Exploring the relationships between perceived umami intensity, umami components and electronic tongue responses in food matrices. (30th January 2022) - Zhu, Yiwen; Zhou, Xirui; Chen, Yan Ping; Liu, Ziyuan; Jiang, Shui; Chen, Gaole; Liu, Yuan
Highlights: Perceived umami intensity (PUI) is quantified in foods from seven categories by 2-AFC method. PUI varied in umami component contents, food types and structures. EUC is available in quantifying PUI of meat, dairy, vegetable and mushroom products. Prediction models for PUI and EUC are established with data from E-tongue by BPNN. Abstract: 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 ( R 2 > 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.