Determinação dos perfis químicos e avaliação de blends de cafés arábica e conilon por SHS-GC-MS, FTIR e quimiometria

• Ano de defesa: 2023
• Autor(a) principal: Lyrio, Marcos Valério Vieira
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Espírito Santo; BR; Mestrado em Química; Centro de Ciências Exatas; UFES; Programa de Pós-Graduação em Química
• Palavras-chave em Português: Cromatografia gasosa; Seleção de variáveis; Perfil químico; subject.br-rjbn; Química
• Link de acesso: http://repositorio.ufes.br/handle/10/16694

Considering the great economic importance of Coffea arabica (arabica) associated with the lower production cost of Coffea canephora (conilon), blends of these coffees are commercially available to reduce costs and combine their unique sensory attributes of each species. Thus, the development of blend evaluation methods is important to guarantee the quality and authenticity of commercialized coffees. This work proposes to develop a chromatographic method based on the evaluation of volatile compounds using static headspace extraction and analysis by gas chromatography coupled to mass spectrometry (SHS-GC-MS), in addition to using Fourier transform infrared spectroscopy (FTIR) and chemometric tools in the evaluation of chemical profiles and quantification of arabica and conilon blends. In the study of blends, the integration of peaks was performed in the total ion chromatogram (TIC) and the extracted ion chromatogram (EIC), aiming to evaluate the analytical performance of the built models and compare the two types of integration. The models optimized by partial least squares (PLS) with UVE variable selection and chromatographic data (TIC and EIC) obtained similar accuracies according to the randomized test, with values of prediction errors between 3.3%–4.7% and R2p > 0.98. There was no difference between the univariate models for TIC and EIC, but the FTIR model performed worse than the GC-MS. Multivariate and univariate models based on chromatographic data showed similar performance. For the classification models, the FTIR, TIC, and EIC data showed accuracies between 96-100% and error rates from 0% to 5%. The application of multivariate and univariate analysis combined with chromatographic and spectroscopic data allows the investigation of coffee blends and the determination of their authenticity. Regarding the detailed chemical profiles of the species, 97 compounds were identified, divided into 17 chemical classes, 69 of which are statistically different between species in terms of relative abundance. Conilon coffee has a greater abundance of anhydrides, furans, alcohols, phenols, xanthines, esters, thiazoles, pyrazines, pyrimidines, thiophenes, pyrroles, and pyridines, while arabica is more abundant in the classes of carboxylic acids, ketones, pyranones, and furanones. The main discriminating classes between the species were carboxylic acids, phenols, and furans, while the main compounds were furfuryl alcohol, acetic acid, 4-vinylguaiacol, N-acetyl-4(H)-pyridine and N-furfurylpyrrole.