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Stable isotope and trace element analyses with non-linear machine-learning data analysis improved coffee origin

Joy Sim1, Cushla Mcgoverin2,3, Indrawati Oey1,4

  • 1Department of Food Science, University of Otago, Dunedin, New Zealand.

Journal of the Science of Food and Agriculture
|March 16, 2023
PubMed
Summary
This summary is machine-generated.

Advanced machine learning models accurately classify green coffee bean origins using stable isotope and trace element data. Decision-tree-based methods excelled in identifying key geographical markers for improved coffee traceability.

Keywords:
coffeemachine learningmarker selectionorigin traceabilitystable isotopestrace elements

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Area of Science:

  • Food Science
  • Analytical Chemistry
  • Machine Learning

Background:

  • Investigated geographical origin classification of green coffee beans.
  • Utilized stable isotope and trace element analyses combined with machine learning.
  • Analyzed specialty green coffee beans from three continents, eight countries, and 22 regions.

Purpose of the Study:

  • To improve coffee origin classification and marker selection.
  • To evaluate the effectiveness of stable isotope and trace element analyses for origin determination.
  • To compare traditional and non-linear machine learning approaches for coffee classification.

Main Methods:

  • Measured five isotope ratios (δ13C, δ15N, δ18O, δ2H, δ34S) and 41 trace elements.
  • Applied Partial Least Squares Discriminant Analysis (PLS-DA) for initial classification.
  • Employed non-linear machine learning techniques, including ensemble decision trees, random forest, and extreme gradient boost.

Main Results:

  • PLS-DA showed limitations at continental and Central American regional levels.
  • Non-linear machine learning significantly improved origin prediction accuracy.
  • Ensemble decision trees, random forest, and extreme gradient boost achieved high predictive accuracies (up to 0.94 and 0.89).

Conclusions:

  • Advanced machine learning models enhance coffee origin classification and marker identification.
  • Decision-tree-based models offer superior performance and interpretability.
  • The study demonstrates the potential of integrated analytical and computational methods for coffee traceability.