Caetano Alexandre Marcelo

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.

Methods of multivariate analysis of NIR reflectance spectra for classification of yerba mate

The present article is about a method of classification for yerba mate (Ilex paraguariensis), native to South America. Yerba mate samples were ground in a cryogenic mill and the near-infrared (NIR) reflectance of milled samples was directly measured. Hierarchical cluster analysis (HCA), principal components analysis (PCA), k-nearest neighbour (kNN), soft independent modelling class analogy (SIMCA), partial least square discriminant analysis (PLS-DA), and support vector machine discriminant analysis (SVM-DA) were used for multivariate analysis of the NIR reflectance spectra. Fifty-four brands of yerba mate from Argentina, Brazil, Paraguay and Uruguay were analyzed to classify the commercialized product by country of origin. For all intervals of the NIR reflectance spectrum evaluated (4435–4318 cm−1, 4358–4200 cm−1, 4436–4200 cm−1, and 4673–4200 cm−1), the SVM-DA classification of all brands was 100% correct. The kNN classification was not 100% correct in any interval. Classification via PCA, HCA and SIMCA was 100% correct for the 4435–4318 cm−1 interval. PLS-DA classification was 100% correct for the 4358–4200 cm−1 and 4435–4318 cm−1 intervals.

Seized cannabis seeds cultivated in greenhouse: A chemical study by gas chromatography-mass spectrometry and chemometric analysis.

Cannabis sativa L. is cultivated in most regions of the world. In 2013, the Brazilian Federal Police (BFP) reported 220 tons of marijuana seized and about 800,000 cannabis plants eradicated. Efforts to eradicate cannabis production may have contributed to the development of a new form of international drug trafficking in Brazil: the sending of cannabis seeds in small amounts to urban centers by logistics postal. This new and increasing panorama of cannabis trafficking in Brazil, encouraged the chemical study of cannabis seeds cultivated in greenhouses by gas-chromatography coupled with mass spectrometry (GC-MS) associated with exploratory and discriminant analysis. Fifty cannabis seeds of different varieties and brands, seized by the BFP were cultivated under predefined conditions for a period of 4.5 weeks, 5.5 weeks, 7.5 weeks, 10 weeks and 12 weeks. Aerial parts were analyzed and cannabigerol, cannabinol, cannabidiol, cannabichromene Δ9-tetrahydrocannabinol (THC) and other terpenoids were detected. The chromatographic chemical profiles of the samples were significantly different, probably due to different variety, light exposition and age. THC content increased with the age of the plant, however, for other cannabinoids, this correlation was not observed. The chromatograms were plotted in a matrix with 50 rows (samples) and 3886 columns (abundance in a retention time) and submitted to PCA, HCA and PLS-DA after pretreatment (normalization, first derivative and autoscale). The PCA and HCA showed age separation between samples however it was not possible to verify the separation by varieties and brands. The PLS-DA classification provides a satisfactory prediction of plant age.

Near infrared spectroscopy combined with chemometrics for growth stage classification of cannabis cultivated in a greenhouse from seized seeds.

Cannabis sativa L. (cannabis, Cannabaceae), popularly called marijuana, is one of the oldest plants known to man and it is the illicit drug most used worldwide. It also has been the subject of increasing discussions from the scientific and political points of view due to its medicinal properties. In recent years in Brazil, the form of cannabis drug trafficking has been changing and the Brazilian Federal Police has exponentially increased the number of seizures of cannabis seeds sent by the mail. This new form of trafficking encouraged the study of cannabis seeds seized germinated in a greenhouse through NIR spectroscopy combined with chemometrics. The plants were cultivated in a homemade greenhouse under controlled conditions. In three different growth periods (5.5weeks, 7.5weeks and 10weeks), they were harvested, dried, ground and directly analyzed. The iPCA was used to select the best NIR spectral range (4000-4375cm-1) in order to develop unsupervised and supervised methods. The PCA and HCA showed a good separation between the three groups of cannabis samples at different growth stages. The PLS-DA and SVM-DA classified the samples with good results in terms of sensitivity and specificity. The sensitivity and specificity for SVM-DA classification were equal to unity. This separation may be due to the correlation of cannabinoids and volatile compounds concentration during the growth of the cannabis plant. Therefore, the growth stage of cannabis can be predicted by NIR spectroscopy and chemometric tools in the early stages of indoor cannabis cultivation.

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.

Toxic and nutrient elements in yerba mate (Ilex paraguariensis)

Toxic and nutrient elements were investigated in yerba mate (Ilex paraguariensis) from South America. Fifty-four brands of commercialised yerba mate from Argentina, Brazil, Paraguay and Uruguay were analysed for Al, Ba, Ca, Cu, Fe, K, Mg, Mn, P, Sr, and Zn, using inductively coupled plasma optical emission spectrometry (ICP-OES), and Li, Be, Ti, V, Cr, Ni, Co, As, Se, Rb, Mo, Ag, Cd, Sb, La, Ce, Pb, Bi and U using inductively coupled plasma mass spectrometry (ICP-MS). Antimony, Se, Ag and Bi were not detected in any sample whereas the limits of detection (LODs) of these elements were 0.19, 0.40, 0.003 and 0.001 μg g−1, respectively. Analysis of variance (ANOVA) revealed that the concentrations of Cd, Ti, Ni, As, Mo, U, Li and Be in yerba mate were not statistically different with regard to the country of origin, while those of the other investigated elements differed.

