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Basil (Ocimum basilicum L) extracts showed significant antioxidant activity and represent a rich source of antioxidants that could contribute to its disease-fighting potential. Basil contains functional ingredients due to its rich content of phenolic compounds, characterized as main carriers of its antioxidant activity. The objective of this study was to determine the influence of the conditions and methods of extraction on chemical composition of basil extracts and also to find similarities or dissimilarities of the extraction procedures in the scope of determined content of functional ingredients. The determination of phenolic compounds (phenolic acids and flavonoids) of 100 basil extracts was performed using HPLC. The concentration of phenolic acids (chlorogenic, p-hydroxybenzoic, caffeic, ferulic, vanillic, cinnamic and rosmarinic) varied from 0.01 to 79.88 mg/kg of dried extract (DE). Concentration of flavonoid components (quercetin, naringenin, rutin) ranged from 0.06 to 72.24 mg/kg of DE. Extraction with dichloromethane (10 min), chloroform (10 min), n-hexane (30 min) and infusion provided high content of rosmarinic acid and quercetin, but also a significant content of chlorogenic acid, vanillic acid, naringenin, cinnamic and ferulic acid. Chemometric analysis indicated the most effective experimental procedure and provided an overview of their grouping in the space of the extracted ingredients.

Milk-based kombucha beverages were obtained conducting kombucha lead fermentation of milk. In order to discriminate the analysed samples and to detect similarities or dissimilarities among them in the space of experimentally determined variables, hierarchical cluster analysis (HCA) and principal component analysis (PCA) were applied. Linear discriminant analysis (LDA) was conducted on the raw data set in order to find a rule for allocating a new sample of unknown origin to the correct group of samples. In the space of the variables analysed by HCA, the dominant discriminating factor for the studied samples of kombucha beverages is the milk fat (MF) content, followed by total unsaturated fatty acids content (TUFA), monounsaturated fatty acids content (MUFA) and polyunsaturated fatty acids content (PUFA). The samples with 0.8 and 1.6% milk fat belong to the same cluster in the space of the analysed variables due to similarities in their AADPPH. It was determined by LDA that there was the biggest difference in quality between the groups of products with winter savoury and stinging nettle, while the highest similarity is between groups of products with wild thyme and peppermint regarding their pH values and antioxidant activity expressed as AADPPH.

In this paper, the guidelines for the interpretation of the results of quantitative structure-retention relationship (QSRR) modeling, comparison and assessment of the established models, as well as the selection of the best and most consistent QSRR model were presented. Various linear and non-linear chemometric regression techniques were used to build QSRR models for chromatographic lipophilicity prediction of a series of triazole, tetrazole, toluenesulfonylhydrazide, nitrile, dinitrile and dione steroid derivatives. Linear regression (LR) and multiple linear regression (MLR) were used as linear techniques, while artificial neural networks (ANNs) were applied as non-linear modeling techniques. Generated models were statistically evaluated applying different approaches for model comparison and ranking. Two non-parametric methods (generalized pair correlation method – GPCM and sum of ranking differences – SRD) were used for model ranking and assessment of the best model for chromatographic lipophilicity prediction using experimentally obtained logk values and row average as a reference ranking. Both, GPCM and SRD, provided highly similar model choice regardless on a different background. These results are in agreement with the classical approach.

Human studies have demonstrated the multiple health benefits of fruits and vegetables. Due to its high fiber, mineral and antioxidant content, carrot is an ideal source for the development of nutraceuticals or functional ingredients. Current research assesses accelerated solvent extraction (ASE) traits which affect the antioxidant qualities of carrot extract using response surface methodology (RSM), hierarchical cluster analysis (HCA), and the sum of ranking differences (SRD). A mixture of organic solvents, acetone, and ethanol with or without the addition of 20% water was applied. The total carotenoid and polyphenol contents in extracts, as well as their scavenging activity and reducing power, were used as responses for the optimization of ASE extraction. RSM optimization, in the case of 20% water involvement, included 49% of acetone and 31% of ethanol (Opt1), while in the case of pure organic solvents, pure ethanol was the best choice (Opt2). The results of HCA clearly pointed out significant differences between the properties of extracts with or without water. SRD analysis confirmed ethanol to be optimal as well. RSM, HCA, and SRD analysis confirmed the same conclusion—water in the solvent mixture can significantly affect the extraction efficacy, and the optimal solvent for extracting antioxidants from carrot by ASE is pure ethanol.

© The Royal Society of Chemistry. In this work, new protic ionic liquids (PILs) with 1-ammonium-2-propanol cation and nine different anions: formate (For), acetate (Ac), lactate (Lac), trifluoroacetate (TFA), chloroacetate (ClA), trichloroacetate (TClA), 3-chloropropionate (3-ClP), 4-chlorobutyrate (4-ClB) and mandelate (Man) were prepared in order to study their antimicrobial activity and possible application for fungi and bacteria removal from deteriorated paper heritage. Ten filamentous fungal strains isolated from specific pigmented area of the damaged books: Trichoderma sp., Cladosporium sp., Penicillium sp.(1-3), Penicillium citrinum, Aspergillus sp.(1,2), Aspergillus flavus, Fusarium graminearum, eight Gram positive and Gram negative ATCC bacterial strains: B. subtilis(6633), S. aureus(6538), E. faecalis(19433), K. rhizophila(9341), E. coli(11229), S. enteritidis(13076), P. mirabilis(12453), P. aeruginosa(15692) and two yeast Candida strains: Candida albicans(ATCC 10231) and Candida albicans(L) were used in this study. The results indicated that antimicrobial activity of selected ionic liquids is significantly affected by the size and specific functional groups in the anion structure. These facts opened the possibility for molecular design of new ionic liquids with strong inhibition properties against the specific bacterial, mould and yeast strains. The significant antimicrobial properties observed in this research suggest that studied PILs may have potential applications in the paper art and artefact cleaning and conservation replacing thus, conventional solvents and organic substances that are toxic for humans and environment.

