Modeling the Growth of Escherichia coli under the Effects of Carum copticum Essential Oil, pH, Temperature and NaCl Using Response Surface Methodology

Abstract

AbstractThe effects of temperature (20–42C), pH value (4.5–8.5), concentration of sodium chloride (0–5% w/v) and concentration of Carum copticum essential oil (0–750 ppm) on the growth parameters of Escherichia coli (ATCC 8739) were investigated. The growth curves generated within different conditions were fitted using Baranyi function. To achieve much more useful results in the context of hazard analysis and critical control points and risk analysis studies, the variation on model parameters was obtained using Monte Carlo analysis. A normal distribution over the experimental data was considered. Two growth parameters (growth rate [GR] and lag time [LT]) of the growth curves under combined effects of temperature, pH, sodium chloride and essential oil were modeled using a quadratic polynomial equation of response surface (RS) model. Mathematical evaluation demonstrated that the standard error of prediction (%SEP) and root mean square error obtained by RS model were 13.941% and 0.011 for GR and 0.274% and 0.053 for LT for model establishing. The results showed that RS model provided a useful and accurate method for predicting the growth parameters of E. coli and could be applied to ensure food safety with respect to E. coli control.Practical ApplicationsThe increasing incidence of foodborne outbreaks resulting in social and economic losses still provokes the need to search for novel strategies in order to prevent food contamination. In this panorama, plant extracts have emerged as unique sources of useful metabolites to provide microbiological safety of foods. Carum copticum is an aromatic, grassy, annual plant that geographically grows in Iran, India, Pakistan and Egypt, and its antimicrobial activities have been established in previous studies. In addition, during the recent decades, there has been a dramatic enhancement in research on the development of mathematical models. Growth predictive modeling has been so widespread that it is now one of the most rapidly advancing fields of study. Over the past few years, response surface methodology (RSM) has been proposed as an efficient modeling technique in predictive microbiology. Considering this, we suggest that RSM can be used as a prediction tool in food safety.