RESUMO
Exposure of antimalarial herbal drugs (AMHDs) to ultraviolet radiation (UVR) affects the potency and integrity of the AMHDs. Instant classification of the AMHDs exposed to UVR (UVR-AMHDs) from unexposed ones (Non-UVR-AMHDs) would be beneficial for public health safety, especially in warm regions. For the first time, this work combined laser-induced autofluorescence (LIAF) with chemometric techniques to classify UVR-AMHDs from Non-UVR-AMHDs. LIAF spectra data were recorded from 200 ml of each of the UVR-AMHDs and Non-UVR-AMHDs. To extract useful data from the spectra fingerprint, principal components (PCs) analysis was used. The performance of five chemometric algorithms: random forest (RF), neural network (NN), support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbour (KNN), were compared after optimization by validation. The chemometric algorithms showed that KNN, SVM, NN, and RF were superior with a classification accuracy of 100% for UVR-AMHDs while LDA had a classification accuracy of 98.8% after standardization of the spectra data and was used as an input variable for the model. Meanwhile, a classification accuracy of 100% was obtained for KNN, LDA, SVM, and NN when the raw spectra data was used as input except for RF for which a classification accuracy of 99.9% was obtained. Classification accuracy above 99.74 ± 0.26% at 3 PCs in both the training and testing sets were obtained from the chemometric models. The results showed that the LIAF, combined with the chemometric techniques, can be used to classify UVR-AMHDs from Non-UVR-AMHDs for consumer confidence in malaria-prone regions. The technique offers a non-destructive, rapid, and viable tool for identifying UVR-AMHDs in resource-poor countries.
Assuntos
Antimaláricos , Raios Ultravioleta , Quimiometria , Análise Discriminante , Lasers , Máquina de Vetores de SuporteRESUMO
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.
Assuntos
Antimaláricos/química , Fluorescência , Geografia , Medicina Herbária/classificação , Lasers , Análise Discriminante , Análise Multivariada , Máquina de Vetores de SuporteRESUMO
Laser-induced autofluorescence (LIAF), combined with multivariate techniques, has been used to discriminate a cataractous lens from healthy lens tissues. In this study, 405 nm and 445 nm were used as excitation sources to induce the autofluorescence. Results show higher autofluorescence intensity in cataractous lens tissues than in healthy ones. Cataractous lens tissues show a red shift of 0.9 nm and 1.2 nm at 405 nm and 445 nm excitations, respectively. Using principal component analysis (PCA), three principal components (PCs) gave more than 99% variability for both 405 nm and 445 nm excitation sources. Based on the three PCs, Fisher's linear discriminant model was developed. An accuracy of 100% was obtained in classifying the lens tissues using Fisher's linear discriminant analysis (FLDA). The LIAF technique assisted by PCA and FLDA may be used for objective discrimination of cataractous lens from healthy lens tissues of Sprague-Dawley rats.
Assuntos
Catarata/diagnóstico , Catarata/metabolismo , Fluorescência , Lasers , Cristalino/metabolismo , Animais , Análise Discriminante , Análise Multivariada , Análise de Componente Principal , Ratos , Ratos Sprague-DawleyRESUMO
An onsite technique for determining drug integrity in sub-Saharan Africa is needed to ensure drug integrity and enhance public health. This current study presents the application of handheld NIR spectroscopic and multivariate techniques for the accurate identification of unexpired drugs from expired ones. A total of 150 drugs comprising 75 drug samples each of antimalarial (40 unexpired and 35 expired) and antibiotics (40 unexpired and 35 expired) were used in the study. Principal component (PC) analysis was used to extract relevant information from the spectral fingerprint and pre-processed using different techniques comparatively to observe the best cluster trends. The performance of three multivariate algorithms: RF, SVM, and PLS-DA were compared after optimization by cross-validation. The results revealed that SVM and PLS-DA were superior with an identification rate for both antimalarial and antibiotic authenticity prediction above 98% at 5 PCs in both the prediction set and calibration set. For simultaneous prediction of expired and unexpired drugs, we achieved a 100% identification rate. Generally, the results show that handheld NIR spectrometers coupled with smartphone devices could successfully be used to identify unexpired antimalarial and antibiotic drugs from expired antimalarial and antibiotic drugs for effective quality assurance in poor-resource countries. This offers positive feasibility for an affordable and user-friendly approach to reducing drug fraud in Africa.