Abstract
To evaluate the effects of protein oxidation and denaturation on the fish texture and moisture loss during frozen storage, we measured the changes of protein oxidation and denaturation (salt-soluble protein (SSP), total sulfhydryl (SH), disulfide (SS), carbonyl contents and Ca2+ -ATPase activity), texture (hardness), and moisture loss (drip loss) of bighead carp fillets stored at -12, -20 and -28°C during 16weeks. These data were employed to develop partial least squares regression (PLSR) model, radial basis function neural network (RBFNN) model, PLSR-RBFNN (PR) model and RBFNN-PLSR (RP) model. The results showed that the RP model provided no enhancement to RBFNN model because it had the exactly same root mean square error (RMSE) and R2 . PLSR model showed better performance than other models when predicting hardness. More appropriate linear or linearity-dominant hybrid model needed to be explored to establish the relationship between protein oxidation and denaturation and texture. The PR model performed better than other models in predicting drip loss with its lower RMSE and higher R2 , which revealed both linear and nonlinear relationship between protein oxidation and denaturation and moisture loss. Therefore, the PR model was a promising and encouraging tool to provide a more comprehensive understanding of the relationship between protein oxidation and denaturation and moisture loss of fish during frozen storage. PRACTICAL APPLICATION: The study explored the effects of protein oxidation and denaturation on the texture and moisture loss of bighead carp during frozen storage (-12 to -28°C). PLSR model showed better performance than other models when predicting the relationship between protein oxidation and denaturation and texture. The PR model was an available tool for manufacturers to predict the relationship between protein oxidation and denaturation and moisture loss.
Published Version
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