Establishing chemical profiling for ecstasy tablets based on trace element levels and support vector machine
Autor: | Vanessa Cristina de Oliveira Souza, Jose Luiz Costa, Fernando Barbosa, Loraine Rezende Togni, Camila Maione, Andres D. Campiglia, Rommel Melgaço Barbosa |
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Rok vydání: | 2016 |
Předmět: |
Drug
Computer science media_common.quotation_subject medicine.medical_treatment 010401 analytical chemistry Ecstasy 02 engineering and technology 01 natural sciences 0104 chemical sciences Support vector machine Stimulant Artificial Intelligence Statistics 0202 electrical engineering electronic engineering information engineering medicine Profiling (information science) 020201 artificial intelligence & image processing Club drug Software media_common |
Zdroj: | Neural Computing and Applications. 30:947-955 |
ISSN: | 1433-3058 0941-0643 |
Popis: | Ecstasy is an amphetamine-type substance that belongs to a popular group of illicit drugs known as “club drugs” whose consumption is rising in Brazil. The effects caused by this substance in the human organism are mainly psychological, including hallucinations, euphoria and other stimulant effects. The distribution of this drug is illegal, and effective strategies are required in order to detain its growth. One possible way to obtain useful information on ecstasy trafficking routes, sources of supply, clandestine laboratories and synthetic protocols is by its chemical components. In this paper, we present a data mining and predictive analysis for ecstasy tablets seized in two cities of Sao Paulo state (Brazil), Campinas and Ribeirao Preto, based on their chemical profile. We use the concentrations of 25 elements determined in the ecstasy samples by ICP-MS as our descriptive variables. We develop classification models based on support vector machines capable of predicting in which of the two cities an arbitrary ecstasy sample was most likely to have been seized. Our best model achieved a 81.59% prediction accuracy. The F-score measure shows that Se, Mo and Mg are the most significant elements that differentiate the samples from the two cities, and they alone are capable of yielding an SVM model which achieved the highest prediction accuracy. |
Databáze: | OpenAIRE |
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