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A method for predicting the five species contents of cadmium was developed by combining the back-propagation artificial neural network with graphite furnace atomic absorption spectrometry(BP-ANN-GF-AAS).Based on the strong learning function and the features of the information distributed storage of artificial neural network(ANN),a single ANN was constituted in which only one determination point of every sample was required.The exchangeable,carbonated,Fe-Mn oxidable,organic and residual species of cadmium for 20 kinds of soil samples from the two sections of Changchun(China) were determined by BP-ANN-GF-AAS.The detection limit of the method is 0.024 μg/L and the limit of quantification is 0.080 μg/L.t-Test indicates that there is not any systemic error of the results obtained by the Tessier sequential extraction graphite furnace atomic absorption spectrometry method(Tessier-GF-AAS) and BP-ANN-GF-AAS.Compared with those of the Tessier-GF-AAS,the prediction errors of BP-ANN-GF-AAS are less than 10%.The proposed method is fast,convenient,sensitive,and can eliminate the interference among various species.
A method for predicting the five species contents of cadmium was developed by combining the back-propagation artificial neural network with graphite furnace atomic absorption spectrometry (BP-ANN-GF-AAS). Based on the strong learning function and the features of the information distributed storage of artificial neural network (ANN), a single ANN was constituted in which only one determination point of every sample was required. the exchangeable, carbonated, Fe-Mn oxidable, organic and residual species of cadmium for 20 kinds of soil samples from the Two sections of Changchun (China) were determined by BP-ANN-GF-AAS. The detection limit of the method is 0.024 μg / L and the limit of quantification is 0.080 μg / Lt-Test indicates that there is not any systemic error of the results obtained by the Tessier sequential extraction graphite furnace atomic absorption spectrometry method (Tessier-GF-AAS) and BP-ANN-GF-AAS. Compared with those of the Tessier-GF-AAS, the prediction errors of BP-ANN-GF -AAS are l ess than 10%. The proposed method is fast, convenient, sensitive, and can eliminate the interference among various species.