基于拉曼光谱的汽油牌号快速识别

提出了一种基于拉曼光谱的汽油牌号快速识别方法。该方法首先基于已知牌号的成品汽油样本进行建模,获取不同牌号成品汽油建模样本的拉曼谱图,经过谱图预处理后对谱图进行主成分分析(principal component analysis,简称PCA),获得载荷矩阵和得分矩阵,同时分别计算不同牌号汽油样本的平均得分向量,即不同牌号的得分中心坐标;然后对未知牌号汽油样本进行识别,先将未知牌号汽油样本拉曼谱图进行相同的谱图预处理,再计算该样品在载荷矩阵上的得分向量,根据PCA类中心最小距离法,计算该得分向量到不同牌号的得分中心坐标的欧式距离,依最小距离直接确定汽油牌号。针对45个取自国内不同炼油厂的成品汽油样本的实验结果表明这些汽油样本拉曼谱图经谱图预处理和PCA后,不同牌号汽油样本在主元得分空间上存在明显类间距,使用PCA类中心最小距离法可实现汽油牌号快速准确的分类。

Fast recognition of gasoline brands based on the Raman spectroscopy

A novel method for fast recognition of gasoline brands based on the Raman spectroscopy is presented. A classification model on the basis of product gasoline samples with known brands was established. The detailed modeling process includes measurement and pretreatment of Raman spectra of these samples, principal component analysis (PCA) to obtain loading vectors and score vectors of all known samples, and calculating each average score vector for all of the samples with the same brand. For a gasoline sample with unknown brand, first measure and preprocess its Raman spectrum with the same pretreatment algorithm, then calculate its score vector on the above loading vectors and its distances to the average score vectors for different brands, and finally determine the brand of the unknown sample by the minimum distance. For 45 product gasoline samples from different refinery, experimental results show that there are significant distances between different brands in the principal component space, and the above classification model can decide the brand of unknown gasoline samples rapidly and accurately.