# High-speed odour sensing using
miniaturised electronic nose

Nik Dennler,1,2,† Damien Drix,1 Tom P. A. Warner,3,4
Shavika Rastogi,1,2 Cecilia Della Casa,3 Tobias Ackels,3,5
Andreas T. Schaefer,3,4 André van Schaik,2 Michael Schmuker,1,6,∗

1Biocomputation Group, University of Hertfordshire, Hatfield AL10 9AB, United
Kingdom
2International Centre for Neuromorphic Systems, Western Sydney University,
Kingswood 2747 NSW, Australia
3Sensory Circuits and Neurotechnology Laboratory, Francis Crick Institute,
London NW1 1AT, United Kingdom
4Departement of Neuroscience, Physiology and Pharmacology, University College
London,
London WC1E 6BT, United Kingdom
5Sensory Dynamics and Behaviour Lab, Institute of Experimental Epileptology
and
Cognition Research (IEECR), University of Bonn Medical Center, 53127 Bonn,
Germany
6BioML Research Services, Berlin, Germany

† E-mail<EMAIL_ADDRESS>
∗ Corresponding author; E-mail<EMAIL_ADDRESS>

> 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.

## Introduction

The sense of olfaction is found all across the animal kingdom, and is crucial
for survival and guiding behaviors such as navigation, food detection,
predator avoidance, and mate selection [1, 2, 3, 4, 5, 6, 7]. Success in these
tasks often hinges on the ability to swiftly and accurately detect and
recognise scents [8, 9, 10, 11], particularly when dealing with odour plumes
characterised by brief and intermittent encounters[12, 13] generated by
turbulent dispersion processes [14, 15, 16]. Concentration fluctuations in
odour plumes can exceed $100\text{\,}\mathrm{Hz}$ [17], while individual odour
encounters can last single milliseconds or less [18] (see Fig. 1a). Many
environmental cues are embedded in the fine structure of the odour plume [19,
20, 21], which various organisms have evolved to use for their advantage. For
instance, Drosophila olfactory receptor neurons can transduce odours in less
than two milliseconds and resolve odour stimuli fluctuations at frequencies
exceeding $100\text{\,}\mathrm{Hz}$ [22]. Similarly, honeybee projection
neurons decode odour identity in tens of milliseconds after stimulus onset
[23], while mosquitoes can identify CO2 packets of just
$30\text{\,}\mathrm{ms}$ [24]. A recent landmark study in mice has revealed
their ability to discriminate rapid odour fluctuations, enabling them to
distinguish temporally correlated from anti-correlated odours at up to
$40\text{\,}\mathrm{Hz}$, which facilitates source separation in complex
environments [25].

Research on mobile olfactory robotics [26] has flourished over the last
decade; driven by promising applications and solutions across various domains
[27], and bootstrapping on the well established field of artificial
olfaction[28]. The latter has demonstrated its effectiveness in domains where
static and slow measurements are sufficient, such as the detection of
hazardous gases or pollutants [29], spoilage alert systems [30], health
monitoring [31], and food sciences [32]. However, many recent applications
call for unmanned ground or aerial vehicles (UGV / UAV) to perform odour
source localisation and navigation tasks [33, 34, 35, 27], which rely heavily
on sensing the environment fast and efficiently, considering plume dynamics
[36].

Typically, mobile olfactory robots incorporate electronic noses; devices that
are characterised by arrays of multiple gas sensors and associated peripheral
electronics [37]. They offer distinct advantages over conventional analytical
methods such as Photoionization Detectors (PID) and mass spectrometers (MS),
notably in terms of portability, power efficiency, cost-effectiveness, and
sensitivity to a wide range of odours and volatile compounds. The most widely
employed sensing components are Metal-oxide (MOx) gas sensors [38], which
offer the significant advantage of a sensing layer that can be tuned through
1., modifications to its chemical structure, and 2., variations in operating
temperature achieved by local heating, allowing for effectively detecting a
diverse range of analyte classes. Their minimal requirements for electronic
peripheral components streamline sensor design, lower costs, and conserve
valuable space. Further reductions in latency, form factor and power
consumption were enabled through latest MEMS-based MOx sensors [39, 40],
facilitating seamless integration into electronic circuits [41].

However, the relatively slow response and recovery times of MOx sensor
electronic noses pose challenges for widespread adoption, and are prohibitive
for many potential robotic applications [42]. For this reason, various studies
have investigated sensor response times and tried to improve them. Recent
advancements in both hardware [43, 44] and software [45, 46, 21, 47, 48, 49,
50, 51] have significantly reduced response and recovery times from the orders
of hours or minutes [52] down to tens-of-seconds or seconds [49, 51].
Nevertheless, those timescales remain orders-of-magnitudes slower than what’s
observed for olfactory sensing in animals, potentially stalling progress on
critical challenges in tracking of greenhouse gas emissions [53], ecological
and environmental monitoring [54, 55], aerial-based wild fire detection [56]
disaster management [57], and more.

