BP neural network to predict shelf life of channel catfish fillets based on near infrared transmittance (NIT) spectroscopy

The objective of this research was to establish shelf-life prediction model of channel catfish fillets by Back-propagation (BP) neural network technology based on near infrared transmittance (NIT). First, freshness prediction model of channel catfish fillets was established based on the chemical analysis data (total volatile basic nitrogen (TVB-N), K value, thiobarbituric acid reactive substance (TBARS) and trimethylamine (TMA)) and NIT spectra (850–1050 nm). The linear correlation coefficient (R2: 0.667–0.887) showed a good performance of the freshness model prediction. Then, BP neural network was applied to establish the shelf-life prediction model of catfish fillets under temperature fluctuation (−6 to −18 °C). The end effective accumulated temperature of frozen catfish fillets was 10,278.4 h °C. The prediction model showed a great stability (above 93 %) and accuracy (above 90 %) as the structure of BP neural network was 4–7–1. Therefore, this study provided a practical basis and technical supports for the establishment of shelf-life prediction model of freshwater fillets by BP neural network based on NIT spectroscopy.