A White-Box Workflow for the Prediction of Food Content From Near-Infrared Data Based on Fourier-Transformation

In this work we propose an white-box workflow for regression related tasks based on near-infrared data in the context of machine learning. The workflow consists of data pre-processing and dimension reduction through (inverse) Fourier-transformation and low-pass filtering of the spectra. Subsequently, a machine learning model shall be applied to predict food contents of the spectra and conclude the workflow. To yield recommendations, we test various standard models, as well as the iterative method of partial-least-squares and the recently proposed Regression (Sensitive) Neural Gas. We shall not only investigate performance aspects, but also discuss theoretical concepts, i.e. interpretability options offered by each model. We show that our pre-processing and reduction approach is able to achieve good results even for signal-to-noise ratio dependent models and that the Regression (Sensitive) Neural Gas offers rich options to gain insights into the data and the model results.