Risco de crédito: uma abordagem utilizando análise discriminante, regressão logística e redes neurais artificiais

Considering the relevance of researches concerning credit risk, model diversity and the existent indicators, this thesis aimed at verifying if the Fleuriet Model contributes in discriminating Brazilian open capital companies in the analysis of credit concession. We specifically intended to i) identify the economic-financial indicators used in credit risk models; ii) identify which economic-financial indicators best discriminate companies in the analysis of credit concession; iii) assess which techniques used (discriminant analysis, logistic regression and neural networks) present the best accuracy to predict company bankruptcy. To do this, the theoretical background approached the concepts of financial analysis, which introduced themes relative to the company evaluation process; considerations on credit, risk and analysis; Fleuriet Model and its indicators, and, finally, presented the techniques for credit analysis based on discriminant analysis, logistic regression and artificial neural networks. Methodologically, the research was defined as quantitative, regarding its nature, and explanatory, regarding its type. It was developed using data derived from bibliographic and document analysis. The financial demonstrations were collected by means of the Economática ® and the BM$FBOVESPA website. The sample was comprised of 121 companies, being those 70 solvents and 51 insolvents from various sectors. In the analyses, we used 22 indicators of the Traditional Model and 13 of the Fleuriet Model, totalizing 35 indicators. The economic-financial indicators which were a part of, at least, one of the three final models were: X1 (Working Capital over Assets), X3 (NCG over Assets), X4 (NCG over Net Revenue), X8 (Type of Financial Structure), X9 (Net Thermometer), X16 (Net Equity divided by the total demandable), X17 (Asset Turnover), X20 (Net Equity Profitability), X25 (Net Margin), X28 (Debt Composition) and X31 (Net Equity over Asset). The final models presented setting values of: 90.9% (discriminant analysis); 90.9% (logistic regression) and 97.8% (neural networks). The modeling in neural networks presented higher accuracy, which was confirmed by the ROC curve. In conclusion, the indicators of the Fleuriet Model presented relevant results for the research of credit risk, especially if modeled by neural networks.

Quantificação de contaminantes em aguardentes de cana/cachaças de alambique e de coluna

In the present study aimed to characterize and quantify four contaminants (ethyl carbamate, 2,3-butanedione, furfural and 5-hydroxymethylfurfural) present in alembic cachaça and industrial. Were collected forty-four samples of cachaça in the southern regions, the Midwest, southeast of Minas Gerais and São Paulo state, and subsequently subjected to physical, chemical and chromatographic analyzes. The physicochemical analyzes were performed according to the methodology described by the Ministry of Agriculture, Livestock and Supply (MAPA). The ethyl carbamate, 2,3-butanedione, furfuaral and 5 hydroxymethylfurfural were characterized and quantified by high-performance liquid chromatography (HPLC). The results of the ethyl carbamate analysis, it was found that both samples showed column cachaças outside the standards required by law, with the values 245.31 235.53 L-1 ug and none of the liquor samples alembic showed concentration greater than 210.0 ug L-1 , and the method is very sensitive to low limits of detection and quantification. In determining 2,3-butanedione, it was revealed that the column cachaças showed higher levels of contaminants when compared to cachaça alembic. In the quantification of furfural and 5-hydroxymethylfurfural was developed and validated analytical methods employed to high-performance liquid chromatography (HPLC) with DAD detector. Samples column cachaças showed higher values than the limit established by Brazilian legislation and ranged from 7.00 to 5.63 mg / 100 ml of anhydrous alcohol over the alembic cachaça.

Análise do teor de cocaína em amostras apreendidas pela polícia utilizando-se a técnica de cromatografia liquida de alta eficiência com detector UV-Vis

The HPLC technique with UV-Vis detection was employed in the analysis of cocaine content in apprehended samples of cocaine and crack. A peak signal for cocaine was obtained in 3.5 minutes run by using acetonitrile/water (95:5v/v) as a mobile phase. Optimized spectrophotometric signal was obtained at a wavelength of 224 nm. The analytical curve from 1.0 to 40.0 ppm of cocaine was obtained, showing a linear correlation coefficient of 0.9989, with detection and quantification limits of 0.75 ppm and 3.78 ppm, respectively. This methodology was employed at the dosage of confiscated samples of cocaine and crack in the Scientific Police Laboratory of Ribeirão Preto-SP city.

Evolution of the concentration of phenolic compounds in cachaça during aging in an oak (Quercus sp.) barrel

Cachaça is a traditional and popular Brazilian drink obtained by distilling fermented sugar cane juice. Among the steps involved in its production, natural aging in wood containers for a certain period of time can lead to alterations in the chemical composition, aroma, flavor and color of the beverage. The present work sought to determine the concentration of phenolic compounds after different periods of aging of the cachaça in an oak (Quercus sp.) barrel. Periodic collections during the aging period were performed, and thirteen selected phenolic compounds were determined by high performance liquid chromatography with a diode-array detector (HPLC-DAD). A progressive increase in the concentration of the compounds analyzed was observed, with syringaldehyde and gallic acid as the compounds encountered in the highest concentration.