Machine Learning-Assisted Lanthanide Fluorescence Sensor Array Based on pH Regulation for Pattern Recognition of Metal Ions in Biological Fluids

There is a strong correlation between physical health and metal ion levels in biological fluids, making them an attractive analyte for noninvasive health monitoring. Given the pressing need for multiple metal ions recognition and simultaneous detection within the human body, this study devised a multi-emission fluorescence sensor based on ATA luminescent ligands and lanthanide complexes. It responded differently to metal ion attack at various pH (at 6, 7, 8) conditions, generating multivariate cross-reactions. Through principal component analysis and Extra Trees algorithm analysis, a superior analytical performance pH-regulated lanthanide fluorescence sensor array was developed, capable of accurately identifying and quantifying ten kinds of metal ions, discriminating binary/ternary mixtures, and maintaining high selectivity in the presence of interference substances with an accuracy of up to 100%. This platform sidesteps the synthesis of multiple transducers, providing a novel strategy for constructing facile and feasible fluorescence sensing arrays based on lanthanide complexes. Notably, the proposed sensor array can enable noninvasive detection of metal ions in biological fluids, highlighting its potential application for diagnosing metal ion-related diseases.