文献类型：期刊文献

英文题名：Effect of water redistribution on the rheological behavior of MP-PSs and 3D printability of surimi: An investigation using LF-NMR

作者：Liu, Shucheng[1,2];Wang, Limeng[1];Pan, Yanmo[1];Sun, Qinxiu[1,2];Wei, Shuai[1,2];Xia, Qiuyu[1,2];Wang, Zefu[1,2];Han, Zongyuan[1,2];Liu, Yang[1,2]

机构：[1]Guangdong Ocean Univ, Coll Food Sci & Technol, Guangdong Prov Engn Technol Res Ctr Seafood, Guangdong Prov Engn Technol Res Ctr Prefabricated, Zhanjiang 524088, Peoples R China;[2]Dalian Polytech Univ, Collaborat Innovat Ctr Seafood Deep Proc, Dalian 116034, Peoples R China

年份：2025

卷号：208

外文期刊名：FOOD RESEARCH INTERNATIONAL

收录：SCI-EXPANDED(收录号：WOS:001453390000001)、、EI(收录号：20251218061906)、WOS

语种：英文

外文关键词：3D printing; Surimi; LF-NMR; Water migration; Rheological properties

外文摘要：This study explored the effect of interaction between starch and water on 3D printing adaptability of surimi, a topic that has received limited attention in previous research. The 3D printability of surimi-starch systems was examined under controlled moisture conditions, and notable enhancements in printing quality were observed when starch concentrations were adjusted to 8-12 % (with a fixed water content of 70 %) or reduced to 4 % with a corresponding increase in water to 80 %. To gain further insights, we investigated myofibrillar protein-potato starch blend system and manipulated the proportions of the constituents and water content, using LF-NMR technology to analyze the competitive adsorption of water molecules among the components. Our findings, validated through polymer blending theory, revealed that within a water content range of 60-80 % and a protein-to-starch mass ratio of 9/1 to 4/6, the system exhibited a bicontinuous phase structure characterized by pronounced competition for water molecules between protein and starch. This competitive adsorption facilitated the redistribution of water within the system, ultimately altering its rheological properties and influencing the 3D printability of the surimi. Moreover, by employing a full factorial design approach, we explored the influence of protein, starch, and water content on the rheological characteristics. To capture these variations, we constructed regression models that underscored the predictive probability of nonlinear surface regression fitting. Our work not only establishes a theoretical foundation for optimizing formulations of 3D-printed minced products but also has profound practical implications for broadening the application of 3D printing technology within the food industry.