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dc.contributor.authorCardoso Schwindt, Virginia-
dc.contributor.authorColetto, Mauricio Miguel-
dc.contributor.authorDíaz, Mónica-
dc.contributor.authorPonzoni, Ignacio-
dc.date.accessioned2023-07-27T16:40:59Z-
dc.date.available2023-07-27T16:40:59Z-
dc.date.issued2022-07-02-
dc.identifier.citationCardoso Schwindt, V., Coletto, M. M., Diaz, M. F., & Ponzoni, I. (2023). Could QSOR modelling and machine learning techniques be useful to predict wine aroma?. Food and Bioprocess Technology, 16(1), 24-42.es_ES
dc.identifier.issn2213-7793es_ES
dc.identifier.urihttp://rid.unrn.edu.ar/handle/20.500.12049/10573-
dc.description.abstractFood informatics is having an increasing impact on the food industry and improving the quality of end products, as well as the efficiency of manufacturing processes. In the case of winemaking, a particular application of interest for food informatics is the sensory analysis of wines. This problem can benefit from the strong development that machine learning has achieved in recent decades. However, these data-driven techniques require accurate and sufficient information to generate models capable of predicting the sensory profile of wines. A review of the sensory analysis and volatile composition of wines is presented in this work, along with significant studies on the use of machine learning models to predict wine-related characteristics such as the antioxidant activity of polyphenols of wine and aroma compounds. In this sense, data from a sensory panel and analytical technology were gathered. This literature review reveals the lack of a homogeneous and sufficiently large database of sensory analysis related to the volatile composition of wines to develop machine learning models. However, among artificial intelligence approaches, the application of quantitative structure-odour relationship (QSOR) models is currently gaining importance. Recent studies show that it would be possible to predict quantitatively the sensory analysis of wines by QSOR models, using general volatile composition information. Therefore, the purpose of this review is to identify key aspects and guidelines for the development of this area.es_ES
dc.format.extentp. 24-42es_ES
dc.language.isoenes_ES
dc.publisherSrpingeres_ES
dc.relation.urihttps://www.springer.com/journal/11947es_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/-
dc.titleCould QSOR Modelling and Machine Learning Techniques Be Useful to Predict Wine Aroma?es_ES
dc.typeArticuloes_ES
dc.rights.licenseCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-ND 4.0)-
dc.description.filiationCardoso Schwindt, Virginia. Universidad Nacional de Río Negro (UNRN), Centro de Investigación y Transferencia Río Negro (CONICET- UNRN), Villa Regina, Río Negro, Argentinaes_ES
dc.description.filiationColetto, Mauricio Universidad Nacional de Río Negro (UNRN), Centro de Investigación y Transferencia Río Negro (CONICET- UNRN), Villa Regina, Río Negro, Argentinaes_ES
dc.description.filiationDíaz, Mónica. Planta Piloto de Ingeniería Química (PLAPIQUI), Universidad Nacional del Sur (UNS) -Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bahía Blanca, Argentinaes_ES
dc.description.filiationPonzoni, Ignacio. Instituto de Ciencias e Ingeniería de la Computación (ICIC), Universidad Nacional del Sur (UNS) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bahía Blanca, Argentinaes_ES
dc.subject.keywordMachine learninges_ES
dc.subject.keywordQSORes_ES
dc.subject.keywordVolatile compositiones_ES
dc.subject.keywordWine aromaes_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.subject.materiaCiencias Exactas y Naturaleses_ES
dc.origin.lugarDesarrolloUniversidad Nacional de Río Negroes_ES
dc.origin.lugarDesarrolloUniversidad Nacional de Sures_ES
dc.origin.lugarDesarrolloCentro de Investigación y Transferencia Río Negroes_ES
dc.origin.lugarDesarrolloPlanta Piloto de Ingeniería Química (PLAPIQUI)es_ES
dc.origin.lugarDesarrolloInstituto de Ciencias e Ingeniería de la Computación (ICIC)es_ES
dc.relation.journalissue16 (1)es_ES
dc.description.reviewtruees_ES
dc.description.resumen-es_ES
dc.relation.journalTitleFood and Bioprocess Technologyes_ES
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