Metodologias analíticas para a identificação de não conformidades em amostras de álcool combustível

• Ano de defesa: 2013
• Autor(a) principal: Silva, Adenilton Camilo da
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraíba; Brasil; Química; Programa de Pós-Graduação em Química; UFPB
• Palavras-chave em Português: Espectroscopia no infravermelho; Voltametria; Classificação multivariada; Seleção de variáveis; Infrared spectroscopy; Voltammetry; Multivariate classification; Variables selection; CIENCIAS EXATAS E DA TERRA::QUIMICA
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/tede/8167

In Brazil, ethanol fuel is marketed in the hydrated form (HEAF– Hydrated Ethyl Alcohol Fuel). The adulterations found in HEAF can generate fines, and possible risks to society. With this perspective, this work proposes developing new analytical methods based on the use of infrared spectroscopy (NIR and MIR), and Cyclic Voltammetry (copper electrode), and chemometric pattern recognition techniques, to identify HEAF adulterations (with water or methanol). A total of 184 HEAF samples collected from different gasoline stations were analyzed. These samples were divided in three classes: (1) unadulterated, (2) adulterated with water (0.5% to 10%mm-1), and (3) adulterated with methanol (2% to 13% mm-1). Principal Components Analysis (PCA) was applied, permitting verification of a tendency to form clusters for unadulterated and adulterated samples. Classification models based on Linear Discriminant Analysis (LDA), with variable selection algorithms: SPA (Successive Projections Algorithm), GA (Genetic Algorithm), and SW (Stepwise) were employed. PLS-DA (Discriminant Analysis by Partial Least Squares) was applied to the data. Assessing the MIR spectra, 100% correct classification was achieved for all models. For NIR data, SPA-LDA and LDA-SW achieved a correct classification rate (RCC) of 84.4%, and 97.8%, respectively, while PLS-DA and GALDA correctly classified all test samples. In the evaluation of voltammetric data, as SPA-LDA as PLS-DA achieved a 93% RCC, but the GA-LDA and SW-LDA models showed better results, correctly classifying all test samples. The results suggest that the proposed methods are promising alternatives for identifying HEAF samples adulterated with water or methanol both quickly and securely.