Chemical characterisation of South African young wines

Louw, Leanie (2007-12)

Thesis (MscAgric (Viticulture and Oenology))--University of Stellenbosch, 2007.


The rapid expansion of the world wine industry has increased the pressure on wine producers to produce high quality, distinguishable wines. The use of sensory evaluation alone as a tool to distinguish between wines is limited by its subjective nature. Chemical characterisation using analytical methods and data analysis techniques are increasingly being used in conjunction with sensory analysis for comprehensive profiling of wine. Analytical chemistry and chemometric techniques are important and inextricable parts of the chemical characterisation of wine. Through this process insight into the inherent composition of wines, be it in a general sense or related to a particular wine category is gained. Data generated during chemical characterisation are typically compiled into electronic databases. The application of such information towards wine quality control includes the establishment of industry benchmarks and authentication. The current project is part of The South African Young Wine Aroma Project, a long term research initiative funded by the South African Wine Industry with the ultimate aim to establish a comprehensive, up-to-date, database of the volatile composition of young wines. The data generated during this thesis represent the first contribution towards realising this ambition. Three clearly defined aims were set for this project, the first of which is the chemical characterisation of South African young wines in terms of selected volatile and non-volatile compounds and Fourier transform infrared spectra, with particular focus on the volatile composition. FTMIR spectra are information rich and non-specific instrumental signals that could provide invaluable information of the inherent composition of the wines. The second aim is the evaluation of the analytical methods used to generate the data and in the last instance, the optimisation of FTMIR spectroscopy for rapid quantification of major wine parameters and volatile compounds. The concentrations of 27 volatile compounds in South African young wines were determined by gas chromatography coupled to flame ionisation detection (GC-FID) using liquid-liquid extraction of the analytes. Wine samples of the 2005 and 2006 vintages produced from six of the most important cultivars in the South African wine industry, namely Sauvignon blanc, Chardonnay, Pinotage, Cabernet Sauvignon, Merlot and Shiraz were used. The producing cellars were from four major South African wine producing regions, namely Stellenbosch, Paarl, Robertson and Worcester. The data captured made a significant contribution to the establishment of the Aroma Project Database. Univariate statistics showed wide variations in the chemical composition of the wines. Red wines were generally characterised by high levels of higher alcohols and white wines by high levels of esters. Most of the differences between vintages were cultivar dependent and phenological differences between cultivars were suggested as a possible cause. Fusel alcohols, iso-acids and esters of fusel alcohols were particularly responsible for differences between red wines. A combination of fatty acids and higher alcohols were responsible for differences between production regions. However, using univariate statistics alone was limited in identifying characteristic features of the chemical composition of the wines. In order to explore the correlations between the volatile components, FTMIR spectra and nonvolatile components the data were further investigated with multivariate data analysis. Principal component analysis was successfully employed to distinguish between wines of different vintages and cultivars. The role of the volatile composition was more influential in the separation of vintage and red wine cultivar groupings than the non-volatile components or the FTMIR spectra. Almost all the individual volatile components contributed to the separation between the vintages and cultivars, thereby highlighting the multivariate nature required to establish the distinguishing features pertaining to each of these categories. The FTMIR spectra and the non-volatile components were more important than the volatile components to characterise the differences between the white cultivars. It was not surprising that both the volatile components and the FTMIR spectra were needed to distinguish between both red and white cultivars simultaneously. It was of interest the full spectrum, including all wavenumbers were required for a powerful classification model. This finding supports the initial expectation that the non-selective but information rich signal captured in the FTMIR spectra is indispensable. No distinction could be made between the production regions, which was not surprising since the wines used in this study was not of guaranteed origin. Furthermore, no clear correlation could be established between the chemical composition or the FTMIR spectra and the quality ratings of the wines. Limitations in the dataset were pointed out that must be taken into account during further investigations in the future. The liquid-liquid extraction method used during the analysis of the volatile components was evaluated for precision, accuracy and robustness. Generally good precision and accuracy were observed. There