Wavelength selection framework for classifying food and pharmaceutical samples into multiple classes

Near infrared (NIR) spectroscopy is an efficient, low‐cost analytical technique widely applied to identify the origin of food and pharmaceutical products. NIR spectra‐based classification strategies typically use thousands of equally spaced wavelengths as input information, some of which may not carry relevant information for product classification. When that is the case, the performance of predictive and exploratory multivariate techniques may be undermined by such noisy information. In this paper, we propose an iterative framework for selecting subsets of NIR wavelengths aimed at classifying samples into categories. For that matter, we integrate Principal Components Analysis (PCA) and three classification techniques: k‐Nearest Neighbor (KNN), Probabilistic Neural Network (PNN) and Linear Discriminant Analysis (LDA). PCA is first applied to NIR data, and a wavelength importance index is derived based on the PCA loadings. Samples are then categorized using the wavelength with the highest index and the classification accuracy is calculated; next, the wavelength with the second highest index is inserted into the dataset and a new classification is performed. This forward‐based iterative procedure is carried out until all original wavelengths are inserted into the dataset used for classification. The subset of wavelengths leading to the maximum accuracy is chosen as the recommended subset. Our propositions performed remarkably well when applied to four datasets related to food and pharmaceutical products. Copyright

Near infrared spectroscopy and element concentration analysis for assessing yerba mate (Ilex paraguariensis) samples according to the country of origin

Yerba mate (Ilex paraguariensis) is used to produce a beverage typically consumed in South America countries, and presents peculiar land-based characteristics due to geographical origin. Such characteristics have recently become a matter of interest for many producers as specific features of yerba mate tend to influence product acceptance in new markets, prices and commercial advantages. This scenario justifies the developing of frameworks tailored to correctly classify products according to their authenticity. This paper uses Near Infrared (NIR) spectroscopy and data describing concentration of chemical elements to classify commercial yerba mate samples according to their place of origin. Aimed at enhancing data interpretability, we propose a novel variable selection method that applies quadratic programming to reduce redundant information among the retained variables and maximize their relationship regarding the sample place of origin; sample categorization is then performed using alternative classification techniques. When applied to the NIR dataset, the proposed method retained average 8.79% of the original wavenumbers, while leading to 1.9% more accurate classifications when compared to categorization using the full spectra. As for the elements dataset, we increased average classification accuracy by 3.5% and retained 47.22% of the original elements. The proposed method also outperformed two other approaches for variable selection from the literature. Our findings suggest that variable selection frameworks help to correctly identify the origin and authenticity of yerba mate samples, making model construction and interpretation easier.

Determination of cocaine and its main adulterants in seized drugs from Rio Grande do Sul, Brazil, by a Doehlert optimized LC-DAD method

The main reason for the increasing cocaine consumption in South America is the high consumption of drugs in Brazil, which is the largest market on the continent. In light of this, the Brazilian Federal Police (BFP) started implementing its own drug chemical profiling program, the PeQui project, aiming to provide useful technical-scientific information about the drug scenario in the country. In this article, a liquid chromatography with a diode array detector (LC-DAD) method was developed through Doehlert optimization for the analysis of cocaine seized in the state of Rio Grande do Sul, Brazil, by the Brazilian Federal Police. In addition to cocaine, the main cocaine adulterants (diltiazem, benzocaine, levamisole, caffeine, phenacetin, lidocaine and dipyrone) were also evaluated. Through Doehlert optimization relating to the resolution and total area, a mobile phase consisting of acetonitrile : water (isocratic mode) with phosphate buffer (pH 8.3) was chosen. Fifty eight cocaine samples seized in 2013–2015 were analyzed. The average cocaine content was 45% of the drug weight and the only adulterants detected were levamisole, phenacetin and caffeine. Levamisole was detected only in salt cocaine samples and low concentrations (below 0.1 mg g−1), whereas phenacetin was detected in base form cocaine in higher concentrations. Caffeine was the only adulterant detected in both the salt and base forms, and was also at low concentrations. These results showed that the drugs seized in this Brazilian state had, on average, a lower cocaine content in relation to the rest of the country.

Development of an inexpensive, practical and non-destructive methodology based on digital images from a scanner for the classification of commercial tannins from Acacia mearnsii

Among the oldest applications of tannins is leather tanning. Skin, as it is when removed from slaughtered animals, is not suitable for use by humans due to its susceptibility to attack and decomposition by bacteria. Widely found in the vegetable kingdom, condensed tannins constitute more than 90% of the total world production of commercial tannins. The bark extract from black wattle is superior to other natural tannins due to its high solubility in water, light colour and low viscosity. In this study, a methodology for the identification and classification of seven commercial tannins from Acacia mearnsii using multivariate analysis of digital images acquired through a commercial scanner was developed. The first three principal components of the principal component analysis showed a well-defined separation of the extracts into seven distinct classes. A hierarchical cluster analysis corroborates this separation. The PLS-DA supervised method showed excellent results regarding classification, with a sensitivity and specificity of 100%, with the exception of product A. Product A presented 91% sensitivity. From the results, we concluded that this method may be useful for the quality control of commercial tannins from Acacia mearnsii and correct identification, with equivalent results to those obtained with NIR without the need to invest in expensive and sophisticated equipment, according to green chemistry principles.