Derivatives are common preprocessing tools, typically implemented as Savitzky-Golay (SG) smoothing derivatives. This work discusses the implementation and optimization of fourth-order gap derivatives (GDs) as an alternative to SG derivatives for processing infrared spectra before multivariate calibration. Gap derivatives approximate the analytical derivative by calculating finite differences of spectra without curve fitting. Gap derivatives offer an advantage of tunability for spectral data as the distance (gap) over which this finite difference is calculated can be varied. Gap selection is a compromise between signal attenuation, noise amplification, and spectral resolution. A method and discussion of the importance of fourth derivative gap selections are presented as well as a comparison to SG preprocessing and lower-order GDs in the context of multivariate calibration. In most cases, we found that optimized GDs led to calibration models performing comparably to or better than SG derivatives, and that optimized fourth-order GDs behaved similarly to matched filters.

Detection limits (DL) for blood on four fabric types were estimated for calibrations derived using partial least squares regression applied to infrared (IR) diffuse reflection spectra. Samples were prepared by dip-coating acrylic, cotton, nylon, and polyester fabrics from solutions of diluted rat blood. While DLs often appear in terms of dilution factor in the forensic community, mass percentage, coverage (mass per unit area), or film thickness are often more relevant when comparing experimental methods. These alternate DL units are related to one another and presented here. The best IR diffuse reflection DLs for blood on acrylic and cotton fabrics were in the mid-IR spectral window corresponding to the protein Amide I/II absorption bands. These DLs were dilution by a factor of 2300 (0.019% w/w blood solids) for acrylic and a factor of 610 (0.055% w/w blood solids) for cotton. The best DL for blood on polyester was found in the mid-IR spectral window corresponding to the protein Amide A absorption band at dilution by a factor of 900 (0.034% w/w blood solids). Because of the similarity between the IR spectra of blood solids and nylon fabrics, no satisfactory IR DLs were determined for the calibration of blood on nylon. We compare our values to DLs reported for blood detection using the standard luminol method. The most commonly reported luminol DLs are of the order of 1000-fold dilution, which we estimate are a factor of 2-7 lower than our reported IR DLs on a coverage basis.

No abstract

No abstract

The effectiveness of material to emit energy as thermal radiation is important in determining the apparent temperature in infrared thermographic measurements. For this reason, a number of measurements of the thermal emissivity in the mid-infrared thermographic (8–12 µm) region have been reported for fabrics. However, many fabrics adsorb moisture from the air, and condensed water has a relatively high thermal emissivity. In this manuscript, we report measurements of adsorption isotherms and mid-infrared thermal emissivity for nylon, cotton, polyester, and acrylic as a function of their moisture content in weight percent at temperatures just above ambient. We find that the order of water mass percentage gain for the fabrics in high humidity conditions are polyester < acrylic < nylon < cotton. The thermal emissivity is ∼0.88 independent of moisture content for the fabrics polyester, cotton, and nylon, while acrylic shows a pronounced increase in thermal emissivity as moisture content increases, ranging from ɛ ∼ 0.81 at low humidity conditions to ɛ ∼ 0.88 under high humidity conditions. In this work, emissivity measurements are made by imaging through a novel infrared window made from household cling wrap and interpreted with equations that are independent of window transmittance and sample temperature.