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.
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.
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.
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