Geologic and geomorphologic features applied for identification of wine terroir units by digital image processing, spectroradiometric and GIS techniques in Encruzilhada do Sul, RS, Brazil

Implementing artificial intelligence (AI) in viticulture and enology is a rapidly growing field of research with an essential number of potential practical applications.

Environmental awareness is globally rising among scientific community, politicians and general public. Biodiversity conservation is becoming a concern for farmers

Enhancing the mineral wine profile: from authentication to identification by artificial intelligence for enhanced security. Analysis of a wine’s mineral concentration profile provides a distinctive fingerprint for each cuvée. Unlike organic profiles, this identification signature remains stable over time and can be deciphered using direct analysis by inductively coupled mass spectrometry (icp-ms).

Tannins are a heterogenous class of polymeric phenolics found in grapes, oak barrels and wine. In red wine tannins are primarily responsible for astringency, though they also have an important role in reacting with and stabilizing pigments. There are numerous sub-classes of tannins found in wine but they all share structural heterogeneity within each sub-class, with varied polymer composition, configuration and length.

Numerous methodologies exist for the quantification of tannins, however, protein precipitation using bovine serum albumin has proved itself useful due to its strong correlation to the sensory perception of astringency and the basic instruments required for the method. Though the method can yield valuable insights into tannin composition, it cannot be automated easily and necessitates well-trained personnel.

The aim was to study the impact of structural features in the polysaccharide moiety of mannoproteins on their interaction with polyphenols and the formation of colloidal aggregates.