Rational Selection of Minimal Sensor Arrays for Analyte Fingerprinting.

High-dimensional, cross-reactive sensor arrays enable powerful chemical fingerprinting, but practical use is often limited by the difficulty of identifying a small, reliable subset of sensors from screening data that contain only a few replicates per analyte. In many experimental settings, collecting large data sets is infeasible, and sensor selection must be performed under conditions of substantial variability and partial overlap between analyte responses. Here, we present a transparent and data-efficient analysis framework that enables rational reduction of sensor arrays using limited experimental data. Rather than relying solely on black-box accuracy metrics, our approach constructs simple, analyte-specific decision regions from measured responses and evaluates how individual sensors contribute to the separation of analytes that are most difficult to distinguish. Sensors are ranked by their contribution to resolving these difficult cases, producing stable and reproducible selections even when replicate numbers are small. An intuitive trade-off curve directly identifies the smallest sensor subset required to reach a desired classification performance. The robustness of the approach is demonstrated using controlled inflation alteration of experimental variability and by application to three independent fluorescence-based sensor libraries. In all cases, we show that only a few selected sensors are sufficient to achieve low classification error while retaining clear, interpretable decision maps that reveal how sensor responses give rise to analyte discrimination. This work provides an open-source, general, platform-agnostic strategy for screening data analysis and sensor-array design, offering compact and interpretable solutions suited to data-limited conditions common in chemical and biological sensing.