Chemistry Department, Faculty of Sciences, KN Toosi University of Technology, Tehran, Iran.
Environ Monit Assess. 2011 Dec;183(1-4):57-63. doi: 10.1007/s10661-011-1905-1. Epub 2011 Mar 17.
The partial least squares modeling is a powerful multivariate statistical tool applied to the spectrophotometric simultaneous determination of the divalent ions of zinc, cadmium, and lead based on the formation of their complexes with 4-(2-thiazolylazo) resorcinol in surfactant media. The linear concentration range for zinc, cadmium, and lead were 0.10-1.31, 0.148-1.92, and 0.148-3.70 mg L( -1), respectively. The experimental calibration set was composed of 36 sample solutions using a mixture design for three component mixtures. The absorption spectra were recorded from 380 through 650 nm. The effect of pH on the sensitivity in determination of zinc, cadmium, and lead was studied in order to choose the optimum pH (pH = 8) for determination. The root-mean-square errors of predictions for zinc, cadmium, and lead were 0.0466, 0.0282, and 0.050, respectively. The proposed method was successfully applied for the determination of zinc, cadmium, and lead in water samples.
偏最小二乘建模是一种强大的多元统计工具，应用于分光光度法同时测定锌、镉和铅的二价离子，基于它们与 4-(2-噻唑基偶氮)间苯二酚在表面活性剂介质中形成的配合物。锌、镉和铅的线性浓度范围分别为 0.10-1.31、0.148-1.92 和 0.148-3.70mgL(-1)。实验校准集由 36 个样品溶液组成，使用三元混合物的混合设计。吸收光谱从 380 到 650nm 记录。研究了 pH 值对锌、镉和铅测定灵敏度的影响，以选择最佳测定 pH 值(pH=8)。锌、镉和铅的预测均方根误差分别为 0.0466、0.0282 和 0.050。该方法成功应用于水样中锌、镉和铅的测定。
