Application of visible and near-infrared spectroscopy to classification of Miscanthus species
The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models ba...
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| creator | Jin, Xiaoli Chen, Xiaoling Xiao, Liang Shi, Chunhai Chen, Liang Yu, Bin Yi, Zili Yoo, Ji Hye Heo, Kweon Yu, Chang Yeon Yamada, Toshihiko Sacks, Erik J Peng, Junhua |
| description | The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species. |
| doi_str_mv | 10.1371/journal.pone.0171360 |
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Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0171360</identifier><identifier>PMID: 28369059</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>60 APPLIED LIFE SCIENCES ; absorption spectroscopy ; Agricultural production ; Agriculture ; Alternative energy sources ; BASIC BIOLOGICAL SCIENCES ; Biology and Life Sciences ; Biomass ; Calibration ; Cellulose ; Chemistry ; China ; Classification ; Computer and Information Sciences ; detergents ; Discriminant Analysis ; Feasibility studies ; Food ; Fourier transforms ; Grasses ; Identification ; Identification and classification ; Infrared radiation ; Infrared spectroscopy ; Laboratories ; Least-Squares Analysis ; leaves ; linear discriminant analysis ; Methods ; Miscanthus ; Models, Biological ; Morphology ; Multivariate analysis ; Near infrared spectroscopy ; Neural networks ; Neural Networks (Computer) ; People and Places ; Physical Sciences ; Physiology ; Plants ; Poaceae - chemistry ; Poaceae - classification ; Poaceae - growth & development ; Principal Component Analysis ; Principal components analysis ; Raw materials ; Research and Analysis Methods ; Saccharification ; Science ; Species Specificity ; Spectrophotometers ; Spectroscopy, Near-Infrared ; Spectrum Analysis ; Support Vector Machine ; Taxonomy ; Trees ; Vocabularies & taxonomies</subject><ispartof>PloS one, 2017-04, Vol.12 (4), p.e0171360-e0171360</ispartof><rights>COPYRIGHT 2017 Public Library of Science</rights><rights>2017 Jin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2017 Jin et al 2017 Jin et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c785t-ed9ef3e940f204defe27550c2edb82b4f61525aaa69ef27e1831a955b01372fe3</citedby><cites>FETCH-LOGICAL-c785t-ed9ef3e940f204defe27550c2edb82b4f61525aaa69ef27e1831a955b01372fe3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5378329/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5378329/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2095,2914,23846,27903,27904,53770,53772,79347,79348</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28369059$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://www.osti.gov/servlets/purl/1368397$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><contributor>Nychas, George-John</contributor><creatorcontrib>Jin, Xiaoli</creatorcontrib><creatorcontrib>Chen, Xiaoling</creatorcontrib><creatorcontrib>Xiao, Liang</creatorcontrib><creatorcontrib>Shi, Chunhai</creatorcontrib><creatorcontrib>Chen, Liang</creatorcontrib><creatorcontrib>Yu, Bin</creatorcontrib><creatorcontrib>Yi, Zili</creatorcontrib><creatorcontrib>Yoo, Ji Hye</creatorcontrib><creatorcontrib>Heo, Kweon</creatorcontrib><creatorcontrib>Yu, Chang Yeon</creatorcontrib><creatorcontrib>Yamada, Toshihiko</creatorcontrib><creatorcontrib>Sacks, Erik J</creatorcontrib><creatorcontrib>Peng, Junhua</creatorcontrib><creatorcontrib>Univ. of Illinois, Urbana-Champaign, Urbana, IL (United States)</creatorcontrib><title>Application of visible and near-infrared spectroscopy to classification of Miscanthus species</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.</description><subject>60 APPLIED LIFE SCIENCES</subject><subject>absorption spectroscopy</subject><subject>Agricultural production</subject><subject>Agriculture</subject><subject>Alternative energy sources</subject><subject>BASIC BIOLOGICAL SCIENCES</subject><subject>Biology and Life Sciences</subject><subject>Biomass</subject><subject>Calibration</subject><subject>Cellulose</subject><subject>Chemistry</subject><subject>China</subject><subject>Classification</subject><subject>Computer and Information Sciences</subject><subject>detergents</subject><subject>Discriminant Analysis</subject><subject>Feasibility studies</subject><subject>Food</subject><subject>Fourier transforms</subject><subject>Grasses</subject><subject>Identification</subject><subject>Identification and 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one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jin, Xiaoli</au><au>Chen, Xiaoling</au><au>Xiao, Liang</au><au>Shi, Chunhai</au><au>Chen, Liang</au><au>Yu, Bin</au><au>Yi, Zili</au><au>Yoo, Ji Hye</au><au>Heo, Kweon</au><au>Yu, Chang Yeon</au><au>Yamada, Toshihiko</au><au>Sacks, Erik J</au><au>Peng, Junhua</au><au>Nychas, George-John</au><aucorp>Univ. of Illinois, Urbana-Champaign, Urbana, IL (United States)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application of visible and near-infrared spectroscopy to classification of Miscanthus species</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2017-04-03</date><risdate>2017</risdate><volume>12</volume><issue>4</issue><spage>e0171360</spage><epage>e0171360</epage><pages>e0171360-e0171360</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>28369059</pmid><doi>10.1371/journal.pone.0171360</doi><tpages>e0171360</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2017-04, Vol.12 (4), p.e0171360-e0171360 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1883868251 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS); EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | 60 APPLIED LIFE SCIENCES absorption spectroscopy Agricultural production Agriculture Alternative energy sources BASIC BIOLOGICAL SCIENCES Biology and Life Sciences Biomass Calibration Cellulose Chemistry China Classification Computer and Information Sciences detergents Discriminant Analysis Feasibility studies Food Fourier transforms Grasses Identification Identification and classification Infrared radiation Infrared spectroscopy Laboratories Least-Squares Analysis leaves linear discriminant analysis Methods Miscanthus Models, Biological Morphology Multivariate analysis Near infrared spectroscopy Neural networks Neural Networks (Computer) People and Places Physical Sciences Physiology Plants Poaceae - chemistry Poaceae - classification Poaceae - growth & development Principal Component Analysis Principal components analysis Raw materials Research and Analysis Methods Saccharification Science Species Specificity Spectrophotometers Spectroscopy, Near-Infrared Spectrum Analysis Support Vector Machine Taxonomy Trees Vocabularies & taxonomies |
| title | Application of visible and near-infrared spectroscopy to classification of Miscanthus species |
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