Predictive Modeling and Validation on Growth, Production of Asexual Spores and Ochratoxin A of Aspergillus Ochraceus Group under Abiotic Climatic Variables

This study aimed to generate predictive models for growth, sporulation, and ochratoxin A (OTA) production under abiotic climatic variables, including temperatures (15-35 °C) and water activity levels (0.99-0.90 a ) by group. The data were divided into three sets: one for training, one for testing, a...

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Veröffentlicht in:Microorganisms (Basel) 2021-06, Vol.9 (6), p.1321
Hauptverfasser: Abdel-Hadi, Ahmed, Alshehri, Bader, Waly, Mohammed, Aboamer, Mohammed, Banawas, Saeed, Alaidarous, Mohammed, Palanisamy, Manikandan, Awad, Mohamed, Baazeem, Alaa
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Sprache:eng
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Zusammenfassung:This study aimed to generate predictive models for growth, sporulation, and ochratoxin A (OTA) production under abiotic climatic variables, including temperatures (15-35 °C) and water activity levels (0.99-0.90 a ) by group. The data were divided into three sets: one for training, one for testing, and the third one for model validation. Optimum growth occurred at 0.95 a and 25 °C and 0.95 a and 30 °C for and , respectively. Significantly improved and spore production occurred at 0.95 a and 20 °C and 0.90 a and 35 °C, respectively. and produced the majority of OTA at 35 °C and 0.95 a and 25-30 °C at 0.95-0.99 a , respectively. The accuracy of the third-order polynomial regression model reached 96% in growth cases, 94.7% in sporulation cases, and 90.9% in OTA production cases; the regression coefficients (R2) ranged from 0.8819 to 0.9978 for the group. A reliable agreement was reached between the predicted and observed growth, sporulation, and OTA production. The effects of abiotic climatic variables on growth, sporulation, and OTA production of group have been effectively defined, and the models generated were responsible for adequately predicted and validated models against data from other strains within group that had been published in the literature under the current treatments. These models could be successfully implemented to predict fungal growth and OTA contamination on food matrices for these strains under these conditions.
ISSN:2076-2607
2076-2607
DOI:10.3390/microorganisms9061321