Towards chemical profiling of ignitable liquids with comprehensive two-dimensional gas chromatography: Exploring forensic application to neat white spirits.

The application of GC×GC-FID and GC×GC-MS for the chemical analysis and profiling of neat white spirit is explored and the benefit of the enhanced peak capacity offered by comprehensive two-dimensional gas chromatography is demonstrated. An extensive sampling exercise was conducted throughout The Netherlands and the production and logistics in terms of bottling and distribution of white spirits were studied. An exploratory approach based on target-peak tables and principal component analysis was employed to study the brand-to-brand differences and production variations over time. Despite the complex chemical composition of white spirit samples this study shows that chemical variation during productions is actually quite limited. Hence care has to be taken with the chemical comparison for forensic purposes. Although some clustering was noticed on brand level, the large scale production process leads to a very consistent composition across stores and brands. However, because of the broad specifications of this commodity product, substantial chemical variation was found over time. This temporal discrimination could be of forensic value when considering white spirits supplies in individual households.

Leveraging probabilistic peak detection to estimate baseline drift in complex chromatographic samples.

Accurate analysis of chromatographic data often requires the removal of baseline drift. A frequently employed strategy strives to determine asymmetric weights in order to fit a baseline model by regression. Unfortunately, chromatograms characterized by a very high peak saturation pose a significant challenge to such algorithms. In addition, a low signal-to-noise ratio (i.e. s/n<40) also adversely affects accurate baseline correction by asymmetrically weighted regression. We present a baseline estimation method that leverages a probabilistic peak detection algorithm. A posterior probability of being affected by a peak is computed for each point in the chromatogram, leading to a set of weights that allow non-iterative calculation of a baseline estimate. For extremely saturated chromatograms, the peak weighted (PW) method demonstrates notable improvement compared to the other methods examined. However, in chromatograms characterized by low-noise and well-resolved peaks, the asymmetric least squares (ALS) and the more sophisticated Mixture Model (MM) approaches achieve superior results in significantly less time. We evaluate the performance of these three baseline correction methods over a range of chromatographic conditions to demonstrate the cases in which each method is most appropriate.

Class-conditional feature modeling for ignitable liquid classification with substantial substrate contribution in fire debris analysis.

Forensic chemical analysis of fire debris addresses the question of whether ignitable liquid residue is present in a sample and, if so, what type. Evidence evaluation regarding this question is complicated by interference from pyrolysis products of the substrate materials present in a fire. A method is developed to derive a set of class-conditional features for the evaluation of such complex samples. The use of a forensic reference collection allows characterization of the variation in complex mixtures of substrate materials and ignitable liquids even when the dominant feature is not specific to an ignitable liquid. Making use of a novel method for data imputation under complex mixing conditions, a distribution is modeled for the variation between pairs of samples containing similar ignitable liquid residues. Examining the covariance of variables within the different classes allows different weights to be placed on features more important in discerning the presence of a particular ignitable liquid residue. Performance of the method is evaluated using a database of total ion spectrum (TIS) measurements of ignitable liquid and fire debris samples. These measurements include 119 nominal masses measured by GC-MS and averaged across a chromatographic profile. Ignitable liquids are labeled using the American Society for Testing and Materials (ASTM) E1618 standard class definitions. Statistical analysis is performed in the class-conditional feature space wherein new forensic traces are represented based on their likeness to known samples contained in a forensic reference collection. The demonstrated method uses forensic reference data as the basis of probabilistic statements concerning the likelihood of the obtained analytical results given the presence of ignitable liquid residue of each of the ASTM classes (including a substrate only class). When prior probabilities of these classes can be assumed, these likelihoods can be connected to class probabilities. In order to compare the performance of this method to previous work, a uniform prior was assumed, resulting in an 81% accuracy for an independent test of 129 real burn samples.

Objective color classification of ecstasy tablets by hyperspectral imaging.

The general procedure followed in the examination of ecstasy tablets for profiling purposes includes a color description, which depends highly on the observers' perception. This study aims to provide objective quantitative color information using visible hyperspectral imaging. Both self-manufactured and illicit tablets, created with different amounts of known colorants were analyzed. We derived reflectance spectra from hyperspectral images of these tablets, and successfully determined the most likely colorant used in the production of all self-manufactured tablets and four of five illicit tablets studied. Upon classification, the concentration of the colorant was estimated using a photon propagation model and a single reference measurement of a tablet of known concentration. The estimated concentrations showed a high correlation with the actual values (R(2) = 0.9374). The achieved color information, combined with other physical and chemical characteristics, can provide a powerful tool for the comparison of tablet seizures, which may reveal their origin.

Automated shape annotation for illicit tablet preparations: a contour angle based classification from digital images.

In order to facilitate forensic intelligence efforts in managing large collections of physical feature data pertaining to illicit tablets, we have developed an automated shape classification method. This approach performs categorical shape annotation for the domain of illicit tablets. It is invariant to scale, rotation and translation and operates on digital images of seized tablets. The approach employs two processing levels. The first (coarse) level is being based on comparing the contour curvature space of tablet pairs. The second (fine) level is a rule based approach, implemented as a classification tree, that exploits characteristic similarities of shape categories. Annotation is demonstrated over a collection of 169 tablets selected for their diverse shapes with an accuracy of 97.6% when 19 shape categories are defined.