Ultraviolet-visible (UV-VIS) spectroscopy and cluster analysis as a rapid tool for classification of medicinal plants

The ultraviolet-visible (UV-Vis) spectroscopy coupled with cluster analysis (CA) was evaluated for the classification of some medicinal plants of different geographical growing area. To have a deeper view, the experiment was carried out on herbs belonging to different families. The UV-Vis spectra of hydroalcoholic extracts were acquired in the range of 200-800 nm. The hierarchical clustering analysis (HCA) was applied to the data matrix provided by unprocessed, normalized and standardized spectra respectively. Different types of distance measuring of (dis)similarity between the samples as well as different kinds of linkage or amalgamation rule were taken into account. The best results for the classification of the selected medicinal plants were obtained using Ward’s method as the amalgamation rule combined with 1-Pearson r clustering distance measurement. The obtained results reveal the ability of HCA with Ward and 1-Pearson r algorithm to identify plant species even when the raw material has different provenience areas and different pedoclimatic growing conditions. In addition, this methodology revealed a direct link between herbs from different families

Use of Secondary Metabolites Profiling and Antioxidant Activity to Unravel the Differences between Two Species of Nettle

In recent years, the interest in natural remedies has increased, so it is important to analyze the plants widely distributed in nature but whose composition is little known. The main objective of the present work is to obtain information based on the profiles of secondary metabolites and antioxidant activity in Lamium album, a very widespread but little studied plant, with the aim of revealing the differences compared to Urtica dioica. First, the optimization of enzymatic extraction assisted by ultrasound was carried out by the Box–Behnken method. The optimized parameters were: concentration of the enzyme—3.3% cellulase, temperature—55 °C, and the extraction time—40.00 min. The efficiency was estimated based on the content of iridoids, the main class of secondary metabolites from Lamium album. Second, the secondary metabolites profiles of the nettle extracts were obtained by thin-layer chromatography using both normal and reverse phases and by RP-UHPLC. The antioxidant activity was evaluated using DPPH and ABTS+ radicals. The obtained results revealed significant differences between the two nettle species, both in terms of the phytochemical compounds, as well as the antioxidant activity, confirming the fact that Lamium album has a high potential to be used in phytomedicine

Application of HPTLC Multiwavelength Imaging and Color Scale Fingerprinting Approach Combined with Multivariate Chemometric Methods for Medicinal Plant Clustering According to Their Species

In the current study, multiwavelength detection combined with color scales HPTLC fingerprinting procedure and chemometric approach were applied for direct clustering of a set of medicinal plants with different geographical growing areas. The fingerprints profiles of the hydroalcoholic extracts obtained after single and double development and detection under 254 nm and 365 nm, before and after selective spraying with specific derivatization reagents were evaluated by chemometric approaches. Principal component analysis (PCA) with factor analysis (FA) methods were used to reveal the contribution of red (R), green (G), blue (B) and, respectively, gray (K) color scale fingerprints to HPTLC classification of the analyzed samples. Hierarchical cluster analysis (HCA) was used to classify the medicinal plants based on measure of similarity of color scale fingerprint patterns. The 1-Pearson distance measurement with Ward’s amalgamation procedure proved to be the most convenient approach for the correct clustering of samples. Data from color scale fingerprints obtained for double development procedure and multiple visualization modes combined with appropriate chemometric methods proved to detect the similar medicinal plant extracts even though they are from different geographical regions, have different storage conditions and no specific markers are individually extracted. This approach could be proposed as a promising tool for authentication and identification studies of plant materials based on HPTLC fingerprinting analysis

Regional pattern and characteristics of essential elements in several medicinal plants using spectrometric methods combined with multivariate statistical approaches

The aim of this study was to provide a regional pattern and characteristics of essential elements in several medicinal plants from North Macedonia and Romania. The content of Ca, Mg, Al, Fe, Cu, Ba and Zn was determined by ICP-OES while Na and K by FAES in some medicinal plants belonging to sixteen families. Similar profiles of elements with a high content of Ca, Mg and K were observed. Peppermint and blackberry from both countries showed extreme content in Al and Fe. A symmetric distribution for K, Ca and Zn and an asymmetric one for Na, Al, Fe and Ba were found in medicinal plants from both countries. Potassium, Ca, Mg, Al and Fe could be considered as markers for growing area. Principal Component Analysis highlighted that the variability of elements content was described by four factors (83.4%) in North Macedonia and three factors (70.0%) in Romania. The first factor could explain the influence of soil nature upon variability of elemental composition, calcareous in North Macedonia (Mg and Ca - 29.4% variance) and a rich one in hydroxides in Romania (Al and Fe - 33.1 % variance). Keywords: essential element, medicinal plant, inductively coupled plasma optical emission spectrometry, flame atomic emission spectrometry, Principal Component Analysis, two-way joining Cluster Analysi

Application of Inductively Coupled Plasma Spectrometric Techniques and Multivariate Statistical Analysis in the Hydrogeochemical Profiling of Caves—Case Study Cloșani, Romania

The aim of the study was to develop the hydrogeochemical profiling of caves based on the elemental composition of water and silty soil samples and a multivariate statistical analysis. Major and trace elements, including rare earths, were determined in the water and soil samples. The general characteristics of water, anions content, inorganic and organic carbon fractions and nitrogen species (NO3− and NH4+) were also considered. The ANOVA—principal component analysis (PCA) and two-way joining analysis were applied on samples collected from Cloșani Cave, Romania. The ANOVA-PCA revealed that the hydrogeochemical characteristics of Ca2+-HCO3− water facies were described by five factors, the strongest being associated with water-carbonate rock interactions and the occurrence of Ca, Mg and HCO3− (43.4%). Although organic carbon fractions have a lower influence (20.1%) than inorganic ones on water characteristics, they are involved in the chemical processes of nitrogen and of the elements involved in redox processes (Fe, Mn, Cr and Sn). The seasonal variability of water characteristics, especially during the spring, was observed. The variability of silty soil samples was described by four principal components, the strongest influence being attributed to rare earth elements (52.2%). The ANOVA-PCA provided deeper information compared to Gibbs and Piper diagrams and the correlation analysis

Application of Inductively Coupled Plasma Spectrometric Techniques and Multivariate Statistical Analysis in the Hydrogeochemical Profiling of Caves—Case Study Cloșani, Romania

The aim of the study was to develop the hydrogeochemical profiling of caves based on the elemental composition of water and silty soil samples and a multivariate statistical analysis. Major and trace elements, including rare earths, were determined in the water and soil samples. The general characteristics of water, anions content, inorganic and organic carbon fractions and nitrogen species (NO3− and NH4+) were also considered. The ANOVA—principal component analysis (PCA) and two-way joining analysis were applied on samples collected from Cloșani Cave, Romania. The ANOVA-PCA revealed that the hydrogeochemical characteristics of Ca2+-HCO3− water facies were described by five factors, the strongest being associated with water-carbonate rock interactions and the occurrence of Ca, Mg and HCO3− (43.4%). Although organic carbon fractions have a lower influence (20.1%) than inorganic ones on water characteristics, they are involved in the chemical processes of nitrogen and of the elements involved in redox processes (Fe, Mn, Cr and Sn). The seasonal variability of water characteristics, especially during the spring, was observed. The variability of silty soil samples was described by four principal components, the strongest influence being attributed to rare earth elements (52.2%). The ANOVA-PCA provided deeper information compared to Gibbs and Piper diagrams and the correlation analysis