A novel Terahertz spectral imaging technique for non-destructive quantitative analysis of alginate in Alaria esculenta Extracts
Year: 2026
Type: article
Abstract: 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.
Source: Applied Food Research
Authors: Qingxia Li, Weihao Meng, Yuqiao Ren, Marco García-Vaquero, Da-Wen Sun
Institutions: University College Dublin, National University of Ireland
Language: English
Subfield: Analytical Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,050
OpenAlex ID: W7154287722