Robert T. Kroutil

Automated detection of methanol vapour by open path Fourier transform infrared spectrometry

Abstract Signal processing techniques are developed to detect the presence of methanol vapour in an open path Fourier transform infrared (FTIR) measurement. An automated detection algorithm is implemented through the direct application of digital filtering and pattern recognition methods to short segments of FTIR interferograms. To test the data analysis methodology, a pollutant source of methanol vapour is simulated by the use of open air active bistatic, passive terrestrial, and passive laboratory spectrometer configurations. Approximately 30,000 interferograms collected from these experiments are used in optimizing and testing the digital filtering and pattern recognition techniques. Interferogram segment lengths ranging from 40 to 150 points are evaluated, along with different segment starting positions and filter bandpass widths. Interferogram segments as short as 40 points (0.01 cm optical retardation) are found to yield detection percentages approaching 100%. These results are achieved while maintaining an extremely low rate of false detections (0.5%).

Experimental design protocol for the pattern recognition analysis of bandpass filtered Fourier transform infrared interferograms

Abstract Signal processing techniques are described for open path Fourier transform infrared (FTIR) measurements that overcome fundamental limitations in conventional data analysis strategies. Bandpass digital filters are applied directly to FTIR interferograms to isolate spectral frequencies of interest, followed by pattern recognition analysis of segments of the filtered interferogram to provide an automated means for detecting a target analyte. In this work, four experimental variables are significant: (1) interferogram segment length, (2) interferogram segment position, (3) filter bandpass position, and (4) filter bandpass width. The limit of detection of a compound is directly related to the ability to choose optimal settings for these variables. Laboratory data collected when SF 6 was present in the optical path of the spectrometer are employed to perform a full factorial experimental design study of these four variables. Analysis of variance techniques are employed to provide a statistical means of interpreting the main and interaction effects existing among the variables. Based on these results, a protocol for designing a near-optimal bandpass filter is developed. Interferogram segment starting position, filter bandpass position, and filter bandpass width interaction effects are found to be statistically significant. It is concluded that these factors must be optimized jointly. Interferogram segment length is found to have the most overall influence on detection performance, but can be studied independently from the other variables. To validate the filter generation protocol developed with the laboratory data, additional work is performed with open path SF 6 data collected during a series of field trials. The results of this study demonstrate that the protocol is valid and can be extended to develop detection schemes for other compounds.

Automated detection of sulfur dioxide in stack emissions by passive Fourier transform infrared spectrometry

Abstract Passive Fourier transform infrared (FT-IR) spectrometry is used in the automated qualitative determination of sulfur dioxide (SO 2 ) in a stack monitoring application. Digital filtering and pattern recognition techniques are optimized and applied to short sections of interferograms in a methodology developed to minimize effects of background variation. Two data sets are investigated that were collected with four similarly configured FT-IR emission spectrometers positioned to monitor stack releases of SO 2 against low-angle sky backgrounds. In the two data sets, 98.2% of 39,058 and 99.58% of 386,260 FT-IR interferograms collected are correctly classified into analyte-active or analyte-inactive categories, respectively, representing the presence or absence of SO 2 in the field-of-view of the spectrometer. This work demonstrates the validity of the methodology with data collected from stack emissions, and shows that the methodology allows training and subsequent prediction of data sets composed of data collected with multiple spectrometers.

Discriminant analysis techniques for the identification of atmospheric pollutants from passive Fourier transform infrared interferograms

Abstract Discriminant analysis techniques are developed for the detection of analyte signals directly from passive Fourier transform infrared (FT-IR) interferograms. The interferograms are preprocessed through the use of digital filters to isolate the spectral frequencies associated with a targeted analyte band. Subsequent application of the discriminant results in a yes/no decision regarding the presence of the analyte. Interferograms collected by a mobile FT-IR remote sensor are used in developing this methodology. Simplex optimization, coupled with a novel objective function, is found to produce the optimum discriminant for the prediction of the presence of the test analyte, SF 6 . The developed discriminant is able to detect low levels of SF 6 , while exhibiting a false alarm rate of less than 1%. This discriminant-based detection scheme can be implemented through the use of only a short interferogram segment, thereby decreasing the size of the interferogram that must be collected. This reduction in data collection requirements has great potential impact in the design of future FT-IR remote sensors.

