RESUMO
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.
RESUMO
The differentiation of 25 automotive clear coats was evaluated using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). The samples were selected from eight different groups of samples which slightly differ in their infrared spectra. Most of the samples could be differentiated by visual inspection of the pyrograms. As an objective mean for evaluation a new software based on the comparison of chromatograms was tested for automatic classification considering retention times as well as mass spectra. The database was formed by the triplicate results of the set of the 25 samples. Normally a replicate measurement of a sample yields the best fit by library search. In addition, for most groups classification with moderate fits are obtained for samples belonging to the same group. Some samples are completely rearranged forming a new group of similar samples containing five samples from three different IR groups and four samples of three other groups, respectively. Furthermore detailed visual recognition of individual pyrolysis products allows subgrouping. Therefore, most samples can be differentiated from each other by Py-GC/MS. The exception were three sample groups containing two samples each, which could not be differentiated from each other neither by library search nor by recognition of minor individual pyrolysis products.
