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 , 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. . 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 ), as well as the dynamic model .
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 , I. monocytogenes  and CI. botulinum . 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
thermal death models
Aeromonas hydrophila Bacillus cereus
Clostridium botulinum Clostridium perfringens Escherichia coli 0157:H7 Listeria monocytogenes Staphylococcus aureus
E. coli 0157: H7
7.5 Table. Some models for pathogens program Pathogen Modeling Program
E.coli 0157: H7
Salmonella разновидности Shigella flexneri
E.coli 0157: Н7
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 . 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  have also been developed. In , 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 , 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 stability
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  and their inclusion in decision support systems in the field of microbiological quality and safety . 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.
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) , which can be used in combination with other systems, for example, with general risk analysis . 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.
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