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Related Experiment Video

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Integration of surface-enhanced Raman spectroscopy (SERS) and machine learning tools for coffee beverage

Qiang Hu1, Chase Sellers1, Joseph Sang-Il Kwon1,2

  • 1The Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77845, USA.

Digital Chemical Engineering
|March 6, 2023
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Summary

This study introduces a new sensor for identifying coffee types using surface-enhanced Raman spectroscopy (SERS) and machine learning. The sensor effectively overcomes challenges in complex sample analysis for quality control.

Keywords:
ClassificationCoffeeFeature extractionMachine learningSurface-enhanced Raman spectroscopy (SERS)

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Chemometrics

Background:

  • Surface-enhanced Raman spectroscopy (SERS) offers powerful molecule identification capabilities.
  • Complex samples present challenges in SERS due to overlapping peaks and signal variability from non-uniform substrates.
  • Machine learning (ML) techniques can address SERS data complexity, similar to facial recognition applications.

Purpose of the Study:

  • To develop and evaluate a sensor system for classifying coffee beverages.
  • To integrate SERS, feature extraction, and ML classifiers for robust analysis.
  • To demonstrate a practical quality-control tool for the food industry.

Main Methods:

  • Utilized a low-cost nanopaper substrate for enhanced SERS signals of coffee compounds.
  • Employed Principal Component Analysis (PCA) and Discriminant Analysis of Principal Components (DAPC) for feature extraction.
  • Evaluated various ML classifiers, including Support Vector Machine (SVM) and K-Nearest Neighbor (KNN).

Main Results:

  • The combination of DAPC with SVM or KNN achieved the highest performance in coffee beverage classification.
  • Nanopaper demonstrated effectiveness as a versatile SERS substrate for dilute analytes.
  • The integrated system successfully addressed challenges of peak overlap and signal variability.

Conclusions:

  • The developed sensor system provides a user-friendly and versatile approach for coffee classification.
  • The integration of SERS with DAPC and ML classifiers offers a promising solution for complex sample analysis.
  • This technology has significant potential as a practical quality-control tool in the food industry.