Pattern-Coupled Baseline Correction Method for Near-Infrared Spectroscopy Multivariate Modeling

In near-infrared (NIR) modeling, the baseline drift tends to affect the qualitative and quantitative performance of the multivariate calibration model. The baseline correction method based on the sparse representation of independent wavelength is a recent method for pure spectra estimation. In fact, the measured NIR generally exhibit local wavelength coupling sparse properties, and the existing baseline correction methods suffer from performance degradation due to ignoring the wavelength coupling effect on baseline correction. In this article, the pattern-coupled learning framework considering the local coupling property is proposed to achieve pure spectrum fitting and baseline correction simultaneously. Specifically, the pattern-coupled hierarchical Gaussian prior model is introduced to characterize the local sparse structure corresponding to characteristic peaks with wavelength coupling. The adaptive coupling method is further proposed to achieve robust learning of NIR under different noise interference. Unlike conventional frameworks where wavelength or hyperparameters are learned independently, the proposed mode coupling framework enables accurate baseline estimation without prior knowledge of spectral structure by characterizing wavelength coupling properties. Compared with the state-of-the-art methods, the root mean square error (RMSE) and $R^{2}$ of the proposed method are reduced and increased by 14.41% and 1.53%, 14.12% and 2.01%, 12.86% and 12.93% in three measured NIR datasets, respectively.