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With a view to maintaining the reputation of wine-producing regions among consumers, minimising economic losses caused by wine fraud, and achieving the purpose of data-driven terroir classification, the use of an absorbance–transmission and fluorescence excitation–emission matrix (A-TEEM) technique has shown great potential based on the molecular fingerprinting of a sample. The effects of changes in wine composition due to ageing and the stability of A-TEEM models over time had not been addressed, however, and the classification of wine blends required investigation. Thus, A-TEEM data were combined with an extreme gradient boosting discriminant analysis (XGBDA) algorithm to build classification models based on a range of Shiraz research wines (n = 217) from five Barossa Valley sub-regions over four vintages that had aged in bottle for several years. This spectral fingerprinting and machine learning approach revealed a 100% class prediction accuracy based on cross-validation (CV) model results for vintage year and 98.8% for unknown sample prediction accuracy when splitting the wine samples into training and test sets to obtain the classification models. The modelling and prediction of sub-regional production area showed a class CV prediction accuracy of 99.5% and an unknown sample prediction accuracy of 93.8% when modelling with the split dataset. Inputting a sub-set of the current A-TEEM data into the models generated previously for these Barossa sub-region wines yielded a 100% accurate prediction of vintage year for 2018–2020 wines, 92% accuracy for sub-region for 2018 wines, and 91% accuracy for sub-region using 2021 wine spectral data that were not included in the original modelling. Satisfactory results were also obtained from the modelling and prediction of blended samples for the vintages and sub-regions, which is of significance when considering the practice of wine blending.

Rootstock can significantly alter the concentration of methoxypyrazines (MPs) in the bunch stem (rachis) of Vitis vinifera L. cv. Cabernet Sauvignon and Shiraz, which has implications for winemaking and wine style. The distribution of MPs across the rachis is an important consideration, but such information was not available. This study aimed to address this research question by comparing MP concentrations in different rachis components throughout grape maturation and in the absence of ambient light. Shiraz and Cabernet Sauvignon bunches were sampled throughout development, segmented into four components (peduncle, top rachis, bottom rachis, and pedicel), and 3-isobutyl-2-methoxypyrazine (IBMP) was quantified in each. For both cultivars, IBMP showed a negative correlation with grape maturity, with concentrations in pedicel at harvest being significantly higher than other rachis components. Additionally, light exclusion significantly increased IBMP concentrations in all rachis segments. The concentration of IBMP varied significantly between different rachis components. The greatest concentrations were measured in the pedicel, which also contributed the largest proportion among the components to total rachis by weight. Due to elevated IBMP concentrations in rachis and the difficulties in excluding matter other than grape from a fermentor, the presence of pedicel during fermentation could produce Shiraz and Cabernet Sauvignon wines with higher concentrations of MPs, thereby potentially increasing vegetal sensory characteristics.

Background and Aims. An OIV resolution provides guidelines on using glutathione as a prefermentation additive when the amount of yeast assimilable nitrogen (YAN) of a juice or must is adequate, to avoid the metabolism of glutathione by the yeast. The effect of YAN concentration on glutathione metabolism by yeast had not been determined. This study explored whether nitrogen management could be used to control glutathione consumption during fermentation. Methods and Results. An HPLC-UV method was developed to quantify reduced L-glutathione (GSH) and oxidised glutathione (GSSG) and used to monitor yeast GSH metabolism during alcoholic fermentation with two yeast strains (AWRI 1688 and AWRI 2861). The addition of GSH had no impact on the fermentation rate of the chemically defined medium, even in a limited YAN environment; however, a decrease in glutathione concentration occurred regardless of YAN concentration. The effect of GSH on volatile sulfur compound formation was yeast strain-dependent. Conclusions. Increasing the YAN status of a chemically defined medium led to a decrease in GSH consumption during fermentation, but the loss of GSH could not be prevented entirely, even with a low initial GSH concentration and high initial YAN. Significance of the Study. In the presence of higher concentrations of GSH during fermentation, there is a risk of forming undesirable fermentative sulfur compounds that are not mitigated through nitrogen supplementation. Thus, it seems unlikely that an argument could be made for the inclusion of GSH in relevant food standards codes as a wine additive especially if a lack of GSH metabolism was a criterion.

