube. Nature 171:37–38Google Scholar
  77. 77.
     Curtis GDW, Lee WH (1995) Culture media and methods for the isolation of Listeria monocytogenes. Int J Food Microbiol 26:1–13CrossRefPubMedGoogle Scholar
  78. 78.
     Cusick SM, O′Sullivan DJ (2000) Use of a single, triplicate arbitrarily primed PCR-procedure for molecular fingerprinting on lactic acid bacteria. Appl Environ Microbiol 66:2227–2231CrossRefPubMedGoogle Scholar
  79. 79.
     Daffonchio D, Borin S, Consolandi A, Mora D, Manachini PL, Sorlini C (1998a). 16S–23S rRNA internal transcribed spacers as molecular markers for the species of the 16S rRNA group I of the genus Bacillus. FEMS Microbiol Lett 163:229–236CrossRefPubMedGoogle Scholar
  80. 80.
     Daffonchio D, Borin S, Frova G, Manachini PL, Sorlini C (1998b) PCR fingerprinting of whole genomes: the spacers between the 16S and 23S rRNA genes and of intergenic tRNA genes reveal a different intraspecific genomic variability of Bacillus cereus and Bacillus licheniformis. Int J Syst Bacteriol 48:107–116PubMedGoogle Scholar
  81. 81.
     Daffonchio D, Borin S, Frova G, Gallo R, Mori E, Fani R, Sorlini C (1999) A randomly amplified polymorphic DNA marker specific for the Bacillus cereus group is diagnostic for Bacillus anthracis. Appl Environ Microbiol 65:1298–1303PubMedGoogle Scholar
  82. 82.
     Da Silva KR, Rabinovitch L, Seldin L (1999) Phenotypic and genetic diversity among Bacillus sphaericus strains isolated in Brazil, potentially useful as biological control agents against mosquito larvae. Res Microbiol 150:153–160CrossRefPubMedGoogle Scholar
  83. 83.
     Datta AR, Wentz BA (1989) Identification and enumeration of virulent Listeria strains. Int J Food Microbiol 8:259–264CrossRefPubMedGoogle Scholar
  84. 84.
     Datta AR, Moore MA, Wentz BA, Lane J (1993) Identification and enumeration of Listeria monocytogenes by nonradioactive DNA probe colony hybridization. Appl Environ Microbiol 59:144–149PubMedGoogle Scholar
  85. 85.
     Daube G, Simon P, Limbourg B, Manteca C, Mainil J, Kaeckenbeeck A (1996) Hybridization of 2,659 Clostridium perfringens isolates with gene probes for seven toxins (alpha, beta, epsilon, iota, theta, mu, and enterotoxin) and for sialidase. Am J Vet Res 57:496–501PubMedGoogle Scholar
  86. 86.
     Dauphin G, Ragimbeau C, Malle P (2001) Use of PFGE typing for tracing contamination with Listeria monocytogenes in three cold-smoked salmon processing plants. Int J Food Microbiol 64:51–61CrossRefPubMedGoogle Scholar
  87. 87.
     Davies CM (1991) A comparison of fluorochromes for direct viable counts by image analysis. Lett Appl Microbiol 13:58–61Google Scholar
  88. 88.
     De Beer D, Stoodley P, Roe F, Lewandowski Z (1994) Effects of biofilm structures on oxygen distribution and mass transport. Biotechnol Bioeng 43:1131–1138Google Scholar
  89. 89.
     De Cesare A, Bruce JL, Dambaugh TR, Guerzoni ME, Wiedmann M (2001) Automated ribotyping using different enzymes to improve discrimination of Listeria monocytogenes isolates, with a particular focus on serotype 4b strains. J Clin Microbiol 39:3002–3005Google Scholar
  90. 90.
     Degnan AJ, Kaspar CW, Otwell WS, Tamplin ML, Luchansky JB (1994) Evaluation of lactic acid fermentation products and food-grade chemicals to control Listeria monocytogenes in blue crab (Callinectes sapidus) meat. Appl Environ Microbiol 60:3198–3203PubMedGoogle Scholar
  91. 91.
    Delbés C, Moletta R, Godon JJ (2000) Monitoring of activity dynamics of an anaerobic digester bacterial community using 16S rRNA polymerase chain reaction—single strand conformation polymorphism analysis. Environ Microbiol 2:506–515CrossRefPubMedGoogle Scholar
  92. 92.
     Delbés C, Moletta R, Godon JJ (2001) Bacterial and archeal 16S rDNA and 16S rRNA dynamics during an acetate crisis in an anaerobic digestor ecosystem. FEMS Microbiol Ecol 35:19–26CrossRefPubMedGoogle Scholar
  93. 93.
     Delgado da Silva MC, Destro MT, Hofer E, Tibana A (2001) Characterization and evaluation of some virulence markers of Listeria monocytogenes strains isolated from Brazilian cheeses using molecular, biochemical and serotyping techniques. Int J Food Microbiol 63:275–280CrossRefPubMedGoogle Scholar
  94. 94.
     DeLong EF, Wickham GS, Pace NR (1989) Phylogenetic stains: ribosomal RNA-based probes for the identification of single cells. Science 243:1360–1363PubMedGoogle Scholar
  95. 95.
     Delpech M (2000) DNA chips. Ann Biol Clin 58:29–38Google Scholar
  96. 96.
     Deneer HG, Boychuk I (1991) Species specific detection of Listeria monocytogenes by DNA amplification. Appl Environ Microbiol 57:606–609PubMedGoogle Scholar
  97. 97.
     Destro MT, Leitao MFF, Farber JM (1996) Use of molecular typing methods to trace the dissemination of Listeria monocytogenes in a shrimp processing plant. Appl Environ Microbiol 62:705–711PubMedGoogle Scholar
  98. 98.
     Diaper JP, Edwards C (1994a) Flow cytometric detection of viable bacteria in compost. FEMS Microbiol Ecol 14:213–220CrossRefGoogle Scholar
  99. 99.
     Diaper JP, Edwards C (1994b) Survival of Staphylococcus aureus in lakewater monitored by flow cytometry. Microbiology 140:35–42PubMedGoogle Scholar
  100. 100.
     Diaper JP, Tither K, Edwards C (1992) Rapid assessment of bacterial viability by flow cytometry. Appl Microbiol Biotechnol 38:268–272PubMedGoogle Scholar
  101. 101.
     Dickinson JH, Kroll RG, Grant KA (1995) The direct application of the polymerase chain reaction to DNA extracted from foods. Lett Appl Microbiol 20:212–216PubMedGoogle Scholar
  102. 102.
     Duffy G, Sheridan JJ, Hofstra H, McDowell DA, Blair IS (1997) A comparison of immunomagnetic and surface adhesion immunofluorescent techniques for the rapid detection of Listeria monocytogenes and Listeria innocua in meat. Lett Appl Microbiol 24:445–450PubMedGoogle Scholar
  103. 103.
     Dufour P, Colon M (1992) The tetrazolium reduction method for assessing the viability of individual bacterial cells in aquatic environments: improvements, performance and applications. Hydrobiology 232:211–218Google Scholar
  104. 104.
     Duineveld BM, Kowalchuk GA, Keijzer A, Elsas JD van, Veen JA van (2001) Analysis of bacterial communities in the rhizosphere of chrysanthemum via denaturing gradient gel electrophoresis of PCR-amplified 16S rRNA as well as DNA fragments coding for 16S rRNA. Appl Environ Microbiol 67:172–178PubMedGoogle Scholar
  105. 105.
     Dunbar J, White S, Forney L (1997) Genetic diversity through the looking glass: effect of enrichment bias. Appl Environ Microbiol 63:1326–1331Google Scholar
  106. 106.
     Duncan S, Glover LA, Killham K, Prosser J (1994) Luminescence-based detection of activity of starved and viable but non-culturable bacteria. Appl Environ Microbiol 60:1308–1316PubMedGoogle Scholar
  107. 107.
     Dyer J (1996) Odor control in recycled fiber mills. Prog Pap Recycling 5:87–90Google Scholar
  108. 108.
     Dykstra G, Stoner M (1997) Enzymes and biodispersants. In: TAPPI (ed) Papermaking. (Biological sciences symposium 1997) TAPPI Press, Atlanta, Ga., pp 117–124Google Scholar
  109. 109.
     Edidin M (1989) Fluorescent labeling of cell surfaces. In: Wang Y-L, Taylor DL (eds) Fluorescence microscopy of living cells in culture, part A. Fluorescent analogs, labeling cells and basic microscopy. (Methods in cell biology, vol 29) Academic Press, San Diego, Calif., pp 87–102Google Scholar
  110. 110.
     Eisgruber H, Schalch B, Sperner B, Stolle A (2000) Comparison of four routine methods for the confirmation of Clostridium perfringens in food. Int J Food Microbiol 57:135–140CrossRefGoogle Scholar
  111. 111.
     Ely B, Gerardot CJ (1988) Use of pulsed-field-gradient gel electrophoresis to construct a physical map of the Caulobacter crescentus genome. Gene 7:323–333CrossRefGoogle Scholar
  112. 112.
     Eneroth A, Svensson B, Molin G, Christiansson A (2001) Contamination of pasteurized milk by Bacillus cereus in the filling machine. J Dairy Res 68:189–196PubMedGoogle Scholar
  113. 113.
     Entis P, Lerner I (2000) Twenty-four-hour direct presumptive enumeration of Listeria monocytogenes in food and environmental samples using the ISO-GRID method with LM-137 agar. J Food Prot 3:354–363Google Scholar
  114. 114.
     Erlandson K, Batt CA (1997) Strain-specific differentiation of lactococci in mixed starter culture populations using randomly amplified polymorphic DNA-derived probes. Appl Environ Microbiol 63:2702–2707PubMedGoogle Scholar
  115. 115.
     European hygienic equipment design group (1993) Hygienic equipment design criteria. Trends Food Sci Technol 4:225–229Google Scholar
  116. 116.
     Exner M, Tuschewitzki G-J, Scharnagel J (1987) Influence of biofilms by chemical disinfectants and mechanical cleaning. Zentralbl Bakteriol Mikrobiol Hyg B 183:549–563PubMedGoogle Scholar
  117. 117.
    Fach P, Popoff MR (1997) Detection of enterotoxigenic Clostridium perfringens in food and fecal samples with a duplex PCR and the slide latex agglutination test. Appl Environ Microbiol 63:4332–4236Google Scholar
  118. 118.
     Farber JM, Daley E (1994) Presence and growth of Listeria monocytogenes in naturally contaminated meats. Int J Food Microbiol 22:33–42CrossRefPubMedGoogle Scholar
  119. 119.
     Farber JM, Peterkin PI (1991) Listeria monocytogenes, a food-borne pathogen. Microbiol Rev 55:476–511PubMedGoogle Scholar
  120. 120.
     Farber JM, Daley EM, MacKie MT, Limerick B (2000) A small outbreak of listeriosis potentially linked to the consumption of imitation crab meat. Lett Appl Microbiol 31:100–104CrossRefPubMedGoogle Scholar
  121. 121.
     Favier CF, Vaughan EE, Vos WM de, Akkermans ADL (2002) Molecular monitoring of succession of bacterial communities in human neonates. Appl Environ Microbiol 68:219–226CrossRefPubMedGoogle Scholar
  122. 122.
     Felske A (1999) Reviewing the DA001 files: a 16S rRNA chase on suspect X99967, a Bacillus and Dutch underground activist. J Microbiol Methods 36:77–93CrossRefPubMedGoogle Scholar
  123. 123.
     Felske A, Engelen B, Nübel U, Backhaus H (1996) Direct ribosomal isolation from soil to extract bacterial rRNA for community analysis. Appl Environ Microbiol 62:4162–4167PubMedGoogle Scholar
  124. 124.
     Felske A, Rheims A, Wolterink E, Stackebrandt E, Akkermans ADL (1997) Ribosome activity reveals prominent activity of an uncultured number of the class Actinobacteria in grassland soils. Microbiology 143:2983–2989PubMedGoogle Scholar
  125. 125.
     Felske A, Akkermans ADL, Vos WM de (1998a) Quantification of 16S rRNAs in complex bacterial communities by multiple competitive reverse transcription-PCR in temperature gradient gel electrophoresis fingerprints. Appl Environ Microbiol 64:4581–4587PubMedGoogle Scholar
  126. 126.
     Felske A, Akkermans ADL, Vos WM de (1998b) In situ detection of an uncultured predominant Bacillus in Dutch grassland soils. Appl Environ Microbiol 64:4588–4590PubMedGoogle Scholar
  127. 127.
     Felske A, Wolterink A, Lis R van, Akkermans ADL (1998c) Phylogeny of the main bacterial 16S rRNA sequences in Drentse A grassland soils (The Netherlands). Appl Environ Microbiol 64:871–879PubMedGoogle Scholar
  128. 128.
