Analytical and Chemometric Investigation of Phenolic Content of South African Red Wines

Conflicting messages about the consumption of alcoholic beverages – including wine – continue to dominate the media, causing increasing uncertainty among consumers and health professionals.

The traceability of agricultural products, including grapes, is essential for ensuring food safety, quality control, and supply chain transparency. This paper investigates the implementation of GS1 standards in enhancing the traceability of grapes from grower to consumer.

Aims: Establishing an image of fine wine through the Geographical Indication (GI) system is of interest to the Australian wine sector. Beyond provenance, the sensory experience of fine wine is often linked to consumption with appropriate foods. For this purpose, studies were undertaken to understand consumer perceptions of what

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.

Il settore primario, ed in particolare quello agricolo, sta attraversando un periodo partico­larmente delicato. Sia gli aspetti della produzione che quelli della commercializzazione ven­gono infatti messi in discussione da nuovi indirizzi economici e tecnologici.