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具有高维电化学传感器信号的顺序注射系统 第2部分。用于测定碱性离子的电位型电子舌。

Sequential injection system with higher dimensional electrochemical sensor signals Part 2. Potentiometric e-tongue for the determination of alkaline ions.

作者信息

Cortina M, Gutés A, Alegret S, Del Valle Manuel

机构信息

Sensors and Biosensors Group, Department of Chemistry, Autonomous University of Barcelona, Edifici Cn, 08193 Bellaterra, Spain.

出版信息

Talanta. 2005 Jun 15;66(5):1197-206. doi: 10.1016/j.talanta.2005.01.023.

DOI:10.1016/j.talanta.2005.01.023
PMID:18970109
Abstract

An intelligent, automatic system based on an array of non-specific-response chemical sensors was developed. As a great amount of information is required for its correct modelling, we propose a system generating it itself. The sequential injection analysis (SIA) technique was chosen as it enables the processes of training, calibration, validation and operation to be automated simply. Detection was carried out using an array of potentiometric sensors based on PVC membranes of different selectivity. The diluted standard solutions needed for system learning and response modelling are automatically prepared from more concentrated standards. The electrodes used were characterised with respect to one and two analytes, by means of high-dimensionality calibrations, and the response surface of each was represented; this characterisation enabled an interference study of great practical utility. The combined response was modelled by means of artificial neural networks (ANNs), and thus it was possible to obtain an automated electronic tongue based on SIA. In order to identify the ANN which provided the best model of the electrode responses, some of the network's parameters were optimised and its usefulness in determining NH(4)(+), K(+) and Na(+) ions in synthetic samples was then tested. Finally, it was used to determine these ions in commercial fertilisers, the obtained results being compared with reference methods.

摘要

开发了一种基于非特异性响应化学传感器阵列的智能自动系统。由于其正确建模需要大量信息,我们提出了一种能自行生成信息的系统。选择顺序注射分析(SIA)技术是因为它能简单地实现训练、校准、验证和操作过程的自动化。使用基于不同选择性PVC膜的电位传感器阵列进行检测。系统学习和响应建模所需的稀释标准溶液由更浓缩的标准溶液自动制备。通过高维校准对所用电极针对一种和两种分析物进行表征,并表示每个电极的响应面;这种表征使得具有很大实用价值的干扰研究成为可能。通过人工神经网络(ANN)对组合响应进行建模,从而有可能获得基于SIA的自动化电子舌。为了确定能提供最佳电极响应模型的人工神经网络,对网络的一些参数进行了优化,然后测试了其在测定合成样品中NH(4)(+)、K(+)和Na(+)离子方面的实用性。最后,将其用于测定商业肥料中的这些离子,并将所得结果与参考方法进行比较。

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