Data fusion approaches for the characterization of musts and wines based on biogenic amine and elemental composition

Samples from various winemaking stages of the production of sparkling wines using different grape varieties were characterized based on the profile of biogenic amines (BAs) and the elemental composition. Liquid chromatography with fluorescence detection (HPLC-FLD) combined with precolumn derivatizat...

ver descrição completa

Detalhes bibliográficos
Autores: Mir-Cerdà, Aina, Granell Geli, Biel, Izquierdo-Llopart, Anais, Sahuquillo Estrugo, Àngels, López Sánchez, José Fermín, Saurina, Javier, Sentellas, Sonia
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2022
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositório:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/191926
Acesso em linha:https://hdl.handle.net/2445/191926
Access Level:Acceso aberto
Palavra-chave:Anàlisi del vi
Vi
Amines biogèniques
Analysis of wine
Wine
Biogenic amines
Descrição
Resumo:Samples from various winemaking stages of the production of sparkling wines using different grape varieties were characterized based on the profile of biogenic amines (BAs) and the elemental composition. Liquid chromatography with fluorescence detection (HPLC-FLD) combined with precolumn derivatization with dansyl chloride was used to quantify BAs, while inductively coupled plasma (ICP) techniques were applied to determine a wide range of elements. Musts, base wines, and sparkling wines were analyzed accordingly, and the resulting data were subjected to further chemometric studies to try to extract information on oenological practices, product quality, and varieties. Although good descriptive models were obtained when considering each type of data separately, the performance of data fusion approaches was assessed as well. In this regard, low-level and mid-level approaches were evaluated, and from the results, it was concluded that more comprehensive models can be obtained when joining data of different natures.