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Title: Application of visible and near-infrared spectroscopy to classification of Miscanthus species

Here, 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% ofmore » 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.« less
Authors:
 [1] ;  [1] ;  [2] ;  [1] ;  [3] ;  [4] ;  [5] ;  [6] ;  [6] ;  [6] ;  [7] ;  [8] ;  [9] ;  [10]
  1. Zhejiang Univ., Hangzhou (China)
  2. Huna Agricultural Univ., Hunan Changsha (China)
  3. Chinese Academy of Sciences (CAS), Hubei (China)
  4. Wuhan Junxi Horticultural Science and Technology Co., Ltd., Hubei (China)
  5. Hunan Agricultural Univ., Hunan Changsha (China)
  6. Kangwon National Univ., Gangwon (South Korea)
  7. Hokkaido Univ., Hokkaido (Japan)
  8. Univ. of Illinois, Urbana-Champaign, Urbana, IL (United States)
  9. China National Seed Group Co., Ltd., Hubei (China)
  10. Agricultural Univ. of Athens (Greece)
Publication Date:
Grant/Contract Number:
SC0006634; SC0012379
Type:
Accepted Manuscript
Journal Name:
PLoS ONE
Additional Journal Information:
Journal Volume: 12; Journal Issue: 4; Journal ID: ISSN 1932-6203
Publisher:
Public Library of Science
Research Org:
Univ. of Illinois, Urbana-Champaign, Urbana, IL (United States)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 59 BASIC BIOLOGICAL SCIENCES; near-infrared spectroscopy; China; leaves; principal component analysis; absorption spectroscopy; neural networks; detergents; linear discriminant analysis
OSTI Identifier:
1368397

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, and Nychas, George -John. Application of visible and near-infrared spectroscopy to classification of Miscanthus species. United States: N. p., Web. doi:10.1371/journal.pone.0171360.
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, & Nychas, George -John. Application of visible and near-infrared spectroscopy to classification of Miscanthus species. United States. doi:10.1371/journal.pone.0171360.
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, and Nychas, George -John. 2017. "Application of visible and near-infrared spectroscopy to classification of Miscanthus species". United States. doi:10.1371/journal.pone.0171360. https://www.osti.gov/servlets/purl/1368397.
@article{osti_1368397,
title = {Application of visible and near-infrared spectroscopy to classification of Miscanthus species},
author = {Jin, Xiaoli and Chen, Xiaoling and Xiao, Liang and Shi, Chunhai and Chen, Liang and Yu, Bin and Yi, Zili and Yoo, Ji Hye and Heo, Kweon and Yu, Chang Yeon and Yamada, Toshihiko and Sacks, Erik J. and Peng, Junhua and Nychas, George -John},
abstractNote = {Here, 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 = {10.1371/journal.pone.0171360},
journal = {PLoS ONE},
number = 4,
volume = 12,
place = {United States},
year = {2017},
month = {4}
}