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Adulteration, the intentional addition of inferior or harmful substances to food products, is a critical issue in the spice industry, particularly with high-demand spices like turmeric and chili powder. These spices are commonly targeted for adulteration due to their widespread use and distinctive colors, which can be easily mimicked with synthetic dyes or other substances. This study employs a combination of spectroscopic and non-spectroscopic techniques, alongside advanced machine learning algorithms to detect adulterants with high accuracy. The future of this research lies in refining these models for real-time industrial applications and expanding the approach to safeguard a broader range of spices, ensuring a safer and more transparent food supply chain.

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Adulteration Detection in Spices Using Machine Learning Algorithms: A Review

  • 会议论文

  • 发表日期：

    2026年

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  • Spandan De Sarkar,
  • Pratibha Mahto,
  • Sowmya Natarajan

摘要

Adulteration, the intentional addition of inferior or harmful substances to food products, is a critical issue in the spice industry, particularly with high-demand spices like turmeric and chili powder. These spices are commonly targeted for adulteration due to their widespread use and distinctive colors, which can be easily mimicked with synthetic dyes or other substances. This study employs a combination of spectroscopic and non-spectroscopic techniques, alongside advanced machine learning algorithms to detect adulterants with high accuracy. The future of this research lies in refining these models for real-time industrial applications and expanding the approach to safeguard a broader range of spices, ensuring a safer and more transparent food supply chain.