Spectral partition correlation based on Voigt function for Raman spectral library search

Raman spectral library search revolves around matching unknown query spectra with reference spectra in library based on similarity metrics, which plays an important role in the field of rapid detection. However, in spectral library search, query spectra and reference spectra are normally obtained by different spectrometers, so peaks within the same range always suffer inconsistent intensities, which exerts a negative influence on calculating spectral similarity metrics. In this paper, a new method named spectral partition correlation based on Voigt function (SPCV) is proposed to advance Raman spectral library search. After normal spectral preprocessing, Voigt function is utilized to fit and partition each unknown query spectrum and its corresponding reference spectrum into segments according to the distribution of Raman peaks and the spectral regions not containing any peaks are ignored. Subsequently, univariate linear regression is applied to correct each pair of segments to decrease the inconsistent peak intensity. The spectra of hexane measured by two different Raman spectrometers are selected as a case study to demonstrate that SPCV can remarkably and effectively increase the values of Pearson's correlation coefficient and ameliorate the performance of spectral matching. Moreover, 252 Raman query spectra and the pre-built library with 14033 reference spectra are also used to further verify the performance of SPCV. As a result, the matching accuracy rate of SPCV is the highest compared with hit quality index (HQI) and weighted segmental hit quality index (SHQI). Thus, SPCV can be a promising candidate for enhancing the performance of librarybased Raman spectral matching.

Automated summary

A new method named SPCV is proposed in this study to advance Raman spectral library search by partitioning spectra based on Voigt function and correcting inconsistent peak intensity. The results demonstrate that SPCV can significantly increase Pearson's correlation coefficient values and improve the performance of spectral matching. Furthermore, SPCV outperforms other indexes in terms of matching accuracy rate, making it a promising candidate for enhancing library-based Raman spectral matching.