were slight indications of inconsistencies in the recoveries of analytes between the red and white wine matrices. Certain parameters of the protocol, namely sample volume, solvent volume, sonication temperature and sonication time, were identified as factors that had a major influence on quantification. The results obtained in this study made a major contribution towards establishing this technique for routine GC-FID analysis in our environment. Due to the high sample throughput in wine laboratories, the use of rapid quantitative analytical methods such as FTMIR spectroscopy is becoming increasingly important. Enzymatic-linked spectrophotometric assays and high performance liquid chromatography (HPLC) methods were evaluated for their suitability to serve as reference methods for optimising and establishing FTMIR calibrations for glucose, fructose, malic acid, lactic acid and glycerol. Pigmented and phenolic compounds were identified as sources of interference in the determination of organic acids in red wines with both enzymatic assays and HPLC. The use of fining treatments for the decolourisation of red wine samples was investigated. Activated charcoal was more efficient in terms of colour removal than polyvinyl polypyrrolidone (PVPP), but neither were compatible with the specific enzymatic method used in this study. Solid phase extraction (SPE), a method commonly used during sample clean-up prior to HPLC analysis of organic acids in wine, and PVPP fining were evaluated as sample preparation methods for HPLC analysis to optimise the quantification of organic acids in red wine. Four different types of SPE cartridges were evaluated and the SPE method was optimised in order to recover the maximum amount of organic acids. However, low recoveries, in some instance less than 50%, for the organic acids in wine were reported for the optimised SPE method. In this respect one was the worst. On average, excellent recoveries were observed for the organic acids using the PVPP method that were in excess of 90%. This method therefore provides a very valuable and simple alternative to SPE for sample-cleanup prior to HPLC analysis. One aspect that still needs to be investigated is the reproducibility of the method that should still be optimised. In general, enzymatic analysis was more suitable for the determination of glucose and fructose, while HPLC analysis were more suitable for the quantification of organic acids. Efficient glycerol quantification was observed with both enzymatic and HPLC analysis, although a lower measurement error was observed during the HPLC analysis. Apart from reliable reference methods, successful FTMIR calibrations also rely on the variability present in the reference sample set. The reference sample set used to establish FTMIR calibrations must ideally be representative of the samples that will be analysed in the future. Commercial, or so-called global, FTMIR calibrations for the determination of important wine parameters were evaluated for their compatibility to a South African young wine matrix. The prediction pH, titratable acidity, malic acid, glucose, fructose, ethanol and glycerol could be improved by establishing a brand new FTMIR calibration, thereby clearly indicating that the South African young wine matrices were significantly different from the samples of European origin that were used to establish the commercial calibrations. New preliminary calibration models were established for a young wine sample matrix and were validated using independent test sets. On average the prediction errors were considered sufficient for at least screening purposes. The effect of wavenumber selection was evaluated. Relatively successful models could be established for all the compounds except glucose. Wavenumber selection had an influence on the efficiency of the calibration models. Some models were more effective using a small amount of highly correlated wavenumbers, while others were more effective using larger wavenumber regions. Preliminary FTMIR calibration models for the screening of volatile compound groups in young wines were evaluated. Compound groups were compiled based on chemical similarity and flavour similarity. Good linearity were observed for the “total alcohol”, “total fatty acids”, “esters” models while an interesting polynomial trend was observed for the “total esters” model. Relatively high prediction errors indicated the possibility of spectral interferences, but the models were nevertheless considered suitable for screening purposes. These findings are a valuable contribution to our environment where fermentation flavour profiles must often be examined. The important role sound and validated analytical methods to generate high quality analytical data, and the subsequent application of chemometric techniques to model the data for the purpose of wine characterisation has been thoroughly explored in this study. After a critical evaluation of the analytical methods used in this study, various statistical methods were used to uncover the chemical composition of South African young wines. The use of multivariate data analysis has revealed some limitations in the dataset and therefore it must be said that wine characterisation is not just reliant on sophisticated analytical chemistry and advanced data analytical techniques, but also on high quality sample sets.

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