基于拉曼光谱的食用植物油快速鉴别

提出了一种基于拉曼光谱的食用植物油快速鉴别方法。基于已知类别的食用植物油样本进行建模,首先对原始拉曼谱图进行基线校正和标准归一化等预处理,并选取食用油不饱和度特征的两处拉曼峰值作为特征向量,计算训练样本特征空间上各个植物油类别的中心坐标;然后,将食用植物油测试样本的拉曼谱图经过相同预处理和特征提取,获取测试样本的特征向量,计算其与各类别中心坐标的欧式距离,根据类中心最小距离法,取欧式距离最小的那一类作为预测样本的类别。针对7类43个食用植物油样本的实验结果表明,采用食用油不饱和度两点描述法进行特征提取,各类别样本聚集效果比PCA好,类间距更大。上述鉴别方法可以准确地实现食用植物油品种的快速分类。

Fast Discrimination of Edible Vegetable Oil Based on Raman Spectroscopy

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.