Journal
APPLIED OPTICS
Volume 57, Issue 20, Pages 5794-5799Publisher
OPTICAL SOC AMER
DOI: 10.1364/AO.57.005794
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Funding
- Key Research of Frontier Science Programs of the Chinese Academy of Sciences (CAS) [QYZDY-SSW-DQC016]
- National Key Research and Development Program of China [2016YFC0201000, 2016YFC0803000]
- National Key Scientific Instrument and Equipment Development Project [2013YQ22064302]
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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. (C) 2018 Optical Society of America
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