Next generation of microbiological risk assessment: Potential of omics data for exposure assessment.

In food safety and public health risk evaluations, microbiological exposure assessment plays a central role as it provides an estimation of both the likelihood and the level of the microbial hazard in a specified consumer portion of food and takes microbial behaviour into account. While until now mostly phenotypic data have been used in exposure assessment, mechanistic cellular information, obtained using omics techniques, will enable the fine tuning of exposure assessments to move towards the next generation of microbiological risk assessment. In particular, metagenomics can help in characterizing the food and factory environment microbiota (endogenous microbiota and potentially pathogens) and the changes over time under the environmental conditions associated with processing, preservation and storage. The difficulty lies in moving up to a quantitative exposure assessment, because the development of models that enable the prediction of dynamics of pathogens in a complex food ecosystem is still in its infancy in the food safety domain. In addition, collecting and storing the environmental data (metadata) required to inform the models has not yet been organised at a large scale. In contrast, progress in biomarker identification and characterization has already opened the possibility of making qualitative or even quantitative connection between process and formulation conditions and microbial responses at the strain level. In term of modelling approaches, without changing radically the usual model structure, changes in model inputs are expected: instead of (or as well as) building models upon phenotypic characteristics such as for example minimal temperature where growth is expected, exposure assessment models could use biomarker response intensity as inputs. These new generations of strain-level models will bring an added value in predicting the variability in pathogen behaviour. Altogether, these insights based upon omics techniques will increase our (quantitative) knowledge on pathogenic strains and consequently will reduce our uncertainty; the exposure assessment of a specific combination of pathogen and food will be then more accurate. This progress will benefit the whole community of safety assessors and research scientists from academia, regulatory agencies and industry.

Quantifying the effect of water activity and storage temperature on single spore lag times of three moulds isolated from spoiled bakery products.

The inhibitory effect of water activity (aw) and storage temperature on single spore lag times of Aspergillus niger, Eurotium repens (Aspergillus pseudoglaucus) and Penicillium corylophilum strains isolated from spoiled bakery products, was quantified. A full factorial design was set up for each strain. Data were collected at levels of aw varying from 0.80 to 0.98 and temperature from 15 to 35°C. Experiments were performed on malt agar, at pH5.5. When growth was observed, ca 20 individual growth kinetics per condition were recorded up to 35days. Radius of the colony vs time was then fitted with the Buchanan primary model. For each experimental condition, a lag time variability was observed, it was characterized by its mean, standard deviation (sd) and 5th percentile, after a Normal distribution fit. As the environmental conditions became stressful (e.g. storage temperature and aw lower), mean and sd of single spore lag time distribution increased, indicating longer lag times and higher variability. The relationship between mean and sd followed a monotonous but not linear pattern, identical whatever the species. Next, secondary models were deployed to estimate the cardinal values (minimal, optimal and maximal temperatures, minimal water activity where no growth is observed anymore) for the three species. That enabled to confirm the observation made based on raw data analysis: concerning the temperature effect, A. niger behaviour was significantly different from E. repens and P. corylophilum: Topt of 37.4°C (standard deviation 1.4°C) instead of 27.1°C (1.4°C) and 25.2°C (1.2°C), respectively. Concerning the aw effect, from the three mould species, E. repens was the species able to grow at the lowest aw (awmin estimated to 0.74 (0.02)). Finally, results obtained with single spores were compared to findings from a previous study carried out at the population level (Dagnas et al., 2014). For short lag times (≤5days), there was no difference between lag time of the population (ca 2000 spores inoculated in one spot) and mean (nor 5th percentile) of single spore lag time distribution. In contrast, when lag time was longer, i.e. under more stressful conditions, there was a discrepancy between individual and population lag times (population lag times shorter than 5th percentiles of single spore lag time distribution), confirming a stochastic process. Finally, the temperature cardinal values estimated with single spores were found to be similar to those obtained at the population level, whatever the species. All these findings will be used to describe better mould spore lag time variability and then to predict more accurately bakery product shelf-life.

Quantifying Effect of Lactic, Acetic, and Propionic Acids on Growth of Molds Isolated from Spoiled Bakery Products.

The combined effect of undissociated lactic acid (0 to 180 mmol/liter), acetic acid (0 to 60 mmol/liter), and propionic acid (0 to 12 mmol/liter) on growth of the molds Aspergillus niger, Penicillium corylophilum, and Eurotium repens was quantified at pH 3.8 and 25°C on malt extract agar acid medium. The impact of these acids on lag time for growth (λ) was quantified through a gamma model based on the MIC. The impact of these acids on radial growth rate (µ) was analyzed statistically through polynomial regression. Concerning λ, propionic acid exhibited a stronger inhibitory effect (MIC of 8 to 20 mmol/liter depending on the mold species) than did acetic acid (MIC of 23 to 72 mmol/liter). The lactic acid effect was null on E. repens and inhibitory on A. niger and P. corylophilum. These results were validated using independent sets of data for the three acids at pH 3.8 but for only acetic and propionic acids at pH 4.5. Concerning µ, the effect of acetic and propionic acids was slightly inhibitory for A. niger and P. corylophilum but was not significant for E. repens. In contrast, lactic acid promoted radial growth of all three molds. The gamma terms developed here for these acids will be incorporated in a predictive model for temperature, water activity, and acid. More generally, results for µ and λ will be used to identify and evaluate solutions for controlling bakery product spoilage.

