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.

Multivariate color scale image analysis - Thin layer chromatography for comprehensive evaluation of complex samples fingerprint.

A chemometric evaluation of the information provided by different color scale fingerprints in thin layer chromatographic analysis of complex samples is proposed for the correct classification of a set of medicinal plant extracts. The fingerprints of the samples were acquired on HPTLC Silica gel 60 F254 and HPTLC Silica gel 60 plates using multiple levels of visualization under UV light. Images processing on red (R), green (G), blue (B) and respectively grey (K) color scale selection was used in order to evaluate the complete chromatographic profile of the extracts. Combination of Principal Component Analysis (PCA) and Factor Analysis (FA) method was applied in order to reveal the individual contribution of each color scales in the analysis of chromatographic fingerprints. The suggested technique provides an applicable strategy to screen for efficacy-associated color scale for grouping/classification of the extracts exploiting the information provided by HPTLC fingerprints. The principal component analysis and linear discriminant analysis (PCA-LDA) method was applied for the evaluation of numerical data provided by color scale fingerprints digitization and for samples classification. A correct classification of the analyzed extracts according to the plants phylum was revealed by color scale fingerprints analysis. The proposed methodology could be considered as a promising tool with future applications in plant material investigations even from the taxonomic perspective classification.

A comprehensive classification of edible oils according to their radical scavenging spectral profile evaluated by advanced chemometrics.

A comprehensive study concerning the characterization and classification of 30 cold-pressed edible oils according to their UV-Vis spectra and radical scavenging profiles using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay is presented. Considering the principal component analysis (PCA) and fuzzy-principal component analysis (FPCA) loadings profiles, the characteristic spectral regions with a significant influence in oil samples classification were identified and associated with characteristic factors in each group. Much more, the oils with high antiradical capacity were revealed. The scores corresponding to the first principal component and the canonical scores corresponding to the first discriminant function derived from radical scavenging spectral profiles allowed a relevant classification of oils in well-defined groups associated with their high, medium and low radical scavenging capacity. The FPCA-LDA method applied on DPPH radical scavenging spectral profiles of edible oils appeared to be the most efficient method with a correct classification rate of 96.7%.

Classification of Romanian medicinal plant extracts according to the therapeutic effects using thin layer chromatography and robust chemometrics.

Thin layer chromatography in combination with image analysis and advanced chemometric methods were successfully used to classify the medicinal herbs according to their therapeutic effects and usage. The investigations were conducted using two types of plates (HPTLC Silica gel 60 and HPTLC Silica gel 60 F254) which were evaluated in UV light at 254 and 365 nm. The holistic evaluation of the numerical data corresponding different image processing channels (blue, grey, red, green) was performed by employing appropriate multivariate methods: hierarchical cluster analysis (HCA), principal component analysis (PCA), fuzzy principal component analysis (FPCA) and linear discriminant analysis (LDA) applied to the first relevant principal components. The results obtained by applying LDA method indicate a highly accurate separation of the medicinal herbs within the four groups, in good agreement with therapeutic effects and usage. According to this classification, the best image processing channels were identified for each of the investigated HPTLC plates: blue channel for HPTLC Silica gel 60 F254 (with 92.9% percent of discrimination in case of PCA and FPCA) and respectively red channel for HPTLC Silica gel 60 (with 93.9% percent of discrimination in case of FPCA). The 2D and 3D score scatterplots illustrate also the accurate and reliable discrimination between the four distinct groups.

The lipophilicity of parabens estimated on reverse phases chemically bonded and oil-impregnated plates and calculated using different computation methods.

The chromatographic behaviour of the parabens has been investigated on RP-18F(254S), RP-18WF(254S), CNF(254S), Diol F(254S) and silica gel 60F(254) plates impregnated with different oils (paraffin, olive, sunflower and corn) using methanol-water mixtures in different volume proportions as mobile phases, the regression determination coefficients being excellent (higher than 0.98 for the majority of compounds). Moreover, highly significant correlations were obtained between different experimental indices of lipophilicity (R(M0), b and scores corresponding to the first principal component (PC1)) and computed log P values. All types of stationary phases investigated appear to be highly suited for estimating the lipophilicity of the parabens.