Machine Learning Assisted Fluorescent Sensor Array for Sensing Applications

ABSTRACT Fluorescent sensor arrays provide pattern‑based, multidimensional optical fingerprints for detecting chemically and biologically diverse analytes across complex matrices. By leveraging orthogonal readouts (intensity, ratiometric channels, lifetime, and excitation–emission matrices) from cross‐reactive and target‐specific elements, fluorescent sensor arrays achieve sensitive, rapid measurements suitable for environmental, biomedical, and food‑safety applications. The data richness of fluorescent sensor arrays, however, exceeds the capabilities of traditional analytical approaches. Classical chemometrics, exemplified by principal component analysis for exploratory visualisation and linear discriminant analysis for baseline classification, assumes linear structure and homoscedasticity, and therefore struggles with non‑linear photophysical responses, multicollinearity, and mixture quantification. This review surveys machine‑learning methods that address these limitations for both discrimination and quantification, including support vector machines and k‑nearest neighbours, tree ensembles, Gaussian process and support‑vector regression, and neural/deep‑learning models tailored for spectra, excitation–emission matrices, and images. Practical guidance is provided on acquisition and pre‑processing, rigorous validation (nested cross‑validation, external tests), uncertainty quantification, and interpretability to inform array design and deployment. Case studies demonstrate improved sensitivity, selectivity, robustness, and calibration transfer. Remaining challenges, dataset size, drift, and matrix effects, are discussed alongside opportunities in excitation‑multiplexed “virtual arrays”, active learning, and explainable AI for next‑generation, data‑driven fluorescent sensing.