Modelling the microbial quality and safety of foods

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.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.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; Lactobacillus curvatus. Microbiological food safety is modelled by assessing growth behaviour of a pathogenic bacterium; Listeria monocytogenes.Models that describe the effect of acidity, temperature, and the combined effect of these variables on the growth parameters of Lactobacillus curvatus 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, aw ). Evidence is presented for the existence of a lower and upper acidity boundary value for bacterial growth.The effect of temperature, acidity, and water activity on bacterial growth rate of Lactobacillus curvatus is 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.Bacterial growth rate of Listeria monocytogenes is 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.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.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

Dormancy within Staphylococcus epidermidis biofilms : a transcriptomic analysis by RNA-seq

The proportion of dormant bacteria within Staphylococcus epidermidis biofilms may determine its inflammatory profile. Previously, we have shown that S. epidermidis biofilms with higher proportions of dormant bacteria have reduced activation of murine macrophages. RNA-sequencing was used to identify the major transcriptomic differences between S. epidermidis biofilms with different proportions of dormant bacteria. To accomplish this goal, we used an in vitro model where magnesium allowed modulation of the proportion of dormant bacteria within S. epidermidis biofilms. Significant differences were found in the expression of 147 genes. A detailed analysis of the results was performed based on direct and functional gene interactions. Biological processes among the differentially expressed genes were mainly related to oxidation-reduction processes and acetyl-CoA metabolic processes. Gene set enrichment revealed that the translation process is related to the proportion of dormant bacteria. Transcription of mRNAs involved in oxidation-reduction processes was associated with higher proportions of dormant bacteria within S. epidermidis biofilm. Moreover, the pH of the culture medium did not change after the addition of magnesium, and genes related to magnesium transport did not seem to impact entrance of bacterial cells into dormancy.The authors thank Stephen Lorry at Harvard Medical School for providing CLC Genomics software. This work was funded by Fundacao para a Ciencia e a Tecnologia (FCT) and COMPETE grants PTDC/BIA-MIC/113450/2009, FCOMP-01-0124-FEDER-014309, FCOMP-01-0124-FEDER-022718 (FCT PEst-C/SAU/LA0002/2011), QOPNA research unit (project PEst-C/QUI/UI0062/2011), and CENTRO-07-ST24-FEDER-002034. The following authors had an individual FCT fellowship: VC (SFRH/BD/78235/2011) and AF (2SFRH/BD/62359/2009)

行政だより

Research on social inequalities in sports participation and unstructured physical activity among young children is scarce. This study aimed to assess the associations of family socioeconomic position (SEP) and ethnic background with children's sports participation and outdoor play. Methods: We analyzed data from 4726 ethnically diverse 6-year-old children participating in the Generation R Study. Variables were assessed by parent-reported questionnaires when the child was 6 years old. Low level of outdoor play was defined as outdoor play <1 hour per day. Series of multiple logistic regression analyses were performed to assess associations of family SEP and ethnic background with children's sports participation and outdoor play. Results: Socioeconomic inequalities in children's sports participation were found when using maternal educational level (p<0.05), paternal educational level (p<0.05), maternal employment status (p<0.05), and household income (p<0.05) as family SEP indicator (less sports participation among low SEP children). Socioeconomic inequalities in children's outdoor play were found when using household income only (p<0.05) (more often outdoor play <1 hour per day among children from low income household). All ethnic minority children were significantly more likely to not to participate in sports and play outdoor <1 hour per day compared with native Dutch children. Adjustment for family SEP attenuated associations considerably, especially with respect to sports participation. Conclusion: Low SEP children and ethnic minority children are more likely not to participate in sports and more likely to display low levels of outdoor play compared with high SEP children and native Dutch children, respectively. In order to design effective interventions, further research, including qualitative studies, is needed to explore more in detail the pathways relating family SEP and ethnic background to children's sports participation and outdoor play

A computerised system for the identification of lactic acid bacteria.

A generic computerised system for the identification of bacteria was 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 groups of bacteria by following a decision tree with general identification tests. The second step in the identification is the distinction of species within a group on the basis of biochemical fermentation patterns. During grouping, 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 cases where the test result is inconclusive. The probabilities of test failure were 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 group. The described identification system has the ability 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

A decision support system for the prediction of microbial food safety and food quality.

The development of a method to predict microbial food safety and quality is described. The manufacture of a food from its ingredients is simulated, using a recipe. Food engineering heuristics are combined with models developed in predictive microbiology. Parameter values of ingredients of foods, such as water activity and pH, and models for microbial growth and decay are used for the prediction of the kinetics of microorganisms generally found in ingredients. The values of these parameters are collected in databases. If required information is lacking, methods are described for making reliable guesses of the parameters. Food quality can be calculated as a function of fluctuating temperature in time. Several food distribution chains can be simulated in order to assess the influence of distribution chains on food quality. The described methods were implemented into a computerised decision support system that can be used in food production, product development and training. In the future it may be possible to apply specific expert knowledge in production and development of foods to improve the quality of prediction