Predictive modelling approach applied to spoilage fungi: growth of Penicillium brevicompactum on solid media

Membré, Jeanne‐Marie; Kubaczka, Martine

doi:10.1046/j.1365-2672.2000.00805.x

Cited by 19 publications

(12 citation statements)

References 18 publications

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“…Usually in the literature mould growth in kinetic models is expressed as radial growth rate in μm h −1 (Membre and Kubaczka 2000; Meletiadis et al. 2001) or total mycelium biomass.…”

Section: Introductionmentioning

confidence: 99%

Mathematical models of mycelium growth and ergosterol synthesis in stationary mould culture

Gutarowska

Żakowska

2009

Letters in Applied Microbiology

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Aims: To develop mathematical models for mycelium growth and ergosterol formation in conditions of periodic stationary culture; to verify possibilities of applying a model describing the relationship between ergosterol content and mycelium quantity. Methods and Results: The mould growth and ergosterol formation models covering all phases of mould growth are described using a modified logistic equation with the addition of an exponential function. The correlation between ergosterol and mycelium biomass depended on the growth phase of mould. Meaningful relation was obtained for initial two phases, when both parameters were growing equally. The quadratic function for estimation of the biomass based on ergosterol content was formulated. The error resulting from the application of this function in initial phases of moulds growth was small at 5–7%, in the following phases it was at 11–31%. Conclusions: Mycelium biomass can be precisely determined basing on the ergosterol content, when we know the moulds growth phase. In natural environments, without the information about growth phases, it will be possible, but with the higher error. Significance and Impact of the Study: Presented model based on the ergosterol content making possible to estimate the quantity of mycelium in moulds cultures and natural environments.

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“…Usually in the literature mould growth in kinetic models is expressed as radial growth rate in μm h −1 (Membre and Kubaczka 2000; Meletiadis et al. 2001) or total mycelium biomass.…”

Section: Introductionmentioning

confidence: 99%

Mathematical models of mycelium growth and ergosterol synthesis in stationary mould culture

Gutarowska

Żakowska

2009

Letters in Applied Microbiology

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“…1996; Valík et al . 1999; Membré and Kubaczka 2000). Despite its greater complexity, the latter model did not exhibit a better fitting than the linear correlation.…”

Section: Introductionmentioning

confidence: 99%

“…This method, which usually exhibits regression coefficients close to 1, is probably the simplest and most direct measure. A more complicated model dedicated to describing bacterial growth rate (Baranyi et al 1993) has been applied to fungal growth (Baranyi et al 1996;Valík et al 1999;Membré and Kubaczka 2000). Despite its greater complexity, the latter model did not exhibit a better fitting than the linear correlation.…”

Section: Introductionmentioning

confidence: 99%

Relationship between spore germination kinetics and lag time during growth of Mucor racemosus

Dantigny

Mansur

Sautour

et al. 2002

Lett Appl Microbiol

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Aims: Spore germination requires microscopic observation whereas fungal growth results in a macroscopic examination. This paper aims at establishing a relationship between the percentage of germinated spores and parameters easily available from visible development. Methods and Results: About 225 spores of Mucor racemosus were inoculated on PDA medium and incubated at 15°and 25°C. Germination kinetics were modelled by a logistic function. Fungal development provided two parameters, a growth rate, l, and a lag period, k, defined as the slope of the straight line of the graph radius (mm) vs time (h) and the intercept of this line with the X-axis, respectively. Conclusions: It was found that the lag period coincided with the completion of the germination process, although the number of spores inoculated should be controlled carefully. Significance and Impact of the Study: Providing that this result can be generalized, this procedure would constitute a significant breakthrough for predicting food spoilage by moulds.

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“…To prevent these kinds of food from mold spoilage, manufacturers should take into consideration in their pasteurization processes the D-values and z-values either of the actual contaminants or of a model. B. fulva is a good model candidate because it has been 3, 31,32,71,72,75,83,109,111,127,128 Linear 5, 13,14,18,21,44,47,51,52,57,58,60,80,85,87,88,90,110,114,115 Two-phase linear (eq. 5)…”

Section: Process and Environmental Factors Influencing Mold Growthmentioning

confidence: 99%

Predicting and Preventing Mold Spoilage of Food Products

Dagnas

Membré

2013

Journal of Food Protection

109

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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.

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Predictive modelling approach applied to spoilage fungi: growth of Penicillium brevicompactum on solid media

Cited by 19 publications

References 18 publications

Mathematical models of mycelium growth and ergosterol synthesis in stationary mould culture

Mathematical models of mycelium growth and ergosterol synthesis in stationary mould culture

Relationship between spore germination kinetics and lag time during growth of Mucor racemosus

Predicting and Preventing Mold Spoilage of Food Products

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