Rapid and environmentally friendly wine analysis using Vis/NIR Spectroscopy and Support Vector Machine regression.

Rapid and environmentally friendly wine analysis using Vis/NIR Spectroscopy and Support Vector Machine regression.

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The aim of this study was to calibrate and validate models that can be used to determine quality parameters in red wine using Vis/NIR spectroscopy and the Least Squares Support Vector Machine (LS-SVM) regression.

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Bibliographic Details
Main Authors: MARQUES, E. J. N., BIASOTO, A. C. T., FREITAS, S. T. de, MENEZES, T. R. de, PEREIRA, G. E., NASSUR, R. de C. M. R., MEDEIROS, E. P. de
Other Authors: EMANUEL JOSÉ NASCIMENTO MARQUES, UFPE; ALINE TELLES BIASOTO MARQUES, CPATSA; SERGIO TONETTO DE FREITAS, CPATSA; THIAGO REIS DE MENEZES; GIULIANO ELIAS PEREIRA, CNPUV / CPATSA; RITA DE CÁSSIA MIRELA RESENDE NASSUR; EVERALDO PAULO DE MEDEIROS, CNPA.
Format: Parte de livro biblioteca
Language:English
eng
Published: 2015-12-09
Subjects:Qualidade do vinho, Vis NIR, Parâmetro de qualidade, Vitivinicultura, Wine., Quality parameters, Uva, Vinho,
Online Access:http://www.alice.cnptia.embrapa.br/alice/handle/doc/1031129
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Summary:The aim of this study was to calibrate and validate models that can be used to determine quality parameters in red wine using Vis/NIR spectroscopy and the Least Squares Support Vector Machine (LS-SVM) regression.

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