After the elution of the papers with CH 3OH:NH 4OH mixture at 99.5:0.5 (%, v/v), Dragendorff reagent was applied, revealed, and the reddish spots were formed. Samples containing reference standard cocaine supplied by the Brazilian Federal Police were doped with levamisole, caffeine, phenacetin, and lidocaine, and posteriorly applied on the paper chromatography method.
Crack is the smocked cocaine, which is obtained from hydrochloride salt (see Scheme 15.2), and sold as small pieces commonly called rock. This is a large and complex field that is evolving very rapidly with developments in advanced algorithms as well as dedicated integrated circuits capable of encoding neural networks.Ī low cost, direct, efficient, and simple analysis of cocaine chemical composition was evaluated applying an ambient PSI-FT-ICR MS on chromatographic surface spots revealed with Dragendorff reagent which was used to identify adulterant compounds in crack samples. Supervised methods involve the learning of data based on advance knowledge of the classification, whereas unsupervised methods make no prior assumption about the sample classes but try to separate groups or clusters.
Two types of non-parametric learning or classification methods are available: supervised and non-supervised ( De Vito, Fattoruso, Pardo, Tortorella, & Di Francia, 2012 Hines, Llobet, & Gardner, 1999 Pardo & Sberveglieri, 2005). Conversely, non-parametric methods require no assumption about the fundamental statistical distributions of data. Parametric methods rely upon obtaining or estimating the probability density function of the parameters used to characterize the response of a system. Two main approaches to pattern recognition are commonly used: parametric and non-parametric. Pattern recognition in an “electronic nose” system may be regarded as a branch of artificial intelligence that involves the mimicking of human intelligence to solve chemical problems. The appropriate data processing methods for gas sensor arrays are reviewed in detail by Persaud (2013). The generated pattern of responses can be used to discriminate a range of different volatile chemicals or complex mixtures such as tobacco. The responses of individual OBPs are very different dependent of the analyte components reflecting the diversity of interactions that may be possible. S1–S8 are individual SAW devices with different immobilized proteins (A) responses to a saturated vapor of alpha-pinene (B) responses to tobacco headspace vapor. Responses of an OBP-SAW array comprising eight OBPs from insect and mammalian origin.
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