Resumen: Wine aroma is the result of complex interactions between volatile compounds and non-volatile ones and individual perception phenomenon. In this work, an aroma network approach, that links volatile composition (chromatographic data) with its corresponding aroma descriptors was used to explain the wine aroma properties. This concept was applied to six monovarietal wines from Bairrada Appellation (Portugal) and used as a case study. A comprehensive determination of the wines’ volatile composition was done (71 variables, i.e., volatile components), establishing a workflow that combines extraction techniques and gas chromatographic analysis. Then, a bipartite network-based approach consisting of two different nodes was built, one with 19 aroma descriptors, and the other with the corresponding volatile compound(s). To construct the aroma networks, the odor active values were calculated for each determined compound and combined with the bipartite network. Finally, the aroma network of each wine was compared with sensory descriptive analysis. The analysis of the specific aroma network of each wine revealed that Sauvignon Blanc and Arinto white wines present higher fruity (esters) and sweet notes (esters and C13 norisoprenoids) than Bical wine. Sauvignon Blanc also exhibits higher toasted aromas (thiols) while Arinto and Bical wines exhibit higher flowery (C13 norisoprenoids) and herbaceous notes (thiols), respectively. For red wines, sweet fruit aromas are the most abundant, especially for Touriga Nacional. Castelão and Touriga Nacional wines also present toasted aromas (thiols). Baga and Castelão wines also exhibit fusel/alcohol notes (alcohols). The proposed approach establishes a chemical aroma fingerprint (aroma ID) for each type of wine, which may be further used to estimate wine aroma characteristics by projection of the volatile composition on the aroma network. Idioma: Inglés DOI: 10.3390/molecules25020272 Año: 2020 Publicado en: Molecules 25, 2 (2020), 272 [17 pp] ISSN: 1420-3049 Factor impacto JCR: 4.411 (2020) Categ. JCR: CHEMISTRY, MULTIDISCIPLINARY rank: 63 / 178 = 0.354 (2020) - Q2 - T2 Categ. JCR: BIOCHEMISTRY & MOLECULAR BIOLOGY rank: 116 / 297 = 0.391 (2020) - Q2 - T2 Factor impacto SCIMAGO: 0.782 - Analytical Chemistry (Q1) - Chemistry (miscellaneous) (Q1) - Drug Discovery (Q1) - Physical and Theoretical Chemistry (Q1) - Molecular Medicine (Q1) - Organic Chemistry (Q1) - Pharmaceutical Science (Q1) - Medicine (miscellaneous) (Q1)