The automated detection of chemical spectral signatures using a passive infrared Fourier Transform
Infrared (FT-IR) Spectrometer mounted in an aircraft is a difficult challenge due to the small total infrared
energy contribution of a particular chemical species compared to the background signature. The detection
of spectral signatures is complicated by the fact that a large, widely varying infrared background is
present that is coupled with the presence of a number of chemical interferents in the atmosphere. This
paper describes a mathematical technique that has been successfully demonstrated to automatically detect
specific chemical species in an automated processing environment. The data analysis methodology has
been demonstrated to be effective using data of low spectral resolution at low aircraft altitudes. An
overview of the implementation and basic concepts of the approach are presented.
Optical properties of whole bovine blood are examined under conditions of different glucose loadings. Partial least-squares (PLS) is used to compute calibration models for glucose from spectra collected over the combination spectral region (5000 - 4000 cm- 1) and first overtone - short wavelength spectral regions (9000 - 5400 cm-1). These models achieve a prediction accuracy of approximately 1mM. Calibration models built for specific glucose absorption regions perform better than models generated strictly from the short wavelength region in which light scattering effects dominate. Net analyte signal (NAS) analysis is employed to investigate the spectral information that forms the basis for the models. The NAS reveals the portion of the glucose spectrum that is orthogonal to the spectral variance induced by the blood matrix. To investigate the selectivity of the spectral measurements, the glucose NAS is compared to residual absorbance spectra formed after subtraction of the non-glucose variance (estimated by application of principal component analysis to a set of blood samples with endogenous glucose concentrations). A match between the NAS and the residual spectra reveals that direct information associated with absorption of light by the glucose molecule is present in the measured data. A similar comparison is made with the regression vector associated with the PLS model. A match between the NAS and regression vector confirms that the correlations encoded in the calibration model do, in fact, arise from glucose absorption information. The results obtained through this work demonstrate that NAS analysis is a valuable tool for use in investigating the selectivity of multivariate calibration models.
Optical properties of whole bovine blood are examined under conditions of different glucose loadings. A strong dependency is established between the scattering properties of the whole blood matrix and the concentration of glucose. This dependency is explained in terms of variations in the refractive index mismatch between the scattering bodies (predominately red blood cells) and the surrounding plasma, and also by variations in the size and shape of the red blood cells. Measurements in the presence of a well-known glucose transport inhibitor indicate that variations in refractive index mismatch are related to the penetration of glucose into the red blood cells. In addition, results measure the glucose dependent aggregation properties of red blood cells. In this experiment, pulsations in transmitted light intensity are explained by cycles of aggregation and disaggregation of red blood cells in response to a propagating pump wave through the blood matrix. Magnitude of these pulsations depends on the concentration of glucose in the sample. Results are also presented to characterize the time-dependent variation in light transmission in response to a step change in glucose concentration. Finally, multivariate calibration models are presented for the measurement of glucose in a whole blood matrix. These models are based on near infrared spectra and Kromoscopy data collected from eighty different samples prepared from a single whole blood matrix. The best model is generated for combination near infrared spectra, which provides a standard error of prediction is less than 1 mM over a concentration range of 3 to 30 mM.
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.
The ability of Kromoscopy to measure glucose selectively is demonstrated in solutions composed glucose, urea, triacetin, bovine serum albumin (BSA), cholesterol, and hemoglobin (Hb). Kromoscopic measurements are made with a four-channel instrument designed for measuring light between 1500 and 1900 nm. The channels are configured to respond to four individual bands of near infrared light centered at 1600, 1700, 1750, and 1800 nm. An equation is proposed that describes the relative response for each channel as a function of relevant experimental parameters. This equation predicts the linear response observed for these types of measurements as a function of solute concentration. In addition, molar absorptivities are provided for glucose, urea, triacetin, BSA, Hb, and water. The non-negligible absorptivity of water demands the consideration of water displacement caused by solute dissolution. Channel responses are measured for a series of thirty-one samples. The chemical composition of these samples is designed to minimize the correlations between glucose concentration and the concentrations of all other solutes. Likewise, these samples provide negligible correlation between the concentration of glucose and the extent of water displacement. A calibration model is constructed for glucose by using a conventional P-matrix multiple linear regression analysis of the four-channel information. The resulting model demonstrates selectivity for glucose with values of 1.27 and 1.34 mM for the standard errors of calibration and prediction, respectively, over a glucose concentration range of 1.9 to 19 mM.
The detection of airborne chemicals is a key capability in a variety of environmental monitoring scenarios. For these applications, passive IR remote sensors collect IR emissions form natural and man-made sources such as the radiant emission from the earth or emissions from the stacks of a chemical plant. Chemical compounds absorb or emit IR energy at characteristic wavelengths, and the profile of these absorption or emission signatures can be used to identify a chemical and to estimate the amount present. Passive IR remote sensors can be implemented in either imaging or non- imaging configurations and can be constructed to acquire IR emission data in either multispectral or hyperspectral modes. Implementing these measurements successfully requires the construction of rugged, portable instruments and the development of computer processing techniques that allow the automated analysis of the large quantities of data acquired by these sensors. The research presented here describes the development of novel signal processing and pattern recognition methodology for application to multispectral imaging data and to non-imaging data acquired with a hyperspectral instrument. Remote sensing data were collected with these instruments mounted on an aircraft platform. Data acquired at an industrial site are used to demonstrate the characteristics of each sensor and the data analysis methodology.
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 IR (FT-IR) 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 signicant finite impulse responses matrix 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-IR 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.
The selectivity of a four-channel Kromoscopic analysis is demonstrated for the measurement of glucose in separate binary and tertiary matrices. A novel virtual search procedure is used to identify three different sets of four, overlapping transmission filters. The first filter set includes filters centered at 900, 1300, 1410, and 1538 nm and is selected to differentiate glucose and urea in a series of binary mixtures. These binary mixtures were prepared with independent levels of 1-10 mM glucose and 9- 213 mM urea dissolved in an aqueous phosphate buffer solution. A second filter set contains filters centered at 1064, 1100, 1224, and 1290 nm and is used to measure glucose in a series of tertiary mixtures composed of glucose, urea and bovine serum albumin. This tertiary matrix consists of 2-13 mM glucose, 13-129 mM urea and 0.05-0.46 g/L bovine serum albumin dissolved in the same type of buffer. Multilinear regression is used to relate the relative Kromoscopic responses to the concentration of glucose in these sample solutions. In both cases, the prediction errors are on the order of 0.6-0.8 mM. The impact of solution temperature is also investigated by examining glucose responses obtained from solutions maintained at temperatures ranging from 35 to 39 degree(s)C. The filter set used in this experiment is composed of filters centered at 1100, 1150, 1254, and 1300 nm. Results from this particular filter set indicate that the directionality and magnitude of the glucose responses are independent of solution temperature. Finally, accurate glucose measurements are demonstrated when a same-temperature blank is used to generate the relative channel response.
In many open-air monitoring applications of Fourier transform spectrometry (FTS), the lack of a valid background reference spectrum limits the ability to perform quantitative measurements. Suppression of the broad band detector envelope overcomes this limitation. Interferogram processing provides a means of suppressing the broad band background, while maintaining the target spectral signatures of interest. A combination of interferogram segment selection, digital filtering, and pattern discrimination techniques achieve the background suppression of the variable broad band detector envelope. The spectral band position, width, and strength of the target vapor determine the parameters that are used for background suppression. Interferogram segment selection depends primarily on spectral band width. Digital filter design requires inputs of both spectral band position and width. The pattern discrimination techniques compensate for variation in the spectral shape with band strength.