DOI: 10.5061/dryad.pg4f4qrxz
title: High-speed odour sensing using miniaturised electronic nose

description: Animals have evolved to rapidly detect and recognise brief and
 intermittent encounters with odour packages, exhibiting recognition
 capabilities within milliseconds. Artificial olfaction has faced
 challenges in achieving comparable results-existing solutions are either
 slow; or bulky, expensive, and power-intensive-limiting applicability in
 real-world scenarios for mobile robotics. Here we introduce a miniaturised
 high-speed electronic nose; characterised by high-bandwidth sensor
 readouts, tightly controlled sensing parameters and powerful algorithms.
 The system is evaluated on a high-fidelity odour delivery benchmark. We
 showcase successful classification of tens-of-millisecond odour pulses,
 and demonstrate temporal pattern encoding of stimuli switching with up to
 60 Hz. Those timescales are unprecedented in miniaturised low-power
 settings, and demonstrably exceed the performance observed in mice. For
 the first time, it is possible to match the temporal resolution of animal
 olfaction in robotic systems. This will allow for addressing challenges in
 environmental and industrial monitoring, security, neuroscience, and
 beyond.
description: # High-speed odour sensing using miniaturised electronic nose
 [https://doi.org/10.5061/dryad.pg4f4qrxz](https://doi.org/10.5061/dryad.pg4f4qrxz) The data comprises of recordings of complex odour stimuli using a high-speed a miniaturised electronic nose (e-nose). The odour stimuli were presented with a custom and high-fidelity olfactometer that was most prominently used in a recent mammalian olfaction study [1]. A photo-ionisation detector (PID) was used for ground-truth recordings and performance evaluations. Details about the experimental protocol and the specificities of the used devices can be found in the manuscript [2]. ## Description of the data and file structure This data repository consists of a nested directory structure (sensor modality - sensor mode - stimuli type - data files), containing a total of 4875 individual e-nose and 2670 PID recording trials. Each set of experiments is accompanied with an `index.csv` file, which specifies the trial names and experimental conditions, thus allowing for fast access of the trials-of-interest. #### Index files The index files are saved as comma-separated values (CSV), containing the following columns: `trial_idx`: Index column, unique across all experiments `trial`_`id`: Unique trial identifier combining 'trialidx', 'condition', 'kind', 'shape', 'concentration', 'gas1', 'gas2' `condition:` Sensor mode. For the e-nose, this describes the sensor heater profiles. `block_idx:` Index with respect to experiment start `kind:` Type of stimulus. 'pulse': single gas odour pulse; 'corr': two correlated pulse trains; 'acorr': two anti-correlated pulse trains; 'plume': replayed odour plume (not used in the paper) `shape:` Temporal structure of stimulus. 'pulse': pulse duration; 'corr': modulation frequency; 'acorr': modulation frequency; 'plume': mixed/co-located odour source or separated odour sources (not used in the paper) concentration: Odour stimulus concentration, between 20% and 100% `gas1:` Gas 1 `gas2:` Gas 2; empty for 'pulse' `t_stimulus`: Timestamp of stimulus w.r.t. experiment initialisation, in the format 'd days hh:mm:ss' `n_saturated:` Number of saturated sensors #### E-nose trial files The e-nose recording trials are saved as comma-separated values (CSV), containing the following columns: `index`: Index column, unique across all experiments  `timestamp`: Timestamp from device boot `time_s`: Time (s) with respect to odour onset `time_ms`: Time (ms) with respect to odour onset `control_cycle_step_left`: Heater power phase for sensor 1-4 (for cycles only) `control_cycle_step_right`: Heater power phase for sensor 1-4 (for cycles only) `R_gas_1`-`R_gas_8`: Gas sensor resistance values, in Ohm `T_heat_1`-`T_heat_8`: Sensor heater temperature, in degree Celsius `p_mbar`: Ambient pressure, in millibar `t_celsius`: Ambient temperature, in degree Celsius `rh_percent`: Ambient relative humidity, in percent `EB`, `IA`, `Eu`, `2H`, `b1`, `b2`, `b_comp`: Flow valve control values for the different odourants, values between 0.0 and 1.0  `total`: Sum of all flow valve control values, should add up to 1.0 at any time #### PID trial files The PID recording trials are saved as comma-separated values (CSV), containing the following columns: `index`: Index column, unique across all experiments  `time_ms`: Time (ms) with respect to odour onset `timestamp`: Timestamp from device boot `pid_V`: PID output, in Volts `EB`, `IA`, `Eu`, `2H`, `b1`, `b2`, `b_comp`: Flow valve control values for the different odourants, values between 0.0 and 1.0  `total`: Sum of all flow valve control values, should add up to 1 at any time ## Code/Software For loading and displaying the data, please refer to the code in the following repository: [https://github.com/nkdnnlr/High-Speed-Odour-Sensing-Using-Miniaturised-Electronic-Nose](https://github.com/nkdnnlr/High-Speed-Odour-Sensing-Using-Miniaturised-Electronic-Nose) In particular, run the jupyter notebook called [00_display_data.ipynb](https://github.com/nkdnnlr/High-Speed-Odour-Sensing-Using-Miniaturised-Electronic-Nose/blob/main/00_display_data.ipynb), which will guide you through the necessary steps. ## References [1] Ackels, T., Erskine, A., Dasgupta, D., Marin, A.C., Warner, T.P., Tootoonian, S., Fukunaga, I., Harris, J.J. and Schaefer, A.T., 2021. Fast odour dynamics are encoded in the olfactory system and guide behaviour. Nature, *593 *(7860), pp.558-563. [2] Dennler, N., Drix, D., Warner, T., Rastogi, S., Della Casa, C., Ackels, T., Schaefer, A.T., van Schaik, A. and Schmuker, M., 2024. High-speed odour sensing using miniaturised electronic nose. Science Advances (2024). preprint: arXiv:2406.01904.