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A machine learning workflow for raw food spectroscopic classification in a future industry.

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This study developed an automated raw food categorization system using machine learning and Fourier-transform infrared spectroscopy (FTIR). The system efficiently classifies seven raw food types, enhancing digital transformation in the food industry.

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Area of Science:

  • Food Science and Technology
  • Spectroscopy
  • Machine Learning

Background:

  • Technological advancements are transforming food production and access.
  • The food industry needs automated systems for quality, affordability, and efficiency.
  • Digital transformation requires robust food classification methods.

Purpose of the Study:

  • To develop a machine learning workflow for automated raw food categorization.
  • To utilize Fourier-transform infrared (FTIR) spectroscopy for food analysis.
  • To create a robust classifier for diverse food types and storage conditions.

Main Methods:

  • Supervised Partial Least Squares (PLS) regression.
  • Support Vector Machine (SVM) classification.
  • Fourier-transform infrared (FTIR) spectroscopy for data acquisition.

Main Results:

  • High efficiency in multi-class classification of seven raw food types.
  • A robust classifier capable of handling variations in storage conditions and batches.
  • Demonstrated potential for real-world application in the food industry.

Conclusions:

  • Automated raw food categorization using FTIR and machine learning is feasible.
  • The developed system offers robustness and efficiency for digital food industry transformation.
  • This technology can improve food quality control and supply chain management.