Zhou Xiu-Jun, Dai Lian-Kui, Li Sheng
State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2012 Jul;32(7):1829-33.
A novel method to fast discriminate edible vegetable oils by Raman spectroscopy is presented. The training set is composed of different edible vegetable oils with known classes. Based on their original Raman spectra, baseline correction and normalization were applied to obtain standard spectra. Two characteristic peaks describing the unsaturated degree of vegetable oil were selected as feature vectors; then the centers of all classes were calculated. For an edible vegetable oil with unknown class, the same pretreatment and feature extraction methods were used. The Euclidian distances between the feature vector of the unknown sample and the center of each class were calculated, and the class of the unknown sample was finally determined by the minimum distance. For 43 edible vegetable oil samples from seven different classes, experimental results show that the clustering effect of each class was more obvious and the class distance was much larger with the new feature extraction method compared with PCA. The above classification model can be applied to discriminate unknown edible vegetable oils rapidly and accurately.
提出了一种利用拉曼光谱快速鉴别食用植物油的新方法。训练集由已知类别的不同食用植物油组成。基于它们的原始拉曼光谱，进行基线校正和归一化以获得标准光谱。选择两个描述植物油不饱和程度的特征峰作为特征向量；然后计算所有类别的中心。对于未知类别的食用植物油，使用相同的预处理和特征提取方法。计算未知样品的特征向量与每个类别的中心之间的欧几里得距离，最终通过最小距离确定未知样品的类别。对于来自七个不同类别的43个食用植物油样品，实验结果表明，与主成分分析相比，新的特征提取方法使每个类别的聚类效果更明显，类间距离更大。上述分类模型可用于快速、准确地鉴别未知食用植物油。
