Molecular approaches to analysing the microbial composition of raw milk and raw milk cheese

The availability and application of culture-independent tools that enable a detailed investigation of the microbiota and microbial biodiversity of food systems has had a major impact on food microbiology. This review focuses on the application of DNA-based technologies, such as denaturing gradient gel electrophoresis (DGGE), temporal temperature gradient gel electrophoresis (TTGE), single stranded conformation polymorphisms (SSCP), the polymerase chain reaction (PCR) and others, to investigate the diversity, dynamics and identity of microbes in dairy products from raw milk. Here, we will highlight the benefits associated with culture-independent methods which include enhanced sensitivity, rapidity and the detection of microorganisms not previously associated with such products.

Control of Listeria monocytogenes in raw-milk cheeses

The development of Listeria monocytogenes in cheeses made with raw-milk originating from six different farms and according to the Saint-Nectaire cheesemaking technology was studied. Milk was inoculated with two strains of L. monocytogenes at 5 to 10 CFU/25 ml. Microbial and chemical analyses were carried out at appropriate intervals during ripening. L. monocytogenes did not grow in the cores of cheeses prepared with milk originating from three farms. That inhibition could be partially attributed to the pH values and L-lactate content. There was no growth in cheeses with pH below 5.2 and lactate content around 14 mg/g. In all cheeses, L. monocytogenes stopped growing in the cores of cheeses after eight days and some other factors may be involved in the inhibition. No relation was found between L. monocytogenes count and other microbial counts. Growth occurred on cheese surfaces between eight and eighteen days, when the pH significantly increased. The lowest L. monocytogenes growth was found on the surface of cheeses with the lowest pH and without any core growth. Further studies will be performed to clarify the involvement of the microbial community in L. monocytogenes inhibition, in particular during the ripening period.

Stochastically modeling Listeria monocytogenes growth in farm tank milk

This article presents a Listeria monocytogenes growth model in milk at the farm bulk tank stage. The main objective was to judge the feasibility and value to risk assessors of introducing a complex model, including a complete thermal model, within a microbial quantitative risk assessment scheme. Predictive microbiology models are used under varying temperature conditions to predict bacterial growth. Input distributions are estimated based on data in the literature, when it is available. If not, reasonable assumptions are made for the considered context. Previously published results based on a Bayesian analysis of growth parameters are used. A Monte Carlo simulation that forecasts bacterial growth is the focus of this study. Three scenarios that take account of the variability and uncertainty of growth parameters are compared. The effect of a sophisticated thermal model taking account of continuous variations in milk temperature was tested by comparison with a simplified model where milk temperature was considered as constant. Limited multiplication of bacteria within the farm bulk tank was modeled. The two principal factors influencing bacterial growth were found to be tank thermostat regulation and bacterial population growth parameters. The dilution phenomenon due to the introduction of new milk was the main factor affecting the final bacterial concentration. The results show that a model that assumes constant environmental conditions at an average temperature should be acceptable for this process. This work may constitute a first step toward exposure assessment for L. monocytogenes in milk. In addition, this partly conceptual work provides guidelines for other risk assessments where continuous variation of a parameter needs to be taken into account.

Raw cow milk bacterial population shifts attributable to refrigeration

We monitored the dynamic changes in the bacterial population in milk associated with refrigeration. Direct analyses of DNA by using temporal temperature gel electrophoresis (TTGE) and denaturing gradient gel electrophoresis (DGGE) allowed us to make accurate species assignments for bacteria with low-GC-content (low-GC%) (<55%) and medium- or high-GC% (>55%) genomes, respectively. We examined raw milk samples before and after 24-h conservation at 4 degrees C. Bacterial identification was facilitated by comparison with an extensive bacterial reference database ( approximately 150 species) that we established with DNA fragments of pure bacterial strains. Cloning and sequencing of fragments missing from the database were used to achieve complete species identification. Considerable evolution of bacterial populations occurred during conservation at 4 degrees C. TTGE and DGGE are shown to be a powerful tool for identifying the main bacterial species of the raw milk samples and for monitoring changes in bacterial populations during conservation at 4 degrees C. The emergence of psychrotrophic bacteria such as Listeria spp. or Aeromonas hydrophila is demonstrated.

Combining fuzzy querying of imprecise data and predictive microbiology using category-based reasoning for prediction of the possible microbial spoilage in foods: application to Listeria monocytogenes

Various predictive models of microbial behavior have been created and extensive data collection has been done by numerous private or public laboratories. However, significant differences between predicted and observed values in foods have been observed and need to be stressed, understood and explained as much as possible. In this paper, we present a software tool (currently at the level of a prototype) able: (i) to store in a database all relevant information expressed on one hand as qualitative or quantitative data and on the other hand as precise or imprecise data; (ii) to retrieve the more relevant information from the database using queries where criteria may be expressed as fuzzy values in order to enhance the flexibility of the search: (iii) to compute, in addition to the nearest data, an estimation of searched values using statistical models. The architecture of this software tool is structured as a category-based reasoning system. Example queries about Listeria monocytogenes (L. monocytogenes) illustrate the functionalities of this tool.

Predictions of growth for Listeria monocytogenes and Salmonella during fluctuating temperature

We studied the predictive performance of a dynamic modelling approach, combined with predictions from the Food MicroModel software, applied to the growth of Listeria monocytogenes and Salmonella in pasteurised milk, chicken liver pâté and minced chicken, under constant as well as fluctuating temperatures. We found that, in general, the accuracy of a prediction under fluctuation temperature was similar to that under constant temperature. Generally, there was a good agreement between predictions and observations. However, the growth of Listeria monocytogenes in pasteurised milk was inhibited largely by the natural flora present.