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

Idwasi, Patrick O.; Small, Gary W.; Combs, Roger J.; Knapp, Robert B.; Kroutil, Robert T.

doi:10.1366/0003702011953784

Cited by 10 publications

(5 citation statements)

References 21 publications

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“…The methodology employed in this work was based on the application of signal processing and pattern recognition techniques directly to short segments of the interferogram data collected by the spectrometer. We have demonstrated this approach previously with data from heated plumes. , This work extends the methodology by evaluating the robustness of the algorithm to the presence of chemical species not included in the development of the classifier.…”

Section: Resultsmentioning

confidence: 99%

Robust Classifier for the Automated Detection of Ammonia in Heated Plumes by Passive Fourier Transform Infrared Spectrometry

Wabomba

Small

2003

Anal. Chem.

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An automated classification algorithm is implemented for the detection of ammonia vapor in heated plumes by passive Fourier transform infrared (FT-IR) spectrometry. This classification methodology allows the real-time detection of chemical signatures in gaseous effluents such as those generated from industrial processes. The characteristics of real-time implementation and excellent robustness are achieved by an analysis strategy based on the application of band-pass digital filters to short segments of the interferogram data collected by the FT-IR spectrometer, followed by the use of piecewise linear discriminant analysis to obtain a yes/no classification regarding the presence of the analyte signature in the filtered data. The optimal classifier developed through this work is based on only 110 interferogram points and employs a single band-pass filter centered at 945 cm(-)(1) with a pass-band full width at half-maximum of 93 cm(-)(1). The average stop-band attenuation of the optimal filter is 42.1 dB. The robustness of the algorithm is tested by exposing it to chemical releases of sulfur hexafluoride, ethanol, methanol, sulfur dioxide, and hydrogen chloride that were not included in the development of the classifier. Excellent classification performance is demonstrated, with missed ammonia detections occurring at a rate of approximately 1%. The occurrence of false detections is less than 0.1% for SF(6) and less than 0.02% for the other interferences tested.

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Section: Resultsmentioning

confidence: 99%

Robust Classifier for the Automated Detection of Ammonia in Heated Plumes by Passive Fourier Transform Infrared Spectrometry

Wabomba

Small

2003

Anal. Chem.

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“…In a series of studies, our laboratory has demonstrated that using short segments of digitally ltered interferograms in the analysis can help to overcome these technical dif culties. [5][6][7][8][9][10] The interferogram representation of broad background spectral features decays much faster than the corresponding representation of a narrower analyte feature, thereby allowing extraction of analyte information by selecting an appropriate interferogram segment. Furtherm ore, the use of short interferogram seg-ments restricts the mirror movement of the interferometer to a shorter range, m aking faster interferogram collection possible.…”

Section: Introductionmentioning

confidence: 99%

Remote Detection of Heated Ethanol Plumes by Airborne Passive Fourier Transform Infrared Spectrometry

et al. 2003

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Methodology is developed for the automated detection of heated plumes of ethanol vapor with airborne passive Fourier transform infrared spectrometry. Positioned in a fixed-wing aircraft in a downward-looking mode, the spectrometer is used to detect ground sources of ethanol vapor from an altitude of 2000-3000 ft. Challenges to the use of this approach for the routine detection of chemical plumes include (1) the presence of a constantly changing background radiance as the aircraft flies, (2) the cost and complexity of collecting the data needed to train the classification algorithms used in implementing the plume detection, and (3) the need for rapid interferogram scans to minimize the ground area viewed per scan. To address these challenges, this work couples a novel ground-based data collection and training protocol with the use of signal processing and pattern recognition methods based on short sections of the interferogram data collected by the spectrometer. In the data collection, heated plumes of ethanol vapor are released from a portable emission stack and viewed by the spectrometer from ground level against a synthetic background designed to simulate a terrestrial radiance source. Classifiers trained with these data are subsequently tested with airborne data collected over a period of 2.5 years. Two classifier architectures are compared in this work: support vector machines (SVM) and piecewise linear discriminant analysis (PLDA). When applied to the airborne test data, the SVM classifiers perform best, failing to detect ethanol in only 8% of the cases in which it is present. False detections occur at a rate of less than 0.5%. The classifier performs well in spite of differences between the backgrounds associated with the ground-based and airborne data collections and the instrumental drift arising from the long time span of the data collection. Further improvements in classification performance are judged to require increased sophistication in the ground-based data collection in order to provide a better match to the infrared backgrounds observed from the air.

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“…Therefore ethanol detection and discrimination requires a significantly different interferogram segment selection and digital filter implementation to attenuate unwanted background spectral features. 16 The aircraft mounted FT-IR sensor observed a wide-range of ethanol concentration profiles for over flights of the vapor generator site. The resultant analysis demonstrates both absorption and emission ethanol signatures against a variety of backgrounds.…”

Section: Ethanol Detectionmentioning

confidence: 99%

Airborne passive FT-IR spectrometry

et al. 2002

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Rapid airborne identification and quantification of vapor hazards is an environmentally important capability for a variety of open-air scenarios. This study demonstrates the use of a commercially available passive Fourier transform infrared (FTlR) spectrometer to detect, identify, and quantify ammonia and ethanol vapor signatures depending on the appropriate signal processing strategy. The signal-processing strategy removes the need for a representative background spectrum and it consists of three steps to extract the spectral information associated with the target vapor. The first step is optimal interferogram segment selection which depends on the bandwidth of the target spectral feature. The second step applies the statistically significant finite impulse response matrix (FIRM) filter to the optimal interferogram segment to attenuate spectral interferences. The third step quantifies the FIRM filter results with a discriminant analysis. The signal processing results prove that low-altitude airborne passive FT-lR spectrometry allows rapid quantitative detection of ammonia and ethanol vapor generated plumes. This effort also documents the direct interferogram analysis of data from the fast scanning airborne passive FT-IR spectrometer.

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

Cited by 10 publications

References 21 publications

Robust Classifier for the Automated Detection of Ammonia in Heated Plumes by Passive Fourier Transform Infrared Spectrometry

Robust Classifier for the Automated Detection of Ammonia in Heated Plumes by Passive Fourier Transform Infrared Spectrometry

Remote Detection of Heated Ethanol Plumes by Airborne Passive Fourier Transform Infrared Spectrometry

Airborne passive FT-IR spectrometry

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