Predicting pectin performance strength using near‐infrared spectroscopic data: A comparative evaluation of 1‐D convolutional neural network, partial least squares, and ridge regression modeling

Abstract

Abstract We compare the application of different modeling strategies in order to predict physical properties of five different industrial pectin formulations based on near‐infrared spectral data. Methods from the chemometric toolbox, such as partial least squares regression (PLS1 and PLS2) and ridge regression, were employed and compared to the performance of a 1‐D convolutional neural network (CNN). The pectin formulations were modeled in two major scenarios, individually using local models, and jointly using global models, which resulted in better prediction performance of the 1‐D CNN.