Application of a combination model based on an error-correcting technique to predict quality changes of vacuum-packed bighead carp (Aristichthys nobilis) fillets

摘要

A combination model based on an error-correcting technique was developed to predict changes in sensory scores, TAC, and K-values of vacuum-packed bighead carp fillets during storage at different temperatures (12, 9, 6, 3, and 0 degrees C). The combination model included a kinetic model and an artificial neuronal network (ANN). TAC, K-values, and sensory scores were modelled by zero -order kinetics, and residual errors generated were simulated by ANN. Then, error corrections obtained by ANN were used to revise results of kinetic models. Relative errors of kinetic models exceeded 10% on some days, by contrast, the proposed combination models performed better with relative errors all within 5%. Therefore, combination models were more satisfactory than single kinetic models. (C) 2016 Published by Elsevier Ltd.