The volatile-mediated interplay between stressed and non-stressed plants has been described in many studies involving both biotic and abiotic stresses as a one-way channel. However, very little is known about the molecular basis and mechanisms by which volatile organic compounds (VOCs) mediate plant communication between drought-stressed ‘emitter’ plants and non-stressed ‘receiver’ neighbours for the defence against impending stress challenges. Aiming to address this in grapevine, this study investigated the effect of two-way VOC exchange between stressed and non-stressed Vitis vinifera L. cv. Shiraz during drought and recovery using four treatments: isolated well-watered (WW) vines, isolated drought-stressed (DS) vines, and co-located DS ‘emitter’ and WW ‘receiver’ vines in a growth room. The results obtained from solid-phase microextraction (SPME) gas chromatography mass spectrometry (GC-MS) analysis showed a synchronised decline in α-pinene concentration in the co-located treatment vines and higher isoprene levels in the DS emitters compared to the isolated DS vines. Targeted gene expression analysis further identified the over-expression of a key gene, allene oxide synthase (AOS), in the jasmonic acid (JA) biosynthesis pathway during peak drought in the DS emitter. Transcript expression of chorismate synthase (CHORS) and α-pinene synthase (VvPNaPin1) showed similar trends in the DS emitter. The results suggest that isoprene and α-pinene may be interplant signalling molecules used by grapevine during drought. To the best of our knowledge, this is the first report of a bi-directional interaction in grapevine between the emitters and receivers under drought stress mediated by the JA and terpenoid biosynthesis pathways.

Despite Xinomavro (Vitis vinifera L.) being a well-known noble red grape variety of northern Greece, little is known about its ‘‘bouquet’’ typicity. Volatile compounds of Xinomavro wines produced using a common vinification protocol were analyzed by gas chromatography–mass spectrometry and sensory descriptive analysis was carried out with a trained panel. Wines were characterized by the presence of fatty acids, ethyl and acetate esters, and alcohols, with contributions from terpenes and a volatile phenol. The most active aroma compounds were determined to be 3-methylbutyl acetate, β-damascenone, ethyl esters of octanoic and hexanoic acids, and eugenol. Those compounds positively correlated with fruity and spicy odor descriptors, with the wines being mostly characterized by five typical aroma terms: strawberry, berry fruit, spices, tomato, and green bell pepper. Partial least squares regression (PLSR) analysis was used to visualize relationship between the orthonasal sensory attributes and the volatile aroma compounds with calculated OAVs > 1. Key aroma-active volatiles in the wines were identified using GC-MS/olfactometry, providing a list of 40 compounds, among which 13 presented a modified detection frequency > 70%. This study is the first of its kind and provided strong indications regarding the aroma compounds defining the sensory characteristics of Xinomavro wines.

Digital control systems are well established in many industries and could find application in the wine sector. Of critical importance to red wine quality, the efficient and targeted extraction of polyphenols from red grape solids during alcoholic fermentation could be a focus for automation. Smart technologies such as model predictive control (MPC) or fuzzy logic appear ideal for application in a complex process such as wine polyphenol extraction, but require mathematical models that accurately describe the system. The aim of this study was to derive and validate a model describing the extraction of catechin (a representative polyphenol) from red grape solids under simulated fermentation conditions. The impact of ethanol, fermentable sugar, and temperature on extraction rate was determined, with factor conditions chosen to emulate those present in industry practice. A first-order approach was used to generate an extraction model based on mass conservation that incorporated temperature and sugar dependency. Coefficients of determination (R2) for all test scenarios exceeded 0.94, indicating a good fit to the experimental data. Sensitivity analysis for the extraction rate and internal cross-validation showed the model to be robust, with a small standard error in cross-validation (SECV) of 0.11 and a high residual predictive deviation (RPD) of 17.68. The model that was developed is well suited to digital technologies where low computational overheads are desirable, and industrial application scenarios are presented for future implementation of the work.

