A machine learning and chemometrics assisted interpretation of spectroscopic data: a NMR-based metabolomics platform for the assessment of Brazilian propolis

In this work, a metabolomics dataset from 1H nuclear magnetic resonance spectroscopy of Brazilian propolis was analyzed using machine learning algorithms, including feature selection and classification methods. Partial least square-discriminant analysis (PLS-DA), random forest (RF), and wrapper meth...

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Detalhes bibliográficos
Autor principal: Maraschin, Marcelo (author)
Outros Autores: Somensi-Zeggio, A. (author), Oliveira, S. K. (author), Kuhnen, S. (author), Tomazzoli, M. M. (author), Zeri, A. C. M. (author), Carreira, Rafael (author), Rocha, Miguel (author)
Formato: conferencePaper
Idioma:eng
Publicado em: 2012
Assuntos:
Supervised classification techniques
Evolutionary algorithms
Random forest
PLS-DA
Wrapper methods
NMR-based metabolomics
Science & Technology
Texto completo:http://hdl.handle.net/1822/23883
País:Portugal
Oai:oai:repositorium.sdum.uminho.pt:1822/23883

Texto Completo

http://hdl.handle.net/1822/23883

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