Categories
Chilled and frozen foods

Prognosticheskaya microbiology

As noted above, low temperatures do not prevent the growth of microorganisms completely,

and to increase the period of time before the onset of substantial microbial growth may require additional preservative factors (for example, low pH and aw). Traditionally, the effect of a combination of preservative factors on certain microorganisms was tested in laboratory studies. Such tests play an important role, but they are often expensive, long-lasting and give results limited to certain test conditions. If any conditions change, the test must be repeated. However, the market for chilled products is very dynamic (with a strong demand for new products [106], which need to be quickly developed and delivered to the market).

Prognostic microbiology is a tool that can quickly provide reliable answers to questions regarding the probability of growth of certain microorganisms under given conditions, including conditions that have not been previously tested. Models can be used to predict the likelihood of growth, the time before growth or the growth rate of microorganisms The use of prognostic models for describing the dynamics of the development of microorganisms is not new, and references to such methods can be found in publications dating back to 1920. [33]. A review of microbiological modeling is given in [49,81].

When developing microbiological models typically use the following steps:

  • careful selection and appropriate preparation of the test microorganism;
  • inoculation of the test microorganism into the growth medium (microbiological medium or foodstuff) with certain characteristics;
  • storage medium in a controlled environment;
  • Sampling of the medium at appropriate intervals for determination of a predetermined test organism;
  • construction of a model describing the reaction of the target organism;
  • validation of the predictions of the model - preferably in the product, to ensure the validity of the forecast;
  • clarification or further improvement of the model.

A variety of models were used for prediction, including the Arrhenius equation, non-linear Arrhenius models, Belegrad models or quadratic, polynomial and mechanistic models (all of which were considered in [81]), as well as the dynamic model [9].

food pathogens

Over the past decade, considerable work has been done on the predictive modeling of the behavior of a wide range of pathogenic bacteria, for example, kinetic growth models for Salmonella [43], I. monocytogenes [35] and CI. botulinum [54]. For such models to be available to food manufacturers, user-friendly software must be created based on them.

To predict the growth of food pathogens, there are currently two main systems. In the UK, the largest and most complete system is FoodMicroModel, which was developed as part of a research program funded by the Ministry of Agriculture, Fisheries and Food. Software can be purchased from the Leatherhead Food Research Association (Leatherhead). In this system there are many models of pathogens, including those presented in Table. 7.4.

The models in the Food MicroModel system are based on data obtained in laboratory conditions of cultivation and verified by comparing the predictions obtained on the models with data based on studies of products after inoculation.

Another comprehensive modeling program created by the US Department of Agriculture and is called the Pathogen Modeling Program. Among the models used in this system are the models given in table. 7.5.

The program can be obtained free of charge via the Internet. The models in this program are derived from extensive growth data in laboratory growing conditions, but have not been tested on real products.

7.4 Table. Some models of pathogens under the program Food MicroModel  

growth Models

thermal death models

Aeromonas hydrophila Bacillus cereus

Clostridium botulinum Clostridium perfringens Escherichia coli 0157:H7 Listeria monocytogenes Staphylococcus aureus

CI. botulinum

E. coli 0157: H7

L. monocytogenes

Salmonella

Y. enterocolitica

Salmonella

Yersinia enterocolitica

7.5 Table. Some models for pathogens program Pathogen Modeling Program

growth Models

survival Models

А.hydrophila

B. cereus

E.coli 0157: H7

Salmonella разновидности Shigella flexneri

S. aureus

Y. enterocolitica

E.coli 0157: Н7

 L. monocytogenes

Salmonella

S. aureus


Food spoilage

There are quite a few systems for modeling the behavior of microorganisms that cause spoilage of products, although many particular models have been published. As a result of working in Tasmania, a predictive model for Pseudomonas was developed, suitable for milk and raw meat [80]. The Campden and Chorlivud Food Research Association has developed a set of models that can be used to assess the rate of damage or the likely stability of products, including chilled ones. This set of models is called Forecast (“Forecast”), and potential users can use it through the help desk (tel. + 44 (0) 1386 842000), which launches the model for the client after carefully ascertaining his needs. The advisory nature of this approach also makes possible the subsequent qualified interpretation of results and analysis of the adequacy of the model. A number of models currently available through Forecast are shown in Table. 7.6. All models in this system are built on the basis of data obtained in laboratory conditions of cultivation, and tested for the relevant products according to literature data or by using resistance tests after inoculation of microorganisms.

