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A sequential injection electronic tongue employing the transient response from potentiometric sensors for anion multidetermination

机译:顺序注射电子舌,采用电位传感器的瞬态响应进行阴离子多测定

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Intelligent and automatic systems based on arrays of non-specific-response chemical sensors were recently developed in our laboratory. For multidetermination applications, the normal choice is an array of potentiometric sensors to generate the signal, and an artificial neural network (ANN) correctly trained to obtain the calibration model. As a great amount of information is required for the proper modelling, we proposed its automated generation by using the sequential injection analysis (SIA) technique. First signals used were steadystate: the equilibrium signal after a step-change in concentration. We have now adapted our procedures to record the transient response corresponding to a sample step. The novelty in this approach is therefore the use of the dynamic components of the signal in order to better discriminate or differentiate a sample. In the developed electronic tongue systems, detection is carried out by using a sensor array formed by five potentiometric sensors based on PVC membranes. For the developed application we employed two different chloride-selective sensors, two nitrate-selective sensors and one generic response sensor. As the amount of raw data (fivefold recordings corresponding to the five sensors) is excessive for an ANN, some feature extraction step prior to the modelling was needed. In order to attain substantial data reduction and noise filtering, the data obtained were fitted with orthonormal Legendre polynomials. In this case, a third-degree Legendre polynomial was shown to be sufficient to fit the data. The coefficients of these polynomials were the input information fed into the ANN used to model the concentrations of the determined species (Cl-, NO3-and HCO3-). Best results were obtained by using a backpropagation neural network trained with the Bayesian regularisation algorithm; the net had a single hidden layer containing three neurons with the tansig transfer function. The results obtained from the time-dependent response were compared with those obtained from steady-state conditions, showing the former superior performance. Finally, the method was applied for determining anions in synthetic samples and real water samples, where a satisfactory comparison was also achieved.
机译:最近在我们实验室中开发了基于非特定响应化学传感器阵列的智能和自动系统。对于多测定应用,通常的选择是使用电位计传感器阵列来生成信号,以及经过正确训练的人工神经网络(ANN)以获取校准模型。由于要进行正确的建模需要大量信息,因此我们建议使用顺序注入分析(SIA)技术自动生成信息。最初使用的信号是稳态信号:浓度逐步变化后的平衡信号。现在,我们已调整程序以记录与采样步骤相对应的瞬态响应。因此,这种方法的新颖之处在于使用信号的动态分量,以便更好地区分或区分样本。在已开发的电子舌系统中,通过使用由五个基于PVC膜的电位传感器形成的传感器阵列进行检测。对于开发的应用程序,我们使用了两种不同的氯离子选择性传感器,两种硝酸盐选择性传感器和一种通用响应传感器。由于ANN的原始数据量(对应于五个传感器的五个记录)的数量过多,因此需要在建模之前进行一些特征提取步骤。为了获得大量的数据减少和噪声过滤,将获得的数据与正交勒让德多项式拟合。在这种情况下,显示了三次勒让德多项式足以拟合数据。这些多项式的系数是输入到ANN中的输入信息,该信息用于对所确定物种(Cl-,NO3-和HCO3-)的浓度进行建模。使用经过贝叶斯正则化算法训练的反向传播神经网络可获得最佳结果。该网具有包含三个具有tansig传递函数的神经元的单个隐藏层。从时变响应获得的结果与从稳态条件获得的结果进行了比较,显示了以前的优异性能。最终,该方法用于测定合成样品和实际水样品中的阴离子,也获得了令人满意的比较。

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