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URI permanente desta comunidadehttps://thoth.dti.ufv.br/handle/123456789/3352
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Item Chemometric analysis of UV characteristic profile and infrared fingerprint variations of Coffea arabica green beans under different space management treatments(Sociedade Brasileira de Química, 2016) Terrile, Amélia E.; Marcheafave, Gustavo G.; Oliveira, Guilherme S.; Rakocevic, Miroslava; Bruns, Roy E.; Scarminio, Ieda S.Ultraviolet characteristic profiles and infrared spectroscopic (FTIR) fingerprints of green bean extracts of Coffea arabica L., cv. IAPAR 59, cultivated in two planting patterns, rectangular and square, and at two different densities, 10,000 and 6,000 plants ha-1, identified as R10,R6,S10, and S6 were analyzed with principal component and hierarchical cluster analyses. A simplex centroid design for four solvents (ethanol, acetone, dichloromethane, hexane) was used for sample extraction. The largest chlorogenic acid (CGA) contents were found at the lower planting density. The dichloromethane extracts of the S10 treatment showed the highest levels of unsaponifiable lipids (cafestol and kahweol). The R6 treatment showed a slightly higher content of cafestol and kahweol. Cluster analysis of FTIR fingerprints confirmed that the CGA and caffeine levels differentiate the spatial arrangements. The FTIR fingerprints suggest that green beans from S6 and R10 were richer in lipids and the other two treatments had more sugars and proteins.Item Integrated chemometric approach to optimize sample preparation for detecting metabolic changes provoked by abiotic stress in Coffea arabica L. leaf fingerprints(Sociedade Brasileira de Química, 2019) Marcheafave, Gustavo G.; Tormena, Cláudia D.; Afoso, Sabrina; Rakocevic, Miroslava; Bruns, Roy E.; Scarminio, Ieda S.The effects of water-deficit stress on irrigated and unirrigated field plants of Coffea arabica L. genotype IAPAR 59 were investigated. Plant extracts were obtained following an ethanoldichloromethane-hexane statistical mixture design. Proton nuclear magnetic resonance (1 H NMR) fingerprints of the extracts were discriminated using factor analysis (FA) and hierarchical clustering techniques. Extracts from the 1:1:1 ternary mixture presented the largest discriminations compared with those from the pure solvents or their 1:1 binary mixtures. Metabolites resulting from fermentation processes and nutritional deficiencies as well as senescence and abscission precursors such as lactate, arginine and methionine were prevalent in unirrigated plants that can provoke expressive decreases in bean productivity as well as premature plant aging. Amino acids that control regulatory, physiological processes and soil salinization have higher concentrations in the irrigated plants. The NMR assignments of eighteen substances observed here were confirmed by electrospray ionization mass spectrometry.Item Irrigation and light acess effects on Coffea arabica L. leaves by FTIR-chemometric analysis(Sociedade Brasileira de Química, 2018) Sanchez, Patrícia M.; Pauli, Elis D.; Scheel, Guilherme L.; Rakocevic, Miroslava; Brunsc, Roy E.; Scarminio, Ieda S.Coffee bean chemical compositions has been extensively studied. However, there is a small amount of research on other parts of the coffee plant, including leaves. Fourier transform infrared (FTIR) spectral profiles of Coffea arabica L. cv. IAPAR 59 leaf extracts from a simplex-centroid design were studied by principal component analysis (PCA) to evaluate the effect of solvente extractor on its metabolites. PCA indicated that the extractor solvents containing ethanol were the most suitable for this study. FTIR spectra in conjunction with orthogonal signal correction and partial least squares-discrimination analysis (OSC-PLS-DA) were used to classify and discriminate the leaves of irrigated and non-irrigated plants by bands related to carbohydrates, amino acids and lipids. Leaves receiving different intensities of solar radiation were also discriminated by bands corresponding to caffeine, carbohydrates and lipids. FTIR spectral profile analyzed with chemometric tools showed to be a useful, powerful and simple procedure to discriminate coffee leaves collected from different microclimate conditions.