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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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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Summary:[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 reproduction algorithm. A dual-mode microscopic hyperspectral imager (DMHI) combined with a machine learning algorithm for the purpose of classifying origins and varieties of Tetrastigma hemsleyanum (T. hemsleyanum) was developed. By switching the illumination source, the DMHI can operate in reflection imaging and fluorescence detection modes. The DMHI system has excellent performance with spatial and spectral resolutions of 27.8 μm and 3 nm, respectively. To verify the capability of the DMHI system, a series of classification experiments of T. hemsleyanum were conducted. Captured hyperspectral datasets were analyzed using principal component analysis (PCA) for dimensional reduction, and a support vector machine (SVM) model was used for classification. In reflection microscopic hyperspectral imaging (RMHI) mode, the classification accuracies of T. hemsleyanum origins and varieties were 96.3% and 97.3%, respectively, while in fluorescence microscopic hyperspectral imaging (FMHI) mode, the classification accuracies were 97.3% and 100%, respectively. Combining datasets in dual mode, excellent predictions of origin and variety were realized by the trained model, both with a 97.5% accuracy on a newly measured test set. The results show that the DMHI system is capable of T. hemsleyanum origin and variety classification, and has the potential for non-invasive detection and rapid quality assessment of various kinds of medicinal herbs.
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ISSN:1386-1425
1873-3557
1873-3557
DOI:10.1016/j.saa.2021.120054