Baseline correction of Raman spectral data using triangular deep convolutional networks

Raman spectroscopy requires baseline correction to address fluorescence and instrumentation-related distortions. Existing baseline correction methods can be broadly classified into traditional mathematical approaches and deep learning-based techniques. While traditional methods often require manual parameter tuning for different spectral datasets, deep learning methods offer greater adaptability and enhance automation. Recent research on deep learning-based baseline correction has primarily focused on optimizing existing methods or designing new network architectures to improve correction performance. This study proposes a novel deep learning network architecture to further enhance baseline correction effectiveness, building upon prior research. Experimental results demonstrate that the proposed method outperforms existing approaches by achieving superior correction accuracy, reducing computation time, and more effectively preserving peak intensity and shape.