Modelling the microbial quality and safety of foods

T. Wijtzes

Research output: Thesisinternal PhD, WU

Abstract

Quality and safety of foods are often influenced by the presence and growth of microorganisms. Microorganisms in foods can be divided into two groups: pathogenic organisms, causing illness, and microorganisms that are not harmful to health, but that can spoil a product. Presence and growth of pathogenic organisms should be avoided as much as possible. Growth of spoilage organisms is allowed to a certain extent. Presence and growth of pathogenic microorganisms largely influences food safety, whereas growth of spoilage organisms, generally determines shelf life of a food product, Food quality is assumed to be influenced by both pathogenic organisms and spoilage organisms.<p>A method to predict microbial safety and quality of foods is presented. The construction of a food product from its ingredients is simulated, following a recipe. Food processing heuristics are combined with models developed in predictive microbiology. Parameter values of ingredients of foods, such as water activity and acidity, and models for microbial growth and decay are used for prediction. The values of these parameters are collected and present in databases. If required information is lacking, methods to make reliable guesses of the parameters are developed. Furthermore, expert knowledge in production and development of foods can be applied to improve the quality of prediction. Shelf life can be calculated as a function of fluctuating temperature in time. Several food distribution chains can be simulated to assess the influence of distribution chains on food quality. The described methods are implemented into a computerised decision support system.<p>Mathematical models for microbial growth, implemented in the decision support system, are given more attention to. Microbiological food quality is examined by modelling bacterial growth of a spoilage bacterium; <em>Lactobacillus curvatus.</em> Microbiological food safety is modelled by assessing growth behaviour of a pathogenic bacterium; <em>Listeria monocytogenes.</em><p>Models that describe the effect of acidity, temperature, and the combined effect of these variables on the growth parameters of <em>Lactobacillus curvatus</em> are developed and validated. Growth parameters (lag time, specific growth rate, and maximum population density) are calculated from growth data at various temperature-acidity combinations. The effect of acidity is monitored at several constant temperature values. Models are set up and fitted to the data. The same procedure is used at constant acidity values to model the effect of temperature. For lag time, specific growth rate, and maximum population density, the effect of temperature can be multiplied with the effect of acidity. The models are equipped with parameters suggesting that organisms cease growing at minimal or maximal values for controlling variables (Temperature, pH, <em>a</em><sub><font size="-2">w</font></sub> ). <em></em> Evidence is presented for the existence of a lower and upper acidity boundary value for bacterial growth.<p>The effect of temperature, acidity, and water activity on bacterial growth rate of <em>Lactobacillus curvatus is</em> modelled in an extended model. The model is based on two, earlier developed models, one for growth rate as a function of temperature and water activity and the earlier mentioned model. It is assumed that combinatory effects between acidity and water activity do not exist. Therefore, the two models are multiplied to result into one model. The resulting model is fitted to data sets measured earlier, and the parameters of the model are determined. A new data set with values for controlling variables outside the data range where the model is developed, is used to validate the developed model. The model is found very well able to predict outside the measured data range.<p>Bacterial growth rate of <em>Listeria monocytogenes is</em> modelled as a function of temperature, acidity and water activity, for which two equations are developed. The first equation predicts growth rate at sub optimal acidity values, sub optimal temperatures and sub optimal water activities, the second model predicts growth throughout the entire acidity range. The models are validated statistically and by comparing model predictions with values reported in literature.<p>Finally, a computerised system for the identification of bacteria is developed. The system is equipped with a key to the identification of lactic acid bacteria. The identification is carried out in two steps. The first step distinguishes classes of bacteria by following a decision tree with general identification tests. The second step in the identification is the distinction of species within a class on the basis of biochemical fermentation patterns. During group classification, probabilities for test failure are used. These probabilities can be used for assessing the quality of a given test answer. The probabilities are also used to select the most probable test answer in case of an inconclusive test result. The probabilities of test failure are determined by a group of experts and a group of potential users of the identification system. During species identification, similarity indices are calculated for all bacteria in a class. The described identification system is able to "learn" from different sessions in the species identification step, improving both identification speed and accuracy. Because of the versatile way in which the system is set up, it can very easily be expanded with identification keys to other organisms.<p>Structured models and modelling methods are used to predict changes in quality and safety of foods. This thesis shows that even complex problems such as the prediction of the quality of foods, can be modelled through the combination of several models. Model systems are developed giving insight into the processes that are of importance in the determination of food quality and safety.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
Supervisors/Advisors
  • van 't Riet, K., Promotor
  • Huis in 't Veld, J.H.J., Promotor, External person
  • Zwietering, Marcel, Promotor
Award date29 Oct 1996
Place of PublicationS.l.
Publisher
Print ISBNs9789054855781
Publication statusPublished - 1996

Keywords

  • foods
  • food quality
  • quality controls
  • food contamination
  • food microbiology
  • quality
  • performance
  • models
  • research

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