Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Sustainable Perennial Crops Laboratory » Research » Publications at this Location » Publication #345866

Research Project: Genetic Diversity Assessment of Cacao and Other Tropical Tree Crop Genetic Resources

Location: Sustainable Perennial Crops Laboratory

Title: Accurate differentiation of green beans of Arabica and Robusta coffee using nanofluidic array of single nucleotide polymorphism (SNP) markers

Author
item Zhang, Dapeng
item Vega, Fernando
item HUAWEI, TAN - Nanjing Agricultural University
item JOHNSON, ELIZABETH - The Inter-American Institute For Cooperation On Agriculture
item SOLANO, WILLIAM - Catie Tropical Agricultural Research
item Meinhardt, Lyndel

Submitted to: Journal of AOAC International
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/28/2019
Publication Date: 4/26/2020
Citation: Zhang, D., Vega, F.E., Huawei, T., Johnson, E., Solano, W., Meinhardt, L.W. 2020. Accurate differentiation of green beans of Arabica and Robusta coffee using nanofluidic array of single nucleotide polymorphism (SNP) markers. Journal of AOAC International. 103:315–324.

Interpretive Summary: Green (unroasted) coffee is one of the most traded agricultural commodities in the world. The yearly value of the global coffee industry has been estimated at US$173 billion. Arabica and Robusta are the two main types of coffees, accounting for 99% of the commercial coffee market. In general, Arabica coffee has more favorable sensory characteristics, thus higher commercial value. The accurate differentiation of green beans of these two species, is needed to safeguard the economic interest ofthe coffee industry. Using a newly developed DNA fingerprinting technology known as Single Nucleotide Polymorphisms (SNPs), we validated a protocol that correctly differentiated green coffee beans from these two coffee species. In addition, this method can identify different coffee cultivars or genotypes through the same procedure. The major advantage of this method is that the genotyping is established on a single bean basis, and it can be carried out in high-throughput fashion, and thus it has a good potential for practical application in the coffee industry. This method can also be used to support management of coffee genetic resources and coffee breeding. This information will be used by the coffee industry, researchers, extension staff and farmers to improve coffee quality control in the value chain.

Technical Abstract: Green (unroasted) coffee is one of the most traded agricultural commodities in the world. The Arabica (Coffea Arabica L.) and Robusta (Coffea canephora L.) species are the two main types of coffees for commercial production. In general, Arabica coffee is known to have better quality in terms of sensory characteristics; thus, it has higher market value than Robusta coffee. Accurate differentiation of green beans of the two species is, therefore, of commercial interest in the coffee industry. Using the newly developed single nucleotide polymorphism (SNP) markers, we analyzed a total of 50 single green bean samples, representing five Arabica and five Robusta cultivars or accessions. Reliable SNP fingerprints were generated for all tested samples. Unambiguous differentiation between Robusta and Arabica coffee was achieved using multivariant analysis and Bayesian assignment test. The SNP marker panel and the genotyping protocol are sufficiently robust to detect admixture of green coffee in a high-throughput fashion. Moreover, the multilocus SNP approach can potentially differentiate different cultivars or genotypes with each species. This advantage, together with the single-bean sensitivity, suggests a significant potential for practical application in coffee industry.