Abstract
The combination of computational methods and the techniques of Thin Layer Chromatography (TLC), Ultraviolet (UV) and Gas Chromatography (GC), brings significant improvements in the speed and accuracy of the analysis and identification of drugs of abuse. In the case of the TLC technique, the processing is fully automatic, through a sequence of algorithms that are run on the images of the resulting plates. By means of this process, the spots corresponding to each substance are characterized with the determination of the respective value of the retardation factor (Rf), and the descriptions of their shape and color. The identification of a sample is made by calculating its dissimilarity with respect to the previously stored patterns. During this process, the quality of the plate is also evaluated. For the analysis of Ultraviolet data, the computation of dissimilarities is performed by taking into account the shape of the spectra. The analyzed sample will take the class of the closest pattern. Spectra are previously standardized in order to eliminate the variation of the slope of the curves caused by the dispersion and the variation in the particle size. With this purpose, the standard normal variate pre-processing method is applied. When using the GC technique, the identification process is based on the detection of peaks that belong to each drug according to their retention times. The drugs are identified by comparing both, the absolute and the relative retention times (with respect to two internal standards) of the detected peaks, with those of the patterns stored in the system.
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Mata, F.J.S. et al. (2014). Integrating Computational Methods for Forensic Identification of Drugs by TLC, GC and UV Techniques. In: Muda, A., Choo, YH., Abraham, A., N. Srihari, S. (eds) Computational Intelligence in Digital Forensics: Forensic Investigation and Applications. Studies in Computational Intelligence, vol 555. Springer, Cham. https://doi.org/10.1007/978-3-319-05885-6_10
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DOI: https://doi.org/10.1007/978-3-319-05885-6_10
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