Beyond Pretty Clusters: Toward Standardized Validation of Fluorescence Sensor Arrays for Multi-Analyte Discrimination: A Mini Review

Abstract

Fluorescence sensor arrays built on lanthanide metal-organic frameworks (Ln-MOFs) have rapidly become a standard approach for discriminating structurally similar analytes. These range from antibiotic congeners and their metabolites to co-occurring metal ions in food and biological matrices. The typical workflow pairs multi-channel fluorescence titration with principal component analysis (PCA) or hierarchical cluster analysis (HCA). A visually separated score plot is then routinely presented as sufficient evidence of discriminatory power. This paper audits that convention across the recent Ln-MOF sensor array literature. We identify five recurring evidentiary gaps. First, circular validation: a model is tested only on the data used to build it. Second, the absence of a quantitative separation metric to replace descriptive language such as “well separated”. Third, a concentration-dependence blind spot, where discrimination is demonstrated at a single analyte concentration rather than across the working range. Fourth, the near-total absence of robustness testing under simulated noise or real-world perturbation. Fifth, the conflation of classification claims with quantification claims that are rarely separately validated. Drawing on studies that have already addressed one or more of these gaps individually, we show that closing them requires no new sensing chemistry. Only a different treatment of data most studies already collect is needed. We propose a nine-item minimum reporting checklist and illustrate it against two representative published arrays. We argue for its adoption as a low-cost first step toward comparable, reproducible discrimination claims, drawing an explicit parallel to earlier minimum-reporting movements such as MIQE and STARD.