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PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis
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Explainable Artificial Intelligence for Coffee Quality Control: From Coffee Origins to Aroma Intensity.

Giorgio Felizzato1, Eloisa Bagnulo1, Giorgia Botta1

  • 1Dipartimento di Scienza e Tecnologia del Farmaco, Università di Torino, Via Pietro Giuria 9, 10125 Torino, Italy.

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Summary
This summary is machine-generated.

This study used explainable AI to link coffee

Keywords:
SHAParoma intensitycoffee qualityexplainable AIorigin identityspecialty coffee

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Area of Science:

  • Food Science
  • Analytical Chemistry
  • Sensory Science
  • Artificial Intelligence

Background:

  • Coffee quality and sensory characteristics are significantly influenced by origin (terroir), impacting chemical composition.
  • Specialty coffee values authenticity, traceability, and distinct flavors, necessitating understanding the molecular basis of sensory attributes like intensity.

Purpose of the Study:

  • To explore the relationship between volatile compounds, coffee origin, and perceived sensory intensity using analytical chemistry and explainable AI.
  • To identify key volatile compounds responsible for differentiating coffee origins and predicting sensory intensity.

Main Methods:

  • Analyzed volatile composition of single-origin coffees using headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME/GC-MS).
  • Employed Support Vector Machine (SVM) for origin classification and SHapley Additive exPlanations (SHAP) for identifying key differentiating volatile compounds.
  • Utilized Ridge Regression (RR) to predict sensory intensity scores provided by an expert panel.

Main Results:

  • The SVM model achieved 91% accuracy in classifying coffee origins based on volatile profiles.
  • SHAP analysis successfully identified key volatile compounds contributing to origin differentiation.
  • Ridge Regression accurately predicted sensory intensity (R² = 0.88), linking molecular profiles to expert-assigned scores.

Conclusions:

  • The explainable AI approach establishes a transparent and reproducible link between coffee's molecular profile, sensory traits, and perceived quality.
  • This method offers an objective and traceable quality assessment system by connecting analytical data with sensory expertise.
  • The findings support the evolution of quality control in the coffee industry through data-driven insights.