Optimization of NIR Calibration Models for Multiple Processes in the Sugar Industry
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Optimization of NIR Calibration Models for Multiple Processes in the Sugar IndustryDate
2016-10-14Citation
Ramírez-Morales I, Rivero D, Fernández-Blanco E, Pazos A. Optimization of NIR calibration models for multiple processes in the sugar industry. Chemometr Intell Lab Syst. 2016; 159:45-57
Abstract
[Abstract] The measurements of Near-Infrared (NIR) Spectroscopy, combined with data analysis techniques, are widely used for quality control in food production processes.
This paper presents a methodology to optimize the calibration models of NIR spectra in four different stages in a sugar factory. The models were designed for quality monitoring, particularly °Brix and Sucrose, both common parameters in the sugar industry.
A three stage optimization methodology, including pre-processing selection, feature selection and support vector machines regression metaparameters tuning, were applied to the spectral data divided by repeated cross-validation. Global models were optimized while endeavoring to ensure they are able to estimate both quality parameters with a single calibration, for the four steps of the process.
The proposed models improve the prediction for the test set (unseen data) compared to previously published models, resulting in a more accurate quality assessment of the intermediate products of the process in the sugar industry.
Keywords
NIR
Chemometrics
Calibration models
Machine learning
Support vector machines
Agro-industry
Chemometrics
Calibration models
Machine learning
Support vector machines
Agro-industry
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Atribución-NoComercial-SinDerivadas 3.0 España
ISSN
0169-7439