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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

Fluorescence spectroscopy utilizing benchtop and portable spectrometers with light-emitting diodes (LEDs) as a fixed excitation source has been used as a method for detecting food adulteration in various products, including honey, extra virgin olive oil, tea, and coffee  It is cost-effective, rapid, and sensitive, allowing for intact measurement. LED-based fluorescence spectroscopy is fast, accurate, and cheaper than using a laser.. Recent advancements in semiconductor technology have enabled the delivery of LEDs with commercially available wavelengths ranging from 370 to 470 nm, exhibiting significant light intensity.

In this paper (purchase required), the authors used the technique to develop a classification model to detect ground soy in ground-roasted Arabica coffee, and to differentiate Robusta and Liberica varieties.  The abstract gives no details of the reference samples used to construct or validate the model but it was limited to 2024 season samples harvested in Indonesia.

Photo by Nathan Dumlao on Unsplash