Modelling bacterial spoilage in cold-filled ready to drink beverages by Acinetobacter calcoaceticus and Gluconobacter oxydans

Battey, Alyce Stiles; Schaffner, Donald W.

doi:10.1046/j.1365-2672.2001.01381.x

Cited by 34 publications

(14 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fermented beverages pH is significant to determine microbiological stability against food-borne pathogens, and is directly correlated with the products' taste. It has been reported that at a pH below 3.8 the growth of most food spoilage bacteria can be inhibited (Battey & Schaffner, 2001).…”

Section: Physicochemical Analysesmentioning

confidence: 99%

Effect of potentially probiotic lactic acid bacteria on the physicochemical composition and acceptance of fermented cereal beverages

Salmerón

Thomas

Pandiella

2015

Journal of Functional Foods

137

View full text Add to dashboard Cite

Section: Physicochemical Analysesmentioning

confidence: 99%

Effect of potentially probiotic lactic acid bacteria on the physicochemical composition and acceptance of fermented cereal beverages

Salmerón

Thomas

Pandiella

2015

Journal of Functional Foods

137

View full text Add to dashboard Cite

“…Natural habitats of G. oxydans are flowers and fruits 8 . The organism is also found in alcoholic beverages (wines and beers) and soft drinks where it causes off-flavors and spoilage 9 .…”

mentioning

confidence: 99%

Complete genome sequence of the acetic acid bacterium Gluconobacter oxydans

et al. 2005

View full text Add to dashboard Cite

“…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).…”

mentioning

confidence: 99%

Modeling Yeast Spoilage in Cold-Filled Ready-To-Drink Beverages with Saccharomyces cerevisiae , Zygosaccharomyces bailii , and Candida lipolytica

Battey

Duffy

Schaffner

2002

Appl Environ Microbiol

View full text Add to dashboard Cite

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 ...

show abstract

Modelling bacterial spoilage in cold-filled ready to drink beverages by Acinetobacter calcoaceticus and Gluconobacter oxydans

Cited by 34 publications

References 23 publications

Effect of potentially probiotic lactic acid bacteria on the physicochemical composition and acceptance of fermented cereal beverages

Effect of potentially probiotic lactic acid bacteria on the physicochemical composition and acceptance of fermented cereal beverages

Complete genome sequence of the acetic acid bacterium Gluconobacter oxydans

Modeling Yeast Spoilage in Cold-Filled Ready-To-Drink Beverages with Saccharomyces cerevisiae , Zygosaccharomyces bailii , and Candida lipolytica

Contact Info

Product

Resources

About