Modelling quality changes in Songpu mirror carp (Cyprinus carpio) fillets stored at chilled temperatures: comparison between Arrhenius model and log‐logistic model

Abstract

Summary To understand the quality changes in Songpu mirror carp (Cyprinus carpio) fillets during chilled storage, sensory score, total aerobic counts (TAC), total volatile basic nitrogen (TVB‐N) and electrical conductivity (EC) of the fillets stored at 270, 273, 276, 282 and 288 K were investigated. Furthermore, both Arrhenius model and log‐logistic model were established to predict the quality changes. The high regression coefficients (R2 > 0.90) represented the acceptability of both Arrhenius model and log‐logistic model. Relative errors between predicted values and observed values were all within ±5% for models based on EC, within ± 25% for models based on TAC and TVB‐N (except the TVB‐N value on 9th and 15th day). The relative errors of sensory score were all within ±35% in the first 9 days. It is concluded that both Arrhenius model and log‐logistic model based on EC, TAC or TVB‐N can be applied in modelling quality changes in Songpu mirror carp fillets during storage within 270–288 K.

Establishment of the Arrhenius Model and the Radial Basis Function Neural Network (RBFNN) Model to Predict Quality of Thawed Shrimp (S olenocera melantho ) Stored at Different Temperatures

“…In addition, relative errors for TAC and Hx were all within ± 12 and ± 18%, respectively, and relative errors for TVB‐N and SA were within ± 30%, except for the predictive values for the 12th day, which was similar to the result of Bao et al . (). However, the relative errors for K values reached ± 37%, which was not well described by the Arrhenius model.…”

“…Thus, the Arrhenius model could make reliable predictions for changes in quality of thawed shrimp stored at −3 to 6C in view of the variability of materials as well as the complicacy of spoilage reactions (Bao et al . ; Wu et al . ).…”

“…For instance, the log‐logistic model was established to predict quality changes in Songpu mirror carp ( Cyprinus carpio ), and the back‐propagation artificial neuronal network model was constructed to predict quality of rainbow trout ( Oncorhynchus mykiss ) fillets (Bao et al . ; Liu et al . ).…”

“…Thus, more types of predictive models have been used to predict quality. For instance, the log-logistic model was established to predict quality changes in Songpu mirror carp (Cyprinus carpio), and the back-propagation artificial neuronal network model was constructed to predict quality of rainbow trout (Oncorhynchus mykiss) fillets (Bao et al 2013;Liu et al 2015). The RBFNN model is a new model, which is applied rarely to food currently, but its predictive accuracy has proved to be nearly perfect.…”

Modeling Quality Changes in Brined Bream (Megalobrama amblycephala) Fillets During Storage: Comparison of the Arrhenius Model, BP, and RBF Neural Network

“…The Arrhenius model was used successfully to describe reaction ratetemperature relations in food systems, but several studies proved that the Arrhenius model was unsuitable to predict changes in aquatic products at later stages of spoilage during low-temperature storage (Bao et al 2013;Yao et al 2011). Artificial neural networks (ANN), including backpropagation neural networks (BP-NN) and radial basis function neural networks (RBF-NN), are a set of mathematical models whose main algorithmic features were inspired by the functioning of human brains (Marini 2009).…”