Karimvand Somaiyeh Khodadadi, Abdollahi Hamid
Department of Chemistry, Institute for Advanced Studies in Basic Sciences, P.O. Box, Zanjan, 45195-1159, Iran.
Mikrochim Acta. 2024 Jun 25;191(7):420. doi: 10.1007/s00604-024-06506-x.
In a sensor array system with the ability to design multiple sensor elements, selecting the optimal sensor elements can maximize the efficiency of the sensor array in responding to various analytes. This paper proposes the application of hard chemical modeling as a means to identify the optimal subset of indicator displacement assay (IDA)-based sensors in the array, aiming to achieve maximum performance for detection or quantification. The model governing all reactions in the IDA sensor and the model of the pure spectrum of active species are first determined. Next, by applying the model of the pure spectrum of active species (including the indicator and indicator-receptor complex) to each sensor element and taking into account the system's nonlinearity, corrected concentration profiles of active species are derived using the generalized classical least square (G-CLS) method. These corrected concentration profiles are utilized as the output signal for each sensor element. Finally, the dynamic ranges (DR) of each sensor element and subsequently the DR for all possible sensor arrays are determined.To assess the effectiveness of the sensor array through dynamic range analysis, an IDA-based sensor system comprising five different elements was designed. It was observed that sensors with a larger dynamic range, when arranged together in an array, are more efficient for the quantitative identification of analytes. However, simply increasing the number of elements in the sensor array may not necessarily enhance its effectiveness; instead, it could amplify the noise within the system. Additionally, multivariate fitting regression with Gaussian function (MFRG), a nonlinear calibration method, was applied to assess the prediction ability of all possible designed sensor arrays.
在具有设计多个传感元件能力的传感器阵列系统中，选择最佳传感元件可使传感器阵列对各种分析物的响应效率最大化。本文提出应用硬化学建模作为一种手段，以识别阵列中基于指示剂置换分析（IDA）的传感器的最佳子集，旨在实现检测或定量的最大性能。首先确定控制IDA传感器中所有反应的模型以及活性物种纯光谱的模型。接下来，通过将活性物种（包括指示剂和指示剂-受体复合物）的纯光谱模型应用于每个传感元件，并考虑系统的非线性，使用广义经典最小二乘法（G-CLS）得出活性物种的校正浓度分布。这些校正后的浓度分布用作每个传感元件的输出信号。最后，确定每个传感元件的动态范围（DR），进而确定所有可能的传感器阵列的DR。为了通过动态范围分析评估传感器阵列的有效性，设计了一个由五个不同元件组成的基于IDA的传感器系统。观察到，动态范围较大的传感器在阵列中一起排列时，对分析物的定量识别效率更高。然而，仅仅增加传感器阵列中的元件数量不一定能提高其有效性；相反，这可能会放大系统内的噪声。此外，还应用了具有高斯函数的多元拟合回归（MFRG），一种非线性校准方法，来评估所有可能设计的传感器阵列的预测能力。
