Modelling the Effect of Salt Concentration on the Fate of Listeria monocytogenes Isolated from Costa Rican Fresh Cheeses.

"Turrialba cheese" is a Costa Rican fresh cheese highly appreciated due to its sensory characteristics and artisanal production. As a ready-to-eat dairy product, its formulation could support Listeria monocytogenes growth. L. monocytogenes was isolated from 14.06% of the samples and the pathogen was able to grow under all tested conditions. Due to the increasing demand for low-salt products, the objective of this study was to determine the effect of salt concentration on the growth of pathogen isolates obtained from local cheese. Products from retail outlets in Costa Rica were analyzed for L. monocytogenes. These isolates were used to determine growth at 4 °C for different salt concentration (0.5-5.2%). Kinetic curves were built and primary and secondary models developed. Finally, a validation study was performed using literature data. The R2 and Standard Error of fit of primary models were ranked from 0.964-0.993, and 0.197-0.443, respectively. An inverse relationship was observed between growth rate and salt concentration. A secondary model was obtained, with R2 = 0.962. The model was validated, and all values were Bf > 1, thus providing fail-safe estimations. These data were added to the free and easy-to-use predictive microbiology software "microHibro" which is used by food producers and regulators to assist in decision-making.

Influence of Acid Adaptation on the Probability of Germination of Clostridium sporogenes Spores Against pH, NaCl and Time.

The Clostridium sp. is a large group of spore-forming, facultative or strictly anaerobic, Gram-positive bacteria that can produce food poisoning. The table olive industry is demanding alternative formulations to respond to market demand for the reduction of acidity and salt contents in final products. while maintaining the appearance of freshness of fruits. In this work, logistic regression models for non-adapted and acid-adapted Clostridium sp. strains were developed in laboratory medium to study the influence of pH, NaCl (%) and time on the probability of germination of their spores. A Clostridium sporogenes cocktail was not able to germinate at pH < 5.0, although the adaptation of the strains produced an increase in the probability of germination at 5.0-5.5 pH levels and 6% NaCl concentration. At acidic pH values (5.0), the adapted strains germinated after 10 days of incubation, while those which were non-adapted required 15 days. At pH 5.75 and with 4% NaCl, germination of the adapted strains took place before 7 days, while several replicates of the non-adapted strains did not germinate after 42 days of storage. The model was validated in natural green olive brines with good results (>81.7% correct prediction cases). The information will be useful for the industry and administration to assess the safety risk in the formulation of new processing conditions in table olives and other fermented vegetables.

'MicroHibro': A software tool for predictive microbiology and microbial risk assessment in foods.

A tool able to quantitatively assess the fate of potential pathogenic microorganisms in foods along the food chain and their impact on public health is highly valuable for food safety decision-makers. The aim of this work was to present an overview of the Predictive Microbiology software MicroHibro, which is able to assess the evolution of potential pathogens and spoilage microorganisms along the food chain, providing estimates for the exposure level and risk associated with a food product. The application is built on an extensive Predictive Microbiology Model Data Base (PMDB) including kinetic processes like growth, inactivation, transfer as well as dose-response models. PMDB can be populated with new models by using an on-line tool in combination with a standardized method for describing Predictive Microbiology models. This enables MicroHibro to be easily updated, increasing its applicability and use. Estimation of microbial risk associated with a food product can be achieved, in MicroHibro, by describing steps in any food chain using four different microbial processes (growth, inactivation, transfer and partitioning). As a result, an estimate of the concentration and prevalence of microorganisms in the food of interest as well as attendant risk are provided. Also, MicroHibro allows comparing different predictive models and validate them by introducing user's data. In this paper, examples are provided to illustrate how predictive models can be incorporated in MicroHibro, and then, used to develop a Quantitative Microbial Risk Assessment model. The use of expert computational systems is a powerful tool for supporting food safety and quality activities by Health Authorities and the food industry. They represent a breakthrough in the assessment and management of food safety based on scientific evidence.

