Application of Convolutional Neural Network Model Based on Combined NIR-Raman Spectra in Feed Composition Analysis

Abstract

AbstractThe maturity of machine learning algorithms such as convolutional neural networks have made it possible to use them in feed spectroscopy. This paper compares convolutional neural network (CNN) and multivariate scattering processing support vector machine (MSC-SVM) modeling, including NIR spectroscopy, Raman spectroscopy modeling, and NIR-Raman spectroscopy modeling, to predict the protein content in feed. The experiments were based on measured NIR (wave number 4000–12000 wavenumbers) and Raman spectral (500–3000 wavenumbers) data due to the complementary roles of NIR and Raman spectroscopy techniques. The organic combination of the two spectral data adds useful information in model building. The CNN and MSC-SVM models based on NIR-Raman spectra, the predictions are better than single spectra.KeywordsFeedConvolutional neural networkSupport vector machineRaman spectroscopy