A review of wine authentication using spectroscopic approaches in combination with chemometrics

Published: 17 July 2021In a global context where trading of wines involves considerable economic value, the requirement to guarantee wine authenticity can never be underestimated. With the ever-increasing advancements in analytical platforms, research into spectroscopic methods is thriving as they offer a powerful tool for rapid wine authentication. In particular, spectroscopic techniques have been identified as a user-friendly and economical alternative to traditional analyses involving more complex instrumentation that may not readily be deployable in an industry setting. Chemometrics plays an indispensable role in the interpretation and modelling of spectral data and is frequently used in conjunction with spectroscopy for sample classification. Considering the variety of available techniques under the banner of spectroscopy, this review aims to provide an update on the most popular spectroscopic approaches and chemometric data analysis procedures that are applicable to wine authentication.Ranaweera K. R. Ranaweera, Dimitra L. Capone, Susan E. P. Bastian, Daniel Cozzolino and David W. Jeffer

Preliminary investigation of potent thiols in Cypriot wines made from indigenous grape varieties Xynisteri, Maratheftiko and Giannoudhi

Polyfunctional thiols have previously been shown to be key aroma compounds in Sauvignon blanc and more recently in Chardonnay wines. Their role in other wine varieties such as those made from three popular indigenous Cypriot grape varieties has remained unexplored. As an extension of a previous project that profiled the sensory and chemical characteristics of Cypriot wines and their comparison to Australian wines, this study aimed to investigate five potent thiols in Xynisteri, Maratheftiko, Giannoudhi, Pinot gris, Chardonnay and Shiraz wines. Wines were analysed utilising Stable Isotope Dilution Assay (SIDA) with derivatisation and High-Performance Liquid Chromatography–Tandem Mass Spectrometry (HPLC-MS/MS). The varietal thiols measured were 4-methyl-4-sulfanylpentan-2-one (4MSP) that has an aroma of “boxwood” and “cat urine” at high concentration, 3-sulfanylhexan-1-ol (3SH) which has been described as having a “grapefruit/tropical fruit” aroma, and 3-sulfanylhexyl acetate (3SHA) that has also been described as having an aroma of “passionfruit”. Additionally, two other potent thiols were measured including benzyl mercaptan (BM) that has an aroma of “smoke and meat” and furfuryl thiol (FFT) that has been described as having a “roasted coffee” like aroma. The reason these thiols are known as potent thiols are due to their very low aroma detection thresholds in the low ng/L (ppt) range. Of the thiols that were measured, 3SH was the only varietal thiol detected in the red wine samples. All of the white wine samples contained 3SH, BM and 3SHA, whereas 4MSP was only detected in Pinot gris and three Xynisteri wines. The potent thiol, FFT, was detected only in the Chardonnay and four of the Xynisteri wines. Interestingly the thiols that were present in the samples were found at concentrations above their aroma detection thresholds (determined in hydroalcoholic solutions), especially 3SH which was found in an order of magnitude above its aroma detection threshold. These findings provide early knowledge of the presence of these thiols in Cypriot wines, compared with Australian wines and establish any relationships between this chemical data with previous wine sensory profile data.Alexander Willem Copper, Cassandra Collins, Susan E. P. Bastian, Trent E. Johnson, Dimitra L. Capon

Using content analysis to characterise the sensory typicity and quality judgements of Australian Cabernet Sauvignon wines

Understanding the sensory attributes that explain the typicity of Australian Cabernet Sauvignon wines is essential for increasing value and growth of Australia's reputation as a fine wine producer. Content analysis of 2598 web-based wine reviews from well-known wine writers, including tasting notes and scores, was used to gather information about the regional profiles of Australian Cabernet Sauvignon wines and to create selection criteria for further wine studies. In addition, a wine expert panel evaluated 84 commercial Cabernet Sauvignon wines from Coonawarra, Margaret River, Yarra Valley and Bordeaux, using freely chosen descriptions and overall quality scores. Using content analysis software, a sensory lexicon of descriptor categories was built and frequencies of each category for each region were computed. Distinction between the sensory profiles of the regions was achieved by correspondence analysis (CA) using online review and expert panellist data. Wine quality scores obtained from reviews and experts were converted into Australian wine show medal categories. CA of assigned medal and descriptor frequencies revealed the sensory attributes that appeared to drive medal-winning wines. Multiple factor analysis of frequencies from the reviews and expert panellists indicated agreement about descriptors that were associated with wines of low and high quality, with greater alignment at the lower end of the wine quality assessment scale.Lira Souza Gonzaga, Dimitra L. Capone, Susan E.P. Bastian, Lukas Danner and David W. Jeffer

