The Molecular Recognition of Wine and Urine

描述(由申请人提供)：大多数味料和气味是通过非特定相互作用的反应组合来识别的。由一系列受体的同时反应产生的模式对一组特定的刺激是特定的。最近，德州大学开发了一种模拟哺乳动物味觉的设备，它使用了一系列树脂珠，这些树脂珠通过分子传感器进行化学衍生化，放置在微机械平台上。来自阵列中所有珠子的光谱图案被发现是特定分析物混合物所特有的。为了将这项技术带入下一阶段，需要开发一种策略，当不同类别的分析物的受体连接时，实现来自珠子的光学响应。 这项建议需要进行研究，以解决两个方面的问题。首先，我们建议测试用于光信号的指示器位移分析的效率。对pH效应和Ca(11)结合作出反应的指示剂将用于发出分析物结合的信号。其次，我们打算探索使用不同的受体对复杂的分析物混合物中存在的成分进行指纹识别的能力。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。之所以选择葡萄酒和尿液作为我们的试验台解决方案，是因为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、单宁和葡萄糖苷。这些课程对指纹识别提出了不同的挑战。我们将使用“手中”受体来识别羧酸盐、糖和葡萄糖苷。或者，果胶、单宁和葡萄糖苷的受体将来自组合化学。 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，在阵列传感器应用中，当与模式识别协议相结合时，合成受体阵列的能力是无法超越的。合成受体具有天然的交叉反应，这正是阵列设置中所需的属性。此外，在创造非天然受体时使用合成组合化学自然地满足了这种交叉反应的要求。我们的目标是向超分子和分析化学社区教授这一一般性的课程。

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.