A systematic review highlights advanced detection methods for adulteration in pulses, suggesting improved quality assessment techniques.
Graphical abstract highlights the nutritional value of pulses and key adulteration types. It summarizes traditional and advanced detection methods, emphasizing computer vision and machine learning for rapid, non-destructive quality assessment.
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Dharni et al. (2026) studied this question.
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