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<p>Solving the complexities of coffee aroma is vital for the industry, especially since different growing regions produce distinct coffee volatile profiles which are influenced by variations in several factors such as climate, soil, and cultivation practices. Discriminating these profiles enables the authentication of coffee origin, helping protect consumers and producers. In this study, the Self-Organizing Map (SOM) was employed to analyze the volatile profile of high-quality coffee from various geographical regions, including Honduras, Ecuador, Costa Rica, Guatemala, El Salvador, and Brazil. The volatile profile of 311 Coffee arabica “specialty” samples was obtained using a Proton Transfer Reaction-Time of Flight Mass Spectrometer (PTR-ToF-MS). Subsequently, by employing a SOM technique, coupled with classifier neural networks, the research focuses on discerning geographical origins, resulting in a two-dimensional map that enhances data visualization and interpretation. This approach also identified which volatile organic compounds (VOCs) play a significant role in identifying different origins across the map. The results demonstrated that samples from Honduras, Ecuador, Costa Rica, and Guatemala were uniformly grouped in specific areas whilst samples from El Salvador and Brazil exhibited more fragmented distributions. This analysis contributes valuable insights into understanding flavor complexities of high-quality coffee, ensuring origin authentication and valorization.</p>

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Discriminating volatile profiles of roasted Arabica specialty coffee beans from different countries using a self-organizing map

  • 期刊论文

  • 发表日期：

    2025年7月11日

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  • Diego Comparini,
  • Corrado Costa,
  • Francesca Antonucci,
  • Simona Violino,
  • Chiara Fini,
  • Cosimo Taiti,
  • Stefano Mancuso,
  • Camilla Pandolfi

摘要

Solving the complexities of coffee aroma is vital for the industry, especially since different growing regions produce distinct coffee volatile profiles which are influenced by variations in several factors such as climate, soil, and cultivation practices. Discriminating these profiles enables the authentication of coffee origin, helping protect consumers and producers. In this study, the Self-Organizing Map (SOM) was employed to analyze the volatile profile of high-quality coffee from various geographical regions, including Honduras, Ecuador, Costa Rica, Guatemala, El Salvador, and Brazil. The volatile profile of 311 Coffee arabica “specialty” samples was obtained using a Proton Transfer Reaction-Time of Flight Mass Spectrometer (PTR-ToF-MS). Subsequently, by employing a SOM technique, coupled with classifier neural networks, the research focuses on discerning geographical origins, resulting in a two-dimensional map that enhances data visualization and interpretation. This approach also identified which volatile organic compounds (VOCs) play a significant role in identifying different origins across the map. The results demonstrated that samples from Honduras, Ecuador, Costa Rica, and Guatemala were uniformly grouped in specific areas whilst samples from El Salvador and Brazil exhibited more fragmented distributions. This analysis contributes valuable insights into understanding flavor complexities of high-quality coffee, ensuring origin authentication and valorization.