Modeling for Predicting the Time to Detection of Staphylococcal Enterotoxin A in Cooked Chicken Product

• Published in: Frontiers in microbiology Vol. 9; p. 1536
• Main Authors: Hu, Jieyun, Lin, Lu, Chen, Min, Yan, Weiling
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 13.07.2018
• Subjects: cooked chicken; Microbiology; predictive microbiology; Staphylococcal enterotoxins; Staphylococcus aureus; time to detection; predictive microbiology; time to detection; Staphylococcus aureus; Staphylococcal enterotoxins; cooked chicken
• ISSN: 1664-302X; 1664-302X
• DOI: 10.3389/fmicb.2018.01536

Staphylococcal enterotoxins (SEs) produced by ( ) are the cause of Saphylococcal food poisoning (SFP) outbreaks. Thus, estimation of the time to detection (TTD) of SEs, that is, the time required to reach the SEs detection limit, is essential for food preservation and quantitative risk assessment. This study was conducted to explore an appropriate method to predict the TTD of SEs in cooked chicken product under variable environmental conditions. An strain that produces staphylococcal enterotoxin A (SEA) was inoculated into cooked chicken meat. Initial inoculating concentrations (approximately 10 , 10 , 10 CFU/g) of and incubation temperatures (15 ± 1, 22 ± 1, 29 ± 1, and 36 ± 1°C) were chosen as environmental variables. The counting of colonies and the detection of SEA were performed every 3 or 6 h during the incubation. The TTD of SEA was considered a response of to environmental variables. Linear polynomial regression was used to model the effects of environmental variables on the TTD of SEA. Result showed that the correlation coefficient ( ) of the regressed equation is higher than 0.98, which means the obtained equation was reliable. Moreover, the minimum concentration of for producing a detectable amount of SEA under various environmental conditions was approximately 6.32 log CFU/g, which was considered the threshold for to produce SEA. Hence, the TTD of SEA could be obtained by calculating the time required to reach the threshold by using an established growth predictive model. Both established methods were validated through internal and external validation. The results of graphical comparison, , and showed that the accuracy of both methods were acceptable, and linear polynomial regression method showed more accurately.