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  • Author or Editor: Petar M. Ristivojević x
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The quality of three types of beer (dark, light and non-alcoholic) was assessed using high-performance thin-layer chromatography (HPTLC) combined with high-resolution mass spectrometry and chemometrics. An HPTLC separation of the polar beer components in the ethyl acetate extract was developed. The polar components were detected either by the in situ 2,2-diphenyl-1-pic-rylhydrazyl (DPPH*) assay or by derivatization with the Neu’s reagent, followed by the PEG solution. This directly allowed the visual comparison and evaluation of the phenolic/flavonoid or radical scavenging (antioxidative) beer profile. Although the three types of beer showed a very similar chemical HPTLC pattern, the signal intensities were different. Detected by the Neu's reagent, the dark beer extracts contained a high amount of phenolic compounds, and the light beer extracts showed a moderate content, while the non-alcoholic beer extracts had the lowest phenolic content. The HPTLC-DPPH* assay confirmed the higher radical scavenging activity of dark beer extracts, if compared to light and non-alcoholic beer extracts. The most active bands with regard to the radical scavenging property were identified to be desdimethyl-octahydro-iso-cohumulone and iso-n/ad-humulone. The use of pattern recognition techniques showed a clear differentiation between dark and non-alcoholic beer extracts, while light beer extracts did overlap with both beer types. This HPTLC screening allowed the (1) direct comparison of beer samples/types via classification and pattern recognition, (2) the assessment of the beer quality with regard to its antioxidative potential, and (3) the reference to single components.

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Planar chromatography is commonly used for the quality control of herbal medicines due to its many advantages. Its combination with chemometrics was proven to be a fast and reliable tool for the extraction of even more analytical information, such as similarity or dissimilarity between samples, and the identification of marker compounds. To date, depending on image processing procedures, different variables have been obtained as input data, and thus, various preprocessing procedures have been applied. In this study, we converted the chromatogram images of high-performance thin-layer chromatography to form a data matrix, by digitization of the chromatograms. Further, principal component analysis was applied on raw data and investigated after different preprocessing techniques. The proposed preprocessing techniques were successfully applied to improve the differentiation between two types of German propolis. The best multivariate models were observed in the case of warping, standard normal variate, and mean centering/autoscaling.

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