Metals play vital roles in biological processes and environment, but their imbalance is associated with several biological and environmental problems, making the detection of toxic metals inevitable.1 However, detection of toxic metals in biological and environmental samples remains a challenge, especially in complex environments where multiple metal ions coexist.
The development of small fluorescent molecules has become increasingly appealing due to their versatility, tuneable photophysical properties, and ability to monitor analytes in real time.2 While selective sensors—designed to detect only a single analyte— have been the focus of many studies, cross-reactive sensors (also called as sensor array) that respond to multiple analytes offer distinct advantages, particularly when analysing complex mixtures. Our approach involves the use of small fluorescent sensors within an array-based platform for identification and quantification of metals. By employing multivariate statistical methods such as linear discriminant analysis (LDA), we aim to distinguish between different toxic metals based on the fluorescent responses of our sensor array. We have developed a library of fluorescent sensors capable of distinguishing different toxic metals, which play vital roles in both biological systems and the environment. We are mainly focused on detection of metals with similar characteristics (e.g. Cu²⁺, Co²⁺, Ni²⁺), and those to which limited attention has been given to date (e.g. Cr⁶⁺, Mo⁶⁺, As³⁺). More, we used a ML-assisted approach to determine a subset of five sensors that could accurately classify 15 metals and quantify concentrations in solutions. Optimised array enables quantification of selected metals and distinguishes metals with common characteristics in mixtures. Our ongoing work focuses on applying this system to real samples. This study demonstrates how array-based sensing provides a powerful, low-cost strategy for metal ion detection and quantification.