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

Abstract

Changes in quality of thawed shrimp (Solenocera melantho) stored at −3, 0, 3 and 6C were determined and modeled by the Arrhenius and the radial basis function neural network (RBFNN) models, based on total volatile base nitrogen , total aerobic counts, K value, hypoxanthine, pH, electrical conductivity (EC) and sensory assessment. A significant inhibition of spoilage was found in shrimp stored at −3C compared to those stored at 0, 3 and 6C. The prediction accuracy of the Arrhenius model was satisfactory for indicators of EC and pH (relative errors within ± 10%), and relative errors for other indicators surpassed ± 10% on some days; the relative errors of RBFNN model were all within ± 2% for each indicator. The RBFNN model was more suitable for predicting the quality of thawed shrimp stored at −3 to 6C. PRACTICAL APPLICATIONSThe Arrhenius model and the radial basis function neural network (RBFNN) model established in this work provide a convenient way to predict quality changes of thawed shrimp during storage. Timely and useful information about quality of shrimp stored at different temperatures in different storage period can be obtained by these models. People can take out measures to reduce the loss of shrimp resources based on these information. The RBFNN model is a potential tool in predicting the quality of shrimp during storage, distribution and processing, which is of great practical value to the shrimp industry.

Comparison between the Arrhenius model and the radial basis function neural network (RBFNN) model for predicting quality changes of frozen shrimp (Solenocera melantho)

“…With extended storage time, the K-value increased significantly (P < 0.05), but it increased faster after 56 days. Moreover, the K-values of shrimp stored at higher temperatures increased more quickly, which was consistent with the conclusion of Xu et al [21] That is, lower temperatures can slow down the autolysis rate of shrimp and inhibit the activity of 5'-nucleotidase, and the slower speed in ATP degradation can lead to a lower increasing rate of K-value. [29] Hx The process of ATP degradation in shrimp is as follows: ATP → ADP → AMP → IMP → HxR → Hx.…”

Protein and lipid changes of mud shrimp (Solenocera melantho) during frozen storage: chemical properties and their prediction

“…[9][10][11] Radial basis function neural network (RBFNN) is a feed-forward neural network and has been proved to have a nearly perfect predictive accuracy. [12] RBFNN can develop a meaningful relationship between inputs and outputs through a learning process and obtain accurate predictive results by mimicking the real neural activity in the human brain. [13] Several previous studies have reported the benefits of using RBFNN model for quality prediction of aquatic products.…”

“…[13] Several previous studies have reported the benefits of using RBFNN model for quality prediction of aquatic products. Xu et al [12] used the RBFNN model for predicting the quality of thawed shrimp stored at different temperatures (−3, 0, 3, and 6°C) and reported the accurate prediction. Wang et al [13] and Kong et al [14] applied RBFNN model to predict the quality changes of bream and common carp fillets during storage.…”

Modeling relationship between protein oxidation and denaturation and texture, moisture loss of bighead carp (Aristichthys Nobilis) during frozen storage

“…However, PLSR model may not consider the nonlinear relationships between protein oxidation and denaturation parameters and texture and moisture loss, and this deficiency could be compensated by radial basis function neural network (RBFNN) model. RBFNN can establish nonlinear function between input data and output data with a nearly perfect predictive accuracy during the fit-ting process (Xu et al, 2016). Shi et al (2017) used RBFNN model to predict the quality changes of frozen mud shrimp and obtained satisfactory predicting results.…”