Complex matrixes typically cannot be analyzed directly to obtain the selectivity and sensitivity required for most trace analysis applications. To circumvent this problem, solid-phase microextraction (SPME) techniques were used to preconcentrate analytes selectively prior to gas chromatographic/ion trap mass spectrometric analysis. This approach was applied to the trace analysis of explosives and their metabolites in seawater. The choice of SPME sorbent phase was shown to be important especially for the amino metabolites of trinitrotoluene (TNT) and RDX, which were extracted better on polar phases. Although equilibration times were quite lengthy, on the order of 30 min or greater, a sampling time of only 10 min was shown to be sufficient for achieving low part-per-billion (ppb) to part-per-trillion (ppt) detection limits for TNT and the amino metabolites in real seawater samples. While SPME was ideal for rapid screening of explosives in seawater samples, methods for improving the reproducibility and accuracy of quantification are still being investigated.

This study was conducted to characterize the chemistry associated with the decomposition of human remains with the objective of identifying time-dependent biomarkers of decomposition. The purpose of this work was to develop an accurate and precise method for measuring the postmortem interval (PMI) of human remains. Eighteen subjects were placed within a decay research facility throughout a four-year time period and allowed to decompose naturally. Field autopsies were performed and tissue samples were regularly collected until the tissues decomposed to the point where they were no longer recognizable (encompassing a cumulative degree hour (CDH) range of approximately 1000 (3 weeks)). Analysis of the biomarkers (amino acids, neurotransmitters, and decompositional by-products) in various organs (liver, kidney, heart, brain, muscle) revealed distinct patterns useful for determining the PMI when based on CDHs. Proper use of the methods described herein allow for PMIs so accurate that the estimate is limited by the ability to obtain correct temperature data at a crime scene rather than sample variability.

DISCLAIMER ABSTRACT:The purpose of this work was to investigate the utility of electronic aroma detection technologies for the detection and identification of accelerant residues in suspected arson debris. Through the analysis of known accelerant residues, a trained neural network was developed for classifjling suspected arson samples. Three unknown fire debris samples were classified using this neural network. The item corresponding to diesel fuel was correctly identified every time. For the other two items, wide variations in sample concentration and excessive water content, producing high sample humidities, were shown to influence the sensor response. Sorbent