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Cocoa bean fingerprinting via correlation networks.

Santhust Kumar1, Roy N D'Souza2, Marcello Corno3

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Network science reveals cocoa

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Area of Science:

  • Food science
  • Analytical chemistry
  • Network science

Background:

  • Cocoa bean fermentation lacks standardization, complicating food authenticity and quality assessments.
  • Existing methods struggle with the chemical complexity of cocoa products.

Purpose of the Study:

  • To demonstrate how network science can enhance food fingerprinting and authenticity research for cocoa.
  • To develop a data-driven approach for classifying cocoa based on its chemical profile.

Main Methods:

  • Utilized liquid chromatography-mass spectrometry (LC-MS) to profile 140 cocoa samples.
  • Constructed correlation networks based on LC-MS data to analyze sample relationships.
  • Applied network visualization, statistics, and machine learning for data interpretation.

Main Results:

  • Correlation network topology successfully distinguished cocoa samples by fermentation/processing stage and country of origin.
  • Increasing correlation thresholds revealed hierarchical clustering, first by processing stage, then by country.
  • The network-based approach provided both qualitative and quantitative classification evidence.

Conclusions:

  • Network science offers a powerful tool for analyzing complex mass spectrometry data in food authenticity studies.
  • This approach can effectively fingerprint cocoa, differentiating processing stages and origins.
  • The methodology shows potential for broad application in food analysis beyond cocoa.