An AI-Ready Pipeline for Impedance-Resolved QCM Biosensor: Interpretable Line-Shape Features, Redundancy Control, and Robust Regression

TL;DR

This paper introduces an AI-ready impedance-resolved workflow for QCM biosensors that preserves resonance line-shape information, enabling more accurate and interpretable concentration predictions compared to traditional models.

Contribution

It develops a novel pipeline that converts full resonance spectra into interpretable features, improving prediction accuracy and robustness in QCM biosensing.

Findings

Impedance line-shape features reduce prediction error by over 3 times.

The pipeline outperforms the classical Kanazawa model in concentration prediction.

Features provide a robust, interpretable basis for machine-learning in spectral sensing.

Abstract

Accurate inference from quartz crystal microbalance (QCM) measurements in liquids is often limited by reducing resonance behavior to two scalar endpoints (frequency and dissipation shifts, $\Delta f$ and $\Delta D$) or by relying on single-equation analytical models (Kanazawa-model). We propose an AI-ready, impedance-resolved workflow that preserves full resonance line-shape information and converts it into compact, physically interpretable features for supervised regression. A passive microfluidic mixer generates glycerol--water concentration gradients under a constant total flow rate (50~$\mu$L/min), while complex impedance spectra of a 10~MHz AT-cut quartz crystal are recorded in real time. Each sweep of nine spectra is parameterized via constrained Gaussian/Lorentzian models to yield 52 line-shape descriptors spanning extrema of $|Z|$, $X$, and $B$ and peaks of $R$, phase, and $G$. The pipeline integrates consensus outlier handling, redundancy-aware feature ranking (mRMR), and cross-validated regression across linear, kernel, and ensemble models. Compared with the classical Kanazawa baseline, the impedance line-shape approach reduces concentration-prediction error from 0.456 to 0.148~\%v/v RMSE (3.09$\times$ lower). The results demonstrate that impedance-resolved line-shape features provide a robust and interpretable basis for machine-learning-assisted QCM inference and illustrate a generalizable pattern for AI-enabled spectral sensing.