Modeling red cabbage seed extract effect on Penicillium corylophilum: Relationship between germination time, individual and population lag time.

The inhibitory effect of a red cabbage seed extract on germination time, individual (single spore) and population lag time of Penicillium corylophilum was studied. First, to compare the biological variability of single spore germination and lag times under stressful conditions, data were collected at levels of red cabbage seed extract varying from 0 to 10 mg/g (150 spores observed in each trial of germination, ca 50 spores in each individual lag experiment). Experiments were performed on malt agar at 25 °C, pH 5.2, aw 0.99. The data, without any transformation, were statistically analyzed; several probability distribution functions were used to fit the cumulated germination times and the individual lag times of spores. In both cases, the best fit was obtained with the Normal distribution. In parallel, lag times at the population level (ca 2000 spores per trial) were collected for the same range of plant extract. Not surprisingly, the difference between individual and population lag times could be explained by a stochastic process. More interestingly, it was shown that under stressful conditions, the population lag time did not correspond to the time required for germination of 95% of spores, but to a much longer time. Finally, it was deduced from the statistical analysis, completed by microscopic observations, that the plant extract affected mainly the hyphal elongation (and then the lag time) and not the germination. Next, secondary models were developed to quantify the effect of red cabbage seed extract on the median of germination times, individual and population lag times. The Minimum Inhibitory Concentrations (MICs) were estimated. It was shown that the red cabbage seed extract MIC for P. corylophilum lag time did not depend on the inoculum load. Application of the secondary models allowed us to conclude that under the conditions of our experiment, the addition of 10 mg/g of red cabbage seed extract enabled extension of lag time to two weeks.

Modeling growth of three bakery product spoilage molds as a function of water activity, temperature and pH.

The objective of this study was to quantify the effect of water activity, pH and storage temperature on the growth of Eurotium repens, Aspergillus niger and Penicillium corylophilum, isolated from spoiled bakery products. Moreover, the behaviors of these three mold species were compared to assess whether a general modeling framework may be set and re-used in future research on bakery spoilage molds. The mold growth was modeled by building two distinct Gamma-type secondary models: one on the lag time for growth and another one on the radial growth rate. A set of 428 experimental growth curves was generated. The effect of temperature (15-35 °C), water activity (0.80-0.98) and pH (3-7) was assessed. Results showed that it was not possible to apply the same set of secondary model equations to the three mold species given that the growth rate varied significantly with the factors pH and water activity. In contrast, the temperature effect on both growth rate and lag time of the three mold species was described by the same equation. The equation structure and model parameter values of the Gamma models were also compared per mold species to assess whether a relationship between lag time and growth rate existed. There was no correlation between the two growth responses for E. repens, but a slight one for A. niger and P. corylophilum. These findings will help in determining bakery product shelf-life and guiding future work in the predictive mycology field.

Predicting and preventing mold spoilage of food products.

This article is a review of how to quantify mold spoilage and consequently shelf life of a food product. Mold spoilage results from having a product contaminated with fungal spores that germinate and form a visible mycelium before the end of the shelf life. The spoilage can be then expressed as the combination of the probability of having a product contaminated and the probability of mold growth (germination and proliferation) up to a visible mycelium before the end of the shelf life. For products packed before being distributed to the retailers, the probability of having a product contaminated is a function of factors strictly linked to the factory design, process, and environment. The in-factory fungal contamination of a product might be controlled by good manufacturing hygiene practices and reduced by particular processing practices such as an adequate air-renewal system. To determine the probability of mold growth, both germination and mycelium proliferation can be mathematically described by primary models. When mold contamination on the product is scarce, the spores are spread on the product and more than a few spores are unlikely to be found at the same spot. In such a case, models applicable for a single spore should be used. Secondary models can be used to describe the effect of intrinsic and extrinsic factors on either the germination or proliferation of molds. Several polynomial models and gamma-type models quantifying the effect of water activity and temperature on mold growth are available. To a lesser extent, the effect of pH, ethanol, heat treatment, addition of preservatives, and modified atmospheres on mold growth also have been quantified. However, mold species variability has not yet been properly addressed, and only a few secondary models have been validated for food products. Once the probability of having mold spoilage is calculated for various shelf lives and product formulations, the model can be implemented as part of a risk management decision tool.