Handheld near infrared spectrometer and machine learning methods applied to the monitoring of multiple process stages in industrial sugar production.

This work aimed to evaluate the performance of a handheld NIR spectrometer in developing calibration models to quantify brix and pol at various stages of an industrial sugar production process. Because of sample variability, collected over two harvesting seasons, NIR measurements were acquired either in transmittance or diffuse reflectance. For modelling purpose, partial least squares (PLS), also combined with variable selection techniques, and support vector machine regression (SVR) were investigated.