Bibliographic Details
| Title: |
Stability and 3D-printing performance of high-internal-phase emulsions based on ultrafine soybean meal particles. |
| Authors: |
Liao, Haiqiang1,2 (AUTHOR), Jiang, Tianshu1,3 (AUTHOR), Chen, Lei1,2 (AUTHOR), Wang, Guozhen1,2 (AUTHOR), Shen, Qian1,2 (AUTHOR), Liu, Xiuying1,2 (AUTHOR), Ding, Wenping1,2 (AUTHOR), Zhu, Lijie1,2 (AUTHOR) lijiezhu@whpu.edu.cn |
| Source: |
Food Chemistry. Aug2024, Vol. 449, pN.PAG-N.PAG. 1p. |
| Subjects: |
Soybean meal, Emulsions, Soy proteins, Whey proteins, Three-dimensional printing, Viscoelasticity |
| Abstract: |
There are numerous studies on the application of soybean whey protein in three-dimensional (3D) printing. In this study, the effects of soybean meal particles (5%, 6%, 7%, 8%, 9%, and 10%) and oil-phase concentrations (70%, 72%, 74%, 76%, and 78%) on the stability and 3D-printing performance of a soybean-meal-based high-internal-phase emulsion were investigated. The results showed that the particle size of the emulsion decreased with increasing soybean meal particle concentration, and that increasing the concentration of the oil phase improved the viscoelasticity of the emulsion. Rheological tests further showed that the higher storage modulus of the emulsion indicated better support and stability. The emulsion with 8% soybean meal-particles and 76% oil-phase concentration exhibited the best printing effect. This study provides an effective solution for the preparation of stabilized high-internal-phase emulsions of soybean meal particles suitable for 3D printing. • Ultrarefined soybean meal was used to prepare a high-internal-phase emulsion. • Emulsion stability was evaluated by adjusting the particle and oil concentrations. • Storage modulus was higher at high concentrations of particles and oil phase. [ABSTRACT FROM AUTHOR] |
|
Copyright of Food Chemistry is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |