Baseline Correction of Fourier Transform Infrared Spectroscopy Signals Based on Cubic Splines

In order to solve the problem of baseline drift caused by the influence of environment, temperature, noise, and other factors in Fourier infrared spectrum data, we propose a baseline correction method based on the cubic spline, which is compared with other existing baseline correction techniques. Since baseline correction is a pre-processing step for subsequent substance composition identification, we can compare the baseline correction effect with the accuracy of substance composition identification. In order to exclude the influence of other factors on the accuracy of substance component identification, we will use the same method in subsequent noise reduction treatment and substance composition identification to effectively compare various baseline correction techniques. Furthermore, we compare different kinds of baseline correction method from two other aspects of the spectrum and root mean square error. The numerical results show that the proposed method has strong comparability with the existing methods in the spectral map and root mean square error, keeping the best performance in substance composition identification.