Discrimination between Shiraz wines from different Australian regions: the role of spectroscopy and chemometrics.

This study reports the use of UV-visible (UV-vis), near-infrared (NIR), and midinfrared (MIR) spectroscopy combined with chemometrics to discriminate among Shiraz wines produced in five Australian regions. In total, 98 commercial Shiraz samples (vintage 2006) were analyzed using UV-vis, NIR, and MIR wavelength regions. Spectral data were interpreted using principal component analysis (PCA), linear discriminant analysis (LDA), and soft independent model of class analogy (SIMCA) to classify the wine samples according to region. The results indicated that wine samples from Western Australia and Coonawarra can be separated from the other wines based on their MIR spectra. Classification results based on MIR spectra also indicated that LDA achieved 73% overall correct classification, while SIMCA 95.3%. This study demonstrated that IR spectroscopy combined with chemometric methods can be a useful tool for wine region discrimination.

Use of attenuated total reflectance midinfrared for rapid and real-time analysis of compositional parameters in commercial white grape juice.

A simple and fast midinfrared (MIR) spectroscopy method was developed for simultaneously determining total soluble solids (TSS, degrees Brix), pH, total phenolics, ammonia, free amino nitrogen (FAN), and yeast assimilable nitrogen (YAN) contents in grape juice samples using attenuated total reflectance (ATR). Results from this study demonstrated the capability of ATR-MIR coupled with partial least-squares regression to measure TSS and pH and to monitor FAN, ammonia, and YAN in a wide range of grape juice samples. The standard error in cross-validation and the residual predictive deviation obtained were 0.20 degrees Brix and 9 for TSS, 0.07 and 3.3 for pH, 14.8 mg/L and 2 for ammonia, 28.3 mg/L and 2 for FAN, and 36.9 mg/L and 2 for YAN, respectively. Both the time of analysis and the volume of sample required were considerably reduced as compared to the transmission MIR measurements currently used by the wine industry.

Classification of Tempranillo wines according to geographic origin: combination of mass spectrometry based electronic nose and chemometrics.

Rapid methods employing instruments such as electronic noses (EN) or gas sensors are used in the food and beverage industries to monitor and assess the composition and quality of products. Similar to other food industries, the wine industry has a clear need for simple, rapid and cost effective techniques for objectively evaluating the quality of grapes, wine and spirits. In this study a mass spectrometry based electronic nose (MS-EN) instrument combined with chemometrics was used to predict the geographical origin of Tempranillo wines produced in Australia and Spain. The MS-EN data generated were analyzed using principal components analysis (PCA), partial least squares discriminant analysis (PLS-DA) and stepwise linear discriminant analysis (SLDA) with full cross validation (leave-one-out method). The SLDA classified correctly 86% of the samples while PLS-DA 85% of Tempranillo wines according to their geographical origin. The relative benefits of using MS-EN will provide capability for rapid screening of wines. However, this technique does not provide the identification and quantitative determination of individual compounds responsible for the different aroma notes in the wine.

Geographical origin of Sauvignon Blanc wines predicted by mass spectrometry and metal oxide based electronic nose.

Analysis of 34 Sauvignon Blanc wine samples from three different countries and six regions was performed by gas chromatography-mass spectrometry (GC-MS). Linear discriminant analysis (LDA) showed that there were three distinct clusters or classes of wines with different aroma profiles. Wines from the Loire region in France and Australian wines from Tasmania and Western Australia were found to have similar aroma patterns. New Zealand wines from the Marlborough region as well as the Australian ones from Victoria were grouped together based on the volatile composition. Wines from South Australia region formed one discrete class. Seven analytes, most of them esters, were found to be the relevant chemical compounds that characterized the classes. The grouping information obtained by GC-MS, was used to train metal oxide based electronic (MOS-Enose) and mass spectrometry based electronic (MS-Enose) noses. The combined use of solid phase microextraction (SPME) and ethanol removal prior to MOS-Enose analysis, allowed an average error of prediction of the regional origins of Sauvignon Blanc wines of 6.5% compared to 24% when static headspace (SHS) was employed. For MS-Enose, the misclassification rate was higher probably due to the requirement to delimit the m/z range considered.

Measurement of condensed tannins and dry matter in red grape homogenates using near infrared spectroscopy and partial least squares.

