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Repositorio de la Universidad de Oviedo. > Producción Bibliográfica de UniOvi: RECOPILA > Artículos >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10651/52911

Title: Artificial Neural Network and Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy to identify the chemical variables related to ripeness and variety classification of grapes for Protected Designation of Origin wine production
Author(s): Murru, Clarissa
Chimeno Trinchet, Christian
Díaz García, Marta Elena
Badía Laíño, Rosana
Fernández González, Alfonso
Issue date: 2019
Publisher version: http://dx.doi.org/10.1016/j.compag.2019.104922
Citation: Computers and Electronics in Agriculture, 164, p. 104922- (2019); doi:10.1016/j.compag.2019.104922
Format extent: p. 104922-
Embargo date: 2021-09
URI: http://hdl.handle.net/10651/52911
ISSN: 0168-1699
Sponsored: We would like to acknowledge the Ministerio de Economía yCompetitividad and European Regional Development Fund (MINECO/FEDER) by thefinancial support under the project MAT2015-66747-R.
Project id.: MINECO/FEDER/MAT2015-66747-R
Appears in Collections:Química Física y Analítica
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