Linear and Nonlinear Learning from Spectroelectrochemical Data: Interrogation of PLS and CNN Behavior Under Experimental Scarcity.

Spectroelectrochemistry (SEC) provides unique information-rich datasets by coupling molecular spectroscopic fingerprints with electrochemical activity. Despite its richness, direct machine-learning (ML) analysis of SEC data under realistic experimental constraints and scarcity remains unexplored. This work examines how much spectroscopically encoded electrochemical information can be learned from minimal SEC training data in a chemically reversible two-electron redox system, and how model choice interacts with limited experimental diversity across scan rate. Using purely experimental SEC datasets collected at four scan rates (2, 3, 5, and 7 mV/s), partial least squares (PLS) and convolutional neural networks (CNNs) regressors are evaluated under several SEC-level training, validation, and test configurations. Model performance is assessed across three targets of increasing physical complexity, including species concentrations, derivative cyclic voltabsorptometry (DCVA) current, and experimental cyclic voltammetry (CV) current. Under single-SEC training, both models achieve the expected near-quantitative concentration prediction (R