Uniform grape maturity can be sought by producers to minimise underripe and/or overripe proportions of fruit and limit any undesirable effects on wine quality. Considering that grape heterogeneity is a multifaceted phenomenon, a composite index summarising overall grape heterogeneity was developed to benefit vineyard management and harvest date decisions. A grape heterogeneity index (GHI) was constructed by aggregating the sum of absolute residuals multiplied by the range of values from measurements of total soluble solids, pH, fresh weight, total tannins, absorbance at 520 nm (red colour), 3-isobutyl-2-methoxypyrazine, and malic acid. Management of grape heterogeneity was also studied, using Cabernet Sauvignon grapes grown under four viticultural regimes (normal/low crop load, full/deficit irrigation) during the 2019/2020 and 2020/2021 seasons. Comparisons of GHI scores showed grape variability decreased throughout ripening in both vintages, then significantly increased at the harvest time point in 2020, but plateaued on sample dates nearing the harvest date in 2021. Irrigation and crop load had no effect on grape heterogeneity by the time of harvest in both vintages. Larger vine yield, leaf area index, and pruning weight significantly increased GHI score early in ripening, but no significant relationship was found at the time of harvest. Differences in the Ravaz index, normalised difference vegetation index, and soil electrical conductivity did not significantly change the GHI score.

Generations of sensors have been developed for predicting food sensory profiles to circumvent the use of a human sensory panel, but a technology that can rapidly predict a suite of sensory attributes from one spectral measurement remains unavailable. Using spectra from grape extracts, this novel study aimed to address this challenge by exploring the use of a machine learning algorithm, extreme gradient boosting (XGBoost), to predict twenty-two wine sensory attribute scores from five sensory stimuli: aroma, colour, taste, flavour, and mouthfeel. Two datasets were obtained from absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) spectroscopy with different fusion methods: variable-level data fusion of absorbance and fluorescence spectral fingerprints, and feature-level data fusion of A-TEEM and CIELAB datasets. The results for externally validated models showed slightly better performance using only A-TEEM data, predicting five out of twenty-two wine sensory attributes with R2 values above 0.7 and fifteen with R2 values above 0.5. Considering the complex biotransformation involved in processing grapes to wine, the ability to predict sensory properties based on underlying chemical composition in this way suggests that the approach could be more broadly applicable to the agri-food sector and other transformed foodstuffs to predict a product’s sensory characteristics from raw material spectral attributes.

As 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.

Understanding how wine compositional traits can be related to sensory profiles is an important and ongoing challenge. Enhancing knowledge in this area could assist producers to select practices that deliver wines of the desired style and sensory specifications. This work reports the use of spectrofluorometry in conjunction with chemometrics for prediction, correlation, and classification based on sensory descriptors obtained using a rate-all-that-apply sensory assessment of Cabernet-Sauvignon wines (n = 26). Sensory results were first subjected to agglomerative hierarchical cluster analysis, which separated the wines into five clusters represented by different sensory profiles. The clusters were modelled in conjunction with excitation-emission matrix (EEM) data from fluorescence measurements using extreme gradient boosting discriminant analysis. This machine learning technique was able to classify the wines into the pre-defined sensory clusters with 100 % accuracy. Parallel factor analysis of the EEMs identified four main fluorophore components that were tentatively assigned as catechins, phenolic aldehydes, anthocyanins, and resveratrol (C1, C2, C3, and C4, respectively). Association of these four components with different sensory descriptors was possible through multiple factor analysis, with C1 relating to ‘dark fruits’ and ‘savoury’, C2 with ‘barnyard’, C3 with ‘cooked vegetables’ and ‘vanilla/chocolate’, and C4 with ‘barnyard’ and a lack of C1 descriptors. Partial least squares regression modelling was undertaken with EEM data and sensory results, with a model for perceived astringency being able to predict the panel scores with 68.1 % accuracy. These encouraging outcomes pave the way for further studies that relate sensory traits to fluorescence data and move research closer to the ultimate goal of predicting wine sensory expression from a small number of compositional factors.