The results presented in this study include the prediction of the antifungal activity of 24 oxazolo derivatives based in their topological and electrostatic molecular descriptors, derived from the 2D molecular structures. The artificial neural network (ANN) method was applied as a regression tool. The input data for ANN modeling were selected by stepwise selection (SS) procedure. The ANN modeling resulted in three networks with the outstanding statistical characteristics. High predictivity of the established networks was confirmed by comparisons of the predicted and experimental data and by the residuals analysis. The obtained results indicate the usefulness of the formed ANNs in precise prediction of minimum inhibitory concentrations of the analyzed compounds towards Candida albicans . The Sum of Ranking Differences (SRD) method was used in this study to reveal possible grouping of the compounds in the space of the variables used in ANN modeling. The obtained results can be considered to be a contribution to development of new antifungal drugs structurally based on oxazole core, particularly nowadays when there is a lack of highly efficient antimycotics.

© 2018, CI and CEQ. All rights reserved. Yerba mate is a natural source of purine alkaloids and other bioactive compounds with prominent therapeutic activity. In this work, the influence of operating variables on yield and purine alkaloids contents upon supercritical carbon dioxide extraction was examined. High-performance liquid chromatography (HPLC) was applied to determine the contents of caffeine and theobromine. The evaluation of the experimental results was performed by applying hierarchical cluster analysis (HCA) and principal component analysis (PCA), followed by analysis of variance (ANOVA). HPLC analysis of yerba mate showed that the total yield and the contents of theobromine and caffeine rapidly increased with time and pressure of the extraction.

Screening of physicochemical properties should be considered one of the essential steps in the drug discovery pipeline. Among the available methods, biomimetic chromatography with an immobilized artificial membrane is a powerful tool for simulating interactions between a molecule and a biological membrane. This study developed a quantitative structure–retention relationships model that would predict the chromatographically determined affinity of xenobiotics to phospholipids, expressed as a chromatographic hydrophobicity index determined using immobilized artificial membrane chromatography. A heterogeneous set of 261 molecules, mostly showing pharmacological activity or toxicity, was analyzed chromatographically to realize this goal. The chromatographic analysis was performed using the fast gradient protocol proposed by Valko, where acetonitrile was applied as an organic modifier. Next, quantitative structure–retention relationships modeling was performed using multiple linear regression (MLR) methods and artificial neural networks (ANNs) coupled with genetic algorithm (GA)-inspired selection. Subsequently, the selection of the best ANN was supported by statistical parameters, the sum of ranking differences approach with the comparison of rank by random numbers and hierarchical cluster analysis.

Naturally occurring molecules are excellent sources of lead compounds. A series of oleanolic acid (OA) derivatives previously synthesized in our laboratory, which show promising antitumor activity, have been analyzed in terms of lipophilicity evaluation applying chromatographic and computational approaches. Retention data obtained on three reversed-phase liquid chromatography stationary phases (RP-HPLC) and immobilized artificial membrane chromatography (IAM-HPLC) were compared with computational methods using chemometric tools such as cluster analysis, principal component analysis and sum of ranking differences. To investigate the molecular mechanism of retention quantitive structure retention relationship analysis was performed, based on the genetic algorithm coupled with multiple linear regression (GA-MLR). The obtained results suggested that the ionization potential of studied molecules significantly affects their retention in classical RP-HPLC. In IAM-HPLC additionally, polarizability-related descriptors also play an essential role in that process. The lipophilicity indices comparison shows significant differences between the computational lipophilicity and chromatographically determined ones.

peer-reviewed, Physicochemical characterization of steroid analogs (triazole, tetrazole, toluenesulfonylhydrazide, nitrile,dinitrile and dione) is considered to be a very important step in further drug selection. This study applies to the determination of lipophilicity of previously synthesized steroid derivatives using reversed-phase high-performance liquid chromatography (RP HPLC). Chemometric aspect of chromatographic lipophilicity is given throughout multiple linear regression (MLR) quantitative structure-retention relationships (QSRR) approach. Minimal inhibitory concentration (MIC) is determined for two steroid derivatives possessing antimicrobial activity against Staphylococcus aureus. Molecular docking study was performed in order to identify the compound with the most promising potential as human cytochrome P450 CYP17A1 inhibitor. Identified 3β-hydroxyandrost-5-eno[16,17-d]-1,2,3-triazole (I.2.) could be recommended for further trials for anticancer drugs and subjected to the absorption, distribution, metabolism, excretion and toxicity (ADMET) evaluation., These results are the part of the projects No. 172025, No. 172012and No. 31055 financially supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia.

