An Effective Baseline Correction Algorithm Using Broad Gaussian Vectors for Chemical Agent Detection with Known Raman Signature Spectra

Abstract

Raman spectroscopy, which analyzes a Raman scattering spectrum of a target, has emerged as a key technology for non-contact chemical agent (CA) detection. Many CA detection algorithms based on Raman spectroscopy have been studied. However, the baseline, which is caused by fluorescence generated when measuring the Raman scattering spectrum, degrades the performance of CA detection algorithms. Therefore, we propose a baseline correction algorithm that removes the baseline, while minimizing the distortion of the Raman scattering spectrum. Assuming that the baseline is a linear combination of broad Gaussian vectors, we model the measured spectrum as a linear combination of broad Gaussian vectors, bases of background materials and the reference spectra of target CAs. Then, we estimate the baseline and Raman scattering spectrum together using the least squares method. Design parameters of the broad Gaussian vectors are discussed. The proposed algorithm requires reference spectra of target CAs and the background basis matrix. Such prior information can be provided when applying the CA detection algorithm. Via the experiment with real CA spectra measured by the Raman spectrometer, we show that the proposed baseline correction algorithm is more effective for removing the baseline and improving the detection performance, than conventional baseline correction algorithms.