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.

Deep Learning Techniques to Improve the Performance of Olive Oil Classification.

The olive oil assessment involves the use of a standardized sensory analysis according to the "panel test" method. However, there is an important interest to design novel strategies based on the use of Gas Chromatography (GC) coupled to mass spectrometry (MS), or ion mobility spectrometry (IMS) together with a chemometric data treatment for olive oil classification. It is an essential task in an attempt to get the most robust model over time and, both to avoid fraud in the price and to know whether it is suitable for consumption or not. The aim of this paper is to combine chemical techniques and Deep Learning approaches to automatically classify olive oil samples from two different harvests in their three corresponding classes: extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO). Our Deep Learning model is built with 701 samples, which were obtained from two olive oil campaigns (2014-2015 and 2015-2016). The data from the two harvests are built from the selection of specific olive oil markers from the whole spectral fingerprint obtained with GC-IMS method. In order to obtain the best results we have configured the parameters of our model according to the nature of the data. The results obtained show that a deep learning approach applied to data obtained from chemical instrumental techniques is a good method when classifying oil samples in their corresponding categories, with higher success rates than those obtained in previous works.

Chemometric studies of thymol binding with bovine serum albumin: A developing strategy for the successful investigation of pharmacological activity.

Thymol (Thy) is a hydrophobic active ingredient present in thyme essential oils. It is marginally soluble in water which is one of the challenging limitations for its application in foods or as drugs. An important aspect of our understanding from this system is to quantitatively comprehend how albumin interacts with Thy. Herein, this study has been focused on the interactions between thymol and bovine serum albumin (BSA) using electrochemical methods and ultraviolet and visible (UV-Vis) spectroscopy. Due to the overlap between obtained signals of existing species, it is apparent that multivariate methods can resolve overlapping signal trough unique decomposition. These obtained profiles are then analyzed to give the thermodynamic parameters, concentration and structural information of BSA binds to Thy. The thermodynamic results show that hydrogen bonding formation and van der Waals forces play major role in the binding process. Also, docking studies suggested that Thy binds mainly to the subdomain IIA of BSA through the formation of hydrogen bonding with Arg 217. Good agreement was found between the results obtained from experimental and theoretical studies. Thus, the current approaches seem to be promising that are not only for the transport of thymol in blood but also for its effective action in food applications.

Investigating the structure-activity relationships of N'-[(5-nitrofuran-2-yl) methylene] substituted hydrazides against Trypanosoma cruzi to design novel active compounds.

Chagas disease, caused by the protozoan Trypanosoma cruzi, is a neglected chronic tropical infection endemic in Latin America. New and effective treatments are urgently needed because the two available drugs - benznidazole (BZD) and nifurtimox (NFX) - have limited curative power in the chronic phase of the disease. We have previously reported the design and synthesis of N'-[(5-nitrofuran-2-yl) methylene] substituted hydrazides that showed high trypanocidal activity against axenic epimastigote forms of three T. cruzi strains. Here we show that these compounds are also active against a BZD- and NFX-resistant strain. Herein, multivariate approaches (hierarchical cluster analysis and principal component analysis) were applied to a set of thirty-six formerly characterized compounds. Based on the findings from exploratory data analysis, novel compounds were designed and synthesized. These compounds showed two-to three-fold higher trypanocidal activity against epimastigote forms than the previous set and were 25-30-fold more active than BZD. Their activity was also evaluated against intracellular amastigotes by high content screening (HCS). The most active compounds (BSF-38 to BSF-40) showed a selective index (SI') greater than 200, in contrast to the SI' values of reference drugs (NFX, 16.45; BZD, > 3), and a 70-fold greater activity than BZD. These findings indicate that nitrofuran compounds designed based on the activity against epimastigote forms show promising trypanocidal activity against intracellular amastigotes, which correspond to the predominant parasite stage in the chronic phase of Chagas disease.

Investigating the structure-activity relationships of N '-[(5-nitrofuran-2-yl) methylene] substituted hydrazides against Trypanosoma cruzi to design novel active compounds

Chagas disease, caused by the protozoan Trypanosoma cruzi, is a neglected chronic tropical infection endemic in Latin America. New and effective treatments are urgently needed because the two available drugs - benznidazole (BZD) and nifurtimox (NFX) - have limited curative power in the chronic phase of the disease. We have previously reported the design and synthesis of N'-[(5-nitrofuran-2-yl) methylene] substituted hydrazides that showed high trypanocidal activity against axenic epimastigote forms of three T cruzi strains. Here we show that these compounds are also active against a BZD- and NFX-resistant strain. Herein, multivariate approaches (hierarchical cluster analysis and principal component analysis) were applied to a set of thirty-six formerly characterized compounds. Based on the findings from exploratory data analysis, novel compounds were designed and synthesized. These compounds showed two-to three-fold higher trypanocidal activity against epimastigote forms than the previous set and were 25-30-fold more active than BZD. Their activity was also evaluated against intracellular amastigotes by high content screening (HCS). The most active compounds (BSF-38 to BSF-40) showed a selective index (SI') greater than 200, in contrast to the SI' values of reference drugs (NFX, 16.45; BZD, > 3), and a 70-fold greater activity than BZD. These findings indicate that nitrofuran compounds designed based on the activity against epimastigote forms show promising trypanocidal activity against intracellular amastigotes, which correspond to the predominant parasite stage in the chronic phase of Chagas disease.

Geochemical behaviour of PM10 aerosol constituents under the influence of succeeding anticyclonic/cyclonic situations: case of Sfax City, southern Tunisia.

The present study investigates the geochemical behaviour of PM10 aerosol constituents (Cl, Na, Si, Al, Ca, Fe, Mg, Mn, Pb, Zn, S) at Sfax City (Tunisia) under succeeding meteorological conditions, including short-lived anticyclonic, cyclonic and prolonged anticyclonic situations. The results revealed daily total concentrations fluctuating between 4.07 and 88.51 µg/m(3). The highest level recorded was noted to occur under the effect of the short-lived anticyclonic situation characterized by low wind speeds. It was 1.5 times higher than those recorded during cyclonic and long-lived anticyclonic situations characterized by moderate to high wind speeds. During the cyclonic situation, the marked increase of (Na and Cl) concentrations is associated with relatively high sea wind speeds (6 to 9 m/s), which are in turn responsible for a slight increase of crustal elements such as Al, Ca, Si, Fe and Mg, by the entrainment in the air of dust from roads and undeveloped areas. During the two anticyclonic situations, the simultaneous increase (due to communal transport) of crustal (Ca, Si, Al, Fe, Mg) and man-made (Mn, S, Pb, Zn) elements was noted to be associated with the dominance of terrigenious wind flows with speeds varying between 1.5 and 4 m/s. However, the significant contribution rates observed for Cl under the prevalence of such winds as compared to other crustal elements such as Fe suggested the influence of the sebkhas of Southern Tunisia.

Multi-elemental analysis of Ziziphora clinopodioidesfrom different regions, periods and parts using atomic absorption spectrometry and chemometric approaches

ABSTRACTIn this study, ten trace elements in Ziziphora clinopodioidesLam., Lamiaceae, from different regions, periods and parts in Xinjiang were determined by atomic absorption spectrometry following microwave-assisted acid digestion. The decreasing sequence of elements levels was K > Ca > Mg > Fe > Cu > Zn > Na > Mn > Cd > Pb. Chemometric approaches, such as correlation analysis, principal component analysis, and hierarchical cluster analysis were applied to classify Z. clinopodioides according to its elements contents. Principal component analysis revealed 83.51% of the variance with the first four principal component variables. Hierarchical cluster analysis indicated five groups from the eighteen regions, and the result of classification can correspond to the geographical distribution for the most regions. Variation in the elements exhibited a decreasing trend, but of different types in the studied periods. Elemental contents distributed in leaves were higher than those in flowers and stems. Therefore, chemometric approaches could be used to analyze data to accurately classify Z. clinopodioides according to origins. This study provided some elemental information on chemotaxonomy, diversity, changing pattern, distribution, and metabolism of Z. clinopodioides at spatial and temporal levels, and could be used as a reference of planting and quality standards.