该研究采集了六类(38个品牌)常见植物油的551份拉曼光谱,并根据光谱数据分别建立了正交偏最小二乘判别和支持向量机模型,对比了连续投影法和竞争性自适应重加权采样法对模型识别正确率的影响。基于算法改进的偏最小二乘判别模型的总体预测准确率为82.53%、83.13%,低于基于全光谱数据建立的偏最小二乘判别模型。竞争性自适应重加权采样法结合支持向量机对玉米油、橄榄油、葵花籽油和芝麻油的品牌分类测试集正确率均达到100%;椰子油和花生油的测试集正确率为22.22%、63.64%。两类特征提取算法均可以减少建立分类模型所需的变量数目和计算资源,但以提取后变量建立分类模型可能会导致识别正确率下降。在解决样本间相似度较高的多分类问题时,支持向量机模型优于正交偏最小二乘判别模型。正确率差异可能和生产商所使用的生产工艺以及植物油原料相关。面对案件侦办中品牌种类多样的油脂物证,基于拉曼光谱分析和特征提取算法的支持向量机模型可为可食用植物油的无损快速检验提供一定的参考与借鉴。
Based on 551 Raman spectral data of 6 categories (38 brands) of vegetable oils, orthogonal partial least squares discriminant analysis (OPLS-DA), and support vector machine (SVM) models were developed to compare the effects of successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS).The accuracy of the improved OPLS-DA model based on two feature extraction algorithms was 82.53% and 83.13%, respectively, which was lower than the model based on full spectrum and SVM models.Brand classification of vegetable oils was investigated using CARS combined with SVM.For the test set, the accuracy for corn, olive, sunflower, and sesame oils was 100%, while the accuracy for coconut oil and peanut oil brands was 22.22% and 63.64%, respectively.Both algorithms could reduce the number of variables and computational resources required to build the classification model.Replacing full-spectrum data with lower spectral data may lead to a decrease in recognition accuracy.The SVM outperformed OPLS-DA in solving multiple classification problems with high sample similarity.The difference in accuracy between vegetable oils may be related to the production process and vegetable oil ingredients used by the manufacturer.The SVM model based on Raman spectroscopy and feature extraction algorithm provides a reference for the nondestructive and rapid inspection of vegetable oils.
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