EnglishNear 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.
| Data di pubblicazione: | 2019 |
| Titolo: | Multi-target prediction of wheat flour quality parameters with near infrared spectroscopy |
| Autore/i: | Barbon Junior, S.; Mastelini, S. M.; Barbon, A. P. A. C.; Barbin, D. F.; Calvini, R.; Lopes, J. F.; Ulrici, A. |
| Autore/i UNIMORE: | |
| Digital Object Identifier (DOI): | 10.1016/j.inpa.2019.07.001 |
| Rivista: | |
| Pagina iniziale: | 1 |
| Pagina finale: | 13 |
| Codice identificativo Scopus: | 2-s2.0-85069547988 |
| Citazione: | Multi-target prediction of wheat flour quality parameters with near infrared spectroscopy / Barbon Junior, S.; Mastelini, S. M.; Barbon, A. P. A. C.; Barbin, D. F.; Calvini, R.; Lopes, J. F.; Ulrici, A.. - In: INFORMATION PROCESSING IN AGRICULTURE. - ISSN 2214-3173. - (2019), pp. 1-13. |
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