Rapid Quantitative Analysis Based on Lorentzian Fitting of Hyperspectral Stimulated Raman Scattering Imaging Data

Current Hyperspectral stimulated Raman scattering (hsSRS) data analysis methods face challenges when it comes to rapidly and reliably quantifying different lipid subtypes, and cannot fully leverage the information in hsSRS data. Here, we present a rapid and reliable quantitative algorithm for quantitative analysis that fully extracts chemical information by using adaptive selection of Lorentzian basis functions to fit the spectra in hsSRS data in bulk. We demonstrated that, by utilizing the ratio relationships between fitted bands, quantitative comparisons of specific lipid subtypes can be achieved. Moreover, we applied our method for the quantitative analysis of lipid composition in lipid droplets based on hsSRS data of liver cancer tissues and confirmed our method has a better fitting effect and a faster solving speed compared to MCR. This suggests that our method has the potential for great utility in the quantitative analysis of hsSRS imaging data for biomedical specimens.