     Felske A, Wolterink A, Lis R van, Vos WM de, Akkermans ADL (1999) Searching for predominant soil bacteria: 16S rDNA cloning versus strain cultivation. FEMS Microbiol Ecol 30:137–145PubMedGoogle Scholar
  129. 129.
     Fischer SG, Lerman LS (1983) DNA fragments differing by single base-pair substitutions are separated in denaturing gradient gels: correspondence with melting theory. Proc Natl Acad Sci USA 80:1579–1583PubMedGoogle Scholar
  130. 130.
     Fitter S, Heuzenroeder M, Thomas CJ (1992) A combined PCR and selective enrichment method for rapid detection of Listeria monocytogenes. J Appl Microbiol 73. 53–59Google Scholar
  131. 131.
     Fleet GH (1999) Microorganisms in food ecosystems. Int J Food Microbiol 50:101–117CrossRefPubMedGoogle Scholar
  132. 132.
     Flemming H-C, Schaule G, McDonogh R (1992) Biofouling on membranes—a short review. In: Melo LF, Bott TR, Fletcher M, Capdeville B (eds) Biofilms—science and technology. Kluwer, Dordrecht, pp 487–497Google Scholar
  133. 133.
     Fluit AC, Torensma R, Visser MJ, Aarsman CJ, Poppelier MJ, Keller BH, Klapwijk P, Verhoef J (1993) Detection of Listeria monocytogenes in cheese with the magnetic immuno-polymerase chain reaction assay. Appl Environ Microbiol 59:1289–1293PubMedGoogle Scholar
  134. 134.
     Fodor SP, Rava RP, Huang XC, Pease AC, Holmes CP, Adams CL (1993) Multiplexed biochemical assays with biological chips. Nature 364:555–556PubMedGoogle Scholar
  135. 135.
     Fonnesbech Vogel B, Huss HH, Ojeniyu B, Ahrens P, Gram L (2001a) Eludication of Listeria monocytogenes contamination routes in cold-smoked salmon processing plants detected by DNA-based typing methods. Appl Environ Microbiol 67:2586–2595CrossRefPubMedGoogle Scholar
  136. 136.
     Fonnesbech Vogel B, Jorgensen LV, Ojeniyi B, Huss HH, Gram L (2001b) Diversity of Listeria monocytogenes isolates from cold-smoked salmon produced in different smokehouses as assessed by random amplified polymorphic DNA analyses. Int J Food Microbiol 65:83–92CrossRefPubMedGoogle Scholar
  137. 137.
     Francis KP, Mayr R, Stetten F von, Steward GSAB, Scherer S (1998) Discrimination of psychrotrophic and mesophilic strains of the Bacillus cereus group by PCR targeting of major cold shock protein genes. Appl Environ Microbiol 64:3525–3529PubMedGoogle Scholar
  138. 138.
     Franks AH, Harmsen HJM, Raangs GC, Jansen GJ, Schut F, Welling GW (1998) Variations in bacterial populations in human feces measured by fluorescent in situ hybridization with group-specific 16S rRNA-targeted oligonucleotide probes. Appl Environ Microbiol 64:3336–3345PubMedGoogle Scholar
  139. 139.
     Fraser JA, Sperber WH (1988) Rapid detection of Listeria spp. in food and environmental samples by esculin hydrolysis. J Food Prot 51:762–765Google Scholar
  140. 140.
     Furrer B, Candrian U, Hoeflein C, Luethy J (1991) Detection and identification of Listeria monocytogenes in cooked sausage product and in milk by in vitro amplification of haemolysin gene fragments. J Appl Bacteriol 70:372–379PubMedGoogle Scholar
  141. 141.
     Ganesh Kumar C, Anand SK (1998) Significance of microbial biofilms in food industry: a review. Int J Food Microbiol 42:9–27PubMedGoogle Scholar
  142. 142.
     Gendel SM, Ulaszek J (2000) Ribotype analysis of strain distribution in Listeria monocytogenes. J Food Prot 63:179–185PubMedGoogle Scholar
  143. 143.
     Genigeorgis C, Carniciu M, Dutulescu D, Farver TB (1991) Growth and survival of Listeria monocytogenes in market cheeses stored at 4 to 30 °C. J Food Prot 54: 662–668Google Scholar
  144. 144.
     Ghelardi E, Celandroni F, Salvetti S, Barsotti C, Baggiani A, Senesi S (2002) Identification and characterization of toxigenic Bacillus cereus isolates responsible for two food-poisoning outbreaks. FEMS Microbiol Lett 208:129–134CrossRefPubMedGoogle Scholar
  145. 145.
     Gibson G (2001) Microarrays in ecology and evolution: a preview. Mol Ecol 11:17–24CrossRefGoogle Scholar
  146. 146.
     Gibson JR, Slater E, XerryJ, Tompkins DS, Owen RJ (1998) Use of an amplified-fragment length polymorphism technique to fingerprint and differentiate isolates of Helicobacter pylori. J Clin Microbiol 36:2580–2585PubMedGoogle Scholar
  147. 147.
     Gikas P, Livingston AG (1993) Use of ATP to characterize biomass viability in freely suspended and immobilized cell bioreactors. Biotechnol Bioeng 42:1337–1351Google Scholar
  148. 148.
     Gillan DC, Speksnijder AGCL, Zwart G, Ridder C de (1998) Genetic diversity of the biofilm covering Montacuta ferruginosa (Mollusca, Bivalvia) as evaluated by denaturing gradient gel electrophoresis analysis and cloning of PCR-amplified gene fragments coding for 16S rRNA. Appl Environ Microbiol 64:3464–3472PubMedGoogle Scholar
  149. 149.
     Gilot P, Content J (2002) Specific identification of Listeria welshimeri and Listeria monocytogenes by PCR assays targeting a gene encoding a fibrinoctin-binding protein. J Clin Microbiol 40:698–703CrossRefPubMedGoogle Scholar
  150. 150.
     Giovannoni SJ, Britschgi TB, Moyer CL Field KG (1990) Genetic diversity in Sargasso Sea bacterioplancton. Nature 345:60–63PubMedGoogle Scholar
  151. 151.
     Giovannacci I, Ragimbeau C, Queguiner S, Salvat G, Vendeuvre JL, Carlier V, Ermel G (1999) Listeria monocytogenes in pork slaughtering and cutting plants. Use of RAPD, PFGE and PCR-REA for tracing and molecular epidemiology. Int J Food Microbiol 53:127–140CrossRefPubMedGoogle Scholar
  152. 152.
     Giraffa G, Neviani E (2001) DNA-based, culture-independent strategies for evaluating microbial communities in food-associated ecosystems. Int J Food Microbiol 67: 19–34CrossRefPubMedGoogle Scholar
  153. 153.
     Glaser P, Frangeul L, Buchrieser C, Rusniok C, Amend A, Baquero F, Berche P, Bloecker H, Brandt P, Chakraborty T, Charbit A, Chetouani F, Couvé E, Daruvar A de, Dehoux P, Domann E, Domínguez-Bernal G, Duchaud E, Durant L, Dussurget O, Entian KD, Fsihi H, Portillo FG, Garrido P, Gautier L, Goebel W, et al (2001) Comparative genomics of Listeria species. Science 294:849–852CrossRefPubMedGoogle Scholar
  154. 154.
     Glover DJ, Harris WJ (1998) The use of alkaline phosphatase-labelled oligonucleotide probes as culture confirmation reagents for identification of commercially important bacteria. Lett Appl Microbiol 27:116–120PubMedGoogle Scholar
  155. 155.
     Graham T, Golsteyn-Thomas EJ, Gannon VP, Thomas JE (1996) Genus- and species-specific detection of Listeria monocytogenes using polymerase chain reaction assays targeting the 16S/23S intergenic spacer region of the rRNA operon. Can J Microbiol 42:1155–1162PubMedGoogle Scholar
  156. 156.
     Grau FH, Vanderline PB (1992) Occurrence, numbers growth of Listeria monocytogenes on some vacuum-packaged processed meats. J Food Prot 55:4–7Google Scholar
  157. 157.
     Graves LM, Swaminathan B, Reeves MW, Hunter SB, Weaver RE, Plikaytis BD, Schuchat A (1994) Comparison of ribotyping and multilocus enzyme electrophoresis for subtyping Listeria monocytogenes isolates. J Clin Microbiol 32:2936–2943PubMedGoogle Scholar
  158. 158.
     Greenwood MH, Roberts D, Burden P (1991) The occurrence of Listeria species in milk and dairy products: a national survey in England and Wales. Int J Food Microbiol 12:197–206CrossRefPubMedGoogle Scholar
  159. 159.
     Greisen K, Loeffenlholz M, Purohit A, Leong D (1994) PCR primers and probes for the 16S rRNA gene of most species of pathogenic bacteria, including bacteria found in cerebrospinal fluid. J Clin Microbiol 32:335–351PubMedGoogle Scholar
  160. 160.
     Grimont F, Grimont PAD (1986) Ribosomal ribonucleic acid gene restriction patterns as potential taxonomic tools. Ann Inst Pasteur Microbiol 137B:165–175PubMedGoogle Scholar
  161. 161.
     Gubash SL, L Ingham L (1997) Comparison of a new, bismuth-iron-sulfite-cycloserine agar for isolation of Clostridium perfringens with the tryptose-sulfite-cycloserine and blood agars. Zentralbl Bakteriol 285:397–402PubMedGoogle Scholar
  162. 162.
     Guinebretiere M-H, Berge O, Normand P, Morris C, Carlin F, Nguyen-The C (2001) Identification of bacteria in pasteurized zucchini purées stored at different temperatures and comparison with those found in other pasteurized vegetable purées. Appl Environ Microbiol 67:4520–4530CrossRefPubMedGoogle Scholar
  163. 163.
     Gürtler V, Stanisich VA (1996) New approaches to typing and identification of bacteria using the 16S-23S rDNA spacer region. Microbiology 142:3–16PubMedGoogle Scholar
  164. 164.
     Ha K-S, Park S-J, Seo S-J, Park J-H, Chung D-H (2002) Incidence and polymerase chain reaction assay of Listeria monocytogenes from raw milk in Gyeongnam province in Korea. J Food Prot 65:111–115PubMedGoogle Scholar
  165. 165.
     Hacia JG, Makalowski W, Edgemon K, Erdos MR, Robbins CM, Fodor SP, Brody LC, Collins FS (1998) Evolutionary sequence comparisons using high-density oligonucleotide arrays. Nat Genet 18:155–158PubMedGoogle Scholar
  166. 166.
     Hansen BM, Hendriksen NB (2001) Detection of enterotoxic Bacillus cereus and Bacillus thuringiensis strains by PCR analysis. Appl Environ Microbiol 67:185–189CrossRefPubMedGoogle Scholar
  167. 167.
     Hansen BM, Leser TD, Hendriksen NB (2001. Polymerase chain reaction assay for the detection of Bacillus cereus group cells. FEMS Microbiol Lett 202:209–213CrossRefPubMedGoogle Scholar
  168. 168.
     Harju-Jeanty P, Väätänen P (1984) Detrimental microorganisms in paper and cardboard mills. Pap Puu 3:245–259Google Scholar
  169. 169.
     Harry M, Gambier B, Bourezgui Y, Garnie-Sillam E (1999) Evaluation of purification procedures for DNA extracted from organic rich samples: interference with humic substances. Analusis 27:439–442Google Scholar
  170. 170.
     Hayashi K (1991) PCR-SSCP: a simple and sensitive method for detection of mutations in a genomic DNA. PCR Methods Appl 1:34–38PubMedGoogle Scholar
  171. 171.
     Hayes PS, Grawes LM, Swaminathan B, Ajello GW, Malcolm GB, Weaver RE, Ransom R, Deaver K, Plikaytis BD, Schuvhat A, Wenger JD, Pinner RW, Broome CV, Listeria study group (1992) Comparison of three selective enrichment methods for the isolation of Listeria monocytogenes from naturally contaminated foods. J Food Prot 55:952–959Google Scholar
  172. 172.
     Heisick JE, Rosas-Marty LI, Tatini SR (1995) Enumeration of viable Listeria species and Listeria monocytogenes in foods. J Food Prot 58:733–736Google Scholar
  173. 173.
     Henderson I, Dongzheng Y, Turnbull PCB (1995) Differentiation of Bacillus anthracis and other ′Bacillus cereus group′ bacteria using IS231-derived sequences. FEMS Microbiol Lett 128:113–118PubMedGoogle Scholar
  174. 174.
     Herman L, Heyndrickx M (2000) The presence of intagenically located REP-like elements is sufficient for REP-PCR typing. Res Microbiol 151:255–261CrossRefPubMedGoogle Scholar
  175. 175.