The present study is focused on a series of newly synthesized 1-aryl-3-ethyl-3-methylsuccinimide derivatives, as potential anticonvulsants. The retention behavior of eleven succinimide derivatives was determined by using reversed phase high performance liquid chromatography (RP-HPLC) and reversed phase high performance thin layer chromatography (RP-HPTLC). The estimated retention behavior was correlated with partition (logP) and distribution coefficients (logD). These high correlations pointed out that the determined retention parameters (logK(0) and R-M(0)) can be considered chromatographic (anisotropic) lipophilicity of the studied succinimide derivatives. The structural properties, which dominantly affect the chromatographic lipophilicity, were determined as well. The significant correlations between the chromatographic lipophilicity and plasma protein binding (PPB), Madin-Darby Canine Kidney (MDCK) cells permeability, volume of distribution (Vd) and absorption constant (Ka) indicate the strong influence of lipophilicity on pharmacokinetics of 1-aryl-3-ethyl-3-methylsuccinimide derivatives. These derivatives have also been tested applying Comprehensive Medicinal Chemistry (CMC) drug-like rules which confirmed their drug-like properties. Besides, their blood-brain penetration (BBB) ability has been estimated applying the set of Clark's rules and by using Pre-ADMET software. Regarding toxicity, it was predicted that only one compound from the set might have toxic effects by blocking the hERG potassium channel. The present study reveals which molecular features in the structure of novel succinimide derivatives could be crucial for their lipophilicity, and consequently for their pharmacokinetic properties. The results indicate that the newly synthesized series of succinimide derivatives should be further considered in design of novel anticonvulsants.

Waffles, cookies, and crackers are considered to be one of the most often consumed confectionery products. Their complex composition in terms of raw materials used for their production often implies more than one source of heavy metals that end up in the final product. Therefore, the analysis of the heavy metals in these types of products is necessary. The present study deals with the determination of the correlations between the content of essential metals with potential toxic effects (copper, iron, zinc, manganese, and cobalt) determined by inductively coupled plasma‐atomic emission spectrometry in waffles, cookies, and crackers collected from the market in Autonomous Province of Vojvodina, Republic of Serbia. The concentrations determined were in the following ranges: Cu (0.299–0.5015 mg/kg d.w.), Fe (3.301–65.35 mg/kg d.w.), Zn (1.962–15.58 mg/kg d.w.), Mn (1.406–27.7 mg/kg d.w.), and Co (0.01–0.26 mg/kg d.w.). The mutual dependence of their concentrations was determined by chemometric linear and nonlinear regression methods which showed to be useful in the fast and easy determination of iron, manganese, zinc, and cobalt concentrations in the studied groups of confectionery products. PRACTICAL APPLICATIONS: The practical application of the established chemometric models is reflected in the possibility of predicting the concentration of one metal in waffles, cookies, and crackers based on the concentration of the other one. In this way, the additional experiments can be avoided and the concentration of particular metal can be precisely determined, which is confirmed by the internal and external statistical validation procedures of the established chemometric models.

In this paper, 52 common bean (Phaseolus vulgaris L.) genotypes were analyzed regarding their phytochemical composition and antioxidant activity, as well as other characteristics (grain coat color, grain size, and growth size). The total phenols content (TPC), determined according to the Folin–Ciocalteu method, varied from 0.166 to 0.772 mg/100 g DM, while total flavonoids content (TFC), determined by aluminum chloride colorimetric assay, varied from 4.861 to 161.861 mg/100 g DM. Antioxidant activity was assessed by 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH) scavenging capacity and ranged from 1.419 to 12.917 mg/ml. The antioxidant activity was predicted using multiple linear regression (MLR). The obtained results revealed certain influence of grain coat color on common bean phytochemical composition and antioxidant activity that was confirmed throughout classification method—principal component analysis (PCA). White grain coat color samples were distinguished from colored ones on the basis of their phytochemical composition and antioxidant activity. PRACTICAL APPLICATIONS: The antioxidant activity of 52 common bean (Phaseolus vulgaris L.) genotypes was predicted using MLR. A certain influence of grain coat color on common bean phytochemical composition and antioxidant activity was detected applying classification chemometric techniques.

Physicochemical properties and nature of the interactions in methanol and ethanol solutions of 1-butyl-3-ethylimidazolium bromide ionic liquid, [C4C2im]Br, have been investigated experimentally measuring density over the whole composition range at selected temperatures and atmospheric pressure. From the experimental densities, the excess properties and partial molar volumes of the components at infinite dilution were calculated, as well as the excess molar expansibility at infinite dilution and isobaric thermal expansion coefficients. Obtained negative values of excess molar volumes with pronounced minimum at xIL ≈ 0.3 indicate strong ion–alcohol interactions and better packing due to differences in the size and shape of the molecules. Results are compared with those obtained for aqueous solutions of investigated ionic liquid and discussed in terms of hydrogen bond formation. Also, apparent molar volumes for {[C4C2im]Br + alcohol} systems show more pronounced deviations comparing to aqueous solutions of [C4C2im]Br confirming stronger self-association of the water molecules.

The problem with trial-and-error approach in organic synthesis of targeted anticancer compounds can be successfully avoided by computational modeling of molecules, docking studies and chemometric tools. It has been proven that A- and B- modified d-homo lactone and d-seco androstane derivatives are compounds with significant antiproliferative activity against estrogen-independent breast adenocarcinoma (ER-, MDA-MB-231) and androgen-independent prostate cancer cells (AR-, PC-3). This paper presents the quantitative structure-activity relationship (QSAR) models based on artificial neural networks (ANNs) which are able to predict whether d-homo lactone and/or d-seco androstane-based compounds will express antiproliferative activity against breast cancer cells (MDA-MB-231) or not. Also, the present paper describes the molecular docking study of 3β-acetoxy-5α,6α-epoxy- (3) and 6α,7α-epoxy-1,4-dien-3-one (24) d-homo lactone androstane derivatives, as well as 4-en-3-one (15) d-seco androstane derivative, which are compounds with strong or moderate antiproliferative activity against prostate cancer cells (PC-3), and compares them with commercially available medicament for prostate cancer - abiraterone. The obtained promising results can be used as guidelines in further syntheses of novel d-homo lactone and d-seco androstane derivatives with antiproliferative activity against breast and prostate cancer cells.

