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Street cocaine is often mixed with various substances that intensify its harmful effects. This paper proposes a framework to identify attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) intervals that best predict the concentration of adulterants in cocaine samples. Wavelengths are ranked according to their relevance through ReliefF and mRMR feature selection approaches, and an iterative process removes less relevant wavelengths based on the ranking suggested by each approach. Gaussian Process (GP) regression models are constructed after each wavelength removal and the prediction performance is evaluated using RMSE. The subset balancing a low RMSE value and a small percentage of retained wavelengths is chosen. The proposed framework was validated using a dataset consisting of 345 samples of cocaine with different amounts of levamisole, caffeine, phenacetin, and lidocaine. Averaged over the four adulterants, the GP regression coupled with the mRMR retained 1.07 % of the 662 original wavelengths, outperforming PLS and SVR regarding prediction performance.

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The commerce of falsified drugs has substantially grown in recent years due to facilitated access to technologies needed for copying authentic pharmaceutical products. Attenuated Total Reflectance coupled with Fourier Transform Infrared (ATR-FTIR) spectroscopy has been successfully employed as an analytical tool to identify falsified products and support legal agents in interrupting illegal operations. ATR-FTIR spectroscopy typically yields datasets comprised of hundreds of highly correlated wavenumbers, which may compromise the performance of classical multivariate techniques used for sample classification. In this paper we propose a new wavenumber interval selection method aimed at selecting regions of spectra that best discriminate samples of seized drugs into two classes, authentic or falsified. The discriminative power of spectra regions is represented by an Interval Importance Index (III) based on the Two-Sample Kolmogorov-Smirnov test statistic, which is a novel proposition of this paper. The III guides an iterative forward approach for wavenumber selection; different data mining techniques are used for sample classification. In 100 replications using the best combination of classification technique and wavenumber intervals, we obtained average 99.87% accurate classifications on a Cialis® dataset, while retaining 12.5% of the authentic wavenumbers, and average 99.43% accurate classifications on a Viagra® dataset, while retaining 23.75% of the authentic wavenumbers. Our proposition was compared with alternative approaches for individual and interval wavenumber selection available in the literature, always leading to more consistent and easier to interpret results.

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Street cocaine is typically altered with several compounds that increase its harmful health-related side effects, most notably depression, convulsions, and severe damages to the cardiovascular system, lungs, and brain. Thus, determining the concentration of cocaine and adulterants in seized drug samples is important from both health and forensic perspectives. Although FTIR has been widely used to identify the fingerprint and concentration of chemical compounds, spectroscopy datasets are usually comprised of thousands of highly correlated wavenumbers which, when used as predictors in regression models, tend to undermine the predictive performance of multivariate techniques. In this paper, we propose an FTIR wavenumber selection method aimed at identifying FTIR spectra intervals that best predict the concentration of cocaine and adulterants (e.g. caffeine, phenacetin, levamisole, and lidocaine) in cocaine samples. For that matter, the Mutual Information measure is integrated into a Quadratic Programming problem with the objective of minimizing the probability of retaining redundant wavenumbers, while maximizing the relationship between retained wavenumbers and compounds' concentrations. Optimization outputs guide the order of inclusion of wavenumbers in a predictive model, using a forward-based wavenumber selection method. After the inclusion of each wavenumber, parameters of three alternative regression models are estimated, and each model's prediction error is assessed through the Mean Average Error (MAE) measure; the recommended subset of retained wavenumbers is the one that minimizes the prediction error with maximum parsimony. Using our propositions in a dataset of 115 cocaine samples we obtained a best prediction model with average MAE of 0.0502 while retaining only 2.29% of the original wavenumbers, increasing the predictive precision by 0.0359 when compared to a model using the complete set of wavenumbers as predictors.

