Maleki N, Safavi A, Sedaghatpour F
Department of Chemistry, College of Sciences, Shiraz University, Shiraz 71454, Iran.
Talanta. 2004 Nov 15;64(4):830-5. doi: 10.1016/j.talanta.2004.02.041.
An artificial neural network (ANN) model is developed for simultaneous determination of Al(III) and Fe(III) in alloys by using chrome azurol S (CAS) as the chromogenic reagent and CCD camera as the detection system. All calibration, prediction and real samples data were obtained by taking a single image. Experimental conditions were established to reduce interferences and increase sensitivity and selectivity in the analysis of Al(III) and Fe(III). In this way, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. Sigmoid transfer functions were used in the hidden and output layers to facilitate nonlinear calibration. Both Al(III) and Fe(III) can be determined in the concentration range of 0.25-4mugml(-1) with satisfactory accuracy and precision. The proposed method was also applied satisfactorily to the determination of considered metal ions in two synthetic alloys.
开发了一种人工神经网络（ANN）模型，用于以铬天青S（CAS）作为显色剂、电荷耦合器件（CCD）相机作为检测系统，同时测定合金中的铝（III）和铁（III）。所有校准、预测和实际样品数据均通过拍摄单张图像获得。建立了实验条件，以减少干扰并提高铝（III）和铁（III）分析的灵敏度和选择性。通过这种方式，应用反向传播学习规则训练了一个由三层节点组成的人工神经网络。在隐藏层和输出层使用了Sigmoid传递函数以促进非线性校准。铝（III）和铁（III）在0.25 - 4μg ml⁻¹的浓度范围内均可测定，具有令人满意的准确度和精密度。所提出的方法也成功应用于两种合成合金中上述金属离子的测定。
