title: Rapid analysis of diesel fuel properties by near infrared reflectance spectra.
abstract: In this study, based on near infrared reflectance spectra (NIRS) of 441 samples from four diesel groups (-10# diesel, -20# diesel, -35# diesel, and inferior diesel), three spectral analysis models were established by using partial least square (PLS) regression for the six diesel properties (i.e., boiling point, cetane number, density, freezing temperature, total aromatics, and viscosity) respectively. In model 1, all the samples were processed as a whole; in model 2 and model 3, samples were firstly classified into four groups by least square support vector machine (LS-SVM), and then partial least square regression models were applied to each group and each property. The main difference between model 2 and model 3 was that the latter used the direct orthogonal signal correction (DOSC), which helped to get rid of the non-relevant variation in the spectra. Comparing these three models, two results could be concluded: (1) models for grouped samples had higher precision and smaller prediction error; (2) models with DOSC after LS-SVM classification yielded a considerable error reduction compared to models without DOSC.
authors.affiliation: Electronic Information School, Wuhan University, Wuhan 430072, Hubei, China.
authors.forename: Fei
authors.initials: F
authors.lastname: Feng
authors.affiliation: Electronic Information School, Wuhan University, Wuhan 430072, Hubei, China. Electronic address: qswu@whu.edu.cn.
authors.forename: Qiongshui
authors.initials: Q
authors.lastname: Wu
authors.affiliation: Electronic Information School, Wuhan University, Wuhan 430072, Hubei, China.
authors.forename: Libo
authors.initials: L
authors.lastname: Zeng
keywords: Direct orthogonal signal correction; Near infrared reflectance spectra; Partial least square regression; Support vector machine