Detection of Chemical Warfare Agents by Colorimetric Sensor Arrays.

We report the successful use of colorimetric arrays to identify chemical warfare agents (CWAs). Methods were developed to interpret and analyze a 73-indicator array with an entirely automated workflow. Using a cross-validated first-nearest-neighbor algorithm for assessing detection and identification performances on 632 exposures, at 30 min postexposure we report, on average, 78% correct chemical identification, 86% correct class-level identification, and 96% correct red light/green light (agent versus non-agent) detection. Of 174 total independent agent test exposures, 164 were correctly identified from a 30 min exposure in the red light/green light context, yielding a 94% correct identification of CWAs. Of 149 independent non-agent exposures, 139 were correctly identified at 30 min in the red light/green light context, yielding a 7% false alarm rate. We find that this is a promising approach for the development of a miniaturized, field-portable analytical equipment suitable for soldiers and first responders.

Improving the linearity of infrared diffuse reflection spectroscopy data for quantitative analysis: an application in quantifying organophosphorus contamination in soil.

Diffuse reflection data are presented for ethyl methylphosphonate in a fine Utah dirt sample as a model system for organophosphate-contaminated soil. The data revealed a chemometric artifact when the spectra were represented in Kubelka-Munk units that manifests as a linear dependence of spectral peak height on variations in the observed baseline position (i.e., the position of the observed transmission intensity where no absorption features occur in the sample spectrum). We believe that this artifact is the result of the mathematical process by which the raw data are converted into Kubelka-Munk units, and we developed a numerical strategy for compensating for the observed effect and restoring chemometric precision to the diffuse reflection data for quantitative analysis while retaining the benefits of linear calibration afforded by the Kubelka-Munk approach. We validated our Kubelka-Munk correction strategy by repeating the experiment using a simpler system--pure caffeine in potassium bromide. The numerical preprocessing includes conventional multiplicative scatter correction coupled with a baseline offset correction that facilitates the use of quantitative diffuse reflection data in the Kubelka-Munk formalism for the quantitation of contaminants in a complex soil matrix, but is also applicable to more fundamental diffuse reflection quantitative analysis experiments.