Leakage-Controlled Deep Learning for Multi-Analyte Classification and Quantification in Fast-Scan Cyclic Voltammetry

Fast-scan cyclic voltammetry (FSCV) is a widely used electrochemical technique for real-time monitoring of neurotransmitters; however, quantitative interpretation remains dependent on calibration curves generated in simplified in vitro environments that may not accurately reflect the complexity of biological matrices. To address the need for automated FSCV interpretation, we developed a two-stage artificial intelligence (AI) framework integrating analyte classification and concentration prediction. FSCV data were collected for serotonin (5-HT), dopamine (DA), ascorbic acid (AA), and copper (Cu2+) using analyte-specific waveforms, generating a dataset of 30,811 cyclic voltammograms from 192 independent experimental runs. Classification models were evaluated using strict run-level partitioning, while regression performance was additionally assessed using run-level and leave-one-electrode-out (LOEO) validation. Four classification architectures were evaluated: a lightweight image-based convolutional neural network (CNN), a feature-engineered multilayer perceptron (MLP), a transfer-learning ResNet18 model, and a one-dimensional CNN (1D-CNN) trained directly on raw voltammograms. The 1D-CNN achieved the highest classification accuracy (99.8%), outperforming transfer learning (98.68%), the lightweight CNN (97.38%), and the feature-based MLP (76.3 ± 0.6%) while maintaining substantially lower model complexity than ResNet18. Input-channel ablation indicated that waveform-specific acquisition protocols contributed to classification performance. For quantitative analysis, analyte-specific artificial neural networks (ANNs) were trained to predict concentrations from voltammetric morphology, achieving coefficients of determination (R2) of 0.9958, 0.9970, 0.9849, and 0.9561 for 5-HT, DA, Cu2+, and AA, respectively, under the original random sample-level partitioning. However, mean R2 values decreased to 0.74–0.88 under run-level validation and 0.61–0.86 under LOEO validation, with conventional peak-current calibration outperforming the ANNs under LOEO conditions. Integration of classification and regression models into an automated inference pipeline enabled sequential analyte identification and concentration estimation from FSCV data. In an additional end-to-end evaluation using the 1D-CNN classifier and analyte-specific ANN regressors, incorrect analyte routing, although infrequent (approximately 0.8%), produced substantial concentration-prediction errors. These results demonstrate the potential of morphology-aware deep learning for automated FSCV interpretation while highlighting the need for improved quantitative reliability and further validation using independent electrodes, mixed-analyte samples, and complex biological matrices.