The sessile plant has developed mechanisms to survive the “rough and tumble” of its natural surroundings, aided by its evolved innate immune system. Precise perception and rapid response to stress stimuli confer a fitness edge to the plant against its competitors, guaranteeing greater chances of survival and productivity. Plants can “eavesdrop” on volatile chemical cues from their stressed neighbours and have adapted to use these airborne signals to prepare for impending danger without having to experience the actual stress themselves. The role of volatile organic compounds (VOCs) in plant–plant communication has gained significant attention over the past decade, particularly with regard to the potential of VOCs to prime non-stressed plants for more robust defence responses to future stress challenges. The ecological relevance of such interactions under various environmental stresses has been much debated, and there is a nascent understanding of the mechanisms involved. This review discusses the significance of VOC-mediated inter-plant interactions under both biotic and abiotic stresses and highlights the potential to manipulate outcomes in agricultural systems for sustainable crop protection via enhanced defence. The need to integrate physiological, biochemical, and molecular approaches in understanding the underlying mechanisms and signalling pathways involved in volatile signalling is emphasised.

In 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.

Low color stability of Rubired food and beverage coloring negatively impacts color yield during production and storage while also limiting the use of this type of food colorant in applications where color stability is a key requirement. This study investigated the impact on color stability of using flash détente (FD) for Rubired color extraction in comparison to a conventional must heating (CMH) extraction process, in conjunction with the use of commercial seed tannin, acetaldehyde, or acid to lower the pH. Rubired concentrate color was evaluated under accelerated aging conditions at 50, 60, and 70 °C, over zero, three, six, and nine days for the different treatments. FD concentrate had lower color stability, with a half-life of 203.3 h and activation energy of 59.2 kJ/mol at 50 °C compared to the CMH concentrate with 233.9 h and 65.2 kJ/mol. FD concentrate generated less 5-hydroxymethylfurfural (5-HMF) during accelerated aging regardless of treatment. Acetaldehyde, low pH, and the combination of these two treatments increased red color stability as well as violet and brown color, whereas seed tannin had no effect. Low pH treatments increased 5-HMF formation and browning, which was detrimental to concentrate quality. Although promising in terms of color stabilization, implementation of these treatments will require development of solutions to mitigate the production of 5-HMF.

Changes to regulations by Food Standards Australia New Zealand have permitted the adjustment of must sugar levels with the addition of water in order to ensure a sound fermentation progress as well as mitigating excessive wine–alcohol levels. This study assessed the implications for Shiraz wine quality following a pre-fermentative must dilution (changing liquid-to-solid ratios), in comparison to juice substitution with water (constant liquid-to-solid ratios) that has previously been deemed a promising way to adjust wine–alcohol levels. While working within the legal limit of water addition to grape must, the effects of both approaches on wine quality parameters and sensory characteristics were rather similar, and of negligible nature. However, different implications between substitution and dilution appeared to be driven by grape maturity, and dilution was found to have a greater impact than substitution on some parameters at higher water implementation rates. In line with previous observations, longer hang-time followed by alcohol adjustments via pre-fermentation water addition were of limited merit compared to simply picking grapes earlier. This work provided further knowledge that supports informed decision making regarding the recently permitted approach of using water during winemaking.

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.

Novel Shiraz red wine products enriched with Ganoderma lucidum (GL) extract, a traditional Asian medicinal mushroom, were developed and characterized. GL extract was added at different levels prior to and after primary fermentation to investigate its impact on the juice fermentation kinetics, and the chemical composition and sensory properties of the resulting wines. The fermentation kinetics of red grape juice were not significantly different between ferments. Basic chemical analyses plus headspace solid-phase micro-extraction (HS-SPME), gas chromatography‒mass spectrometry (GC-MS), and a rate-all-that-apply (RATA) (n = 65) sensory panel were used to investigate the influence of GL extract additions on wine composition and sensory characteristics. Of the 54 sensory attributes assessed, 39 significantly differentiated the wines. A clear separation between GL wine treatments was evident with PLS regression, where specific volatiles were correlated with relevant sensory attributes that dominated the wines. These products could be promising for emerging wine markets.

Grape composition affects wine flavour and aroma not only through varietal compounds, but also by influencing the production of volatile compounds by yeast. C9 and C12 compounds that potentially influence ethyl ester synthesis during fermentation were studied using a model grape juice medium. It was shown that the addition of free fatty acids, their methyl esters or acyl-carnitine and acyl-amino acid conjugates can increase ethyl ester production in fermentations. The stimulation of ethyl ester production above that of the control was apparent when lower concentrations of the C9 compounds were added to the model musts compared to the C12 compounds. Four amino acids, which are involved in CoA biosynthesis, were also added to model grape juice medium in the absence of pantothenate to test their ability to influence ethyl and acetate ester production. β-Alanine was the only one shown to increase the production of ethyl esters, free fatty acids and acetate esters. The addition of 1 mg∙L−1 β-alanine was enough to stimulate production of these compounds and addition of up to 100 mg∙L−1 β-alanine had no greater effect. The endogenous concentrations of β-alanine in fifty Cabernet Sauvignon grape samples exceeded the 1 mg∙L−1 required for the stimulatory effect on ethyl and acetate ester production observed in this study.

