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 in the selected cultivars to primary metabolite 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, and 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, free sterols, 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 from primary metabolites of natural products 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 the development of new breeding programs.

Keywords: berry seeds; chemical fingerprint; chromatography techniques; sugar, lipid and fatty acid identification.

Fig. 1

High-performance thin-layer chromatograms of lipids in berry seeds of: a) strawberry, b) blackberry (1–3) and black currant (4–15), and c) raspberry (1–4), gooseberry (5 and 6), chokeberry (7), goji berry (8), cape gooseberry (9) and blueberry (10–14) cultivars

Fig. 2

Fatty acid methyl ester chromatograms of analyzed berry species: a) strawberry, black currant, blueberry, blackberry and raspberry cultivars, and b) cape gooseberry, goji berry, chokeberry and gooseberry cultivars

Fig. 3

Principal component analysis: a) score plot, b and c) loading plots for lipid profile, d) score plot, e and f) loading plots for fatty acid profile, g) score plot, and h and i) loading plots for sugar profile of seeds from berry fruit cultivars. Number of 1200 variables represent the intensities of pixels along the 1200 length lines obtained by digitization of lipid chromatograms (using green channel of profiles as the most informative one), 2384 variables represent intensities of analytical signals on fatty acid chromatograms, and 1797 variables represent intensities of analytical signals on sugar chromatograms. Intensities of signals were transformed in R_F and t_R scale in order to determine the most influential variables

Fig. S1

Map with marked locations of the orchards of berry fruits

Fig. S2

Chromatograms adjusted to gray scale for: a) raspberry, b) gooseberry, c) chokeberry, goji berry and cape gooseberry, d) blackberry, e) blueberry, f) black currant, and g) strawberry

Fig. S3

Partial least square-discriminant analysis of lipid profiles of berry seeds: a-d) score plots of data, e-h) plots of the variables (intensity of pixels) versus variable importance in the projection scores, i-l) plot of the coefficients of parameters

Fig. S4

Partial least square-discriminant analysis of fatty acid profiles of berry seeds: a-d) score plots of data, e-h) plots of the variables (intensities of analytical signals) versus variable importance in the projection scores, i-l) plot of the coefficients of parameters

Fig. S5

Partial least square-discriminant analysis of sugar profiles of berry seeds: a-d) score plots of data, e-h) plots of the variables (intensities of analytical signals) versus variable importance in the projection scores, i-l) plot of the coefficients of parameters