拉曼光谱和MLS-SVR的食用油脂肪酸含量预测研究

Research on Prediction Method of Fatty Acid Content in Edible Oil Based on Raman Spectroscopy and Multi-Output Least Squares Support Vector Regression Machine

为实现食用植物油中饱和脂肪酸、油酸、亚油酸含量的快速预测，对一批纯食用油以及不同比例两两混合油共91个样品进行了拉曼光谱检测，在800～2000cm。范围内，通过基于寻峰算法的自动确定支点的基线拟合方法，对获得的光谱数据进行预处理，提取八个特征峰作为拉曼光谱的特征值。以这些特征值为输入，以样品油中实际饱和脂肪酸、油酸、亚油酸含量为输出，运用偏最小二乘回归（PLS）和多输出最小二乘支持向量回归机（MLS-SVR）方法，分别建立了可以同时预测三种脂肪酸含量的数学模型，结果表明MLS-SVR方法具有较好的效果。将MLS-SVR模型的预测结果与气相色谱法结果相比较，可得到三种脂肪酸的预测均方根误差分别为0．4967％，0．840ooA和1．0199％，相关系数分别为0．8133，0．9992和0．9981；对未知样品三种脂肪酸的预测均方根误差不超过5％。表明，拉曼光谱和MLS-SVR相结合的食用油脂肪酸含量预测方法，具有快速、简便、无损、准确等优点，为食用油脂肪酸含量分析提供了一种可行的方法。

For the purpose of the rapid prediction of saturated fatty acid, oleic acid, linoleic acid content in edible vegetable oil, the Raman spectra of a batch of edible vegetable oils and their one-one mixtures with different ratios were measured in the range of 800～2 000 cm-1 , 91 samples were measured totally in this research, the obtained Raman spectra data were preprocessed by a new method proposed in this paper called auto-set fulcrums baseline fitting method based on peak-seeking algorithm, and 8 characteristic peak values （872 cm^-1 [v（C-C）], 972 em^-1 [θ（C=C） trans], 1 082 cm^-1 Iv（C-C）], 1 267 cm^-1 [3（=C-H） cis], 1 303 cm-1 [3（CHz）twist[ng], 1 442 cm-l[3（CHz） scissoring], 1 658 cm-1 [v（C-C） cis], 1 748 em1 [v（C=O）]） were extracted to be the eigenvalues for the whole spectra, among the 8 peaks there are three peaks（972, 1 267, 1 658 cm 1） that play an important role in the establishment of mathematical model, they are closely concerned with C--C band which distinguishes the three fatty acid types. By using these eigenvalues as inputs, and actual saturated fatty acid, oleic acid, linoleic acid contents of sample oils as outputs, a prediction mathematical model that predicts simultaneously the three fatty acid contents was established using multiple regression analysis： multi-output least squares support vector regression machine （MLS-SVR） and partial least squares（PLS）. Results show that the MLS-SVR has better effects. The predicting results are compared with results of gas chromatography（GC）, and the obtained root mean square error of prediction（RMSEP） for saturat- ed fatty acid, oleic acid, linoleic acid are 0. 496 7 %, 0. 840 0 % and 1. 019 9 %, and the correlation coefficients （r） are 0. 813 3, 0. 999 2 and 0. 998 1, respectively. When this model is applied in the detection of new unknown oil samples, the prediction error does not exceed 5%. Results show that the Raman spectra analysis technology based on MLS-SVR can be a convenient, fast, non-destructive, and precise new method for oil detection.