Partial models for spoilage microorganisms are found in the aforementioned Food MicroModel program (for example, the Brochothrix thermosphacta,

7.6 Table. Existing options for Forecast Models (Assotsiatsiyapischevyh research Campden and Chorleywood station)

Model pH Salt, wt.% Temperature, ° C
Bacillus Species 4,0 7,0 0,5 10,0 5 25
Species Pseudomonas 5,5 7,0 0,0 4,0 0 15
Enterobacteriaceae 4,0 7,0 0,5 10 0 30
Yeast (OHL). 2,5 6,3 0,5 10 1 22
Lactic acid bacteria 2,9 5,8 0,5 10 2 30

Saccharomyces cerevisiae, Lactobacillus plantarum, Zygosaccharomyces bailii). In addition to bacteria and yeast, models for mold growth [109] have also been developed. In [83], similar models were applied to the analysis of the deterioration of product quality (destruction of pectin), and not to the analysis of the growth of microorganisms.

Practical application of models

In fig. 7.3 shows how the model is used in practice by comparing predicted values ​​with established standards for expiration. There are many other potential goals for their use, for example, to answer the following questions:

  • What level of microorganism content will be at different storage temperatures ?;
  • how much salt is needed to limit the number of microorganisms at a given level after storage for one week at 8 ° C ?;
  • what is the effect of increasing the pH of the product from the 5,0 5,4 to?

Some [26,65] researchers have noted the flaws or inaccuracies of such predictions in that they predict growth faster than observed in real products. Nevertheless, many models, especially models for pathogens, are designed to be “safe”, and the product may contain additional antimicrobial factors missing from the model that can suppress or prevent the predicted growth. Therefore, it is important to ensure that any model used takes into account important preservative factors that are essential to the study, and that the model has been tested on the relevant products. Most of the developed models are built for individual microorganisms or their species in pure cultures and can therefore not take into account the effects of interaction and competition of microorganisms, which are very likely in real products.

In [90], modeling techniques were applied to study the interactions between bacteria that cause spoilage. Information provided by prognostic models, if used improperly by unqualified people, can cause serious consequences. To obtain useful information it is important to set the task correctly.

Using predictive models provides many advantages for the development and production of chilled products. In developing the product, they can help to concentrate resources to assess the microbiological safety and stabilityA graphic representation of the results obtained using the Forecast system (Campden and Chorlivud Food Research Association) for the following conditions: pH 6,0; salt 3 wt.%, storage temperature 6 ° C. The user tolerance ratio for the Enterobactericeae ssp family is clearly visible. Pseudomonas and Bacillus and projected shelf life

Fig. 7.3. A graphic representation of the results obtained using the Forecast system (Campden and Chorlivud Food Research Association) for the following conditions: pH 6,0; salt 3 wt.%, storage temperature 6 ° C. The user tolerance ratio for the Enterobactericeae ssp family is clearly visible. Pseudomonas and Bacillus and projected shelf life

hundreds of different combinations of ingredients before the start of practical work "in the kitchen." Predictive models can serve as a decision-making tool, allowing you to effectively concentrate on the development of technological processes and products, as well as on risk assessment. When used correctly, these models can be very valuable in comprehensive quality studies using the HACCP method. Following their use, targeted practical tests and tests of resistance to certain microorganisms should be performed. With this use, prognostic models can serve as powerful tools for microbiologists in production. Several researchers have proposed the development of predictive models in the framework of computer neural networks [59] and their inclusion in decision support systems in the field of microbiological quality and safety [121]. Predictive models can also be used in education and training, since they allow the behavior of microorganisms to be demonstrated and the risks present without the need for expensive laboratory work.

It should be emphasized that microbiological models will never completely eliminate the need for microbiological expertise, microbiological testing of the resistance of a product to certain microorganisms and research on its shelf life, but they can be very useful as indicators of the safety and stability of chilled products and ingredients.

conclusions

Chilled products form a complex group of diverse consumer products containing many ingredients. The nomenclature and the number of microorganisms present in them depends on the natural microflora, on microorganisms infecting products before and after processing, on growth rates and properties of microorganisms, on the ability of microorganisms to cause spoilage, on the properties of the product itself, on the impact of processing and packaging methods, and from terms and temperatures of storage. That is why the issues of microbiological safety and damage to refrigerated products are very complex. In this case, one can be guided by certain general principles:

  •  the microbiological state of all raw materials should be known, and only good quality raw materials should be used;
  •  all processing steps must be clearly described; treatment regimes should be monitored and adjusted to ensure proper operation at each stage, which is of particular importance for products whose microbiological stability is provided by a combination of several factors;
  •  temperature refrigeration storage of the product should be monitored at all stages - from raw materials and materials until the use of the product at home, not forgetting about retail; the lower the temperature, the slower the rate of microbial development;
  •  To ensure minimal microbiological contamination, great attention should be paid to hygienic conditions throughout the entire process.

