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 pre-diction 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 degrees C). The end effective accumulated temperature of frozen catfish fillets was 10,278.4 h degrees 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.