LF-NMR as a tool for predicting the 3D printability of surimi-starch systems

Yang Liu; Qinxiu Sun; Shuai Wei; Qiuyu Xia; Yanmo Pan; Shucheng Liu; Hongwu Ji; Chujin Deng; Jiming Hao

doi:10.1016/j.foodchem.2021.131727

LF-NMR as a tool for predicting the 3D printability of surimi-starch systems

Food Chem. 2022 Apr 16:374:131727. doi: 10.1016/j.foodchem.2021.131727. Epub 2021 Dec 1.

Authors

Yang Liu¹, Qinxiu Sun², Shuai Wei³, Qiuyu Xia³, Yanmo Pan¹, Shucheng Liu⁴, Hongwu Ji³, Chujin Deng¹, Jiming Hao¹

Affiliations

¹ College of Food Science and Technology, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Product Processing and Safety, Guangdong Province Engineering Laboratory for Marine Biological Products, Guangdong Provincial Engineering Technology Research Center of Seafood, Key Laboratory of Advanced Processing of Aquatic Product of Guangdong Higher Education Institution, Zhanjiang 524088, China.
² College of Food Science and Technology, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Product Processing and Safety, Guangdong Province Engineering Laboratory for Marine Biological Products, Guangdong Provincial Engineering Technology Research Center of Seafood, Key Laboratory of Advanced Processing of Aquatic Product of Guangdong Higher Education Institution, Zhanjiang 524088, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Zhanjiang 524088, China; Collaborative Innovation Center of Seafood Deep Processing, Dalian Polytechnic University, Dalian 116034, China. Electronic address: [email protected].
³ College of Food Science and Technology, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Product Processing and Safety, Guangdong Province Engineering Laboratory for Marine Biological Products, Guangdong Provincial Engineering Technology Research Center of Seafood, Key Laboratory of Advanced Processing of Aquatic Product of Guangdong Higher Education Institution, Zhanjiang 524088, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Zhanjiang 524088, China; Collaborative Innovation Center of Seafood Deep Processing, Dalian Polytechnic University, Dalian 116034, China.
⁴ College of Food Science and Technology, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Product Processing and Safety, Guangdong Province Engineering Laboratory for Marine Biological Products, Guangdong Provincial Engineering Technology Research Center of Seafood, Key Laboratory of Advanced Processing of Aquatic Product of Guangdong Higher Education Institution, Zhanjiang 524088, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Zhanjiang 524088, China; Collaborative Innovation Center of Seafood Deep Processing, Dalian Polytechnic University, Dalian 116034, China. Electronic address: [email protected].

PMID: 34915372
DOI: 10.1016/j.foodchem.2021.131727

Abstract

In this study, surimi from golden pompanos was mixed with starch to form a surimi-starch system. The water properties, rheological properties, and three-dimensional (3D) printability of the surimi-starch were measured. Cluster analysis results showed that the 3D printability was closely related to the type and addition content of starch, and the water and rheological properties. The low-field nuclear magnetic resonance (LF-NMR) parameters were used to predict 3D printability using polynomial regression models. The correlation coefficients (R²) for 3D printing accuracy and stability were 0.88 and 0.93, and the root mean square error (RMSE) values were 0.20% and 4.59%, respectively. In the verification test, the R² for the two models were 0.85 and 0.89, and the RMSE values were 0.20% and 1.06%, respectively. The nonlinear surface regression fitting exhibited superior predictive performance. Therefore, LF-NMR is a good non-destructive tool for quickly and accurately predicting the 3D printability of the surimi-starch systems.

Keywords: 3D printing; LF-NMR; Rheological properties; Surimi-starch system; Water distribution.

MeSH terms

Animals
Fishes*
Magnetic Resonance Spectroscopy
Rheology
Starch*
Water

Substances

Water
Starch