Effect of chlorogenic acid on the quality of golden pomfret during refrigerated storage: Predictive model using artificial neural network

Abstract

This study investigated the effects of chlorogenic acid (CGA) on the quality of golden pomfret fillets during refrigerated storage. An artificial neural network (ANN) model was developed to predict changes in the quality of CGA-treated golden pomfret fillets. The results revealed that CGA effectively inhibited the texture deterioration and protein degradation of golden pomfret fillets during storage. After golden pomfret fillets were treated by 2 g/L CGA, the value of total volatile basic nitrogen (TVB-N) (25.90 mg N/100 g) was significantly decreased after 15 d, compared to the control group (31.86 mg N/100 g). In addition, the proportion of α-helix of myofibrillar protein (MP) in the 2 g/L CGA-treated group increased to 32.60 %, while the proportion of random coils decreased to 29.18 %, suggesting that CGA effectively protected the secondary structure of MP. Moreover, the established ANN model could accurately predict the quality changes of CGA-treated golden pomfret fillets during storage with relative errors of less than 3 %. This study provides a theoretical basis for the application of CGA in the golden pomfret processing industry and modeling parameters of ANN for the simulation of other food processing.