     Herman LMF, Ridder FMHFM de, Vlaemynck GMM (1995a) A multiplex PCR method for the identification of Listeria spp. and Listeria monocytogenes in dairy samples. J Food Prot 58:867–872Google Scholar
  176. 176.
     Herman LMF, Block JHGE de, Moermans RJB (1995b) Direct detection of Listeria monocytogenes in 25 milliliters of raw milk by a two-step PCR with nested primers. Appl Environ Microbiol 61:817–819PubMedGoogle Scholar
  177. 177.
     Hoffman AD, Wiedmann M (2001) Comparative evaluation of culture- and BAX polymerase chain reaction-based detection for Listeria spp. and Listeria monocytogenes in environmental and raw fish samples. J Food Prot 64:1521–1526PubMedGoogle Scholar
  178. 180.
     Holah JT, Betts RP, Thorpe RH (1988) The use of direct epifluorescent microscopy DEM and the direct epifluorescent filter technique DEFT to assess microbial populations on food contact surfaces. J Appl Bacteriol 65:215–221PubMedGoogle Scholar
  179. 181.
     Holah JT, Betts RP, Thorpe RH (1989) The use of epifluorescence microscopy to determine surface hygiene. Int Biodeterior 25:147–154CrossRefGoogle Scholar
  180. 178.
     Holah JT (1992) Industrial monitoring: hygiene in food processing. In: Melo LF, Bott TR, Fletcher M, Capdeville B (eds) Biofilms—science and technology. Kluwer, Dordrecht, pp 645–659Google Scholar
  181. 179.
     Holah J, Timperley A (1999) Hygienic design of food processing facilities and equipment. In: Wirtanen G, Salo S, Mikkola A (eds) R3-Nordic contamination control symposium 30. (VTT symposium 193) Libella Painopalvelu Oy, Espoo, pp 11–39Google Scholar
  182. 182.
     Holbrook R, Andersson JM (1980) An improved selective and diagnostic medium for the isolation and enumeration of Bacillus cereus in foods. Can J Microbiol 26:753–759Google Scholar
  183. 183.
     Hood AM, Tuck A, Dane CR (1990) A medium for the isolation, enumeration and rapid presumptive identification of injured Clostridium perfringens and Bacillus cereus. J Appl Bacteriol 69:359–372PubMedGoogle Scholar
  184. 184.
     Hood SK, Zottola EA (1995) Biofilms in food processing. Food Control 6:9–18CrossRefGoogle Scholar
  185. 185.
     Hood SK, Zottola EA (1997) Adherence to stainless steel by foodborne microorganisms during growth in model food systems. Int J Food Microbiol 37:145–153CrossRefPubMedGoogle Scholar
  186. 186.
     Hough AJ, Harbison S-A, Savill MG, Melton LD, Fletcher G (2002) Rapid enumeration of Listeria monocytogenes in artificially contaminated cabbage using real-time polymerase chain reaction. J Food Prot 65:1329–1332PubMedGoogle Scholar
  187. 187.
     Hsieh YM, Sheu SJ, Chen YL, Tsen HY (1999) Enterotoxigenic profiles and polymerase chain reaction detection of Bacillus cereus group cells and B. cereus strains from foods and food-borne outbreaks. J Appl Microbiol 87:481–490CrossRefPubMedGoogle Scholar
  188. 188.
     Hsih TY, Tsen HY (2001) Combination of immunomagnetic separation and polymerase chain reaction for the simultaneous detection of Listeria monocytogenes and Salmonella spp. in food samples. J Food Prot 64:1744–1750PubMedGoogle Scholar
  189. 189.
     Hsuch PR, Teng LJ, Yang PC, Pan HL, Ho SW, Luh KT (1999) Nosocomial pseudoepidemic caused by Bacillus cereus traced to contaminated ethyl alcohol from a liquor factory. J Clin Microbiol 37:2280–2284PubMedGoogle Scholar
  190. 190.
     Huang C-T, Yu FP, McFeters GA, Stewart PS (1995) Nonuniform spatial patterns of respiratory activity within biofilms during disinfections. Appl Environ Microbiol 61:2252–2256PubMedGoogle Scholar
  191. 191.
     Hudson JA, Lake RJ, Savill MG, Scholes P, McCormick RE (2001) Rapid detection of Listeria monocytogenes in ham samples using immunomagnetic separation followed by polymerase chain reaction. J Appl Microbiol 90:614–621CrossRefPubMedGoogle Scholar
  192. 192.
     Hughes JB, Hellmann JJ, Ricketts TH, Bohannan BJM (2001) Counting the uncountable: statistical approaches to estimating microbial diversity. Appl Environ Microbiol 67:4399–4406CrossRefPubMedGoogle Scholar
  193. 193.
     Hughes MC (1993) The effect of some papermaking additives on slime microflora composition. Appita 46:194–197Google Scholar
  194. 194.
     Hughes-van Kregten MC (1988) Slime flora of New Zealand paper mills. Appita 41:470–474Google Scholar
  195. 195.
     Hurt RA, Qiu X, Wu L, Roh Y, Palumbo AV, Tiedje JM, Zhou J (2001) Simultaneous recovery of RNA and DNA from soils and sediments. Appl Environ Microbiol 67:4495–4503PubMedGoogle Scholar
  196. 196.
     Husmark U, Rönner U (1992) The influence of hydrophobic electrostatic and morphologic properties on the adhesion of Bacillus spores. Biofouling 5:335–344Google Scholar
  197. 197.
     Hyytiä E, Björkroth J, Hielm S, Korkeala H (1999) Characterization of Clostridium botulinum groups I and II by randomly amplified polymorphic DNA analysis and repetitive element sequence-based PCR. Int J Food Microbiol 48:179–189CrossRefPubMedGoogle Scholar
  198. 198.
     Ibekwe AM, Papiernik SH, Gan J, SR Yates, Yang C-H, Crowley DE (2001) Impact of fumigants on soil microbial communities. Appl Environ Microbiol 67:3245–3257CrossRefPubMedGoogle Scholar
  199. 199.
     Inoue S, Katagiri K, Terao M, Maruyama T (2001) RAPD- and actA gene-typing of Listeria monocytogenes isolates of human listeriosis, the intestinal contents of cows and beef. Microbiol Immunol 45:127–133PubMedGoogle Scholar
  200. 200.
     In't Veld PH, Havelaar AH, Strijp-Lockefeer NGWM van (1999) The certification of a reference material for the evaluation of methods for the enumeration of Bacillus cereus. J Appl Microbiol 86:266–274CrossRefPubMedGoogle Scholar
  201. 201.
     Ishii K, Fukui M, Takii S (2000) Microbial succession during a composting process as evaluated by denaturing gradient gel electrophoresis analysis. J Appl Microbiol 89:768–777PubMedGoogle Scholar
  202. 202.
     Jacobsen CN (1999) The influence of commonly used selective agents on the growth of Listeria monocytogenes. Int J Food Microbiol 50:221–226CrossRefGoogle Scholar
  203. 203.
     Jacobsen CN, Rasmussen J, Jakobsen M (1997) Viability staining and flow cytometric detection of Listeria monocytogenes. J Microbiol Methods 28:35–43CrossRefGoogle Scholar
  204. 204.
     Jacquet C, Catimel B, Brosch R, Buchrieser C, Dehaumont P, Goulet V, Lepoutre A, Veit P, Rocourt J (1995) Investigations related to the epidemic strain involved in the French listeriosis outbreak in 1992. Appl Environ Microbiol 61:2242–2246PubMedGoogle Scholar
  205. 205.
     Jansen GJ, Wildeboer-Veloo AC, Tonk RH, Franks AH, Welling GW (1999) Development and validation of an automated, microscopy-based method for enumeration of groups of intestinal bacteria. J Microbiol Methods 37:215–221CrossRefGoogle Scholar
  206. 206.
     Janssen P, Coopman R, Huys G, Swings J, Bleeker M, Vos P, Zabeau M, Kersters K (1996) Evaluation of the DNA fingerprinting method AFLP as a new tool in bacterial taxonomy. Microbiology 142:1881–1893PubMedGoogle Scholar
  207. 207.
     Jaradat ZW, Schutze GE, Bhunia AK (2002) Genetic homogeneity among Listeria monocytogenes strains from infected patients and meat products from two geographic locations determined by phenotyping, ribotyping and PCR analysis of virulence genes. Int J Food Microbiol 76:1–10CrossRefPubMedGoogle Scholar
  208. 208.
     Jass J, Walker JT (2000) Biofilms and biofouling. In: Walker J, Surman S, Jass J (eds) Industrial biofouling; detection, prevention and control, vol 1. Wiley, Chichester, pp 1–12Google Scholar
  209. 209.
     Jeffers GT, Bruce JL, McDonough PL, Scarlett J, Boor KJ, Wiedmann M (2001) Comparative genetic characterization of Listeria monocytogenes isolates from human and animal listeriosis cases. Microbiology 147:1095–1104PubMedGoogle Scholar
  210. 210.
     Jensen MA, Webster JA, Straus N (1993) Rapid identification of bacteria on the basis of polymerase chain reaction-amplified ribosomal DNA spacer polymorphisms. Appl Environ Microbiol 59:945–952PubMedGoogle Scholar
  211. 211.
     Jepras RI, Carter J, Pearson , FE Paul MJ Wilkinson (1995) Development of a robust flow cytometric assay for determining numbers of viable bacteria. Appl Environ Microbiol 61:2696–2701Google Scholar
  212. 212.
     Jersek B, Tcherneva E, Rijpens N, Herman L (1996) Repetitive element sequence-based PCR for species and strain discrimination in the genus Listeria. Lett Appl Microbiol 23:55–60PubMedGoogle Scholar
  213. 213.
     Jersek B, Gilot P, Gubina M, Klun N, Mehle J, Tcherneva E, Rijpens N, Herman L (1999) Typing of Listeria monocytogenes strains by repetitive element sequence-based PCR. J Clin Microbiol 37:103–109PubMedGoogle Scholar
  214. 214.
     Johansson T (1998) Enhanced detection and enumeration of Listeria monocytogenes from foodstuffs and food-processing environment. Int J Food Microbiol 40:77–85CrossRefPubMedGoogle Scholar
  215. 215.
     Johnsrud SC (1997) Biotechnology for solving slime problems in the pulp and paper industry. In: Scherper P (ed) Advances in biochemical engineering/biotechnology, vol 57. Springer, Berlin Heidelberg New York, pp 311–328Google Scholar
  216. 216.
     Josephson KL, Gerba CP, Pepper LL (1993) Polymerase chain reaction detection of nonviable bacterial pathogens. Appl Environ Microbiol 59:3513–3515PubMedGoogle Scholar
  217. 217.
     Kadra B, Guillou JP, Popoff M, Bourlioux P (1999) Typing of sheep clinical isolates and identification of enterotoxigenic Clostridium perfringens strains by classical methods and by polymerase chain reaction (PCR). FEMS Immunol Med Microbiol 24:259–266CrossRefGoogle Scholar
  218. 218.
     Karpiskova R, Pejchalova M, Mokrosova J, Vytrasova J, Smuharova P, Ruprich J (2000) Application of a chromogenic medium and the PCR method for the rapid confirmation of L. monocytogenes in foodstuffs. J Microbiol Methods 41:267–271CrossRefPubMedGoogle Scholar
  219. 219.
     Karwoski M, Venelampi O, Linko P, Mattila-Sandholm T (1995) A staining procedure for viability assessment of starter culture cells. Food Microbiol 12:21–29Google Scholar
  220. 220.
     Kato N, Kim SM, Kato H, Tanaka K, Watanabe K, Ueno K, Chong Y (1993) Identification of enterotoxin-producing Clostridium perfringens by the polymerase chain reaction. J Jpn Assoc Infect Dis 67:724–729Google Scholar
  221. 221.
     Keevil CW, Walker JT (1992) Normaski DIC microscopy and image analysis of biofilms. Binary 4:92–95Google Scholar
  222. 222.
     Kell DB, Kaprelyants AS, Weichart DH, Harwood CR, Barer MR (1998) Viability and activity in readily culturable bacteria: a review and discussion of the practical issues. Antonie Van Leeuwenhoek 73:169–187CrossRefPubMedGoogle Scholar
  223. 223.
     Kepner RLJ, Pratt JR (1994) Use of fluorochromes for direct enumeration of total bacteria in environmental samples: past and present. Microbiol Rev 58:603–615PubMedGoogle Scholar
  224. 224.
     Kerouanton A, Brisabois A, Denoyer E, Dilasser F, Grout J, Salvat G, Picard B (1998) Comparison of five typing methods for the epidemiological study of Listeria monocytogenes. Int J Food Microbiol 43:61–71CrossRefPubMedGoogle Scholar
  225. 225.
