Liquid-phase quartz crystal microbalance (QCM) biosensors are commonly quantified using scalar readouts such as frequency shift, dissipation, or bandwidth. Although these descriptors are useful, they can discard resonance line-shape morphology that contains additional information for quantitative inference. Here, we present a machine-learning-assisted impedance line-shape workflow for temporally robust quantitative 10 MHz QCM biosensing. A passive microfluidic mixer was used to generate controlled glycerol–water concentration gradients under constant total flow while complex impedance spectra were acquired in real time. Each sweep was parameterized using constrained Gaussian/Lorentzian models to obtain 52 physically interpretable descriptors spanning resistance, reactance, impedance magnitude, admittance components, and phase. The pipeline combines consensus outlier handling, training-fold mRMR feature ranking, and regression models evaluated under both corrected shuffled five-fold cross-validation and a stricter temporally blocked validation protocol. The shuffled reference model achieved R²=0.948 and RMSE = 0.162 %v/v, while seven-block temporal validation achieved R²=0.764, RMSE = 0.343 %v/v, and MAE = 0.246 %v/v. Under the same row-matched comparison, a Kanazawa frequency-shift baseline derived from the |Z| trough position yielded R²=0.331, RMSE = 0.578 %v/v, and MAE = 0.416 %v/v. Feature-set ablation further indicated that phase and conductance line-shape descriptors contain particularly strong temporally generalizable information. To demonstrate relevance for recognition-based biosensing, the workflow was also evaluated in an experimental DNA hybridization QCM assay in 1× PBS. Using all replicate-level predictions, the impedance line-shape ML pipeline reduced mean absolute percentage concentration error from 21.02% for conventional Δf calibration to 10.73%, corresponding to a 48.9% reduction in prediction error. These results show that impedance-resolved resonance morphology can improve quantitative QCM biosensing beyond scalar frequency-shift calibration and provides an interpretable basis for AI-assisted electronic biosensors.

Liquid-phase quartz crystal microbalance (QCM) biosensors are commonly quantified using scalar readouts such as frequency shift, dissipation, or bandwidth. Although useful, these descriptors discard resonance line-shape morphology that carries additional information for quantitative inference. Here, we present a machine-learning-assisted impedance line-shape workflow for temporally robust quantitative 10 MHz QCM biosensing. A passive microfluidic mixer generated controlled glycerol–water concentration gradients under constant total flow while complex impedance spectra were acquired in real time, and each sweep was parameterized using constrained Gaussian/Lorentzian models to obtain 52 physically interpretable descriptors spanning resistance, reactance, impedance magnitude, admittance components, and phase. The pipeline combines consensus outlier handling, training-fold mRMR feature ranking, and regression models evaluated under both shuffled five-fold cross-validation and a stricter temporally blocked protocol in which contiguous portions of the experiment are held out. The workflow achieved $R^2=0.948$ under shuffled validation and $R^2=0.823$ under seven-block temporal validation, the latter reducing prediction error by 48.4% relative to a row-matched Kanazawa frequency-shift baseline evaluated on the same sweeps. Feature-set ablation indicated that phase and conductance line-shape descriptors carry particularly strong temporally generalizable information. Applied to an experimental DNA hybridization assay in $1\times$ PBS, the workflow reduced mean absolute percentage concentration error from 21.02% to 10.73%, a 48.9% improvement over conventional $\Delta f$ calibration. Retaining resonance morphology therefore improves quantitative QCM biosensing beyond scalar frequency-shift calibration and provides an interpretable basis for AI-assisted electronic biosensors.