GP Algorithm-Based Fourier Transform Infrared Spectrum Trend Term Removal Model

Trend term removal is a key step in Fourier transform infrared spectroscopy(FTIR) data pre-processing. The most commonly used least squares (LS) method, although satisfying the real-time requirement, has many problems such as highly correlated initial values of the expression parameters, the need to pre-estimate the trend term shape, and poor fitting accuracy at low signal-to-noise ratios. In order to achieve real-time and robust trend term removal, a new trend term removal method using genetic programming (GP) in symbolic regression is constructed in this paper, and the FTIR simulation interference results and experimental measurement data for common volatile organic compounds (VOCs) gases are analyzed. The results show that the genetic programming algorithm can both reduce the initial value requirement and greatly improve the trend term accuracy by 20%–30% in three evaluation indicators, which is suitable for gas FTIR detection in complex scenarios.