peer-reviewed, Recent studies have confirmed the possibility of an insoluble material to carry out a marked antioxidant activity by a solid–liquid interaction, and in such way opened a new chapter for dietary fiber application in food industry as a functional unit with radical scavenging capacity. Therefore, this paper investigates the possibility of improving the antioxidant activity of cookies with addition of sugar beet fibers. The chemometric analysis was carried out on the experimentally obtained data of the antioxidant activity of the cookies with modified and non-modified sugar beet fibers compared to the cookies with commercially available dietary fiber (Fibrex®) produced in the Nordic Sugar A/S factory, Sweden. The hierarchical cluster analysis and the sum of ranking differences were applied. The introduction of modified and non-modified sugar beet fibers in the cookies formulation showed promising results regarding the cookies EC50 values decline compared to the control samples. Cookies with addition of modified sugar beet fibers showed the best antioxidant activity in the first 4 weeks. Cookies with Fibrex® fibers exhibited the highest antioxidant activity., The authors acknowledge the financial support of the Ministry of Science and Technological Development of the Republic of Serbia.

Numerous structurally different amides and imides including succinimide derivatives exhibit diverse bioactive potential. The development of new compounds requires rationalization in the design in or- der to provide structural changes that guarantee favorable physico-chemical properties, pharmacological activity and safety. In the present research, a comprehensive study with comparison of the chromato- graphic lipophilicity and other physico-chemical properties of five groups of 1-arylsuccinimide derivatives was conducted. The chemometric analysis of their physico-chemical properties was carried out by us- ing unsupervised (hierarchical cluster analysis and principal component analysis) and supervised pattern recognition methods (linear discriminant analysis), while the correlations between the in silico molecular features and chromatographic lipophilicity were examined applying linear and non-linear Quantitative Structure–Retention Relationship (QSRR) approaches. The main aim of the conducted research was to determine similarities and dissimilarities among the studied 1-arylsuccinimides, to point out the molec- ular features which have significant influence on their lipophilicity, as well as to establish high-quality QSRR models which can be used in prediction of chromatographic lipophilicity of structurally similar 1-arylsuccinimides. This study is a continuation of analysis and determination of the physico-chemical properties of 1-arylsuccinimides which could be important guidelines in further in vitro and eventually in vivo studies of their biological potential.

The main raw material for cookies production is wheat flour (white or wholemeal/integral) as a nutritionally highly valuable component. In addition to the benefits it brings, this raw material is also a potential source of contamination with residues of heavy metals originating from soil and plant protection agents. Therefore, it is necessary to analyze their concentrations in both wheat flour and final products, since the wheat flour is often present in cookies and related confectionery products in a proportion >60 or 70%. The aim of this paper was to determine the content of heavy metals, including highly toxic ones (As, Cd, Hg and Pb) and essential metals with potential toxic effects (Fe, Cu, Cr, Co, Mn, Ni, Zn) in cookies, waffles and crackers available in local markets in the Autonomous Province of Vojvodina, Republic of Serbia. The present study is focused on chemometric estimation of the risk group of confectionery products containing wheat flour in an easy and efficient way. Hierarchical cluster analysis, principal component analysis, linear discriminant analysis and the sum of ranking differences analysis were applied for this purpose as chemometric tools with substantially different theoretical bases. The obtained results indicated that there is a specific group of cookies with a relatively high content of heavy metals. The group of crackers contained lower concentrations of heavy metals than the other groups of studied products. The results of the sum of ranking differences analysis indicate that there is no strict separation between the samples regarding the ratio among heavy metals contents.

The present study is aimed to analyze lipophilicity and ADMET profiles, and to develop field based 3D-QSAR and ligand-based pharmacophore hypothesis for a series of 17α-picolyl and 17(E)-picolinylidene androstane derivatives in order to give detailed structural insights and to highlight important binding features of novel androstane derivatives, as compounds with antiproliferative activity toward breast adenocarcinoma cells. This study can provide guidelines for the rational design of novel potent compounds. Sum of ranking differences (SRD), as a non-parametric method, was applied for compounds ranking. 3D-QSAR methods, including comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), were applied to predict the antiproliferative effect on breast adenocarcinoma cells and provide the regions in space where interactive fields may influence the activity. The compounds are ranked so the compounds with the most favorable ADME and lipophilicity features together with significant anticancer activity can be distinguished. The established 3D-QSAR model could be used for design of new compounds with antiproliferative activity on the human ER– breast adenocarcinoma cells. The pharmacophore model is able to accurately predict antiproliferative activity. Generally, the present study provides significant guidelines for further selection, synthesis and rational design of new highly potential androstane derivatives as anticancer drugs.

The experimental measurements of important physico-chemical properties can be quite complex task especially in the case of the large group of chemistries such as ionic liquids. The main goal of the present study is to define the relationships between the structural characteristics of the analyzed ionic liquids described by molecular descriptors and their important physico-chemical property — density. For this purpose the multiple linear regression and artificial neural networks analysis were applied. Also, principal component analysis was performed in order to gain an overview of similarities and dissimilarities among the studied ILs. In addition, the detailed statistical validation procedures were done to select the best model which can be used for prediction of the density of ILs.

