Comparison between the Arrhenius model and the radial basis function neural network (RBFNN) model for predicting quality changes of frozen shrimp (Solenocera melantho)

Changes in quality indices [total volatile base nitrogen (TVB-N), salt extractable protein (SEP), hypoxanthine (Hx), K-value, sensory assessment (SA), and electrical conductivity (EC)] for shrimp (Solenocera melantho) stored at -28, -20, and -12 degrees C for 112 days were investigated in this study. The Arrhenius model and the radial basis function neural network (RBFNN) model were established to predict changes in the quality of shrimp during storage. Quality of shrimp stored at -12 degrees C changed more quickly during 56-112 days, but those stored at -28 degrees C deteriorated slowly during the entire storage period. Additionally, the indicators SEP, EC, and SA all fitted to the Arrhenius model well (relative errors within +/- 10%), but this model did not perform well in the prediction of K-value, Hx, and TVB-N on some days. However, the RBFNN model showed excellent accuracy for all indicators (relative errors within +/- 0.5%). The RBFNN model performed better than the Arrhenius model in predicting the quality of shrimp stored at -28 degrees C to -12 degrees C.