Establishing chemical profiling for ecstasy tablets based on trace element levels and support vector machine

作者：Maione Camila; de Oliveira Souza Vanessa C; Togni Loraine R; da Costa Jose L; Campiglia Andres D; Barbosa Fernando Jr; Barbosa Rommel M^*

来源：Neural Computing & Applications, 2018, 30(3): 947-955.

DOI：10.1007/s00521-016-2736-3

• Ecstasy
• Inductively coupled plasma mass spectrometry
• Classification
• Data mining
• Support vector machines

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 So Paulo state (Brazil), Campinas and Ribeiro 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.

• 出版日期2018-8