Ramírez-Morales, IvánRivero, DanielFernández-Blanco, EnriquePazos, A.2017-04-242016-10-14Ramírez-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; 159:45-570169-7439http://hdl.handle.net/2183/18419[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.engAtribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/NIRChemometricsCalibration modelsMachine learningSupport vector machinesAgro-industryjournal articleopen access