Optimization of NIR calibration models for multiple processes in the sugar industry

•Single calibration models for multiple process in the sugar industry were developed.•Feature selection techniques and Support Vector Regression were used to develop the models.•A total of 1797 NIR spectra ranging between 400.0 nm and 1888 nm, were analyzed.•The proposed models for Brix and Sucrose performed better in test set data compared to those published.•A R-squared of 0.99 and a RMSE of 0.305 were achieved for °Brix model. A R-squared of 0.99, RMSE of 0.486 for were achieved for Sucrose model.

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.