Application of digital filtering and pattern recognition techniques to interferogram-based fourier transform infrared qualitative analysis

Small, Gary W.; Barber, Abigail S.

doi:10.1016/0169-7439(92)85010-z

Cited by 6 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Research in our laboratory is focused on overcoming this limitation by use of novel data analysis strategies for suppressing the contributions of the infrared background without performing an actual background measurement. − This methodology is based on the direct analysis of short segments of FT-IR interferogram data and combines bandpass digital filtering and pattern recognition techniques to achieve the detection of VOCs. The filtering step extracts the analyte signature from collected interferograms, while the pattern recognition procedure uses the filtered interferogram data as unique patterns to determine the presence or absence of targeted VOCs.…”

mentioning

confidence: 99%

“…Previous research in our laboratory has demonstrated the ability of this methodology to extract analyte signatures from interferogram data and thereby eliminate the contributions of terrain backgrounds and adjacent spectral bands of interfering compounds. − In the work presented here, this compound detection problem is made significantly more challenging by combining sky and water backgrounds with the terrain backgrounds used previously. The ability of the methodology to implement an automated detection of TCE in the presence of this extreme background variation is assessed.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Automated Detection of Trichloroethylene by Fourier Transform Infrared Remote Sensing Measurements

et al. 1997

Self Cite

View full text Add to dashboard Cite

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.

show abstract

mentioning

confidence: 99%

mentioning

confidence: 99%