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首页> 外文期刊>Talanta: The International Journal of Pure and Applied Analytical Chemistry >Single-step calibration,prediction and real samples data acquisition for artificial neural network using a CCD camera
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Single-step calibration,prediction and real samples data acquisition for artificial neural network using a CCD camera

机译:使用CCD相机进行人工神经网络的单步校准,预测和实际样品数据采集

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摘要

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 0.25-4 mug ml~(-1)with satisfactory accuracy and precision.The proposed method was also applied satisfactorily to the determination of considered metal ions in two synthetic alloys.
机译:以铬天青S(CAS)为显色剂,以CCD相机为检测系统,建立了一种用于同时测定合金中Al(III)和Fe(III)的人工神经网络模型。通过拍摄单张图像获得真实的样品数据。建立了实验条件以减少分析Al(III)和Fe(III)的干扰并提高灵敏度和选择性。这样,一个由三层膜组成的人工神经网络通过使用反向传播学习规则训练节点,在隐藏层和输出层中使用S型传递函数以促进非线性校准。可以在0.25-4马克杯的浓度范围内确定Al(III)和Fe(III) ml〜(-1)具有良好的准确度和精密度。该方法也令人满意地用于两种合成合金中考虑的金属离子的测定。

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