Optimization of on-line microwave flow injection analysis system by artificial neural networks for the determination of ruthenium

• Published in: Analytica chimica acta Vol. 429; no. 2; pp. 207 - 213
• Main Authors: Wang, Huaiwen, Zhou, Yongyao, Zhao, Yunkun, Li, Qianfeng, Chen, Xingguo, Hu, Zhide
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 23.02.2001; Elsevier
• Subjects: Analytical chemistry; Artificial neural network; Chemistry; Exact sciences and technology; On-line microwave flow injection analysis; Optimization; Ruthenium; Spectrometric and optical methods; On-line microwave flow injection analysis; Ruthenium; Artificial neural network; Optimization; Data analysis; Chemical analysis; Ruthenium ion; Backpropagation algorithm; Transition metal Ions; Sample preparation; Feedforward neural nets; Microwave oven; Acid digestion; Platinoid Ions; Learning algorithm; Chemometrics; Quantitative analysis; Flow injection; Ultraviolet visible spectrometry
• ISSN: 0003-2670; 1873-4324
• DOI: 10.1016/S0003-2670(00)01291-5

A methodology based on the coupling of experimental design and artificial neural networks (ANNs) was proposed in the optimization of a new on-line microwave flow injection system (FIA) for the determination of ruthenium, grounded on its catalytic effect on the oxidation of dibromocarboxyarsenazo (DBM-AsA) by potassium periodide under the microwave irradiation. The response function (RF) used was a weighted linear combination of two variables related to sensitivity and sampling rate. A neural network with extended delta-bar-delta (EDBD) learning algorithm was applied to predict the maximal RF, according to which the optimized conditions were obtained. The optimized new on-line microwave FIA system is able to determine ruthenium in 5–200 ng ml −1 range with a detection limit of 2.1 ng ml −1 and a recovery of 94.6%. A sampling rate of 58 h −1 was obtained. In contrast to traditional methods, the use of this methodology has advantages in terms of a reduction in analysis time and an improvement in the ability of optimization.