Profile of explosives's use in ATMs/cash safes robberies in Brazil.

This retrospective study reports data obtained by the Federal Police's National Institute of Criminalistics (INC-PF) relating to chemical analysis aimed at identifying explosives used in Automated teller machines (ATMs)/cash safes robberies between 2014 and 2020 in Brazil. 93 Real cases were studied and, based on the analysis carried out on the materials related to these cases, focusing on the type of explosive used, the following distribution profile was obtained: I) explosive mixtures based on chlorates and/or perchlorates (53%); explosive emulsion (22%); black gunpowder (13%); negative/inconclusive (11%) and organic - pentaerythritol tetranitrate (PETN) (1%). These results can contribute to investigations related to diversion/loss of explosives for criminal purposes, indicating, for example, through relationships between prevalence in the use of a certain type of explosive with a certain location, its possible origin (mining industry, explosive industries, fireworks factories, among others). The profile observed in the results can guide the selection of explosives to be studied in future research, as the possibilities are vast. Furthermore, despite the expressive number of occurrences in Brazil in the period of interest of this study, only a small fraction of samples was sent to the forensic chemistry laboratory to identify the explosive involved, which suggests that expanding chemical analysis should be encouraged in this field. In combination with an increase in professional training and collaboration trials between the laboratories, these activities can improve the chemical explosive's profile in Brazil, enabling the search for correlations between occurrences and contributing to the growth and development of this area.

Optimization of a saccharin molecularly imprinted solid-phase extraction procedure and evaluation by MIR hyperspectral imaging for analysis of diet tea by HPLC.

Saccharin was determined based on a new molecularly imprinted solid-phase extraction (MISPE) procedure. The polymer was synthesized with a hybrid monomer of metacrylic acid and 3-amino propril tetraethoxysilane and saccharin as template. After the synthesis, the saccharin removal from the MIP was verified by the UV analysis of the solutions used in the template removal procedure, as well as by the direct MIP analysis using FTIR hyperspectral image and chemometrics. The residual saccharin concentrations observed in the image analysis revealed a narrow concentration distribution consistent with a homogenous material. The MISPE was performed with homemade cartridges containing 200 mg of the MIP. The results obtained with standards and diet tea samples confirmed high affinity, adsorption capacity and selectivity of the MIP. The MISPE cartridge exhibited recoveries of 100 ± 3% in six extraction cycles. The diet tea analysis showed a significant reduction of the interferences, which can considerable simplifies the HPLC-UV analysis.

Discrimination of white automotive paint samples using ATR-FTIR and PLS-DA for forensic purposes.

The consequences of a hit-and-run car crash are significant and may include serious injuries to the victims, health system overload and even victim's death. The vehicle and driver identification are often challenging for local law enforcement. The aim of this study was to develop a methodology to discriminate between automotive paint samples according to the make of the vehicle and its color shade. 143 white samples (collected at traffic accident scenes) were analyzed in situ by Fourier transform infrared spectroscopy with attenuated total reflectance (ATR-FTIR) and coupled microscopy. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed for data analysis. The samples were split into three groups: calibration set, validation set and external test set. The figures of merit were calculated to assess the quality of the model. Sensitivity, specificity, and efficiency rates were, respectively, 98,9%, 98.4% and 98.6%, for the calibration set. For the validation group, the classification accuracy was 100%. Correct classification rates for the internal validation set and external test set were 100% and 79.1% respectively. The technique is clean, fast, relatively low-cost, and non-destructive. Damaged regions of the samples were avoided by using the attached microscope. Limiting the age of the samples to a maximum of 10 years was enough to avoid misclassifications due to the natural degradation and weathering of the sample. Since the external test group is formed by underrepresented classes, its correct classification rate (79.1%) can be potentially improved at any time, by including and analyzing more samples.

Discrimination of whisky brands and counterfeit identification by UV-Vis spectroscopy and multivariate data analysis.

The discrimination of whisky brands and counterfeit identification were performed by UV-Vis spectroscopy combined with partial least squares for discriminant analysis (PLS-DA). In the proposed method all spectra were obtained with no sample preparation. The discrimination models were built with the employment of seven whisky brands: Red Label, Black Label, White Horse, Chivas Regal (12years), Ballantine's Finest, Old Parr and Natu Nobilis. The method was validated with an independent test set of authentic samples belonging to the seven selected brands and another eleven brands not included in the training samples. Furthermore, seventy-three counterfeit samples were also used to validate the method. Results showed correct classification rates for genuine and false samples over 98.6% and 93.1%, respectively, indicating that the method can be helpful for the forensic analysis of whisky samples.

Determination of vegetable oils and fats adulterants in diesel oil by high performance liquid chromatography and multivariate methods.

The current legislation requires the mandatory addition of biodiesel to all Brazilian road diesel oil A (pure diesel) marketed in the country and bans the addition of vegetable oils for this type of diesel. However, cases of irregular addition of vegetable oils directly to the diesel oil may occur, mainly due to the lower cost of these raw materials compared to the final product, biodiesel. In Brazil, the situation is even more critical once the country is one of the largest producers of oleaginous products in the world, especially soybean, and also it has an extensive road network dependent on diesel. Therefore, alternatives to control the quality of diesel have become increasingly necessary. This study proposes an analytical methodology for quality control of diesel with intention to identify and determine adulterations of oils and even fats of vegetable origin. This methodology is based on detection, identification and quantification of triacylglycerols on diesel (main constituents of vegetable oils and fats) by high performance liquid chromatography in reversed phase with UV detection at 205nm associated with multivariate methods. Six different types of oils and fats were studied (soybean, frying oil, corn, cotton, palm oil and babassu) and two methods were developed for data analysis. The first one, based on principal component analysis (PCA), nearest neighbor classification (KNN) and univariate regression, was used for samples adulterated with a single type of oil or fat. In the second method, partial least square regression (PLS) was used for the cases where the adulterants were mixtures of up to three types of oils or fats. In the first method, the techniques of PCA and KNN were correctly classified as 17 out of 18 validation samples on the type of oil or fat present. The concentrations estimated for adulterants showed good agreement with the reference values, with mean errors of prediction (RMSEP) ranging between 0.10 and 0.22% (v/v). The PLS method was efficient in the quantification of mixtures of up to three types of oils and fats, with RMSEP being obtained between 0.08 and 0.27% (v/v), mean precision between 0.07 and 0.32% (v/v) and minimum detectable concentration between 0.23 and 0.81% (v/v) depending on the type of oil or fat in the mixture determined.