Indirect prediction of the 3D printability of polysaccharide gels using multiple machine learning (ML) models

In this paper, the capabilities of NIR spectroscopy and LF-NMR data were compared for rapidly predicting the rheological properties of polysaccharide gels and assessing their printability. Seven machine learning (ML) models were established for rheological property prediction based on partial least squares regression (PLSR), support vector regression (SVR), back propagation artificial neural network (BPANN), one-dimensional convolutional neural network (1D CNN), recurrent neural network (RNN), long short-term memory (LSTM), and Transformer. The results showed that among the seven models, the SVR, BPANN, and 1D CNN models based on NIR spectroscopy effectively predicted the rheological parameters of polysaccharide gels, with the highest R² in the prediction set reaching 0.9796 and the highest RPD reaching 7.0708. For most polysaccharide gels, using the LF-NMR relaxation time distribution curves provided better predictions of rheological properties than using transverse relaxation time and peak area. Among the seven models, the PLSR, SVR, 1D CNN, and Transformer models effectively predicted the rheological characteristics based on LF-NMR parameters, with the highest R² in the prediction set reaching 0.9869 and the highest RPD reaching 8.7220. This study successfully established a prediction system for the rheological behaviors and 3D printing performance of polysaccharide gels using NIR spectroscopy and LF-NMR data combined with ML methods, achieving an intelligent assessment of the 3D printing behavior of polysaccharide gels.