The skin's interstitial fluid is rich in composition and easily accessible for the monitoring of systemic biomarkers, however, THz-based molecular detection in biological media is challenging. Machine learning can provide solutions, but strict data engineering is required to avoid confounding trends and ensure large training datasets. We propose an experimental framework to mimic interstitial fluid diffusion in ex vivo pig skin to detect analytes via THz-ATR spectroscopy. We evaluate the applicability of the protocol for controlled studies of THz-ATR spectroscopy-based biomolecular detection in skin. Our findings can significantly contribute to the field of ML-reinforced biosensing.
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