Abstract
The formation of mycotoxins and potentially allergenic spores associated with fungal growth can cause spoilage of food and animal feed. This study integrated an improved genetic algorithm (IGA) in an adaptive neuro-fuzzy inference system (ANFIS) for predicting the presence of foodborne fungi and modeling their growth. The IGA enhanced the performance of the ANFIS model in predictive microbiology. Based on temperature, pH, and water quantity, the proposed IGA-ANFIS model can accurately predict the maximum specific growth rate of the ascomycetous fungus
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