食品科学 ›› 2022, Vol. 43 ›› Issue (3): 25-32.doi: 10.7506/spkx1002-6630-20210309-120

• 基础研究 • 上一篇    下一篇

大豆蛋白水解物苦味评价方法

田霄艳,郑斐庭,冯涛,喻晨,宋诗清,孙敏,姚凌云   

  1. (1.上海应用技术大学香料香精技术与工程学院,上海 201418;2.安琪酵母股份有限公司,湖北 宜昌 443003)
  • 出版日期:2022-02-15 发布日期:2022-03-08
  • 基金资助:
    国家自然科学基金面上项目(31771942)

Comparative Analysis of Bitter Evaluation Methods for Soybean Protein Hydrolysate

TIAN Xiaoyan, ZHENG Feiting, FENG Tao, YU Chen, SONG Shiqing, SUN Min, YAO Lingyun   

  1. (1. School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai 201418, China; 2. Angel Yeast Co., Ltd., Yichang 443003, China)
  • Online:2022-02-15 Published:2022-03-08

摘要: 目的:研究大豆蛋白水解物的苦味程度,寻找苦味评价方法。方法:采用感官评价和电子舌分析大豆蛋白水解物的苦味程度;采用高效液相色谱法分析游离氨基酸含量和肽分子质量分布;采用偏最小二乘回归分析(partial least squares regression,PLSR)研究游离氨基酸含量、感官苦味强度以及电子舌苦味响应值评价的相关性,同时建立苦味值预测模型。结果:电子舌评价蛋白水解物苦味响应值结果优于感官评价结果,可以很好分辨样品间苦味值差异。F1样品苦味最强,其呈苦味游离氨基酸、分子质量小于1 000 Da肽的含量高于其他样品。不同质量浓度样品的感官评价苦味强度与电子舌苦味响应值具有良好线性关系,利用PLSR建立了电子舌响应值与感官评价苦味强度预测分析模型,得回归方程:Y=0.484X1+0.537X2+0.509X3+0.522X4(Y为感官苦味强度预测值;X1、X2、X3、X4分别为电子舌传感器苦味、鲜味、丰富度、咸味响应值),所建立模型校正数据集和验证数据集的决定系数R2分别为0.93和0.97,校正均方根误差分别为0.18和0.10,说明模型的拟合效果较好,可靠性很高。结论:感官评价与电子舌模型具有很高的可靠性,电子舌评价可以代替感官评价对植物蛋白水解物进行苦味值评价,可为后续植物蛋白水解物苦味值研究提供一种新的评价方法。

关键词: 苦味肽;电子舌;感官评价;偏最小二乘回归分析建模;苦味评价

Abstract: Objective: To evaluate the degree of bitterness of soybean protein hydrolysates and to find out a more suitable method of bitterness evaluation. Methods: Sensory evaluation and electronic tongue were used to analyze the bitterness of soybean protein hydrolysate. The contents of free amino acids and the molecular mass distribution of peptides were analyzed by high performance liquid chromatography (HPLC). Partial least squares regression (PLSR) was used to study the correlation between free amino acids, sensory bitterness intensity and electronic tongue response, and a prediction model for bitterness value was established. Results: The electronic tongue was superior to the sensory evaluation, and could clearly distinguish the difference in bitterness value among samples. Sample F1 had the strongest bitterness value, and its contents of bitter free amino acids and peptides with molecular mass less than 1 000 Da were higher than those of other samples. There was a good linear relationship between the sensory evaluation of samples with different concentrations and the electronic tongue evaluation, and the regression equation describing this relationship was obtained as follows: Y = 0.484X1 + 0.537X2 + 0.509X3 + 0.522X4, where Y is predicted sensory bitterness intensity, and X1, X2, X3 and X4 are electronic tongue sensor responses for bitterness, umami taste, richness and salt taste, respectively. The determination coefficients (R2) for the calibration and validation sets were 0.93 and 0.97, and the root mean square error of calibration (RMSEC) were 0.18 and 0.10, respectively, indicating that the model has good fitness and high reliability. Conclusion: Both sensory evaluation and electronic tongue model have high reliability. Electronic tongue evaluation can be used instead of sensory evaluation to evaluate the bitterness of plant protein hydrolysates. This study can provide a new evaluation method for further studies on the bitterness of plant protein hydrolysates.

Key words: bitter peptide; electronic tongue; sensory evaluation; partial least squares regression model; bitter taste evaluation

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