Authenticity tests for coffee tend to focus on the variety (Arabica vs Rustica) or adulteration of roasted ground coffee (e.g. with chicory). There has been relatively little focus on authenticating the origin of green beans, for example to underpin Fair Trade traceability.
Proteomics has previously shown differences among cultivars. This paper (subscription required) built on previous studies that had showed that long-term adaptation to a distinct climate (associated with the geographical location), are likely to significantly affect various metabolic processes and thus protein profiles. Most proteins in beans are likely to be enzymes, such as oxidases and peroxidases. Previous researchers had identified 531 proteins in C. arabica cultivars in high-altitude African and low-altitude South American samples. Further analysis pointed out that only a few proteins were significantly different between them, plausibly corresponding to the concentration of certain compounds (e.g., flavonoids) alongside the adaptation to the environmental niches (e.g., colder climate or predominant pathogens). Post-harvest processing modifies proteomic profile.
This study used a combination of proteomic profiling with linear discriminant analysis for the classification of the geographical origin of green specialty coffee beans from well-known harvesting regions in Central America, South America, Africa, and Asia. Out of 1596 identified proteins, the authors selected the top 30 target markers ranked by ANOVA. They report that the model's prediction performance using leave-one-out cross-validation reached 85.3 %, with the lowest accuracy in the prediction rate for Asian samples. Model performance and prediction sensitivity to random states were tested using 5-fold cross-validation. After 20 iterations, the model performance slightly decreased to 84.0 %. Specificity and sensitivity confirmed that the model appears to be reliable at distinguishing Asian and African samples.
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