Infrared spectroscopy is commonly applied to the analysis of small gas-phase molecules. One of the limitations of using Fourier transform infrared (FT-IR) spectroscopy for these applications is the time response of long path length gas cells. Hollow waveguides (HW) that transmit in the mid-infrared spectral range have higher optical efficiencies compared to long path length cells due to smaller cell volumes. This study characterizes a silver coated, 2 mm inner diameter HW for the analysis of carbon monoxide (CO) and nitric oxide (NO) and compares the performance to a 3 m gas cell and traditional gas analyzers. The HW was found to have a CO response time less than the NDIR analyzer and approximately one-tenth of the response time on the FT-IR system equipped with a 3 m gas cell. The utility of the increased response time was demonstrated by measuring CO concentrations in sidestream cigarette smoke at the same temporal resolution as an NDIR analyzer. A 10 to 60% increase in sensitivity using various frequencies for both CO and NO was observed using the HW compared to the 3 m multipass gas cell. However, cost savings for gas-sensing applications can be achieved on a per analyte basis by using FT-IR spectroscopy, especially in combination with a HW gas-sensing module, which is significantly less expensive than a multipass gas cell.

Fourier transform infrared (FT-IR) spectroscopy was compared directly to independent standard analytical techniques for the routine measurement of carbon monoxide (CO) and nitric oxide (NO) yields from cigarette sidestream smoke. The FT-IR instrument was configured in-line with a nondispersive infrared (NDIR) analyzer for CO analysis and a chemiluminescence (CL) analyzer for NO analysis to monitor the sidestream smoke from a single port of a linear smoking machine. A cold trap was inserted prior to the FT-IR to minimize the levels of vapor phase interferents, such as water. Univariate and multivariate regression analysis were evaluated for the prediction of cigarette yield from time-resolved spectral data at 1, 2, 4 and 8 cm-1 spectral resolution. Regressions were developed using three different spectral ranges including unique rotation-vibration lines, the R-branch, and the entire absorption band. As per standard methods, yields were calculated from the concentration traces generated during the smoke runs for five different cigarettes spanning the expected range of mainstream total particulate matter deliveries. The FT-IR traces for the smoke runs revealed improved temporal resolution yielding analytical information from smoke generated in between puffs. The performance between the validation methods and the FT-IR calibrations was statistically compared. In general, for the determination of CO, the FT-IR calibrations underestimated the yield measured by NDIR by less than 10%. For the NO measurement, the univariate FT-IR calibrations overestimated the NO yield measured by the CL analyzer, whereas the partial least squares (PLS) calibrations showed good agreement. PLS calibrations were developed for both analytes providing no significant difference when compared to the respective standard analytical techniques. Results for sidestream CO and NO yields for Kentucky reference cigarette 1R4F utilizing 8 cm-1 calibrations compared favorably to values reported elsewhere in the literature. Hence, calibration of the FT-IR system at 8 cm-1 spectral resolution clearly revealed the potential of this method, providing enhanced temporal resolution, simultaneous determination of several smoke components, and reduced complexity of the experimental setup in contrast to the standard techniques.

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Imaging technology has extended itself from performing gauging on machined parts, to verifying labeling on consumer products, to quality inspection of a variety of man-made and natural materials. Much of this has been made possible by faster computers and algorithms used to extract useful information from the image. In the application of agricultural material, specifically tobacco leaves, the tremendous amount of natural variability in color and texture creates new challenges to image feature extraction. As with many imaging applications, the problem can be expressed as "I see it in the image, how can I get the computer to recognize it?" In this application, the goal is to measure the amount of thick stem pieces in an image of tobacco leaves. By backlighting the leaf, the stems appear dark on a lighter background. The difference in lightness of leaf versus darkness of stem is dependent on the orientation of the leaf and the amount of folding. Because of this, any image thresholdmg approach must be adaptive. Another factor that allows us to identify the stem from the leaf is shape. The stem is long and narrow, while dark folded leaf is larger and more oblate. These criteria under the image collection limitations create a good application for fuzzy logic. Several generalized classification algorithms, such as fuzzy c-means and fuzzy learning vector quantization, are evaluated and compared. In addition, fuzzy thresholdmg based on image shape and compactness are applied to this application.