ArticleMolecules (Basel, Switzerland)2023
Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS) Fingerprinting and Chemometrics for Coffee Classification and Authentication.
Article in Molecules (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 11 citations in OpenAlex.
- Review
- High-Throughput Testing for Unknown Mutagens and Cytotoxica via Duplex Planar Ames-Cytotoxicity Bioassay Including Metabolic S9 Activation.Analytical chemistry · 2026Article
- Metabolomic-based promising technique for fruit-based natural products analysis.Food chemistry: X · 2025Review
- Mass Spectrometric Fingerprinting to Detect Fraud and Herbal Adulteration in Plant Food Supplements.Molecules (Basel, Switzerland) · 2025Article
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Authors and funding
3 authors at 2 institutions in 1 country.
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Abstract
Nowadays, the quality of natural products is an issue of great interest in our society due to the increase in adulteration cases in recent decades. Coffee, one of the most popular beverages worldwide, is a food product that is easily adulterated. To prevent fraudulent practices, it is necessary to develop feasible methodologies to authenticate and guarantee not only the coffee's origin but also its variety, as well as its roasting degree. In the present study, a C18 reversed-phase liquid chromatography (LC) technique coupled to high-resolution mass spectrometry (HRMS) was applied to address the characterization and classification of Arabica and Robusta coffee samples from different production regions using chemometrics. The proposed non-targeted LC-HRMS method using electrospray ionization in negative mode was applied to the analysis of 306 coffee samples belonging to different groups depending on the variety (Arabica and Robusta), the growing region (e.g., Ethiopia, Colombia, Nicaragua, Indonesia, India, Uganda, Brazil, Cambodia and Vietnam), and the roasting degree. Analytes were recovered with hot water as the extracting solvent (coffee brewing). The data obtained were considered the source of potential descriptors to be exploited for the characterization and classification of the samples using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). In addition, different adulteration cases, involving nearby production regions and different varieties, were evaluated by pairs (e.g., Vietnam Arabica-Vietnam Robusta, Vietnam Arabica-Cambodia and Vietnam Robusta-Cambodia). The coffee adulteration studies carried out with partial least squares (PLS) regression demonstrated the good capability of the proposed methodology to quantify adulterant levels down to 15%, accomplishing calibration and prediction errors below 2.7% and 11.6%, respectively.
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Registered trials
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