     Kilic U, Schalch B, Stolle A (2002) Ribotyping of Clostridium perfringens from industrially produced ground meat. Lett Appl Microbiol 34:238–243CrossRefPubMedGoogle Scholar
  226. 226.
     Kim Y-R, Czajka J Batt CA (2000) Development of a fluorogenic probe-based PCR assay for detection of Bacillus cereus in nonfat dry milk. Appl Environ Microbiol 66:1453–1459CrossRefPubMedGoogle Scholar
  227. 227.
     Kim W, Hong Y, Jae-hyung T, Won-bok L, Choi C, Chung S (2001) Genetic relationships of Bacillus anthracis and closely related species based on variable-number tandem repeat analysis and BOX-PCR genomic fingerprinting. FEMS Microbiol Lett 207:21–27CrossRefGoogle Scholar
  228. 228.
     Kinniment S, Wimpenny JWT (1990) Biofilms and biocides. Int Biodeterior 26:181–194CrossRefGoogle Scholar
  229. 229.
     Klaassen CHW, Haren HA van, Horrevorts AM (2002) Molecular fingerprinting of Clostridium difficile isolates: pulsed-field gel electrophoresis versus amplified fragment length polymorphism. J Clin Microbiol 40:101–104CrossRefPubMedGoogle Scholar
  230. 230.
     Kokai-Kun JF, Songer JG, Czeczulin JR, Chen F, McClane BA (1994) Comparison of Western immunoblots and gene detection assays for identification of potentially enterotoxigenic isolates of Clostridium perfringens. J Clin Microbiol 32:2533–2539PubMedGoogle Scholar
  231. 231.
     Kurisu F, Satoh H, Mino T, Matsuo T (2002) Microbial community analysis of thermophilic contact oxidation process by using ribosomal RNA approaches and the quinone method. Water Res 36:429–438Google Scholar
  232. 232.
     Lampel KA, Orlandi PA, Kornegay L (2000) Improved template preparation for PCR-based assays for detection of food-borne bacterial pathogens. Appl Environ Microbiol 66:4539–4542CrossRefPubMedGoogle Scholar
  233. 233.
     Lantz P-G, Hahn-Hägerdal B, Rådström P (1994) Sample preparation methods in PCR-based detection of food pathogens. Trends Food Science Technol 5:384–398Google Scholar
  234. 234.
     Lawrence JR, Korber DR, Hoyle BD, Costerton JW, Caldwell DE (1991) Optical sectioning of microbial biofilms. J Bacteriol 173:6558–6567PubMedGoogle Scholar
  235. 235.
     Lawrence JR, Wolfaardt GM, Neu TR (1998) The study of biofilms using confocal laser scanning microscope. In: Wilkinson MHF, Schut F (eds) Digital image analysis of microbes. Wiley, Chichester, pp 431–465Google Scholar
  236. 236.
     Lawrence LM, Gilmour A (1994) Incidence of Listeria spp. and Listeria monocytogenes in a poultry processing environment and in poultry products and their rapid confirmation by multiplex PCR. Appl Environ Microbiol 60:4600–4604Google Scholar
  237. 237.
     Lawrence LM, Gilmour A (1995) Characterization of Listeria monocytogenes isolated from poultry products and from the poultry-processing environment by random amplification of polymorphic DNA and multilocus enzyme electrophoresis. Appl Environ Microbiol 61:2139–2144PubMedGoogle Scholar
  238. 238.
     Lechner S, Mayr R, Francis KP, Pruss BM, Kaplan T, Wiessner-Gunkel E, Stewart GSAB, Scheler S (1998) Bacillus weihenstephanensis sp. nov. is a new psychrotolerant species of the Bacillus cereus group. Int J Syst Bacteriol 48:1373–1382PubMedGoogle Scholar
  239. 239.
     Lee D-H, Zo Y-G, Kim S-J (1996) Nonradioactive method to study genetic profiles of natural bacterial communities by PCR-single-strand-conformation polymorphism. Appl Environ Microbiol 62:3112–3120PubMedGoogle Scholar
  240. 240.
     Lee N, Nielsen PH, Andreasen KH, Juretschko S, Nielsen JL, Schleiffer K-H, Wagner M (1999) Combination of fluorescent in situ hybridization and microradiography—a new tool for structure-function analyses in microbial ecology. Appl Environ Microbiol 65:1289–1297PubMedGoogle Scholar
  241. 241.
     Lehner A, Loncarevic S, Wagner M, Kreike J, Brandl E (1999) A rapid differentiation of Listeria monocytogenes by use of PCR-SSCP in the listeriolysin O (hlyA) locus. J Microbiol Methods 34:165–171CrossRefGoogle Scholar
  242. 242.
     Lei XH, Promadej N, Kathariou S (1997) DNA fragments from regions involved in surface antigen expression specifically identify Listeria monocytogenes serovar 4 and a subset thereof: cluster IIB (serotypes 4b, 4d,and 4e). Appl Environ Microbiol 63:1077–1082PubMedGoogle Scholar
  243. 243.
     Leung K, Topp E (2001) Bacterial community dynamics in liquid swine manure during storage: molecular analysis using DGGE/PCR of 16S rDNA. FEMS Microbiol Ecol 38:169–177CrossRefGoogle Scholar
  244. 244.
     Linton RH, Pierson MD, Bishop JR (1990) Increase in heat resistance of Listeria monocytogenes Scott A by sublethal heat shock. J Food Prot 53:924–927Google Scholar
  245. 245.
     Lipski A, Friedrich U, Altendorf K (2001) Application of rRNA-targeted oligonucleotide probes in biotechnology. Appl Microbiol Biotechnol 56:40–57CrossRefPubMedGoogle Scholar
  246. 246.
     Liu PY, Ke SC, Chen SL (1997) Use of pulsed-field gel electrophoresis to investigate a pseudo-outbreak of Bacillus cereus in a pediatric unit. J Clin Microbiol 35:1533–1535PubMedGoogle Scholar
  247. 247.
     Liu WT, Mirzabekov AD, Stahl DA (2001) Optimization of an oligonucleotide microchip for microbial identification studies: a non-equilibrium dissociation approach. Environ Microbiol 3:619–629CrossRefPubMedGoogle Scholar
  248. 248.
     Lockhart DJ, Dong H, Byrne MC, Folletti MT, Gallo MV, Chee MS, Mittmann M, Wang C, Kobayashi M, Horton H, Brown EL (1996) Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol 14:1675–1680PubMedGoogle Scholar
  249. 249.
     Loncarevic S, Danielsson-Tham M-L, Tham W (1995) Occurrence of Listeria monocytogenes in soft and semi-soft cheeses in retail outlets in Sweden. Int J Food Microbiol 26:245–250CrossRefPubMedGoogle Scholar
  250. 250.
     Louie M, Jayaratne P, Luchsinger I, Devenish J, Yao J, Schlech W, Simor A (1996) Comparison of ribotyping, arbitrarily primed PCR pulsed field gel electrophoresis for molecular typing of Listeria monocytogenes. J Clin Microbiol 34:15–19PubMedGoogle Scholar
  251. 251.
     Low JC, Donachie W (1997) A review of Listeria monocytogenes and listeriosis. Vet J 153:9-29PubMedGoogle Scholar
  252. 252.
     Lucchini S, Thompson A, Hinton JCD (2001) Microarrays for microbiologists. Microbiology 147:1403–1414PubMedGoogle Scholar
  253. 253.
     Lukinmaa S, Takkunen E, Siitonen A (2002) Molecular epidemiology of Clostridium perfringens related to food-borne outbreaks of disease in Finland from 1984 to 1999. Appl Environ Microbiol 68:3744–3749CrossRefPubMedGoogle Scholar
  254. 254.
     Lundén JM, Miettinen MK, Autio TJ, Korkeala HJ (2000) Persistent Listeria monocytogenes strains show enhanced adherence to food contact surface after short contact times. J Food Prot 63:1204–1207PubMedGoogle Scholar
  255. 255.
     Lundén JM, Autio TJ, Korkeala HJ (2002) Transfer of persistent Listeria monocytogenes contamination between food-processing plants associated with a dicing machine. J Food Prot 65:1129–1133PubMedGoogle Scholar
  256. 256.
     Lunge VR, Miller BJ, Livak KJ, Batt CA (2002) Factors affecting the performance of 5´nuclease PCR assays for Listeria monocytogenes detection. J Microbiol Met 51:361–368CrossRefGoogle Scholar
  257. 257.
     MacDonald F, Sutherland AD (1994) Important differences between the generation times of Listeria monocytogenes and Listeria innocua in two Listeria enrichment broths. J Dairy Res 61:433–436PubMedGoogle Scholar
  258. 258.
     MacNaughton SJ, O′Donnell AG, Embley TM (1994) Permeabilization of mycolic-acid-containing actinomycetes for in situ hybridization with fluorescently labelled oligonucleotide probes. Microbiology 140:2859–2865PubMedGoogle Scholar
  259. 259.
     Malak M, Vivier A, Andre P, Decallonne J, Gilot P (2001) RAPD analysis, serotyping,and esterase typing indicate that the population of Listeria monocytogenes strains recovered from cheese and from patients with listeriosis in Belgium are different. Can J Microbiol 47:883–887CrossRefPubMedGoogle Scholar
  260. 260.
     Manafi M (2000) New developments of chromogenic and fluorogenic culture media. Int J Food Microbiol 60:205–218CrossRefPubMedGoogle Scholar
  261. 261.
     Mäntynen V, Lindström K (1998) A rapid PCR-based DNA test for enterotoxic Bacillus cereus. Appl Environ Microbiol 64:1634–1639PubMedGoogle Scholar
  262. 262.
     Manz W, Eisenbrecher M, Neu TR, Szewzyk U (1998) Abundance and spatial organization of Gram-negative sulfate-reducing bacteria in activated sludge investigate by in situ probing with specific 16S rRNA targeted oligonucleotides. FEMS Microbiol Ecol 25:43–61CrossRefGoogle Scholar
  263. 263.
     Manz W, Wendt-Potthoff K, Neu TR, Szewzyk U, Lawrence JR (1999) Phylogenetic composition, spatial structure dynamics of lotic bacterial biofilms investigated by fluorescent in situ hybridization and confocal laser scanning microscopy. Microbial Ecol 37:225–237CrossRefGoogle Scholar
  264. 264.
     Manzano M, Cocolin L, Ferroni P, Gasparini V, Narduzzi D, Cantoni C, Comi G (1996) Identification of Listeria species by a semi-nested polymerase chain reaction. Res Microbiol 147:637–640CrossRefGoogle Scholar
  265. 265.
     Manzano M, Cocolin L, Cantoni C, Comi G (1997) Detection and identification of Listeria monocytogenes from milk and cheese by a single-step PCR. Mol Biotechol 7: 85–88Google Scholar
  266. 266.
     Manzano M, Cocolin L, Pipan C, Falasca E, Botta GA, Cantoni C, Comi G (1997) Single-strand conformation polymorphism (SSCP) analysis of Listeria monocytogenes iap-gene as tool to detect different serogroups. Mol Cell Prob 11:459–462CrossRefGoogle Scholar
  267. 267.
     Manzano M, Cocolin L, Cantoni C, Comi G (1998) A rapid method for the identification and partial serotyping of Listeria monocytogenes in food by PCR and restriction enzyme analysis. Int J Food Microbiol 42:207–212CrossRefGoogle Scholar
  268. 268.
     Marks SL, Kather EJ, Kass PH, Melli AC (2002) Genotypic and phenotypic characterization of Clostridium perfringens and Clostridium difficile in diarrheic and healthy dogs. J Vet Intern Med 16:533–540PubMedGoogle Scholar
  269. 269.
     Maslanka SE, Kerr JG, Williams G, Barbaree JM, Carson LA, Miller JM, Swaminathan B (1999) Molecular subtyping of Clostridium perfringens by pulsed-field gel electrophoresis to facilitate food-borne-disease outbreak investigations. J Clin Microbiol 37:2209–2214PubMedGoogle Scholar
  270. 270.
     Mattila-Sandholm T, Wirtanen G (1992) Biofilm formation in the industry, a review. Food Rev Int 8:573–603Google Scholar
  271. 271.
     Maukonen J, Mattila-Sandholm T, Wirtanen G (2000) Metabolic indicators for assessing bacterial viability in hygiene sampling using cells in suspension and swabbed biofilm. Lebensm Wiss Technol 33:225–234CrossRefGoogle Scholar
  272. 272.
     McDonald K, Sun D-W (1999) Predictive food microbiology for the meat industry: a review. Int J Food Microbiol 52:1–27CrossRefPubMedGoogle Scholar
  273. 273.
     McDougald D, Rice SA, Weichart D, Kjelleberg S (1998) Nonculturability: adaptation or debilitation? FEMS Microbiol Ecol 25:1–9Google Scholar
  274. 274.
