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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.

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.

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.