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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
Author(s) -
Xu Zihan,
Liu Xiaochang,
Wang Huiyi,
Hong Hui,
Yu Xunpei,
Luo Yongkang
Publication year - 2016
Publication title -
journal of food processing and preservation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.511
H-Index - 48
eISSN - 1745-4549
pISSN - 0145-8892
DOI - 10.1111/jfpp.12666
Subject(s) - shrimp , arrhenius equation , biological system , artificial neural network , food spoilage , environmental science , statistics , chemistry , mathematics , computer science , biology , machine learning , fishery , activation energy , genetics , organic chemistry , bacteria
Changes in quality of thawed shrimp ( S olenocera melantho ) stored at −3, 0, 3 and 6C were determined and modeled by the A rrhenius 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 Applications The 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.