     McFeters GA, Yu FP, Pyle BH, Stewart PS (1995) Physiological assessment of bacteria using fluorochromes. J Microbiol Met 21:1–13Google Scholar
  275. 275.
     McLauchlin J, Ripabelli G, Brett MM, Threlfall EJ (2000) Amplified fragment length polymorphism (AFLP) analysis of Clostridium perfringens for epidemiological typing. Int J Food Microbiol 56:21–28PubMedGoogle Scholar
  276. 276.
     Mead GC (1985) Selective and differential media for Clostridium perfringens. Int J Food Microbiol 9:89–98CrossRefGoogle Scholar
  277. 277.
     Mereghetti L, Lanotte P, Savoye-Marczuk V, Marquet–Van der Mee N, Audurier A, Quentin R (2002) Combined ribotyping and random multiprimer DNA analysis to probe the population structure of Listeria monocytogenes. Appl Environ Microbiol 68:2849–2857CrossRefPubMedGoogle Scholar
  278. 278.
     Mettler E, Carpentier B (1998) Variations over time of microbial load and physicochemical properties of floor materials after cleaning in food industry premises. J Food Prot 61:57–65PubMedGoogle Scholar
  279. 279.
     Meunier J-R, Grimont PAD (1993) Factors affecting reproducibility of random amplified polymorphic DNA fingerprinting. Res Microbiol 144:373–379PubMedGoogle Scholar
  280. 280.
     Miettinen MK, Björkroth KJ, Korkeala HJ (1999) Characterization of Listeria monocytogenes from an ice cream plant by serotyping and pulsed-field gel electrophoresis. Int J Food Microbiol 46:187–192CrossRefPubMedGoogle Scholar
  281. 281.
     Miettinen MK, Siitonen A, Heiskanen P, Haajanen H, Björkroth KJ, Korkeala HJ (1999) Molecular epidemiology of an outbreak of febrile gastroenteritis caused by Listeria monocytogenes in cold-smoked rainbow trout. J Clin Microbiol 37:2358–2360PubMedGoogle Scholar
  282. 282.
     Miettinen MK, Palmu L, Björkroth KJ, Korkeala H (2001) Prevalence of Listeria monocytogenes in broilers at the abattoir, processing plant, and retail level. J Food Prot 64:994–999PubMedGoogle Scholar
  283. 283.
     Miwa N, Masuda T, Kwamura A, Terai K, Akiyma M (2002) Survival and growth of enterotoxin-positive and enterotoxin-negative Clostridium perfringens in laboratory media. Int J Food Microbiol 72:233–238CrossRefPubMedGoogle Scholar
  284. 284.
     Møller S, Kristensen CS, Poulsen LK, Carstensen JM, Molin S (1995) Bacterial growth on surfaces: automated image analysis for quantification of growth rate-related parameters. Appl Environ Microbiol 61:741–748Google Scholar
  285. 285.
     Moran MA, Torsvik VL, Torsvik T, Hodson RE (1993) Direct extraction and purification of rRNA for ecological studies. Appl Environ Microbiol 59:915–918PubMedGoogle Scholar
  286. 286.
     Mosteller TM, Bishop JR (1993) Sanitizer efficacy against attached bacteria in a milk biofilm. J Food Prot 56:34–41Google Scholar
  287. 287.
     Mucchetti G (1995) Biological fouling and biofilm formation on membranes. Int Dairy Fed Spec 9504:118–124Google Scholar
  288. 288.
     Mullis K (1987a) US patent 4,683,195Google Scholar
  289. 289.
     Mullis K (1987b) US patent 4,683,202Google Scholar
  290. 290.
     Mullis K, Faloona F, Scharft S, Saiki R, Horn G, Erlich H (1986) Specific enzymatic amplification of DNA in vitro: the polymerase chain reaction. Cold Spring Harbor Symp Quant Biol 51:263–273Google Scholar
  291. 291.
     Murray AE, Hollibaugh JT, Orrego C (1996) Phylogenetic compositions of bacterioplankton from two California estuaries compared by denaturing gradient gel electrophoresis of 16S rDNA fragments. Appl Environ Microbiol 62:2676–2680PubMedGoogle Scholar
  292. 292.
     Muyzer G, Waal EC de, Uitterlinden AG (1993) Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl Environ Microbiol 59:695–700PubMedGoogle Scholar
  293. 293.
     Muyzer G, Brinkhoff T, Nübel U, Santegoeds C, Schäfer H, Wawer C (1998) Denaturing gradient gel electrophoresis (DGGE) in microbial ecology. In: Akkermans ADL, Elsas DJ van, Bruijn FJ de (eds) Molecular microbial ecology manual. Kluwer, Dordrecht, pp–27Google Scholar
  294. 294.
     Muyzer G, Smalla K (1998) Application of denaturing gradient gel electrophoresis (DGGE) and temperature gradient gel electrophoresis (TGGE) in microbial ecology. Antonie Van Leeuwenhoek 73:127–141PubMedGoogle Scholar
  295. 295.
     Myers RM, Fischer SG, Maniatis T, Lerman LS (1985) Modification of the melting properties of duplex DNA by attachment of a GC-rich DNA sequence as determined by denaturing gradient gel electrophoresis. Nucleic Acids Res 13:3111–3129PubMedGoogle Scholar
  296. 296.
     Myers RM, Maniatis T, Lerman LS (1987) Detection and localization of single base changes by denaturing gradient gel electrophoresis. Nucleic Acids Res 13:3131–3145Google Scholar
  297. 297.
     Nadkarni MA, Martin FE, Jacques NA, Hunter N (2002) Determination of bacterial load by real-time PCR using a broad-range (universal) probe and primers set. Microbiology 148:257–266PubMedGoogle Scholar
  298. 298.
     Nichols WW (1991) Biofilms, antibiotics and penetration. Rev Med Microbiol 2:177–181Google Scholar
  299. 299.
     Niederhauser C, Candrian U, Hofelein C, Jermini M, Buhler HP, Luthy J (1992) Use of polymerase chain reaction for detection of Listeria monocytogenes in food. Appl Environ Microbiol 58:1564–1568PubMedGoogle Scholar
  300. 300.
     Niederhauser C, Höfelein C, Lüthy J, Kaufmann U, Bühler HP, Candrian U (1993) Comparison of ″Gen-Probe″ DNA probe and PCR for detection of Listeria monocytogenes in naturally contaminated soft cheese and semi-soft cheese. Res Microbiol 144:47–54CrossRefPubMedGoogle Scholar
  301. 301.
     Nilsson J, Svensson B, Ekelund K, Christiansson A (1998) A RAPD-PCR method for large-scale typing of Bacillus cereus. Lett Appl Microbiol 27:168–172PubMedGoogle Scholar
  302. 302.
     Nogva HK, Rudi K, Naterstad K, Holck A, Lillehaug D (2000) Application of 5′-nuclease PCR for quantitative detection of Listeria monocytogenes in pure cultures, water, skim milk, and unpasteurized whole milk. Appl Environ Microbiol 66:4266–4271CrossRefPubMedGoogle Scholar
  303. 303.
     Norton DM, McCamey MA, Gall KL, Scarlett JM, Boor KJ, Wiedmann M (2001) Molecular studies on the ecology of Listeria monocytogenes in the smoked fish processing industry. Appl Environ Microbiol 67:198–205CrossRefPubMedGoogle Scholar
  304. 304.
     Norton DM, Scarlett JM, Horton K, Sue D, Thimothe J, Boor KJ, Wiedmann M (2001) Characterization and pathogenic potential of Listeria monocytogenes isolates from the smoked fish industry. Appl Environ Microbiol 67:646–653CrossRefPubMedGoogle Scholar
  305. 305.
     Notermans SH, Dufrenne J, Leimeister-Wächter M, Domann E, Chakraborty T (1991) Phosphatidylinositol-specific phospholipase C activity as a marker to distinguish between pathogenic and nonpathogenic Listeria species. Appl Environ Microbiol 57:2666–2670PubMedGoogle Scholar
  306. 306.
     Nübel U, Englen B, Felske A, Snaidr J, Wieshuber A, Amann R, Ludwig W, Backhaus H (1996) Sequence heterogeneities of genes encoding 16S rRNAs in Paenibacillus polymyxa detected by temperature gradient gel electrophoresis. J Bacteriol 178:5636–5643PubMedGoogle Scholar
  307. 307.
     O′Connor L, Joy J, Kane M, Smith T, Maher M (2000) Rapid polymerase chain reaction/DNA probe membrane-based assay for the detection of Listeria and Listeria monocytogenes in food. J Food Prot 63:337–342PubMedGoogle Scholar
  308. 308.
     Ojeniyi B, Wegener HC, Jensen NE, Bisgaard M (1996) Listeria monocytogenes in poultry and poultry products: epidemiological investigations in seven Danish abattoirs. J Appl Bacteriol 80:395–401PubMedGoogle Scholar
  309. 309.
     Ojeniyi B, Christensen J, Bisgaard M (2000) Comparative investigation of Listeria monocytogenes isolated from turkey processing plant, turkey products and from human cases of listeriosis in Denmark. Epidemiol Infect 125:303–308CrossRefPubMedGoogle Scholar
  310. 310.
     Olsen GJ, Lane DJ, Giovannoni SJ, Pace NR, Stahl DA (1986) Microbial ecology and evolution: a ribosomal RNA approach. Annu Rev Microbiol 40:337–365CrossRefPubMedGoogle Scholar
  311. 311.
     Olsen JE, Aabo S, Hill W, Notermans S, Wernars K, Granum PE, Popvic T, Rasmussen HN, Olsvik O (1995) Probes and polymerase chain reaction for detection of food-borne bacterial pathogens. Int J Food Microbiol 28:1–78CrossRefPubMedGoogle Scholar
  312. 312.
     Olsvik O, Popovic T, Skjerve E, Cudjoe KS, Hornes E, Ugelstad J, Uhlen M (1994) Magnetic separation techniques in diagnostic microbiology. Clin Microbiol Rev 7:43–54PubMedGoogle Scholar
  313. 313.
     Orita M, Iwahana H, Kanazawa H, Hayashi K, Sekiya T (1989) Detection of polymorphisms of human DNA by gel electrophoresis as single-strand conformation polymorphisms. Proc Natl Acad Sci USA 86:2766–2770PubMedGoogle Scholar
  314. 314.
     Ouverney CC, Fuhrman JA (1999) Combined microautoradiography—16S rRNA probe technique for determination of radioisotope uptake by specific microbial cell types in situ. Appl Environ Microbiol 65:1746–1752PubMedGoogle Scholar
  315. 315.
     Palleroni NJ (1997) Prokaryotic diversity and the importance of culturing. Antonie Van Leeuwenhoek 72:3–19PubMedGoogle Scholar
  316. 316.
     Paziak-Domaska B, Bogusawska E, Wieckowska-Szakiel M, Kotowski R, Rózalska B, Chmiela M, Kur J, Dabrowski W, Rudnicka W (1999) Evaluation of the API test, phosphatidylinositol-specific phospholipase C activity and PCR method in identification of Listeria monocytogenes in meat foods. FEMS Microbiol Lett 171:209–214CrossRefPubMedGoogle Scholar
  317. 317.
     Pedro MS, Haruta S, Hazaka M, Shimada R, Yoshida C, Hiura K, Ishii M, Igarashi y (2001) Denaturing gradient gel electrophoresis analyses of microbial community from field-scale composter. J Biosci Bioeng 91:159–165Google Scholar
  318. 318.
     Peng J-S, Tsai W-C, Chou C-C (20010) Surface characteristics of Bacillus cereus and its adhesion to stainless steel. Int J Food Microbiol 65:105–111CrossRefGoogle Scholar
  319. 319.
     Petersen L, Madsen M (2000) Listeria spp. in broiler flocks: recovery rates and species distribution investigated by conventional culture and the EiaFoss method. Int J Food Microbiol 58:113–116CrossRefPubMedGoogle Scholar
  320. 320.
     Petrone G, Polidoro M, Donnarumma G, Conte MP, Papi E, Seganti L, Valenti P (1997) Identification of Listeria monocytogenes by colony hybridization test using the virulence-associated hly and inlA genes as probes. Ann Ig 9:281–288PubMedGoogle Scholar
  321. 321.
     Pinna A, Sechi LA, Zanetti S, Usai D, Delogu G, Cappuccinelli P, Carta F (2001) Bacillus cereus keratitis with contact lens wear. Ophthalmology 108:1830–1834CrossRefPubMedGoogle Scholar
  322. 322.
     Pirbazari M, Voice TC, Weber WJ Jr (1990) Evaluation of biofilm development on various natural and synthetic media. Hazard Waste Hazard Mater 7:239–250Google Scholar
  323. 323.
