title: Leuconostoc mesenteroides growth in food products: prediction and sensitivity analysis by adaptive-network-based fuzzy inference systems.
abstract: BACKGROUND
  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°C), 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 growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions.
  
  
  METHODS
  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 (R (2)). Graphical plots were also used for model comparison.
  
  
  CONCLUSIONS
  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 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 growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions.
authors.affiliation: Department of Pharmacy, Chi Mei Medical Center, Tainan, Taiwan.
authors.forename: Hue-Yu
authors.initials: HY
authors.lastname: Wang
authors.forename: Ching-Feng
authors.initials: CF
authors.lastname: Wen
authors.forename: Yu-Hsien
authors.initials: YH
authors.lastname: Chiu
authors.forename: I-Nong
authors.initials: IN
authors.lastname: Lee
authors.forename: Hao-Yun
authors.initials: HY
authors.lastname: Kao
authors.forename: I-Chen
authors.initials: IC
authors.lastname: Lee
authors.forename: Wen-Hsien
authors.initials: WH
authors.lastname: Ho
mesh_terms: D000332:Aerobiosis; D000693:Anaerobiosis; D000704:Analysis of Variance; D005506:Food Contamination; D005516:Food Microbiology; D017143:Fuzzy Logic; D007934:Leuconostoc / Q000254:growth & development*; D016571:Neural Networks, Computer; D016011:Normal Distribution; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity