Jelena Trifković

Phenolic profile and antioxidant activity of Serbian polyfloral honeys.

A total of 58 polyfloral honey samples from different regions in Serbia were studied to determine their phenolic profile, total phenolic content and antioxidant capacity. UHPLC-LTQ OrbiTrap MS made possible the identification of 36 compounds: 24 flavonoids, two abscisic acids, and 10 phenolic acids and their derivatives. Quantification was done using 14 available standards. Data on phenolics and abscisic acids allowed the discrimination and classification of honeys in accordance to their geographical origin, using pattern recognition techniques, principal component analysis and partial least squares discriminant analysis. Samples originated from Vojvodina and Zlatibor region were clearly distinguished from those from the rest of Serbia because of the presence of dicaffeoylquinic acid, ellagic acid, caffeic acid phenethyl ester, and chlorogenic acid, among others. A good correlation (r = 0.865) was observed between total phenolic content and radical-scavenging activity. Total phenolic content ranged from 0.03 to 1.39 mg GAE/g and radical scavenging activity ranged from 1.31% to 25.61%.

Pattern recognition methods and multivariate image analysis in HPTLC fingerprinting of propolis extracts

High‐performance thin‐layer chromatography (HPTLC) combined with image analysis and pattern recognition methods were used for fingerprinting and classification of 52 propolis samples collected from Serbia and one sample from Croatia. Modern thin‐layer chromatography equipment in combination with software for image processing and warping was applied for fingerprinting and data acquisition. The three mostly used chemometric techniques for classification, principal component analysis, cluster analysis and partial least square‐discriminant analysis, in combination with simple and fast HPTLC method for fingerprint analysis of propolis, were performed in order to favor and encourage their use in planar chromatography. HPTLC fingerprint analysis of propolis was for the first time performed on amino silica plates. All studied propolis samples have been classified in two major types, orange and blue, supporting the idea of existence of two types of European propolis. Signals at specific RF values responsible for classification of studied extracts have also been isolated and underlying compounds targeted for further investigation. Copyright

Ultrahigh‐performance Liquid Chromatography and Mass Spectrometry (UHPLC–LTQ/Orbitrap/MS/MS) Study of Phenolic Profile of Serbian Poplar Type Propolis

INTRODUCTION Propolis is a resinous natural substance collected by honeybees from different plant sources. Due to the presence of various phytochemicals, this bee-product exhibits numerous biological activities, including anti-bacterial, anti-viral, anti-inflammatory, anti-oxidant, immunostimulating and anti-tumour effects. As the chemical composition and biological activity of propolis depend on its botanical and geographical origin, searching for new bioactive substances in various types of propolis from unexplored regions is of great importance. OBJECTIVE The aim of this study is the evaluation of the phenolic profile of poplar propolis samples in order to characterise Serbian propolis, to identify possible new constituents and to specify the phenolic components relevant for differentiation of poplar propolis samples into two subgroups through simultaneous analysis of poplar bud extracts. METHODS Ethanolic extracts of propolis and poplar buds were comprehensively analysed using ultrahigh-performance liquid chromatography coupled with hybrid mass spectrometry, which combines the linear trap quadrupole and Orbitrap MS/MS mass analyser together with chemometric methods. RESULTS Extensive fingerprint analysis of Serbian propolis was achieved for the first time. Seventy-five phenolic compounds were detected. Eight of them were identified in propolis for the first time. Pattern-recognition methods applied to the content of ten quantified phenolics verified the existence of two subgroups of propolis, with galangin, chrysin and pinocembrin as the most influential distinguishing factors. CONCLUSION The phenolic composition of the analysed propolis samples confirm their affiliation to the European poplar type propolis and the existence of two subgroups according to botanical origin.

