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Improving Raman spectroscopic identification of rice varieties by feature extraction
Author(s) -
Sha Min,
Zhang Ding,
Zhang Zhengyong,
Wei Jinhong,
Chen Yuan,
Wang Mengtian,
Liu Jun
Publication year - 2020
Publication title -
journal of raman spectroscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.748
H-Index - 110
eISSN - 1097-4555
pISSN - 0377-0486
DOI - 10.1002/jrs.5828
Subject(s) - principal component analysis , raman spectroscopy , pattern recognition (psychology) , artificial intelligence , support vector machine , identification (biology) , feature extraction , biological system , feature (linguistics) , analytical chemistry (journal) , mathematics , computer science , chemistry , chromatography , physics , optics , botany , biology , linguistics , philosophy
Raman spectroscopy combined with pattern recognition can identify rice varieties excellently. In this study, a method was established to select the key feature for identification model. Seventy‐two Raman spectra of three varieties of rice were analyzed. Seventy‐one independent variables and characteristic bands (420–560, 820–980, 1,000–1,200, and 1,300–1,500 cm −1 ) were obtained by principal component analysis (PCA). Window analysis further narrowed the range of characteristic bands (451–550, 951–1,000, and 1,351–1,450 cm −1 ). Hierarchical cluster analysis (HCA) obtained 30 wavenumbers with small correlation. The prediction accuracy was 91.71%, whereas the time was reduced by 10 times when these 30 wavenumbers were used to establish the identification model. The method combined PCA, window analysis, and HCA with support vector machine can be used as an effective feature extraction method to improve the efficiency for identification of rice varieties. Under the circumstances of large sample size or relatively complex data, the screening of Raman spectrum information is an important means to simplify the model and improve the prediction efficiency.