Real-Sample Validation of Microchemical Sensors: Matrix Effects, Chemometric Reliability, and Field-Ready Analytical Performance

Microchemical sensors are increasingly used in food safety, clinical diagnostics, environmental monitoring, agriculture, forensics, and industry because they offer rapid, sensitive, miniaturized, and portable detection. However, performance is often judged mainly by limit of detection, linear range, sensitivity, selectivity, and recovery in spiked samples, which do not establish reliability in complex matrices. Real samples contain proteins, lipids, salts, pigments, enzymes, humic substances, particles, microbes, and variable pH or ionic strength that can alter recognition, calibration, signal generation, and interpretation. This review proposes a real-sample validation framework integrating sampling, pretreatment, matrix-effect assessment, realistic interference testing, calibration, reproducibility, inter-device variation, stability, authentic sample analysis, reference-method comparison, uncertainty, greenness, portability, and field readiness. It also evaluates chemometrics and artificial intelligence for electrochemical, optical, spectral, image-based, and sensor-array data, emphasizing risks of overfitting, data leakage, small datasets, weak external validation, and poor transferability. Unlike reviews centered on materials or ultralow detection limits, this review identifies validation intelligence as the missing link between laboratory sensitivity and real-world reliability. Ranking sensors by real-sample readiness rather than sensitivity alone provides a practical roadmap for robust, transparent, sustainable, and decision-ready sensing systems.