A Non-Destructive Distinctive Method for Discrimination of Automobile Lubricant Variety by Visible and Short-Wave Infrared Spectroscopy

A novel method which is a combination of wavelet packet transform (WPT), uninformative variable elimination by partial least squares (UVE-PLS) and simulated annealing (SA) to extract best variance information among different varieties of lubricants is presented. A total of 180 samples (60 for each v...

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Main Authors:	Jiang, Lulu, Liu, Fei, He, Yong
Format:	Online
Language:	English
Published:	Molecular Diversity Preservation International (MDPI) 2012
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3376611/

id	pubmed-3376611
recordtype	oai_dc
spelling	pubmed-33766112012-06-25 A Non-Destructive Distinctive Method for Discrimination of Automobile Lubricant Variety by Visible and Short-Wave Infrared Spectroscopy Jiang, Lulu Liu, Fei He, Yong Article A novel method which is a combination of wavelet packet transform (WPT), uninformative variable elimination by partial least squares (UVE-PLS) and simulated annealing (SA) to extract best variance information among different varieties of lubricants is presented. A total of 180 samples (60 for each variety) were characterized on the basis of visible and short-wave infrared spectroscopy (VIS-SWNIR), and 90 samples (30 for each variety) were randomly selected for the calibration set, whereas, the remaining 90 samples (30 for each variety) were used for the validation set. The spectral data was split into different frequency bands by WPT, and different frequency bands were obtained. SA was employed to look for the best variance band (BVB) among different varieties of lubricants. In order to improve prediction precision further, BVB was processed by UVE-PLS and the optimal cutoff threshold of UVE was found by SA. Finally, five variables were mined, and were set as inputs for a least square-support vector machine (LS-SVM) to build the recognition model. An optimal model with a correlation coefficient (R) of 0.9850 and root mean square error of prediction (RMSEP) of 0.0827 was obtained. The overall results indicated that the method of combining WPT, UVE-PLS and SA was a powerful way to select diagnostic information for discrimination among different varieties of lubricating oil, furthermore, a more parsimonious and efficient LS-SVM model could be obtained. Molecular Diversity Preservation International (MDPI) 2012-03-12 /pmc/articles/PMC3376611/ /pubmed/22737021 http://dx.doi.org/10.3390/s120303498 Text en © 2012 by the authors; licensee MDPI, Basel, Switzerland This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Jiang, Lulu Liu, Fei He, Yong
spellingShingle	Jiang, Lulu Liu, Fei He, Yong A Non-Destructive Distinctive Method for Discrimination of Automobile Lubricant Variety by Visible and Short-Wave Infrared Spectroscopy
author_facet	Jiang, Lulu Liu, Fei He, Yong
author_sort	Jiang, Lulu
title	A Non-Destructive Distinctive Method for Discrimination of Automobile Lubricant Variety by Visible and Short-Wave Infrared Spectroscopy
title_short	A Non-Destructive Distinctive Method for Discrimination of Automobile Lubricant Variety by Visible and Short-Wave Infrared Spectroscopy
title_full	A Non-Destructive Distinctive Method for Discrimination of Automobile Lubricant Variety by Visible and Short-Wave Infrared Spectroscopy
title_fullStr	A Non-Destructive Distinctive Method for Discrimination of Automobile Lubricant Variety by Visible and Short-Wave Infrared Spectroscopy
title_full_unstemmed	A Non-Destructive Distinctive Method for Discrimination of Automobile Lubricant Variety by Visible and Short-Wave Infrared Spectroscopy
title_sort	non-destructive distinctive method for discrimination of automobile lubricant variety by visible and short-wave infrared spectroscopy
description	A novel method which is a combination of wavelet packet transform (WPT), uninformative variable elimination by partial least squares (UVE-PLS) and simulated annealing (SA) to extract best variance information among different varieties of lubricants is presented. A total of 180 samples (60 for each variety) were characterized on the basis of visible and short-wave infrared spectroscopy (VIS-SWNIR), and 90 samples (30 for each variety) were randomly selected for the calibration set, whereas, the remaining 90 samples (30 for each variety) were used for the validation set. The spectral data was split into different frequency bands by WPT, and different frequency bands were obtained. SA was employed to look for the best variance band (BVB) among different varieties of lubricants. In order to improve prediction precision further, BVB was processed by UVE-PLS and the optimal cutoff threshold of UVE was found by SA. Finally, five variables were mined, and were set as inputs for a least square-support vector machine (LS-SVM) to build the recognition model. An optimal model with a correlation coefficient (R) of 0.9850 and root mean square error of prediction (RMSEP) of 0.0827 was obtained. The overall results indicated that the method of combining WPT, UVE-PLS and SA was a powerful way to select diagnostic information for discrimination among different varieties of lubricating oil, furthermore, a more parsimonious and efficient LS-SVM model could be obtained.
publisher	Molecular Diversity Preservation International (MDPI)
publishDate	2012
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3376611/
_version_	1611537228374212608

A Non-Destructive Distinctive Method for Discrimination of Automobile Lubricant Variety by Visible and Short-Wave Infrared Spectroscopy

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