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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

Paper-based analytical devices (PADs) have the potential for low-cost, rapid point-of-use testing with easier and cheaper fabrication than (for example) 3D laser-printed microfluidics.

This review states that it covers cutting edge applications for food authenticity analysis and includes a section on how close some of the applications are to commercialisation.  There is no detail in the publicly-available abstract as to what topics or applications the review covers.  Purchase of the article would be needed to ascertain its use or relevancy.  It is published in a reputable peer-reviewed journal.