Predicting fungal growth: The effect of water activity on four species of Aspergillus

CSIRO Division of Food Science and Technology, North Ryde, N.S.W., Australia.
International Journal of Food Microbiology (Impact Factor: 3.08). 12/1994; 23(3-4):419-31. DOI: 10.1016/0168-1605(94)90167-8
Source: PubMed


Growth of four species belonging to Aspergillus Section Flavi (A. flavus, A. oryzae, A. parasiticus and A. nomius) was studied at 30 degrees C at ten water activities (aw) between 0.995 and 0.810 adjusted with equal mixtures of glucose and fructose. Colony diameters were measured at intervals and plotted against time. A flexible growth model describing the change in colony diameter (mm) with respect to time was first fitted to the measured growth data and from the fitted curves the maximum colony growth rates were calculated. These values were then fitted with respect to aw to predict colony growth rates at any aw within the range tested. The optimum aw for each species and time to reach a colony diameter of 3 mm were also calculated.

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    • "The effect of water activity and pH, on the growth of H. burtonii, P. anomala, and S. fibuligera was examined on Malt Extract Agar by Deschuyffeleer et al. (2011). In this study, the authors modelled the effect of a w on yeast growth by the Gibson et al. (1994) model. As stated by Lahlali et al. (2008), any extrapolation from synthetic medium to food may be hazardous because of the involvement of other factors. "
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    ABSTRACT: The present study focused on the effects of temperature, T, and water activity, aw, on the growth of Hyphopichia burtonii, Pichia anomala, and Saccharomycopsis fibuligera on Sabouraud Agar Medium. Cardinal values were estimated by means of cardinal models with inflection. All the yeasts were xerophilic, and they exhibited growth at 0.85 aw. The combined effects of T, aw, and pH on the growth of these species were described by the gamma-concept and validated on bread in the range of 15-25°C, 0.91-0.97 aw, and pH4.6-6.8. The optimum growth rates on bread were 2.88, 0.259, and 1.06mm/day for H. burtonii, P. anomala, and S. fibuligera, respectively. The optimal growth rate of S. fibuligera on bread was about 2 fold that obtained on Sabouraud. Due to reproduction by budding, P. anomala exhibited low growth on Sabouraud and bread. However, this species is of major concern in the baker's industry because of the production of ethyl acetate in bread. Copyright © 2015 Elsevier B.V. All rights reserved.
    International journal of food microbiology 03/2015; 204:47-54. DOI:10.1016/j.ijfoodmicro.2015.03.026 · 3.08 Impact Factor
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    • "Fungal growth rate, or rather, the increase in colony diameter over a time period, can be empirically described by the Baranyi and Roberts (1994) model, which was originally developed for bacterial growth. The model has been successfully used to describe the growth of bacteria, yeast and filamentous fungi (Gibson et al., 1994; Valik et al., 1999; Marín et al., 2008). From this primary model, the detection time (λ) and the maximum growth rate (μ max ) parameters are estimated, which subsequently may be used for a secondary modeling if growth curves for different constant conditions are available. "
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    ABSTRACT: B. fulva and N. fischeri are heat-resistant fungi which are a concern to food industries (e.g. apple juice industry) since their growth represents significant economic liabilities. Although the most common method used to assess fungal growth in solid substrates is by measuring the colony’s diameter, it is difficult to apply this method to food substrates. Alternatively, ergosterol contents have been used to quantify fungal contamination in some types of food. The current study aimed at modeling the growth of the heat-resistant fungi B. fulva and N. fischeri by measuring the colony diameter and ergosterol content, fitting the Baranyi and Roberts model to the results, and finally establishing a correlation between the parameters of the two analytical methods. Whereas the colony diameter was measured daily, the quantification of ergosterol was performed when the colonies reached the diameters 30, 60, 90, 120 and 150 mm. Results showed that B. fulva and N. fischeri were able to grow successfully on solidified apple juice at 10, 15, 20, 25 and 30 °C, and the Baranyi and Roberts model showed good ability to describe growth data. The correlation curves between the parameters of colony diameter and ergosterol content were obtained with satisfactory statistical indexes.
    International Journal of Food Microbiology 10/2014; In press. DOI:10.1016/j.ijfoodmicro.2014.10.006 · 3.08 Impact Factor
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    • "The gained symmetric convex pattern of the data lends itself to a parabolic fitting that can be achieved by linear regression, with all its numerical and statistical advantages regarding the robustness of the estimates. Gibson et al. (1994) "
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    ABSTRACT: The purpose of this paper was to develop a predictive model for the effect of temperature and water activity on the growth rate of Aspergillus niger and to determine the sources of the error when the model is used for prediction. Parallel mould growth curves, derived from the same spore batch, were generated and fitted to determine their growth rate. The variances of replicate ln(growth-rate) estimates were used to quantify the experimental variability, inherent to the method of determining the growth rate. The environmental variability was quantified by the variance of the respective means of replicates. The idea is analogous to the "within group" and "between groups" variability concepts of ANOVA procedures. A (secondary) model, with temperature and water activity as explanatory variables, was fitted to the natural logarithm of the growth rates determined by the primary model. The model error and the experimental and environmental errors were ranked according to their contribution to the total error of prediction. Our method can readily be applied to analysing the error structure of predictive models of bacterial growth models, too.
    International journal of food microbiology 11/2013; 170C:78-82. DOI:10.1016/j.ijfoodmicro.2013.10.018 · 3.08 Impact Factor
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