This work presents an adaptive sensor signal-processing approach to enable quantification, using a single gas sensor or a small sensor array, of multianalyte mixtures of aromatic hydrocarbons in the presence of various interferents and humidity for environmental-monitoring applications. Dynamic sensor responses are analyzed by extracting multivariable sensing parameters to provide necessary sensitivity and selectivity. This is achieved by integrating the Levenberg–Marquardt-modified, exponentially weighted, recursive-least-squares-estimation (LM-modified EW-RLSE) algorithm and principal-component analysis (PCA). Achieving measured detection limits as low as 3 μg/L (≤1 ppm by volume) for 6 target analytes, the system exhibits excellent PCA cluster separation for all analytes in the mixtures, with reliable identification and accurate quantification, even in the presence of various interferents. Concentration errors of approximately ±5% are obtained for mixtures containing up to 6 BTEX compounds (including chemical isomers) and up to 4 interferents. Additionally, the study investigates the impact of humidity on the polymer/plasticizer-coated shear-horizontal surface acoustic wave (SH-SAW) sensors, demonstrating accurate concentration estimation in a relative humidity range from dry nitrogen to 65%. This sensing-and-multivariate-signal-processing approach is a promising candidate for reliable environmental monitoring in real-world applications.

中文翻译：

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分析存在干扰物和湿度条件下多变量传感器对多分析物气体样品的响应


这项工作提出了一种自适应传感器信号处理方法，可以在存在各种干扰物和湿度的情况下，使用单个气体传感器或小型传感器阵列对芳烃的多种分析物混合物进行量化，用于环境监测应用。通过提取多变量传感参数来分析动态传感器响应，以提供必要的灵敏度和选择性。这是通过集成 Levenberg-Marquardt 修改的、指数加权的递归最小二乘估计 （LM-modified EW-RLSE） 算法和主成分分析 （PCA） 来实现的。该系统对 6 种目标分析物的检测限低至 3 μg/L（≤1 ppm（体积计），对混合物中的所有分析物均表现出出色的 PCA 簇分离，即使在存在各种干扰物的情况下也能实现可靠的鉴定和准确定量。对于含有多达 6 种 BTEX 化合物（包括化学异构体）和多达 4 种干扰物的混合物，浓度误差约为 ±5%。此外，该研究还调查了湿度对聚合物/增塑剂涂层的剪切水平表面声波 （SH-SAW） 传感器的影响，证明了在干燥氮气到 65% 的相对湿度范围内可以准确估计浓度。这种传感和多变量信号处理方法是在实际应用中进行可靠环境监测的有前途的候选者。