Terroir 1996 banner
IVES 9 IVES Conference Series 9 Caractérisation des productions vitivinicoles des terroirs du Barolo (Piemonte, Italie)

Caractérisation des productions vitivinicoles des terroirs du Barolo (Piemonte, Italie)

Abstract

Le projet “Caractérisation des productions vitivinicoles du Barolo” est né par la volonté de la Région Piémont de créer une équipe multidisciplinaire de recherche pour l’individuation des différences du vin Barolo en relation avec le terroir, dans la perspective d’arriver à l’identification de sous-zones à l’intérieur de la zone de production du Barolo A.O.C .. La description de la méthodologie utilisée se trouve dans les acts du symposium de Siena 1998. L’expérience fait partie du Programme de recherche Viticolture-Oenologie mis en oeuvre par le Ministère Politiques Agricoles et la Région Piémont.

DOI:

Publication date: February 24, 2022

Issue: Terroir 2000

Type: Article

Authors

M. Soster (1), A. Cellino (1), F. Spanna (1), R. Salandin (2), M. Piazzi (2), I. Boni (2), F. Mannini (3),
N. Argamante (3), A. Schubert (4), C. Lovisolo (4), M. Ubigli (5), V. Gerbi (6), G. Zeppa (6), L. Rolle (6), M. Gily (7)

(1) Regione Piemonte, Assessorato Agricoltura – Corso Stati Uniti,21 – 10128 – TORINO
(2) Istituto per le Piante da Legno e l’Ambiente, Corso Casale 476 – 10132 TORINO
(3) Centro Miglioramento genetico e Biologia della Vite del CNR, Via Leonardo da Vinci,44 – 10095 – GRUGLIASCO (TO)
(4) Dipartimento Colture Arboree – Università di Torino, Via Leonardo da Vinci, 44 – 10095 GRUGLIASCO (TO)
(5) Istituto Sperimentale per l’Enologia – MIPAF, Via Pietro Micca, 35 – 14100 ASTI
(6) Dipartimento Valorizzazione delle Produzioni e Risorse Agroforestali- Università di Torino, Via Leonardo da Vinci, 44 – 10095 GRUGLIASCO (TO)
(7) Associazione produttori Vignaioli Piemontesi, Via Alba, 15 – 12051 CASTAGNITO (CN)

Tags

IVES Conference Series | Terroir 2000

Citation

Related articles…

Thermal risk assessment for viticulture using monthly temperature data

Temperature extremes affect grapevine physiology, as well as grape quality and production. In most grape growing regions, frost or heat wave events are rare and as such conducting a risk analysis using robust statistics makes the use of long term daily data necessary.

Identification of QTLS for sunburn resilience in grapevine berries

Context and purpose of the study – Grape sunburn is an abiotic stress response triggered by high temperatures.

New biological tools to control and secure malolactic fermentation in high pH wines

Originally, the role of the malolactic fermentation (MLF) was simply to improve the microbial stability of wine via biological deacidification. However, there is an accumulation of evidence to support the fact that lactic acid bacteria (LAB) also contribute positively to the taste and aroma of wine. Many different LAB enter into grape juice and wine from the surface of grape berries, cluster stems, vine leaves, soil and winery equipment. Due to the highly selective environment of juices and wine, only a few types of LAB are able to grow.

Prediction of sauvignon blanc quality gradings with static headspace−gas chromatography−ion mobility spectrometry (SHS−GC−IMS) and machine learning

The main goal of the current study is the development of a cost-effective and easy-to-use method suitable for use in the laboratory of commercial wineries to analyze wine aroma. Additionally, this study attempted to establish a prediction model for wine quality gradings based on their aroma, which could reveal the important aroma compounds that correlate well with different grades of perceived quality METHODS: Parameters of the SHS−GC−IMS instrument were first optimized to acquire the most desirable chromatographic resolution and signal intensities. Method stability was then exhibited by repeatability and reproducibility. Subsequently, compound identification was conducted. After method development, a total of 143 end-ferment wine samples of three different quality gradings from vintage 2020 were analyzed with the SHS−GC−IMS instrument. Six machine learning methods were employed to process the results and construct a quality prediction model. Techniques that aim to explain the model to extract useful insights were also applied.

Development of breeding of PIWI varieties in the Czech Republic

Context and purpose of the study. The Czech Republic is one of the most important grape growers of PIWI varieties in the Europe, as the total area planted with PIWI varieties is almost 1000 ha.