Model for Listeria monocytogenes inactivation by high hydrostatic pressure processing in Spanish chorizo sausage.

A central composite design was implemented to study the effect of three factors on HHP-induced L. monocytogenes inactivation in Spanish chorizo sausage, in order to increase its effectiveness: product aw (0.79-0.92), pressure intensities (349-600 MPa, at 18 °C) and holding time (0-12.53 min). Response surface methodology was implemented with backward stepwise regression to generate a model that best fitted to the experimental data. All the three factors studied significantly influenced HHP inactivation of L. monocytogenes (p < 0.05). Pathogen reductions increased as the pressure and duration of HHP treatments rose. Low values of aw seemed to exert a protective effect on L. monocytogenes and pressures below 400 MPa did not lead to significant pathogen reductions. The model was validated with independent published data. Accuracy and bias factors were also determined to evaluate the performance of the developed model, which was considered acceptable for prediction purposes. The model generated represents a mathematical tool that will help food manufacturers to improve the efficacy of HHP processing of chorizo sausage and observe the regulatory authority's specifications regarding L. monocytogenes levels while maintaining food safety.

Next day Salmonella spp. detection method based on real-time PCR for meat, dairy and vegetable food products.

The microbiological standard for detection of Salmonella relies on several cultural steps and requires more than 5 days for final confirmation, and as consequence there is a need for an alternative rapid methodology for its detection. The aim of this study was to compare different detection strategies based on real-time PCR for a rapid and sensitive detection in an ample range of food products: raw pork and poultry meat, ready to eat lettuce salad and raw sheep milk cured cheese. Three main parameters were evaluated to reduce the time and cost for final results: the initial sample size (25 and 50 g), the incubation times (6, 10 and 18 h) and the bacterial DNA extraction (simple boiling of the culture after washing the bacterial pellet, the use of the Chelex resin, and a commercial silica column). The results obtained demonstrate that a combination of an incubation in buffered peptone water for 18 h of a 25 g-sample coupled to a DNA extraction by boiling and a real-time PCR assay detected down to 2-4 Salmonella spp.CFU per sample in less than 21 h in different types of food products. This RTi-PCR-based method is fully compatible with the ISO standard, providing results more rapidly and cost-effectively. The results were confirmed in a large number of naturally contaminated food samples with at least the same analytical performance as the reference method.

Microbiological criteria for Campylobacter in broiler carcasses in Italy: a possible approach to derive them.

The aim of this paper was to provide suitable microbiological criteria (MC) for Campylobacter in broiler carcasses and a sampling plan to verify compliance with such criteria. Data were gathered in the presence and concentration of Campylobacter in broiler carcasses collected in three different Italian slaughterhouses, labelled as A, B and C. The sampling plan to be validated in each slaughterhouse included the analysis of three different carcasses collected immediately after chilling from 30 different lots, for a total of 90 samples per slaughterhouse. The number of positive samples containing above 100 CFU/g and above 1,000 CFU/g throughout the 30 tested lots was determined to estimate between-lot variability. Based on this information, the performance of four MC was evaluated for lot compliance: i) n=3; c=0; m=100 CFU/g; ii) n=3; c=0; m=1,000 CFU/g; iii) n=3; c=1; m=1,000 CFU/g and iv) n=3; c=2; m=1,000 CFU/g. Positive Campylobacter samples were found in 60% of the lots tested in slaughterhouses A and C and in 73.3% of lots from slaughterhouse B. The differences among the three slaughterhouses in the mean Campylobacter levels found in positive samples were not significant and were used to evaluate the performance of the MC. The level of lot compliance to different MC was calculated and for the most stringent one (n=3; c=0; m=100 CFU/g) was 40% at slaughterhouses A and C but only 26.7% at slaughterhouse B. The results of this study show an alternative approach to establish MC for Campylobacter in broilers. According to (1) Campylobacter prevalence and concentration in Italy, (2) applied experimental plan and (3) selected slaughterhouses, the number of compliant lots to the suggested MC ranged between 26.7 and 100%. The selection of the fit for purpose MC is a risk manager decision, based on a reasonable balance between public health and cost for poultry industries.

