Simultaneous Denoising and Baseline Correction of Microplate Raman Spectra Using a Dual-Branch U-Net

Abstract We present a dual-branch U-Net architecture for simultaneous baseline correction and denoising of Raman spectra. The network features a shared encoder that diverges into two specialized decoding heads for the Raman signal and for the baseline. The two heads are coupled with a cross-branch gating mechanism. The model can cross-confirm the peaks by comparing the recovered Raman signal with the baseline-corrected spectrum. Moreover, the model supports quantitative analysis by integrating the deep Raman-head reconstruction, Ideep, expressed in the detector’s intensity units. The model was trained entirely using a custom synthetic data engine explicitly designed to emulate automated HTS acquisitions from microplates. Comprehensive validation demonstrates robust peak recovery on synthetic spectra with signal-to-noise ratios (SNRs) as low as 5. The model recovers the Raman signal from highly noisy glycerol samples and from moderately noisy adenine sulfate samples. Quantitative analysis is conducted on guanine by integrating Ideep at a deep decoder layer. Transfer is tested on l-tyrosine, which is chemically unrelated to the training distribution.