Identification of Commercial Antimalarial Herbal Drugs Using Laser-Induced Autofluorescence Technique and Multivariate Algorithms.
Antimalaria Herbal Drugs (AMHDs)
Discrimination
Laser-Induced Autofluorescence (LIAF)
Multivariate algorithm
Secondary metabolites
Journal
Journal of fluorescence
ISSN: 1573-4994
Titre abrégé: J Fluoresc
Pays: Netherlands
ID NLM: 9201341
Informations de publication
Date de publication:
01 Jul 2023
01 Jul 2023
Historique:
received:
19
04
2023
accepted:
13
06
2023
medline:
1
7
2023
pubmed:
1
7
2023
entrez:
1
7
2023
Statut:
aheadofprint
Résumé
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.
Identifiants
pubmed: 37392364
doi: 10.1007/s10895-023-03309-w
pii: 10.1007/s10895-023-03309-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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