peer-reviewed, The selection of the most promising anticancer compounds from the pool of the huge number of synthesized molecules is a quite complex task. There are many compounds characterization approaches which can suggest the best structural features of a molecule with the highest antiproliferative effect on the certain type of cancer cell lines. One of these approaches is the lipophilicity determination of compounds and the analysis of its correlation with the anticancer activity. Since the importance of the lipophilicity is underlined in many earlier studies, this study is focused on determination of lipophilicity of previously synthesized 17α-picolyl and 17(E)-picolinylidene androstane derivatives by using reversed-phase high performance liquid chromatography (RP-HPLC) as a very fast, effective and relatively cheap method. Determination of the chromatographic lipophilicity of the studied androstanes can be considered as the part of their physicochemical characterization, which is a very important step in their further selection as drug candidates. The present study does not neglect the in silico approach. The determined chromatographic lipophilicity was analyzed by quantitative structure-retention relationship (QSRR) approach in order to reveal which molecular characteristics contribute mostly to the typical behavior of the androstanes in the applied chromatographic system, and thus to their lipophilicity. Classical statistical approach and Sum of Ranking Differences method were used for selection of the best QSRR models which should be used in prediction of chromatographic lipophilicity of studied androstane derivatives., This study is financially supported by the research projects of the Ministry of Education, Science and Technological Development of the Republic of Serbia (No. 172012 and No. 172021) and the research project of the Provincial Secretariat for Science and Technological Development of Vojvodina (No. 114-451-347/2015-02).

Model discrimination is still not a resolved task. The classical statistical approaches lead to different results (for the same models) and at the same time a lot of models seem to be statistically equivalent. The authors deliberately select such conditions when their algorithm is superior. Hence, it is better to apply different approaches to compare and rank the models fairly. This paper presents the application of methodology called sum of ranking differences (SRD) to rank the artificial neural network models [quantitative structure–activity relationship (QSAR) models] designed for prediction of anticancer activity of 17-picolyl and 17-picolinylidene androstane derivatives toward androgen receptor negative prostate cancer cells (AR-, PC-3). The SRD method suggests the consistent models, in terms of compounds order and proximity to the golden standard, which should preferably be used in the prediction of anticancer activity of studied androstane derivatives.

Quantitative structure–retention relationship analysis has been performed in order to correlate the retention of carboxydrate derivatives with their calculated lipophilicity, using normal-phase thin-layer chromatography. Principal component analysis followed by hierarchical cluster analysis and multiple linear regression was performed. Statistically significant and physically meaningful structure–retention relationships were obtained. Very good predictive ability of the established mathematical models allows us to estimate the retention behavior of structurally similar compounds. Sum of ranking differences was applied for comparing values obtained using different chromatographic systems and for comparing calculated lipophilicity descriptors of investigated derivatives.

Display Omitted 64 beetroot samples were cultivated under different conditions.Novel approach to samples similarity analysis was applied.The differences between the samples were detected.ANN-R model best predicts antioxidant activity of beetroot extracts. The estimation of influences of different cultivation conditions on chemical composition of crops can be quite complex task. Usually, the classical statistic approach (comparison of statistical characteristics of different populations) is applied. However, this study presents the analysis of the influence of different cultivation conditions (cultivation with or without foil cover, different preceding crops and fertilization) on the contents of dry matter, betalains and phenolic compounds in 64 beetroot (Beta vulgaris L. ssp. vulgaris) samples and their antioxidant potential (IC50) by using powerful chemometric tools, such as Wald-Wolfowitz run test, cluster analysis and sum of ranking differences, in order to obtain groupings of the samples which share similar properties or to test if the samples come from the same population. Besides the aforementioned classification or pattern recognition analysis, the regression methods (linear, multiple linear and artificial neural network regressions) were carried out in order to establish reliable linear and non-linear relationships between IC50 and contents of betalains, phenolic compounds and dry matter. The results reveal certain influence of foil cover, used during the cultivation, on beetroot antioxidant potential and content of the determined betalains and phenolic compounds, as well as the strong non-linear relationship between the analyzed variables. Certain influence of preceding crops on phenolic compounds content is detected by Wald-Wolfowitz run test.

The present paper gives an insight into the linear and nonlinear relationships between the retention behavior in reversed-phase high performance liquid chromatography (RP-HPLC) of several classes of pesticides isolated from groundwater, and their in silico physicochemical, topological and lipophilicity molecular descriptors. The quantitative structure-retention relationship (QSRR) chemometric approach was applied for this purpose on a large set of compounds (77 pesticides). The selection of the most appropriate molecular descriptors was achieved by stepwise selection procedure coupled with partial least squares method and the variance inflation in projection parameter (SS-PLS-VIP). QSRR included the linear regression (LR), multiple linear regession (MLR) and artificial neural networks regression (ANN-R). In order to select the optimal QSRR model, statistical validation parameters were used. Besides, a relatively new chemometric method called sum of ranking differences (SRD) was applied in order to select the optimal regression model. The obtained results showed that certain models can successfully be used for precise prediction of the retention time of the studied compounds.

In this paper correlations between molecular lipophilicity of a twelve 2,4-dioxotetrahydro-1,3-thiazole derivatives and their retention characteristics obtained by reversed-phase thin-layer chromatography are presented. The regression models were defined applying two chemometric approaches: linear regression and multiple linear regression. The studied thizole derivatives were described with molecular lipophilicity descriptors that were used for prediction of their retention characteristic RM. Statistically significant and physically meaningful structure–retention relationships were obtained and cross-validated. The predicted results are very well correlated with the experimental data. Very good predictive ability of the established mathematical models allows us to estimate the retention behavior of structurally similar compounds.

Quantitative structure-retention relationships (QSRR) was developed for eleven 1,6-anhydrohexose and D-aldopentose derivatives using their retention data (RM0 values), obtained by normal phase (NP) thin-layer chromatography (TLC) and partition coefficients for n-octanol/water bi-phase system–logP. In order to select molecular descriptors that best describe the retention behavior of the investigated molecules, principal component analysis (PCA) was carried out, followed by hierarchical cluster analysis (HCA) and multiple linear regression (MLR). The best QSRR models were validated by leave-one-out technique and by the calculation of statistical parameters. Established mathematical models are meaningful and statistically significant and can successfully predict retention behavior of investigated 1,6-anhydrohexose and D-aldopentose derivatives.

