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Proteomics coupled machine learning-innovative approach in geographical origin authentication of green Coffea arabica.

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Title: Proteomics coupled machine learning-innovative approach in geographical origin authentication of green Coffea arabica.
Authors: Belej Ľ; The Slovak University of Agriculture in Nitra, The Faculty of Biotechnology and Food Sciences, Institute of Food Sciences, Tr. A. Hlinku 2, 94976 Nitra, Slovakia., Demianová A; The Slovak University of Agriculture in Nitra, The Faculty of Biotechnology and Food Sciences, Institute of Food Sciences, Tr. A. Hlinku 2, 94976 Nitra, Slovakia., Danchenko M; Institute of Plant Genetics and Biotechnology, Plant Science and Biodiversity Centre, Slovak Academy of Sciences, Akademická 2, 95007 Nitra, Slovakia., Mishra S; Institute of Plant Genetics and Biotechnology, Plant Science and Biodiversity Centre, Slovak Academy of Sciences, Akademická 2, 95007 Nitra, Slovakia., Baráth P; Institute of Chemistry, Slovak Academy of Sciences, Dúbravská cesta 9, 84538 Bratislava, Slovakia., Jurčaga L; The Slovak University of Agriculture in Nitra, The Faculty of Biotechnology and Food Sciences, Institute of Food Sciences, Tr. A. Hlinku 2, 94976 Nitra, Slovakia., Lidiková J; The Slovak University of Agriculture in Nitra, The Faculty of Biotechnology and Food Sciences, Institute of Food Sciences, Tr. A. Hlinku 2, 94976 Nitra, Slovakia., Bobko M; The Slovak University of Agriculture in Nitra, The Faculty of Biotechnology and Food Sciences, Institute of Food Sciences, Tr. A. Hlinku 2, 94976 Nitra, Slovakia., Poláková K; The Slovak University of Agriculture in Nitra, The Faculty of Biotechnology and Food Sciences, Institute of Food Sciences, Tr. A. Hlinku 2, 94976 Nitra, Slovakia., Švecová T; The Slovak University of Agriculture in Nitra, The Faculty of Biotechnology and Food Sciences, Institute of Food Sciences, Tr. A. Hlinku 2, 94976 Nitra, Slovakia., Bobková A; The Slovak University of Agriculture in Nitra, The Faculty of Biotechnology and Food Sciences, Institute of Food Sciences, Tr. A. Hlinku 2, 94976 Nitra, Slovakia. Electronic address: alica.bobkova@uniag.sk.
Source: Food chemistry [Food Chem] 2025 Nov 30; Vol. 493 (Pt 2), pp. 145784. Date of Electronic Publication: 2025 Aug 05.
Publication Type: Journal Article; Evaluation Study
Language: English
Journal Info: Publisher: Elsevier Applied Science Publishers Country of Publication: England NLM ID: 7702639 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-7072 (Electronic) Linking ISSN: 03088146 NLM ISO Abbreviation: Food Chem Subsets: MEDLINE
Imprint Name(s): Publication: Barking : Elsevier Applied Science Publishers
Original Publication: Barking, Eng., Applied Science Publishers.
MeSH Terms: Coffea*/chemistry , Coffea*/classification , Coffea*/genetics , Coffea*/metabolism , Proteomics*/methods , Plant Proteins*/chemistry , Plant Proteins*/genetics , Plant Proteins*/metabolism , Machine Learning* , Food Contamination*/analysis, Africa ; Seeds/chemistry ; Seeds/classification ; Asia ; Geography
Abstract: Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
The geographical authentication of green specialty coffee is an economically sensitive analytical task that is not yet fully resolved. We used an innovative combination of proteomic profiling with linear discriminant analysis for the authentication 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, we selected the top 30 target markers ranked by ANOVA. 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. Besides, 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 %. This research contributes to advancing traceability tools in the coffee industry, ensuring product authenticity, and promoting fair trade practices. Specificity and sensitivity confirmed that the model appears to be reliable at distinguishing Asian and African samples.
(Copyright © 2025 Elsevier Ltd. All rights reserved.)
Contributed Indexing: Keywords: Authentication of origin; Green coffee; Linear discriminant analysis; Proteome profiling; Specialty beans
Substance Nomenclature: 0 (Plant Proteins)
Entry Date(s): Date Created: 20250807 Date Completed: 20250915 Latest Revision: 20250915
Update Code: 20250916
DOI: 10.1016/j.foodchem.2025.145784
PMID: 40774217
Database: MEDLINE
Description
Abstract:Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br />The geographical authentication of green specialty coffee is an economically sensitive analytical task that is not yet fully resolved. We used an innovative combination of proteomic profiling with linear discriminant analysis for the authentication 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, we selected the top 30 target markers ranked by ANOVA. 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. Besides, 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 %. This research contributes to advancing traceability tools in the coffee industry, ensuring product authenticity, and promoting fair trade practices. Specificity and sensitivity confirmed that the model appears to be reliable at distinguishing Asian and African samples.<br /> (Copyright © 2025 Elsevier Ltd. All rights reserved.)
ISSN:1873-7072
DOI:10.1016/j.foodchem.2025.145784