These goals can best be achieved by using a quality control system that includes risk analysis at critical control points (HACCP) [74], which can be used in combination with other systems, for example, with general risk analysis [69]. Using appropriate validated models can greatly assist in the decision-making process. And finally, improving the training of people involved in food production, marketing and retail, as well as increasing consumer awareness in the field of hygiene and temperature control when working with chilled products can be of great benefit.

Literature

  1.  ABEYTA, С. and WEKELL, М. М., (1988) Potential sources of Aeromonas hydrophila //J.Food Safety, 9, pp. 11-22.
  2.  ADVISORY COMMITTEE ON THE MICROBIOLOGICAL SAFETY OF FOOD (ACMSF). (1995) Report on Verocytoxin-producing Escherichia coli. - HMSO, London.
  3.  ALCOCK, S. J., (1984) Growth characteristics of food-poisoning organisms at suboptimal temperatures. II Salmonellae. - Campden Food Preservation Research Association Memorandum No 364.
  4.  ALCOCK, SJ, (1987) Growth characteristics of food-poisoning organisms at suboptimal temperatures. - Campden Food Preservation Research Association Technical Memorandum No. 440.
  5.  ANGELOTTI, R., FOTER, M. J. and LEWIS, K. H., (1961) Time-temperature effects of salmonellae and staphylococci in foods, Am.J. Pub. Health, 36, pp. 559-563.
  6.  ANON, (1989) Guidelines for Cook-Chill and Cook-Freeze Catering Systems, HMSO, London.
  7.  ANON, (1991a) The Microbiological Safety of Food, Part II, HMSO, London.
  8.  ANON, (1991b) Principles and Practices for the Safe Processing of Foods, Butterworth- Heinemann, Oxford.
  9.  BARANYI, J. and ROBERTS, T. A., (1994) A dynamic approach to predicting bacterial growth in food, InternationalJournal of Food Microbiology, 23, pp.277-294.
  10.  BAYNE, H. G. and MICHENER, H. D., (1979) Heat resistance of Byssochlamys ascospores Appl. Environ. Microbiol., 37, pp. 449-453.
  11.  BELL, C. and KYRIAKIDES, A., (1998a) E. coli: a practical approach to the organism and its control in foods, Blackie Academic and Professional, London.
  12.  BELL, C. and KYRIAKIDES, A., (1998b) Listeria: a practical approach to the organism and its control in foods, Blackie Academic and Professional, London.
  13.  BETTS, G. D., (1992) The microbiological safety of sous-vide processing, Campden and Chorleywood Food Research Association Technical Manual, No. 39.
  14.  BETTS, G. D., (1996) A code of practice for the manufacture of vacuum and modified atmosphere packaging chilled foods, Campden and Chorleywood Food Research Association, CCFRA Guideline No. 11.
  15.  BLACK, R. E., JACKSON, R. L., TSAI, T., MEDVESKY, M., SHAYEGANI, M., FEELEY, J. C., MACLEOD, K. I. E., and WAKELEE, A. W., (1978) Epidemic Yersinia enterocolitica infection due to contaminated chocolate milk, New Engl.J. Med., 298, p. 76-79.
  16.  BORCH, E., KANT-MUERMANS, M.-L., and BLIXT, Y., (1996) Bacterial spoilage of meat and cured meat products. InternationalJournal of Food Microbiology, 33, pp. 103-120.
  17.  BORDER, P. and NORTON, M., (1997) Safer eating: microbiological food poisoning and its prevention. The Parliamentary Office of Science and Technology, London.
  18.  BRADSHAW, J. G., PEELER, J. T. and TWEDT, R. M., (1991) Thermal resistance of Listeria spp. in milk,/. FoodProt., 54, pp. 12-14.
  19.  BUTZLER, J. P. and OOSTEROM, J., (1991) Campylobacter pathogenicity and significance in foods, Int.J. Food Microbiol., 12, pp. 1-8.
