Monte Carlo band selection with residual network for noise-robust near-infrared spectral quantitative analysis

A noise-robust Monte Carlo continuous band selection and light residual network (MCC-LRNet) framework is proposed for robust quantitative analysis of industrial near-infrared (NIR) spectra. The framework integrates three innovations, including a parameterized preprocessing module for noise and outlier removal; a Monte Carlo Continuous (MCC) band selection algorithm that extracts informative contiguous wavelength intervals; and a custom Residual Network (LRNet) for regression. Experiments on four industrial datasets (two tobacco and two beer, covering 18 indices) show that compared with PLS, SVM, and ResNet18, the complete MCC-LRNet framework achieves the lowest RMSE on 10 of the 18 indices, with RMSE ranging from 0.065 (Nitrogen) to 0.750 (Total sugar) and R² values ranging from 0.882 (K) to 0.988 (Moisture). The proposed framework can serve as a competitive baseline for spectral quantitative analysis.