Development of multivariate calibration method for simultaneous determination of nickel, lead and zinc in tap water

Chau Minh Huynh,Mai Anh Nguyen,Minh Khanh Duong,Thach Thai Pham,Thu Du Ly,Tran Thi Bao Pham

doi:10.32508/stdj.v18i2.1176

Abstract

Conventional spectrophotometric methods for simultaneous determination of nickel, lead and zinc in forms of complexes with a reagent is not feasible due to the overlap of their absorption spectra. A multivariate calibration method was used to overcome this problem. In this study, the calibration model was constructed based on absorption spectra of 30 mixture standards in the range from 490 to 600 nm. Factors influencing experimental results such as amount of reagents, pH, and color development time were optimized. The standard calibration ranges for determination of nickel, lead and zinc were found at 0.5-5 ppm. The method was applied for determination of these ions in tap water samples at ppm level, with recoveries (and RSD) of nickel, lead and zinc were 103.3 % (3.0 %), 74.9 % (11.5 %) and 104.6 % (4.6 %), respectively.

Highlights

Nickel, lead and zinc co-exist in many samples
This research depicted a method for simultaneous determination of nickel, lead and zinc by combining spectrophotometry with multivariate calibration technique to increase the speed and accuracy of the analysis
On a fixed amount of xylenol orange (XO) was used for complexation and this resulted in the ratio varied from 1:2-1:20 depending on the M2+ level

Summary

Introduction

Lead and zinc co-exist in many samples. Several techniques such as XFS [1], polarography [2], AAS [3], ICP-OES [4] were used to determine these metal ions. This research depicted a method for simultaneous determination of nickel, lead and zinc by combining spectrophotometry with multivariate calibration technique to increase the speed and accuracy of the analysis. Complexes of these ions with xylenol orange (XO) in synthetic and tap water samples were analyzed by spectrophotometry without any prior treatment. Achieved data was treated by partial least square method (PLS) to build a correlation vector to predict concentration of each component in the samples from their spectral signals.

Results

Conclusion