Remote detection of heated ethanol plumes by airborne passive Fourier transform infrared spectrometry.

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.

Quantitative Analysis of Sulfur Dioxide with Passive Fourier Transform Infrared Remote Sensing Interferogram Data

Multivariate calibration models are developed for the determination of sulfur dioxide (SO2) by passive Fourier transform infrared (FT-IR) remote sensing measurements. In a series of experiments designed to simulate the measurement of SO2 from industrial stack emissions, low-angle sky backgrounds are viewed through the windows of a heated flow-through gas cell. With this apparatus, infrared emission from the hot SO2 is measured against the cold background of the sky. The FT-IR interferogram data collected are analyzed directly in the construction of the calibration models. Bandpass digital filters are applied to the interferograms to isolate the modulated infrared frequencies corresponding to either the asymmetric or symmetric S–O stretching vibrations at 1361 and 1151 cm−1, respectively. Quantitative calibration models are constructed by submitting short segments of the filtered interferograms to partial least-squares regression analysis. The experimental design allows the impact of variation in the temperature of the SO2 to be evaluated for its effect on the calibration models. Three data sets are constructed consisting of data with increasing temperature variation. When the temperature variation in the data is less than 30 °C, the calibration models are able to achieve a cross-validation standard error of prediction (CV-SEP) of approximately 27 ppm-m across the 185 to 727 ppm-m range of density-corrected, path-averaged concentration. These calibration models are applied to an interferogram segment of only 250 points, and do not require any separate measurement of the infrared background. A comparison of the results from the interferogram-based analyses with those obtained in an analysis of single-beam spectral data reveals similar performances for the models computed with both types of data.

Infrared detection and analysis of vapor plumes using an airborne sensor

An airborne infrared (IR) line-scanner and a Fourier transform infrared (FT-IR) spectrometer operating in the 3- 5micrometers and 8-12micrometers spectral regions provide a rapid wide- area surveillance capability. The IR scene containing target vapors is mapped remotely with the wide fields of view (FOV) multi-spectral IR line-scanner using 14 bands. The narrow FOV FT-IR spectrometer permits remote verification of target vapor plume contents within the IR scene. The IR image and FT-IR interferogram analysis supply a near real-time detection that provides visual monitoring of potential downwind vapor hazards. This capability is demonstrated using the target vapor methanol. An active mono-static FT-IR configuration furnishes ground-truth monitoring for methanol released from an industrial stack and a nearby ground-level area. The airborne and ground-truth results demonstrate the usefulness of this approach in alerting first responders to potential downwind vapor hazards from an accidental release.

Calibration Transfer in the Automated Detection of Acetone by Passive Fourier Transform Infrared Spectrometry

The qualitative determination of acetone is performed by passive Fourier transform infrared (FT-IR) spectrometry with data spanning two spectrometers. Digital filtering and piecewise linear discriminant analysis techniques are optimized and applied directly to short interferogram segments to eliminate background and instrument variation and then perform pattern recognition. Once optimized, this methodology classifies remote sensing data into categories representing the presence or absence of the analyte in an automated fashion. The addition to the training set of small numbers of interferogram data from a second spectrometer is evaluated in the creation of qualitative models robust with respect to differences between the instruments. Results of these experiments show that classification percentages averaged across all tested interferogram segments are improved from 76.2 ± 6.4% to 95.1 ± 2.2% with the addition of as few as 10 background interferograms collected in the field with the secondary instrument. The results also demonstrate that a broader, more optimal range of segments in the interferogram can be utilized when these background data are added from the secondary instrument. It is also found possible to standardize the data from the secondary instrument with blackbody background interferograms collected in the laboratory.

Infrared interferogram analysis for ammonia detection with passive FTIR spectrometry

The detection of gaseous ammonia with open-path Fourier transform spectrometry furnishes a means of accessing fugitive emissions from various industrial production processes by stack and site monitoring. This study relies on direct interferogram analysis of passive infrared data from open-air industrial and controlled laboratory environments. Direct interferogram analysis permits the identification of ammonia emissions by detection of the (nu) 2 spectral bands in the interferogram time domain.

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

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.