Quantitative structure-retention relationship (QSRR) analysis has been performed in order to correlate the retention of selected estradiol derivatives with their calculated molecular lipophilicity. The lipophilicity descriptors were derived computationally and most important were selected. Linear regression (LR) Wad used for model establishing. Statistical quality of the generated models was determined by standard statistical and crossvalidation statistical parameters. Statistically significant and physically meaningful models were obtained. The prediction results are very well correlated with the experimentally observed data. Given predictive ability of the established models indicates that they could be used for predicting the chromatographic behavior of the similar molecules in normal-phase high-performance thin-layer chromatography. [Projekat Ministarstva nauke Republike Srbije, br. 172012, br. 172014 i br. 31055]

Quantitative structure-retention relationship (QSRR) method was applied to study the chromatographic behaviour of D-homo-androstene derivatives 1-7. Retention constants (RM 0) of the analysed derivatives were determined by reversed-phase high-performance thin-layer chromatography (RP HPTLC) on C18 plates by using four mobile phase mixtures: methanol-water, acetone-water, acetonitrile-water, and dioxane-water. Correlation analysis based on multiple regression method was applied in order to model chromatographic retention by means of nine different lipophilicity descriptors (logP). The developed QSRR models were cross-validated and high-quality validation parameters were obtained by leave-one-out method. It was found that the derived QSRR models have a good predictive ability. [Projekat Ministarstva nauke Republike Srbije, br. 172012 i br. 172014]

The present study is based on the quantitative structure-activity relationship (QSAR) analysis of binding affinity toward human prion protein (huPrPC) of quinacrine, pyridine dicarbonitrile, diphenylthiazole and diphenyloxazole analogs applying different linear and non-linear chemometric regression techniques, including univariate linear regression, multiple linear regression, partial least squares regression and artificial neural networks. The QSAR analysis distinguished molecular lipophilicity as an important factor that contributes to the binding affinity. Principal component analysis was used in order to reveal similarities or dissimilarities among the studied compounds. The analysis of in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) parameters was conducted. The ranking of the studied analogs on the basis of their ADMET parameters was done applying the sum of ranking differences, as a relatively new chemometric method. The main aim of the study was to reveal the most important molecular features whose changes lead to the changes in the binding affinities of the studied compounds. Another point of view on the binding affinity of the most promising analogs was established by application of molecular docking analysis. The results of the molecular docking were proven to be in agreement with the experimental outcome.

This study is based on the analysis of the retention behavior of some anhydro-D-aldose derivatives in reversed-phase thin-layer chromatography (RP-TLC). Chromatographic separations were achieved applying a methanol/water mobile phase and three different stationary phases: C18-bonded silica gel, silica gel impregnated with paraffin oil, and silica gel impregnated with squalane. Retention behavior of the analyzed molecules was defined by constant and correlated with in silico molecular descriptors. According to the statistical validation parameters, obtained results showed that the established multiple-linear quantitative structure-retention relationship models (MLR-QSRR) can successfully predict the chromatographic lipophilicity of structurally similar compounds. In the present study the influence of some functional groups on the total lipophilicity of studied derivatives was determined as well. Principal component analysis (PCA) was applied in order to obtain an overview of similarity or dissimilarity among the analyzed compounds and hierarchical cluster analysis (HCA) was applied in order to compare separation characteristics of the applied stationary phases.

In the present work, relationships between the textural characteristics of fermented milk products obtained by kombucha inoculums with various teas were investigated by using chemometric analysis. The presented data which describe numerically the textural characteristics (firmness, consistency, cohesiveness and index of viscosity) were analysed. The quadratic correlation was determined between the textural characteristics of fermented milk products obtained at fermentation temperatures of 40 and 43 °C, using milk with 0.8, 1.6 and 2.8% milk fat and kombucha inoculums cultivated on the extracts of peppermint, stinging nettle, wild thyme and winter savory. Hierarchical cluster analysis (HCA) was performed to identify the similarities among the fermented products. The best mathematical models predicting the textural characteristics of investigated samples were developed. The results of this study indicate that textural characteristics of sample based on winter savory have a significant effect on textural characteristics of samples based on peppermint, stinging nettle and wild thyme, which can be very useful in the determination of products texture profile.

The nutritional requirements for antimicrobial agent production using Streptomyces hygroscopicus were analyzed in shake flask experiments. Antimicrobial activity was tested against Staphylococcus aureus and Bacillus cereus. The mathematical models have been generated with relative high complexity in order to give an adequate fit to the data. All the results suggest a high dependence of produced antimicrobial agent quantities on the amount of carbon, nitrogen, and phosphorus in cultivation medium. The statistical results of the generated models reflect the high predictive ability. The derived models were validated using leave-one-out cross-validation technique, and from statistical point of view, they have significantly high values of the cross-validation parameters.

Quantitative structure-retention relationship (QSRR) was developed for a series of estrane derivatives, on the basis of their retention data, obtained in reversed-phase thin-layer chromatography (RP TLC), and in silico molecular descriptors. Physicochemical and topological descriptors, as well as molecular bulkiness descriptors, were calculated from the optimized molecular structures. Full geometry optimization was achieved by using Austin Model 1 (AM1) semi-empirical molecular orbital method. In the present study, QSRR analysis was based on principal component analysis (PCA), multiple linear regression (MLR) and partial least squares (PLS) method. PCA was applied in order to reveal similarities or dissimilarities between analytes, and MLR and PLS regression methods were carried out in order to identify the most important in silico molecular descriptors and quantify their influence on the retention behaviour of studied compounds. Physically meaningful and statistically significant structure-retention relationships were established.

