Chemical characterisation of South African young wines

[摘要] The rapid expansion of the world wine industry has increased the pressure on wine producers toproduce high quality, distinguishable wines. The use of sensory evaluation alone as a tool todistinguish between wines is limited by its subjective nature. Chemical characterisation usinganalytical methods and data analysis techniques are increasingly being used in conjunction withsensory analysis for comprehensive profiling of wine. Analytical chemistry and chemometrictechniques are important and inextricable parts of the chemical characterisation of wine. Throughthis process insight into the inherent composition of wines, be it in a general sense or related to aparticular wine category is gained. Data generated during chemical characterisation are typicallycompiled into electronic databases. The application of such information towards wine qualitycontrol includes the establishment of industry benchmarks and authentication.The current project is part of The South African Young Wine Aroma Project, a long termresearch initiative funded by the South African Wine Industry with the ultimate aim to establish acomprehensive, up-to-date, database of the volatile composition of young wines. The datagenerated 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 chemicalcharacterisation of South African young wines in terms of selected volatile and non-volatilecompounds and Fourier transform infrared spectra, with particular focus on the volatilecomposition. FTMIR spectra are information rich and non-specific instrumental signals that couldprovide invaluable information of the inherent composition of the wines. The second aim is theevaluation of the analytical methods used to generate the data and in the last instance, theoptimisation of FTMIR spectroscopy for rapid quantification of major wine parameters and volatilecompounds.The concentrations of 27 volatile compounds in South African young wines were determinedby gas chromatography coupled to flame ionisation detection (GC-FID) using liquid-liquidextraction of the analytes. Wine samples of the 2005 and 2006 vintages produced from six of themost 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 fourmajor South African wine producing regions, namely Stellenbosch, Paarl, Robertson andWorcester. The data captured made a significant contribution to the establishment of the AromaProject Database. Univariate statistics showed wide variations in the chemical composition of thewines. Red wines were generally characterised by high levels of higher alcohols and white winesby high levels of esters. Most of the differences between vintages were cultivar dependent andphenological differences between cultivars were suggested as a possible cause. Fusel alcohols,iso-acids and esters of fusel alcohols were particularly responsible for differences between redwines. A combination of fatty acids and higher alcohols were responsible for differences betweenproduction regions. However, using univariate statistics alone was limited in identifyingcharacteristic features of the chemical composition of the wines. In order to explore the correlations between the volatile components, FTMIR spectra and nonvolatilecomponents the data were further investigated with multivariate data analysis. Principalcomponent analysis was successfully employed to distinguish between wines of different vintagesand cultivars. The role of the volatile composition was more influential in the separation of vintageand red wine cultivar groupings than the non-volatile components or the FTMIR spectra. Almost allthe individual volatile components contributed to the separation between the vintages and cultivars,thereby highlighting the multivariate nature required to establish the distinguishing featurespertaining to each of these categories. The FTMIR spectra and the non-volatile components weremore important than the volatile components to characterise the differences between the whitecultivars. It was not surprising that both the volatile components and the FTMIR spectra wereneeded to distinguish between both red and white cultivars simultaneously. It was of interest thefull spectrum, including all wavenumbers were required for a powerful classification model. Thisfinding supports the initial expectation that the non-selective but information rich signal captured inthe 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 theFTMIR spectra and the quality ratings of the wines. Limitations in the dataset were pointed out thatmust be taken into account during further investigations in the future.The liquid-liquid extraction method used during the analysis of the volatile components wasevaluated for precision, accuracy and robustness. Generally good precision and accuracy wereobserved. There were slight indications of inconsistencies in the recoveries of analytes betweenthe 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 amajor influence on quantification. The results obtained in this study made a major contributiontowards 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 analyticalmethods such as FTMIR spectroscopy is becoming increasingly important. Enzymatic-linkedspectrophotometric assays and high performance liquid chromatography (HPLC) methods wereevaluated for their suitability to serve as reference methods for optimising and establishing FTMIRcalibrations for glucose, fructose, malic acid, lactic acid and glycerol. Pigmented and phenoliccompounds were identified as sources of interference in the determination of organic acids in redwines with both enzymatic assays and HPLC. The use of fining treatments for the decolourisationof red wine samples was investigated. Activated charcoal was more efficient in terms of colourremoval than polyvinyl polypyrrolidone (PVPP), but neither were compatible with the specificenzymatic method used in this study. Solid phase extraction (SPE), a method commonly usedduring sample clean-up prior to HPLC analysis of organic acids in wine, and PVPP fining wereevaluated as sample preparation methods for HPLC analysis to optimise the quantification oforganic acids in red wine. Four different types of SPE cartridges were evaluated and the SPEmethod was optimised in order to recover the maximum amount of organic acids. However, lowrecoveries, in some instance less than 50%, for the organic acids in wine were reported for theoptimised 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 methodtherefore provides a very valuable and simple alternative to SPE for sample-cleanup prior to HPLCanalysis. One aspect that still needs to be investigated is the reproducibility of the method thatshould still be optimised. In general, enzymatic analysis was more suitable for the determination ofglucose and fructose, while HPLC analysis were more suitable for the quantification of organicacids. 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 thevariability present in the reference sample set. The reference sample set used to establish FTMIRcalibrations 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 wineparameters were evaluated for their compatibility to a South African young wine matrix. Theprediction pH, titratable acidity, malic acid, glucose, fructose, ethanol and glycerol could beimproved by establishing a brand new FTMIR calibration, thereby clearly indicating that the SouthAfrican young wine matrices were significantly different from the samples of European origin thatwere used to establish the commercial calibrations. New preliminary calibration models wereestablished for a young wine sample matrix and were validated using independent test sets. Onaverage the prediction errors were considered sufficient for at least screening purposes. The effectof wavenumber selection was evaluated. Relatively successful models could be established for allthe compounds except glucose. Wavenumber selection had an influence on the efficiency of thecalibration models. Some models were more effective using a small amount of highly correlatedwavenumbers, while others were more effective using larger wavenumber regions.Preliminary FTMIR calibration models for the screening of volatile compound groups in youngwines were evaluated. Compound groups were compiled based on chemical similarity and flavoursimilarity. Good linearity were observed for the 'total alcohol, 'total fatty acids, 'esters modelswhile an interesting polynomial trend was observed for the 'total esters model. Relatively highprediction errors indicated the possibility of spectral interferences, but the models werenevertheless considered suitable for screening purposes. These findings are a valuablecontribution to our environment where fermentation flavour profiles must often be examined.The important role sound and validated analytical methods to generate high quality analyticaldata, and the subsequent application of chemometric techniques to model the data for the purposeof wine characterisation has been thoroughly explored in this study. After a critical evaluation of theanalytical methods used in this study, various statistical methods were used to uncover thechemical composition of South African young wines. The use of multivariate data analysis hasrevealed some limitations in the dataset and therefore it must be said that wine characterisation isnot just reliant on sophisticated analytical chemistry and advanced data analytical techniques, butalso on high quality sample sets.

[发布机构] Stellenbosch University