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April 10, 2026Scientific ReportsOpen Access

Hybrid near-infrared and chemical-based machine learning enhances the reliability of chili powder origin classification

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Demonstrates enhanced origin classification accuracy in chili powder, indicating improved consumer trust and regulatory compliance.

The research aims to enhance the reliability of chili powder origin classification using hybrid techniques.
Integrated near-infrared spectroscopy and chemical composition analysis with machine learning
Classified domestic and imported chili powder samples from Korea, China, and Vietnam
Used support vector machine models with LASSO-selected NIR bands for analysis
Evaluated the performance of hybrid models against standalone NIR models
Hybrid models achieved 100% origin classification accuracy using NIR and chemical variables
NIR alone reached high accuracy with as few as four bands
Organic acids and proximate components were emphasized in hybrid models
Shapley additive explanation analysis improved interpretability of results

Yang et al. (2026) studied this question.

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