Machine learning classification of origins and varieties of Tetrastigma hemsleyanum using a dual-mode microscopic hyperspectral imager

[Display omitted] •A dual-mode microscopic hyperspectral imager (DMHI) was developed.•Machine learning was proposed for the classification of Tetrastigma hemsleyanum.•Prediction accuracies of origins and varieties reached both 97.5%.•True tone was reconstructed using the spectra and color reproducti...

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Bibliographic Details
Published in:	Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Vol. 261; p. 120054
Main Authors:	Jiao, Changwei, Xu, Zhanpeng, Bian, Qiuwan, Forsberg, Erik, Tan, Qin, Peng, Xin, He, Sailing
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 15.11.2021
Subjects:	Dual-mode microscopic hyperspectral imager Geographical origin Machine learning classification Medicinal variety Tetrastigma hemsleyanum LCTF SVM LPP T. hemsleyanum RMHI Tetrastigma hemsleyanum CMF HPLC HSI PCs Machine learning classification FMHI Geographical origin Medicinal variety DMHI PLS-DA ROI PCA RF NIR FWHM PGP Dual-mode microscopic hyperspectral imager LVF AOTF TCM OLDA
ISSN:	1386-1425, 1873-3557, 1873-3557
Online Access:	Get full text
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