The subject of this paper is to determine the post-mortem interval (PMI) using the data obtained by potentiometric measurements of the electrolyte concentrations (potassium and sodium) in human vitreous humour. The data were processed by linear least squares (LLS) and artificial neural network (ANN) procedures. The LLS mathematical models have been developed as calibration models for prediction of the PMI. The quality of the models was validated by the leave one out (LOO) technique and by using an external data set. High agreement between experimental and predicted PMI values indicated the good quality of the derived models. Additionally, we analysed the influence of various factors (the cause of death, sex, differences between electrolyte concentrations in left and right eye) on the accuracy and reliability of obtained PMI. The ANN method was based on 174 forensic cases with different causes of death and known PMI ranging from 3.1–24.1 hours. The external data sets corresponding to 40 selected forensic c...

This study is based on the analyses of the retention behavior of selected natural styryl lactones and their synthetic analogues in reversed-phase high-performance liquid chromatography. Chromatographic separations were achieved applying ZORBAX SB-C18 column and two different mobile phases: methanol-water and acetonitrile-water. Chromatographic lipophilicity of the analyzed compounds was defined by logk0 constant and correlated with in silico molecular descriptors. According to the statistical validation parameters, obtained results indicate that the presented linear and multiple quantitative structure-retention relationship models can successfully predict the chromatographic lipophilicity of structurally similar compounds. Hierarchical cluster analyses (HCA) was applied in order to group similar compounds according to their chromatographic and in silico lipophilicity. It can be concluded that chromatographic systems with methanol-water were better for modelling of logk0. Modelling was performed in order to characterize compounds regarding their lipophilicity profiles as future drug candidates.

peer-reviewed, The present paper deals with chromatographic lipophilicity determination of twenty-nine selected steroid derivatives using reversed-phase high-performance liquid chromatography (RP HPLC) combined with two mobile phase, acetonitrile-water and methanol-water. Chromatographic behavior of four groups (triazole and tetrazole, toluenesulfonylhydrazide, nitrile and dinitrile and dione) of selected steroid derivatives was studied. Investigated compounds were grouped using principal component analysis (PCA) according to their logk values for both mobile phases. Grouping was in the very good accordance with the polarity and lipophilicity ofthe investigated compounds. QSRR (quantitative structure-retention relationship) approach was used to model chromatographic lipophilicity behavior using molecular descriptors. Modeling was performed using linear regression (LR) and multiple linear regression (MLR) methods. The most influential molecular descriptors were lipophilicity descriptors that are important for molecules ability to pass through biological membranes and geometrical descriptors. All established LR-QSRR and MLR-QSRR models were statistically validated by standards, cross- and external validation parameters as well as with two graphical methods. According to all these assessments, MLR models were better for chromatographic lipophilicity prediction. It was shown that chromatographic systems with methanol-water were better for modeling of logk than systems with acetonitrile-water, as well as the systems that contained lower volume fractions of organic component in mobile phase. Modeling was performed in order to obtain lipophilicity profiles of investigated compounds as future drug candidates of biomedical importance., These results are the part of the projects Nos. 172025, 31055 and 172012, supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia.

Main objective of this work was to investigate the influence of pressure and temperature on supercritical carbon dioxide extraction of Satureja montana in terms of extraction yield and chemical composition. The most dominant compound in all investigated extracts was oxygenated monoterpene-carvacrol. The kinetics of the supercritical carbon dioxide extraction of S. montana as well as the solubility data were investigated by modelling the extraction curves using different empirical models and all models used showed similar deviation from experimental data. Hierarchical cluster analysis was employed in order to reveal possible similarities and dissimilarities among the extracts obtained at different extraction conditions.

In this paper, quantitative structure-retention relationship study has been applied in order to correlate obtained retention parameter R(M)(0) and two groups of molecular descriptors, for eleven investigated benzimidazole derivatives. Principal component analysis (PCA), followed by hierarchical cluster analysis (HCA) and multiple linear regression (MLR), was applied in order to identify the most important molecular descriptors. Mathematical models were established and the best models were further validated by leave-on-out (LOO) technique as well as by the calculation of the statistical parameters. Statistically significant models were established.

The relationships between the contents of various metals in different types of chocolates were studied using chemometric approach. Chemometric analysis was based on the application of artificial neural networks (ANN). ANN was performed in order to select the significant models for predicting the metal contents. ANN equations, that represent the content of one metal as a function of the contents of other metals were established. The statistical quality of the generated mathematical models was determined by standard statistical measures and cross-validation parameters. High agreement between experimental and predicted values, obtained in the validation procedure, indicated the good quality of the models. The obtained results indicate the possibility of predicting the metal contents in different types of chocolate.

The relationship between retention behavior of eleven 1,6-anhydrohexose and D-aldopentose derivatives and their molecular characteristics was studied using quantitative structure–retention relationships (QSRR) approach. Retention parameters RM0, obtained by normal-phase thin-layer chromatography, were correlated with molecular and in silico absorption, distribution, metabolism and excretion (ADME) descriptors. For describing the retention behavior of investigated molecules and determining the similarities between molecules, principal component analysis (PCA) was performed, followed by hierarchical cluster analysis (HCA) and multiple linear regression (MLR). For both sets of descriptors, PCA resulted in a model with the two significant principal components (PCs). HCA was conducted to confirm the grouping of the compounds already obtained by PCA. MLR equations were established for both sets of descriptors and statistical quality of the generated models was determined by standard statistical measures and cross-validation parameters. According to statistical validation, two very good models with molecular and one with in silico ADME descriptors were obtained. Very good predictive ability of the established mathematical models allows us to estimate retention behavior of structurally similar compounds and to understand their behavior in similar chromatographic systems.

