Predicting and Preventing Mold Spoilage of Food Products

International audienceThis 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

Modelling microbial responses: application to food spoilage

Chapitre 4International audienceRoughly, one-third of food produced for human consumption is lost or wasted globally, which amounts to about 1.3 billion tons per year. For instance, in the European Union, over 100 million tons of food are wasted annually. Food is lost or wasted throughout the supply chain, from initial agricultural production down to final household consumption. One of the causes of food waste is spoilage, leading to food deterioration up to the point in which the food can be no longer consumed. This overall loss of quality could be due to various reasons, which could be sorted into two types: either a change in physico-chemical properties (e.g. textural evolution and production of off-flavor by lipid degradation) or a microbial growth

Quantifying effect of lactic, acetic and propionic acids on growth of molds isolated from spoiled bakery products

International audienceThe 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

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

9th International Conference on Predictive Modelling in Food (ICPMF); Rio de Janeiro, BRAZILThe inhibitory effect of water activity (a(w)) 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 a(w) varying from 0.80 to 0.98 and temperature from 15 to 35 degrees C. Experiments were performed on malt agar, at pH 5.5. When growth was observed, ca 20 individual growth kinetics per condition were recorded up to 35 days. 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 a(w) 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: Tops of 37.4 degrees C (standard deviation 1.4 degrees C) instead of 27.1 degrees C (1.4 degrees C) and 25.2 degrees C (1.2 degrees C), respectively. Concerning the a(w) effect, from the three mould species, E. repens was the species able to grow at the lowest a(w) (aw(min), 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 days), 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