为研究双斑东方鲀在冷藏过程中滋味物质的变化规律，并建立一种快速检测冷藏双斑东方鲀的鲜度的方法，本文通过对不同冷藏期（0 ℃和4 ℃）双斑东方鲀的滋味物质进行测定分析，并以菌落总数（TVC）的测定结果为鱼肉新鲜度的判别标准，电子舌传感器响应值为因变量，运用偏最小二乘法（PLSR）和多元线性回归（MLR）法建立能够用于区分不同冷藏期双斑东方鲀新鲜度的TVC预测模型，并对模型进行验证。结果表明PLSR和MLR模型都能对不同冷藏时期鱼肉的新鲜度进行预测，其中MLR模型的拟合度较高，0 ℃和4 ℃组TVC预测模型的训练集（Rc2）分别为0.98和0.99，预测集的决定系数（Rv2）为0.97和0.99，训练集的均方根误差（RMSEV）为0.40和0.08，预测及的均方根误差（RMSEP）为0.44和0.08；外部验证结果显示，该模型测试集的正确率为100%，具有较好的预测能力。以上结果表明电子舌技术可做为双斑东方鲀在冷藏过程中新鲜度的快速检测方法。

In order to study the changes of taste substances of Fugu bimaculatus during cold storage, a rapid method to detect the freshness of Fugu bimaculatus during cold storage (0 ℃ and 4 ℃) was established. The results of total bacterial count (TVC) were used as the criterion of fish freshness, and the response value of electronic tongue sensor was used as the dependent variable. The partial least squares (PLSR) and multiple linear regression (MLR) methods were used to establish the TVC prediction model, which can be used to distinguish the freshness of Fugu bimaculatus in different cold storage periods, and the model was verified. The results showed that both PLSR and MLR models can predict the freshness of fish at different refrigeration periods, but the MLR model had a better fit. The training sets (Rc2) of the TVC prediction models for the 0 ℃ and 4 ℃ groups were 0.98 and 0.99, respectively, The coefficient of determination (Rv2) of the prediction set was 0.97 and 0.99, the root means square error (RMSEV) of the training set was 0.40 and 0.08, and the root mean square error (RMSEP) of the prediction is 0.44 and 0.08. External verification results showed that the accuracy of the model test set was 100%, and it had a good predictive ability. The above results showed that the electronic tongue technology could be used as a rapid detection method for the freshness of the Fugu bimaculatus in the cold storage process. Taken together, these results suggest that electronic tongue technology can be used as a rapid detection method for the freshness of the Fugu bimaculatus in the cold storage process.

福建省种业创新与产业化工程项目（2017FJSCZY03）；福建省海洋渔业结构调整专项（2017HTJG07）