J Opt Soc Am A Opt Image Sci Vis. 2020 Nov 1;37(11):C103-C110. doi: 10.1364/JOSAA.396701.
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.
基于地理来源,激光诱导荧光(LIF)与多元技术结合已被用于识别抗疟草药植物(AMHPs)。AMHP 样本取自加纳海岸角都会区的四个地理来源(Abrafo、Jukwa、Nfuom 和 Akotokyere)。记录了 AMHP 样本的 LIF 光谱数据。利用多元技术,通过使用 K-最近邻(KNN)、支持向量机(SVM)和线性判别分析(LDA)方法,对 AMHP 光谱数据的前两个主成分的训练集进行建模。SVM 和 KNN 方法对预测数据的成功率达到 100%,而 LDA 的成功率为 99%。建议使用 KNN 和 SVM 方法根据地理来源识别 AMHPs。对所有 AMHP 样本的 LIF 光谱进行去卷积后发现,槲皮素和小檗碱等化合物存在于所有 AMHP 样本中。