Search

Cold stabilisation is a widely used industrial process to prevent tartrate instability in bottled wines. In this article, current knowledge regarding performance and cost of cold stabilisation and alternative technologies for tartrate stabilisation is reviewed. Whilst there have been occasional cost comparisons between cold stabilisation and alternative technologies published in the literature, existing data is not satisfactory to permit an accurate economic evaluation of the different process options. Therefore, alternative technologies to cold stabilisation, including the Westfalia process, nanofiltration and electrodialysis were compared for both technical and economic performance. The engineering calculations and conceptual cost estimates were based on real world data from an Australian winery. Product loss was a key cost driver in differentiating tartrate stabilisation processes. Cold stabilisation was found to be the most economic treatment process irrespective of scale or winery size. Westfalia process and nanofiltration were the next most cost effective options.

The effect of sample processing with three different blenders, sample freezing and storage time on the determination of berry quality parameters - total anthocyanins, total soluble solids (TSS), total phenolics (TP) and pH - in homogenates of red grape berries has been examined. Samples of Cabernet Sauvignon, Merlot and Shiraz from four regions of South Australia were homogenised using Ultra-Turrax, Waring or Retsch commercial blenders applied to fresh samples, and to samples held in frozen storage overnight or for 1, 3, 6 and 12 months. All samples were analysed immediately after homogenisation. The results showed that neither homogeniser type nor freezing had any significant effect on total anthocyanins or TSS. However, freezing had an effect on total anthocyanins concentrations in samples with higher colour. The homogeniser type had a significant effect on TP and pH for all varieties analysed together. Freezing had no significant effect on TP, but significant effect on pH, the value being greater in samples at the lower end of the pH range. Frozen storage time of up to 3 months had no significant effect on total anthocyanins, TSS or TP. Statistically significant differences were observed in the concentration of total anthocyanins in samples after 3 months of frozen storage.

The aim of this work was to investigate the potential of a head space mass spectrometry electronic nose (MS e_nose) instrument combined with chemometrics as a rapid and low cost technique to monitor spoilage induced by Brettanomyces yeast in two commercial red wine varieties in Australia (i.e. Cabernet Sauvignon and Shiraz). Red wine samples of Cabernet Sauvignon and Shiraz grape varieties sourced from barrels and bottled wines were analyzed using a MS e_nose instrument. The MS e_nose data generated were analyzed based on principal component analysis (PCA) and stepwise linear discriminant analysis (SLDA) using full cross validation ( leave one out method). The SLDA classified correctly 67% of the samples in three categories based on the concentration of 4-ethylphenol (4EP) namely high (higher than 500 μg L −1 ), medium (between 500 and 200 μg L −1 ) and low (lower than 100 μg L −1 ) in respect of Brettanomyces yeast spoilage in the set of red wines analyzed. The work reported here is a feasibility study and further work is needed in order to improve the calibration specificity, accuracy and robustness.

The objective of this study was to investigate the effects of some commonly used sample preparation procedures, including overnight freezing, and the type of homogeniser on the near-infrared (NIR) spectra of red grape homogenates. Homogenates (n = 284) of three red grape varieties were prepared using one of three types of homogenisers after one of two types of short term storage (fresh and overnight freezing) and then scanned in a FOSS NIRSystems6500 instrument (400–2500 nm). The NIR spectral data were then analysed using various discrimination techniques, namely Penalized Discriminant Analysis (PDA), Multivariate Adaptive Regression Splines discriminant analysis (MARS-DA) and Random Forests (RF) yielding correct classification rates (CCR) of 63.4%, 58.6% and 45.6%, respectively. To improve the CCR of the discrimination models, feature extraction from the NIR spectral data was performed using an adaptive discrete wavelet transformation (DWT). The DWT algorithm employs an adaptive wavelet basis function that maximizes the discrimination between the different combinations of homogenisers, storage and grape varieties. The results after adaptive DWT on the NIR spectra resulted in CCRs of 99.93%, 99.2% and 76.4% for PDA, MARS-DA and RF, respectively. Further analysis of adaptive DWT PDA via MANOVA revealed significant differences in the main and interaction effects of the three treatments, which were then associated with specific regions within the NIR spectrum.

The combination of mass spectrometry (MS) based electronic nose (eNose) with visible (VIS) and near infrared spectroscopy (NIR) was explored as an objective tool to measure sensory attributes in commercial Riesling wines grown in Australia. Calibration models were developed between instrumental data and sensory scores using partial least squares (PLS) regression with full cross validation. Good correlations (r > 0.70, root mean square standard error in cross validation (RMSECV): 0.66) were found for developed and floral; intermediate (0.70 > r > 0.60, RMSECV: 0.84 and 0.63) for tropical and low (r < 0.50, RMSECV: 0.98) for green characters measured by a sensory panel and the combination of both techniques. The results suggested that data from instrumental techniques coupled with chemometrics might be related with sensory scores measured by a trained panel. The study is considered a starting point in order to evaluate useful sources of information generated by different instrumental techniques with the objective to select combination of sensors for specific wine quality attributes.

Samples of distillates derived from the production of wine-fortifying spirit were analyzed for methanol by gas chromatography (GC) and near-infrared spectroscopy (NIRS). NIRS calibration models were developed which could accurately predict methanol concentrations in samples of fortifying spirit that had been produced over a period of three years from four different commercial distillation facilities. The best accuracy of the predictive models, as measured by the standard error of prediction value, was 0.06 g/L methanol. Other distillation fractions, produced during preparation of commercial fortifying spirit, were also examined. The most useful NIRS calibration models used partial least squares regression on continuous spectra from a scanning instrument, but it was demonstrated that calibrations could also be developed with a smaller number of fixed wavelengths, using multiple linear regression models. NIRS offers the advantages of rapid analysis, with simple routine operation, and may offer the potential for in-line process control in the operation of a commercial distillation facility.

