Mathematical models were developed to predict the probability of yeast spoilage of cold-filled ready-to-drink beverages as a function of beverage formulation. A Box-Behnken experimental design included five variables, each at three levels: pH (2.8, 3.3, and 3.8), titratable acidity (0.20, 0.40, and 0.60%), sugar content (8.0, 12.0, and 16.0°Brix), sodium benzoate concentration (100, 225, and 350 ppm), and potassium sorbate concentration (100, 225, and 350 ppm). Duplicate samples were inoculated with a yeast cocktail (100 l/50 ml) consisting of equal proportions of Saccharomyces cerevisiae, Zygosaccharomyces bailii, and Candida lipolytica (ϳ5.0 ؋ 10 4 CFU/ml each). The inoculated samples were plated on malt extract agar after 0, 1, 2, 4, 6, and 8 weeks. Logistic regression was used to create the predictive models. The pH and sodium benzoate and potassium sorbate concentrations were found to be significant factors controlling the probability of yeast growth. Interaction terms for pH and each preservative were also significant in the predictive model. Neither the titratable acidity nor the sugar content of the model beverages was a significant predictor of yeast growth in the ranges tested.The high water activity (A w ) of most ready-to-drink beverages typically allows microbial growth. Hurdles such as pH, sugar content, and chemical preservatives prevent the growth of most organisms in ready-to-drink beverages (1). Spoilage yeasts, such as Saccharomyces cerevisiae, Candida lipolytica, and Zygosaccharomyces bailii, are sometimes able to overcome these hurdles. These organisms tolerate acidic environments and are resistant to chemical preservatives, such as potassium sorbate and sodium benzoate (1,12).Challenge studies are conducted in order to assess the ability of organisms to grow in a particular foodstuff. Challenge studies require considerable labor, time, and materials, and the number of parameters that can be tested is often limited. Validated predictive models, however, can provide rapid information about the microbial stability of a product and can be used in conjunction with challenge studies to improve product stability and reduce costs.The focus of predictive microbiology has been in the creation of pathogen models with polynomial regression, such as the Food MicroModel (25) and the U.S. Department of Agriculture's Pathogen Modeling Program (40). Spoilage models are less prevalent in the literature (28, 39) but include models for yeasts (31), molds (18), and bacteria (2, 9, 22). Logisticregression models are also less prevalent in predictive food microbiology but are gaining importance (42).Our research applies the techniques of predictive food microbiology to the spoilage of a model cold-filled beverage system. Several models for Z. bailii have been developed (5, 7, 11), but the focus of our research was to create a single mathematical model to describe spoilage by three common spoilage yeasts, in order to aid in the product development of readyto-drink beverages. The resulting model demonstrates which ...

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Aims: Mathematical models were created which predict the growth of spoilage bacteria in response to various preservation systems. Methods and Results: A Box-Behnken design included ®ve variables: pH (2á8, 3á3, 3á8), titratable acidity (0á20%, 0á40%, 0á60%), sugar (8á0, 12á0, 16á0°Brix), sodium benzoate concentration (100, 225, 350 ppm), and potassium sorbate concentration (100, 225, 350 ppm). Duplicate samples were inoculated with a bacterial cocktail (100 ll 50 ml ±1 ) consisting of equal proportions of Acinetobacter calcoaceticus and Gluconobacter oxydans (5´10 5 cfu ml ±1 each). Bacteria from the inoculated samples were enumerated on malt extract agar at zero, one, two, four, six, and eight weeks. Conclusions: The pH, titratable acidity, sugar content, sodium benzoate, and potassium sorbate levels were all signi®cant factors in predicting the growth of spoilage bacteria. Signi®cance and Impact of the Study: This beverage spoilage model can be used to predict microbial stability in new beverage product development and potentially reduce the cost and time involved in microbial challenge testing.