最小二乘支持向量机结合红外光谱法测定润滑油酸值  

Determination of Acid Value of Lubricating Oil by Infrared Spectrometry with Least Square Vector Machine

作　　者：史令飞 瞿军[2] 邢志娜[2] 

机构地区：[1]海军航空工程学院研究生管理大队,烟台264001 [2]海军航空工程学院飞行器工程系,烟台264001

出　　处：《理化检验（化学分册）》2018年第2期200-203,共4页Physical Testing and Chemical Analysis(Part B:Chemical Analysis)

摘　　要：在润滑油酸值进行红外光谱法测定中,采用最小二乘支持向量机(LS-SVM)建立了酸值的定量预测模型。用Kennard-Stone方法将30个样本划分为训练集(24个样本)和验证集(6个样本),进行定量预测,并与偏最小二乘法和径向基函数神经网络所建模型的预测进行比较。结果表明:LS-SVM所建模型的预测标准偏差(SEP)最小,仅为0.002;预测值的相对误差为1.3%~5.3%。由此认为LS-SVM所建模型的训练和预测结果均优于其余两种方法所建模型。对5个未知样品的分析结果表明:LS-SVM模型的预测值与化学法实测值之间的相对误差(1.2%~3.1%)也较少。In the infrared spectrometric determination of acid value of lubricating oil, quantitative prediction model for the acid value was established by the least square vector machine （LS-SVM） method. By applying the Kennard-Stone method, 30 samples were divided into 2 groups, the training group （with 24 samples） and the testing group （with 6 samples）, and quantitative prediction was performed. As compared with the models developed by methods of PLS and RBF-NH, the model of LS-SVM gave the smallest value of SEP （0. 002）, and values of relative error of the predicted values were found in the range of 1.3 %--5.3 %. It was shown that the results of training and prediction obtained by the model of LS-SVM were better than those obtained by the other 2 models. In the analysis of 5 unknown samples, small relative errors （ranged from 1.2% to 3.1%） were obtained by the model of LS-SVM between the predicted values and the true values.

关 键 词：最小二乘支持向量机 红外光谱 润滑油 酸值 

分 类 号：O657.33[理学—分析化学]