Effect of dry heat treatment of soy protein powder on aligned structure formation in soy protein-based food gels during freezing

大豆蛋白 化学 化学工程 变性(裂变材料) 含水量 食品科学 流变学 泥浆 冷冻干燥 凝结 色谱法 纹理(宇宙学) 结晶学 材料科学 复合材料 热力学 核化学 物理 岩土工程 图像(数学) 人工智能 计算机科学 工程类
作者
Ratchanon Chantanuson,Shinsuke Nagamine,Takashi Kobayashi,Kyuya Nakagawa
出处
期刊:Journal of Food Engineering [Elsevier]
卷期号:363: 111779-111779 被引量:1
标识
DOI:10.1016/j.jfoodeng.2023.111779
摘要

The formation of aligned structures in a protein-based food gel through the freezing process depends on protein content, as proteins are crucial in creating rheological properties that closely resemble meat. These proteins contribute to the cohesive and resilient nature of plant-based meat substitutes by binding water, enabling the replication of the texture of real meat. This study was conducted to explore the impact of the water-binding ability of soy protein isolate (SPI) on the formation of aligned porous structures in soy protein-based food gels using the freeze alignment technique. Protein powders with different denaturation degrees can be prepared by dry heating SPI, resulting in powders with different amounts of soluble fraction and water-binding capacity. In this study, the frozen ratio, which quantifies the ratio of freezable water in the slurry, was used to assess the water-binding ability of the formulations. The degree of dry heating significantly affected the formation of ice crystals during freezing. As the solid content and water-binding ability increased, aligned pore formation was restricted. Fast Fourier transform (FFT) analysis was conducted on the microscopic images of freeze aligned products, revealing a significant impact of the water-binding ability on anisotropic pore structure formations. In the present study, the application of the dry heat treatment at 70 °C and 70% relative humidity, along with a formulation having 10% solid content, showed a higher potential for replicating the anisotropic structure of meat analogs.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
2秒前
2秒前
4秒前
逐风给逐风的求助进行了留言
5秒前
科研通AI5应助灌饼采纳,获得30
5秒前
Owen应助Zzzzzzzzzzz采纳,获得10
6秒前
7秒前
8秒前
巫马秋寒应助笑点低可乐采纳,获得10
8秒前
xuex1完成签到,获得积分10
8秒前
情怀应助阳光的雁山采纳,获得10
10秒前
斯文败类应助jy采纳,获得10
10秒前
10秒前
日月轮回发布了新的文献求助10
11秒前
36456657应助木香采纳,获得10
12秒前
无花果应助ns采纳,获得30
12秒前
刘铭晨完成签到,获得积分10
12秒前
13秒前
YY发布了新的文献求助10
13秒前
Rrr发布了新的文献求助10
14秒前
学术蠕虫发布了新的文献求助10
14秒前
14秒前
miumiuka完成签到,获得积分10
15秒前
个性的薯片应助lyt采纳,获得20
17秒前
sweetbearm应助寒涛先生采纳,获得10
18秒前
wanci应助YY采纳,获得10
19秒前
19秒前
20秒前
20秒前
21秒前
HC完成签到 ,获得积分10
22秒前
姚姚的赵赵完成签到,获得积分10
22秒前
JamesPei应助大豪子采纳,获得30
23秒前
jy发布了新的文献求助10
23秒前
23秒前
陆靖易发布了新的文献求助10
23秒前
LQW完成签到,获得积分20
24秒前
25秒前
plant完成签到,获得积分10
25秒前
lyt完成签到,获得积分10
25秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527928
求助须知:如何正确求助?哪些是违规求助? 3108040
关于积分的说明 9287614
捐赠科研通 2805836
什么是DOI,文献DOI怎么找? 1540070
邀请新用户注册赠送积分活动 716904
科研通“疑难数据库(出版商)”最低求助积分说明 709808