New voltammetry-based technique for the determination of tebuthiuron in crystal and brown sugar samples

Sugar is widely consumed worldwide and Brazil is the largest producer, consumer, and exporter of this product. To guarantee proper development and productivity of sugar cane crops, it is necessary to apply large quantities of agrochemicals, especially herbicides and pesticides. The herbicide tebuthiuron (TBH) prevents pre- and post-emergence of infesting weed in sugarcane cultures. Considering that it is important to ensure food safety for the population, this paper proposes a reliable method to analyse TBH in sugar matrixes (brown and crystal) using square wave voltammetry (SWV) and differential pulse voltammetry (DPV) at bare glassy carbon electrode and investigate the electrochemical behavior of this herbicide by cyclic voltammetry (CV). Our results suggest that TBH or the product of its reaction with a supporting electrolyte is oxidized through irreversible transfer of one electron between the analyte and the working electrode, at a potential close to +1.16 V vs. Ag |AgClsat in 0.10 mol L-1 KOH as supporting electrolyte solution. Both DPV and SWV are satisfactory for the quantitative analysis of the analyte. DPV is more sensitive and selective, with detection limits of 0.902, 0.815 and 0.578 mg kg-1, and quantification limits of 0.009, 0.010 and 0.008 mg kg-1 in the absence of the matrix and in the presence of crystal and brown sugar matrix, respectively. Repeatability lay between 0.53 and 13.8%, precision ranged between 4.14 and 15.0%, and recovery remained between 84.2 and 113% in the case of DPV conducted in the absence of matrix and in the presence of the crystal sugar matrix, respectively.

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.