  20.  CAHILL, M. M., (1990) Virulence factors in motile Aeromonas species: a review,/ Appl. Bacterial, 69, pp. 1-16.
  21.  COGHILL, D. and JUFFS, H. S., (1979) Incidence of psychrotrophic spore-forming bacteria in pasteurised milk and cream products and effect of temperature on their growth, Australian J. Dairy Technol., 3, pp. 150-153.
  22.  CONNER, D. E. and KOTROLA, J. S., (1995) Growth and survival of Escherichia coli 0157:H7 under acidic conditions, Applied and Environmental Microbiology, 61, pp. 382-385.
  23.  COUSIN, M. A., (1982) Presence and activity of psychrotrophic microorganisms in milk and dairy products: a review\J. Food Prot., 45, pp. 172-207.
  24.  COX, L. J., KLEISS, T., CORDIER, J. L., CORDELLANA, C., KONKEL, P., PEDRAZZINI, C., BEUMER, R. and SIEBENGA, A., (1989) Listeria spp. in food processing, non-food processing and domestic environments, Food Microbiol, 6, pp. 49-61.
  25.  DAINTY, R. H., (1996) Chemical/biochemical detection of spoilage, International Journal of Food Microbiology, 33, pp. 19-34.
  26.  DALGAARD, P. and JORGENSEN, L. V., (1998) Predicted and observed growth of Listeria monocytogenes in seafood challenge tests and naturally contaminated cold smoked salmon, InternationalJournal of Food Microbiology, 40, pp. 105-115.
  27.  DAY, BPF, (2000) Chilled food packaging // Chilled Foods: a comprehensive guide / Stringer, MF and Dennis, C. (ed.). - 2nd edn. - Cambridge: Woodhead Publishing Ltd. - P. 137-150.
  28.  D'AOUST, JY, (1991) Psychrotrophy and foodborne Salmonella //Int.J. Food Microbiol., 13, pp. 207-216.
  29.  DENG, Y., RYU, JH, and BEUCH AT, LR, (2000), Escherichia coli 0157: Accepted acidic acid. pp. 7-2000.
  30.  DOYLE, M. P., (1990) Pathogenic Escherichia coli, Yersinia enterocolitica and Vibrio parahae- molyticus//Th.e Lancet, № 336, pp. 1111-1115.
  31.  ECKLUND, MW, WIELER, DL and POYSKY, FT, (1967) Clostridium botulinum at 3,3 to 5,6 ° C // J. Bacterial, 93, pp. 1461-1462.
  32.  EDDY, BP, (1960) “Psychrophilic” // J. Appl. Bacterial., 23, pp. 189-190.
  33.  ESTY, J. R. and MEYER, K. F., (1922) The heat resistance of spores of Cl botulinum and allied anaerobes //J. Infect. Dis., 31, pp. 650-663.
  34.  FAITH, NG, WIERZBA, RK, IHNOT, AM, ROERING, AM, LORANG, TD, KASPER, CW and LUCHANSKY, JB, (1998) at 0157 ° or 4 ° C under 21, 135 and 191 ° C // J. of Food Protection, 246, pp.61-383.
  35.  FÄRBER, JM, CAI, Y. and ROSS, WH, (1996) Predictive modeling of the growth of Listeria monocytogenes in C02 environments // Int.J. of Food Microbiology, 32, pp. 133-144.
  36.  FILTENBORG, O., FRISVAD, J. C. and THRANE, U., (1996) Moulds in food spoilage //Int. J. of Food Microbiology, 33, pp. 85-102.
  37.  FRICKER, C. R. and TOMPSETT, S., (1989) Aeromonas spp. in foods: a significant cause of food poisoning // Int.J. Food Microbiol., 9, pp. 17-23.
  38.  GARDNER, GA, (1981) Brochothrix thermosphacta (Microbacterium thermo-sphactum): a review // Psychrotrophic Microorganisms in Spoilage and Pathogenicity / Roberts, TA et al. (ed.) - London: Academic Press, 1981. - P. 139-173.
  39.  GARDNER, GA, (1983) Microbial spoilage of cured meats // Food Microbiology: Advances and Prospects / Roberts, TA and Skinner, FA (ed.). - London: Academic Press, 1983. - P. 179-202.
  40.  GAZE, JE, (1992) Food pasteurisation treatments. - Campden Food & Drink Research Association Technical Manual, No. 27.
