Effect of Input Data Variability on Estimations of the Equivalent Constant Temperature Time for Microbial Inactivation by HTST and Retort Thermal Processing

Abstract

In spite of novel technologies, commercialized or under development, thermal processing continues to be the most reliable and cost-effective alternative to deliver safe foods. However, the severity of the process should be assessed to avoid under- and over-processing and determine opportunities for improvement. This should include a systematic approach to consider variability in the parameters for the models used by food process engineers when designing a thermal process. The Monte Carlo procedure here presented is a tool to facilitate this task for the determination of process time at a constant lethal temperature.

Mathematical modeling and Monte Carlo simulation of thermal inactivation of non-proteolytic Clostridium botulinum spores during continuous microwave-assisted pasteurization

“…Once the heat transfer parameters were obtained and validated, Monte Carlo simulation (Sokolowski, 2010) was used to conduct a probabilistic analysis of the effect of the variations in the process parameters on the total lethality in the products (Chotyakul et al, 2011;Poschet et al, 2003;Salgado et al, 2011). In thermal processing of foods, the time and temperature used to kill C. botulinum spore are often precisely controlled to prevent inadequate cooking.…”

“…Monte Carlo simulation can be used to evaluate the uncertainties of food safety and quality estimations associated with process variations (Chotyakul et al, 2011) and to analyze the impact of input data variability on estimations of the equivalent constant temperature time for microbial inactivation during HTST and retort processing (Salgado et al, 2011). MAPS is a new continuous thermal processing technology.…”

Monte Carlo analysis of the product handling and high-pressure treatment effects on the Vibrio vulnificus risk to raw oysters consumers

“…More recently, regulatory agencies have begun to require evidence that these process objectives are met with a high probability typically set at a 95% confidence interval (95CI) considering all relevant variability factors (Fernandez et al, 1999;Rieu et al, 2007;Smout et al, 2000). Monte Carlo based calculations have been used to meet this new regulatory requirement for different food products and processes (Chotyakul et al, 2011a,b;Salgado et al, 2011). In this work, the impact factors and their variability involved in the handling of raw oysters from harvest to consumption were analyzed with a Monte Carlo procedure to estimate the V. vulnificus load with 95CI at consumption.…”

Inclusion of the variability of model parameters on shelf-life estimations for low and intermediate moisture vegetables

“…(1) and (2), the effect of the parameter variability on the shelf-life estimate can be considered by using the Monte Carlo procedure, a statistical method that generates a frequency distribution of all possible outcomes from a predictive model (Chotyakul, Pérez Lamela, & Torres, in press;Chotyakul, Velazquez, & Torres, 2011;Salgado, Torres, Welti-Chanes, & Velazquez, 2011; SermentMoreno, Su, Torres, & Welti Chanes, submitted for publication; Torres, Chotyakul, Velazquez, Saraiva, & Pérez Lamela, 2010). The development of a Monte Carlo calculation strategy requires first a brief description of this method.…”

“…The development of a Monte Carlo calculation strategy requires first a brief description of this method. A Monte Carlo procedure (Cassin, Paoli, & Lammerding, 1998) uses a vector corresponding to a set of measurements, or data summarized as statistical probability distributions (Salgado et al, 2011;Serment-Moreno et al, submitted for publication). Calculations are repeated many times, using each time a randomly selected value for the input parameters, and thus yielding each time a slightly different outcome depending on the variability of the input parameters.…”