To meet the growing demand for complementary and alternative treatment for malaria, manufacturers produce several antimalarial herbal medicinal products. Herbal medicinal products regulation is difficult due to their complex chemical nature, requiring cumbersome, expensive, and time-consuming methods of analysis. The aim of this study was to develop a simple spectroscopic method together with a chemometric model for the classification and the identification of expired liquid antimalarial herbal medicinal products. Principal component analysis model was successfully used to distinguish between different herbal medicinal products and identify expired products. Principal component analysis showed a clear class separation between all five herbal medicinal products (HMP) studied, with explained variance for first and second principal components as 37.51% and 26.38%, respectively, while the third principal component had 18.74%. Support vector machine classification gave specificity and accuracy of 1.00 (100%) for training set data for all the products. The validation set HMP1, HMP2, and HMP3 had sensitivity, specificity, and accuracy of 1.00. HMP4 and HMP5 had sensitivity and specificity of 0.90 and 1.00, respectively, and an accuracy of 0.98. The support vector machine classification and principal component analysis models were successfully used to identify expired herbal medicinal products. This strategy can be used for rapid field detection of expired liquid antimalarial herbal medicinal products.
A Method for Fault Detection in Wireless Sensor Network Based on Pearson’s Correlation Coefficient and Support Vector Machine Classification
