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

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Abstract

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 São Paulo state (Brazil), Campinas and Ribeirão 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.

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References

  1. Almeida SP, Silva MTA (2013) Ecstasy (MDMA): effects and patterns of use reported by users in São Paulo. Rev Bras Psiquiatr 25:11–17

    Article  Google Scholar 

  2. Barbosa R, Nelson D (2016) The use of support vector machine to analyze food security in a region of brazil. Appl Artif Intell 30(4):318–330

    Article  Google Scholar 

  3. Batista BL, Rodrigues JL, Souza VCO, Barbosa F Jr (2009) A fast ultrasound-assisted extraction procedure for trace elements determination in hair samples by ICP-MS for forensic analysis. Forensic Sci Int 192:88–93

    Article  Google Scholar 

  4. Brown SD, Melton TC (2011) Trends in biological methods for the determination and quantification of club drugs: 2000–2010. Biomed Chromatogr 25:300–321

    Article  Google Scholar 

  5. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2:121–167

    Article  Google Scholar 

  6. Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40:16–28

    Article  Google Scholar 

  7. Chen Y, Lin C (2006) Combining SVMs with various feature selection strategies. In: Guyon I, Nikravesh N, Gunn S, Zadeh LA (eds) Feature extraction, studies in fuzziness and soft computing, vol 207. Springer, Berlin Heidelberg, pp 315–324

    Google Scholar 

  8. Comment S, Lock E, Zingg C, Jakob A (2001) The analysis of ecstasy tablets by ICP-MS and ICP/AES. Probl Forensic Sci 46:131–146

    Google Scholar 

  9. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297

    MATH  Google Scholar 

  10. Dash M, Liu H (1997) Feature selection for classification. Intell Data Anal 1:131–156

    Article  Google Scholar 

  11. Delen D, Cogdell D, Kasap N (2012) A comparative analysis of data mining methods in predicting NCAA bowl outcomes. Int J Forecast 28:543–552

    Article  Google Scholar 

  12. Delen D, Walker G, Kadam A (2005) Predicting breast cancer survivability: a comparison of three data mining methods. Artif Intell Med 34:113–127

    Article  Google Scholar 

  13. Duda RO, Hart PE, Stork DG (2000) Pattern classification, 2nd edn. Wiley-Interscience, New York

    MATH  Google Scholar 

  14. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874

    Article  Google Scholar 

  15. Fierro I, Deban L, Pardo R, Tascón M, Vázquez D (2007) Analysis of heavy metals in ecstasy tablets by electrochemical methods. Toxicol Environ Chem 89:411–419

    Article  Google Scholar 

  16. French HE, Went MJ, Gibson SJ (2013) Graphite furnace atomic absorption elemental analysis of ecstasy tablets. Forensic Sci Int 231:88–91

    Article  Google Scholar 

  17. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

    MATH  Google Scholar 

  18. Hamel LH (2009) Knowledge discovery with support vector machines. Wiley-Interscience, New York

    Book  Google Scholar 

  19. Koper C, van den Boom C, Wiarda W, Schrader M, de Joode P, van der Peijl G, Bolck A (2007) Elemental analysis of 3,4-methylenedioxymethamphetamine (MDMA): a tool to determine the synthesis method and trace links. Forensic Sci Int 171:171–179

    Article  Google Scholar 

  20. Maione C, de Paula ES, Gallimberti M, Batista BL, Campiglia AD, Barbosa F Jr, Barbosa RM (2016) Comparative study of data mining techniques for the authentication of organic grape juice based on ICP-MS analysis. Expert Syst Appl 49:60–73

    Article  Google Scholar 

  21. Maione C, Batista BL, Campiglia AD, Barbosa F Jr, Barbosa RM (2016) Classification of geographic origin of rice by data mining and inductively coupled plasma mass spectrometry. Comput Electron Agric 121:101–107

    Article  Google Scholar 

  22. Nigam K, Mccallum AK, Thrun S, Mitchell T (2000) Text classification from labeled and unlabeled documents using EM. Mach Learn 39:103–134

    Article  MATH  Google Scholar 

  23. Polat K, Güneş S (2007) Breast cancer diagnosis using least square support vector machine. Digit Signal Process 17:694–701

    Article  Google Scholar 

  24. R Core Team (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Viena. http://www.R-project.org/

  25. Schäffer M, Dieckmann S, Pütz M, Kohles T, Pyell U, Zimmermann R (2013) Impact of reaction parameters on the chemical profile of 3,4-methylenedioxymethamphetamine synthesized via reductive amination: target analysis based on GC-qMS compared to non-targeted analysis based on GCxGC-TOF-MS. Forensic Sci Int 233:201–211

    Article  Google Scholar 

  26. Schneider K (2003) A comparison of event models for Naive Bayes anti-spam e-mail filtering. In: Proceedings of the tenth conference on european chapter of the association for computational linguistics—volume 1, EACL ‘03, Budapest, Hungary. Association for Computational Linguistics, Stroudsburg, pp 307–314

  27. Tan P, Steinbach M, Kumar V (2005) Introduction to data mining, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston

    Google Scholar 

  28. Tong S, Koller D (2002) Support vector machine active learning with applications to text classification. J Mach Learn Res 2:45–66

    MATH  Google Scholar 

  29. Waddell RJ, NicDaéid N, Littlejohn D (2004) Classification of ecstasy tablets using trace metal analysis with the applications of chemometric procedures and artificial neural network algorithms. Analyst 129:235–240

    Article  Google Scholar 

  30. Zain SM, Behkami S, Bakirdere S, Koki IB (2016) Milk authentication and discrimination via metal content clustering—a case of comparing milk from Malaysia and selected countries of the world. Food Control 66:306–314

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for financial support.

Funding

This study was funded by São Paulo Research Foundation (FAPESP) and the National Council for Technological and Scientific Development (CNPq).

Author information

Authors and Affiliations

  1. Instituto de Informática, Universidade Federal de Goiás, Goiânia, Goiás, Brazil

    Camila Maione & Rommel M. Barbosa

  2. Laboratório de Toxicologia e Essencialidade de Metais, Depto. de Análises Clínicas, Toxicológicas e Bromatológicas, Faculdade de Ciências Farmacêuticas de Ribeirão Preto-USP, Ribeirão Prêto, São Paulo, Brazil

    Vanessa C. de Oliveira Souza & Fernando Barbosa Jr.

  3. Laboratório de Química e Toxicologia Forense, Instituto de Criminalística de São Paulo, São Paulo, São Paulo, Brazil

    Loraine R. Togni & Jose L. da Costa

  4. Centro de Controle de Intoxicação, Universidade de Campinas, Campinas, São Paulo, Brazil

    Jose L. da Costa

  5. Department of Chemistry, University of Central Florida, Orlando, FL, USA

    Andres D. Campiglia

  6. Center for Forensic Science, University of Central Florida, Orlando, FL, USA

    Andres D. Campiglia

Authors
  1. Camila Maione
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  2. Vanessa C. de Oliveira Souza
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  3. Loraine R. Togni
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  4. Jose L. da Costa
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  5. Andres D. Campiglia
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  6. Fernando Barbosa Jr.
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  7. Rommel M. Barbosa
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Corresponding author

Correspondence to Rommel M. Barbosa.

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All authors declare that they have no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Maione, C., Souza, V.C.O., Togni, L.R. et al. Establishing chemical profiling for ecstasy tablets based on trace element levels and support vector machine. Neural Comput & Applic 30, 947–955 (2018). https://doi.org/10.1007/s00521-016-2736-3

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  • DOI: https://doi.org/10.1007/s00521-016-2736-3

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