Can varietal ‘apricot’ aroma of Viognier wine be controlled with clonal selection and harvest timing?

Multiple Factor Analysis (MFA) According to the Chenin blanc Association of South Africa, there are three recognized dry wine styles, Fresh and Fruity (FF), Rich and Ripe Unwooded (RRU), and Rich and Ripe Wooded (RRW), classically attributed with the help of sensory evaluation. One of the “rapid methods” has drawn our attention for the purpose of simplifying and making style attribution for large sample sets, evaluated during different sessions, more robust. Polarized Projective Mapping (PPM) is a hybrid of Projective Mapping (PM) and Polarised Sensory Positioning (PSP). It is a reference-based method in which poles
(references) are used for the evaluation of similarities and dissimilarities between samples.

The global consumer demand for low or non-alcoholic wine is growing steadily in recent years, driven by health concerns, religious beliefs, and personal taste preferences etc.. Consequently, the removal of alcohol from wine can significantly alter its chemical and sensory properties, including color, aroma, and taste, which make a significant challenge for consumer to accept these products. Ethanol plays a crucial role in various chemical reactions and interactions that contribute to the development of wine’s characteristics.

Lately, rosé wine is rapidly increasing its popularity worldwide. Short-time macerations with the red skin of the grapes cause the partial extraction of anthocyanins, which are responsible for the pinki-sh-salmon hue of rosé wines. However, the low quantity of tannins (antioxidants) and richness in phenolic acids, which can be easily oxidized into yellowish pigments, tend to predispose rosé wines to an undesirable browning. Although the use of SO₂ for the prevention of oxidation is highly extended, this practice is expected to be reduced. Therefore, the search for alternative oenological adjuvants that prevent the oxidation and browning of rosé wines is highly desired.

Dealcoholization of wine has gained increasing attention as consumer preferences shift toward lower-alcohol or
alcohol-free beverages. This process meets key demands, including health-conscious lifestyles, regulatory
compliance, and the expanding non-alcoholic market [1-3].

A large varietal collection including over 1700 varieties was maintained in Conegliano, ITA, since the 1950s. Phenological data on a subset of 400 grape varieties including wine grapes, table grapes, and raisins were acquired at bud break, flowering, veraison, and ripening since 1964. Despite the efforts in maintaining and acquiring data over such an extensive collection, the data set has varying degrees of missing cases depending on the variety and the year. This is ubiquitous in phenology datasets with significant size and length. In this work, we evaluated four state-of-the-art methods to estimate missing values in this phenological series: k-Nearest Neighbour (kNN), Multivariate Imputation by Chained Equations (mice), MissForest, and Bidirectional Recurrent Imputation for Time Series (BRITS). For each phenological stage, we evaluated the performance of the methods in two ways. 1) On the full dataset, we randomly hold-out 10% of the true values for use as a test set and repeated the process 1000 times (Monte Carlo cross-validation). 2) On a reduced and almost complete subset of varieties, we varied the percentage of missing values from 10% to 70% by random deletion. In all cases, we evaluated the performance on the original values using normalized root mean squared error. For the full dataset we also obtained performance statistics by variety and by year. MissForest provided average errors of 17% (3 days) at budbreak, 14% (4 days) at flowering, 14.5% (7 days) at veraison, and 17% (3 days) at maturity. We completed the imputations of the Conegliano dataset, one of the world’s most extensive and varied phenological time series and a steppingstone for future climate change studies in grapes. The dataset is now ready for further analysis, and a rigorous evaluation of imputation errors is included.