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

Abstract

Staphylococcal enterotoxins (SEs) produced by Staphylococcus aureus (S. aureus) 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 S. aureus strain that produces staphylococcal enterotoxin A (SEA) was inoculated into cooked chicken meat. Initial inoculating concentrations (approximately 102, 103, 104 CFU/g) of S. aureus and incubation temperatures (15 ± 1, 22 ± 1, 29 ± 1, and 36 ± 1°C) were chosen as environmental variables. The counting of S. aureus 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 S. aureus 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 (R2) of the regressed equation is higher than 0.98, which means the obtained equation was reliable. Moreover, the minimum concentration of S. aureus for producing a detectable amount of SEA under various environmental conditions was approximately 6.32 log CFU/g, which was considered the threshold for S. aureus to produce SEA. Hence, the TTD of SEA could be obtained by calculating the time required to reach the threshold by using an established S. aureus growth predictive model. Both established methods were validated through internal and external validation. The results of graphical comparison, RMSE, SEP, Af, and Bf showed that the accuracy of both methods were acceptable, and linear polynomial regression method showed more accurately.

Review of Quantitative Microbial Risk Assessment in Poultry Meat: The Central Position of Consumer Behavior

“…These latter additional studies covered the following topics: Predictive microbiology including modeling of microbial growth, inactivation. and survival along the meat chain [ 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 ]; Estimation of the prevalence of contamination at several steps [ 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , ...…”

“…Predictive microbiology including modeling of microbial growth, inactivation. and survival along the meat chain [ 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 ];…”

Alternative to Animal Use for Detecting Biologically Active Staphylococcal Enterotoxin Type A

“…The ability to discern active toxin is also important for development of food treatment and processing methods. Cooking and pasteurization are forms of heat treatment that inactivate SEA [22]. Pulsed ultraviolet (UV) light has also been applied to inactivate SEA [23].…”

Dimensional Analysis Model Predicting the Number of Food Microorganisms

“…For external evaluation ( Giffel and Zwietering, 1999 ), new data from the storage of NGB samples selected randomly within the range of experimental design were used. The accuracy of the models describing microbial growth was evaluated using the following seven criteria: coefficient of determination ( R 2 ), adjusted coefficient of determination ( R 2 adj ), median relative error (RE) (Equation 12) of model predictions, root mean square error (RMSE) ( Hu et al, 2018 ; Antunes-Rohling et al, 2019 ; Park et al, 2020 ), %SEP, accuracy factor ( A f ), and bias factor ( B f ) ( Baranyi et al, 1993 ; Ross, 1996 ; Tarlak et al, 2018 ), expressed as Equations 7–13, respectively.…”