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The present study aims to understand an ill-defined sensory concept by a long-term memory-based strategy with Spanish winemakers from four wine regions using "green wine"as a case study. A total of 77 Spanish winemakers from four Spanish wine regions carried out a non-tasting free description task. The description task yielded terms belonging to two main categories including origin-related terms as well as sensory terms. Sensory terms belonged to aroma, taste, trigeminal, colour, multimodal and hedonic subcategories, which elucidates the multidimensionality of the studied concept. The most cited specific terms were "vegetal aroma", "bitter"and "unpleasant". Despite these commonalities, a certain idiosyncrasy linked to taste ("excessive sourness") and trigeminal ("astringency") subcategories as well as to wine components ("tannins") was evidenced as they were cited distinctly by experts belonging to separate wine regions. The capacity of approaches based on long-term memory to decipher multidimensional and ill-defined concepts is highlighted. The regional effect is also explained in terms of cognitive processes (i.e., knowledge and experience), which is linked to the use of sensory concepts by wine experts.

Aims: The present work aims to predict sensory astringency from wine chemical composition using machine learning algorithms. Material and results: Moristel grapes from different vineblocks and at different stages of ripening were collected. Eleven different wines were produced in 75 L tanks in triplicate, and further sensory factors were described by the rate-all-that-apply method with a trained panel of participants. The polyphenolic composition was characterised in wines by measuring the concentration and activity of tannins using UHPLC-UV/VIS, the mean degree of polymerisation (mDP. and the composition of tannins using thiolysis followed by UHPLC-MS. Conventional oenological parameters were analysed using FTIR and UV-Vis. Machine learning was applied to build models for predicting a wines astringency from its chemical composition. The best model was obtained using the support vector regressor (radial kernel) algorithm presenting a root-mean-square error (RMSE) value of 0.190. Conclusions: The main variables of the astringency model were the % of procyanidins constituting tannins and ethanol content, followed by other eight variables related to tannin structure and acidity. Significance of the study: These results increase the knowledge of chemical variables related to the perception of wine astringency and provide tools to control and optimise grape and wine production stages to modulate astringency and maximise quality and the consumer appeal of wines.