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Cocaine ATR-FTIR spectra consist of a large number of wavenumbers that typically decreases the performance of exploratory and predictive multivariate techniques. This paper proposes a framework for selecting the most relevant wavenumbers to classify cocaine samples into two categories regarding chemical composition, i.e. salt and base. The proposed framework builds a wavenumber importance index based on the Bhattacharyya distance (BD) followed by a procedure that removes wavenumbers from the spectra according to the order suggested by the BD index. The recommended wavenumber subset is chosen based on multiple criteria assessing classification performance, which are recalculated after each wavenumber is eliminated. The method was applied to ATR-FTIR spectra from 513 samples of cocaine, remarkably reducing the percent of retained wavenumbers and yielding near to perfect classifications in the testing set. In addition, we compared our propositions with other methods tailored to wavenumber selection; we found that the proposed framework, which relies on simple mathematical fundamentals, yielded competitive results.

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In this article, five hundred and thirteen cocaine seizures of the State of Rio Grande do Sul (Brazil) were analyzed by Fourier transform infrared spectroscopy (FT-IR) in the fingerprint region (1800-650 cm(-1)) to profiling and evaluate the pharmaceutical products used as adulterants. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used to identify patterns among the samples whereas partial least square discriminant analysis (PLS-DA) and support vector machines discriminant analysis (SVM-DA) were used to classification the cocaine between base and salt. Spectra of standard solid mixtures of cocaine (salt and base), phenacetin, lidocaine and caffeine were used associated with PCA to predict qualitatively the profile of cocaine seizure. In HCA and PCA, salt and base group were formed correctly. Accordingly with predicted profile of the salt samples, they were majority adulterated with caffeine and lidocaine whereas base cocaine was adulterated only with phenacetin. In the discrimant analysis, all methods have classified the cocaine samples correctly with sensitivity and specificity equal to one between salt and base.

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As intoxicações por medicamentos são predominantes no Brasil e frequentes na faixa etária de 0 a 14 anos. O ácido valproico (AV) vem se destacando em virtude do aumento do seu espectro de utilização na terapêutica clínica, porém, a hepatotoxicidade pode ser desencadeada por altas concentrações desse fármaco, apresentando alta incidência em crianças. Logo, tornam-se importantes métodos rápidos de quantificação desse fármaco, a fim de auxiliar o clínico no tratamento da intoxicação. Diante desse cenário, os objetivos deste trabalho foram comparar metodologias analíticas para quantificação de AV por CLAE-F (fluorescência) e CG/DIC (detecção por ionização de chama) em relação à sua potencial aplicação em toxicologia clínico-laboratorial de urgência. Para quantificação de AV por fluorescência, realizou-se a derivatização do AV com 4-(Bromometil)-7-metoxicumarina, sendo o produto da reação analisado em λ de emissão de 325 e detecção de 398 nm, na faixa de calibração de 1-300miug/mL. Com relação à CG/DIC, esta apresentou-se linear na faixa de 100-2000 miug/mL, sem necessidade de derivatização prévia. A técnica de CG/DIC mostrou-se mais apropriada para análises toxicológicas de urgência em casos de intoxicação com AV, tendo em vista o menor tempo de corrida, a linearidade obtida, menor custo, rapidez e praticidade, além de utilizar um equipamento robusto, disponível na grande maioria dos laboratórios de toxicologia de pequeno e médio porte.

Poisoning by drugs is rather frequent in Brazil in the age range of 0 to 14 years. Intoxication by valproic acid (VA) stands out because of an increase in its spectrum of clinical use; hepatotoxicity is an important reaction that can be triggered by high concentrations of this drug and there is a high incidence of toxic events in children. Therefore, fast methods of analysing this drug are essential, in order to help the clinician to treat the intoxication. Given this scenario, the objective of this study was to compare analytical methods to determine VA, by HPLC-F (fluorescence) and GC/FID (flame ionization detection), assessing their potential application in the urgent toxicology clinic. For the fluorometric analysis, the VA was first derivatized with 4-bromomethyl-6, 7-dimethoxycoumarin, and the resulting compound was excited at λ = 325 nm and detected by the emission at 398 nm. The calibration range was 1-300 miug/mL. The GC/FID method showed a linear response in the range 100-2000 miug/mL, without requiring prior derivatization. The technique of GC/FID proved more appropriate for the urgent toxicological analysis of VA, in view of the shorter time of analysis, linearity, lower cost, speed, efficiency and the use of robust equipment that is already available in the great majority of small and medium-sized toxicological clinics.

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