Sensor array for qualitative and quantitative analysis of metal ions and metal oxyanion based on colorimetric and chemometric methods

Abstract Gold nanoparticle (AuNP)-based colorimetric sensor is sensitive for the detection of metal ions and metal oxyanion in aqueous solution. However, this method is usually not suitable for multi-objective analysis in complex mixture systems because it is suffering from interference of co-existents. In the present paper, we proposed a sensitive, flexible, low-cost, and multi-units sensor method for the qualitative and quantitative analysis of metal ions and metal oxyanion based on the global ultraviolet and visible (UV–Vis) spectra of amino acid-gold nanoparticles (amino acid-AuNPs) sensors in the range of 230–800 nm. Different amino acids (L-Histidine, L-Lysine, L-Methionine, D-Penicillium) which can prevent the aggregation of the AuNPs in NaCl solution, were investigated to build sensor arrays responding to different ions induced AuNPs aggregation. The UV–Vis spectra that Cd 2+ , Ba 2+ , Mn 2+ , Ni 2+ , Cu 2+ , Fe 3+ , Cr 3+ , Cr 2 O 7 2- , Sn 4+ , Pb 2+ induce amino acid-AuNPs displayed different characteristics and the ions were classified correctly by using partial least squares-discriminant analysis (PLS-DA). Taking the advantage of the multivariate analysis and sensor arrays, we simultaneously quantified the ions in binary and ternary mixture systems (Cr 3+ /Cr 2 O 7 2- , Fe 3+ /Cd 2+ , Fe 3+ /Cr 3+ /Cr 2 O 7 2- ). Data fusion methods further improved the prediction accuracy of the chemometric models built on multi-amino acids-AuNPs sensors. The proposed method has a potential for analyzing metal ions and metal oxyanion in much more complex mixture systems.