Flexible querying of Web data to simulate bacterial growth in food.

A preliminary step in microbial risk assessment in foods is the gathering of experimental data. In the framework of the Sym'Previus project, we have designed a complete data integration system opened on the Web which allows a local database to be complemented by data extracted from the Web and annotated using a domain ontology. We focus on the Web data tables as they contain, in general, a synthesis of data published in the documents. We propose in this paper a flexible querying system using the domain ontology to scan simultaneously local and Web data, this in order to feed the predictive modeling tools available on the Sym'Previus platform. Special attention is paid on the way fuzzy annotations associated with Web data are taken into account in the querying process, which is an important and original contribution of the proposed system.

Validation of a stochastic modelling approach for Listeria monocytogenes growth in refrigerated foods.

A stochastic modelling approach was developed to describe the distribution of Listeria monocytogenes contamination in foods throughout their shelf life. This model was designed to include the main sources of variability leading to a scattering of natural contaminations observed in food portions: the variability of the initial contamination, the variability of the biological parameters such as cardinal values and growth parameters, the variability of individual cell behaviours, the variability of pH and water activity of food as well as portion size, and the variability of storage temperatures. Simulated distributions of contamination were compared to observed distributions obtained on 5 day-old and 11 day-old cheese curd surfaces artificially contaminated with between 10 and 80 stressed cells and stored at 14°C, to a distribution observed in cold smoked salmon artificially contaminated with approximately 13 stressed cells and stored at 8°C, and to contaminations observed in naturally contaminated batches of smoked salmon processed by 10 manufacturers and stored for 10 days a 4°C and then for 20 days at 8°C. The variability of simulated contaminations was close to that observed for artificially and naturally contaminated foods leading to simulated statistical distributions properly describing the observed distributions. This model seems relevant to take into consideration the natural variability of processes governing the microbial behaviour in foods and is an effective approach to assess, for instance, the probability to exceed a critical threshold during the storage of foods like the limit of 100 CFU/g in the case of L. monocytogenes.

Semantic annotation of Web data applied to risk in food.

A preliminary step to risk in food assessment is the gathering of experimental data. In the framework of the Sym'Previus project (http://www.symprevius.org), a complete data integration system has been designed, grouping data provided by industrial partners and data extracted from papers published in the main scientific journals of the domain. Those data have been classified by means of a predefined vocabulary, called ontology. Our aim is to complement the database with data extracted from the Web. In the framework of the WebContent project (www.webcontent.fr), we have designed a semi-automatic acquisition tool, called @WEB, which retrieves scientific documents from the Web. During the @WEB process, data tables are extracted from the documents and then annotated with the ontology. We focus on the data tables as they contain, in general, a synthesis of data published in the documents. In this paper, we explain how the columns of the data tables are automatically annotated with data types of the ontology and how the relations represented by the table are recognised. We also give the results of our experimentation to assess the quality of such an annotation.

Stochastic, compartmental, and dynamic modeling of cross-contamination during mechanical smearing of cheeses.

Cheese smearing is a complex process and the potential for cross-contamination with pathogenic or undesirable microorganisms is critical. During ripening, cheeses are salted and washed with brine to develop flavor and remove molds that could develop on the surfaces. Considering the potential for cross-contamination of this process in quantitative risk assessments could contribute to a better understanding of this phenomenon and, eventually, improve its control. The purpose of this article is to model the cross-contamination of smear-ripened cheeses due to the smearing operation under industrial conditions. A compartmental, dynamic, and stochastic model is proposed for mechanical brush smearing. This model has been developed to describe the exchange of microorganisms between compartments. Based on the analytical solution of the model equations and on experimental data collected with an industrial smearing machine, we assessed the values of the transfer parameters of the model. Monte Carlo simulations, using the distributions of transfer parameters, provide the final number of contaminated products in a batch and their final level of contamination for a given scenario taking into account the initial number of contaminated cheeses of the batch and their contaminant load. Based on analytical results, the model provides indicators for smearing efficiency and propensity of the process for cross-contamination. Unlike traditional approaches in mechanistic models, our approach captures the variability and uncertainty inherent in the process and the experimental data. More generally, this model could represent a generic base to use in modeling similar processes prone to cross-contamination.

Temperature effect on bacterial growth rate: quantitative microbiology approach including cardinal values and variability estimates to perform growth simulations on/in food.

Temperature effect on growth rates of Listeria monocytogenes, Salmonella, Escherichia coli, Clostridium perfringens and Bacillus cereus, was studied. Growth rates were obtained in laboratory medium by using a binary dilutions method in which 15 optical density curves were generated to determine one mu value. The temperature was in the range from 2 to 48 degrees C, depending on the bacterial species. Data were analysed after a square root transformation. No large difference between the strains of a same species was observed, and therefore all the strains of a same species were analysed together with the same secondary model. The variability of the residual error, including both measurements errors and biological strain difference, was homogenous for sub-optimal temperature values. To represent this variability in bacterial kinetic simulation, the 95% confidence interval based on an asymptotic Normal distribution, around the growth rate value was determined. With this modelling approach, the behaviour of bacterial species on food, irrespective of the strain or the laboratory, was described. This growth simulation with confidence limits has several applications, such as to facilitate comparisons between a challenge-test and simulation results, and, to appreciate if the temperature change has or has not a significant effect on a bacterial growth profile, with regard to the uncontrolled factors. The integration of this piece of work in the Sym'Previus software is now in process. Results obtained in five French laboratories will be extended by working on new food and new microbial species and improved by further work on variability estimation.

Development and validation of experimental protocols for use of cardinal models for prediction of microorganism growth in food products.

An experimental protocol to validate secondary-model application to foods was suggested. Escherichia coli, Listeria monocytogenes, Bacillus cereus, Clostridium perfringens, and Salmonella were observed in various food categories, such as meat, dairy, egg, or seafood products. The secondary model validated in this study was based on the gamma concept, in which the environmental factors temperature, pH, and water activity (aw) were introduced as individual terms with microbe-dependent parameters, and the effect of foodstuffs on the growth rates of these species was described with a food- and microbe-dependent parameter. This food-oriented approach was carried out by challenge testing, generally at 15 and 10 degrees C for L. monocytogenes, E. coli, B. cereus, and Salmonella and at 25 and 20 degrees C for C. perfringens. About 222 kinetics in foods were generated. The results were compared to simulations generated by existing software, such as PMP. The bias factor was also calculated. The methodology to obtain a food-dependent parameter (fitting step) and therefore to compare results given by models with new independent data (validation step) is discussed in regard to its food safety application. The proposed methods were used within the French national program of predictive microbiology, Sym'Previus, to include challenge test results in the database and to obtain predictive models designed for microbial growth in food products.