A Comprehensive Machine Learning Framework for Detection of Red Sumac (Rhus coriaria) Adulteration with Barberry Using Hyperspectral Imaging

Document Type : Original Research

Authors

1 Biomedical Engineering Group, Department of Electrical Engineering and Information Technology, Iranian Research Organization for Science and Technology (IROST)

2 Information Technology and Intelligent Systems Group, Department of Electrical Engineering and Information Technology, Iranian Research Organization for Science and Technology (IROST), 33535111, Tehran, Iran

3 Biomedical Engineering Group, Department of Electrical Engineering and Information Technology, Iranian Research Organization for Science and Technology (IROST), 33535111, Tehran, Iran

4 Department of Chemical Technologies, Iranian Research Organization for Science and Technology (IROST), 33535111, Tehran, Iran

10.22103/bbr.2026.27386.1157

Abstract

This research proposes a comprehensive machine learning framework for detecting red sumac adulteration with barberry using hyperspectral imaging. Various features including statistical features, Discrete Cosine Transform (DCT) features, and shape features were extracted and analyzed for classification performance. Results demonstrate that among traditional classifiers, Random Forest achieved the highest accuracy (91.11%) when utilizing all features, with statistical features having the most significant impact on classification. Among deep learning approaches, LSTM networks outperformed all other methods with accuracy exceeding 96%, demonstrating the importance of sequential spectral information in adulteration detection. Further analysis revealed that even with aggressive spectral down sampling (30×), the LSTM model maintained high accuracy (93.9%), suggesting potential for implementation in cost-effective systems. These findings advance food authentication technology by offering both high-performance detection methods and practical implementation strategies for identifying red sumac adulteration, contributing to food safety and quality assurance efforts.

Keywords

References

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Biomechanism and Bioenergy Research
Volume 5, Issue 2
June 2026
Pages 84-101

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History

  • Receive Date: 11 March 2026
  • Revise Date: 08 April 2026
  • Accept Date: 01 May 2026

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