Masters Degrees (Viticulture and Oenology)

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Browsing Masters Degrees (Viticulture and Oenology) by Subject "Aromatic compounds"

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    The quantification of white grape juice phenolics using various spectroscopic methods and chemometrics
    (Stellenbosch : Stellenbosch University, 2021-12) Clarke, Sarah; Aleixandre-Tudo, Jose Luis; Du Toit, Wessel J.; Bosman, Gurthwin W. ; Stellenbosch University. Faculty of AgriSciences. Dept. of Viticulture and Oenology.
    ENGLISH ABSTRACT: Phenolic compounds are aromatic, secondary metabolites found in plant tissues. They have a number of bioactive properties as well as positive effects on health. Phenolic compounds, although found at lower levels in Méthode Cap Classique (MCC) and white wines, contribute to the mouthfeel and flavour as well as having antimicrobial and antioxidant properties. The levels of the phenolic compounds present in a wine depend on the variety, ripeness of grapes at harvest, soil type and the vinification processes applied. During the pressing stages, juice is obtained from the grapes and skins in contact with the juice. This maceration time, although limited, allows for phenolic compounds to be extracted and dissolved in the juice. The ability to monitor the phenolic concentration during the pressing stages of MCC and white wine could potentially increase the yield recovery of quality juice, furthermore, allowing increased control in the vinification process and can lead to benefits such as improved and consistent wine quality. Current phenolic compound analysis methods can be outdated and unreliable due to interferences. In order for phenolic monitoring techniques to be useful in the wine industry they must be compatible with process control methods. Spectroscopy techniques, alongside chemometrics, for the quantification of phenolics have the potential to be implemented into wineries as in-line and on-line systems. These techniques provide increased accuracy and reliability. This research explores a range of analytical techniques which may be applied to the quantification of phenolic compounds with the use of calibration models. Infrared (IR), Raman and fluorescence spectroscopy were the analytical methods explored and the reference total phenolic index (TPI) data was collected using Ultra-Violet/Visible (UV/Vis) spectrophotometry. These spectroscopic techniques were chosen as they are suited for the implementation into portable devices and hence could be of use to the wine industry for process control analysis. The spectroscopic analyses performed are: - Attenuated Total Reflectance Mid-Infrared Spectrometer (ATR-MIR). - Multi-Purpose Analyser (MPA) Transmission Fourier Transform Near-Infrared Spectrometer (T-FT-NIR). - Matrix F Diffuse Reflectance Fourier Transform Near-Infrared Spectrometer (DF-FT-NIR). - Raman spectroscopy with a central wavelength of 532nm. - Fluorescence spectroscopy with emission spectra between 300nm and 575nm and excitation wavelengths between 300nm and 575nm. Partial Least Squares (PLS) regression models were built for all analytical methods explored and the robustness of these models were examined using a range of statistical parameters. Further techniques, such as machine learning, were explored for the data obtained in the fluorescence spectroscopy. T-FT-NIR provided the best model for TPI with 0.547 and 2.12 RMSEP and RPDval, respectively. Moreover, high prediction accuracy was observed with DF-FT-NIR for the MCC dataset with 0.457 RMSEP and 2.01 RPD. The models obtain form the Raman and fluorescence spectra underperformed those of the IR instruments. However, improvements in fluorescence model performance were achieved when the use of a machine learning analysis pipeline was explored. The statistical parameters used to determine model robustness did not indicate that all of the predication models constructed are of immediate use to the wine industry. Despite these results, it is believed that the aim of this research is worth further investigation. The observed models do indicate results which could be of potential use for screening purposes. Further research could be the key to unlocking the potential of these spectroscopic methods for phenolic quantification as this would reduce the number of variables which are believed to have caused the results observed.