Polyphenolic compounds proling in guabiroba fruits (Campomanesia xanthocarpa Berg.) by comprehensive two-dimensional liquid chromatography

Significance of the Topic

The characterization of polyphenolic compounds in guabiroba (Campomanesia xanthocarpa Berg.) fruits addresses a gap in the understanding of this under-studied native species. Polyphenols are known for their antioxidant and health-promoting properties, and comprehensive profiling supports the development of functional foods, quality control in the industry and deeper insights into ripening-related metabolic changes.

Objectives and Study Overview

This work aimed to optimize an extraction protocol for polyphenols in unripe and ripe guabiroba fruits using a simplex-centroid design and to apply comprehensive two-dimensional liquid chromatography (LC×LC) for detailed profiling. Key objectives included selecting an optimal solvent mixture, configuring an LC×LC platform and quantifying major phenolic constituents across fruit maturity stages.

Methodology and Instrumentation

• Extraction Optimization: A simplex-centroid experimental design tested methanol, 2% acetic acid and acetonitrile in binary and ternary combinations. Freeze-dried fruit (1.5 g) was extracted with 15 mL solvent, using orbital shaking, ultrasonic treatment and centrifugation. Extracts were evaporated, reconstituted in methanol and filtered prior to analysis.
• LC×LC Platform: A Nexera-e system equipped with dual-dimension switching valves and 10 µL loops connected a RP-Amide column (1D) and a short C18 column (2D). A segmented multi-step gradient in the second dimension and one-minute modulation provided high peak capacity and orthogonality.
• Detection and Quantification: Photodiode array detection (200–450 nm) combined with an ESI-MS triple quadrupole enabled compound identification. Calibration curves for eleven standards (1–100 mg/L) were prepared in methanol; other compounds were semi-quantified against representative standards.

Main Results and Discussion

• Solvent Optimization: The highest total phenolics (274.5 mg GAE/100 g) was achieved with a 50:50 (v/v) mixture of acetonitrile and 2% acetic acid, confirmed by model validation (R² = 0.92).
• LC×LC Performance: The method yielded effective peak capacities of 803 (unripe) and 808 (ripe) with orthogonality values of 65% and 52%, respectively, supporting separation of complex polyphenol mixtures.
• Compound Profiling: Thirty-seven phenolic compounds were identified, including flavonols, flavones, phenolic acids, flavan-3-ols, phenones and flavanones. Ripe fruit showed marked increases in methyl galangin isomers (up to 151.7 mg/100 g) and geraldone, reflecting ripening-related biosynthetic activity.

Benefits and Practical Applications

The optimized extraction and LC×LC methodology enables detailed quality assessment of guabiroba fruit extracts for nutraceutical development, standardization in functional food production and comparative studies of maturity-dependent phytochemical changes.

Future Trends and Possibilities

Further research may integrate high-resolution mass spectrometry for structural elucidation, explore bioactivity assays to link specific polyphenols with health effects and apply the workflow to other native or under-utilized fruit species. Advances in automation and data processing will enhance throughput and discovery of novel compounds.

Conclusions

A simplex-centroid design efficiently defined an extraction solvent that maximizes phenolic yield in guabiroba. The first comprehensive LC×LC-PDA-MS method identified and quantified 37 polyphenols, revealing clear compositional differences between unripe and ripe fruits. This approach lays a foundation for functional ingredient development and further bioactivity investigations.

References

• [1] Pereira MC, Steffens RS, Jablonski A, Hertz PF, De Rios A, Vizzotto M, Flôres SH. Journal of Agricultural and Food Chemistry. 2012;60:3061–3067.
• [2] Abozed SS, El-Kalyoubi M, Abdelrashid A, Salama MF. Annals of Agricultural Sciences. 2014;59:63–67.
• [3] Uma DB, Ho CW, Wan Aida WMM. Sains Malaysiana. 2010;39:119–128.
• [4] Stoll DR, Carr PW. Analytical Chemistry. 2017;89:519–531.
• [5] Camenzuli M, Schoenmakers PJ. Analytica Chimica Acta. 2014;838:93–101.
• [6] Li X, Stoll DR, Carr PW. Analytical Chemistry. 2009;81:845–850.
• [7] Barauna SC, Delwing-Dal Magro D, Brueckheimer MB, Maia TP, Sala GABN, Döhler AW, Harger MC, de Melo DFM, de Gasper AL, Alberton MD, Siebert DA, Micke GA, de Albuquerque CAC, Delwing-De Lima D. Metabolic Brain Disease. 2018;33:1985–1994.
• [8] de Araújo FF, Neri-Numa IA, de Paulo Farias D, da Cunha GRM, Pastore GM. Food Research International. 2019;121:57–72.
• [9] Luque A, Sebai SC, Santiago-Schübel B, Le Coz Y, Jenot D, Ramaen O, Sauveplane V, Pandjaitan R. Metabolic Engineering. 2014;23:123–135.
• [10] Lee HN, Shin SA, Choo GS, Kim HJ, Park YS, Kim BS, Kim SK, Cho SD, Nam JS, Choi CS, Che JH, Park BK, Jung JY. International Journal of Molecular Medicine. 2018;41:888–898.