  41.  GAZE, J. E., Brown, G. D., GASKELL, D. E. and BANKS, J. G., (1989) Heat resistance of Listeria monocytogenes in homogenates of chicken, beef steak and carrot // Food Microbiol., 6, pp. 251-259.
  42.  GEORGE, S. M., LUND, B. M. and BROCKLEHURST, T. F., (1988) The effect of pH and temperature on initiation of growth of Listeria monocytogenes // Letters in Appl. Microbiol., 6, pp. 153-156.
  43.  GIBSON, A. M., BRATCHELL, N. and ROBERTS, T. A., (1988) Predicting microbial growth: growth responses of salmonellae in a laboratory medium as affected by pH, sodium chloride and storage temperature //Int.J. Food Microbiol., 6, pp.155-178.
  44.  GILL, CD and MOLIN, G., (1991) Modified atmospheres and vacuum packaging // Food Preservatives / Russell, NJ and Gould, GW (ed.). - Glasgow: Blackie and Son Ltd. - P. 172-199.
  45.  GILL, C. D., (1983) Meat spoilage and evaluation of the potential storage life of fresh meat // J. Food Prot., 46, pp. 444-452.
  46.  GILMOUR, A. and WALKER, S. J., (1988) Isolation and identification of Yersinia enterocolitica and Yersinia enterocolitica-like bacteria //J. Appl. Bacterial., Suppl., 65, pp. 213S-236S.
  47.  GLASS, K. A. and DOYLE, M. P., (1991) Relationship between water activity of fresh pasta and toxin production by proteolytic Clostridium botulinum. //J. Food Prot., 54, pp. 162-165.
  48.  GOEPFERT, J. M., SPIRA, W. M. and KIM, Ft. U., (1972) Bacillus cereus food poisoning: a review //J. Milk Food Technol., 35, pp. 213-227.
  49.  GOULD, G. W., (1989a). Predictive modelling of microbial growth and survival in foods // Food Sci. Technol. Today, 3, pp. 89-92.
  50.  GOULD, GW, (1989b) Heat-induced injury and inactivation // Mechanisms of Food Preservation Procedures / Gould, GW (ed.). - London: Elsevier Appl. Sci., 1989. - P. 11-42.
  51.  GOULD, G. W., (1996) Methods for preservation and extension of shelf-life // Int. J. of Food Microbiology, 33, pp. 51-64.
  52.  GOULD, GW and JONES, MV (1989) Combination and synergistic effects // Mechanisms of Food Preservation Procedures / Gould, GW (ed.). - London: Elsevier Appl. Sci. - 1989.-P. 400-421.
  53.  GOULD, GW and RUSSELL, NJ, (1991) Major food organisms, food poisoning and food organisms // Food Preservatives / Russell, NJ and Gould, GW (ed.). - Glasgow: Blackie and Son Ltd. - P. 1-21.
  54.  GRAHAM, A. F., MASON, D. R. and PECK, M. W., (1996) Predictive model of the effect of temperature, pH and sodium chloride on growth from spores of non-proteolytic Clostridium botulinum // Int. J. of Food Microbiology, 31, pp. 69-85.
  55.  GRAM, L., and HUSS, H. H., (1996) Microbiological spoilage of fish and fish products // International Journal of Food Microbiology, 53, pp. 121-138.
  56.  GREENWOOD, M. H. and HOOPER, W. L., (1989) Improved methods for the isolation of Yersinia species from milk and foods // Food Microbiol., 6, pp. 99-104.
  57.  GRIFFITHS, M. W. and PHILLIPS, J. D., (1990) Incidence, source and some properties of psychrotrophic Bacillus spp. found in raw and pasteurized milk //J. Soc. Dairy Technol, 43, pp. 62-66.
  58.  GUTIERREZ, R., GAREIA, T., GONZALEZ, I., SANZ, B., HERNANDEZ, P. E., and MARTIN, R. (1997) A quantitative PCR-ELISA for the rapid enumeration of bacteria in refrigerated raw milk //J. of Appl. Microbiol., 83, pp. 518-523.
  59.  HAJMEER, M. N., BASHEER, I. A. and NAJJAR, Y. M., (1997) Computational neural networks for predictive microbiology II. Application to microbial growth // Int. J. of Food Microbiol., 34, pp. 51-66.
