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

2018 ◽  
Vol 10 (2) ◽  
pp. 312 ◽  
Author(s):  
Rui Silva ◽  
Véronique Gomes ◽  
Arlete Mendes-Faia ◽  
Pedro Melo-Pinto
Keyword(s):  
Support Vector Regression ◽  
Hyperspectral Imaging ◽  
Support Vector ◽  
Wine Grape ◽  
Grape Berries

Brix, pH and anthocyanin content determination in whole Port wine grape berries by hyperspectral imaging and neural networks

2015 ◽  
Vol 115 ◽  
pp. 88-96 ◽  
Author(s):  
Armando M. Fernandes ◽  
Camilo Franco ◽  
Ana Mendes-Ferreira ◽  
Arlete Mendes-Faia ◽  
Pedro Leal da Costa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Hyperspectral Imaging ◽  
Anthocyanin Content ◽  
Wine Grape ◽  
Port Wine ◽  
Grape Berries ◽  
Content Determination

Identification of Wine Grape Varieties Based on Near-infrared Hyperspectral Imaging

2019 ◽  
Vol 35 (6) ◽  
pp. 959-967
Author(s):  
Xu Liu ◽  
Enyu Zhang
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Spectral Data ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Variety Identification ◽  
Support Vector ◽  
Wine Grape ◽  
Full Spectrum ◽  
Grape Varieties

Abstract. Wine grape variety is one of the main determinants of wine quality. The objective of this study is to explore the feasibility of using hyperspectral imaging (HSI) to identify six red and six white wine grape cultivars during the ripening period. Abnormal spectral data were removed by the Mahalanobis distance, and six different methods were employed to preprocess the spectral data. Next, the effective wavelengths for the classification of grape varieties were selected using principal component analysis (PCA) loadings to improve the HSI processing speed. Finally, three methods were applied to classify grape samples: a support vector machine (SVM), a random forest (RF), and an AdaBoost model. The results indicated that the model established by Savitzky-Golay (S-G) Filter + PCA + SVM achieves the best classification result. The average calibration and validation accuracy for red grapes reached 93.06% and 90.01%, respectively, and for white grapes, they reached 83.77% and 81.09%, respectively, which are slightly lower than those achieved by the full-spectrum model. This study revealed that hyperspectral imaging has great potential for rapid variety discrimination of different wine grapes. Keywords: Hyperspectral imaging, Random forest, Support vector machine, Variety identification, Wine grape.

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection

Data Analytics to Help Improve Hydraulic Fracture Geometry Prediction Using Support Vector Regression: Case Study

2020 ◽  
Author(s):  
Avinash Wesley ◽  
Bharat Mantha ◽  
Ajay Rajeev ◽  
Aimee Taylor ◽  
Mohit Dholi ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Hydraulic Fracture ◽  
Data Analytics ◽  
Support Vector ◽  
Fracture Geometry
Sign in / Sign up

Export Citation Format

Share Document