Zellers E T, Batterman S A, Han M, Patrash S J
Department of Environmental and Industrial Health, School of Public Health, University of Michigan, Ann Arbor 48109-2029.
Anal Chem. 1995 Mar 15;67(6):1092-106. doi: 10.1021/ac00102a012.
A method for determining the optimal set of polymer sensor coatings to include in a surface acoustic wave (SAW) sensor array for the analysis of organic vapors is described. The method combines an extended disjoint principal components regression (EDPCR) pattern recognition analysis with Monte Carlo simulations of sensor responses to rank the various possible coating selections and to estimate the ability of the sensor array to identify any set of vapor analytes. A data base consisting of the calibrated responses of 10 polymer-coated SAW sensors to each of six organic solvent vapors from three chemical classes was generated to demonstrate the method. Responses to the individual vapors were linear over the concentration ranges examined, and coatings were stable over several months of operation. Responses to binary mixtures were additive functions of the individual component responses, even for vapors capable of strong hydrogen bonding. The EDPCR-Monte Carlo method was used to select the four-sensor array that provided the least error in identifying the six vapors, whether present individually or in binary mixtures. The predicted rate of vapor identification (87%) was experimentally verified, and the vapor concentrations were estimated within 10% of experimental values in most cases. The majority of errors in identification occurred when an individual vapor could not be differentiated from a mixture of the same vapor with a much lower concentration of a second component. The selection of optimal coating sets for several ternary vapor mixtures is also examined. Results demonstrate the capabilities of polymer-coated SAW sensor arrays for analyzing of solvent vapor mixtures and the advantages of the EDPCR-Monte Carlo method for predicting and optimizing performance.
本文描述了一种确定用于分析有机蒸汽的表面声波（SAW）传感器阵列中最佳聚合物传感器涂层组合的方法。该方法将扩展不相交主成分回归（EDPCR）模式识别分析与传感器响应的蒙特卡罗模拟相结合，以对各种可能的涂层选择进行排序，并估计传感器阵列识别任何一组蒸汽分析物的能力。生成了一个数据库，其中包含10个聚合物涂层SAW传感器对来自三个化学类别的六种有机溶剂蒸汽中每一种的校准响应，以证明该方法。在所研究的浓度范围内，对单个蒸汽的响应呈线性，并且涂层在几个月的运行过程中保持稳定。对二元混合物的响应是各个组分响应的加和函数，即使对于能够形成强氢键的蒸汽也是如此。EDPCR - 蒙特卡罗方法用于选择在识别六种蒸汽（无论是单独存在还是以二元混合物形式存在）时提供最小误差的四传感器阵列。蒸汽识别的预测率（87%）经过实验验证，并且在大多数情况下，蒸汽浓度的估计值与实验值的误差在10%以内。当单个蒸汽无法与含有低得多浓度的第二种组分的相同蒸汽混合物区分开时，会出现大多数识别错误。还研究了几种三元蒸汽混合物的最佳涂层组合选择。结果证明了聚合物涂层SAW传感器阵列在分析溶剂蒸汽混合物方面的能力以及EDPCR - 蒙特卡罗方法在预测和优化性能方面的优势。