     Pirttijärvi TSM, Graeffe TH, Salkinoja-Salonen MS (1996) Bacterial contaminants in liquid packaging boards: assessment of potential for food spoilage. J Appl Bacteriol 81:445–458PubMedGoogle Scholar
  324. 324.
     Pirttijärvi T (2000) Contaminant aerobic sporeforming bacteria in the manufacturing processes of food packaging board and food. PhD thesis, University of Helsinki, HelsinkiGoogle Scholar
  325. 325.
     Pirttijärvi TSM, Andersson MA, Salkinoja-Salonen MS (2000) Properties of Bacillus cereus and other bacilli contaminating biomaterial-based industrial processes. Int J Food Microbiol 60:231–239CrossRefPubMedGoogle Scholar
  326. 326.
     Pituch H, Braak N van den, Belkum A van, Leeuwen W van, Obuch-Woszczatynski p, Luczak M, Verbrugh H, Meisel-Mikolajczyk F, Martirosian G (2002) Characterization of Clostridium perfringens strains isolated from Polish patients with suspected antibiotic-associated diarrhea. Med Sci Monit 8:BR85–BR88PubMedGoogle Scholar
  327. 327.
     Ploem JS (1967) The use of a vertical illuminator with interchangeable dichroic mirrors for fluorescence microscopy with incident light. Z Wiss Mikrosk 68:129–142PubMedGoogle Scholar
  328. 328.
     Porter KG, Feig YS (1980) The use of DAPI for identifying and counting of aquatic microflora. Limnol Oceanogr 25:943–948Google Scholar
  329. 329.
     Poulsen LK, Ballard G, Stahl DA (1993) Use of rRNA fluorescence in situ hybridization for measuring the activity of single cells in young and established biofilms. Appl Environ Microbiol 59:1354–1360PubMedGoogle Scholar
  330. 330.
     Power EGM (1996) RAPD typing in microbiology—a technical review. J Hosp Infect 34:247–265PubMedGoogle Scholar
  331. 331.
     Price D, Ahearn DG (1988) Incidence and persistence of Pseudomonas aeruginosa in whirlpools. J Clin Microbiol 26:1650–1654PubMedGoogle Scholar
  332. 332.
     Priest FG, Kaji DA, Rosato YB, Canhos VP (1994) Characterization of Bacillus thuringiensis and related bacteria by ribosomal RNA gene restriction fragment length polymorphism. Microbiology 140:1015–1022PubMedGoogle Scholar
  333. 333.
     Raaska L, Sillanpää J, Sjöberg A-M, Suihko M-L (2002) Potential microbiological hazards in the production of refined paper products for food applications. J Ind Microbiol Biotechnol 28:225–231CrossRefPubMedGoogle Scholar
  334. 334.
     Radhika B, Padmapriya BP, Chandrashekar A, Keshava N, Varadraj MC (2002) Detection of Bacillus cereus in foods by colony hybridization using PCR-generated probe and characterization of isolates for toxins by PCR. Int J Food Microbiol 74:131–138CrossRefPubMedGoogle Scholar
  335. 335.
     Ramsing NB, Fiossing H, Ferdelman TG, Andersen F, Thamdrup B (1996) Distribution of bacterial populations in a stratified fjord (Marianger fjord, Denmark) quantified by in situ hybridization and related to chemical gradients in the water column. Appl Environ Microbiol 62:1391–1404PubMedGoogle Scholar
  336. 336.
     Raskin L, Poulsen LK, Noguera DR, Rittman BE, Stahl DA (1994) Quantification of methanogenic groups in anaerobic biological reactors by oligonucleotide probe hybridization. Appl Environ Microbiol 60:1241–1248PubMedGoogle Scholar
  337. 337.
     Reischl U, Kochanowski B (1995) Quantitative PCR—a survey of the present technology. Mol Biotechnol 3:55–71PubMedGoogle Scholar
  338. 338.
     Restaino L, Frampton EW, Irbe RM, Schabert G, Spitz H (1999) Isolation and detection of Listeria monocytogenes using fluorogenic and chromogenic substrates for phosphatidylinositol-specific phospholipase. Can J Food Prot 62:244–251Google Scholar
  339. 339.
     Rijpens NP, Jannes G, Asbroek M van, Herman LM, Rossau R (1995) Simultaneous detection of Listeria spp. and Listeria monocytogenes by reverse hybridization with 16S-23S rRNA spacer probes. Mol Cell Prob 9:423–432CrossRefGoogle Scholar
  340. 340.
     Ripabelli G, McLauchlin J, Threlfall EJ (2000) Amplified fragment length polymorphism (AFLP) analysis of Listeria monocytogenes. Syst Appl Microbiol 23:132–136PubMedGoogle Scholar
  341. 341.
     Robichaud WT (1991) Controlling anaerobic bacteria to improve product quality and mill safety. TAPPI J 74:149–152Google Scholar
  342. 342.
     Rodriguez GG, Phipps D, Ishiguro K, Ridgway HF (1992) Use of fluorescent redox probe for direct visualization of actively respiring bacteria. Appl Environ Microbiol 58:1801–1808PubMedGoogle Scholar
  343. 343.
     Rodríguez-Romo LA, Heredia NL, Labbé RG, García-Alvarado JS (1998) Detection of enterotoxigenic Clostridium perfringens in spices used in Mexico by dot blotting using a DNA probe. J Food Prot 61:201–204PubMedGoogle Scholar
  344. 344.
     Rolfs A, Schuller I, Finckh U, Weber-Rolfs I (1992) Detection of single base changes using PCR. In: Rolfs A, Schuller I, Finckh U, Weber-Rolfs I (eds) PCR: clinical diagnostics and research. Springer, Berlin Heidelberg New York, pp 149–167Google Scholar
  345. 345.
     Rölleke S, Muyzer G, Wawer C, Wanner G, Lubitz W (1996) Identification of bacteria in a biodegraded wall painting by denaturing gradient gel electrophoresis of PCR-amplified gene fragments coding for 16S rRNA. Appl Environ Microbiol 62:2059–2065PubMedGoogle Scholar
  346. 346.
     Rompré A, Servais P, Baudart J, de-Roubin M-R, Laurent P (2002) Detection and enumeration of coliforms in drinking water: current methods and emerging approaches. J Microbiol Methods 49:31–54CrossRefPubMedGoogle Scholar
  347. 347.
     Roose-Amsaleg CL, Garnier-Sillam E, Harry M (2001) Extraction and purification of microbial DNA from soil and sediment samples. Appl Soil Ecol 18:47–60CrossRefGoogle Scholar
  348. 348.
     Rosenbaum V, Riesner D (1987) Temperature-gradient gel electrophoresis. Thermodynamic analysis of nucleic acids and proteins in purified form in cellular extracts. Biophys Chem 26:235–246CrossRefPubMedGoogle Scholar
  349. 349.
     Rossen L, Holmstrøm K, Olsen JE, Rasmussen OF (1991) A rapid polymerase chain reaction (PCR)-based assay for the identification of Listeria monocytogenes in food samples. Int J Food Microbiol 14:145–151CrossRefGoogle Scholar
  350. 350.
     Rossen L, Norskov P, Holmstrom K, Rasmussen OF (1992) Inhibition of PCR by components of food samples, microbial diagnostic assays and DNA-extraction solutions. Int J Food Microbiol 17:37–45CrossRefPubMedGoogle Scholar
  351. 351.
     Saiki RK, Scharft S, Faloona F, Mullis KB, Horn GT, Erlich HA, Arnheim N (1985) Enzymatic amplification of β-globin genomic sequences and restriction site analysis for diagnosis of sickle cell anemia. Science 230:1350–1354PubMedGoogle Scholar
  352. 352.
     Salo S, Alanko T, Sjöberg A-M, Wirtanen G (2002) Validation of Hygicult E dipslides in surface hygiene control: a Nordic collaborative study. J AOAC Int 85:388–394PubMedGoogle Scholar
  353. 353.
     Salzano G, Pallotta ML, Maddonni MF, Coppola R (1995) Identification of Listeria monocytogenes in food and environment by polymerase chain reaction. J Environ Sci Health A30:63–71Google Scholar
  354. 354.
     Sarkar PK, Hasenack B, Nout MJ (2002) Diversity and functionality of Bacillus and related genera isolated from spontaneously fermented soybeans (Indian Kinema) and locust beans (African Soumbala). Int J Food Microbiol 77:175–186CrossRefPubMedGoogle Scholar
  355. 355.
     Sartory DP, Field M, Curbishley SM, Pritchard AM (1998) Evaluation of two media for the membrane filtration enumeration of Clostridium perfringens from water. Lett Appl Microbiol 27:323–327PubMedGoogle Scholar
  356. 356.
     Satokari RM, Vaughan EE, Akkermans ADL, Saarela M, Vos WM de (2001) Bifidobacterial diversity in human feces detected by genus-specific PCR and denaturing gradient gel electrophoresis. Appl Environ Microbiol 67:504–513CrossRefPubMedGoogle Scholar
  357. 357.
     Sayler GS, Layton AC (1990) Environmental applications of nucleic acid hybridization. Annu Rev Microbiol 44:625–648PubMedGoogle Scholar
  358. 358.
     Schabereiter-Gurtner C, Piñar G, Lubitz W, Rölleke S (2001) An advanced molecular strategy to identify bacterial communities on art objects. J Microbiol Methods 45:77–87PubMedGoogle Scholar
  359. 359.
     Schalch B, Björkroth J, Eisgruber H, Korkeala H, Stolle A (1997) Ribotyping for strain characterization of Clostridium perfringens isolates from food poisoning cases and outbreaks. Appl Environ Microbiol 63:3992–3994Google Scholar
  360. 360.
     Schalch B, Sperner B, Eisgruber H, Stolle A (1999) Molecular methods for the analysis of Clostridium perfringens relevant to food hygiene. FEMS Immunol Med Microbiol 24:281–286CrossRefPubMedGoogle Scholar
  361. 361.
     Schangkuan YH, Yang JF, Lin HC, Shaio MF (2000) Comparison of PCR-RFLP, ribotyping, and ERIC-PCR for typing Bacillus anthracis and Bacillus cereus strains. J Appl Microbiol 89:452–462CrossRefPubMedGoogle Scholar
  362. 362.
     Schaule G, Flemming H-C, Ridgway HF (1993) Use of 5-cyano-2,3-ditolyl tetrazolium chloride for quantifying planctonic and sessile respiring bacteria in drinking water. Appl Environ Microbiol 59:3850–3857PubMedGoogle Scholar
  363. 363.
     Schena M, Shalon D, Davis RW, Brown PO (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270:467–470PubMedGoogle Scholar
  364. 364.
     Scheu PM, Berghof K, Stahl U (1998) Detection of pathogenic and spoilage micro-organisms in food with the polymerase chain reaction. Food Microbiol 15:13–31CrossRefGoogle Scholar
  365. 365.
     Schleifer KH, Ludwig W, Amann R (1993) Nucleic acid probes. In: Goodfellow M, McDonnell O (eds) Handbook of new bacterial systematics. Academic Press, London, pp 463–510Google Scholar
  366. 366.
     Schneegurt MA, Kulpa CF Jr (1998) The application of molecular techniques in environmental biotechnology for monitoring microbial systems. Biotechnol Appl Biochem 27:73–79Google Scholar
  367. 367.
     Schraft H, Griffiths MW (1995) Specific oligonucleotide primers for detection of lecithinase-positive Bacillus spp. by PCR. Appl Environ Microbiol 61:98–102Google Scholar
  368. 368.
     Schwartz T, Hoffmann S, Obst U (1998) Formation and bacterial composition of young, natural biofilms obtained from public bank-filtered drinking water systems. Water Res 32:2787–2797Google Scholar
  369. 369.
     Schwieger F, Tebbe CC (1998) A new approach to utilize PCR-single-strand-conformation polymorphism for 16S rRNA gene-based microbial community analysis. Appl Environ Microbiol 64:4870–4876PubMedGoogle Scholar
  370. 370.
     Sciacchitano CJ (1998) DNA fingerprinting of Listeria monocytogenes using enterobacterial repetitive intergenic consensus (ERIC) motifs–polymerase chain reaction/capillary electrophoresis. Electrophoresis 19:66–70PubMedGoogle Scholar
  371. 371.
     Sekiguchi H, Tomioka N, Nakahara T, Uchiyama H (2001) A single band does not always represent single bacterial strains in denaturing gradient gel electrophoresis analysis. Biotechnol Lett 23:1205–1208CrossRefGoogle Scholar
  372. 372.
     Selvakumar N, Ding BC, Wilson SM (1997) Separation of DNA strands facilitates detection of point mutations by PCR-SSCP. BioTechnology 22:604–606Google Scholar
  373. 373.
