食品科学 ›› 2011, Vol. 32 ›› Issue (12): 296-300.doi: 10.7506/spkx1002-6630-201112065

• 分析检测 • 上一篇    下一篇

发酵乳储存期内风味模型的建立与检验

刘景,王荫榆,郭本恒,张雪洪   

  1. 1. 光明乳业股份有限公司技术中心
    2.上海交通大学生命科学技术学院
  • 出版日期:2011-06-25 发布日期:2011-06-10

Construction and Validation of Flavor Model of Fermented Milk during Storage

LIU Jing1,2, WANG Yin-yu1,GUO Ben-heng1,*,ZHANG Xue-hong2   

  1. 1. Center of Technology, Bright Dairy and Food Co. Ltd., Shanghai 200436, China ; 2. School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
  • Online:2011-06-25 Published:2011-06-10

摘要: 使用多变量统计方法,以发酵乳风味协调感为因变量,主要风味指标羰基化合物(乙醛、双乙酰),酸类物质(挥发酸、总酸度)以及发酵乳成分(糖、脂肪、蛋白质和非脂乳固体)为自变量,建立发酵乳风味模型。对8种风味指标进行多元线性回归和主成分分析,挑选对因变量影响大而相关性较小的风味指标为自变量。最终选择双乙酰、挥发酸、糖和脂肪含量为风味模型的自变量,建立风味协调感与主要风味指标的多元线性回归方程。该模型中各变量系数在统计意义上显著,且各系数无明显多重共线性。最终模型的调整R2为0.447。随机样品对该风味模型验证结果显示,模型观测值与风味协调性预测值有较好的拟合,平均误差为7.18%。

关键词: 发酵乳, 风味, 多元线性回归, 主成分分析, 建模

Abstract: Multivariate statistical methods were used to establish a flavor model of fermented milk. The parameters of major flavor compounds such as carbonyl compounds (aldehyde and diacetyl), acids (volatile acid and total acidity), and fermented milk components (sugar, fat, protein and non-fat milk solids) were analyzed as independent variables, and sensory coordination of flavor as the dependent variable. In order to select the indices with great impact on dependent variable and little correlation with independent variables, multiple linear regression and principal components analysis (PCA) were conducted to analyze 8 flavor indices. Finally, the concentrations of aldehyde, volatile acid, sugar and fat were selected as the independent variables and sensory evaluation score of flavor coordination as dependent variable. The coefficients of variables in the established model revealed statistical significance and weak multicollinearity was observed. The adjusted R2 of the final model was 0.447. The determined values and the predicted values of the tested samples were fitted well with an average error of 7.18%.

Key words: fermented milk, flavor, multiple linear regression, principal components analysis (PCA), modeling

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