Spectral Correlation Information Embedded NIR Modeling Method and Its Industrial Applications

Near-infrared (NIR) technology is an essential online measurement and monitoring technology in modern industrial processes. Although various methods have been developed for spectral multivariate analysis, they often ignore the potential correlation and complementary relationship between wavelengths. To this end, this work proposes the spectral correlation information embedded (SCIE) modeling method to improve NIR model performance by incorporating the underlying correlation structure among spectral wavelengths, in which the structural model is developed to integrate the spectral correlation information matrix into NIR regression analysis. Furthermore, the theory-guided initialization and structural correlation constraint are introduced to assist model learning and avoid overfitting. The effectiveness of the proposed SCIE method is demonstrated on the benchmark dataset and real-world NIR datasets from the refining process. Comparative results indicate that the proposed method effectively selects wavelengths associated with functional groups and achieves accurate prediction of petrochemical product properties.