利用ATR-MIR光谱分析技术对掺杂了不同水平带鱼糜的鲢鱼糜进行了分类。 文中采用了五种化学计量学方法(SIMCA, KNN, SVR, PLS-DA和 ID3决策树)分别结合ATR-MIR光谱数据建立判别模型, 并对各个分类模型性能和分类效果进行了分析和比较。 结果表明: 无论是对生鱼糜样品进行鉴别还是对其熟制品进行鉴别, 基于SIMCA方法建立的分类模型都获得了较好的分类效果。 对鱼糜制品和生鱼糜正确分类率分别达到了96.59%和96.43%, 相应的交互验证均方根误差(RMSECV)值分别为: 0.185 7和0.189 8, 相关系数r值达到了0.988 0和0.994 1。 这表明ATR-MIR结合化学计量学方法可以对掺假的鲢鱼糜进行鉴别, 也表明ATR-MIR在鱼糜品质监测方面具有很广阔的应用前景。

ATR-MIR spectroscopic analysis was used to classify sliver carp surimi and surimi products adulterated with different levels of hairtail surimi. Five chemometric methods, including SIMCA (soft independent modeling class of analogies), KNN (K-nearest neighbor), SVR (support vector machines regression), PLS-DA (partial least squares discriminate analysis) and ID3 (interative dicremiser version 3) Decision tree were used to build the classifying models. And the performances of the models were compared. Results showed that for both cooked and uncooked mixed surimi samples, better classifications were obtained using SIMCA model, the percentage of the correct classification reached 96.59% and 96.43%, and the corresponding RMSECV were 0.185 7 and 0.189 8, r value were 0.988 0 and 0.994 1 respectively. The results of this study demonstrated for the first time that ATR-MIR spectroscopy combined with chemometrics method can be used to classify sliver carp surimi and surimi products adulterated with different levels of hairtail surimi.