A S Battey1, D W Schaffner. 1. Food Risk Analysis Initiative, Rutgers, The State University of New Jersey, New Brunswick, 08901-8520, USA.
Abstract
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 five variables: pH (2.8, 3.3, 3.8), titratable acidity (0.20%, 0.40%, 0.60%), sugar (8.0, 12.0, 16.0 * 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 microl 50 ml(-1)) consisting of equal proportions of Acinetobacter calcoaceticus and Gluconobacter oxydans (5 x 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. CONCLUSION: The pH, titratable acidity, sugar content, sodium benzoate, and potassium sorbate levels were all significant factors in predicting the growth of spoilage bacteria. SIGNIFICANCE 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.
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 five variables: pH (2.8, 3.3, 3.8), titratable acidity (0.20%, 0.40%, 0.60%), sugar (8.0, 12.0, 16.0 * 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 microl 50 ml(-1)) consisting of equal proportions of Acinetobacter calcoaceticus and Gluconobacter oxydans (5 x 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. CONCLUSION: The pH, titratable acidity, sugar content, sodium benzoate, and potassium sorbate levels were all significant factors in predicting the growth of spoilage bacteria. SIGNIFICANCE 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.