Deep learning baseline correction method via multi-scale analysis and regression

Due to the fluorescence or instrument error, the captured spectra are influenced by the baseline, and the baseline may impact the qualitative and quantitative analysis of spectra. Currently, numerous studies have attempted to remove the impact of spectra baseline, among which multi-scale analysis and regression methods are the most representative methods. It is worth noting that deep learning has made progress in data analysis, but remains rare for baseline correction. In this paper, a baseline correction method based on deep learning is proposed. Specifically, we use the mathematical and physical significance of multi-scale analysis and regression to design a deep learning model and propose a non-convex and non-smooth loss function. Furthermore, some deep learning modules are added to the designed network model. The experiments of simulation, real, and application demonstrate the proposed method achieves state-of-the-art performance.