MICROBIAL MODELING AND BIOINFORMATICS FOR FOOD SAFETY AND SECURITY
Location: Residue Chemistry and Predictive Microbiology
Title: Predictive thermal inactivation model for effects of temperature, cinnamaldehyde and carvacrol on stressed multiple Salmonella serotypes in ground chicken
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: February 10, 2011
Publication Date: June 10, 2011
Citation: Juneja, V.K., Yadav, A.S., Hwang, C., Sheen, S. 2011. Predictive thermal inactivation model for effects of temperature, cinnamaldehyde and carvacrol on stressed multiple Salmonella serotypes in ground chicken [abstract]. IFT Annual Meeting, June 11-14, 2011, New Orleans, LA. 1:1.
The use of heat to inactivate foodborne pathogens is a critical control point and the most common means of assuring the microbiological safety of processed foods. A key to optimization of the heating step is defining the heat resistance of target pathogens. Sufficient evidence exists to document that insufficient cooking, reheating and/or subsequent cooling of meat products are often contributing factors in food-poisoning outbreaks. Accordingly, the thermal inactivation of an eight-strain mixture of Salmonella serotypes, starvation-stressed by suspending in Butterfield’s phosphate buffer at 37C for 24 h, in ground chicken as affected by heating temperature (60 – 71.1C), carvacrol (CR; 0 – 1.0%, w/w) and cinnamaldehyde (CN; 0 – 1.0%, w/w) was studied. A complete factorial experimental design was employed to assess the effects and interactions of 54 combinations of heating temperature, CR and CN content. Heating was carried out using a circulating water bath. The recovery medium was tryptic soy agar supplemented with 0.6% yeast extract and 1% sodium pyruvate. Decimal reduction times (D-values) were calculated by fitting a survival model to the data with a curve fitting program. The D-values were analyzed by second order response surface regression for temperature, CR and CN levels. In comparison with CN, CR was more effective in rendering the pathogen more sensitive to the lethal effect of heat. For example, the D-values at 60C were 0.32 min and 2.70 min in chicken supplemented with 1% CR and CN, respectively. The three variables interacted to affect the inactivation of the pathogen. A predictive model was developed, which described the combined effect of temperature, CR and CN levels on thermal resistance of Salmonella in chicken. The model can predict D-values for any combinations of temperature, CR and CN that are within the range of those tested. Using this predictive model, food processors should be able to design adequate thermal regimes to eliminate Salmonella in thermally processed chicken.