FT-IR Remote Sensing of Industrial Atmospheres for Spatial Characterization

Hammaker, Robert M.; Fateley, William G.; Chaffin, Charles T.; Marshall, Timothy L.; Tucker, Melissa D.; Makepeace, V. D.; Poholarz, John M.

doi:10.1366/0003702934067685

Cited by 20 publications

(6 citation statements)

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“…Fourier transform infrared (FT-IR) spectrometry is being investigated as a potential analytical solution in the remote m onitoring scenarios described above. [1][2][3][4][5] These FT-IR m easurements have been implemented in both active and passive modes. In the active mode, a controlled, high-temperature infrared source is used to probe the atmosphere between either the source and spectrometer or between an external retrore ector and the source/spectrometer package.…”

Section: Introductionmentioning

confidence: 99%

Multiple Filtering Strategy for the Automated Detection of Ethanol by Passive Fourier Transform Infrared Spectrometry

et al. 2001

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Digital filtering methods are evaluated for use in the automated detection of ethanol from passive Fourier transform infrared (FT-IR) data collected during laboratory and open-air remote sensing experiments. In applications in which analyte signals are overwhelmed by the overlapping signals of an interference, the use of multiple digital filters is observed to improve the sensitivity of the analyte detection. The detection strategy is based on the application of bandpass digital filters to short segments of the interferogram data collected by the FT-IR spectrometer. To implement the automated detection of a target analyte, the filtered interferogram segments are supplied as input to piecewise linear discriminant analysis. Through the use of a set of training data, discriminants are computed that can subsequently be applied to detect the presence of the analyte in an automated manner. This research focuses on the detection of ethanol vapor in the presence of an ammonia interference. A two-filter detection strategy based on the use of separate ethanol and ammonia filters is compared to an approach based on a single ethanol filter. Bandpass parameters of the digital filters and the interferogram segment location are optimized through the use of laboratory data in which ethanol and ammonia vapors are generated in a gas cell and viewed against various infrared background radiances. The filter and segment parameters obtained through this optimization are subsequently tested with field remote sensing data collected when the spectrometer is allowed to view ethanol and ammonia plumes generated from a heated stack. The two-filter strategy is found to outperform the single-filter approach with both the laboratory and field data in situations in which the ammonia interference dominates the ethanol signature.

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Section: Introductionmentioning

confidence: 99%

Multiple Filtering Strategy for the Automated Detection of Ethanol by Passive Fourier Transform Infrared Spectrometry

et al. 2001

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“…The use of Fourier transform infrared (FT-IR) remote sensing spectrometry for the detection of airborne pollutants is gaining in importance . This trend can be attributed to several advantages inherent to the technique.…”

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confidence: 99%

Calibration Transfer Algorithm for Automated Qualitative Analysis by Passive Fourier Transform Infrared Spectrometry

and¹,

Small²,

Combs³

et al. 2000

Anal. Chem.

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The automated qualitative analysis of passive Fourier transform infrared (FT-IR) remote sensing data is made difficult by the presence in the data of background and instrument-specific variation. For data collected with a single instrument, variation in the data arises from changes in the infrared background radiance, changes in the atmospheric composition within the field-of-view of the spectrometer, and changes in the instrument response function arising from temperature variation in the spectrometer. When more than one spectrometer is used, the variation in detector responses and phase signatures between instruments serves to complicate further the task of implementing an automated processing algorithm for detecting the signature of a target compound. In this work, a combination of signal processing and pattern recognition methodology is applied directly to the interferogram data collected by the FT-IR spectrometer to implement an automated compound detection procedure that is independent of background and instrument-specific variation. The key to this algorithm is the use of highly attenuating digital filters to isolate in the interferogram the frequencies associated with an analyte absorption or emission band while suppressing information at other frequencies. For the test compounds, acetone and sulfur hexafluoride, it is demonstrated that when this digital filtering procedure is coupled with either piecewise linear discriminant analysis or a back-propagation neural network, an automated detection algorithm can be developed with data from a primary instrument and then subsequently used to predict the presence of analyte signatures in data collected with a secondary spectrometer. Correct classification rates in excess of 92% are obtained for both compounds when the algorithm is applied to data collected with the secondary instrument.

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“…Fourier transform infrared (FT-IR) remote sensing measurements are becoming increasingly popular for the remote detection of airborne volatile organic compounds (VOCs) …”

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confidence: 99%

“…This “passive” spectral measurement allows the sensor to be a highly portable air monitor that can interrogate the atmosphere over large distances. The principal limitation of this approach, however, is the sensitivity of the measurements to changes in the infrared background emission present in the field of view (FOV) of the spectrometer. ,− The detected background radiance is the result of contributions from the infrared radiation source, the intervening atmosphere, and the instrumental response function of the spectrometer . The spectral features of analyte species are superimposed on this varying background, and therefore, the background information must be suppressed if the analyte signatures are to be observed reliably.…”

mentioning

confidence: 99%

Automated Detection of Trichloroethylene by Fourier Transform Infrared Remote Sensing Measurements

et al. 1997

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Passive Fourier transform infrared (FT-IR) remote sensing measurements are used to implement the automated detection of trichloroethylene (TCE) vapor in the presence of a variety of infrared background signatures. Through the use of a combination of bandpass digital filtering and piecewise linear discriminant analysis, this detection procedure is applied directly to short segments of the interferogram data collected by the FT-IR spectrometer. Data employed in this work were collected during openair/passive cell terrestrial and passive cell laboratory measurements. Infrared backgrounds employed included terrain, low-angle sky, and water backgrounds, in addition to laboratory blackbody measurements. Other potentially interfering chemical species present were carbon tetrachloride, acetone, methyl ethyl ketone, and sulfur hexafluoride (SF 6 ). These data are used to assemble two data sets of differing complexity. Optimization studies are performed separately with each data set to study the influence of filter bandpass position, bandpass width, interferogram segment location, and segment size on the ability to detect TCE.The optimal parameters found consist of a Gaussian-shaped filter positioned at 939.5 cm -1 , with a width at half-height of 123.4 cm -1 . This filter is applied to interferogram points 111-220 (relative to the centerburst). When applied to a prediction set of 60 000 interferograms, the piecewise linear discriminant developed on the basis of these optimal parameters is found to detect TCE successfully in 96.2% of the cases in which it is present. The overall rate of false detections is 0.5%. The limit of detection of TCE is found to be 102 ppm-m at a temperature difference of 10.5 °C between the infrared background and the analyte. SF 6 is observed to provide the greatest spectral interference among the compounds tested, producing a false detection rate of 8.6%. It is found that this false detection rate can be reduced to 1.5% through the development of a probabilitybased interpretation of the piecewise linear discriminant results. These results are observed to compare favorably with those obtained in a separate analysis of filtered singlebeam spectra.

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FT-IR Remote Sensing of Industrial Atmospheres for Spatial Characterization

Cited by 20 publications

References 4 publications

Multiple Filtering Strategy for the Automated Detection of Ethanol by Passive Fourier Transform Infrared Spectrometry

Multiple Filtering Strategy for the Automated Detection of Ethanol by Passive Fourier Transform Infrared Spectrometry

Calibration Transfer Algorithm for Automated Qualitative Analysis by Passive Fourier Transform Infrared Spectrometry

Automated Detection of Trichloroethylene by Fourier Transform Infrared Remote Sensing Measurements

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