COMPARISON OF RESPONSE SURFACE METHODOLOGY AND THE PROPOSED WEIBULL MODEL FOR INACTIVATION OF LISTERIA INNOCUA BY HIGH HYDROSTATIC PRESSURE

Predictive Modeling of Microbial Behavior in Food

Microorganisms can contaminate food, thus causing food spoilage and health risks when the food is consumed. Foods are not sterile; they have a natural flora and a transient flora reflecting their environment. To ensure food is safe, we must destroy these microorganisms or prevent their growth. Recurring hazards due to lapses in the handling, processing, and distribution of foods cannot be solved by obsolete methods and inadequate proposals. They require positive approach and resolution through the pooling of accumulated knowledge. As the industrial domain evolves rapidly and we are faced with pressures to continually improve both products and processes, a considerable competitive advantage can be gained by the introduction of predictive modeling in the food industry. Research and development capital concerns of the industry have been preserved by investigating the plethora of factors able to react on the final product. The presence of microorganisms in foods is critical for the quality of the food. However, microbial behavior is closely related to the properties of food itself such as water activity, pH, storage conditions, temperature, and relative humidity. The effect of these factors together contributing to permitting growth of microorganisms in foods can be predicted by mathematical modeling issued from quantitative studies on microbial populations. The use of predictive models permits us to evaluate shifts in microbial numbers in foods from harvesting to production, thus having a permanent and objective evaluation of the involving parameters. In this vein, predictive microbiology is the study of the microbial behavior in relation to certain environmental conditions, which assure food quality and safety. Microbial responses are evaluated through developed mathematical models, which must be validated for the specific case. As a result, predictive microbiology modeling is a useful tool to be applied for quantitative risk assessment. Herein, we review the predictive models that have been adapted for improvement of the food industry chain through a built virtual prototype of the final product or a process reflecting real-world conditions. It is then expected that predictive models are, nowadays, a useful and valuable tool in research as well as in industrial food conservation processes.

Application and optimization of the tenderization of pig Longissimus dorsi muscle by adenosine 5'-monophosphate (AMP) using the response surface methodology

Based on single factor experiments, NaCl concentration, adenosine 5'-monophosphate (AMP) concentration and temperature were selected as independent variables for a three-level Box-Behnken experimental design, and the shear force and cooking loss were response values for regression analysis. According to the statistical models, it showed that all independent variables had significant effects on shear force and cooking loss, and optimal values were at the NaCl concentration of 4.15%, AMP concentration of 22.27 mmol/L and temperature of 16.70°C, which was determined with three-dimensional response surface diagrams and contour plots. Under this condition, the observed shear force and cooking loss were 0.625 kg and 8.07%, respectively, exhibiting a good agreement with their predicted values, showing the good applicability and feasibility of response surface methodology (RSM) for improving pork tenderness. Compared with control pig muscles, AMP combined with NaCl treatment demonstrated significant effects on improvement of meat tenderness and reduction of cooking loss. Therefore, AMP could be regarded as an effective tenderization agent for pork.