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Type: Artigo
Title: Multi-target prediction of wheat flour quality parameters with near infrared spectroscopy
Author: Barbon Junior, S.
Mastelini, S.M.
Barbon, A.P.A.C.
Barbin, D.F.
Calvini, R.
Lopes, J.F.
Ulrici, A.
Abstract: Near Infrared (NIR) spectroscopy is an analytical technology widely used for the non-destructive characterisation of organic samples, considering both qualitative and quantitative attributes. In the present study, the combination of Multi-target (MT) prediction approaches and Machine Learning algorithms has been evaluated as an effective strategy to improve prediction performances of NIR data from wheat flour samples. Three different Multi-target approaches have been tested: Multi-target Regressor Stacking (MTRS), Ensemble of Regressor Chains (ERC) and Deep Structure for Tracking Asynchronous Regressor Stack (DSTARS). Each one of these techniques has been tested with different regression methods: Support Vector Machine (SVM), Random Forest (RF) and Linear Regression (LR), on a dataset composed of NIR spectra of bread wheat flours for the prediction of quality-related parameters. By combining all MT techniques and predictors, we obtained an improvement up to 7% in predictive performance, compared with the corresponding Single-target (ST) approaches. The results support the potential advantage of MT techniques over ST techniques for analysing NIR spectra
Subject: Espectroscopia de infravermelho
Country: China
Editor: China Agricultural University
Rights: Fechado
Identifier DOI: 10.1016/j.inpa.2019.07.001
Date Issue: 2019
Appears in Collections:FEA - Artigos e Outros Documentos

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