Surface-enhanced Raman database of 24 metabolites: Stable measurement of spectra, extraction and analysis of the main features

Surface-enhanced Raman spectroscopy (SERS) has been used in Raman-based metabolomics to provide abundant molecular fingerprint information in situ with extremely high sensitivity, without damaging the sample. How-ever, poor reproducibility, caused by the randomness of the adsorption sites, and the short-range effect of SERS have hindered the development of SERS in metabolomics, resulting in very few SERS reference databases for small-molecule metabolites. In this work, our previously proposed large laser spot-swift mapping SERS method was adopted for the measurement of 24 commercially available metabolite standards, to provide reproducible and reliable references for Raman-based metabolomics study. Among these 24 metabolites, 22 contained no Raman data in PubChem. Other than the SERS spectra data, we extracted and explained the molecular vibration information of these metabolites, and combined with the density functional theory (DFT) calculations, we provided a new possibility for the fast Raman recognition of small-molecule metabolites. Accordingly, a large laser spot-swift mapping SERS database of metabolites in human serum was initially established, which contained not only the original spectral data but also other detailed feature information regarding the Raman peaks. With continuous accumulation, this database could play a promising role in Raman-based metabolomics and other Raman-related research.

Automated summary

This study used the large laser spot-swift mapping SERS method to measure 24 metabolite standards, providing reliable references for Raman-based metabolomics. In addition to the SERS spectra data, the study extracted and explained the molecular vibration information of these metabolites, and combined with DFT calculations, providing a new possibility for fast Raman recognition of small-molecule metabolites. The established large laser spot-swift mapping SERS database of metabolites could play an important role in metabolomics and other Raman-related research.