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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Jg. 261; S. 120054
Hauptverfasser:	Jiao, Changwei, Xu, Zhanpeng, Bian, Qiuwan, Forsberg, Erik, Tan, Qin, Peng, Xin, He, Sailing
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 15.11.2021
Schlagworte:	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-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!