NIRDiffusion: A diffusion-model-based framework for enhanced quality assessment of industrial plant materials

Soybean and American ginseng, as industrial plant-derived materials, require precise quantification of soluble proteins and ginsenosides due to their importance in industrial applications such as bio-based materials and pharmaceutical ingredient production. Near-infrared (NIR) spectroscopy, combined with advanced AI models, offers a rapid, non-invasive, and cost-effective approach for evaluating the quality of industrial plant-derived materials. However, its application is limited by challenges such as high spectral dimensionality, environmental noise, and small labeled datasets. To address these issues, this study proposes a diffusion-model-based representation learning framework, NIRDiffusion, which utilizes a probabilistic, iterative process of perturbation and reversal to capture multi-scale non-linear spectral latent features while effectively suppressing noise. The proposed NIRDiffusion combined with a 1D-CNN regression model achieves R 2 values of 0.973 and 0.949 in predicting water-soluble protein (WSP) in soybean seeds and total ginsenosides (TG) in American ginseng, respectively. Comparative analyses reveal significant prediction error reductions of at least 17.89 % (WSP) and 11.21 % (TG) relative to popular unsupervised feature extraction and three self-supervised learning approaches. These results demonstrate that NIRDiffusion effectively captures discriminative features in NIR spectral data, enabling accurate and scalable quality assessment of plant-derived materials through integration with predictive modeling. • NIRDiffusion utilizes diffusion models for robust NIR spectral representation. • Multi-scale feature extraction enhances prediction accuracy for complex spectra. • Framework offers scalable solutions for analyzing industrial plant materials. • Advances chemometric modeling for efficient NIR spectroscopy applications.