This study (open access) used machine learning classification models to identify monosaccharide markers for coffee adulteration. These markers (proposed thresholds for glucose, xylose and mannitol) are suitable for authenticity monitoring vs Brazilian official regulatory standards (SDA Ordinance 570) using High-Performance Anion Exchange Chromatography with Pulsed Amperometric Detection and can flag adulteration with corn, wheat, and barley adulteration from 3%.
The training and validation sets were prepared from verified samples supplied by the Brazilian Ministry of Agriculture and roasted, ground and adulterated in-house. Coffees (157 raw samples) comprised of arabica and canephora species from eight different states. Adulterants were acai, husk, barley, wood fragments, corn and wheat ranging from 1 – 20%.
Photo by Nathan Dumlao on Unsplash
Comments