title: Characterization of neural network generalization in the determination of pH and anthocyanin content of wine grape in new vintages and varieties.
abstract: The generalization ability of hyperspectral imaging combined with neural networks (NN) in estimating pH and anthocyanin content during ripening was evaluated for vintages and varieties not employed in the NN creation. A NN, from a previously published work, trained with grape samples of Touriga Franca (TF) variety harvested in 2012 was tested with TF from 2013 and two new varieties, Touriga Nacional (TN) and Tinta Barroca (TB) from 2013. Each sample contained a small number of whole berries. The present work results suggest that, under certain conditions, it might be possible for the NN to provide for new vintages and varieties results comparable to those of the vintages and varieties employed in the NN training. For pH, the results are state-of-the-art for the new vintage and varieties tested. For anthocyanin, generalization is bad for TB from 2013 but presents state-of-the-art absolute percentage error for TF and TN from 2013.
authors.affiliation: CITAB-Centre for the Research and Technology of Agro-Environmental and Biological Sciences, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal.
authors.forename: Véronique
authors.initials: V
authors.lastname: Gomes
authors.affiliation: Center of Intelligent Systems, IDMEC/LAETA, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal; INOV - INESC Inovação, Rua Alves Redol, 9, 1000-029 Lisboa, Portugal.
authors.forename: Armando
authors.initials: A
authors.lastname: Fernandes
authors.affiliation: BioISI - Biosystems & Integrative Sciences Institute, University of Lisboa, Faculty of Sciences, Campo Grande, Lisboa, Portugal; Departamento de Genética e Biotecnologia, Escola das Ciências da Vida e Ambiente, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal.
authors.forename: Paula
authors.initials: P
authors.lastname: Martins-Lopes
authors.affiliation: BioISI - Biosystems & Integrative Sciences Institute, University of Lisboa, Faculty of Sciences, Campo Grande, Lisboa, Portugal.
authors.forename: Leonor
authors.initials: L
authors.lastname: Pereira
authors.affiliation: Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal; BioISI - Biosystems & Integrative Sciences Institute, University of Lisboa, Faculty of Sciences, Campo Grande, Lisboa, Portugal.
authors.forename: Arlete
authors.initials: A
authors.lastname: Mendes Faia
authors.affiliation: CITAB-Centre for the Research and Technology of Agro-Environmental and Biological Sciences, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal; Departamento de Engenharias, Escola de Ciências e Tecnologia, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal. Electronic address: pmelo@utad.pt.
authors.forename: Pedro
authors.initials: P
authors.lastname: Melo-Pinto
mesh_terms: D000872:Anthocyanins / Q000032:analysis*; D005504:Food Analysis; D006863:Hydrogen-Ion Concentration; D016571:Neural Networks, Computer; D027843:Vitis / Q000737:chemistry*; D014920:Wine / Q000032:analysis*
chemical_list: D000872:Anthocyanins
keywords: Grape berries; Hyperspectral imaging; Neural networks; Prediction; Wine quality