摘要
本文以小麦粉、马铃薯全粉为主要原料,豆腐柴叶为辅料,采用单因素和Box-Behnken实验设计,响应面和主成分分析法优化马铃薯挂面加工工艺。结果表明:第1~第3主成分累计贡献率达到86.10%,足以描述马铃薯挂面质构、感官、烹调损失率和干物质吸水率综合反应的挂面品质。以主成分分析得到的规范化综合评分为响应值建立的二次多项式回归模型回归效果显著,拟合度较好(p<0.0001,R2=0.9699)。偏最小二乘法回归分析预测马铃薯挂面最佳综合评分工艺参数为:马铃薯全粉添加量31%,豆腐柴汁液添加量9%,醒发时间31 min,醒发温度25℃,理论综合评分值达到0.9194,该条件下马铃薯挂面规范化综合评分达0.9116,与模型预测值接近,表明以主成分分析得到的规范化综合评分为响应值建立的回归模型具有良好的预测能力。
In this paper,wheat flour and potato granules were used as main raw materials, and the Premna microphylla Turcz leaves were used as anxiliary materials.The single factor and Box-Behnken experimental design, response surface analysis （RSA） and principal component analysis （PCA）were used to optimize the processing technology of potato noodles in this paper.The results showed that the first to third principal components cumulative percentages contribution was 86.10% ,which could be sufficient to reflect the quality of noodles with comprehensive response of the texture indexes, sensory, cooking loss rate and dry matter water absorption rate. The regression coefficient of the two polynomial regression model established by principal component analysis was significant, and had better fit degree （ p 〈 0.0001, R2 = 0.9699 ） . Partial least squares regression analysis predicted that the best comprehensive score for the process parameters was：potato granules of 31% ,Premna microphylla Turcz Juice of 9% ,fermentation time of 31 min,fermentation temperature of 25 ℃.The comprehensive score of the theory reached 0.9194.The actual measured value was 0.9116 which was consistent with the model predicted value.The results indicated that the regression model established by principal component analysis has had a good predictive ability.
出处
《食品工业科技》
CAS
CSCD
北大核心
2017年第23期143-150,158,共9页
Science and Technology of Food Industry
基金
四川省生物质资源利用与改性工程技术研究中心专职科研创新团队建设基金项目(14tdgc04)
江油市豆腐柴种植与综合利用及示范(2017NFP0178)
关键词
马铃薯
豆腐柴
质构特性
主成分分析
响应面
potato
Premna microphyUa Turcz
texture properties
principal component analysis
response surface methodology
