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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.

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.

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.