Comparison of Arrhenius model and artificial neuronal network for predicting quality changes of frozen tilapia (Oreochromis niloticus).

• Ice crystal formation combined with protein denaturation to predict shelf life was first reported. • The prediction accuracy of the kinetic model was decreased at later stages of storage. • IFI λmax could be accurately described the quality changes of tilapia. • ANN could successfully predict the quality changes throughout the whole period. • It is the first time to use K value to predict the freshness range of other indexes. The objectives of this study were to study the quality changes (ice crystal morphology, Ca2+-ATPase activity, total sulfhydryl [SH] content, intrinsic fluorescence intensity [IFI], and K value [freshness determination]) of tilapia at different storage temperatures for 112 days, and kinetic models and artificial neuronal network (ANN) were developed to predict the changes. There was a dramatic increase in cross-section area and equivalent diameter and a sharp decrease in Ca2+-ATPase activity and SH content during the first 4 weeks (p < 0.05). IFI λmax decreased by 43.95%, 29.77%, 28.97% and 18.58% after 16 weeks at 265 K, 259 K, 253 K, and 233 K. The kinetic model established by IFI λmax could be accurately described the quality changes during storage at 233–265 K. However, the prediction accuracy established by other indices decreased at later stages (14–16 weeks). The ANN model was superior to Arrhenius models and performed better for all indicators. [ABSTRACT FROM AUTHOR]