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Remote Sens. 2018, 10(2), 312; https://doi.org/10.3390/rs10020312

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

1
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
2
WM&B—Laboratory of Wine Microbiology & Biotechnology, Department of Biology and Environment, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
3
BioISI-Biosystems & Integrative Sciences Institute, Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016 Lisbon, Portugal
4
Departamento de Engenharias, Escola de Ciências e Tecnologia, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
*
Author to whom correspondence should be addressed.
Received: 12 December 2017 / Revised: 12 February 2018 / Accepted: 16 February 2018 / Published: 18 February 2018
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Abstract

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 of whole berries and the spectrum of each sample was collected during ripening using hyperspectral imaging in the range of 380–1028 nm. Touriga Franca (TF) variety samples were collected for the 2012–2015 vintages, and Touriga Nacional (TN) and Tinta Barroca (TB) variety samples were collected for the 2013 vintage. These TF vintages were independently used to train, validate and test the SVR methodology; different combinations of TF vintages were used to train and test each model to assess the performance differences under wider and more variable datasets; the varieties that were not employed in the model training and validation (TB and TN) were used to test the generalization ability of the SVR approach. Each case was tested using an external independent set (with data not included in the model training or validation steps). The best R2 results obtained with varieties and vintages not employed in the model’s training step were 0.89, 0.81 and 0.90, with RMSE values of 35.6 mg·L−1, 0.25 and 3.19 °Brix, for anthocyanin concentration, pH index and sugar content, respectively. The present results indicate a good overall performance for all cases, improving the state-of-the-art results for external test sets, and suggesting that a robust model, with a generalization capacity over different varieties and harvest years may be obtainable without further training, which makes this a very competitive approach when compared to the models from other authors, since it makes the problem significantly simpler and more cost-effective. View Full-Text
Keywords: generalization; machine learning; dimensionality reduction; hyperspectral reflectance imaging; sugar content; anthocyanin concentration; pH index; grape berries generalization; machine learning; dimensionality reduction; hyperspectral reflectance imaging; sugar content; anthocyanin concentration; pH index; grape berries
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Silva, R.; Gomes, V.; Mendes-Faia, A.; Melo-Pinto, P. Using Support Vector Regression and Hyperspectral Imaging for the Prediction of Oenological Parameters on Different Vintages and Varieties of Wine Grape Berries. Remote Sens. 2018, 10, 312.

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