Comparison of Arrhenius model and artificial neuronal network for the quality prediction of rainbow trout (Oncorhynchus mykiss) fillets during storage at different temperatures
文献类型： 外文期刊
作者： Liu, Xiaochang 1 ; Jiang, Yan 1 ; Shen, Song 1 ; Luo, Yongkang 1 ; Gao, Liang 2 ;
作者机构： 1.China Agr Univ, Coll Food Sci & Nutr Engn, Beijing Higher Inst Engn Res, Ctr Anim Prod, Beijing 100083, Peoples R China
2.Beijing Fisheries Res Inst, Beijing 100068, Peoples R China
关键词： Rainbow trout fillets;Quality changes;Arrhenius model;Artificial neuronal network
期刊名称：LWT-FOOD SCIENCE AND TECHNOLOGY （ 影响因子：4.952； 五年影响因子：5.383 ）
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年卷期：
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收录情况： SCI
摘要： Quality changes in total aerobic counts (TAC), electrical conductivity (EC), K-value and sensory assessment (SA) of rainbow trout (Oncorhynchus mykiss) fillets during storage at 282, 279, 276, 273 and 270 K were determined. Simultaneously, Arrhenius model and feed-forward artificial neuronal network (ANN) were established to predict changes of rainbow trout fillets during storage, and a comparative study between these two models was also performed. The relative error between predicted and experimental value was used as the comparative parameter. The results showed that TAC, EC and K-value increased with storage time, while SA decreased with time. The change rate of all indicators increased as a function of temperature. Arrhenius models based on EC and TAC were acceptable, while those based on SA and K-value showed poor performances in some days. By contrast, ANN was more effective to predict changes in TAC, EC, K-value and SA throughout the storage, with relative errors all below 10%. Therefore, ANN could be a potential tool in modeling quality changes of rainbow trout fillets within 270-282 K. (C) 2014 Elsevier Ltd. All rights reserved.
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