Comparison of Spectral and Interferogram Processing Methods Using Simulated Passive Fourier Transform Infrared Remote Sensing Data

Abstract

Computer-generated synthetic single-beam spectra and interferograms provide a means of comparing signal processing strategies that are employed with passive Fourier transform infrared (FT-IR) sensors. With the use of appropriate radiance models and spectrometer characteristics, synthetic data are generated for one-, two-, and four-component mixtures of organic vapors (ethanol, methanol, acetone, and methyl ethyl ketone) in two passive FT-IR remote sensing scenarios. The single-beam spectra are processed by using Savitsky–Golay smoothing and first-derivative and second-derivative filters. Interferogram data are processed by Fourier filtering using Gaussian-shaped bandpass digital filters. Pattern recognition is performed with soft independent modeling of class analogy (SIMCA). Quantitative models for the target gas integrated concentration-path-length product are built by using either partial least-squares (PLS) regression or locally weighted regression (LWR). Pattern recognition and calibration models of the filtered spectra or interferograms produced comparable results. Discrimination of target analytes in complex mixtures requires a sufficiently large temperature differential between the infrared background source and analyte cloud. Quantitative analysis is found to be possible only when the temperature of the analyte cloud is stable or known and differs significantly from the background temperature. Net analyte signal (NAS) methods demonstrate that interferogram and spectral processing methods supply identical information for multivariate pattern recognition and calibration.

Quantitative Determination of Methanol and Ethanol with Synthetic Calibration Spectra in Passive Fourier Transform Infrared Remote Sensing Measurements

“…The single-beam spectrum recorded using the spectrometer while viewing the input radiance, L X , can be written mathematically as defined by Shaffer and Combs: 6…”

“…Therefore, a significant temperature differential between the analyte plume and the background is the primary requirement for detection capability. 6 The greater the differential temperature, the more the analyte signal can be observed superposed on the background.…”

Error Analysis for Estimation of Trace Vapor Concentration Pathlength in Stack Plumes

“…Quanti cation of trace analytes in plum es is limited to estimating the concentration-pathlength product because the plume thickness is generally unknown. Algorithms are considered ''single pixel'' 1 (e.g., a single on-plume and off-plume pixel are m easured) or ''multi-pixel'' [2][3][4] (where m ultiple pixels are employed in the algorithm). The analysis discussed in this work can be applied to either estimation approach; however, of primary interest are algorithms that use multi-pixel m easurements.…”

Generalization of rank reduction problems with Wedderburn's formula

“…One of the main developments for the analytical chemist is the calculation of the net analyte signal (NAS). Its interest derives from the large number of potential applications: calculation of analytical figures of merit [1][2][3][4][5][6][7][8]; wavelength selection [7,[9][10][11]; outlier detection [4,10]; calibration model performance assessment [12,13]; interpretation of prediction error and the effect of spectral pretreatment [6]; representation of the multivariate model as a twodimensional plot [6,14]; development of new calibration methods [7,[15][16][17]; development of new preprocessing methods [18][19][20].…”