In this work, we are pushing the limits of artificial olfaction with a high-
speed, miniaturised electronic nose that can resolve odour pulses in the
millisecond range. We propose an integrated e-nose design of MEMS-based MOx
sensors and fast sampling periphery, as well as a set of powerful algorithms
for control, sensing, and signal processing. We demonstrate the systems
ability to operate at unprecedented temporal timescales when classifying short
odour pulses, as well as when discriminating temporal characteristics of
rapidly switching odour pairs. The challenge of deploying rapid and complex
odour stimuli in a controlled and precise fashion [58] is overcome by using a
high temporal-precision olfactometer setup, which most recently has been used
for showcasing the temporal odour recognition capabilities in mice [25, 59].

We first elaborate on the proposed design of the electronic nose and the
feedback control methods, with which we achieve thermal response times that
allow for ultra-fast heater cycles — orders-of-magnitudes faster than what is
suggested in the literature. Later, we show that the electronic nose can
successfully classify the odour of short pulses, with durations down to tens
of milliseconds. This is achieved by rapidly switching the sensor heater
temperature, then extracting phase-locked data features to train machine
learning classifiers. Further, we demonstrate the system’s ability encode and
infer temporal features in a task involving rapidly switching odour pairs, up
to modulation frequencies of $60\text{\,}\mathrm{Hz}$, which we show to match
and even exceed the demonstrated capabilities of mice on equivalent tasks
[25]. This is achieved by controlling the heater temperature to be constant,
permitting for sensor response feature extraction from the frequency domain.
Finally, we discuss our results and its implications, and identify some
example use cases that may benefit highly from using fast sensing modalities.

## Results

### High-speed electronic nose and odour delivery system

We constructed a portable high-speed and high bandwidth electronic nose, which
leverages the advantage of MEMS-based gas sensors and their rapid response
times. We emphasised form factor and power consumption considerations that
allow for sophisticated field measurements under space and power constraints,
such as mobile robotic platforms[35]. Our design (Fig. 1c & Fig. S1a)
consisted of the following elements: a microcontroller for data processing and
storage, eight analogue metal-oxide MEMS gas sensors (Fig. 1d), associated
analogue circuitry and data converters, and a combined pressure, humidity and
temperature sensor.

Figure 1: Electronic nose and odour delivery system. a, Decoding temporal
information of odour plumes requires fast sensing. Top: Two sequential TiCl4
smoke plume photographies, shifted and superimposed, kindly provided by Dr.
Paul Szyszka. Bottom: Dual-PID recordings of source-separated odour plumes,
from Ackels et al. [25]. Plume and sensor location (red) for illustrative
purposes only. b, Experimental setup with odour delivery device and electronic
nose. Adapted from Ackels et al. [25]. c, Electronic nose circuitry. d,
Microscopy image of the MiCS-6814 NH3 sensor with its housing removed. e,
Heater modulation cycle in ambient air. f, PID and flow meter traces for a
$20\text{\,}\mathrm{Hz}$ stimulus. Solid / faded (occluded) traces for mean /
std. of five trials. g, Resulting olfactometer temporal fidelity, for various
frequencies. Odourants abbreviations: IA: isoamyl acetate; EB: ethyl butyrate;
Eu: cineol; 2H: 2-heptanone; blank: odourless control.

Ideal MOx sensor operation requires the sensing site to be heated to several
hundred degrees. The sensor response is highly dependent on the temperature
and its variation over time. Previous studies have shown that a modulation of
the sensor’s operating temperature often leads to better and faster gas
discrimination performances [43], however the suggested sensor heater cycle
durations were on the orders of seconds to minutes [43, 50, 60, 61, 62, 63].
Aiming to achieve ultra-fast heater cycles,

our design couples each sensor with a separate temperature control loop, which
samples the temperature and adjusts the hotplate current at high frequency.
Fig. 1d shows a typical heater modulation cycle in ambient air, where the
sensor resistance follows the hotplate temperature in a low-pass fashion. In
our experiments, we used two different heater temperature control schemes: one
that cycled between low and high temperature values
($150\text{\,}\mathrm{\SIUnitSymbolCelsius}$ and
$400\text{\,}\mathrm{\SIUnitSymbolCelsius}$), and one at a constant high
temperature ($400\text{\,}\mathrm{\SIUnitSymbolCelsius}$).

