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.