Matrix-Aware Electrochemical Sensing: A Neural Network Approach to Deciphering Medium-Specific Surface Dynamics

Area of Science:

• Biomedical Engineering
• Biosensing Technology
• Machine Learning in Healthcare

Background:

• Accurate monitoring in complex biofluids is hindered by unpredictable skin-electrode interference and impedance variations.
• Existing biosensors struggle with matrix-dependent interference, limiting their reliability in real-world applications.
• Developing robust and adaptive sensing platforms is crucial for effective metabolic monitoring.

Purpose of the Study:

• To present an intelligent enzymatic microneedle platform for calibration-efficient, matrix-adaptive, and drift-resilient sensing.
• To enhance high-fidelity glucose monitoring in complex biofluids using a novel dual-mode architecture and explainable AI.
• To demonstrate the platform's ability to distinguish specific analyte signals from environmental and interfacial perturbations.

Main Methods:

• Integration of 2-terminal/4-terminal impedance acquisition with a functionalized Nafion interface for enhanced robustness.
• Development of an explainable feedforward neural network (FFNN) utilizing SHapley Additive exPlanations (SHAP) for model interpretability.
• Few-shot domain adaptation for optimizing sensor performance in complex biological matrices like whole blood.

Main Results:

• The platform demonstrated robustness against contact-related and biofluid-induced perturbations.
• Explainable AI revealed physics-aligned feature relationships, enabling matrix-dependent compensation and interfacial integrity evaluation.
• Achieved a mean absolute relative difference (MARD) of 2.55% in whole blood, indicating high prediction fidelity.
• Ablation studies and principal component analysis validated the effectiveness of the proposed framework.

Conclusions:

• The intelligent microneedle platform offers a scalable and robust framework for minimally invasive metabolic monitoring.
• The combination of electrochemical interface engineering and explainable machine learning provides a significant advancement in biosensing.
• This approach successfully addresses challenges in complex biofluid analysis, paving the way for improved diagnostic tools.