To provide odour stimuli to the e-nose, we used an odour delivery system that
can reliably present gaseous odour samples with a bandwidth beyond
$60\text{\,}\mathrm{Hz}$, described earlier [25, 59] and depicted in Fig. 1b.
The system was based on high-speed micro-valves and incorporated a flow
compensation mechanism [25], ensuring exceptionally high temporal signal
fidelity, and constant flow across the stimuli (Fig. 1f & Fig. 1g and
Methods). As prototypical, simplistic high-frequency odour stimuli, we used
square pulses of different duration and separation times. A set of odourants
that resembles smells encountered in nature was considered: Ethyl butyrate
(pineapple), isoamyl acetate (banana), cineol (eucalyptus) and 2-heptanone
(cheese). The odourants were diluted in odourless mineral oil solvent.
Additionally, we used two (identical) pure solvent samples as controls. The
odours were presented as singular pulses with varying durations
($10\text{\,}\mathrm{ms}$ \- $1\text{\,}\mathrm{s}$) and concentrations
(20%-100%), and as correlated and anti-correlated odour pulse trains
($1\text{\,}\mathrm{s}$) at different modulation frequencies (2Hz - 60Hz).

### Rapid heater modulation enables robust data features

Figure 2:  Rapid heater modulation enables robust data features. a, Sensor
resistance of four MOx sensors with 20 Hz hotplate temperature modulation,
responding to a $1\text{\,}\mathrm{s}$ odour pulse of isoamyl acetate (green
background). b, $50\text{\,}\mathrm{ms}$ data feature for different gases,
selected between $500\text{\,}\mathrm{ms}$ and $550\text{\,}\mathrm{ms}$ after
odour pulse onset. Raw sensor response (upper) and normalised sensor response
(lower, see Methods for normalisation procedure). Time shifted by cycle phase
$\rho$ w.r.t. odour onset, for visual guidance only. c, Principal component
analysis (PCA), explained variance (most left) and projections, and d,
t-distributed stochastic neighbour embedding (t-SNE) visualisation, for the
set of normalised data features extracted between $500\text{\,}\mathrm{ms}$
and $1000\text{\,}\mathrm{ms}$ after odour onset. e, Accuracy scores for a
k-nearest neighbours (k-NN) classifier trained on $50\text{\,}\mathrm{ms}$
data features from $1000\text{\,}\mathrm{ms}$ odour pulses at full
concentration, and tested on $50\text{\,}\mathrm{ms}$ features from
$1000\text{\,}\mathrm{ms}$ odour pulses at different concentration levels
(tuned by adjusting the duty cycle of the micro-valves).

While cycling the sensor heater temperature can yield better odour
classification results, the cycle duration may be restricting the temporal
bandwidth at which a stimulus can be resolved. In recent studies, we tested
the effect of $150\text{\,}\mathrm{ms}$ duty cycles and found evidence for
robust data features [64, 65]. In the current work, we leveraged our system’s
ability to rapidly modulate the sensor temperature, and cycled the heater
temperature between a low step at $150\text{\,}\mathrm{\SIUnitSymbolCelsius}$
and a high step at $400\text{\,}\mathrm{\SIUnitSymbolCelsius}$ with a period
of $50\text{\,}\mathrm{ms}$. Notably, this is orders-of-magnitudes shorter
than what had been suggested in previous studies [63]. The resistance of the
gas sensing elements closely tracks these changes in operating temperature
(Fig. 1e & Fig. 2a), enabling us to extract gas features that are phase-locked
with the heater cycles for subsequent analysis and classification. For this
purpose, we divided the continuous stream of gas sensor samples into
$50\text{\,}\mathrm{ms}$ chunks aligned with the temperature cycles (Fig. 2b,
upper row). The $50\text{\,}\mathrm{ms}$ data features further underwent pre-
stimulus baseline normalisation and scaling (Fig. 2b, lower; from now on
referred to as "normalised data feature"); for details see Methods).

For testing class discriminability and robustness to concentration
fluctuations, data features were extracted by sampling four sensors between
$500\text{\,}\mathrm{ms}$ and $1000\text{\,}\mathrm{ms}$ after the onset of a
$1000\text{\,}\mathrm{ms}$ odour stimulus. Principal Component projections
(Fig. 2c) and t-distributed stochastic neighbour embeddings (t-SNE) (and Fig.
2d) show distinct clustering that coincided with odour classes. Further, a
k-nearest neighbours (k-NN) classifier was trained on data features of one
second long odour pulses at full concentration (100%), and tested on features
of one second long odour pulses at various concentrations (20% - 100%). The
classification performance results are shown in Fig. 2e. Notably, for the
normalised data feature, the model provided $100\%$ classification accuracy at
the trained concentration level, which remained at $(88.7\pm 0.5)\%$ and
$(81.2\pm 0.6)\%$ when tested on $80\%$ and $60\%$ of the trained
concentration level. Accuracy at lower concentration levels dropped
significantly but remained well above chance. This is significantly better
than what is achieved with the raw data feature (see Fig. 2e).

