Application of Adaptive-Network Based Fuzzy Inference Systems in Food Products: Prediction for Microorganism Growth

[[abstract]]本論文在探討腸膜明串珠菌在有氧和厭氧條件下，有溫度（10.5?24.5℃），pH值（5.5?7.5），氯化鈉（0.25％至6.25％）和亞硝酸鈉（0至200 ppm）等四個變數的綜合影響下，由類神經模糊推論系統與人工神經網路來預測生長率。類神經模糊推論系統(ANFIS)和人工神經網路模型(ANN)的比較，應用六個統計指標：平均絕對值誤差百分比（MAPE），均方根誤差（RMSE），預測誤差百分比（SEP），偏差度（Bf），精準度（Af），絕對偏差值（R2），經過預測結果比較與實際數據計算及圖表顯示，在本論文案例中，類神經模糊推論系統(ANFIS)比人工神經網路模型(ANN)有較佳準確度。此外，ANFIS結合高斯歸屬函數所預測的成長率有較高的準確度。在四個環境因素的靈敏度分析顯示：腸膜明串珠菌在有氧和厭氧條件下預測的生長速率，最具顯著影響力的參數是溫度，次顯著影響力的參數是氯化鈉。因此，在有氧和厭氧條件下的腸膜明串珠菌生長率，ANFIS模型是一個可快速預測成長率的工具。[[abstract]]An adaptive network-based fuzzy inference system (ANFIS) was compared with an artificial neural network (ANN) in terms of accuracy in predicting the combined effects of temperature (10.5 to 24.5 ℃), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the specific growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. The ANFIS and ANN models were compared in terms of six statistical indices calculated by comparing their prediction results with actual data: mean absolute percentage error (MAPE), root mean square error (RMSE), standard error of prediction percentage (SEP), bias factor (Bf), accuracy factor (Af), and absolute fraction of variance (R2). Graphical plots were also used for model comparison. The learning-based systems obtained encouraging prediction results. Sensitivity analyses of the four environmental factors showed that temperature and, to a lesser extent, NaCl had the most influence on accuracy in predicting the specific growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. The observed effectiveness of ANFIS for modeling microbial kinetic parameters confirms its potential use as a supplemental tool in predictive mycology. Comparisons between growth rates predicted by ANFIS and actual experimental data also confirmed the high accuracy of the Gaussian membership function in ANFIS. Comparisons of the six statistical indices under both aerobic and anaerobic conditions also showed that the ANFIS model was better than all ANN models in predicting the four kinetic parameters. Therefore, the ANFIS model is a valuable tool for quickly predicting the specific growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions