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Abstract.Near infrared spectroscopy (NIRS) was investigated for measurement of diesel cetane. Three types of pretreatments including standard normal variate (SNV), multiplicative scattering correction (MSC) and Savitzky-Golay smoothing combining first derivative were adopted to eliminate the system noises and external disturbances. Then, partial least squares (PLS) and least squares-support vector machine (LS-SVM) methods were implemented for calibration models. Simultaneously, the performance of least squares-support vector machine (LS-SVM) method was compared with three kinds of dimension reduction input, including principal components (PCs), latent variables (LVs), and effective wavelengths (EWs). The best predictions were obtained with LS-SVM-LVs model for diesel cetane number (R 2 Pre =0.557, RMSEP = 2.169 and residual prediction deviation (RPD) =1.61), which was deemed as good model predictions. It is recommended to adopt LS-SVM-LVs model technique for higher accuracy measurement of the selected diesel cetane number with NIR, in comparison with LS-SVM-PCs and LS-SVM-EWs model techniques.

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