Using Support Vector Regression and Hyperspectral Imaging for the Prediction of Oenological Parameters on Different Vintages and Varieties of Wine Grape Berries

Using Support Vector Regression and Hyperspectral Imaging for the Prediction of Oenological Parameters on Different Vintages and Varieties of Wine Grape Berries

The performance of a support vector regression (SVR) model with a Gaussian radial basis kernel to predict anthocyanin concentration, pH index and sugar content in whole grape berries, using spectroscopic measurements obtained in reflectance mode, was evaluated. Each sample contained a small number o...

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Bibliographic Details
Main Authors: Rui Silva, Véronique Gomes, Arlete Mendes-Faia, Pedro Melo-Pinto
Format: Article
Language:English
Published: MDPI AG 2018-02-01
Series:Remote Sensing
Subjects:
generalization
machine learning
dimensionality reduction
hyperspectral reflectance imaging
sugar content
anthocyanin concentration
pH index
grape berries
Online Access:http://www.mdpi.com/2072-4292/10/2/312

Internet

http://www.mdpi.com/2072-4292/10/2/312

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