This work improves a previously proposed quantification framework by expanding its applicability to quaternary odor mixtures and optimizing the active-sensing process. The approach employs four quartz crystal microbalance (QCM) sensors, where both frequency shifts and resistance changes at multiple odd harmonics—measured by vector network analyzers (VNWA)—serve as virtual sensors to enhance selectivity. A selection strategy based on the condition number was introduced to identify informative harmonic responses. Experiments validated that the proposed method reduced the average root-mean-square error (RMSE) by 47.44%, with a statistically significant improvement (p = 0.0070) compared with the only fundamental responses. Among three feedback control algorithms, finite horizon linear–quadratic regulator (LQR) with time-varying gains achieved the best active-sensing performance. The experimental evaluations further confirmed short-term and long-term repeatabilities, as well as the capability to handle unstable sensor responses. Overall, the proposed method enables accurate and robust quantification of quaternary odor mixtures across a wide recipe range, demonstrating its potential as an easy-to-use solution for future odor recording systems.
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