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Suitability of low‐field nuclear magnetic resonance (LF‐NMR) combining with back propagation artificial neural network (BP‐ANN) to predict printability of polysaccharide hydrogels 3D printing
Author(s) -
Guo Chaofan,
Zhang Min,
Chen Huizhi
Publication year - 2021
Publication title -
international journal of food science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.831
H-Index - 96
eISSN - 1365-2621
pISSN - 0950-5423
DOI - 10.1111/ijfs.14844
Subject(s) - self healing hydrogels , rheology , polysaccharide , piston (optics) , materials science , artificial neural network , viscosity , chemistry , chemical engineering , analytical chemistry (journal) , composite material , chromatography , polymer chemistry , artificial intelligence , computer science , organic chemistry , physics , optics , engineering , wavefront
In this work, a fast and nondestructive method for predicting the printing performance of polysaccharide hydrogels using LF‐NMR combined with BP‐ANN model was established in extrusion‐based food 3D printing process.