Gas-Phase Odorant Fast Quantification by Odor Biosensor Based on Reference Response Model

Gas-phase odor biosensors based on cells expressing olfactory receptors (ORs) show favorable detection characteristics. These biosensors, however, suffer nonlinearity, drift, and aging problems. We previously developed an active sensing method to solve those problems but it took relatively long time to obtain a final result. Here, we have developed a gas-phase odorant fast quantification method to speed up the quantification procedure. The odor intensity was controlled by the stimulation duration and the experiment target was to quantify an unknown intensity. We first focused on the odorant geosmin, and obtained a standard response model by curve fitting with the response data from several odor intensities. This model was used to calculate an unknown odor intensity thus solving the nonlinear issue. During the experiment, known and unknown odor stimulations were alternately supplied. The biosensor response was calibrated based on the known odor stimulation response, thereby overcoming the drift and aging problems. Fast quantification was successfully achieved in 400 s which was much faster than previous research. Furthermore, we studied the effect of stimulation interval on quantification accuracy. Moreover, we presented a skip-enable fast quantification approach which increased the sampling rate especially when the odor stimulation was sparse. In addition, the feasibility of fast quantification method was verified again on odorant 1-octen-3-ol. The fast quantification method demonstrated in this study will benefit the practical application of gas-phase odor biosensors, particularly for variable target odorants.

Automated summary

Gas-phase odor biosensors based on cells expressing olfactory receptors (ORs) have favorable detection characteristics but suffer from nonlinearity, drift, and aging problems. This study developed a gas-phase odorant fast quantification method to speed up the quantification procedure. By controlling the stimulation duration and using curve fitting, an unknown odor intensity can be accurately quantified, overcoming nonlinearity issues. The biosensor response was calibrated based on known odor stimulations to address drift and aging problems. The fast quantification method was successfully achieved in 400 s, and its feasibility was verified on different odorants.