Study on baseline correction methods for the Fourier transform infrared spectra with different signal-to-noise ratios.

Removing the baseline from the spectra, which are measured by a Fourier transform infrared spectrometer (FTIR), is an important preprocessing step for further spectra analysis such as quantitative and qualitative analysis. An automatic baseline correction method named iterative averaging, which is based on the basic knowledge of moving average, is presented. We also compared it to other methods, such as rubber band, adaptive iterative reweight penalized least squares, automatic iterative moving average, and morphological weighted penalized least squares, using simulated and experimental spectra with different signal-to-noise ratios (SNRs) to evaluate the performance of these methods by performance metrics and to select an appropriate method to analyze FTIR spectra. Performance metrics such as root-mean-square error, goodness-of-fit coefficient, and chi-square are calculated. The iterative averaging method achieves the best results, which are judged by performance metrics values, when it is applied to the FTIR spectra with different SNRs. It also can correct the baseline of the FTIR spectra automatically, and improve the capability and adaptability of the unsupervised online analysis of the FTIR system effectively.