Temporally Robust Machine-Learning-Assisted Impedance Line-Shape Analysis for Quantitative QCM Biosensing with DNA Hybridization Validation
BIOSENSORS-BASEL, cilt.16, sa.10, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 16 Sayı: 10
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/bios16100574
- Dergi Adı: BIOSENSORS-BASEL
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Compendex, EMBASE, INSPEC, MEDLINE, Directory of Open Access Journals, Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Health Research Premium Collection (ProQuest)
- Acıbadem Mehmet Ali Aydınlar Üniversitesi Adresli: Evet
Özet
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 R2=0.948 under shuffled validation and R2=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.