Predictive Microbiology for Monitoring Spoilage of Dairy Products with Time‐Temperature Integrators

Abstract

Time/temperature integrators (TTI) have a potential for monitoring time-temperature history of perishable foods, including dairy products. To correlate the end of shelf life of dairy products with different TTI's, kinetic data for growth of a dairy spoilage microorganism was obtained. Both Arrhenius and square root equations were used to model the growth of Pseudomonusfrugii. A significant negative histoty effect was observed for P. fragi growth rate whereas history effect was positive on the lag phase, under certain nonisothermal conditions. A correlation scheme -of P. frugi growth was developed with the TI'I response. The application of TIT's for dairy products is feasible despite the history effects.

Evaluation of a Clostridium perfringens Predictive Model, Developed under Isothermal Conditions in Broth, To Predict Growth in Ground Beef during Cooling

“…Despite repeated attempts to demonstrate a temperature history effect, few examples have been reported (10). Trends seen in this study when comparing nonisothermal experimental data collected in ground beef to predicted values from both single-and dual-rate-cooling experiments suggest that the Juneja 1999 model could be improved and that a temperature history effect may be operating.…”

“…They were maintained and prepared according to procedures described by Juneja et al (11). Appropriate volumes of a spore cocktail (approximately 10 8 CFU/ml) were inoculated into 908 g of ground beef (25% fat), obtained at a retail store, to result in initial inoculum levels of between 10 1 or 10 3 concentrations of spores/g. Initial spore concentration has been shown to influence germination and growth of C. botulinum (21) and other spore-forming bacteria (8,15), so initial concentrations of 10 1 and 10 3 spores/g were used for each cooling time to determine the effect of initial spore concentration.…”

Predictive Modeling of the Shelf Life of Fish under Nonisothermal Conditions

“…Predictions of lag time under nonisothermal conditions based on the mathematical concept of equation 7 have been previously reported for other microorganisms (12,24) Prediction of growth under dynamic temperature conditions. Since temperature changes in a production and distribution chain are usually random and thus no mathematical expression can be used to describe the time-temperature variation, an accepted approach to predict microbial growth is to divide the time-temperature history into short assumed constant temperature time intervals (11).…”

“…However, unlike other factors affecting microbial growth (e.g., pH and water activity), temperature may vary extensively throughout the complete production and distribution chain. In practice, foods are frequently exposed to significant temperature fluctuations during transportation and storage before delivery to the consumer.Several studies have been published predicting microbial growth at fluctuating temperatures (2,12,24,42,48). The aim of these studies was to test whether growth under nonisothermal conditions can be predicted from models based on growth data obtained isothermally.…”

“…Several studies have been published predicting microbial growth at fluctuating temperatures (2,12,24,42,48). The aim of these studies was to test whether growth under nonisothermal conditions can be predicted from models based on growth data obtained isothermally.…”