     Senczek D, Stephan R, Untermann F (2000) Pulsed-field gel electrophoresis (PFGE) typing of Listeria strains isolated from a meat processing plant over a 2-year period. Int J Food Microbiol 62:155-159CrossRefPubMedGoogle Scholar
  374. 374.
     Sheridan JJ, Duffy G, Buchanan RL, MacDowell DA, Blair IS (1994) The use of selective and non-selective enrichment broths for the isolation of Listeria species from meat. Food Microbiol 11:439–446CrossRefGoogle Scholar
  375. 375.
     Shimizu T, Ohtani K, Hirakawa H, Ohshima K, Yamashita A, Shiba T, Ogasawara N, Hattori M, Kuhara S, Hayashi H (2002) Complete genome sequence of Clostridium perfringens, an anaerobic flesh-eater. Proc Nat Acad Sci USA 99:996–1001CrossRefGoogle Scholar
  376. 376.
     Simon MC, Gray DI, Cook N (1996) DNA extraction and PCR method for the detection of Listeria monocytogenes in cold-smoked salmon. Appl Environ Microbiol 62:822–824Google Scholar
  377. 377.
     Simon HM, Smith KP, Dodsworth JA, Guenthner B, Handelsman J, Goodman RM (2001) Influence of tomato genotype on growth of inoculated and indigenous bacteria in the spermosphere. Appl Environ Microbiol 67:514–520CrossRefPubMedGoogle Scholar
  378. 378.
     Smalla K, Wieland G, Buchner A, Zock A, Parzy J, Kaiser S, Roskot N, Heuer H, Berg G (2001) Bulk and rhizosphere soil bacterial communities studied by denaturing gradient gel electrophoresis: plant-dependent enrichment and seasonal shifts revealed. Appl Environ Microbiol 67:4742–4751CrossRefGoogle Scholar
  379. 379.
     Sorrelle PH, Eelgard W (1992) Growth in recycling escalates costs for paper machine biological control. Pulp Pap 66:57–64Google Scholar
  380. 380.
     Southern EM (1975) Detection of specific sequences among DNA fragments separated by gel electrophoresis. J Mol Biol 98:503–517PubMedGoogle Scholar
  381. 381.
     Sparks SG, Carman RJ, Sarker MR, McClane BA (2001) Genotyping of enterotoxigenic Clostridium perfringens fecal isolates associated with antibiotic-associated diarrhea and food poisoning in North America. J Clin Microbiol 39:883–888Google Scholar
  382. 382.
     Spiro A, Lowe M (2002) Quantitation of DNA sequences in environmental PCR products by a multiplexed, bead-based method. Appl Environ Microbiol 68:1010–1013CrossRefPubMedGoogle Scholar
  383. 383.
     Stahl DA (1995) Application of phylogenetically based hybridization probes to microbial ecology. Mol Ecol 4:535–542Google Scholar
  384. 384.
     Stahl DA, Amann RI (1991) Development and application of nucleic acid probes in bacterial systematics. In: Stackebrandt E, Goodfellow M (eds) Sequencing and hybridization techniques in bacterial systematics. Wiley, Chichester, pp 205–248Google Scholar
  385. 385.
     Stahl DA, Capman WC (1994) Applications of molecular genetics to the study of microbial communities. NATO ASI Ser 35:193–206Google Scholar
  386. 386.
     Stahl DA, Flesher B, Mansfield HR, Montgomery L (1988) The use of phylogenetically based hybridization probes for studies of ruminal microbial ecology. Appl Environ Microbiol 54:1079–1084PubMedGoogle Scholar
  387. 387.
     Stephan R (1996) Randomly amplified polymorphic DNA (RAPD) assay for genomic fingerprinting of Bacillus cereus isolates. J Food Microbiol 31:311–316CrossRefGoogle Scholar
  388. 388.
     Stewart PS, Griebe T, Srinivasan R, Chen C-I, Yu FP, Beer D de, McFeters GA (1994) Comparison of respiratory activity and culturability during monochloramine disinfection of binary population biofilms. Appl Environ Microbiol 60:1690–1692PubMedGoogle Scholar
  389. 389.
     Strachan NJ, Gray DI (1995) A rapid general method for the identification of PCR products using a fibre-optic biosensor and its application to the detection of Listeria. Lett Appl Microbiol 21:5–9PubMedGoogle Scholar
  390. 390.
     Stugger S (1948) Fluorescence microscope examination of bacteria in soil. Can J Res 26:188–193Google Scholar
  391. 391.
     Suh J-H, Knabel SJ (2001) Comparison of different enrichment broths and background flora for detection of heat-injured Listeria monocytogenes in whole milk. J Food Prot 64:30–36PubMedGoogle Scholar
  392. 392.
     Suihko M-L, Hoekstra ES (1999) Fungi present in some recycled fibre pulps and paperboards. Nord Pulp Pap Res J 14:199–203Google Scholar
  393. 393.
     Suihko M-L, Salo S, Niclasen O, Gudbjorndottir B, Torkelsson G, Bredholt S, Sjöberg A-M, Gustavsson P (2002) Characterization of Listeria monocytogenes isolates from the meat, poultry and seafood industries by automated ribotyping. Int J Food Microbiol 30:137–146CrossRefGoogle Scholar
  394. 394.
     Swaminathan B, Hunter SB, Desmarchelier PM, Gerner-Smidt P, Graves LM, Harlander S, Hubner R, Jacquet C, Pedersen B, Reineccius K, Ridley A, Saunders NA (1996) WHO-sponsored international collaborative study to evaluate methods for subtyping Listeria monocytogenes: restriction fragment length polymorphism (RFLP) analysis using ribotyping and Southern hybridization with two probes derived from L. monocytogenes chromosome. Int J Food Microbiol 32:263–278CrossRefPubMedGoogle Scholar
  395. 395.
     Tansuphasiri U (2001) Development of duplex PCR assay for rapid detection of enterotoxigenic isolates of Clostridium perfringens. Southeast Asian J Trop Med Public Health 32:105–113Google Scholar
  396. 396.
     Tansuphasiri U, Wongsuvan G, Eampokalap B (2002) PCR detection and prevalence of enterotoxin (cpe) gene in Clostridium perfringens isolated from diarrhea patients. J Med Assoc Thailand 85:624-633Google Scholar
  397. 397.
     Tartakovsky B, Manuel MF, Beaumier D, Greer CW, Guiot SR (2001) Enhanced selection of an anaerobic pentachlorophenol-degrading consortium. Biotechnol Bioeng 73:476–483CrossRefPubMedGoogle Scholar
  398. 398.
     Taylor DL, Salmon ED (1989) Basic fluorescence microscopy. In: Wang Y-L, Taylor DL (eds) Fluorescence microscopy of living cells in culture. Part A: fluorescent analogs, labeling cells and basic microscopy. (Methods in cell biology, vol 29) Academic Press, San Diego, Calif., pp 207–237Google Scholar
  399. 399.
     Te Giffel MC, Beumer RR, Klijn N, Wagendorp A, Rombouts FM (1997) Discrimination between Bacillus cereus and Bacillus thuringiensis using specific DNA probes based on variable regions of 16S rRNA. FEMS Microbiol Lett 146:47–51CrossRefPubMedGoogle Scholar
  400. 400.
     Teske A, Wawer C, Muyzer G, Ramsing NB (1996) Distribution of sulfate-reducing bacteria in a stratified fjord (Mariager fjord, Denmark) as evaluated by most-probable-number counts and denaturing gradient gel electrophoresis of PCR-amplified ribosomal DNA fragments. Appl Environ Microbiol 62:1405–1415PubMedGoogle Scholar
  401. 401.
     Thomas EJG, King RK, Burchak J, Gannon VJP (19910 Sensitive and specific detection of Listeria monocytogenes in milk and ground beef with the polymerase chain reaction. Appl Environ Microbiol 57:2576–2580Google Scholar
  402. 402.
     Thorsen BE, Oivind E, Norland S, Hoff KA (1992) Long-term starvation survival of Yersinia ruckeri at different salinities studied by microscopical and flow cytometric methods. Appl Environ Microbiol 58:1624–1628PubMedGoogle Scholar
  403. 403.
     Ticknor LO, Kolsto A-B, Hill KK, Keim P, Laker MT, Tonks M, Jackson PJ (2001) Fluorescent amplified fragment length polymorphism analysis of Norwegian Bacillus cereus and Bacillus thuringiensis soil isolates. Appl Environ Microbiol 67:4863–4873CrossRefPubMedGoogle Scholar
  404. 404.
     Tsen HY, Chen ML, Hsieh YM, Sheu SJ, Chen YL (2000) Bacillus cereus -group strains, their hemolysin BL activity, and their detection in foods using a 16S RNA and hemolysin BL gene targeted multiplex polymerase chain reaction system. J Food Prot 63:1496–1502PubMedGoogle Scholar
  405. 405.
     Turner SJ, Saul DJ, Rodrigo AG, Lewis GD (2002) A heteroduplex method for detection of targeted sub-populations of bacterial communities. FEMS Microbiol Lett 208:9–13CrossRefPubMedGoogle Scholar
  406. 406.
     Uzal FA, Plumb JJ, Blackall LL, Kelly WR (1997) PCR detection of Clostridium perfringens producing different toxins in faeces of goats. Lett Appl Microbiol 25:339–344PubMedGoogle Scholar
  407. 407.
     Väisänen O, Elo S, Marmo S, Salkinoja-Salonen M (1989) Enzymatic characterization of Bacilli from food packaging paper and board machines. J Ind Microbiol 4:419–428Google Scholar
  408. 408.
     Väisänen O, Nurmiaho-Lassila EL, Marmo S, Salkinoja-Salonen M (1994) Structure and composition of biological slimes on paper and board machines. Appl Environ Microbiol 60:641–653Google Scholar
  409. 409.
     Vallaeys T, Topp E, Muyzer G, Macharet V, Laguerre G, Rigaud A, Soulas G (1997) Evaluation of denaturing gradient gel electrophoresis in the detection of 16S rDNA sequence variation in rhizobia and methanotrophs. FEMS Microbiol Ecol 24:279–285CrossRefGoogle Scholar
  410. 410.
     Van Belkum A, Scherer S, Van Alphen L, Verbrugh H (1998) Short-sequence DNA repeats in prokaryotic genomes. Microbiol Mol Biol Rev 62:275–293PubMedGoogle Scholar
  411. 411.
     Van Damme-Jongsten M, Rodhouse J, Gilbert RJ, Notermans S (1990) Synthetic DNA probes for detection of enterotoxigenic Clostridium perfringens strains isolated from outbreaks of food poisoning. J Clin Microbiol 28:131–133PubMedGoogle Scholar
  412. 412.
     Van Der Zwet WC, Parlevliet GA, Savelkoul PH, Stoof J, Kaiser AM, Van Furth AM, Vandenbroucke-Grauls CM (2000) Outbreak of Bacillus cereus infections in a neonatal intensive care unit traced to balloons used in manual ventilation. J Clin Microbiol 38:4131–4136PubMedGoogle Scholar
  413. 413.
     Vaneechoutte M (1996) DNA fingerprinting techniques for microorganisms. Mol Biotechnol 6:115–142PubMedGoogle Scholar
  414. 414.
     Vaneechoutte M, Rossau R, Vos P De, Gilis M, Janssens D, Paepe N, De Rouck A, Fiers T, Claeys G, Kersters K (1992) Rapid identification of bacteria of the Comamonadaceae with amplified ribosomal DNA-restriction analysis (ARDRA). FEMS Microbiol Lett 15:227–233CrossRefGoogle Scholar
  415. 415.
     Vaneechoutte M, Boerlin P, Tichy H-V, Bannerman E, Jager B, Bille J (1998) Comparison of PCR-based DNA-fingerprinting techniques for the identification of Listeria species and their use for atypical Listeria isolates. Int J Syst Bacteriol 48:127–139PubMedGoogle Scholar
  416. 416.
     Van Netten P, Perales I, Moosdijk A van de, Curtis GDW, Mossel DAA (1989) Liquid and solid selective differential media for the detection and enumeration of L. monocytogenes and other Listeria spp. Int J Food Microbiol 8:299–316CrossRefPubMedGoogle Scholar
  417. 417.
     Van Netten P, Kramer JM (1992) Media for the detection and enumeration of Bacillus cereus in foods: a review. Int J Food Microbiol 17:85–99CrossRefPubMedGoogle Scholar
  418. 418.
     Vela M, Heredia NL, Feng P, García-Alvarado JS (1999) DNA probe analysis for the carriage of enterotoxigenic Clostridium perfringens in feces of a Mexican subpopulation. Diagn Microbiol Infect Dis 35:101–104CrossRefPubMedGoogle Scholar
  419. 419.
     Versalovic J, Koeuth T, Lupski JR (1991) Distribution of repetitive DNA sequences in eubacteria and application to fingerprinting of bacterial genomes. Nucleic Acid Res 19:6823–6831Google Scholar
  420. 420.
