We have investigated the application of near-IR reflectance spectroscopy to the determination of motor oil contamination in sandy loam. Although the present work is concerned with a specific case of contamination, we discuss the possibility of applying the method to other organic contaminants and other types of soil. The spectral region considered was 1600–1900 nm, which contains the first overtone of the CH stretch. Using a commercial Fourier transform spectrometer together with cross-validated partial least-squares data analysis, the one-sigma precision for the determination of motor oil in sandy loam was 0.17 wt % (0.13 to 0.26 wt % at the 95% confidence level). The largest contribution to the precision of the determination was sampling error, or inhomogeneity in each sample. Given the precision limit imposed by the sampling error, we found that the performance of the spectrometer could be lowered without affecting the overall precision. In a modeling exercise, adequate performance was obtained with a spectrometer having only seven spectral channels with a spectral resolution of 10 nm and a spectral noise level of 10−3 absorbance units. A design for an inexpensive miniature instrument is presented.

A specialized hyperspectral imager has been developed that preprocesses the spectra from an image before the light reaches the detectors. This "optical computer" does not allow the flexibility of digital post-processing. However, the processing is done in real time and the system can examine = 2 x lo6 scene pixels/sec. Therefore, outdoors it could search for pollutants, vegetation types, minerals, or man-made objects. On a highspeed production line it could identify defects in sheet products like plastic wrap or film, or on painted or plastic parts.ISIS is a line scan imager. A spectrally dispersed slit image is projected on a Spatial -Light Modulator. The SLM is programmed to take the inner product of the spectral intensity vector and a spectral basis vector. The SLM directs the positive and negative parts of the inner product to different linear detector arrays so the signal difference equals the inner product. We envision a system with one telescope and =4 SLMs. INTRODUCTIONDifferent classes of materials can be separated from one-another because they have different reflectance spectra in the 0.5-pm to 2.4-pm spectral region. Figure 1 a is an image of a scene containing soil, trees, and a tank. Figure IC, can be calculated for these spectra using spectral linear discriminant analysis2. These vectors, reminiscent of eigenvectors, would emphasize the differences between the spectra of the different material classes.One can search for specific materials in an area by scanning it with a hyperspectral imager, processing the data, and comparing the results with known results. The data processing consists of taking the spectra from each image pixel and taking the inner product (dot product) of the pixel's spectra and each of the spectral basis vectors. The constants computed from the inner products can then be compared with known results. As an example, consider searching a scene ( Fig. 1 a) using two basis vectors. For each pixel of the scene, the two inner products have been taken and the results have been plotted, producing the scatter plot shown in Figure Id. Note that the background and the material sought tend to cluster into three different areas, one of which is "interesting".Most hyperspectral instruments use a line-scan-imager architecture. These instruments move an image of the scene across a slit. After passing through the slit, the spectra are DISCLAIMERThis report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, make any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or ...

Recently there has been a surge of interest in spectroscopic sensors operating in the near-IR, although it is recognized that the mid-IR contains more spectral information. The general question addressed in this paper is, How much specificity is lost in choosing the near-IR over the mid-IR for sensor applications? The example considered is the separability among three classes of organic compounds: alkanes, alcohols, and ketones/aldehydes. We use spectra from two sources: the Hummel polymer library (mid-IR) and the library of Buback and Vögele (near-IR). This is the first paper on class separability to make use of this new near-IR library, available in digital form only since July 1995. Five spectral regions are considered: region 5, 10,500 to 6300 cm−1; region 4, 7200 to 5200 cm−1; region 3, 5500 to 3800 cm−1; region 2, 3900 to 2500 cm−1; and region 1, 2500 to 500 cm−1. Class separability is explored both qualitatively and quantitatively with the use of principal component scatter plots, linear discriminant analysis, Bhattacharyya distances, and other methods. We find that the separability is greatest in region 1 and least in region 2, with the three near-IR regions being intermediate. Furthermore, we find that, in the near-IR, there is sufficient class separability to ensure that organic compounds of one class can be determined in the midst of interference from the other classes.

A commercial Fourier transform infrared spectrometer (modified to improve the purge) and a long-path gas cell were used to demonstrate a detection limit of about i0 ppb for water in N2, HCI, and HBr. This technology is being developed as an in-line monitor to extend the life of gas delivery systems and improve wafer yields in the semiconductor industry. The reported detection limit includes an improvement of about a factor of three achieved by applying quantitative multivariate calibration to the problem. Methods are discussed to compensate for background moisture in the beam path. Also, discussed are the choice of operating parameters to optimize the instrumental performance. Surprisingly, resolving the narrow water absorption bands (FWHM = 0.20 cm -1) is not necessary to achieve optimal sensitivity. In fact, optimal sensitivity is achieved at 2 to 4 cm i resolution, allowing the use of an inexpensive interferometer in the final instrument. A calibration over the range of 40 to 2000 ppb of water in N2 has been performed and found to be in good agreement with published spectral data. Finally, an improved instrument design is discussed which is projected to have a lower detection limit and a shorter collection time.

The calculation of an absorbance spectrum depends on the measurement of a blank, or background spectrum. In many cases, such as the determination of atmospheric constituents with the use of open-path Fourier transform infrared spectroscopy (FT-IR) or the determination of water vapor in a gaseous sample, it is very difficult to obtain a good background spectrum. The difficulty is due to the fact that it is nearly impossible in these situations to measure a spectrum with no analyte features present. We present a method of generating a background spectrum based on filtering the analyte features from the sample spectrum. When the filtering method is used, the accuracy of the results obtained is found to be dependent upon the analyte peak width, peak height, and type of filter employed. Guidelines for the use of this background generation technique for quantitative determinations are presented.