It is an important method for predicting the diesel quality parameter by combination of near-infrared spectroscopy and chemometrics. In order to improve the accuracy of prediction model for diesel property, this paper puts forth a modeling method based on training dictionary and its sparse representation, and this method can test the cetane number, boiling point and total aromatics fast. The first stage of the method searches the particular redundant dictionary for diesel oil calibration samples, and the redundant dictionary can be trained by using K-means singular value decomposition algorithm (K-SVD). After finding of this redundant dictionary, it uses the orthogonal matching pursuit algorithm (OMP) to search the sparse representation coefficients of diesel spectroscopy under the training dictionary. This coefficients of the training dictionary, which is similar with the sparse representation coefficients of wavelet dictionary and fourier dictionary, is scale expansion of spectral characteristics that can effectively use to establish the pre-test partial least squares prediction (PLS) model of cetane number, boiling point and total aromatics for diesel. The spectra of diesel samples with different quality parameters from same factory are collected and the quality parameters of diesel samples are measured by ASTM certification method. The sparse representation coefficients of diesel spectroscopy under fourier dictionary and wavelet dictionary are figured in the experiments, inter-compared the performance of prediction model for diesel property under sparse representation coefficients of training dictionary, fourier dictionary and wavelet dictionary. Furthermore, the PLS model correlation coefficient (R) and standard deviation of prediction set (SEP) under sparse representation coefficients in training dictionary achieve 0.99 and 0.05 separately, it has optimal performance and also has considerable improvement to compare with the prediction performance of fourier dictionary and wavelet dictionary, which validated the efficiency of the method in predicting the diesel quality parameter using near-infrared spectroscopy and the proposed method can substantially improve the accuracy and stability of near-infrared spectroscopy prediction method.

Key words: near-infrared spectroscopy, diesel property, sparse representation, redundant dictionary, dictionary training