Laser-induced fluorescence combined with multivariate techniques identifies the geographical origin of antimalarial herbal plants
| Autor: | Charles Lloyd Yeboah Amuah, Benjamin Anderson, Moses J. Eghan, Jerry Opoku-Ansah, Paul K. Buah-Bassuah, Peter Osei-Wusu Adueming |
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| Rok vydání: | 2020 |
| Předmět: |
Multivariate statistics
Support Vector Machine Multivariate analysis Herbal Medicine 01 natural sciences Fluorescence 010309 optics Antimalarials Optics 0103 physical sciences Laser-induced fluorescence Training set Geography business.industry Lasers Discriminant Analysis Pattern recognition Linear discriminant analysis Atomic and Molecular Physics and Optics Electronic Optical and Magnetic Materials Support vector machine Multivariate Analysis Principal component analysis Computer Vision and Pattern Recognition Artificial intelligence business |
| Zdroj: | Journal of the Optical Society of America A. 37:C103 |
| ISSN: | 1520-8532 1084-7529 |
| DOI: | 10.1364/josaa.396701 |
| Popis: | Laser-induced fluorescence (LIF) combined with multivariate techniques has been used in identifying antimalarial herbal plants (AMHPs) based on their geographical origin. The AMHP samples were collected from four geographical origins (Abrafo, Jukwa, Nfuom, and Akotokyere) in the Cape Coast Metropolis, Ghana. LIF spectra data were recorded from the AMHP samples. Utilizing multivariate techniques, a training set for the first two principal components of the AMHP spectra data was modeled through the use of K-nearest neighbor (KNN), support vector nachine (SVM), and linear discriminant analysis (LDA) methods. The SVM and KNN methods performed best with 100% success for the prediction data, while the LDA had a 99% success rate. The KNN and SVM methods are recommended for the identification of AMHPs based on their geographical origins. Deconvoluted peaks from the LIF spectra of all the AMHP samples revealed compounds such as quercetin and berberine as being present in all the AMHP samples. |
| Databáze: | OpenAIRE |
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