AirNet: A Deep Learning-Driven Auto Baseline Correction Algorithm Balancing Global Smoothness and Local Fidelity

quares (Dr-airPLS) under optimized smoothness. On both simulated and experimental Raman spectra, AirNet outperforms algorithms like airPLS, OP-airPLS, and DIRAS in both accuracy and generalizability, with a speed of 0.3 s per spectrum. Furthermore, the Homologous Model of AirNet ensures consistent baseline correction across homologous spectra. The segmented fitting strategy applies a region-specific smoothing parameter, facing a Raman spectrum with drastically changed background gradients. AirNet extracts high-fidelity Raman spectral information, providing a solid basis for reliable spectrum-structure correlation.