The aim of this study was to establish models for the antioxidant activity (IC50) prediction of lettuce samples (Lactuca sativa L.) using their phytochemicals content (total phenols, chlorophylls, total carotenoids, and vitamin C) by using suitable chemometric methods. In order to select phytochemicals that best describe the antioxidant activity, classification, and regression analysis were conducted. Generalized pair correlation method (GPCM) gave insight which phytochemicals are the most important for the antioxidant activity prediction. That was confirmed using multiple linear regression (MLR) and establishing models that were validated. The sum of ranking differences (SRD) was applied in order to obtain and rank models with best predictive power. Both GPCM and MLR showed that the most important phytochemicals for the antioxidant activity prediction are chlorophyll a, chlorophyll a + b, and total carotenoids content. The obtained results should be considered as preliminary for the prediction of the antioxidant activity and further improvement in scientific research on this subject follows. Practical applications The models for the prediction of the antioxidant activity were obtained. Novel approach for variable selection was conducted and new method for the MLR model ranking was applied. Chemometric guidelines for the selection of lettuce cultivars with increased antioxidant activity were defined.

The present paper deals with prediction of cytotoxic activity of 17-picolyl and 17-picolinylidene androstane derivatives toward androgen receptor negative prostate cancer cell line (PC-3). The prediction was achieved applying artificial neural networks (ANNs) method on the basis of molecular descriptors. The most important descriptors (skin permeability (SP), Madin-Darby canine kidney cell permeability (MDCK) and universal salt solubility factor (S+SF)) were selected by using stepwise selection coupled with partial least squares method. The ANN modelling was carried out in order to obtain reliable models which can facilitate further synthesis of androstane derivatives with high antiproliferative activity toward PC-3 cell line. The modelling procedure resulted in three ANN models with the best statistical performance. The obtained results show that the established ANN models can be applied for required purpose.

Soybean oil fractions were obtained by collecting extract at different time intervals during supercritical CO2 extraction. Influences of pressure (300–500 bar), temperature (40–60°C), CO2 flow rate (0.194–0.436 kg/h) and particle size (0.238–1.059 mm) on the concentration of triacylglycerols (TAGs) were investigated. Relationships between retention behavior of seventeen TAGs and their molecular characteristics were studied using chemometric approach. Quantitative Structure–Retention Relationship (QSRR) analysis was carried out on retention time values (tr) obtained by high pressure liquid chromatography to identify structural requirements of different TAGs for their retention. Principal component analysis (PCA) was performed in order to select molecular descriptors that best describe retention behavior of the compounds investigated, and to determine the similarities among molecules. The accurate mathematical models were developed for predicting the retention behaviour of some TAGs. The validity of the models was evaluated by suitable statistical and cross-validation parameters.

The relationships between the contents of various metals (Cu, Ni, Pb and Al) in 38 different milk chocolate samples were studied using a chemometric approach. The chemometric expressions were generated using a training set of 25 chocolate samples and the predictive ability of the resulting models was evaluated against a test set of 13 chocolate samples. The chemometric analysis was based on the application of multiple linear regression analysis (MLR). MLR was performed in order to select the significant models for predicting the metal contents. The MLR equations that represent the content of one metal as a function of the contents of other metals were established. High agreement between experimental and predicted values, obtained in the validation procedure, indicated the good quality of the models. It enables the researchers to establish reliable relationships between the contents of various metals which can be used for their prediction in different types of chocolate prior to their analysis. This can reduce the trial-and-error element and experimental costs in the production.[Projekat Ministarstva nauke Republike Srbije, br. 31055, br. 172012 i br. 172014]

In the present study, principal component analysis (PCA) followed by principal component regression (PCR) and partial least squares (PLS) method was applied in order to identify the most important in silico molecular descriptors and quantify their influence on antifungal activity (expressed as minimal inhibitory concentration) of selected benzoxazole and oxazolo[4,5-b]pyridine derivatives against Candida albicans. PLS regression showed the best statistical performance, according to the lowest value of the standard error (root mean square errors of calibration of 0.02526 and cross-validation of 0.04533), while PCR model was characterized by root mean square errors of calibration of 0.03176 and cross-validation of 0.05661. The most important descriptors in both PLS and PCR model are solubility in water, expressed as AClogS and ABlogS, and lipophilicity, expressed as XlogP2 and ABlogP. Very good predictive ability of the established models, confirmed by corresponding statistical parameters, allows us to estimate antifungal activity of structurally similar compounds.

The properties relevant to lipophilicity of four series of synthesized s-triazine derivatives have been studied by quantitative structure-retention relationship (QSRR) approach. Examination of chromatographic behavior revealed a linear correlation between RM values and the volume fraction of mobile phase modifier. Furthermore, a reliable relationship was defined between the retention constants, RM0, and theoretically calculated bioactivity descriptors for lipophilicity and solubility. Principal component analysis (PCA) followed by multiple linear regression (MLR) and hierarchical cluster analysis (HCA) 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 QSRR models were further validated by leave one out technique as well as by the calculation of statistical parameters for the established theoretical models. The RM0 values of the investigated s-triazine derivatives have been recommended for description of their lipophilicity and evaluation of pharmacokinetic properties.