鲜味
电子舌
味精
品味
食品科学
风味
化学
食品工业
人工智能
生化工程
计算机科学
数学
工程类
作者
Yiwen Zhu,Xi Zhou,Yan Ping Chen,Ziyuan Liu,Shui Jiang,Gaole Chen,Yuan Liu
出处
期刊:Food Chemistry
[Elsevier]
日期:2022-01-01
卷期号:368: 130849-130849
被引量:19
标识
DOI:10.1016/j.foodchem.2021.130849
摘要
Umami intensity promotes food flavor blending and food choice, while a universal quantification procedure is still lacking. To evaluate perceived umami intensity (PUI) in seven categories of foods, modified two-alternative forced choice (2-AFC) method with monosodium glutamate as reference was applied. Meanwhile, we explored whether equivalent umami concentration (EUC) by chemical analysis and electronic tongue (E-tongue) are applicable in PUI quantification. The results indicated that EUC was appropriate in quantifying PUI of samples from meat, dairy, vegetable and mushroom groups (r = 1.00, p < 0.05). Moreover, models with a good prediction capacity for PUI and EUC (R2 > 0.99) were established in separated food categories by back propagation neural networks, where E-tongue data were set as input. This study explored the effectiveness of the three methods in evaluating the PUIs of various foods, which provides multiple choices for the food industry.
科研通智能强力驱动
Strongly Powered by AbleSci AI