Discrimination of Green Coffee (Coffea arabica and Coffea canephora) of Different Geographical Origin Based on Antioxidant Activity, High-Throughput Metabolomics, and DNA RFLP Fingerprinting

Abstract

The genus Coffea is known for the two species C. arabica (CA) and C. canephora (CC), which are used to prepare the beverage coffee. Proper identification of green beans of coffee varieties is based on phenotypic and phytochemical/molecular characteristics. In this work, a combination of chemical (UV/Vis, HPLC-DAD–MS/MS, GC–MS, and GC-FID) and molecular (PCR-RFLP) fingerprinting was used to discriminate commercial green coffee accessions from different geographical origin. The highest content of polyphenols and flavonoids was always found in CC accessions, whereas CA showed lower values. ABTS and FRAP assays showed a significant correlation between phenolic content and antioxidant activity in most CC accessions. We identified 32 different compounds, including 28 flavonoids and four N-containing compounds. The highest contents of caffeine and melatonin were detected in CC accessions, whereas the highest levels of quercetin and kaempferol derivatives were found in CA accessions. Fatty acids of CC accessions were characterized by low levels of linoleic and cis octadecenoic acid and high amounts of elaidic acid and myristic acid. Discrimination of species according to their geographical origin was achieved using high-throughput data analysis, combining all measured parameters. Lastly, PCR-RFLP analysis was instrumental for the identification of recognition markers for the majority of accessions. Using the restriction enzyme AluI on the trnL-trnF region, we clearly discriminated C. canephora from C. arabica, whereas the cleavage performed by the restriction enzymes MseI and XholI on the 5S-rRNA-NTS region produced specific discrimination patterns useful for the correct identification of the different coffee accessions. This work extends our previous studies and provides new information on the complete flavonoid profile, combining high-throughput data with DNA fingerprinting to assess the geographical discrimination of green coffee.