  60.  HAUSCHILD, AHW, (1989) Clostridium botulinum // Foodborne Bacterial Pathogens / Doyle, MP (ed.). - New York: Marcel Dekker, 1989. - P. 111-189.
  61.  HERBERT, RA, (1989) Microbial growth at low temperatures // Mechanisms of Food Preservation Procedures / Gould, GW (ed.). - London: Elsevier Appl. Sci., 1989. - P. 71-96.
  62.  HOLAH, JT, (1999) Effective microbiological sampling of food processing environments. - Campden and Chorleywood Food Research Association Guideline No. 20.
  63.  HOLAH, JT, TAYLOR, J. and HOLDER, JS, (1993) The spread of Listeria by cleaning systems. - Campden Food & Drink Research Association Technical Memorandum No. 673.
  64.  HUIS INT'VELD, JHT (1996) Microbial and biochemical spoilage of foods: an overview // Int. J. of Food Microbiol., 33, pp. 1-18.
  65.  HYGTIA, E., HIELM, S., MOKKILA, M., KINNUNEN, A and KORKEALA, H., (1999) Predicted and observed growth and toxigenesis by Clostridium botulinum type E in vacuum- packaged fishery product challenge tests // Int. J. of Food Microbiol., 47, pp. 161-169.
  66.  JAQUETTE, C. B. and BEUCHAT, L. R., (1998) Combined effects of pH, nisin and temperature on growth and survival of psychrotrophic Bacillus cereus//}. of Food Protection, 61, pp. 563- 570.
  67.  JAY, JM, (1978) Modem Food Microbiology. - 2nd ed. - New York: D. van Nostrand Co., 1978.
  68.  JOHNSON KM (1984) Bacillus cereus foodborne illness - an update // J. Food Prot., 47, pp. 145-153.
  69.  JOUVEJ. L., STRINGER, MF and BAIRD-PARKER, AC, (1998) Food Safety Management Tools. - Brussels: ILSI - Europe, 1998.
  70.  KABARA, JJ and EKLUNDT, (1991) Organic acids and their esters // Food Preservatives / Russell, NJ and Gould, GW (ed.). - Glasgow: Blackie and Son Ltd., 1991. - P. 44-71.
  71.  KAPER, B. and O'BRIEN, AD, (1998) Escherichia coli 0157: H7 and other Shiga toxin-producing E. coli strains. - Washington: ASM Press, 1998.
  72.  KAUPPI, KI, O'SULLIVAN, DJ and TATINIS, R., (1998) Influence of the Escherichia coli // Food Microbiology, 15, pp. 355-364.
  73.  KRAMER, JM and GILBERT, RJ, (1989) Bacillus cereus and other Bacillus species // Foodborne Bacterial Pathogens / Doyle, MP (ed.). - New York: Marcel Dekker, 1989. —P. 21-69.
  74.  LEAPER, S., (1997) HACCP: a practical guide. - 2nd ed. - Campden and Chorleywood Food Research Association Technical Manual No. 38.
  75.  LOGUE, C. M., SHERIDAN, G., WAUTERS, G., MCDOWELL, D. A. and BLAIR, I. S., (1996) Yersinia spp and numbers, with particular reference to Y.enterocolitica occurring on Irish meat and meat products, and the influence of alkali treatment on their isolation // Intern. J. of Food Microbiol., 33, pp. 257-274.
  76.  LOVETT, L., BRADSHAW, J. G. and PEELER, J. T., (1982) Thermal inactivation of Yersinia emerocolitica in milk // Appl. Environ. Microbiol., 44, pp. 517-519.
  77.  LUCKE, FK and EARNSHAW, RG, (1991) Starter cultures // Food Preservatives / Russell, NJ and Gould, GW (ed.). - Glasgow: Blackie and Son Ltd., 1991. - P. 215-234.
  78.  LUND, B. M., (1990) The prevention of foodborne listeriosis // Br. FoodJ., 92, pp. 13-22.
  79.  MCLAUCHLIN, J. A., (1987) A review: Listena monocytogenes, recent advances in the taxonomy and epidemiology of listeriosis in humans //J. Appl. Bacteriol., 63, pp. 1-2.
  80.  MCMEEKIN, T. A. and ROSS, T., (1996) Shelf-life prediction: status and future possibilities // Int.]. of Food Microbiol., 31, pp. 65-84.
