Gaussian process regression coupled with mRMR to predict adulterant concentration in cocaine.

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.

A computer vision chemometric-assisted approach to access pH and glucose influence on susceptibility of Candida pathogenic strains.

Microorganisms adapt to environmental conditions as a survival strategy for different interactions with the environment. The adaptive capacity of fungi allows them to cause disease at various sites of infection in humans. In this study, we propose digital images as responses of a complete factorial 23. Furthermore, we compared two experimental approaches: the experimental design (3D) and the checkerboard assay (2D) to know the influence of pH, glucose, and fluconazole concentration on different strains of the genus Candida. The digital images obtained from the factorial 23 were used as input in the PCA-ANOVA to analyze the results of this experimental design. pH modification in the culture medium modifies the susceptibility in some species less adapted to this type of modification. For the first time, to the best of our knowledge, digital images were used as input to PCA-ANOVA to obtain information on Candida spp.. Therefore, a higher concentration of antifungals is needed to inhibit the same strain at a lower pH. In short, we present an alternative with less use of reagents and time. In addition, the use of digital images allows obtaining information about fungal susceptibility with three or more factors.

Interval importance index to select relevant ATR-FTIR wavenumber Intervals for falsified drug classification.

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.

Wavenumber selection method to determine the concentration of cocaine and adulterants in cocaine samples.

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.

Growth of Paecilomyces variotii in B0 (diesel), B100 (biodiesel) and B7 (blend), degradation and molecular detection.

The introduction of biodiesel to diesel may allow the fuel to be more susceptible to microorganism growth, especially during incorrect storage. To evaluate the effect of adding biodiesel in pure diesel on the growth of Paecilomyces variotii, microcosms containing pure diesel (B0), blend diesel/biodiesel (B7) and pure biodiesel (B100) were used. In microcosm with minimal mineral medium and B0, B7 or B100, after 60 days, the biomass (dry weight) formed at interface oil-water in B7 and B100 was significantly higher when compared to that of B0. Infrared analysis showed reduction of the carbonile fraction in B7 and B100 suggesting formation of intermediate compounds in B7. To monitor possible contamination of fuel storage tank by P. variotii samples were collected and analysed by specific-PCR assay for detection of P. variotii spores in the aqueous phase. This method was able to detect a minimum of 103 spores ml-1, corresponding to 0.0144 ng µl-1 of DNA. Specificity was tested against Aspergillus fumigatus and Pseudallescheria boydii.

Multicriteria wavenumber selection in cocaine classification.

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.

Profiling cocaine by ATR-FTIR.

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.

Determination of intrinsic viscosity of poly(ethylene terephthalate) using infrared spectroscopy and multivariate calibration method.

A methodology was developed to determine the intrinsic viscosity of poly(ethylene terephthalate) (PET) using diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and multivariate calibration (MVC) methods. Multivariate partial least squares calibration was applied to the spectra using mean centering and cross validation. The results were correlated to the intrinsic viscosities determined by the standard chemical method (ASTM D 4603-01) and a very good correlation for values in the range from 0.346 to 0.780dLg(-1) (relative viscosity values ca. 1.185-1.449) was observed. The spectrophotometer detector sensitivity and the humidity of the samples did not influence the results. The methodology developed is interesting because it does not produce hazardous wastes, avoids the use of time-consuming chemical methods and can rapidly predict the intrinsic viscosity of PET samples over a large range of values, which includes those of recycled materials.

Análise de medicamentos antiinflamatórios não esteroidais manipulados empregando espectroscopia de reflexão difusa no infravermelho com transformada de Fourier (DRIFTS) / Analysis of nonsteroidal antiinflammatory drugs in compounded medicines by diffuse-reflectance infrared Fourier transform spectroscopy (DRIFTS)

A análise de dois fármacos diferentes, mas estruturalmente semelhantes, foi realizada, empregando espectros de reflexão difusa no infravermelho médio com transformada de Fourier (DRIFTS), em associação com a técnica de análise por agrupamentos hierárquicos (AAH). Amostras de 3 diferentes farmácias de manipulação, contendo diclofenaco de sódio 50mg, diclofenaco de potássio 50mg e seus respectivos excipientes, foram analisadas em duplicata por dois analistas. Para a análise multivariada foi empregado o aplicativo Pirouette R 2.7 da Infometrix, selecionando-se o conjunto de regiões dos espectros com maior número de informações. Os dendogramas foram construídos com os dados auto-escalados e correção do espalhamento de luz (MSC), utilizando três tipos de construções: simples, flexível e incremental. Com a aplicação da AAH, constatou-se a formação de diferentes grupos obedecendo à discriminação de cada princípio ativo, indicando seus fornecedores e, em separado, outro grupo. Estes resultados demonstram que a técnica DRIFTS, em conjunto com ferramentas quimioterápicas, constitui uma excelente opção para a caracterização de fármacos, gerando uma inovadora metodologia para auxiliar no controle da qualidade de processos industriais de produção de medicamentos