Warmer growing seasons, variations to grape ripening dynamics, and stylistic changes have contributed to increased wine alcohol levels, which can negatively impact sensory properties. As a consequence, winemakers have sought technological innovations to produce reduced alcohol wine (RAW). The sensory methodology used by industry to optimize the ethanol content of RAW is known as ‘alcohol sweetspotting’. However, to date, there is no scientific evidence to support the alcohol sweetspot phenomenon, and the sensory methodology used for alcohol sweetspotting has not been validated. In this study, different methods of presenting wine samples (i.e., ordered vs. randomized, and linear vs. circular) were employed to determine to what extent presentation order influences the outcome of alcohol sweetspotting trials. Two different approaches to statistical analysis of sensory data, i.e., chi-square goodness of fit vs. one proportion tests, were also evaluated. Statistical analyses confirmed alcohol sweetspots were apparent in some sweetspot determination trials, but outcomes were not reproducible in replicate determinations (either by panel or by individual panelists). Analysis of data using the one proportion test improved the likelihood of identifying statistically significant differences between RAWs, but variation in individuals’ sensitivity to differences in sensory properties following ethanol removal prevented validation of the alcohol sweetspot phenomenon based on the wines studied.

Certain volatile thiols are some of the most potent odour-active molecules that are found in nature. Thiols play significant roles in the aroma qualities of a range of foods and beverages, including wine, with extremely low odour detection thresholds (nanogram per litre range). A fundamental understanding of their formation, fate, and impact essentially depends on the development of suitable analytical methods. The analysis of volatile thiols in foods and beverages is a challenging task when considering (1) the complexity of food and beverage matrices and (2) that thiols are highly reactive, low molecular-weight volatiles that are generally present at trace to ultra-trace concentrations. For the past three decades, the analytical evaluation of volatile thiols has been intensively performed in various foods and beverages, and many novel techniques related to derivatisation, isolation, separation, and detection have been developed, particularly by wine researchers. This review aims to provide an up-to-date overview of the major analytical methodologies that are proposed for potent volatile thiol analysis in wine, foods, and other beverages. The analytical challenges for thiol analysis in foods and beverages are outlined, and the main analytical methods and recent advances in methodology are summarised and evaluated for their strengths and limitations. The key analytical aspects reviewed include derivatisation and sample preparation techniques, chromatographic separation, mass spectrometric detection, matrix effects, and quantitative analysis. In addition, future perspectives on volatile thiol research are also suggested.

This study investigated compositional changes in red wines resulting from wine alcohol removal by reverse osmosis-vaporative perstraction (RO-EP) and provides insight into the physical and chemical changes in reduced alcohol wine (RAW). Trial 1 involved RO-EP treatment of three wines that were analyzed pre-treatment, post-treatment, and post-treatment with alcohol adjustment (i.e., addition of ethanol to achieve the original alcohol content). Trial 2 involved partial dealcoholization of two wines and analysis of samples collected during RO-EP treatment, i.e., wine in, wine out, retentate, permeate (pre- and post-EP treatment) and strip water. Wine color was analyzed by spectrophotometric methods, while other compositional changes were determined by WineScan, high performance liquid chromatography (HPLC) and gas chromatography–mass spectrometry (GC–MS) analyses. In general, RAWs were slightly more concentrated than pre-treatment wines, which resulted in greater color intensity and increased phenolics and organic acids. However, partial dealcoholization resulted in lower concentrations of some fermentation volatiles, particularly ethyl esters, which may reflect ester hydrolysis following ethanol removal.

