Elis Daiane Pauli

Detection of roasted and ground coffee adulteration by HPLC by amperometric and by post-column derivatization UV–Vis detection

Coffee is one of the most consumed beverages in the world. Due to its commercial importance, the detection of impurities and foreign matters has been a constant concern in fraud verification, especially because it is difficult to percept adulterations with the naked eye in samples of roasted and ground coffee. In Brazil, the most common additions are roasted materials, such as husks, sticks, corn, wheat middling, soybean, and more recently - acai palm seeds. The performance and correlation of two chromatographic methods, HPLC-HPAEC-PAD and post-column derivatization HPLC-UV-Vis, were compared for carbohydrate analysis in coffee samples. To verify the correlation between the two methods, the principal component analysis for the same mix of triticale and acai seeds in different proportions with coffee was employed. The performance for detecting adulterations in roasted and ground coffee of the two methods was compared.

Método para determinação de carboidratos empregado na triagem de adulterações em café

The objective in this work was to validate a chromatography method for the determination of total carbohydrates in soluble coffee, using a HPLC-UV-VIS with postcolumn derivatization system, in order to verify adulterant additions. The validated method was accurate and robust. Adulteration could be observed by increasing xylose and glucose levels in samples with addition of coffee husks and starchy products while decreasing of galactose and mannose characteristic carbohydrates presenting in high concentration in soluble coffees produced by arabica and robusta coffee beans.

Analytical investigation of secondary metabolites extracted from Camellia sinensis L. leaves using a HPLC-DAD-ESI/MS data fusion strategy and chemometric methods

Considerable attention has been paid to the study of green tea leaves because of their high consume and beneficial effects on human health. In this work, an appropriate strategy is proposed to investigate and resolve the major metabolites extracted from Camellia sinensis tea leaves. Statistical design mixtures of ethanol, ethyl acetate, dichloromethane, and chloroform were used to study the effects of different solvents and their mixtures on the extraction of the secondary metabolites of C. sinensis tea leaves from two different harvest seasons. Extracted samples were analyzed by high performance liquid chromatography‐diode array detection‐electrospray ionization mass spectrometer allowing the resolution of a large amount of tea metabolites with high relative abundances, especially when their extraction was performed in pure ethanol and with solvent mixtures with ethanol. Resolution of the more relevant metabolites was achieved by the simultaneous analysis of the fused diode array detection and mass spectrometer detectors data from the same samples using the multivariate curve resolution‐alternating least squares chemometric method. Peak areas finally resolved were further analyzed by orthogonal signal correction and partial least squares‐discrimination analysis to discriminate among C. sinensis tea samples. Using the Variable Importance in Projection variable selection method, epigallocatechin and caffeine were finally selected as the two more important chemical constituents of tea leaves that were discriminating more between the tea samples from two different harvest seasons. Copyright

Irrigation and Light Access Effects on Coffea arabica L. Leaves by FTIR‑Chemometric Analysis

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 solvent 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.

UV-Vis spectral fingerprinting and chemometric method applied to the evaluation of Camellia sinensis leaves from different harvests

UV-Vis spectral fingerprinting was used to discriminate Camellia sinensis leaves of two different harvests and multivariate data analysis was applied to determine the relevant metabolites for separation. First statistical mixture designs of pure ethanol, ethyl acetate, dichloromethane and chloroform solvents as well as their binary, ternary and quaternary mixtures extracted larger varieties and amounts of C. sinensis leaf metabolites than would be obtained from classical solvent extractions. UV-Vis spectral fingerprints of crude extracts were subjected to Orthogonal Signal Correction and Partial Least Squares-Discrimination Analysis (OSC-PLS-DA) for classification. The spectra were all correctly identified and classified, showing that the OSC-PLS-DA model possesses a good predictive ability to separate spectral fingerprints of different harvests. VIP score values showed that bands at 272, 410 and 663 nm were responsible for separation. These metabolites were identified by HPLC-DAD as caffeine and pheophytin a. According to the mixture model, the maximum values of relative abundances of both caffeine and pheophytin a can be extracted with pure dichloromethane.