| Affected by Temperature and Initial Concentration of Spoilage Organisms (2008) | |||||||||||||
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| Predictive microbiology and quantitative microbial risk assessment are rapidly developing disciplines that use mathematical models to quantitatively estimate the presence and growth of microbes in food products. This report explains how these techniques were used in troubleshooting a milk spoilage problem. The central objective of this article is to demonstrate the concept and value of Monte Carlo simulation in a simple manner, such as could be adapted by interested food processors and used as a guide for their own studies. Generation times, initial concentrations of spoilage organisms found in milk, and storage temperature data were fit to probability distribution functions. Monte Carlo simulation results indicate that slight decreases (of 2.1ºC) in average storage temperatures significantly increased the simulated shelf life of pasteurized milk (more than 50 % less psychrotrophic spoilage by 14 days), especially in those simulation samples contaminated with mesophilic bacteria (almost 75% less spoilage). When the average storage temperature was reduced by 2.1ºC, storage temperature variability by 1ºC, and average initial microbial contamination levels by 0.5 Log CFU/ml, this 10 significantly lengthened shelf life when either mesophilic (1 % spoilage by 14 days) or psychrotrophic (9 % spoiled by 14 days) microbes were present. Microbial simulation tools used primarily for food safety risk assessment can also be used to predict microbial spoilage and may be of value to the food industry. A peer-reviewed article | |||||||||||||
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