     Vogel BF, Huss HH, Ojeniyu B, Ahrens P, Gram L (2001) Eludication of Listeria monocytogenes contamination routes in cold-smoked salmon processing plants detected by DNA-based typing methods. Appl Environ Microbiol 67:2586–2595CrossRefPubMedGoogle Scholar
  421. 421.
     Vogel BF, Jorgensen LV, Ojeniyi B, Huss HH, Gram L (2001) Diversity of Listeria monocytogenes isolates from cold-smoked salmon produced in different smokehouses as assessed by random amplified polymorphic DNA analyses. Int J Food Microbiol 6:83–92CrossRefGoogle Scholar
  422. 422.
     Von Stetten F, Francis KP, Lechner S, Neuhaus K, Schrerer S (1998) Rapid discrimination of psychotolerant and mesophilic strains of the Bacillus cereus group by PCR targeting of 16S rDNA. J Microbiol Methods 34:99–106CrossRefGoogle Scholar
  423. 423.
     Von Wintzingerode F, Göbel UB, Stackebrandt E (1997) Determination of microbial diversity in environmental samples: pitfalls of PCR-based rRNA analysis. FEMS Microbiol Rev 21:213–229PubMedGoogle Scholar
  424. 424.
     Vos P, Hogers R, Bleeker M, Reijans M, Lee T van de, Hornes M, Frijters A, Pot J, Peleman J, Kuiper M, Zabeau M (1995) AFLP: a new technique for DNA fingerprinting. Nucleic Acids Res 23:4407–4414PubMedGoogle Scholar
  425. 425.
     Wagner M, Assmus B, Hartmann A, Hutzler P, Amann R (1994) In situ analysis of microbial consortia in activated sludge using fluorescently labeled, rRNA-targeted oligonucleotide probes and confocal laser scanning microscopy. J Microsc 176:181–187PubMedGoogle Scholar
  426. 426.
     Wagner M, Schmid M, Juretschenko S, Trebesius K-H, Bubert A, Goebel W, Schleifer K-H (1998a) In situ detection of a virulence factor mRNA and 16S rRNA in Listeria monocytogenes. FEMS Microbiol Lett 160:159–168CrossRefPubMedGoogle Scholar
  427. 427.
     Wagner M, P Hutzler R Amann (1998b) Three-dimensional analysis of complex microbial communities by combining confocal laser scanning microscopy and fluorescence in situ hybridization. In: Wilkinson MHF, Schut F (eds) Digital image analysis of microbes. Wiley, Chichester, pp 467–486Google Scholar
  428. 428.
     Wagner M, Maderner A, Brandl E (1999) Development of multiple primer RAPD assay as a tool for phylogenetic analysis in Listeria spp. strains isolated from milk product associated epidemics, sporadic cases of listeriosis and dairy environment. Int J Food Microbiol 52:29–37CrossRefPubMedGoogle Scholar
  429. 429.
     Wagner M, Lehner A, Klein D, Buber A (2000) Single-strand conformation polymorphism in the hly gene and polymerase chain reaction analysis of a repeat region in the aip gene to identify and type Listeria monocytogenes. J Food Prot 63:332–336PubMedGoogle Scholar
  430. 430.
     Wagner R (1994) The regulation of ribosomal RNA synthesis and bacterial cell growth. Arch Microbiol 161:100–109CrossRefPubMedGoogle Scholar
  431. 431.
     Walberg M, Gaustad P, Steen HB (1999) Uptake kinetics of nucleic acid targeting dyes in S. aureus, E. faecalis and B. cereus: a flow cytometric study. J Microbiol Methods 35:167–176CrossRefPubMedGoogle Scholar
  432. 432.
     Wallace DM (1987) Large- and small-scale phenol extractions. Methods Enzymol 152:33–48PubMedGoogle Scholar
  433. 433.
     Wallner G, Erhart R, Amann R (1995) Flow cytometric analysis of activated sludge with rRNA-targeted probes. Appl Environ Microbiol 61:1859–1866PubMedGoogle Scholar
  434. 434.
     Wang R-F, Cao W-W, Johnson MG (1991) Development of a 16S rRNA-based oligomer probe specific for Listeria monocytogenes. Appl Environ Microbiol 57:3666–3670PubMedGoogle Scholar
  435. 435.
     Wang S-Y, Hitchins AD (1994) Enrichment of severely and moderately heat-injured Listeria monocytogenes cells. J Food Saf 14:259–271Google Scholar
  436. 436.
     Wang RF, Cao WW, Franklin W, Campbell W, Cerniglia CE (1994) A 16S rDNA-based PCR method for rapid and specific detection of Clostridium perfringens in food. Mol Cell Probes 8:131–137CrossRefPubMedGoogle Scholar
  437. 437.
     Wang R-F, Cao W-W, Cerniglia CE (1997) A universal protocol for PCR detection of 13 species of foodborne pathogens in foods. J Appl Microbiol 83:727–736PubMedGoogle Scholar
  438. 438.
     Warburton DW, Farber JM, Armstrong A, Caldeira R, Hunt T, Messier S, Plante R, Tiwari NP, Vinet J (1991) A comparative study of the ″FDA″ and ″USDA″ methods for the detection of Listeria monocytogenes in foods. Int J Food Microbiol 13:105–118CrossRefPubMedGoogle Scholar
  439. 439.
     Warburton DW, Farber JM, Armstrong A, Caldeira R, Tiwari NP, Babiuk T, La Casse P, Read S (1991) The Canadian comparative study of modified versions of the ″FDA″ and ″USDA″ methods for the detection of Listeria monocytogenes. J Food Prot 54:669–676Google Scholar
  440. 440.
     Ward DM, Weller R, Bateson M (1990)16S rRNA sequences reveal numerous uncultured microorganisms in a natural community. Nature 35:63–65Google Scholar
  441. 441.
     Weber S, Stubner S, Conrad R (2001) Bacterial populations colonizing and degrading rice straw in anoxic paddy soil. Appl Environ Microbiol 67:1318–1327PubMedGoogle Scholar
  442. 442.
     Welsh J, McClelland M (1990) Fingerprinting genomes using PCR with arbitrary primers. Nucleic Acid Res 25:7212–7218Google Scholar
  443. 443.
     Wernars K, Heuvelman K, Notermans S, Domann E, Leimeister-Wächter M, Chakraborty T (1992) Suitability of the prfA gene, which encodes a regulator of virulence genes in Listeria monocytogenes in the identification of pathogenic Listeria spp. Appl Environ Microbiol 58:765–768PubMedGoogle Scholar
  444. 444.
     Wernars K, Boerlin P, Audurier A, Russell EG, Curtis GDW, Herman L, Mee-Marquet N van der (1996) The WHO sponsored multicenter study of Listeria monocytogenes subtyping: random amplification of polymorphic DNA (RAPD). Int J Food Microbiol 32:325–341CrossRefPubMedGoogle Scholar
  445. 445.
     Wesley IV, Harmon KM, Dickson JS, Schwartz AR (2002) Application of a multiplex polymerase chain reaction assay for the simultaneous confirmation of Listeria monocytogenes and other Listeria species in turkey sample surveillance. J Food Prot 65:780–785Google Scholar
  446. 446.
     Wheeler AE, Stahl DA (1996) Extraction of microbial DNA from aquatic sediments. In: Akkermans ADL, Elsas JD van, Bruijn FJ de (eds) Molecular microbial ecology manual. Kluwer, Dordrecht, pp–29Google Scholar
  447. 447.
     Widjojoatmodjo MN, Fluit AC, Verhoef J (1994) Rapid identification of bacteria by PCR-single-strand conformation polymorphism. J Clin Microbiol 32:3002–3007PubMedGoogle Scholar
  448. 448.
     Widjojoatmodjo MN, Fluit AC, Verhoef J (1995) Molecular identification of bacteria by fluorescence-based PCR-single-strand conformation polymorphism analysis of the 16S rRNA gene. J Clin Microbiol 33:2601–2606PubMedGoogle Scholar
  449. 449.
     Wilkinson MHF (1995) Fluoro-morphometry, adding fluorometry to an image processing system for bacterial morphometry. Rijksuniversiteit Groningen, GroningenGoogle Scholar
  450. 450.
     Wilkinson MHF, Schut F (1998) Digital image analysis of microbes—imaging, morphometry, fluorometry and motility techniques and applications. Wiley, ChichesterGoogle Scholar
  451. 451.
     Wilson KH, Wilson WJ, Radosevich JL, DeSantis TZ, Viswanathan VS, Kuczmarski TA, Andersen GL (2002) High-density microarray of small-subunit ribosomal DNA probes. Appl Environ Microbiol 68:2535–2541CrossRefPubMedGoogle Scholar
  452. 452.
     Winters DK, Maloney TP, Johnson MG (1999) Rapid detection of Listeria monocytogenes by a PCR assay specific for an aminopeptidase. Mol Cell Probes 13:127–131CrossRefPubMedGoogle Scholar
  453. 453.
     Wirtanen G (1995) Biofilm formation and its elimination from food processing equipment. (VTT publication 251) The Technical Research Centre of Finland, EspooGoogle Scholar
  454. 454.
     Wirtanen G, Mattila-Sandholm T (2003) Biofilms in the food industry. In: Bitton G (ed) Encyclopedia of environmental microbiology. Wiley, New York (in press)Google Scholar
  455. 455.
     Wirtanen G, Mattila-Sandholm T (1993) Epifluorescence image analysis and cultivation of foodborne biofilm bacteria grown on stainless steel surfaces. J Food Prot 56:678–683Google Scholar
  456. 456.
     Wirtanen G, Husmark U, Mattila-Sandholm T (1996) Microbial evaluation of the biotransfer potential from surfaces with Bacillus biofilms after rinsing and cleaning procedures in closed food-processing systems. J Food Prot 59:727–733Google Scholar
  457. 457.
     Wirtanen G, Salo S, Maukonen J, Bredholt S, Mattila-Sandholm T (1997). NordFood sanitation in dairies. (VTT publication 309) The Technical Research Centre of Finland, EspooGoogle Scholar
  458. 458.
     Wirtanen G, Saarela M, Mattila-Sandholm T (2000a) Biofilms—impact on hygiene in food industries. In: Bryers J (ed) Biofilms II: process analysis and applications. Wiley–Liss, New York, pp 327-372Google Scholar
  459. 459.
     Wirtanen G, Storgårds E, Saarela M, Salo S, Mattila-Sandholm T (2000b) Detection of biofilms in the food and beverage industry. In: Walker J, Surman S, Jass J (eds) Industrial biofouling—detection, prevention and control. Wiley, Chichester, pp 175–204Google Scholar
  460. 460.
     Wirtanen G, Salo S, Helander IM, Mattila-Sandholm T (2001) Microbiological methods for testing disinfectant efficiency on Pseudomonas biofilm. Colloid Surf B: Biointerface 20:37–50Google Scholar
  461. 461.
     Yamada S, Ohashi E, Agata N, Venkateswaran K (1999) Cloning and nucleotide sequence analysis of gyrB of Bacillus cereus, B. thuringiensis, B. mycoides B. anthracis and their application to the detection of B. cereus in rice. Appl Environ Microbiol 65:1483–1490PubMedGoogle Scholar
  462. 462.
     Ye RW, Wang T, Bedzyk L, Croker KM (2001) Applications of DNA microarrays in microbial systems. J Microbiol Methods 47:257–272CrossRefPubMedGoogle Scholar
  463. 463.
     Yu FP, McFeters GA (1994) Rapid in situ assessment of physiological activities in bacterial biofilms using fluorescent probes. J Microbiol Methods 20:1–10CrossRefPubMedGoogle Scholar
  464. 464.
     Yu FP, McFeters GA (1994) Physiological responses of bacteria in biofilms to disinfection. Appl Environ Microbiol 60:2462–2466PubMedGoogle Scholar
  465. 465.
     Zhang T, Fang HHP (2001) Phylogenetic diversity of a SRB-rich marine biofilm. Appl Microbiol Biotechnol 57:437–440CrossRefGoogle Scholar
  466. 466.
     Zoetendal EG, Akkermans ADL, Vos WM de (1998) Temperature gradient gel electrophoresis analysis of 16S rRNA from human fecal samples reveals stable and host-specific communities of active bacteria. Appl Environ Microbiol 64:3854–3859PubMedGoogle Scholar

Copyright information

© Society for Industrial Microbiology 2003

Authors and Affiliations

  • Johanna Maukonen
    • 1
  • Jaana Mättö
    • 1
  • Gun Wirtanen
    • 1
  • Laura Raaska
    • 1
  • Tiina Mattila-Sandholm
    • 1
  • Maria Saarela
    • 1
  1. 1.VTT BiotechnologyFinland

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