Improving signal-to-noise ratio of Raman measurements based on ensemble learning approach.

Raman spectroscopy is an extensively explored vibrational spectroscopic technique to analyze the biochemical composition and molecular structure of samples, which is often assumed to be non-destructive when carefully using proper laser power and exposure time. However, the inherently weak Raman signal and concurrent fluorescence interference often lead to Raman measurements with a low signal-to-noise ratio (SNR), especially for biological samples. Great efforts have been made to develop experimental approaches and/or numerical algorithms to improve the SNR. In this study, we proposed an ensemble learning approach to recover and denoise Raman measurements with a low SNR. The proposed ensemble learning approach was evaluated on 986 pairs of Raman measurements, each pair of which consists of a low SNR Raman spectrum and a high SNR reference Raman spectrum from the exact same fungal sample but uses 200 times the integration time. Compared with conventional methods, the Raman measurements recovered by the proposed ensemble learning approach are more identical to high SNR reference Raman measurements, with an average RMSE and MAE of only 1.337 × 10 and 1.066 × 10, respectively; thus, the proposed ensemble learning approach is expected to be a powerful tool for numerically improving the SNR of Raman measurements and further benefits rapid Raman acquisition from biological samples.