Department of Chemistry, Faculty of Sciences, Shiraz University, Shiraz 71454, Iran.
Talanta. 2004 Jan 9;62(1):51-6. doi: 10.1016/S0039-9140(03)00399-0.
Artificial neural networks (ANNs) are proposed for the determination of sulfite and sulfide simultaneously. The method is based on the reaction between Brilliant Green (BG) as a colored reagent and sulfite and/or sulfide in buffered solution (pH 7.0) and monitoring the changes of absorbance at maximum wavelength of 628nm. Experimental conditions such as pH, reagents concentrations, and temperature were optimized and training the network was performed using principal components (PCs) of the original data. The network architecture (number of input, hidden and output nodes), and some parameters such as learning rate (eta) and momentum (alpha) were also optimized for getting satisfactory results with minimum errors. The measuring range was 0.05-3.6mugml(-1) for both analytes. The proposed method has been successfully applied to the quantification of the sulfite and sulfide in different water samples.
人工神经网络 (ANNs) 被提议用于同时测定亚硫酸盐和硫化物。该方法基于在缓冲溶液 (pH 7.0) 中亚硫酸盐和/或硫化物与亮绿 (BG) 作为显色试剂之间的反应，并监测在最大波长 628nm 处吸光度的变化。优化了实验条件，如 pH 值、试剂浓度和温度，并使用原始数据的主成分 (PCs) 对网络进行训练。为了获得最小误差的满意结果，还优化了网络结构（输入、隐藏和输出节点的数量）和一些参数，如学习率 (eta) 和动量 (alpha)。两种分析物的测定范围均为 0.05-3.6μgml(-1)。该方法已成功应用于不同水样中亚硫酸盐和硫化物的定量分析。
