Characterization of surface organic components of human hair by on-line supercritical fluid extraction-gas chromatography/mass spectrometry: a feasibility study and comparison with human identification using mitochondrial DNA sequences

This paper discusses results of a supercritical fluid extraction-gas chromatography/mass spectrometry (SFE-GC/MS) study of small samples ( 100 microg to 1 mg) of human scalp hair. The method offers a number of benefits including greater sensitivity than liquid extraction methods because the entire extractable mass is transferred to the analytical system, compared with only a few percent from a conventional liquid extraction/injection. The project's goals were to determine if SFE-GC/MS analyses of the surface-extractable components of an individual's hair yield consistent chemical profiles and to investigate if the profiles are sufficiently different to distinguish them from those of other individuals. In addition, the mtDNA sequences from ten of the same individuals used in the SFE-GC/MS study from four family units were determined, and, while the families were distinguishable, the maternal relations yielded identical sequences. In tandem, SFE-GC/MS and mtDNA techniques may provide valuable complementary data from forensic hair samples.     

Using Ecological Inference Point Estimates as Dependent Variables in Second-Stage Linear Regressions

The practice of using point estimates produced by the King ecologicalinference technique as dependent variables in second-stage linearregressions leads to second-stage results that, in general,are inconsistent. This conclusion holds even when all assumptionsbehind King's ecological technique are satisfied. Second-stageinconsistency is a consequence of the fact that King-based pointestimates of disaggregated quantities contain errors correlatedwith the true quantities the estimates measure. Our findingson second-stage inconsistency, as well as a fix that we propose,follow from econometric theory in conjunction with an analysisof simulated and real ecological data sets.