Primary Metabolite Chromatographic Profiling as a Tool for Chemotaxonomic Classification of Seeds from Berry Fruits

Abstract

Research background. Considering the importance of consumption of berry fruits with proven health-beneficial properties, and difficulties in quality control of products of specific botanical and geographic origin, a fingerprint method was developed, based on advanced data analysis (pattern recognition, classification), in order to relate the variability of nutrients among selected cultivars according to primary metabolites profile. Experimental approach. Forty-five samples of genuine berry fruit cultivars (strawberry, raspberry, blackberry, black currant, blueberry, gooseberry, chokeberry, cape gooseberry, and goji berry) were characterized according to chromatographic profiles of primary metabolites (sugars, lipids and fatty acids) obtained by three chromatographic techniques (high-performance thin-layer chromatography, gas chromatography coupled to mass spectrometry, high-performance anion-exchange chromatography with pulsed amperometric detection). Results and conclusions. Comprehensive analysis allowed monitoring and identification of metabolites belonging to polar lipids, mono-, di- and triacylglycerols, free fatty acids, sterols, and sterol esters, mono- to heptasaccharides, and sugar alcohols. Chemical fingerprint of berry seeds showed the uniformity of primary metabolites within each fruit species, but revealed differences depending on the botanical origin. All three chromatographic methods provided a discriminative, informative, and predictive metabolomics methodology, which proved to be useful for chemotaxonomic classification. Novelty and scientific contribution. A novel methodology for the identification of bioactive compounds corresponding to natural products primary metabolites was described. The proposed untargeted metabolite profiling approach could be used in the future as a routine method for tracing of novel bioactive compounds. The knowledge of metabolite composition obtained in this study, can provide a better assessment of genotypic and phenotypic differences between berry fruit species and varieties, and could contribute to development of new breading programs.

Quality Assessment of Bee Pollen‐Honey Mixtures Using Thin‐Layer Chromatography in Combination with Chemometrics

“…HMF was detected and quantified at 285 nm using the calibration curve of the HMF standard with a concentration range of 1–15 mg/L. Sugars in honey samples were determined by high‐performance anion‐exchange chromatography (HPAEC) with pulsed amperometric detection (PAD) method with Dionex CarboPac PA100 column as described in Krstić et al [31] …”

“…HMF was detected and quantified at 285 nm using the calibration curve of the HMF standard with a concentration range of 1-15 mg/L. Sugars in honey samples were determined by high-performance anion-exchange chromatography (HPAEC) with pulsed amperometric detection (PAD) method with Dionex CarboPac PA100 column as described in Krstić et al [31] Preparation of Bee Pollen-Honey Mixtures Bee pollen-honey mixtures were prepared by adding each bee pollen to each honey at 20 % (B1/1-B1/8, S1/ 1-S1/8, L1/1-L1/8) and 30 % (B2/1-B2/8, S2/1-S2/8, L2/1-L2/8). Corresponding amounts of honey and powdered bee pollen were mixed by mechanical stirring until homogenization.…”