Multiobjective response and chemometric approaches to enhance the phytochemicals and biological activities of beetroot leaves: an unexploited organic waste.

Research on medicinal plants is developing each day due to inborn phytochemicals, which can encourage the progress of novel drugs. Most plant-based phytochemicals have valuable effects on well-being. Among them, beetroot leaves (BL) are known for their therapeutic properties. Here, three solvents, namely, acetonitrile, ethanol, and water, and their combinations were developed for BL extraction and simultaneous assessment of phytochemical compounds and antioxidant and antifoodborne pathogen bacteria activities. By using the augmented simplex-centroid mixture design, 40.40% acetonitrile diluted in water at 38.74% and ethanol at 20.86% favored the recovery of 49.28 mg GAE/mL (total phenolic content (TPC)) and 0.314 mg QE/mL (total flavonoid content (TFC)), respectively. Acetonitrile diluted in water at 50% guarantees the best antioxidant activity, whereas the optimal predicted mixture for the highest antibacterial activity matches 24.58, 50.17, and 25.25% of acetonitrile, ethanol, and water, respectively. These extraction conditions ensured inhibition of Staphylococcus aureus, Salmonella enterica, and Escherichia coli, respectively, at 0.402, 0.497, and 0.207 mg/mL. Under optimized conditions, at three concentrations of BL, minimal inhibitory concentration (MIC), 2 × MIC, and 4 × MIC, a linear model was employed to investigate the inhibition behavior against the three tested bacteria. The early logarithmic growth phase of these bacteria illustrated the bactericidal effect of optimized extracted BL with a logarithmic growth phase inferior to 6 h. Therefore, BL extract at 4 × MIC, which corresponds to 1.608, 1.988, and 0.828 mg/mL, was more efficient against S. aureus, S. enterica, and E. coli.

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.