{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T19:28:11Z","timestamp":1768678091990,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,7,19]],"date-time":"2021-07-19T00:00:00Z","timestamp":1626652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["PD\/BD\/128272\/2017"],"award-info":[{"award-number":["PD\/BD\/128272\/2017"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Processes"],"abstract":"<jats:p>The high quality of Port wine is the result of a sequence of winemaking operations, such as harvesting, maceration, fermentation, extraction and aging. These stages require proper monitoring and control, in order to consistently achieve the desired wine properties. The present work focuses on the harvesting stage, where the sugar content of grapes plays a key role as one of the critical maturity parameters. Our approach makes use of hyperspectral imaging technology to rapidly extract information from wine grape berries; the collected spectra are fed to machine learning algorithms that produce estimates of the sugar level. A consistent predictive capability is important for establishing the harvest date, as well as to select the best grapes to produce specific high-quality wines. We compared four different machine learning methods (including deep learning), assessing their generalization capacity for different vintages and varieties not included in the training process. Ridge regression, partial least squares, neural networks and convolutional neural networks were the methods considered to conduct this comparison. The results show that the estimated models can successfully predict the sugar content from hyperspectral data, with the convolutional neural network outperforming the other methods.<\/jats:p>","DOI":"10.3390\/pr9071241","type":"journal-article","created":{"date-parts":[[2021,7,19]],"date-time":"2021-07-19T04:55:40Z","timestamp":1626670540000},"page":"1241","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Prediction of Sugar Content in Port Wine Vintage Grapes Using Machine Learning and Hyperspectral Imaging"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1281-4760","authenticated-orcid":false,"given":"V\u00e9ronique","family":"Gomes","sequence":"first","affiliation":[{"name":"CITAB\u2014Centre for the Research and Technology of Agro-Environmental and Biological Sciences, Inov4Agro\u2014Institute for Innovation, Capacity Building and Sustainability of Agri-food Production, Universidade de Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4997-8865","authenticated-orcid":false,"given":"Marco S.","family":"Reis","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering, University of Coimbra, CIEPQPF, Rua S\u00edlvio Lima, P\u00f3lo II\u2014Pinhal de Marrocos, 3030-790 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2589-9281","authenticated-orcid":false,"given":"Francisco","family":"Rovira-M\u00e1s","sequence":"additional","affiliation":[{"name":"Agricultural Robotics Laboratory, Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Valencia, Spain"}]},{"given":"Ana","family":"Mendes-Ferreira","sequence":"additional","affiliation":[{"name":"CITAB\u2014Centre for the Research and Technology of Agro-Environmental and Biological Sciences, Inov4Agro\u2014Institute for Innovation, Capacity Building and Sustainability of Agri-food Production, Universidade de Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"WM&B\u2014Laboratory of Wine Microbiology & Biotechnology, Department of Biology and Environment, Universidade de Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"BioISI\u2014Biosystems & Integrative Sciences Institute, Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016 Lisbon, Portugal"}]},{"given":"Pedro","family":"Melo-Pinto","sequence":"additional","affiliation":[{"name":"CITAB\u2014Centre for the Research and Technology of Agro-Environmental and Biological Sciences, Inov4Agro\u2014Institute for Innovation, Capacity Building and Sustainability of Agri-food Production, Universidade de Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Departamento de Engenharias, Escola de Ci\u00eancias e Tecnologia, Universidade de Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.compag.2015.05.013","article-title":"Brix, pH and anthocyanin content determination in whole Port wine grape berries by hyperspectral imaging and neural networks","volume":"115","author":"Fernandes","year":"2015","journal-title":"Comput. 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