The Application of Visible and Near-Infrared Spectroscopy Combined with Chemometrics in Classification of Dried Herbs

The fast differentiation and classification of herb samples are complicated processes due to the presence of many various chemical compounds. Traditionally, separation techniques have been employed for the identification and quantification of compounds present in different plant matrices, but they are tedious, time-consuming and destructive. Thus, a non-targeted approach would be specifically advantageous for this purpose. In the present study, spectroscopy in the visible and near-infrared range and pattern recognition techniques, including the principal component analysis (PCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), regularized discriminant analysis (RDA), super k-nearest neighbor (SKNN) and support vector machine (SVM) techniques, were applied to develop classification models that enabled the discrimination of various commercial dried herbs, including mint, linden, nettle, sage and chamomile. The classification error rates in the validation data were below 10% for all the classification methods, except for SKNN. The results obtained confirm that spectroscopy and pattern recognition methods constitute a good non-destructive tool for the rapid identification of herb species that can be used in routine quality control by the pharmaceutical industry, as well as herbal suppliers, to avoid mislabeling.