Raman spectroscopy is a label-free and non-destructive spectroscopic technique that has been explored for bacterial identification. However, noise often interferes with the interesting Raman peaks because the Raman signal is inherently weak, especially for bacterial samples. Although this problem can be solved by increasing the exposure time or the power of the excitation laser, a longer acquisition time is required or the risk of sample damage is increased. In contrast, short exposure time and low laser power often lead to inadequate acquisition of Raman scattering, in which the Raman spectra with low signal-to-noise ratio (SNR) is difficult to be further analyzed. In order to quickly and accurately characterize biological samples by using low SNR Raman measurements, a weighted spectral reconstruction based method was developed and tested on Raman spectra with low SNR from 20 bacterial samples of two species. Principal component analysis followed by support vector machine was applied on the reference Raman spectra and the spectra recovered from the low SNR Raman measurements by the proposed method, the traditional spectral reconstruction method, and four other commonly used de-noising methods for the discrimination of bacterial species. The results showed that a classification accuracy of 90% was achieved based on our method, which was comparable to that of the reference Raman spectra and showed significant advantages over other spectral recovery methods. Therefore, the weighted spectral reconstruction method can preserve the most biochemical information for the bacterial species' identification while removing the noise from the low SNR Raman spectra, in which the advantages of lesser sample damage and shorter acquisition time would promote wider biomedical applications of Raman spectroscopy.
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