Identification of Commercial Antimalarial Herbal Drugs Using Laser-Induced Autofluorescence Technique and Multivariate Algorithms

In malaria-prone developing countries the integrity of Anti-Malarial Herbal Drugs (AMHDs) which are easily preferred for treatment can be compromised. Currently, existing techniques for identifying AMHDs are destructive. We report on the use of non-destructive and sensitive technique, Laser-Induced-...

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Bibliographic Details
Published in:Journal of fluorescence 2024-03, Vol.34 (2), p.855-864
Main Authors: Boateng, Rabbi, Opoku-Ansah, Jerry, Eghan, Moses Jojo, Adueming, Peter Osei-Wusu, Amuah, Charles Lloyd Yeboah
Format: Article
Language:English
Subjects:
Algorithms
Analytical Chemistry
Antimalarials
Biochemistry
Biological and Medical Physics
Biomedical and Life Sciences
Biomedicine
Biophysics
Biotechnology
Cluster analysis
Clustering
Developing countries
Discriminant Analysis
Drugs
Lasers
LDCs
Malaria
Metabolites
Multivariate analysis
Nondestructive testing
Phenols
Principal Component Analysis
Principal components analysis
Spectra
Support Vector Machine
Support vector machines
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Summary:In malaria-prone developing countries the integrity of Anti-Malarial Herbal Drugs (AMHDs) which are easily preferred for treatment can be compromised. Currently, existing techniques for identifying AMHDs are destructive. We report on the use of non-destructive and sensitive technique, Laser-Induced-Autofluorescence (LIAF) in combination with multivariate algorithms for identification of AMHDs. The LIAF spectra were recorded from commercially prepared decoction AMHDs purchased from accredited pharmacy shop in Ghana. Deconvolution of the LIAF spectra revealed secondary metabolites belonging to derivatives of alkaloids and classes of phenolic compounds of the AMHDs. Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA) were able to discriminate the AMHDs base on their physicochemical properties. Based on two principal components, the PCA- QDA (Quadratic Discriminant Analysis), PCA-LDA (Linear Discriminant Analysis), PCA-SVM (Support Vector Machine) and PCA-KNN (K-Nearest Neighbour) models were developed with an accuracy performance of 99.0, 99.7, 100.0, and 100%, respectively, in identifying AMHDs. PCA-SVM and PCA-KNN provided the best classification and stability performance. The LIAF technique in combination with multivariate techniques may offer a non-destructive and viable tool for AMHDs identification.
ISSN:1053-0509
1573-4994
DOI:10.1007/s10895-023-03309-w