Sensory and chemical drivers of wine consumers' preference for a new shiraz wine product containing ganoderm alucidum extract as a novel ingredient

This study explored wine consumers' preferences towards a novel Australian Shiraz wine product containing Ganoderma lucidum (GL). Wine consumers (n = 124) were asked to complete a questionnaire and participate in a blind tasting of six GL wine products (differing in the amount and timing of GL extract additions). Based on individual liking scores for each GL wine product that was tasted, four hedonic clusters C1 (n = 44, preferred control and low levels of GL additions), C2 (n = 28, preferred control only), C3 (n = 26, generally preferred all GL additions) and C4 (n = 26, preferred 1 g/L additions and 4 g/L post-fermentation) were identified. Sensory attributes of the GL wine products were also profiled with rate-all-that-apply (n = 65) and the 31 sensory attributes that significantly differentiated the wines underwent principal component analysis with the hedonic clusters overlaid to explain consumers' preferences. There was a clear separation between hedonic clusters. Sensory attributes and volatile flavor compounds that significantly differentiated the wines were subjected to partial least squares regression, which indicated the important positive drivers of liking among the hedonic clusters. Pepper and jammy aroma, 3-methylbutanoic acid (linked to fruity notes) and non-fruit aftertaste positively drove C2's preference, whereas spice flavor and hexanoic acid (known for leafy and woody descriptors) drove C3's liking. There were no positive drivers for C1's liking but bitter taste, cooked vegetable, and toasty aromas drove this cluster' dislike. C4 preferred brown appearance, tobacco aroma, and jammy and cooked vegetable flavors. These findings provide the wine industry with deeper insights into consumers' liking towards new GL wine products targeted at the Australasian market.Anh N.H. Nguyen, Trent E. Johnson, David W. Jeffery, Dimitra L. Capone, Lukas Danner and Susan E.P. Bastia

Grape seed extract improves parameters of small intestinal mucositis in a dose responsive manner in rats

KY Cheah, GS Howart, SEP Bastia

Grape seed extract: a potential adjunct to chemotherapy?

Ker Y Cheah, Gordon S Howarth, Susan EP Bastia

Linking the sensory properties of chardonnay grape Vitis vinifera cv. berries to wine characteristics

Formal or informal sensory analyses of grapes are often used to determine when a parcel of fruit should be harvested to produce a certain wine style. This study investigated whether relationships exist between sensory perceptions and basic chemical measures of Chardonnay grape berries and the corresponding wines. Chardonnay grape parcels were harvested at commercial maturity from across South Australia in vintages 2015 and 2016, yielding a total of 25 and 24 samples, respectively. Grapes were evaluated using berry sensory assessment (BSA) and vinified identically using small-scale winemaking, and the resulting wines were evaluated with descriptive sensory analysis. Sensory assessors were trained in the respective sensory evaluation methods. Chardonnay grape and wine samples were discriminated by the panel according to sensory attributes, and the fruit could also be discriminated by basic chemistry measures. However, differences in Chardonnay wines were subtle compared with those in grapes, as indicated by low effect sizes. Moderate validation models (R²Val = 0.53 to 0.81) of partial least squares regression (PLSR) 1 were determined in the 2015 vintage, using BSA attributes as x-variables and wine sensory attributes as y-variables, but poor models were obtained with the 2016 vintage (R²Val < 0.5). In the 2015 models, relationships were found for wine attributes of heat, sourness, and astringency, possibly due to slight variations in ripeness. Strong relationships that revealed wine style from variations in grapes were not found. Overall, relating the sensory characteristics of Chardonnay grapes to the wines was challenging and indicated that variation in style of these varietal wines does not greatly depend on the raw grape material.Jun Niimi, Paul K. Boss, David W. Jeffery and Susan E.P. Bastia

