NIR based sensometric approach for consumer preference evaluation

Disease resistant grapevine varieties (PIWI) are increasingly important for sustainable wine production, yet the impact of different yeasts on their wine profiles remains poorly studied. In this study, nine white interspecies varieties (i.e., caladris blanc, fleurtai, hibernal, johanniter, muscaris, sauvignon kretos, soreli, souvignier gris, and voltis) grown at the faculty of agriculture, university of Zagreb (Croatia) were vinified with three different saccharomyces cerevisiae yeasts (control strain, zymaflore x5, and zymaflore xarom).

Oenococcus oeni is the main Lactic Acid Bacteria responsible for malolactic fermentation, converting malic acid into lactic acid and carbon dioxide in wines. Following the alcoholic fermentation, this second fermentation ensures a deacidification and remains essential for the release of aromatic notes and the improvement of microbial stability in many wines. Nevertheless, wine is a harsh environment for microbial growth, especially because of its low pH (between 2.9 and 3.6 depending on the type of wine) and nutrient deficiency. In order to maintain homeostasis and ensure viability, O. oeni possesses different cellular mechanisms including organic acid metabolisms which represent also the major pathway to synthetize energy in wine.

Grapevines (Vitis vinifera L.) are plants of great economic importance, with over 80% of grape production dedicated to wine production, yielding more than 258 million hectoliters annually [1].

Modern grapevine breeding programs have overcome many challenges using genomic selection, which has allowed breeders to make targeted selections at earlier stages in the breeding process. However, the cost of genetic testing may present a burden for some programs, and markers often struggle to accurately predict quantitative traits. Recent advances in high throughput, high-dimensional data have provoked investigation into the use of high-dimensional phenomics as a low-cost addition to the grape breeder’s toolkit that may offer advantages in predicting quantitative traits. High-dimensional secondary trait (HDST) data has been employed in annual crops for prediction of agriculturally important traits such as yield.

This paper presents is the create, the study and amplographic
description the new white Greek table variety grapes “Aztec”, created in 2013 by breeder P. Zamanidis at
the Athens vineyard of the Institute of Olive, Subtropical Plants and Vine.