LC-MS method development and comparison of sampling materials for the analysis of organic gunshot residues

This study aimed at comparing the efficiency of various sampling materials for the collection and subsequent analysis of organic gunshot residues (OGSR). To the best of our knowledge, it is the first time that sampling devices were investigated in detail for further quantitation of OGSR by LC-MS. Seven sampling materials, namely two "swab"-type and five "stub"-type collection materials, were tested. The investigation started with the development of a simple and robust LC-MS method able to separate and quantify molecules typically found in gunpowders, such as diphenylamine or ethylcentralite. The evaluation of sampling materials was then systematically carried out by first analysing blank extracts of the materials to check for potential interferences and determining matrix effects. Based on these results, the best four materials, namely cotton buds, polyester swabs, a tape from 3M and PTFE were compared in terms of collection efficiency during shooting experiments using a set of 9 mm Luger ammunition. It was found that the tape was capable of recovering the highest amounts of OGSR. As tape-lifting is the technique currently used in routine for inorganic GSR, OGSR analysis might be implemented without modifying IGSR sampling and analysis procedure.

A nonparametric method for the measurement of size diversity with emphasis on data standardization

The most suitable method for estimation of size diversity is investigated. Size diversity is computed on the basis of the Shannon diversity expression adapted for continuous variables, such as size. It takes the form of an integral involving the probability density function (pdf) of the size of the individuals. Different approaches for the estimation of pdf are compared: parametric methods, assuming that data come from a determinate family of pdfs, and nonparametric methods, where pdf is estimated using some kind of local evaluation. Exponential, generalized Pareto, normal, and log-normal distributions have been used to generate simulated samples using estimated parameters from real samples. Nonparametric methods include discrete computation of data histograms based on size intervals and continuous kernel estimation of pdf. Kernel approach gives accurate estimation of size diversity, whilst parametric methods are only useful when the reference distribution have similar shape to the real one. Special attention is given for data standardization. The division of data by the sample geometric mean is proposedas the most suitable standardization method, which shows additional advantages: the same size diversity value is obtained when using original size or log-transformed data, and size measurements with different dimensionality (longitudes, areas, volumes or biomasses) may be immediately compared with the simple addition of ln k where kis the dimensionality (1, 2, or 3, respectively). Thus, the kernel estimation, after data standardization by division of sample geometric mean, arises as the most reliable and generalizable method of size diversity evaluation