The Molecular Recognition of Wine and Urine

描述（由申请人提供）：大多数促味剂和气味剂是通过非特异性相互作用的复合反应来鉴定的。由一系列感受器的同时反应所产生的模式是特定于一组特定刺激的。最近，德克萨斯大学开发了一种模仿哺乳动物味觉的装置。通过使用树脂珠的阵列，这些树脂珠是用分子传感器化学衍生的，放置在微机械平台中。发现来自阵列中所有珠的光谱图案对于分析物的特定混合物是特异性的。为了将这项技术带到下一个阶段，需要开发用于在附着不同类别分析物的受体时实现珠的光学响应的策略。 这一建议需要针对两个领域进行研究。首先，我们建议测试光信号的指示剂置换测定的效率。响应于pH效应和Ca（II）结合的指示剂将用于信号分析物结合。第二，我们打算探索使用差异受体的能力，指纹的成分存在于一个复杂的混合物的分析物。这两个目标将在两个极具挑战性的复杂解决方案的背景下进行探索：葡萄酒和尿液。选择葡萄酒和尿液作为我们的试验台解决方案的原因是由于它们的复杂性和独特的相似化学结构。我们建议针对五类分析物：羧酸，糖，果胶，单宁和葡萄糖酸。这些类别对指纹识别提出了不同的挑战。我们将使用“在手”的受体羧酸盐，糖和碳水化合物。或者，果胶、单宁和果胶的受体将衍生自组合化学。 在实践中，合成受体由于其简单性而受到类似分析物的干扰。然而，我们认为，当与模式识别协议相结合时，合成受体阵列的能力对于阵列传感器应用来说是不可超越的。合成受体具有天然交叉反应性，这正是阵列设置中所需的属性。此外，在非天然受体的产生中使用合成组合化学自然地满足了这种交叉反应性的要求。我们的目标是教这个一般教训的超分子和分析化学社区。

DESCRIPTION (provided by applicant): Most tastants and odorants are identified through a composite of responses from non-specific interactions. The pattern created by the simultaneous response of a series of receptors is specific for a particular set of stimuli. A device that mimics the mammalian sense of taste was recently developed at U.T. by using arrays of resin beads that are chemically derivatized with molecular sensors, placed into a micromachined platform. The spectroscopic pattern from all the beads in the array was found to be specific for a particular mixture of analytes. To take this technology to the next stage, there is a need to develop strategies for achieving optical responses from beads when receptors for different classes of analytes are attached. This proposal entails studies aimed at addressing two areas. First, we propose to test the efficiency of indicator displacement assays for optical signaling. Indicators responsive to pH effects and Ca(ll) binding will be used to signal analyte binding. Second, we intend to explore the ability to use differential receptors to fingerprint the components present in a complex mixture of analytes. These two goals will be explored within the context of two highly challenging complex solutions: wine and urine. The reason for the choice of wine and urine as our test-bed solutions is due to their complexity and the uniquely similar chemical structures. We propose to target five classes of analytes: carboxylic acids, sugars, pectins, tannins, and gluoconorides. These classes present different challenges for fingerprinting. We will use "in-hand" receptors for carboxylates, sugars and gluconorides. Alternatively, the receptors for the pectins, tannins and gluconorides will be derived from combinatorial chemistry. In practice, synthetic receptors suffer interference from similar analytes due to their simplicity. Yet, we feel that the power of an array of synthetic receptors, when coupled with pattern recognition protocols, cannot be exceeded for array sensor applications. Synthetic receptors are naturally cross-reactive, the exact attribute that is desired in an array setting. Furthermore, the use of synthetic combinatorial chemistry in the creation of unnatural receptors naturally compliments this requirement of cross reactivity. Our goal is to teach this general lesson to the supramolecular and analytical chemistry community.