Near infrared spectroscopy (NIR) is employed in the agrifood industry to recover information about the chemical composition and quality parameters of different products in a non-invasive, fast and accurate way. However, the relationships between the spectral measurements and the quality parameters are not trivial, and different machine learning methods are employed for modeling them. In this post, an overview of the commonly employed calibration methods based on machine learning will be provided.
