Espectroscopia no infravermelho associada a quimiometria para a determinação de parâmetros de qualidade e de indicação geográfica de cachaças

• Ano de defesa: 2018
• Autor(a) principal: Oliveira, Sheila Catarina de
• Orientador(a): Veras Neto, Jose Germano
• Banca de defesa: Simões, Simone da Silva , Araujo, Mario Cesar Ugulino de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual da Paraíba
• Programa de Pós-Graduação: Programa de Pós-Graduação em Química - PPGQ
• Departamento: Pró-Reitoria de Pós-Graduação e Pesquisa - PRPGP
• País: BR
• Palavras-chave em Português: Calibração multivariada; Quimiometria; Aguardente
• Palavras-chave em Inglês: Multivariate calibration
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA
• Link de acesso: https://repositorio.uepb.edu.br/handle/123456789/73603

Cachaça is a distilled beverage derived from the sugarcane juice. Considered one of the most consumed alcoholic beverages in Brazil and around the World, this alcoholic beverage has great influence in the economy of several Brazilian regions and it has attracted the interest of researchers from the most diverse science areas. The increase in the consumption and in the production of cachaça made it mandatory to establish a regulation regarding the quality control procedures. However, the methods currently applied have several disadvantages, such as being expensive and time-consuming. In this context, this dissertation aimed to develop an alternative method of analysis, using chemometric methods in association with infrared spectroscopy. The samples of commercial cachaça from different regions of Brazil were analyzed according to the standards established by the current legislation in the country regarding the physical-chemical properties: acidity, density, alcoholic content, lead and copper. Infrared spectroscopy spectra were collected in the range between 12000 and 4000 cm^-1 (NIR) and 4000 and 400 cm^-1(MIR). The unsupervised pattern recognition method of principal component analysis (PCA) was applied for an exploratory analysis of the data in order to identify patterns and clustering among the samples produced in the recently extinct Microregion of Brejo Paraibano. Partial least squares regression (PLSR) was used in the development of calibration models for the determination of physical-chemical parameters. All regression models constructed to predict the parameters of interest showed higher determination coefficients and smaller prediction errors in the spectral region of NIR. The values of R^2 , RMSECV and RMSEP were 0,977, 0,63 and 1,06 for the alcoholic content model and 0,960, 0,0014 e 0,0019 for the density model. The models constructed to predict the total acidity of the samples presented unsatisfactory results (R2=0,588, RMSECV=14,9074) and therefore were not used in the external calibration stage. These results demonstrate the potential of infrared spectroscopy analyzes for accurate prediction of density and alcohol contents of cachaça samples. Comparative tests using the MIR spectra resulted in higher values of values and lower determination coefficients. Therefore, it is possible to conclude that NIRS is a better option for the development of multivariate models of cachaça samples. Regarding the application PCA analysis, three assumptions were evaluated: there is a formation of defined groupings based on the producing region (using NIR and MIR); there is a formation of clusters according to alcoholic content limits (NIR) and finally; the samples can be grouped based on their physical-chemical results. The first two attempts did not present the formation of any group. However, the third application returned interesting results, allowing the grouping of samples in relation to their copper and calcium composition. Density and alcoholic concentration were also responsible for the discrimination of a group of samples.