A novel Terahertz spectral imaging technique for non-destructive quantitative analysis of alginate in Alaria esculenta Extracts

Alginate concentration is an important chemical indicator for evaluating the quality and extraction efficiency of marine biological polysaccharides. In this study, for the first time, a novel Terahertz (THz) spectral imaging technique was applied to the non-destructive quantitative analysis of alginate in Alaria esculenta extract samples . To evaluate the optimal modelling strategy, three machine learning algorithms were developed and compared, including partial least squares regression (PLSR), gaussian process regression (GPR), and a three-dimensional convolutional neural network (3D-CNN). Unlike traditional approaches relying solely on mean spectral features, the 3D-CNN was designed to capture combined spatial-spectral information from the THz imaging data . The results showed that the GPR model achieved the highest prediction performance with an R² of 0.908, while the 3D-CNN model also provided reliable predictions with an R² of 0.841 . This study demonstrates the feasibility and effectiveness of terahertz spectral imaging as a non-destructive method for accurately quantifying biopolymer concentrations in food and pharmaceutical materials, opening new possibilities for rapid and non-invasive quality assessment.