Predicting and Preventing Mold Spoilage of Food Products

Abstract

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.

Solubility, photostability and antifungal activity of phenylpropanoids encapsulated in cyclodextrins

“…Spores generally serve as primary inoculum to initiate plant disease. Moreover, food spoilage could result from a contamination of fungal spores that germinate and grow up to a visible mycelium before the end of the shelf life (Dagnas & Membré, 2013). The spore germination inhibitory activity of free and HP-b-CD encapsulated PPs against F. oxysporum was determined.…”

Evaluation of chlorine dioxide as an antimicrobial against Botrytis cinerea in California strawberries

“…Specifically, mold spoilage of food results from a biological process that begins with contamination by fungal spores which germinate and extend and into hyphae to form a visible mycelium over time resulting in the end of the product's shelf life (Dantigny, Marín, Beyer, & Magan, 2007). Mold growth is affected by environmental factors such as water activity (a w ), pH and temperature, and also by food constituents like proteins, carbohydrates, lipids and organic acids (Dagnas & Membre 2013;Garcia, Ramos, Sanchis, & Marín, 2009). Optimum conditions for mold growth depend on the type of microorganism, but in general, food spoilage will occur more rapidly at temperatures above 25 C and a w above 0.85 as long as the food system is contaminated with spores and that those spores are able to germinate (Dantigny, Guilmart, & Bensoussan, 2005;Gougouli & Koutsoumanis, 2012).…”

Modelling the influence of temperature, water activity and sodium metabisulphite on the growth and OTA production of Aspergillus carbonarius isolated from Greek wine grapes

“…An innovative and promising approach to assess fungal responses and therefore mycotoxin contamination in food products, in relation to key controlling parameters in the food environment, is based on predictive mycology (Dantigny et al, 2005;Garcia et al, 2009;Dagnas and Membr e, 2013). Until now, many studies have been focused in modelling fungal growth under diverse environmental conditions (Dantigny et al, 2005;Samapundo et al, 2005;Tassou et al, 2007;Marín et al, 2008;Gougouli and Koutsoumanis, 2010;Panagou et al, 2010), but few have taken into account the possibility to model toxin production (Medina et al, 2007;Giorni et al, 2011;Garcia et al, 2013).…”