  81.  MCMEEKIN, TA, OLLEY, JN, ROSS, T. and RATKOWSKY, DA, (1993) Predictive microbiology: theory and application. - Somerset: Research Studies Press, 1993.
  82.  MATCHES, J. R. and LISTON, J., (1968) Low temperature growth of Salmonella //J. Food Sci.,33, pp. 641-645.
  83.  MEMBRE, JM and KUBACZKA, M., (1998) Degradation of pectin compounds during pasteurized vegetable juice spoilage by Chryseomonas luteola: a predictive microbiology approach // Int. J. of Food Microbiol., 42, pp. 159-166.
  84.  MICHENER, H. D. and ELLIOTT, R. P., (1964) Minimum growth temperatures for food poisoning, fecal indicator and psychrophilic microorganisms // Adv. in Food Res., 13, pp. 349- 396.
  85.  MITSCHERLICH, E. and MARTH, EH, (1984) Microbial Survival in the Environment. - Berlin: Springer-Verlag, 1984.
  86.  MORITA, R. Y., (1973) Psychrophilic bacteria // Bacterial. Rev., 39, pp. 144-167.
  87.  MURRAY, EGD, WEBB, RA and SWAN, MBR, (1926) Bacterium monocytogenes (n. Sp) // J. Pathol. Bacterial, 29, pp. 407-439.
  88.  NEILL, SD, (1974) A line of milk stored at a low temperature: PhD Thesis / The Queen's University of Belfast, Northern Ireland, 1974.
  89.  PALUMBO, S. A. and BUCHANAN, R. L., (1988) Factors affecting growth or survival of Aeromonas hydrophila in foods //J. Food Safety, 9, pp. 37-51.
  90.  PIN, C. and BARANYI, J. (1998) Predictive models as means to quantify the interactions of spoilage organisms // Int. J. of Food Microbiol., 41, pp. 59-72.
  91.  PITT, JI and HOCKING, AD, (1985) Spoilage of fresh and perishable foods // Fungi and Food Spoilages - Sydney: Academic Press, 1985. - P 365-382.
  92.  PRESCOTT, S. C. and GEER, L. P., (1936) Observations on food poisoning organisms under refrigeration conditions // Refrigeration Engineering, 32, pp. 211-212,282-283.
  93.  RALOVICH, BS, (1987), European countries and / / Listeriosis: Joint WHO / ROI Consultation on Prevention and Control / Schonberg, A. (ed.). - Vet. Med. Hefte, Berlin, pp. 51-55.
  94.  REINHEIMER, J. A. and BARGAGNA, M. L., (1989). Response of psychrotrophic strains of Bacillus to different heat treatments // Microbiol-Aliments-Nutr., 5, pp. 117-122.
  95.  RIDELL, J. and KORKEALA, H., (1997) Minimum growth temperatures of Hafnia alvei and other Enterobacteriaceae isolated from refrigerated meat determined with a temperature gradient incubator. InternationalJournal of Food Microbiology 35, pp. 287-292.
  96.  RUSSELL, NJ and GOULD, GW, (1991) Food preservatives. - Glasgow: Blackie, 1991.
  97.  SCHIEMANN, DA (1989) Yersinia enterocolitica and Yersiniapseudotuberculosis // Foodborne Bacterial Pathogens / Doyle, MP (ed.). - New York: Marcel Dekker. - P. 601-672.
  98.  SCHLIEFSTEIN, J. I. and COLEMAN, M. B., (1939) An unidentified microorganism resembling B. lignieri and Past,pseudotuberculosis, and pathogenic for man //New York State J. Med., 39, pp. 1749-1753.
  99.  SCHMIDT, CF, LECHOWICH, RV and FOLINAZZO, JF, (1961) Growth and toxin production by Type E Clostridium botulinum below 40 ° F // J. Food Sci., 26, pp. 626-634.
  100.  SCHUCHAT, A., SWAMINATHAN, B. and BROOMEC, V., (1991) Epidemiology of human listeriosis // Clin. Microbiol. Rev., 4, pp. 169-183.
  101.  SHAW, R., (1998) Identification of meat and meat products. - Campden and Chorleywood Food Research Association Review No. 8.
  102.  SIMUNOVIC, J., OBLINGER, J. L. and ADAMS, J. P., (1985) Potential for growth of non- proteolytic types of Clostridium botulinum in pasteurized and restructured meat products: a review //J. Food Prot., 48, pp. 265-276.
