Cortina M, Duran A, Alegret S, del Valle M
Sensors and Biosensors Group, Department of Chemistry, Autonomous University of Barcelona, Edifici Cn, 08193, Bellaterra, Barcelona, Spain.
Anal Bioanal Chem. 2006 Aug;385(7):1186-94. doi: 10.1007/s00216-006-0530-2. Epub 2006 Jun 24.
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 steady-state: 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.
最近我们实验室开发了基于非特异性响应化学传感器阵列的智能自动系统。对于多测定应用，通常的选择是使用电位传感器阵列来生成信号，并通过正确训练人工神经网络（ANN）来获得校准模型。由于正确建模需要大量信息，我们提出使用顺序注射分析（SIA）技术自动生成信息。最初使用的信号是稳态信号：浓度阶跃变化后的平衡信号。我们现在已经调整了程序来记录与样品阶跃相对应的瞬态响应。因此，这种方法的新颖之处在于使用信号的动态成分以便更好地区分或鉴别样品。在已开发的电子舌系统中，检测是通过使用由五个基于PVC膜的电位传感器组成的传感器阵列进行的。对于已开发的应用，我们使用了两种不同的氯离子选择性传感器、两种硝酸根离子选择性传感器和一种通用响应传感器。由于对于人工神经网络来说原始数据量（与五个传感器对应的五重记录）过多，因此在建模之前需要进行一些特征提取步骤。为了实现大量的数据缩减和噪声过滤，将获得的数据与正交勒让德多项式进行拟合。在这种情况下，结果表明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用于对所测定物种（Cl-、NO3-和HCO3-）浓度进行建模的人工神经网络的信息。使用贝叶斯正则化算法训练的反向传播神经网络获得了最佳结果；该网络有一个包含三个神经元且具有tansig传递函数的单隐藏层。将从随时间变化的响应中获得的结果与从稳态条件下获得的结果进行比较，结果表明前者具有更好的性能。最后，该方法被应用于测定合成样品和实际水样中的阴离子，并且也取得了令人满意的比较结果。