Structure–retention relationship study of arylpiperazines by linear multivariate modeling

A quantitative structure-retention relationship study has been performed to correlate the retention of 33 newly synthesized arylpiperazines with their molecular characteristics, using thin-layer chromatography. Principal component analysis followed by multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS) was performed to identify the most important factors, to quantify their influences, and to select descriptors that best describe the behavior of the compounds investigated. The best statistical performance was achieved by applying PLS regression, leading to the lowest value of the standard error (root mean square errors of calibration of 0.159 and cross-validated value RMSE cross-validation=0.231 units), followed by the PCR (root mean square errors of calibration=0.195 and RMSE cross-validation=0.305) and MLR (R(adj)(2)=0.9499, F=102.017, mean square error=0.052 and predicted residual error sum of squares=2.23). Two factors of the highest influence: surface tension and hydrophilic-lipophilic balance appear as the part of obtained models. In addition, polar surface area and hydrophilic surface area are included by both PLS and PCR models. Moreover, logP has been added to the PLS model. Besides, PCR model includes following descriptors: hydrogen bond acceptor, hydrogen bond donor and LUMO energy, whereas topological descriptors: connectivity indices 0 and 2, and valence index 3 are included in the MLR model.

Amino acids profile of Serbian unifloral honeys

BACKGROUND The free amino acids profile of 192 samples of seven different floral types of Serbian honey (acacia, linden, sunflower, rape, basil, giant goldenrod, and buckwheat) from six different regions was analysed in order to distinguish honeys by their botanical origin. RESULTS The most abundant amino acids were proline, alanine, phenylalanine, threonine and arginine. Based on the established amino acids profiles, some important differences have been identified among studied honey samples relying on the basic descriptive statistics data, and confirmed by multivariate chemometric methods. Principal component analysis revealed that basil honey samples form a well-defined cluster imposed with phenylalanine content. The model obtained by linear discriminant analysis might be used to distinguish basil honey from the rest of the samples, and has moderate predictive power to separate genuine acacia, linden, sunflower and rape honeys. New data for the amino acids profile of giant goldenrod and buckwheat honey samples are presented. CONCLUSIONS The floral origin of honey could be successfully evaluated by its amino acids profile coupled with chemometric analysis.

Planar Chromatographic Systems in Pattern Recognition and Fingerprint Analysis

An overview of opportunities of contemporary planar chromatography in pattern recognition and fingerprint analysis is presented. The most used chemometric methods are highlighted and their main advantages and drawbacks are underlined. In addition a cross section of the application of planar chromatographic fingerprinting in food, pharmaceutical, environmental, and forensic analysis is given.

Metal accumulation capacity of parasol mushroom (Macrolepiota procera) from Rasina region (Serbia)

During many years of industrial development, soil system was contaminated with large amounts of toxic metals. In order to investigate the mobility and availability of metals from soil to mushrooms, the content of 13 elements (Al, Ba, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Sr, Ti, and Zn), in caps and stipes of wild-grown edible mushroom Macrolepiota procera and soil substrates, collected from five sites in Rasina region in central Serbia, was determined. Soil samples were subjected to the sequential extraction procedure proposed by the Community Bureau of Reference in order to fractionate acid-soluble/exchangeable, reducible, oxidizable, and residual fractions. Metal concentrations were determined by inductively coupled plasma optical emission spectrometer and inductively coupled plasma mass spectrometer and the results subjected to multivariate data analysis. A principal component analysis distinguished mushrooms samples from different geographical areas and revealed the influence of soil composition on metal content in mushrooms. Hierarchical cluster analyses confirmed that the first three phases of extraction were the most important for metal uptake by mushrooms from soil. The bioconcentration factors and translocation factors for each metal were also calculated. These results showed that M. procera could serve as a good dietary source of essential elements, especially Cu, Zn, Mn, and Fe but the consumption of mushrooms may pose a health risk for consumers during the “season of mushrooms,” due to the presence of cadmium.

Determination of the soil-water partition coefficients (log KOC) of some mono- and poly-substituted phenols by reversed-phase thin-layer chromatography.