A survey of 108 alpaca producers registered with the Australian Alpaca Association examined the occurrence and severity of ‘staggers’ (a colloquial term embracing various diseases of the nervous system in animals, characterised by neck tremors and head nodding in the milder alpaca cases and a lack of coordination in moving, a staggering gait and frequent falling in severe cases) in Australian alpaca and the presence of four pasture grasses, during three production seasons in 2004–2006. There have been few studies on the susceptibility of alpaca to staggers and its effect on productivity and animal welfare. The survey found that 23% of alpaca producers had observed staggering animals, with Victoria and South Australia being the most severely affected states. Clinical signs of staggers were most frequent in January–March, with a mean duration of 3 months. Some animals showed clinical signs lasting up to 12 months. A strong correlation was found between the presence of perennial ryegrass (P < 0.001) and phalaris (P < 0.003) and the occurrence of staggers. Based on grass presence and the timing when staggers was observed, it was concluded that perennial ryegrass toxicosis was the main cause of staggers in alpaca. About 12% and 9% of alpaca grazing pasture containing perennial ryegrass exhibited staggers in the two full seasons for which data were collected. Herds with staggering animals were correlated with those exhibiting possible subclinical effects – heat sensitivity and ill-thrift (P < 0.01) and reduced fertility (P < 0.05) – of perennial ryegrass endophyte toxins. Results indicate that weanlings and cria are more likely to stagger than adults. Thirteen of the 15 mixed farms with staggering alpaca did not observe staggers in other livestock, suggesting that alpaca may be more sensitive to the causal toxins than sheep and cattle.

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.

The modern wine industry needs tools for process control and quality assessment in order to better manage fermentation or bottling processes. During wine fermentation it is important to measure both substrate and product concentrations (e.g. sugars, phenolic compounds), however, the analysis of these compounds by traditional means requires sample preparation and in some cases several steps of purification are needed. The combination of visible/near-infrared (Vis/NIR) spectroscopy and chemometrics potentially provides an ideal solution to accurately and rapidly monitor physical or chemical changes in wine during processing without the need for chemical analysis. The aim of this study was to assess the possibility of combining spectral and multivariate techniques, such as principal component analysis (PCA), discriminant partial least squares (DPLS), or linear discriminant analysis (LDA), to monitor time-related changes that occur during red wine fermentation. Samples (n = 652) were collected at various times from several pilot scale fermentations with grapes from either Cabernet Sauvignon or Shiraz varieties, over three vintages (2001-2003) and scanned using a monochromator instrument (Foss-NIRSystems 6500, Silver Spring, MD) in transmission mode (400-2,500 nm). PCA was used to demonstrate consistent progressive spectral changes that occur through the time course of the fermentation. LDA using PCA scores showed that regardless of variety or vintage, samples belonging to a particular time point in fermentation could be correctly classified. This study demonstrates the potential of Vis/NIR spectroscopy combined with chemometrics, as a tool for the rapid monitoring of red wine fermentation.

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.

The use of visible (vis) and near-infrared spectroscopy (NIR) was explored as a tool to discriminate between samples of Australian commercial white wines of different varietal origins (Chardonnay and Riesling). Discriminant models were developed using principal component analysis (PCA), principal component regression (PCR), and discriminant partial least-squares (DPLS) regression. The samples were randomly split into two sets, one used as a calibration set (n = 136) and the remaining samples as a validation set (n = 133). When used to predict the variety of the validation set samples, the DPLS models correctly classified 100% of Riesling and up to 96% of Chardonnay wines. These results showed that vis-NIR might be a suitable and alternative technology that can be easily implemented by the wine industry to discriminate Riesling and Chardonnay commercial wine varieties. However, the relatively limited number of samples and varieties involved in the present work suggests caution in extending the potential of such a technique to other wine varieties.

Vibrational spectroscopic techniques such as near-infrared (NIR) and mid-infrared (mid-IR) offer possibilities for simple, rapid and cost effective analysis in the wine and other alcoholic beverage industries. This chapter highlights recent applications of vibrational spectroscopy to analyze the composition of grapes, juices, wines, and other alcoholic beverages. A range of challenges and requirements for sample presentation and instrument configurations for optimum performance in analyzing these highly variable and complex matrices are discussed. Applications for grapes and wines include compositional analysis (especially alcoholic content), fermentation monitoring, and authenticity and sensory profiling, some of which also have potential for nondestructive and on-line testing, including compositional measurement of bottled product in situ. An outline of similar applications for other fermented products, such as beer and sake, is also presented. Keywords: vibrational spectroscopy; infrared; alcoholic beverages; grapes; wine; spirit; fermentation; composition; analysis; sample presentation

Saccharomyces cerevisiae was immobilised by adsorption to untreated sugar cane bagasse in a packed bed reactor. Complete conversion of glucose to ethanol was obtained at a dilution rate of 0.19 h−1. Continuous ethanol production was maintained for up to 57 days. Reactor productivity increased with increasing packing density of the bagasse. Plugging of void spaces due to cell overgrowth led to channelling of the feed and decreased reactor productivity. Increasing the average column temperature alleviated plugging and restored column performance over a short period; however prolonged exposure to the high temperature resulted in decreased ethanol production rates. Bagasse has advantages as a support material for ethanol production from sugar cane or beet, including negligible cost, ready availability and the capacity to support a high yeast population.