Rapid Determination of Acid Value of Diesel Engine Oil by Infrared Spectroscopy with Application of Algorithm of RF-RVM

Combination of ATR middle IRS with algorithm of random forests-relevance vector machine (RF-RVM) was studied for determination of acid value of diesel engine oil. 48 samples of oil for diesel of vehicles in use under various circumstances were selected in this work, and their acid values were pre-determined by the method given in the standard of GB/T 4945-2002, giving results in the range of 0.59-1.71 mg_(KOH)·g^-1. The original IR spectra were preprocessed by SG smoothing in combination with first-order differential calculus, characteristic wave numbers were selected by applying RF algorithm and method of correlation coefficient, and modeling analysis was made by RVM. Six characteristic wave numbers were selected in RF algorithm, which not only making a remarkable reduction of number of input of variables, but also giving better modeling effect by the RVM algorithm. Correlation coefficient for the model verification set was found to be 0.922 5, with the standard deviation SEP of 0.091 7. It was shown that rapid and accurate determination of acid value of oil in use could be realized by the combination of IRS with algorithm of RF-RVM.