Predictive Modeling for the Growth ofSalmonellaEnteritidis in Chicken Juice by Real-Time Polymerase Chain Reaction

Abstract

The goals of this study were to monitor the growth kinetics of Salmonella Enteritidis in chicken juice using real-time polymerase chain reaction (PCR) and to evaluate its efficacy by comparing the results with an experimental database. Salmonella Enteritidis was inoculated in chicken juice samples at an initial inoculum of 10 CFU/mL with inoculated samples incubated at six different temperatures (10, 15, 20, 25, 30, and 35°C). Sampling was carried out for 36 h to observe the growth of Salmonella Enteritidis. The total DNA was extracted from the samples, and the copy number of the Salmonella invasion gene (invA) was quantified by real-time PCR and converted to Salmonella Enteritidis cell concentration. Growth kinetics data were analyzed by the Baranyi and Roberts model to obtain growth parameters, whereas the Ratkowsky's square-root model was used to describe the effect of the interactions between growth parameters and temperature on the growth of Salmonella Enteritidis. The growth parameters of Salmonella Enteritidis obtained from an experiment conducted at a constant temperature were validated with growth data from chicken juice samples that were incubated under fluctuating temperature conditions between 5°C and 30°C for 30-min periods. A high correlation was observed between maximum growth rate (μ) and storage temperature, indicating that the real-time PCR-monitoring method provides a precise estimation of Salmonella Enteritidis growth in food material with a microbial flora. Moreover, the μ data reflected data from microbial responses viewer database and ComBase. The results of this study suggested that real-time PCR monitoring provides a precise estimation of Salmonella Enteritidis growth in food materials with a background microbial flora.

Predicting the Growth Behavior of Foodborne Pathogenic Bacteria by Real-time Polymerase Chain Reaction Method

“…in pasteurized and unpasteurized milk (Kawasaki et al 2014). The same method was also applied to modeling Salmonella Enteritidis growth in chicken juice as a function of storage time and temperature (Noviyanti et al 2018).…”

“…Such incidences potentially provide conditions facilitating the growth of undesirable bacteria in food products, thereby increasing the deterioration rate of food. Moreover, there is an increase in the potential risk of infection with pathogenic bacteria since the shifts in temperatures enable pathogenic bacteria to attain infectious levels (Noviyanti et al 2018). In this section, the application of real-time PCR techniques for data collection will be discussed from the perspective of constructing microbiological growth models that can predict bacterial responses to dynamic temperature conditions.…”

“…In this study, three dynamic temperature profiles were designed to represent the chilling temperature of milk (2°C), refrigeration temperature (5°C), slight temperature abuse (8°C), severe temperature abuse (12°C and 15°C), and a control temperature near the optimum condition for L. monocytogenes (30°C). The model construction method used for dynamic kinetic analysis of L. monocytogenes in this study was similar to that described for the S. Enteritidis dynamic modeling study in chicken juice samples (Noviyanti et al 2018). In this study, however, the conventional plate count method was also applied to collect growth data for L. monocytogenes under fluctuating temperatures for comparison with the real-time PCR method.…”

“…In recent years, real-time polymerase chain reaction (PCR) analysis has emerged as a novel technique for the rapid, specific, and high-throughput quantification of pathogenic bacteria (Kawasaki et al 2014;Noviyanti et al 2018Noviyanti et al , 2020Noviyanti et al , 2021. Real-time PCR enables the collection of multiple growth data plots of pathogenic bacteria in various food materials containing background flora.…”

Modelling the inactivation, survival and growth of Salmonella enterica under osmotic stress considering inoculum phase and serotype

“…Predictive models for Salmonella spp. growth and inactivation are widely available in the literature (Milkievicz et al, 2020(Milkievicz et al, , 2021Muñoz-Cuevas et al, 2012;Noviyanti et al, 2018;Pin et al, 2011). However, most of them exclusively describe microbial behaviour in response to favourable or unfavourable environmental conditions.…”