  103.  SKIRROW, M. B., (1990) Campylobacter//The Lancet, 336, pp. 921-923.
  104.  SPERBER, W, H., (1983) Influence of water activity on foodborne bacteria - a review // J. Food Prot., 46, pp. 142-150.
  105.  STELMA, GN, (1989) Aeromonas hydrophila // Foodborne Bacterial Pathogens / Doyle, MP (ed.). - New York: Marcel Dekker, 1989. - P. 1-19.
  106.  STRINGER, MF and DENNIS, C. (2000) The market for chilled foods // Chilled Foods: a comprehensive guide. - 2nd ed. - Cambridge: Woodhead Publishing, 2000.
  107.  TACKET, CO, NAVAIN, JP, SATTIN, R., LOFGREN, JR, KÖNIGSBERG, C., RENDTORFF, RC, RAUSA, A., Davis, BR and COHEN, ML, (1984) A multistate outbreak of development, Yersinia enterocolitica transmitted by pasteurized milk // J. American Med. Assoc., 51, pp. 483-486.
  108.  TACKET, C. O., BALLARD, L., HARRIS, N., ALLARD, L, NOLAN, C., QUAN, T. and COHEN, M. L., (1985) An outbreak of Yersinia enterocolitica infections caused by contaminated tofu // American J. Epidemiol., 121, pp. 705-711.
  109.  TERPLAN, G., SCHOEN, R., SPRINGMEYER, W., DEGLE, I., and BECKER, H., (1987) Interstigations on Listeria in cheese // Listeriosis - Joint WHO / ROI Consultation on Prevention and Control / Schonberg, A. (ed.). - Vet. Med. Hefte, Berlin, pp. 98-105.
  110.  TODD, L. S., HARDY, I. C., STRINGER, M. F. and BARTHOLOMEW, B. A., (1989) Toxin production by strains of Aeromonas hydrophila grown in laboratory media and prawn puree // Int. J. Food Microbiol., 9, pp. 145-156.
  111.  VALIK, L., BARANYI, J. and CORNER, F., (1999) Predicting fungal growth: the effect of water activity on Penecillium roquefortii // Int. J. of Food Microbiol., 47, pp. 141-146.
  112.  VAN NETTEN, R., VAN DE MOOSDIJK, A., VAN HOENSEL, P. and MOSSEL, D. A. A., (1990) Psychrotrophic strains of Bacillus cereus producing enterotoxin //J. Appl. Bacteriol, 69, pp. 73-79.
  113.  VENKITANARAYANEN, K. S., FAUSTMAN, C., CRIVELLO, J. F., KHAN, M. I., HOAGLAND, T. A. and BERRY, B. W., (1997) Rapid estimation of spoilage bacterial load in aerobically stored meat by a quantitative polymerase chain reaction // J. Appl. Microbiol., 82, pp. 359-364.
  114.  WALKER, S. J., (1988) Major spoilage microorganisms in milk and dairy products //J. Soc. Dairy Technol, 41, pp. 91-92.
  115.  WALKER, SJ and STRINGER, MF, (1987) Growth of Listeria monocytogenes and Aeromonas hydrophila at chill temperatures. - Campden Food and Drink Research Association Memorandum No 462.
  116.  WALKER, SJ and STRINGER, MF, (1990) Microbiology of chilled foods // Chilled Foods - The State of the Art / Gormley, T. R (ed.). - Barking: Elsevier Appl. Sei., 1990. - P. 269-304.
  117.  WALKER, SJ, (1990) Listeria monocytogenes: an emerging pathogen // Food Technology International Europe / Turner, A. (ed.). - London: Sterling Publications, 1990. - P 237-240.
  118.  WALKER, S. J., ARCHER, P. and BANKS, J. G., (1990a) Growth of Listeria monocytogenes at refrigeration temperatures //J. Appl. Bacteriol., 68, pp. 157-162.
  119.  WALKER, S. J., ARCHER, P. and BANKS, J. G., (1990b) Growth of Yersinia enterocolitica at chill temperatures in milk and other media // Milchwissenschaft, 45, pp. 503-506.
  120.  WHITFIELD, F. B., (1998) Microbiology of food taints // Int. J. of Food Science and Technology, 33, pp. 31-51.
  121.  WIJTZES, T., VAN'T RIET, K., HUIS IN'TVELD, JHJ and ZWIETERING, MH, (1998) Int. J. of Food Microbiology, 42, pp. 79-90.

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