Defining wine typicity: sensory characterisation and consumer perspectives

Wine encapsulates the expression of multiple inputs – from the vineyard location and environment to viticultural and winemaking practices – collectively known as terroir. Each of these inputs influence a wine's chemical composition and sensory traits, which vary depending on cultivar as well as provenance. These aspects underpin the overall concept of wine typicity, an important notion that enables wine from a delimited geographical area to be differentiated and recognisable in national and international wine markets. Indeed, consumers are increasingly more aware of the significance of regionality and may use this to influence their purchasing decisions. Understanding which sensory attributes represent regional typicity and how these are best conveyed to consumers is therefore important for the prosperity and reputation of producers. As reviewed herein, the sensory typicity of wine can be identified using different types of testing methods, with the most effective being a combination of approaches, such as sorting task in combination with descriptive sensory analysis. Consumer perceptions of regionality and wine typicity are then examined to provide insight into their behaviours. This includes consideration of the importance of origin to perceptions of quality and typicity, in terms of meeting expectations and engaging consumers. Based on the literature reviewed, it is proposed that wine typicity can be defined as a juxtaposition of unique traits that define a class of wines having common aspects of terroir involving biophysical and human dimensions that make the wines recognisable, and in theory, unable to be replicated in another territory.L. Souza Gonzaga, D.L. Capone, S.E.P. Bastian, D.W. Jeffer

Dynamic characterization of wine astringency profiles using modified progressive profiling

Wine astringency is important for quality and consumer acceptance. Perception of this mouthfeel is temporal and can be separated further into unique textural sub-qualities. Quantitative data on these astringent sub-qualities in wine however are poorly understood. The aim of this study was to characterize the dynamic astringency profiles of 13 Australian commercial red wines and 2 rosés made from 11 grape varieties using modified progressive profiling by a trained sensory panel (n = 8). Seven attributes generated and defined by the panel (overall astringent intensity and 6 sub-qualities: pucker, mouth coat, dry, grippy, adhesive and graininess) were scored at six time periods (each lasting 10 s), with 20 s gap between each time period. Attributes were rated on 15 cm scales with anchors at 10 and 90% and samples were evaluated in duplicate. The wine composition as well as phenolic profiles were determined. Intensities of astringent sub-qualities were correlated with overall intensity, but the sub-quality profiles at a specific evaluation period and the progression of an attribute varied differently depending on the wine. The discrimination of wines at each time interval was dependent on attribute, and the relative importance of each astringent sub-quality varied at different evaluation periods. Correlations between mouthfeel attributes and chemical measures were established. This study demonstrated the utilisation of modified progressive profiling for wine astringency evaluation, providing a tool to capture quantitative data on astringent sub-qualities in wine.Wenyu Kang, Jun Niimi, Richard A. Muhlack, Paul A. Smith, Susan E.P. Bastia

Spectrofluorometric analysis to trace the molecular fingerprint of wine during the winemaking process and recognise the blending percentage of different varietal wines

Published: 10 March 2022As a robust analytical method, spectrofluorometric analysis with machine learning modelling has recently been used to authenticate wine from different regions, vintages and varieties. This preliminary study investigated whether the molecular fingerprint obtained with this approach is maintained throughout the winemaking process, along with assessing different percentages of wine in a blend. Monovarietal wine samples were collected at different stages of the winemaking process and analysed with the absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) technique. Wines were clustered tightly according to origin for the different winemaking stages, with some clear separation of different regions and varieties based on principal component analysis. In addition, wines were classified with 100 % accuracy according to varietal origin using extreme gradient boosting (XGB) discriminant analysis. The sensitivity of the A-TEEM technique was such that it allowed for accurate modelling of wine blends containing as little as 1 % of Cabernet-Sauvignon or Grenache in Shiraz wine when employing XGB regression, which performed better than partial least squares regression. The overall results indicated the potential for applying A-TEEM and machine learning modelling to wine chemical traceability through production to guarantee the provenance of wine or identify the composition of a blend.Ranaweera K.R. Ranaweera, Adam M. Gilmore, Susan E.P. Bastian, Dimitra L. Capone and David W. Jeffer