Samples (n = 620) of homogenized red grape berries were analyzed using a visible and near-infrared (NIR) spectrophotometer (400-2500 nm) in reflectance. The spectra and the analytical data were used to develop partial least-squares calibrations to predict dry matter (DM) content and condensed tannins (CT) concentrations. The coefficient of determination in cross-validation and the standard error of cross-validation were 0.92 and 0.83% w/w for DM and 0.86 and 0.46 mg/g epicatechin equivalents for CT, respectively. The standard error in prediction was 1.34% w/w for DM and 0.89 mg/g epicatechin equivalents for CT, respectively. By implementing a NIR spectroscopy method to measure DM and CT in red grape homogenates, we have developed an approach that is suited to large-scale compositional analysis in commercial wine production facilities, as it enables the analysis of large numbers of samples needed to stream batches of fruit. From an economical point of view, the calibration models could be achieved with relatively small data sets. Thus, NIR offers a suitable and efficient tool for the simultaneous measurement of DM and CT in addition to other important parameters in red grape homogenates such as total anthocyanins, total soluble solids, and pH, with minimal sample preparation and low cost.

Use of direct headspace-mass spectrometry coupled with chemometrics to predict aroma properties in Australian Riesling wine.

The aim of this study was to investigate the potential use of a direct headspace-mass spectrometry electronic nose instrument (MS e_nose) combined with chemometrics as rapid, objective and low cost technique to measure aroma properties in Australian Riesling wines. Commercial bottled Riesling wines were analyzed using a MS e_nose instrument and by a sensory panel. The MS e_nose data generated were analyzed using principal components analysis (PCA) and partial least squares (PLS1) regression using full cross validation (leave one out method). Calibration models between MS e_nose data and aroma properties were developed using partial least squares (PLS1) regression, yielding coefficients of correlation in calibration (R) and root mean square error of cross validation of 0.75 (RMSECV: 0.85) for estery, 0.89 (RMSECV: 0.94) for perfume floral, 0.82 (RMSECV: 0.62) for lemon, 0.82 (RMSECV: 0.32) for stewed apple, 0.67 (RMSECV: 0.99) for passion fruit and 0.90 (RMSECV: 0.86) for honey, respectively. The relative benefits of using MS e_nose will provide capability for rapid screening of wines before sensory analysis. However, the basic deficiency of this technique is lack of possible identification and quantitative determination of individual compounds responsible for the different aroma notes in the wine.

Comparison of metal oxide-based electronic nose and mass spectrometry-based electronic nose for the prediction of red wine spoilage.

Taints caused by Brettanomyces sp. spoilage are of concern to winemakers and consumers. Typically the taints are described as "barnyard", "sweaty saddle", and "Band-aid" when present in red wine at concentrations of several hundred micrograms per liter or more. The two main components of the taint are 4-ethylphenol (4EP) and 4-ethylguaiacol (4EG), which are metabolites produced by Brettanomyces yeasts. There is a need for a rapid instrumental method to quantify these compounds in wines. In this paper are compared two techniques, the metal oxide sensor-based electronic nose (MOS-Enose) and the mass spectrometry-based electronic nose (MS-Enose). Gas chromatography-mass spectrometry (GC-MS) was used for quantification and prediction purposes. Following ethanol removal, the limits of detection of a MOS-Enose were determined as 44 microg L(-1) for 4EP and 91 microg L(-1) for 4EG, using the SY/gCT sensor. These values are significantly lower than the reported human sensory thresholds. Partial least-squares (PLS) regression of electronic nose signals against known levels of 4EP and 4EG in 46 Australian red wines showed that the MOS-Enose was unable to identify "brett" spoilage reliably because of the response of the gas sensors to intersample variation in volatile compounds other than ethylphenols. Conversely, the MS-Enose was capable of reliably estimating concentrations of 4EP higher than 20 microg L(-1). Correlations (r2) of 0.97 and 0.98 were obtained between estimates of 4EP and 4EG concentrations with the concentrations determined by conventional GC-MS. It is concluded that, following ethanol removal, existing metal oxide sensors are sufficiently sensitive to detect brett taints in wine but lack the selectivity needed to perform this task when the aroma volatile background varies.

Usefulness of chemometrics and mass spectrometry-based electronic nose to classify Australian white wines by their varietal origin.

A combination of mass spectrometry-based electronic nose (MS e_nose) and chemometrics was explored to classify two Australian white wines according to their varietal origin namely Riesling and unwooded Chardonnay. The MS e_nose data were analysed using principal components analysis (PCA), discriminant partial least squares (DPLS) and linear discriminant analysis (LDA) applied to principal components scores and validated using full cross validation (leave one out). DPLS gave the highest levels of correct classification for both varieties (>90%). LDA classified correctly 73% of unwooded Chardonnay and 82% of Riesling wines. Even though the conventional analysis provides fundamental information about the volatile compounds present in the wine, the MS e_nose method has a series of advantages over conventional analytical techniques due to simplicity of the sample-preparation and reduced time of analysis and might be considered as a more convenient choice for routine process control in an industrial environment. The work reported here is a feasibility study and requires further development with considerably more commercial samples of different varieties. Further studies are needed in order to improve the calibration specificity, accuracy and robustness, and to extend the discrimination to other wine varieties or blends.