Open AccessA Study on the Quantitative Viscosity Detection of Automobile Lubricating Oils Based on Near-infrared Spectroscopy
Author(s) -
Xingxing Yang,
Zhui Hu,
Qiang Xu,
Zihao Cai,
Xing Zheng,
Lieqiang Xiong
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1673/1/012022
Subject(s) - partial least squares regression , smoothing , viscosity , particle swarm optimization , interval (graph theory) , infrared spectroscopy , biological system , materials science , spectroscopy , analytical chemistry (journal) , algorithm , mathematics , chemistry , chromatography , physics , statistics , composite material , organic chemistry , combinatorics , quantum mechanics , biology
A quantitative viscosity prediction of automobile lubricating oils was performed. The near-infrared spectra were preprocessed by using three different techniques, namely, multiple scattering correction (MSC), moving average smoothing (MAS), and Savitzky–Golay smoothing (SG). The characteristic wavelength was extracted by using three different techniques, namely, successive projections algorithm (SPA), synergy interval partial least squares (siPLS), and interval partial least squares (iPLS). The initial data of the viscosity of lubricating oils collected by near-infrared (NIR) spectroscopy were preprocessed by using two different optimization methods, namely, particle swarm optimization (PSO) and genetic algorithm (PA). Finally, the quantitative viscosity detection model of the lubricating oils was built by combining with support vector regression (SVR). The optimal technical route for modeling was explored.