Comparison of Arrhenius model and artificial neuronal network for the quality prediction of rainbow trout (Oncorhynchus mykiss) fillets during storage at different temperatures

•Quality changes of rainbow trout fillets during chilled storage were determined.•Arrhenius model was developed to predict freshness of rainbow trout fillets.•ANN was developed to predict freshness of rainbow trout fillets.•ANN was more effective to predict freshness of rainbow trout fillets.

Quality changes in total aerobic counts (TAC), electrical conductivity (EC), K-value and sensory assessment (SA) of rainbow trout (Oncorhynchus mykiss) fillets during storage at 282, 279, 276, 273 and 270 K were determined. Simultaneously, Arrhenius model and feed-forward artificial neuronal network (ANN) were established to predict changes of rainbow trout fillets during storage, and a comparative study between these two models was also performed. The relative error between predicted and experimental value was used as the comparative parameter. The results showed that TAC, EC and K-value increased with storage time, while SA decreased with time. The change rate of all indicators increased as a function of temperature. Arrhenius models based on EC and TAC were acceptable, while those based on SA and K-value showed poor performances in some days. By contrast, ANN was more effective to predict changes in TAC, EC, K-value and SA throughout the storage, with relative errors all below 10%. Therefore, ANN could be a potential tool in modeling quality changes of rainbow trout fillets within 270-282 K.