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Oil Adulteration Identification by Hyperspectral Imaging Using QHM and ICA

To investigate the feasibility of identification of qualified and adulterated oil product using hyperspectral imaging(HIS) technique, a novel feature set based on quantized histogram matrix (QHM) and feature selection method using improved kernel independent component analysis (iKICA) is proposed fo...

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Detalles Bibliográficos
Autores principales: Han, Zhongzhi, Wan, Jianhua, Deng, Limiao, Liu, Kangwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4731151/
https://www.ncbi.nlm.nih.gov/pubmed/26820311
http://dx.doi.org/10.1371/journal.pone.0146547
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author Han, Zhongzhi
Wan, Jianhua
Deng, Limiao
Liu, Kangwei
author_facet Han, Zhongzhi
Wan, Jianhua
Deng, Limiao
Liu, Kangwei
author_sort Han, Zhongzhi
collection PubMed
description To investigate the feasibility of identification of qualified and adulterated oil product using hyperspectral imaging(HIS) technique, a novel feature set based on quantized histogram matrix (QHM) and feature selection method using improved kernel independent component analysis (iKICA) is proposed for HSI. We use UV and Halogen excitations in this study. Region of interest(ROI) of hyperspectral images of 256 oil samples from four varieties are obtained within the spectral region of 400–720nm. Radiation indexes extracted from each ROI are used as feature vectors. These indexes are individual band radiation index (RI), difference of consecutive spectral band radiation index (DRI), ratio of consecutive spectral band radiation index (RRI) and normalized DRI (NDRI). Another set of features called quantized histogram matrix (QHM) are extracted by applying quantization on the image histogram from these features. Based on these feature sets, improved kernel independent component analysis (iKICA) is used to select significant features. For comparison, algorithms such as plus L reduce R (plusLrR), Fisher, multidimensional scaling (MDS), independent component analysis (ICA), and principle component analysis (PCA) are also used to select the most significant wavelengths or features. Support vector machine (SVM) is used as the classifier. Experimental results show that the proposed methods are able to obtain robust and better classification performance with fewer number of spectral bands and simplify the design of computer vision systems.
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spelling pubmed-47311512016-02-04 Oil Adulteration Identification by Hyperspectral Imaging Using QHM and ICA Han, Zhongzhi Wan, Jianhua Deng, Limiao Liu, Kangwei PLoS One Research Article To investigate the feasibility of identification of qualified and adulterated oil product using hyperspectral imaging(HIS) technique, a novel feature set based on quantized histogram matrix (QHM) and feature selection method using improved kernel independent component analysis (iKICA) is proposed for HSI. We use UV and Halogen excitations in this study. Region of interest(ROI) of hyperspectral images of 256 oil samples from four varieties are obtained within the spectral region of 400–720nm. Radiation indexes extracted from each ROI are used as feature vectors. These indexes are individual band radiation index (RI), difference of consecutive spectral band radiation index (DRI), ratio of consecutive spectral band radiation index (RRI) and normalized DRI (NDRI). Another set of features called quantized histogram matrix (QHM) are extracted by applying quantization on the image histogram from these features. Based on these feature sets, improved kernel independent component analysis (iKICA) is used to select significant features. For comparison, algorithms such as plus L reduce R (plusLrR), Fisher, multidimensional scaling (MDS), independent component analysis (ICA), and principle component analysis (PCA) are also used to select the most significant wavelengths or features. Support vector machine (SVM) is used as the classifier. Experimental results show that the proposed methods are able to obtain robust and better classification performance with fewer number of spectral bands and simplify the design of computer vision systems. Public Library of Science 2016-01-28 /pmc/articles/PMC4731151/ /pubmed/26820311 http://dx.doi.org/10.1371/journal.pone.0146547 Text en © 2016 Han et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Han, Zhongzhi
Wan, Jianhua
Deng, Limiao
Liu, Kangwei
Oil Adulteration Identification by Hyperspectral Imaging Using QHM and ICA
title Oil Adulteration Identification by Hyperspectral Imaging Using QHM and ICA
title_full Oil Adulteration Identification by Hyperspectral Imaging Using QHM and ICA
title_fullStr Oil Adulteration Identification by Hyperspectral Imaging Using QHM and ICA
title_full_unstemmed Oil Adulteration Identification by Hyperspectral Imaging Using QHM and ICA
title_short Oil Adulteration Identification by Hyperspectral Imaging Using QHM and ICA
title_sort oil adulteration identification by hyperspectral imaging using qhm and ica
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4731151/
https://www.ncbi.nlm.nih.gov/pubmed/26820311
http://dx.doi.org/10.1371/journal.pone.0146547
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