Probabilistic approach for determining Salmonella spp. and L. monocytogenes concentration in pork meat from presence/absence microbiological data.

In the present study, prevalence and concentration of Salmonella spp. and Listeria monocytogenes in fresh pork cuts were determined through the analysis of twelve lots for one year. Five samples were analyzed at retail and after storage at 4 and 12°C up to the end of shelf-life (7 days). Results obtained for Salmonella spp. indicated that a total of 15 samples (8.33%) were positive, which represents 4 (25%) sampling events positive (i.e. at least one sample was positive in at least one of the sampling scenarios). Salmonella was randomly distributed and direct correlation with storage time and temperature was not obtained. For L. monocytogenes, 26 samples (14.44%) were positive, which represents 5 (41.67%) positive sampling lots. For this pathogen, a group of samples were only positive at the end of the shelf-life but not immediately after purchasing indicating clearly that the contamination was not only heterogeneously distributed but also close to the levels of detection, and in all the cases below the limit of contamination. As neither Salmonella spp. nor L. monocytogenes was enumerated by direct plating (<10 cfu/g) a probabilistic approach basing on Binomial and Poisson distributions was subsequently performed to estimate microbial concentration from presence/absence data. Estimated concentration values were below 40 cfu/kg for both pathogens in more than 80% of the tested lots. The data collected in this study add new knowledge on this very important and difficult to control segment of the farm-to-fork chain.

Study of the effect of post-packaging pasteurization and argon modified atmosphere packaging on the sensory quality and growth of endogenous microflora of a sliced cooked meat product.

The objective of this work was to study the effect of post-packaging pasteurization on the sensory quality and growth of natural microorganisms during refrigerated storage (6 °C) of a cooked meat product considering two packaging atmospheres based on mixture of typical gases (CO(2)/N(2) (22/78%) and novel gases (CO(2)/Ar (17/83%)). Growth of lactic acid bacteria was significantly different between samples with and without post-packaging pasteurization, showing a growth rate >0.44 and equal to 0.28 log cfu/day, respectively. For samples with post-packaging pasteurization, atmosphere CO(2)/Ar resulted in a lower growth of lactic acid bacteria and a better sensory quality. Overall, samples without post-packaging pasteurization did not show a significant reduction of sensory quality during storage time (121 days) while samples with post-packaging pasteurization showed deterioration in their sensory quality. Further investigation is needed to obtain more definitive conclusions about the effect of post-packaging pasteurization and argon-based packaging atmospheres on cooked meat products.

Improving artificial neural networks with a pruning methodology and genetic algorithms for their application in microbial growth prediction in food.

The application of Artificial Neural Networks (ANN) in predictive microbiology is presented in this paper. This technique was used to build up a predictive model of the joint effect of NaCl concentration, pH level and storage temperature on kinetic parameters of the growth curve of Lactobacillus plantarum using ANN and Response Surface Model (RSM). Sigmoid functions were fitted to the data and kinetic parameters were estimated and used to build the models in which the independent variables were the factors mentioned above (NaCl, pH, temperature), and in some models, the values of the optical densities (OD) vs. time of the growth curve were also included in order to improve the error of estimation. The determination of the proper size of an ANN was the first step of the estimation. This study shows the usefulness of an ANN pruning methodology. The pruning of the network is a process consisting of removing unnecessary parameters (weights) and nodes during the training process of the network without losing its generalization capacity. The best architecture has been sought using genetic algorithms (GA) in conjunction with pruning algorithms and regularization methods in which the initial distribution of the parameters (weights) of the network is not uniform. The ANN model has been compared with the response surface model by means of the Standard Error of Prediction (SEP). The best values obtained were 14.04% of SEP for the growth rate and 14.84% for the lag estimation by the best ANN model, which were much better than those obtained by the RSM, 35.63% and 39.30%, respectively. These were very promising results that, in our opinion, open up an extremely important field of research.