利用黄原胶和魔芋胶优化基于刚毛藻(Kai)的3D食品墨水配方以用于吞咽障碍友好型应用
Formulation Optimization of Cladophora glomerata (Kai)-Based 3D Food Inks Using Xanthan Gum and Konjac Gum for Dysphagia-Friendly Applications.
文献信息
| PMID | 42940233 |
|---|---|
| 原文 | 在 PubMed 查看原文 ↗ |
| 发表日期 | 2026 |
| 作者 | Yonn Pwint Phyu |
| 作者单位 | Food Science and Technology Program, School of Agro-Industry, Mae Fah Luang University, Chiang Rai 57100, Thailand. |
| 期刊 | Foods (Basel, Switzerland) |
| SCI 分区 | Q1 |
| IF | 6.5 |
| 研究类型 | 基础研究 · 基础/转化 |
| 所属专科 | 咽喉科 |
中文摘要
三维(3D)食品打印已成为一种有前景的方法,可用于为吞咽障碍个体生产质地改良食品。然而,利用淡水大型藻类开发稳定的食品墨水仍具挑战性,因为需要合适的流变学、结构和质构特性以确保可打印性和吞咽安全性。本研究优化了利用黄原胶(XG)和魔芋胶(KG)配制的基于刚毛藻(Kai)的3D食品墨水配方,以用于吞咽障碍友好型应用。采用响应面法(RSM)最优设计,共16种配方,评估XG和KG对硬度、黏附性、内聚性、打印精度、变形和脱水收缩的影响。根据日本质构标准以及UDF和IDDSI框架,对配方的流变学、微观结构和吞咽障碍适用性进行了表征。RSM模型充分描述了亲水胶体-响应关系(R2 = 0.8456-0.9750)。含≥6%亲水胶体的选定配方,尤其是XG:KG比例在1:2至3:1之间,表现出优异的打印精度(>94%),并满足所有三项吞咽障碍分类。数值优化预测最佳配方为5.263% XG和3.188% KG,总体合意度为0.91。这些发现为利用刚毛藻开发可持续、吞咽障碍友好型3D打印食品提供了配方策略。
英文摘要
Three-dimensional (3D) food printing has emerged as a promising approach for producing texture-modified foods for individuals with dysphagia. However, developing stable food inks from freshwater macroalgae remains challenging due to the need for suitable rheological, structural, and textural properties to ensure printability and swallowing safety. The current study optimized the formulation of Cladophora glomerata (Kai)-based 3D food inks using xanthan gum (XG) and konjac gum (KG) for dysphagia-friendly applications. A response surface methodology (RSM) optimal design with 16 formulations evaluated the effects of XG and KG on hardness, adhesiveness, cohesiveness, print accuracy, deformation, and syneresis. Formulations were characterized for rheology, microstructure, and dysphagia suitability according to Japanese texture criteria, and UDF and IDDSI frameworks. The RSM models adequately described hydrocolloid-response relationships (R2 = 0.8456-0.9750). Selected formulation containing ≥6% hydrocolloid, particularly XG:KG ratios between 1:2 to 3:1 demonstrated superior print accuracy (>94%) and met all three dysphagia classifications. Numerical optimization predicted an optimum formulation of 5.263% XG and 3.188% KG with an overall desirability of 0.91. These findings provide a formulation strategy for developing sustainable, dysphagia-friendly 3D-printed foods from C. glomerata.