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Machine learning-driven hyperspectral imaging for non-destructive origin verification of green coffee beans across continents, countries, and regions

Coffee is a target for geographical origin fraud. More rapid, cost-effective, and sustainable traceability solutions are needed. The potential of hyperspectral imaging-near-infrared (HSI-NIR) and advanced machine learning models for rapid and non-destructive origin classification of coffee was explored for the first time (i) to understand the sensitivity of HSI-NIR for classification across various origin scales (continental, country, regional), and (ii) to identify discriminant wavelength regions. HSI-NIR analysis was conducted on green coffee beans from three continents, eight countries, and 22 regions.

The classification performance of four different machine learning models (PLS-DA, SVM, RBF-SVM, Random Forest) was compared. Linear SVM provided near-perfect classification performance at the continental, country, and regional levels, and enabled a feature selection opportunity.

This study demonstrates the feasibility of using HSI-NIR with machine learning for rapid and nondestructive screening of coffee origin, eliminating the need for sample processing.


Joy Sim, Yash Dixit, Cushla Mcgoverin, Indrawati Oey, Russell Frew, Marlon M. Reis, Biniam Kebede

Published in:

Food Control Volume 156, February 2024, 110159


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