Conduction of a round-robin test on a real sample for the identification of gunshot residues by SEM/EDX.

A round-robin test on the identification of GSR particles by SEM/EDX and involving eleven Institutes was conducted on a real sample, in order to evaluate the possibilities/limitations of using such sample to get additional information (compared to the analysis of the usual synthetic sample used within the framework of the ENFSI proficiency test) about the performances of the SEM/EDX systems. Each Institute was asked to analyse this sample following its own standard operating procedure, and by using all the systems in house, whenever available. Between each Institute, a check of the sample was performed by the organizing Institute (NICC), in order inter alia to monitor any degradation and/or contamination of the sample. A total of about 30 analyses were performed on the sample. For each particle of interest identified on the real sample, the detection effectiveness was monitored, as well as the classification allotted by each Institute. The Institutes were also asked to report some of their measurement parameters, and to send the results as they would have been communicated in their own case report. A quite good agreement was observed with regard to the classification of the particles of interest, since a broad consensus was reached for approximately 75% of these particles. A different classification risk exists for some classes, the barium/antimony classes being probably the most critical, as traces of lead may cause the particles to shift (or not) from the consistent with GSR upper-class to the characteristic of GSR upper-class; in the end, the decision to shift from one class to another strongly depends on local rules. At the end of the campaign, a survey sent to collect experience and lessons learned from this exercise showed that analysing a real sample definitively offers an added value, especially in terms of classification process (during the automatic run and when performing the manual review) of particles.

Survey on batch-to-batch variation in spray paints: a collaborative study.

This study represents the most extensive analysis of batch-to-batch variations in spray paint samples to date. The survey was performed as a collaborative project of the ENFSI (European Network of Forensic Science Institutes) Paint and Glass Working Group (EPG) and involved 11 laboratories. Several studies have already shown that paint samples of similar color but from different manufacturers can usually be differentiated using an appropriate analytical sequence. The discrimination of paints from the same manufacturer and color (batch-to-batch variations) is of great interest and these data are seldom found in the literature. This survey concerns the analysis of batches from different color groups (white, papaya (special shade of orange), red and black) with a wide range of analytical techniques and leads to the following conclusions. Colored batch samples are more likely to be differentiated since their pigment composition is more complex (pigment mixtures, added pigments) and therefore subject to variations. These variations may occur during the paint production but may also occur when checking the paint shade in quality control processes. For these samples, techniques aimed at color/pigment(s) characterization (optical microscopy, microspectrophotometry (MSP), Raman spectroscopy) provide better discrimination than techniques aimed at the organic (binder) or inorganic composition (fourier transform infrared spectroscopy (FTIR) or elemental analysis (SEM - scanning electron microscopy and XRF - X-ray fluorescence)). White samples contain mainly titanium dioxide as a pigment and the main differentiation is based on the binder composition (CH stretches) detected either by FTIR or Raman. The inorganic composition (elemental analysis) also provides some discrimination. Black samples contain mainly carbon black as a pigment and are problematic with most of the spectroscopic techniques. In this case, pyrolysis-GC/MS represents the best technique to detect differences. Globally, Py-GC/MS may show a high potential of discrimination on all samples but the results are highly dependent on the specific instrumental conditions used. Finally, the discrimination of samples when data was interpreted visually as compared to statistically using principal component analysis (PCA) yielded very similar results. PCA increases sensitivity and could perform better on specific samples, but one first has to ensure that all non-informative variation (baseline deviation) is eliminated by applying correct pre-treatments. Statistical treatments can be used on a large data set and, when combined with an expert's opinion, will provide more objective criteria for decision making.