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Machine Learning and the Use of Spectroscopy for Adulteration Detection in Turmeric Powder.

Asma Kisalaei1, Vali Rasooli Sharabiani1, Ahmad Banakar2

  • 1Department of Biosystems Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran.

Molecules (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

This study developed a rapid, non-destructive method using UV/Vis and NIR spectroscopy with machine learning to detect turmeric adulteration. The optimized SVM-LCA model achieved 100% accuracy, ensuring food safety.

Keywords:
feature selection methodsmachine learningspectroscopyturmeric

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Machine Learning

Background:

  • Turmeric adulteration poses risks to consumer health and industry.
  • Existing detection methods can be slow, destructive, or inaccurate.
  • Developing rapid, non-destructive techniques is crucial for quality control.

Purpose of the Study:

  • To develop a rapid, non-destructive, and accurate method for detecting turmeric adulteration.
  • To evaluate the effectiveness of UV/Vis and NIR spectroscopy combined with machine learning algorithms.
  • To optimize feature selection for enhanced model performance and efficiency.

Main Methods:

  • Collected spectral data in UV/Vis (170-870 nm) and NIR (900-2170 nm) ranges.
  • Evaluated four supervised learning algorithms: SVM, LDA, MLP, and Decision Tree.
  • Employed a hybrid feature selection method (SVM-LCA) for dimensionality reduction.

Main Results:

  • Models in the NIR range achieved 100% accuracy on the test set.
  • The SVM-LCA feature selection method identified optimal wavelengths, maintaining or improving classification accuracy.
  • The developed method demonstrated high precision, recall, and F1-score.

Conclusions:

  • UV/Vis and NIR spectroscopy with machine learning are effective for detecting turmeric adulteration.
  • The SVM-LCA approach enhances model efficiency and accuracy.
  • This research supports the development of intelligent quality control systems for food and pharmaceuticals.