Roger J. Combs

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.

EQUILIBRIUM VAPOR CELL FOR QUANTITATIVE IR ABSORBANCE MEASUREMENTS

Infrared absorbance measurements through a gas flow cell are made with the closed-loop circulation of vapor/air mixtures equilibrated with the use of temperature-regulated aqueous solutions. Constant reproducible vapor pressures of organic solutes are established with the equilibrated aqueous solutions. The water solvent depresses the vapor pressure of the pure organic solutes of methanol, ethanol, isopropanol, acetone, and methyl ethyl ketone (MEK). Knowledge of the solution liquid mole fractions, the pure component vapor pressures, and the Wilson coefficients permits determination of the solute vapor pressures to within 2% accuracy. Reliable aqueous solution preparation requires only the correct weighings of pure constituent materials before mixing to achieve the targeted solute liquid mole fractions. Absorbances are measured for four of the five solutes over a range of seven concentrations and for MEK over four concentrations. These concentrations show the absorbance region of adherence to Beers law with an experimental precision of approximately ±2% for the solutes studied. Absorptivities that are calculated from the Beers law slope are compared to the available infrared absorbance data.

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

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.

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.