The colour of red wine is largely determined by the concentration of anthocyanins that are extracted from grape skins during fermentation. Because colour is a key parameter in determining the overall quality of the finished product, understanding the effect of processing variables on anthocyanin extraction is critical for producing a red wine with the desired sensorial characteristics. In this study, the effect of convective conditions (natural and forced) on the mass transfer properties of malvidin-3-glucoside (M3G) from pre-fermentative grape solids was explored at various liquid phase conditions representing stages of fermentation. A mathematical model that separates solid and liquid phase mass transfer parameters was applied to experimental extraction curves, and in all cases, provided a coefficient of determination exceeding 0.97. Calculated mass transfer coefficients indicated that under forced convective conditions, the extraction process was controlled by internal diffusion whereas under natural convection, both internal diffusion and liquid-phase mass transfer were relevant in determining the overall extraction rate. Predictive simulations of M3G extraction during active fermentation were accomplished by incorporating the current results with a previously developed fermentation model, providing insight into the effect of a dynamic liquid phase on anthocyanin extraction.

Extraction of grape components is a key consideration for red winemaking. The impact of changing process variables on mass transfer properties of anthocyanins from fresh pre-fermentative red grape solids under forced convective conditions was explored using the dominant red grape anthocyanin, malvidin-3-glucoside (M3G) as a model solute. A two level full factorial design was implemented to investigate effects of temperature, sugar and ethanol on mass transfer properties. Factor levels were chosen to simulate conditions found at various points during the maceration and fermentation steps of the red winemaking process. A rigorous mathematical model was developed and applied to experimental extraction curves, allowing the separation of mass transport properties in liquid and solid phases in a wine-like system, for the first time. In all cases, the coefficient of determination exceeded 0.92, indicating good agreement between experimental and mathematically-solved M3G concentrations. For the conditions studied, internal mass transfer was found to limit M3G extraction and changes to the liquid phase composition and temperature influence the distribution constant. Surface response models of mass transfer parameters were developed to allow future simulations of fermentation scenarios aimed at maximising the extraction potential of M3G.

3-Isobutyl-2-methoxypyrazine (IBMP), the most prevalent grape-derived methoxypyrazine, can contribute green bell pepper, vegetative and herbaceous aromas and flavours to wines. At elevated concentrations, this potent odorant may mask desirable fruity and floral aromas in wine and may be considered as a fault. A new remediation method for wines with elevated IBMP levels has been trialled using magnetic polymers, prepared in the same way as ordinary polymers but with the incorporation of iron oxide nanoparticles as magnetic substrates. Characterisation by Fourier transform-infrared (FTIR) spectroscopy and scanning electron microscopy (SEM) showed no difference between thermally synthesised and microwave synthesised polymers. Magnetic polymers were found to have removed over 40% of the IBMP present in spiked model wine and white wine within ten minutes. The addition of magnetic nanoparticles and microwave-induced polymerisation did not affect the adsorption properties of the polymer in model wine and the polymer could be regenerated at least five times. Both Langmuir and Freundlich isotherms were found to fit the data for both types of polymer. However, attempts to produce imprinted polymers were not achieved, as they were found not to be differentiated from non-imprinted counterparts via adsorption tests.

The relationship between berry chemical composition, region of origin and quality grade was investigated for Chardonnay grapes sourced from vineyards located in seven South Australian Geographical Indications (GI). Measurements of basic chemical parameters, amino acids, elements, and free and bound volatiles were conducted for grapes collected during 2015 and 2016. Multiple factor analysis (MFA) was used to determine the sets of data that best discriminated each GI and quality grade. Important components for the discrimination of grapes based on GI were 2-phenylethanol, benzyl alcohol and C6 compounds, as well as Cu, Zn, and Mg, titratable acidity (TA), total soluble solids (TSS), and pH. Discriminant analysis (DA) based on MFA results correctly classified 100% of the samples into GI in 2015 and 2016. Classification according to grade was achieved based on the results for elements such as Cu, Na, Fe, volatiles including C6 and aryl alcohols, hydrolytically-released volatiles such as (Z)-linalool oxide and vitispirane, pH, TSS, alanine and proline. Correct classification through DA according to grade was 100% for both vintages. Significant correlations were observed between climate, GI, grade, and berry composition. Climate influenced the synthesis of free and bound volatiles as well as amino acids, sugars, and acids, as a result of higher temperatures and precipitation.