In order to determine the soil-water partition coefficient for eleven mono- and poly-substituted phenolic compounds, for which there is still no literature data available, the possibility of using thin-layer chromatography (TLC) as a means for rapid and reliable logK(OC) estimation was examined. A series of chromatographically derived descriptors: R(M)(0), b, C(0) and PC1 (first principal component), calculated from retention data obtained under reversed-phase conditions, were used for the assessment of models as well as for a direct calibration procedure. The final calibration models are discussed with regard to the achieved accuracy and statistical quality, the type of descriptors used and the corresponding chromatographic conditions. The estimated logK(OC) values of the studied phenols were compared with those obtained by other means: (a) the present OECD guideline based on an HPLC technique; (b) the KOCWIN software package, available free of charge from the US Environmental Protection Agency web site and (c) general LSER models established by Nguyen and coworkers, and Poole and coworkers. The proposed method showed the best agreement with the results obtained by the OECD procedure, followed by the LSER models of Poole and Nguyen. Lower quality correlations were achieved with the KOCWIN calculated values, especially those predicted by molecular connectivity indices.

Antimicrobial Activity of Serbian Propolis Evaluated by Means of MIC, HPTLC, Bioautography and Chemometrics

New information has come to light about the biological activity of propolis and the quality of natural products which requires a rapid and reliable assessment method such as High Performance Thin-Layer Chromatography (HPTLC) fingerprinting. This study investigates chromatographic and chemometric approaches for determining the antimicrobial activity of propolis of Serbian origin against various bacterial species. A linear multivariate calibration technique, using Partial Least Squares, was used to extract the relevant information from the chromatographic fingerprints, i.e. to indicate peaks which represent phenolic compounds that are potentially responsible for the antimicrobial capacity of the samples. In addition, direct bioautography was performed to localize the antibacterial activity on chromatograms. The biological activity of the propolis samples against various bacterial species was determined by a minimum inhibitory concentration assay, confirming their affiliation with the European poplar type of propolis and revealing the existence of two types (blue and orange) according to botanical origin. The strongest antibacterial activity was exhibited by sample 26 against Staphylococcus aureus, with a MIC value of 0.5 mg/mL, and Listeria monocytogenes, with a MIC as low as 0.1 mg/mL, which was also the lowest effective concentration observed in our study. Generally, the orange type of propolis shows higher antimicrobial activity compared to the blue type. PLS modelling was performed on the HPTLC data set and the resulting models might qualitatively indicate compounds that play an important role in the activity exhibited by the propolis samples. The most relevant peaks influencing the antimicrobial activity of propolis against all bacterial strains were phenolic compounds at RF values of 0.37, 0.40, 0.45, 0.51, 0.60 and 0.70. The knowledge gained through this study could be important for attributing the antimicrobial activity of propolis to specific chemical compounds, as well as the verification of HPTLC fingerprinting as a reliable method for the identification of compounds that are potentially responsible for antimicrobial activity. This is the first report on the activity of Serbian propolis as determined by several combined methods, including the modelling of antimicrobial activity by HPTLC fingerprinting.

Comparative study of different approaches for multivariate image analysis in HPTLC fingerprinting of natural products such as plant resin.

Considering the introduction of phytochemical fingerprint analysis, as a method of screening the complex natural products for the presence of most bioactive compounds, use of chemometric classification methods, application of powerful scanning and image capturing and processing devices and algorithms, advancement in development of novel stationary phases as well as various separation modalities, high-performance thin-layer chromatography (HPTLC) fingerprinting is becoming attractive and fruitful field of separation science. Multivariate image analysis is crucial in the light of proper data acquisition. In a current study, different image processing procedures were studied and compared in detail on the example of HPTLC chromatograms of plant resins. In that sense, obtained variables such as gray intensities of pixels along the solvent front, peak area and mean values of peak were used as input data and compared to obtained best classification models. Important steps in image analysis, baseline removal, denoising, target peak alignment and normalization were pointed out. Numerical data set based on mean value of selected bands and intensities of pixels along the solvent front proved to be the most convenient for planar-chromatographic profiling, although required at least the basic knowledge on image processing methodology, and could be proposed for further investigation in HPLTC fingerprinting.