Configuration-Specific Temporal Alignment of a Repurposed Wearable Fuel-Cell Alcohol Sensor with an MQ-3 Timing Proxy in a Laboratory Static-Headspace Chamber

Ethanol emitted from stressed woody tissues is a host-associated cue for ambrosia beetles, motivating investigation of portable ethanol-responsive sensors for future tree monitoring. This laboratory study evaluated temporal alignment between two asynchronously logged volatile organic compound (VOC)-responsive sensors during a denatured-alcohol chamber challenge. We selected the BACtrack Skyn wearable fuel-cell alcohol sensor for its compact form and timestamped output as a candidate for future tree-headspace measurements, and evaluated one device with an MQ-3 metal-oxide timing proxy and environmental logging in a closed static-headspace chamber. The dataset comprised five alcohol-free control runs (B0–B4) and three technical 1 μL denatured-alcohol challenge runs (EtOH1–EtOH3). Signals were processed on a common 1 min grid using a 10–15 min analytical baseline and a 15–45 min response-characterization window. MQ-3 peaks occurred at 17.0–17.3 min from the run origin (T0), whereas Skyn device-output peaks occurred at 18.8–19.2 min; peak-time offsets were 1.5–2.2 min. First-difference analysis selected a 2 min alignment setting on the primary 1 min grid in all three challenge runs; a supplementary 20 s analysis gave run-specific optima of 80–160 s. Across the three challenge runs, run-specific Pearson correlations ranged from 0.686 to 0.751 before alignment and from 0.861 to 0.930 after alignment; each coefficient was calculated from the paired 1 min observations in the 15–45 min response window. Normalized root-mean-square error (RMSE) ranged from 0.208 to 0.272 before alignment and from 0.121 to 0.230 after alignment, whereas rank correlation did not improve consistently. Alcohol-free controls remained below challenge-run peak magnitudes, although MQ-3 traces included drift and positive excursions. These within-run comparisons describe alignment in the tested configuration, not intrinsic sensor latency, concentration calibration, or validated tree-monitoring performance.