Predictive Model of Soymilk Sweetness Based on Protein and Amino Acid Compositions of Soybean Materials
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摘要: 本研究采用电子舌分析了30个大豆品种加工成豆浆的甜度值,运用相关性分析法探究了豆浆甜度值与大豆原料蛋白、氨基酸组成之间的关系,使用逐步回归的方法建立了豆浆甜度的预测模型。结果表明:不同品种的大豆在蛋白质、氨基酸组成上有很大差异。大豆球蛋白(11S)含量(r=0.370)、大豆球蛋白/β-伴大豆球蛋白比率(11S/7S比率)(r=0.436)、丝氨酸(r=0.418)和苏氨酸(r=0.373)含量与豆浆甜度呈显著正相关(p<0.05),α亚基含量(r=-0.460)、β-伴大豆球蛋白(7S)含量(r=-0.428)、蛋氨酸(r=-0.372)和酪氨酸(r=-0.464)含量与豆浆甜度呈显著负相关(p<0.05)。通过逐步回归建立豆浆甜度预测模型的决定系数R2=0.747,方程为:F(甜度预测值)=-0.125×α亚基+3.172×苏氨酸+1.655×丝氨酸-2.894×蛋氨酸-2.097×酪氨酸+9.908,模型验证结果显示,实测值与模型预测值的平均相对误差为4.61%。因此,用本研究模型能准确地预测豆浆甜度。Abstract: In this study,sweetness values of soymilk made from 30 varieties of soybeans were determined by electronic tongues. Correlation analysis was used to explore the relationship between soymilk sweetness value and protein as well as amino acid compositions of soybean raw materials. Stepwise regression was applied to build soymilk sweetness predictive model. Results indicated that great variations of protein and amino acid compositions exist in different varieties of soybeans. Glycinin(11S)contents(r=0.370),glycinin/β-conglycinin ratio(11S/7S ratio)(r=0.436),serine contents(r=0.418)and threonine contents(r=0.373)were significantly positively correlated with soymilk sweetness(p<0.05). The contents of α subunit(r=-0.460),β-conglycinin(7S)(r=-0.428),methionine(r=-0.372)and tyrosine(r=-0.464)were significantly negatively correlated with soymilk sweetness(p<0.05). Predictive model of soymilk sweetness established by stepwise regression had a coefficient of determination R2=0.747,the equation was:F(predicted sweetness)=-0.125×α subunit+3.172×threonine+1.655×serine-2.894×methionine-2.097×tyrosine+9.908. An average relative error of 4.61% was obtained for predicted values compared with measured values. Therefore,the predictive model would have demonstrated great potential in accurately predicting soy milk sweetness values.
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Keywords:
- soybean protein /
- amino acid /
- soymilk sweetness /
- predictive model
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