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

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.