Quantitative prediction of the bitterness suppression of elemental diets by various flavors using a taste sensor
Quantitative prediction of the bitterness suppression of elemental diets by various flavors using a taste sensor
复制标题
DOI：
10.1023/b:pham.0000008039.59875.4f
复制
发表时间：
2003-12-01
影响因子：
3.7
通讯作者：
Uchida, T
中科院分区：
文献类型：
--
作者：
Miyanaga, Y;Inoue, N;Uchida, T
目的。本研究的目的是建立一种定量预测各种口味对元素饮食抑制苦味的方法，并利用多通道味觉传感器预测这些元素饮食的最佳口服配方。我们研究了不同稀释用水量和添加不同数量的五种口味(菠萝、苹果、牛奶咖啡、绿茶粉和香蕉)对基本饮食Aminoreban EN(R)苦味的影响。用人体志愿者(n=9)进行味觉测试和人工味觉传感器测量，将50g氨瑞班EN(R)分别溶于不同体积(140、180、220、260、300、420、660、1140和2100毫升)的水中，以及50g氨瑞班EN(R)溶于180毫升水中并添加3~9克各种口味进行味觉掩蔽。在味觉测试中，用于稀释的水体积的对数值与志愿者给出的苦味强度分数之间的关系被证明是线性的。在味觉测试中，香料的添加也减少了基本饮食的苦味；这种影响的大小依次为苹果、菠萝、牛奶咖啡、绿茶粉和香蕉。在人工味觉传感器中，观察到阿米诺班EN(R)的吸附(CPA值，对应于苦味)引起通道1的膜电位的巨大变化，但对任何一种口味都没有。通道1的CPA值与人体味觉测试结果有很好的相关性，说明该味觉传感器不仅能够评价氨基苯本身的苦味，而且能够评价含有有机酸和风味成分等多种元素的五种香精的抑苦效果，以及水稀释对这种苦味的影响。通过对味觉传感器和人体味觉数据的回归分析，我们能够预测50g氨力班EN(R)溶液在添加或不添加选定香料的情况下，经不同体积的水(140-300毫升)稀释后的苦味。尽管这种预测方法不能提供对人类味觉的完美模拟，但在没有完全味觉测试结果的情况下，人工味觉传感器可能有助于预测含有各种口味的基本饮食的苦味强度。
Purpose. The purpose of the study was to develop a method for the quantitative prediction of the bitterness suppression of elemental diets by various flavors and to predict the optimum composition of such elemental diets for oral administration using a multichannel taste sensor.Methods. We examined the effects of varying the volume of water used for dilution and of adding varying quantities of five flavors ( pineapple, apple, milky coffee, powdered green tea, and banana) on the bitterness of the elemental diet, Aminoreban EN(R). Gustatory sensation tests with human volunteers (n = 9) and measurements using the artificial taste sensor were performed on 50 g Aminoreban EN(R) dissolved in various volumes ( 140, 180, 220, 260, 300, 420, 660, 1140, and 2100 ml) of water, and on 50 g Aminoreban EN(R) dissolved in 180 ml of water with the addition of 3 - 9 g of various flavors for taste masking.Results. In gustatory sensation tests, the relationship between the logarithmic values of the volumes of water used for dilution and the bitterness intensity scores awarded by the volunteers proved to be linear. The addition of flavors also reduced the bitterness of elemental diets in gustatory sensation tests; the magnitude of this effect was, in decreasing order, apple, pineapple, milky coffee, powdered green tea, and banana. With the artificial taste sensor, large changes of membrane potential in channel 1, caused by adsorption (CPA values, corresponding to a bitter aftertaste), were observed for Aminoreban EN(R) but not for any of the flavors. There was a good correlation between the CPA values in channel 1 and the results of the human gustatory tests, indicating that the taste sensor is capable of evaluating not only the bitterness of Aminoreban EN(R) itself but also the bitterness-suppressing effect of the five flavors, which contained many elements such as organic acids and flavor components, and the effect of dilution ( by water) on this bitterness. Using regression analysis of data derived from the taste sensor and from human gustatory data for four representative points, we were able to predict the bitterness of 50 g Aminoreban EN(R) solutions diluted with various volumes of water ( 140 - 300 ml), with or without the addition of a selected flavor.Conclusions. Even though this prediction method does not offer perfect simulation of human taste sensations, the artificial taste sensor may be useful for predicting the bitterness intensity of elemental diets containing various flavors in the absence of results from full gustatory sensation tests.