Removal of the ambient air features from fourier-Transform near-Infrared spectra. (June 2023)

• Record Type: Journal Article
• Authors: Kojić, Dušan; Simonova, Anna A.; Yasui, Masato
• Is Part Of: Journal of quantitative spectroscopy & radiative transfer. Volume 301(2023)
• Publication Date: 2023-06
• Subjects: Near-infrared -- Spectroscopy -- Ambient air -- Vapor -- Water -- Baseline; Spectrum analysis -- Periodicals; Radiation -- Periodicals; Analyse spectrale -- Périodiques; Rayonnement -- Périodiques; Radiation; Spectrum analysis; Periodicals; 543.0858
• DOI: 10.1016/j.jqsrt.2023.108538
• Languages: English
• ISSNs: 0022-4073

Abstract

Highlights: A vast majority of near-infrared spectrometers operate in ambient air conditions. Ambient air bands infiltrate spectra and deteriorate signal extraction. Recent advances in modeling spectra of ambient air allow for precise compensation. We propose a correction procedure based on physics-informed baseline fitting. Removal of ambient air bands is required in demanding spectral applications. Abstract: Presence of ambient air in the optical path of Fourier-Transform single-beam near-infrared spectrometers introduces features that are external to the sample of interest and deteriorate accuracy on the time scale that is, as our data shows, similar to that of spectral acquisition. This necessitates corrective action performed either by controlling the atmosphere within the instrument or by post-processing the spectrum. Since controlling the atmosphere introduces a variety of technical issues a vast majority of spectrometers are equipped with software tools for post-processing the spectrum. A noticeable absence of applied studies on compensation of atmospheric absorption in the near-infrared spectral region prompted us to propose such a procedure from the aspect of recent advances in modeling of the ambient air absorption in this region that, in combination with a comprehensive survey of various baseline-correction algorithms, yielded a procedure for calibrated subtraction of the background single-channel spectrum that accurately models the corrupting features of the ambient air. Comparisons between different models of the total absorption of ambient air showed significant improvements that were characterized collectively, using multivariate analysis, and individually, using our novel de-trending technique, called self-subtraction. We conclude that corrections introduce measurable changes in the spectra and that implementation of this, or similar, algorithms is a prerequisite for performing quantitative analyses in the near-infrared region.