Evaluation of medicinal plants using laser-induced breakdown spectroscopy (LIBS) combined with chemometric techniques

Abstract

Medicinal plants play a vital role in herbal medical field and allopathic medicine field industry. Chemical and spectroscopic studies of Taraxacum officinale, Hyoscyamus niger, Ajuga bracteosa, Elaeagnus angustifolia, Camellia sinensis, and Berberis lyceum are conducted in this paper by using a 532-nm Nd:YAG laser in an open air environment. These medicinal plant's leaves, roots, seed, and flowers are used to treat a range of diseases by the locals. It is crucial to be able to distinguish between beneficial and detrimental metal elements in these plants. We demonstrated how various elements are categorized and how roots, leaves, seeds and flowers of same plants differ from each other on the basis of elemental analysis. Furthermore, for classification purpose, different classification models, partial least square discriminant analysis (PLS-DA), k-nearest neighbors (kNN), and principal component analysis (PCA) are used. We found silicon (Si), aluminum (Al), iron (Fe), copper (Cu), calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), manganese (Mn), phosphorous (P), and vanadium (V) in all of the medicinal plant samples with a molecular form of carbon and nitrogen band. We detected Ca, Mg, Si, and P as primary components in all of the plant samples, as well as V, Fe, Mn, Al, and Ti as essential medicinal metals, and additional trace elements like Si, Sr, and Al. The result's findings show that the PLS-DA classification model with single normal variate (SNV) preprocessing method is the most effective classification model for different types of plant samples. The average correct classification rate obtained for PLS-DA with SNV is 95%. Moreover, laser-induced breakdown spectroscopy (LIBS) was successfully employed to perform rapid, sensitive, and quantitative trace element analysis on medicinal herbs and plant samples.

Keywords: Allopathic medicine; Classification; LIBS; Partially least square discriminant; Preprocessing; Quantitative; Taraxacum officinale.