Automated Background Subtraction Algorithm for Raman Spectra Based on Iterative Weighted Least Squares

A major problem in Raman spectroscopy is that the spectrum is often suffered from intrinsic fluorescence which is orders of magnitude greater than the Raman signal. Background subtraction is essential for further analysis, particularly for quantitative analysis using multivariate calibration. In this paper, we propose a background removal algorithm which approximates the background by a polynomial and estimates the polynomial coefficients by iterative weighted least squares. The performance of the algorithm accompanied with two comparative methods are evaluated both on simulated and real Raman spectra. The results show that the proposed algorithm provides the best result using R(2) between the actual and extracted Raman peaks. It also improves the performance of background removal in quantitative Raman spectroscopy. Further more, the algorithm is least dependent on the choice of polynomial order.