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食品研究与开发:2023,44(15):76-83
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芝麻分离蛋白制备及其白蛋白乳液的稳定性质
(合肥工业大学食品与生物工程学院,安徽 合肥 230009)
Preparation of Sesame Protein Isolate and Its Albumin Emulsion Properties
(School of Food and Biology Engineering,Hefei University of Technology,Hefei 230009,Anhui,China)
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投稿时间:2021-04-01    
中文摘要: 该文采用Osborne 法从脱脂芝麻粕中分级分离芝麻分离蛋白中的白蛋白、球蛋白、醇溶蛋白和谷蛋白组分,对其芝麻分离蛋白及不同组分蛋白理化性质和加工性能进行比较,并制备芝麻白蛋白乳液,探究油相体积分数、白蛋白浓度和离子强度对乳液粒径、流变特性和微观结构的影响。结果表明:Osborne 法比传统碱溶酸沉法芝麻分离蛋白得率更高,白蛋白的溶解度(28.1%)、保水性(3.7 g/g)、起泡性(273.2%)和乳化稳定指数(19.1 min)较好,球蛋白的保油性(2.3 g/g)和起泡稳定性(82.2%)较强,醇溶蛋白的乳化活性指数(23.7 m2/g)较高;在油相体积分数为60%,白蛋白浓度为6.0%,pH 值为3 条件下制备的白蛋白乳液粒径最小且分布均一,表现为非牛顿流体的假塑性行为,激光共聚焦显微镜观察发现白蛋白吸附在油水界面,进一步说明白蛋白乳液具有一定的稳定性。
Abstract:In this study,the Osborne method was used to fractionate sesame protein isolate from defatted sesame meal into albumin,globulin,prolamin,and glutelin fractions.The physicochemical properties and processing characteristics of sesame protein isolate and its different fractions were compared,and sesame albumin emulsion was prepared.The effects of oil phase volume fraction,albumin concentration,and ionic strength on emulsion particle size,rheological properties,and microstructure were investigated.The results showed that the Osborne method yielded a higher sesame protein isolate than the traditional alkali solubility-acid precipitation method.Albumin exhibited the best solubility(28.1%),water-holding capacity(3.7 g/g),foamability(273.2%),and emulsion stability index(19.1 min).Globulin showed the highest oil-holding capacity(2.3 g/g)and foam stability(82.2%).Prolamin had the highest emulsifying activity index(23.7 m2/g).The smallest and most uniform droplet size of the albumin emulsion was achieved under the conditions of 60%oil phase volume fraction,6.0%albumin concentration,and pH3.The emulsion exhibited pseudoplastic behavior as a non-Newtonian fluid.Laser confocal microscopy observation revealed that albumin adsorbed at the oil-water interface,further indicating the stability of the albumin emulsion.
文章编号:202315012     中图分类号:    文献标志码:
基金项目:“十三五”国家重点研发计划子课题(2018YFD0401104-2);安徽省科技重大专项(202003a06020025)
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