Description of Aspergillus flavus growth under the influence of different factors (water activity, incubation temperature, protein and fat concentration, pH, and cinnamon essential oil concentration) by kinetic, probability of growth, and time-to-detection models
详细信息 查看全文
- 作者：Carlos E. Kosegarten ; Nelly Ramí ; rez-Corona ; Emma Mani-Ló ; pez ; Enrique Palou ; Aurelio Ló ; pez-Malo ; aurelio.lopezm@udlap.mx
- 关键词：Gompertz equation ; Logistic regression ; Probabilistic model ; Time-to-detection ; Aspergillus flavus ; Cinnamon essential oil
- 刊名：International Journal of Food Microbiology
- 出版年：2017
- 出版时间：2 January 2017
- 年：2017
- 卷：240
- 期：Complete
- 页码：115-123
- 全文大小：944 K
- 卷排序：240
文摘
A Box-Behnken design was used to determine the effect of protein concentration (0, 5, or 10 g of casein/100 g), fat (0, 3, or 6 g of corn oil/100 g), aw (0.900, 0.945, or 0.990), pH (3.5, 5.0, or 6.5), concentration of cinnamon essential oil (CEO, 0, 200, or 400 μL/kg) and incubation temperature (15, 25, or 35 °C) on the growth of Aspergillus flavus during 50 days of incubation. Mold response under the evaluated conditions was modeled by the modified Gompertz equation, logistic regression, and time-to-detection model. The obtained polynomial regression models allow the significant coefficients (p < 0.05) for linear, quadratic and interaction effects for the Gompertz equation's parameters to be identified, which adequately described (R2 > 0.967) the studied mold responses. After 50 days of incubation, every tested model system was classified according to the observed response as 1 (growth) or 0 (no growth), then a binary logistic regression was utilized to model A. flavus growth interface, allowing to predict the probability of mold growth under selected combinations of tested factors. The time-to-detection model was utilized to estimate the time at which A. flavus visible growth begins. Water activity, temperature, and CEO concentration were the most important factors affecting fungal growth. It was observed that there is a range of possible combinations that may induce growth, such that incubation conditions and the amount of essential oil necessary for fungal growth inhibition strongly depend on protein and fat concentrations as well as on the pH of studied model systems. The probabilistic model and the time-to-detection models constitute another option to determine appropriate storage/processing conditions and accurately predict the probability and/or the time at which A. flavus growth occurs.