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Rapid Classification of Coffee Products by Data Mining Models from Direct Electrospray and Plasma-Based Mass Spectrometry Analyses

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Abstract

In coffee manufacture, analytical methods with high-throughput and cost efficacy are required for process development and quality control. Thus, we investigated the applicability of direct mass spectrometry methods to distinguish coffee products according to species, geographic origin and processing. We tested the performance of the established method direct-injection electrospray mass spectrometry (DIESI-MS) and the emerging method low-temperature plasma ionization mass spectrometry (LTP-MS). Both methods are capable of classifying coffee products, but DIESI-MS and LTP-MS yield complementary information about the chemical composition of the samples. DIESI-MS shows a broad molecular weight range of compounds. In contrast, LTP-MS detects mainly low molecular weight compounds, which correspond to quality-related ingredients, such as caffeine and purines. LTP-MS displays a high potential for rapid quality control measurements and online monitoring, because no sample processing is required. Data mining methods support the discovery of ‘important’ compounds, which are responsible for the discrimination between sample groups, and reveal associated chemical processes.

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Authors and Affiliations

  1. Department of Biotechnology and Biochemistry, Cinvestav Unidad Irapuato, Km. 9.6 Libramiento Norte Carr. Irapuato-León, 36821, Irapuato, GTO, Mexico

    Roberto Gamboa-Becerra, Josaphat Miguel Montero-Vargas, Sandra Martínez-Jarquín, Abigail Moreno-Pedraza & Robert Winkler

  2. Agroindustrias Unidas de México S.A. de C.V. (AMSA), Bosque de Alisos 45A, Colonia Bosques de las Lomas, México, DF, Mexico

    Eligio Gálvez-Ponce

Authors
  1. Roberto Gamboa-Becerra
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  2. Josaphat Miguel Montero-Vargas
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  3. Sandra Martínez-Jarquín
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  4. Eligio Gálvez-Ponce
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  5. Abigail Moreno-Pedraza
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  6. Robert Winkler
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Corresponding author

Correspondence to Robert Winkler.

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Funding

This study was funded by Secretaría de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación (SAGARPA) and the Consejo Nacional de Ciencia y Tecnología (CONACyT), Mexico, with grants AVANCE N° 127280, INNOVATEC PEI 451/2012, FIT S0017-2015-01, I0017/CB2010-01/151596, FINNOVA I010/260/2014 and FRONTERAS 2015-2/814. RGB, JMMV, SMJ and AMP thank CONACYT for their postgraduate fellowships.

Conflict of Interest

SMJ and RW are inventors of the patent application ‘Non-thermal plasma jet device as source of spatial ionization for ambient mass spectrometry and method of application’ (WO 2014/057409, United States Patent 9362100). The micromass ZQ was borrowed from Waters, Mexico. Agroindustrias Unidas de Mexico SA de CV and Garcomex SA de CV provided samples. The sponsors were not involved in the study design, collection, analysis or interpretation of data.

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Gamboa-Becerra, R., Montero-Vargas, J.M., Martínez-Jarquín, S. et al. Rapid Classification of Coffee Products by Data Mining Models from Direct Electrospray and Plasma-Based Mass Spectrometry Analyses. Food Anal. Methods 10, 1359–1368 (2017). https://doi.org/10.1007/s12161-016-0696-y

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  • DOI: https://doi.org/10.1007/s12161-016-0696-y

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