title: 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.
abstract: A Box-Behnken design was used to determine the effect of protein concentration (0, 5, or 10g of casein/100g), fat (0, 3, or 6g of corn oil/100g), 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 400μL/kg) and incubation temperature (15, 25, or 35°C) on the growth of Aspergillus flavus during 50days 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 50days 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.
authors.affiliation: Departamento de Ingeniería Química, Alimentos y Ambiental, Universidad de las Américas Puebla, Cholula, Puebla 72810, Mexico.
authors.forename: Carlos E
authors.initials: CE
authors.lastname: Kosegarten
authors.affiliation: Departamento de Ingeniería Química, Alimentos y Ambiental, Universidad de las Américas Puebla, Cholula, Puebla 72810, Mexico.
authors.forename: Nelly
authors.initials: N
authors.lastname: Ramírez-Corona
authors.affiliation: Departamento de Ingeniería Química, Alimentos y Ambiental, Universidad de las Américas Puebla, Cholula, Puebla 72810, Mexico.
authors.forename: Emma
authors.initials: E
authors.lastname: Mani-López
authors.affiliation: Departamento de Ingeniería Química, Alimentos y Ambiental, Universidad de las Américas Puebla, Cholula, Puebla 72810, Mexico.
authors.forename: Enrique
authors.initials: E
authors.lastname: Palou
authors.affiliation: Departamento de Ingeniería Química, Alimentos y Ambiental, Universidad de las Américas Puebla, Cholula, Puebla 72810, Mexico. Electronic address: aurelio.lopezm@udlap.mx.
authors.forename: Aurelio
authors.initials: A
authors.lastname: López-Malo
mesh_terms: D000348:Aflatoxins / Q000378:metabolism; D001231:Aspergillus flavus / Q000254:growth & development* / Q000302:isolation & purification / Q000378:metabolism*; D002935:Cinnamomum zeylanicum; D005516:Food Microbiology / Q000379:methods*; D006863:Hydrogen-Ion Concentration; D007700:Kinetics; D009822:Oils, Volatile / Q000494:pharmacology; D011336:Probability; D013696:Temperature; D013995:Time; D014867:Water / Q000378:metabolism
chemical_list: D000348:Aflatoxins; D009822:Oils, Volatile; D014867:Water
keywords: Aspergillus flavus; Cinnamon essential oil; Gompertz equation; Logistic regression; Probabilistic model; Time-to-detection