Combined Effects of pH and Sugar on Growth Rate of Zygosaccharomyces rouxii , a Bakery Product Spoilage Yeast

Abstract

The effects of citric acid-modified pH (pH 2.5, 2.75, 3, 3.5, 4, 4.5, 5, and 5.5) and a 30% glucose–70% sucrose mixture (300, 400, 500, 600, 700, 800, 875, and 900 g/liter) on an osmophilic yeast,Zygosaccharomyces rouxii, were determined by using synthetic medium. One hundred experiments were carried out; 50-ml culture flasks were inoculated with 103 CFU ml−1 by using a collection strain and a wild-type strain cocktail. The biomass was measured by counting cell colonies, and growth curves were fitted by using a Baranyi equation. The growth rate decreased linearly with sugar concentration, while the effect of pH was nonlinear. Indeed, the optimal pH range was found to be pH 3.5 to 5, and pH 2.5 resulted in a 30% reduction in the growth rate. Finally, we evaluated the performance of two nonlinear predictive models developed previously to describe bacterial contamination. Equations derived from the Rosso and Ratkowsky models gave similar results; however, the model that included dimensionless terms based on the Ratkowsky equation was preferred because it contained fewer estimated parameters and also because biological interpretation of the results was easier.

Population growth of the floricolous yeastMetschnikowia reukaufii: effects of nectar host, yeast genotype, and host × genotype interaction

“…Between‐host differences in host quality may stem from variation in any of the nectar parameters considered here, as pH, sugar concentration, and sugar composition are all known to affect yeast growth (e.g. Membré et al ., ; Peay et al ., ; Pozo et al ., ). When cultured in single‐sugar artificial media, M. reukaufii growth declines with increasing sugar concentration, and also in the direction sucrose–glucose–fructose (Herrera et al ., ), which supports the interpretation that nectar sugar differences account, at least in part, for between‐host variation in growth found in this study.…”

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

“…1). This model has been successfully used to ®t growth of foodborne bacteria (Membre  et al 1999a), yeast (Membre  et al 1999b), or fungi such as Penicillium roqueforti (Valik et al 1999) and Aspergillus¯avus (Gibson et al 1994). The growth rate, m, estimated by a modelling step, is equivalent to the slope of the straight line observed on the plot.…”

“…Yeast and moulds are found in a wide range of environments due to their capacity to utilize a variety of substrates and to their relative tolerance of low pH, low water activity and low temperature (Huis in't Veld 1996). A few studies concerning yeast or mould colonization have dealt with the predictive modelling approach (Cuppers et al 1997;Membre  et al 1999b). The primary model developed by Baranyi has been successfully adapted to ®t colony diameter growth curves of Penicillium roqueforti (Valik et al 1999) and Aspergillus¯avus (Gibson et al 1994).…”

Accurate Quantification of Fungal Growth in Bread by Using Spectral Analysis

“…When bread is cut, the inner, more susceptible surface is exposed to mould infection. The bakery product's water activity (a w ) is the most important factor affecting the type and rate of spoilage (Membré et al, 1999). Commercially produced and properly handled bread generally lacks sufficient amounts of moisture to allow growth of any micro-organisms except moulds.…”