基于大豆原料蛋白质和氨基酸组成的豆浆甜度预测模型研究
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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R~2=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。
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( 11 S)contents( r = 0.370),glycinin/β-conglycinin ratio( 11 S/7 S 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( 7 S)( 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 R~2= 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.
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( 11 S)contents( r = 0.370),glycinin/β-conglycinin ratio( 11 S/7 S 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( 7 S)( 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 R~2= 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.
引文
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[16]GB 5009.124-2016食品中氨基酸的测定[S].北京:中国标准出版社,2017.
[17]李淑芬,胡敏.碱溶酸沉法提取大豆蛋白条件的优化[J].大豆科学,2014,33(2):274-280.
[18]Ma L,Li B,Han F,et al.Evaluation of the chemical quality traits of soybean seeds,as related to sensory attributes of soymilk[J].Food Chemistry,2015,173:694-701.
[19]乔明武,张莹,杨月,等.感官评定与仪器分析在北豆腐品质评价中的应用[J].大豆科学,2011,30(4):648-651.
[20]Nielsen N C,Floener L,Evans R P,et al.The structure and expression of glycinin genes from soybean[J]. Journal of the American Oil Chemists Society,1986,63(4):458-458.
[21]周宇锋,宋莲军,乔明武,等.大豆蛋白亚基与豆腐的质构特性的相关性[J].中国粮油学报,2014,29(4):22-25.
[22]黄明伟,刘俊梅,王玉华,等.大豆蛋白组分与豆腐品质特性的研究[J].食品工业科技,2015,36(13):94-98.
[23]Wang L,Liu H,Liu L,et al. Prediction of peanut protein solubility based on the evaluation model established by supervised principal component regression[J]. Food Chemistry,2017,218:553-560.
[2]林宇山.感官评价在食品工业中的应用[J].食品工业科技,2006(8):202-203.
[3]巩效伟,朱东来,洪鎏,等.基于电子舌的电子烟甜度评价模型研究[J].中国烟草学报,2017,23(6):22-30.
[4]Kang B S,Lee J E,Park H J. Electronic tongue-based discrimination of Korean rice wines(makgeolli)including prediction of sensory evaluation and instrumental measurements[J].Food Chemistry,2014,151(151):317-323.
[5]Heng L,Vincken J P,Gvan K,et al.Bitterness of saponins and their content in dry peas[J]. Journal of the Science of Food&Agriculture,2006,86(8):1225-1231.
[6]Shigemitsu Kudou,Yvette Fleury,Dieter Welti,et al.Malonyl isoflavone glycosides in soybean seeds(Glycine max Merrill)[J].Journal of the Agricultural Chemical Society of Japan,1991,55(9):2227-2233.
[7]Lee J Y,Min S,Choe E O,et al. Formation of volatile compounds in soy flour by singlet oxygen oxidation during storage under light[J]. Journal of Food Science,2003,68(6):1933-1937.
[8]Min S,Yu Y,Yoo S,et al. Effect of soybean varieties and growing locations on the flavor of soymilk[J]. Journal of Food Science,2005,70(1):C8-C12.
[9]Terhaag M M,Almeida M B,Benassi M D T. Soymilk plain beverages:Correlation between acceptability and physical and chemical characteristics[J].Food Science and Technology,2013,33(2):387-394.
[10]赵静,丁奇,孙颖,等.猪骨汤中的游离氨基酸及其呈味特征分析[J].食品研究与开发,2015,36(18):1-6.
[11]Ardo Y. Flavour formation by amino acid catabolism[J].Biotechnology Advances,2006,24(2):238-242.
[12]Toshihide Nishimura,Hiromichi Kato. Taste of free amino acids and peptides[J]. Food Reviews International,1988,4(2):175-194.
[13]谷镇,杨焱.食用菌呈香呈味物质研究进展[J].食品工业科技,2013,34(5):363-367.
[14]GB 5009.5-2016食品中蛋白质的测定[S].北京:中国标准出版社,2017.
[15]NY/T 1205-2006大豆水溶性蛋白含量的测定[S].北京:中国标准出版社,2007.
[16]GB 5009.124-2016食品中氨基酸的测定[S].北京:中国标准出版社,2017.
[17]李淑芬,胡敏.碱溶酸沉法提取大豆蛋白条件的优化[J].大豆科学,2014,33(2):274-280.
[18]Ma L,Li B,Han F,et al.Evaluation of the chemical quality traits of soybean seeds,as related to sensory attributes of soymilk[J].Food Chemistry,2015,173:694-701.
[19]乔明武,张莹,杨月,等.感官评定与仪器分析在北豆腐品质评价中的应用[J].大豆科学,2011,30(4):648-651.
[20]Nielsen N C,Floener L,Evans R P,et al.The structure and expression of glycinin genes from soybean[J]. Journal of the American Oil Chemists Society,1986,63(4):458-458.
[21]周宇锋,宋莲军,乔明武,等.大豆蛋白亚基与豆腐的质构特性的相关性[J].中国粮油学报,2014,29(4):22-25.
[22]黄明伟,刘俊梅,王玉华,等.大豆蛋白组分与豆腐品质特性的研究[J].食品工业科技,2015,36(13):94-98.
[23]Wang L,Liu H,Liu L,et al. Prediction of peanut protein solubility based on the evaluation model established by supervised principal component regression[J]. Food Chemistry,2017,218:553-560.