摘要
为研究新型牛肉薄饼加工工艺,采用单因素实验讨论大豆分离蛋白、复合磷酸盐、卡拉胶对牛肉薄饼剪切力、水分含量、感官评分的影响。以剪切力、水分含量、感官评分为评价指标,通过L9(3^3)正交试验优化牛肉薄饼加工工艺。结果表明:采用极差法、主成分分析法评价不同试验方案牛肉薄饼品质具有一定的合理性,但这两种方法都存在不足,综合考虑两种评价方法可提高结果的合理性。经过综合分析,最佳方案为A1B2C2,即大豆分离蛋白添加量为2.00%、复合磷酸盐添加量为0.30%、卡拉胶添加量为0.10%,为牛肉薄饼最佳加工工艺。该最佳生产工艺下生产的牛肉薄饼水分含量为43.61、剪切力为7.70、感官总分为84.90。本研究为生产高档牛肉肉糜制品,增加产品种类、提高产品食用品质提供依据。
In order to study the new processing technology of beef pancake, the effects of soybean protein isolate, complex phosphate and carrageenan on shear force, water content and sensory score of beef pancake were studied by single factor experiment. Using shear force, moisture content and sensory score as evaluation indexes, L9(3^3) orthogonal test was used to optimize the processing technology of beef pancake. The results showed that the range method and principal component analysis method were feasible to evaluate the quality of beef pancakes in different test schemes, but both of them had shortcomings. After comprehensive analysis, the best plan was A1 B2 C2, that is, the added amount of soybean protein isolate was 2.00%, the added amount of compound phosphate was 0.30%, and the added amount of carrageenan was 0.10%, which was the best processing technology of beef pancake. The moisture content, shear force and sensory score of the beef pancake were 43.61, 7.70 and 84.90 respectively. This study provides a basis for the production of high-grade beef mince products, increasing product types and improving food quality.
作者
赵改名
焦阳阳
祝超智
李珊珊
李佳麒
银峰
祁兴磊
ZHAO Gai-ming;JIAO Yang-yang;ZHU Chao-zhi;LI Shan-shan;LI Jia-qi;YIN Feng;QI Xing-lei(College of Food Science and Technology,Henan Agricultural University,Zhengzhou 450002,China;Zhumadian Comprehensive Experimental Station,Zhumadian 463000,China)
出处
《现代食品科技》
EI
CAS
北大核心
2019年第11期144-151,共8页
Modern Food Science and Technology
基金
国家现代农业(肉牛牦牛)产业技术体系建设专项(CARS-37)
“十三五”国家重点研发计划重点专项(2018YFD0401200)
河南省高校重点科研项目(18B550006)
河南省重点研发与推广专项(192102110099)
关键词
牛肉薄饼
大豆分离蛋白
复合磷酸盐
卡拉胶
归一值
主成分分析
beef crepes
soybean protein isolate
complex phosphate
carrageenan
return a value
principal component analysis
