Affiliations: [a]
Department of Logistics Management, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
| [b]
Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan
| [c]
Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
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 Monascus ruber. The model uses Gaussian membership functions to minimize the root-mean-square error, which was used as a performance index. Experiments verified that the prediction accuracy of the proposed IGA-ANFIS model is higher than those of existing neural network models and neural fuzzy network models.
