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确定蒸汽传感器阵列的识别极限。

Establishing a limit of recognition for a vapor sensor array.

作者信息

Zellers E T, Park J, Hsu T, Groves W A

机构信息

Department of Environmental and Industrial Health, University of Michigan, Ann Arbor 48109-2029, USA.

出版信息

Anal Chem. 1998 Oct 1;70(19):4191-201. doi: 10.1021/ac980344w.

Abstract

Organic vapor analysis with microsensor arrays relies principally on two output parameters: the response pattern, which provides qualitative information, and the response sensitivity, which determines the limit of detection (LOD). The latter is used to define the operating limit in the low-concentration range, under the implicit assumption that, if a vapor can be detected, it can be identified and differentiated from other vapors on the basis of its response pattern. In this study, the performance of an array of four polymer-coated surface acoustic wave vapor sensors was explored using calibrated response data from 16 solvent vapors in Monte Carlo simulations coupled with pattern recognition analysis. The statistical modeling revealed that the ability to recognize a vapor from its response pattern decreases with decreasing vapor concentration, as expected, but also that the concentration at which errors in vapor recognition become excessive is well above the calculated LOD in most cases, despite the LOD being based on the least sensitive sensor in the array. These results suggest the adoption of a limit of recognition (LOR), defined as the concentration below which a vapor can no longer be reliably recognized from its response pattern, as an additional criterion for evaluating the performance of multisensor arrays. A generalized method for estimating the LOR is presented, as well as a means for improving the LOR via residual error analysis.

摘要

使用微传感器阵列进行有机蒸汽分析主要依赖于两个输出参数

提供定性信息的响应模式,以及决定检测限(LOD)的响应灵敏度。后者用于定义低浓度范围内的操作极限,隐含的假设是,如果一种蒸汽能够被检测到,那么就可以根据其响应模式将其识别并与其他蒸汽区分开来。在本研究中,利用来自16种溶剂蒸汽的校准响应数据,在蒙特卡洛模拟中结合模式识别分析,探索了由四个聚合物涂层表面声波蒸汽传感器组成的阵列的性能。统计建模表明,正如预期的那样,从其响应模式识别蒸汽的能力会随着蒸汽浓度的降低而下降,但在大多数情况下,蒸汽识别错误变得过多时的浓度远高于计算出的检测限,尽管检测限是基于阵列中最不敏感的传感器得出的。这些结果表明应采用识别限(LOR),定义为蒸汽浓度低于该值时就无法再根据其响应模式可靠识别,作为评估多传感器阵列性能的附加标准。本文提出了一种估计LOR的通用方法,以及一种通过残差分析来提高LOR的方法。

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