An FIA-MS Method for Rapid Coffee Adulteration Detection: A Comparative Study with a Non-Targeted LC-MS Approach

Coffee adulteration is a growing concern in the food industry due to economic and quality implications. This study evaluates a rapid, non-targeted fingerprinting method based on flow injection analysis-mass spectrometry (FIA-MS) for detecting common coffee adulterants. A total of 119 samples were analyzed, including 43 coffee samples and 76 samples of common coffee adulterants (16 chicory, 10 barley, and 50 flour samples). FIA-MS combined with chemometric analysis allowed for the classification of pure and adulterated coffee samples with over 95% accuracy. Compared to LC-MS, the FIA-MS method showed a similar performance while offering significantly faster analysis and lower solvent consumption, making it a practical and sustainable option for high-throughput screening. For PLS regression studies, calibration and prediction errors were consistently below 0.91% and 11.7%, respectively. Furthermore, the methodology was compared with a non-targeted LC-MS approach, showing an excellent performance.

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Publication Details

Journal
Foods
Published
2025-08-22
DOI
https://doi.org/10.3390/foods14172931
Primary Topic
Coffee research and impacts
Type
article
Field-Weighted Citation Impact
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article

An FIA-MS Method for Rapid Coffee Adulteration Detection: A Comparative Study with a Non-Targeted LC-MS Approach

Óscar Núñez, Javier Saurina, Nerea Núñez
Foods
Coffee research and impacts
article

An FIA-MS Method for Rapid Coffee Adulteration Detection: A Comparative Study with a Non-Targeted LC-MS Approach

Óscar Núñez, Javier Saurina, Nerea Núñez
article en

Abstract

Coffee adulteration is a growing concern in the food industry due to economic and quality implications. This study evaluates a rapid, non-targeted fingerprinting method based on flow injection analysis-mass spectrometry (FIA-MS) for detecting common coffee adulterants. A total of 119 samples were analyzed, including 43 coffee samples and 76 samples of common coffee adulterants (16 chicory, 10 barley, and 50 flour samples). FIA-MS combined with chemometric analysis allowed for the classification of pure and adulterated coffee samples with over 95% accuracy. Compared to LC-MS, the FIA-MS method showed a similar performance while offering significantly faster analysis and lower solvent consumption, making it a practical and sustainable option for high-throughput screening. For PLS regression studies, calibration and prediction errors were consistently below 0.91% and 11.7%, respectively. Furthermore, the methodology was compared with a non-targeted LC-MS approach, showing an excellent performance.

FoodsVol. 14(17)
Generalitat de Catalunya (ES), Departament de Recerca i Universitats (ES), Universitat de Barcelona (ES)
Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación
Openalex Percentile: Top 76%
Coffee research and impacts
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