Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing, China.
National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing, China.
J Sci Food Agric. 2021 Sep;101(12):4987-4994. doi: 10.1002/jsfa.11142. Epub 2021 Mar 12.
Many new forecasting models have been applied to fish freshness prediction like support vector regression (SVR) and radial basis function neural network (RBFNN). In this study, RBFNN, SVR, and Arrhenius models were established and compared for predicting and evaluating the quality of salmon fillets during storage at different temperatures, based on thiobarbituric acid (TBA), total volatile basic nitrogen (TVB-N), total viable counts (TVCs), K value, and sensory assessment (SA).
The TBA, TVB-N, TVC, and K values increased during storage whereas SA decreased. Residuals of the three models are random and irregular, indicating that these models were suitable for predicting the freshness of salmon fillets. The RBFNN predicted quality of salmon fillets stored at different temperatures with relative errors all within ±5% (except for the TVC value at day 6). Relative errors of the SVR model for predicting TVB-N and K value were within 10%, while the relative errors of the Arrhenius model fluctuated greatly (ranging from ±0.46 to ±38.29%) and most of it exceeded 10%. RBFNN model had the best predictive performance by comparing the residual and relative errors of the three models.
RBFNN is a promising method for predicting the freshness of salmon fillets stored at -2 to 10 °C in the cold chain. © 2021 Society of Chemical Industry.
许多新的预测模型已被应用于鱼的新鲜度预测，如支持向量回归（SVR）和径向基函数神经网络（RBFNN）。本研究基于硫代巴比妥酸（TBA）、总挥发性碱性氮（TVB-N）、总活菌数（TVC）、K 值和感官评价（SA），建立了 RBFNN、SVR 和 Arrhenius 模型，以预测和评估鲑鱼片在不同温度下储存期间的品质。
TBA、TVB-N、TVC 和 K 值在储存过程中增加，而 SA 则下降。三个模型的残差是随机和不规则的，这表明这些模型适用于预测鲑鱼片的新鲜度。RBFNN 预测了不同温度下储存的鲑鱼片的品质，相对误差均在±5%以内（TVC 值在第 6 天除外）。SVR 模型预测 TVB-N 和 K 值的相对误差在 10%以内，而 Arrhenius 模型的相对误差波动较大（范围从±0.46 到±38.29%），大多数超过 10%。通过比较三个模型的残差和相对误差，RBFNN 模型具有最佳的预测性能。
RBFNN 是一种有前途的方法，可用于预测冷链中-2 至 10°C 储存的鲑鱼片的新鲜度。 © 2021 化学工业协会。