### Time-resolved classification of millisecond odour pulses

Figure 3:  Electronic nose can classify short odour pulses based on 50 ms data
features. a, Feature labels for the training set were phase aligned in
relation to odour on- and offset. Features that overlapped with transition
periods were not considered for training ("rejected", see Methods for
parameters). b, Odour stimulus classification over time for odour pulses of
various lengths ($10\text{\,}\mathrm{ms}$ \- $1000\text{\,}\mathrm{ms}$, as
predicted by a RBF-kernel SVM classifier trained on $50\text{\,}\mathrm{ms}$
features from $1000\text{\,}\mathrm{ms}$ second odour pulses. Shown here are
$1000\text{\,}\mathrm{ms}$ pulses. For visual clarity only, the trials are
sorted by odour, and within each odour are sorted by phase w.r.t. stimulus
onset. c, Classification correctness over time (evaluated via the true odour
presence), for different pulse durations. d, Test accuracy, onset time and
offset time for the prediction over time described in b & c. Onset and offset
were extracted using time-to-first non-’blank’ and ’blank’ prediction
respectively, and shown here with respect to theoretical odour onset and
offset.

In natural settings, odour bouts can be as brief as only milliseconds long.
For an agent’s successful interaction with the environment, this requires the
ability to classify odours fast and robustly. We evaluated the ability of the
electronic nose to classify odour pulses of various durations. A Support
vector machine (SVM) with Gaussian radial basis function was trained on
$50\text{\,}\mathrm{ms}$ data features, which were acquired from eight gas
sensors throughout a $1000\text{\,}\mathrm{ms}$ odour stimulus at five
concentration levels. Control trials (’blank’) were included, obtained during
a $1000\text{\,}\mathrm{ms}$ odourless mineral oil stimulation or immediately
after odour pulses. See Fig. 3a for a depiction of the labelled features. The
trained model was deployed to predict the odour presence over time during
exposure to odour stimulations of various durations, ranging from
$10\text{\,}\mathrm{ms}$ to $1000\text{\,}\mathrm{ms}$. Fig. 3b displays the
predicted classes over time on the example of an $1000\text{\,}\mathrm{ms}$
odour pulse, where Fig. 3c summarises the predictions over time for all pulse
durations.

From these predictions, the corresponding accuracy, onset times, and offset
times were derived and shown in Fig. 3d. The classifier attained a 100%
accuracy in predicting the correct class for odour pulse durations ranging
from $1000\text{\,}\mathrm{ms}$ down to $50\text{\,}\mathrm{ms}$, despite not
having been trained on pulses shorter than $1000\text{\,}\mathrm{ms}$.
Accuracy dropped for $20\text{\,}\mathrm{ms}$ and $10\text{\,}\mathrm{ms}$
pulses but remained above chance level. Notably, the classifier accurately and
rapidly predicted the recovery of the sensor site, indicating ’no odour’ when
no odour was present. The time required for the classifier to correctly
identify the odour remained relatively consistent across odour pulse
durations, with an average value of ($87\text{\,}\mathrm{ms}$ ±
$20\text{\,}\mathrm{ms}$). Following odour offset, the classifier robustly
predicted ’no odour’ within ($106\text{\,}\mathrm{ms}$ ±
$24\text{\,}\mathrm{ms}$) for $1000\text{\,}\mathrm{ms}$ odour pulses. For
shorter durations, this time increased inversely proportional to the pulse
duration, which can presumably be attributed to the sensor’s integration time
that may approach or exceed the duration of the short odour pulses.

### Decoding temporal structure of rapidly switching odours

Figure 4:  Decoding temporal structure of rapidly switching odours. a, Odour
valve commands. b, PID response. c, Electronic nose response. d, Discrete
Fourier Transformation (DFT) of first derivative of the sensor log-resistance.
Crosses denote highest-magnitude peaks. DFT bin frequencies were rounded to
nearest integers, for visual clarity only. e, Feature visualisation frequency,
magnitude and phase of the dominant DFT peaks. Thick lines; means of
corresponding trials, thinner lines; single trials. f, Class-balanced
accuracies for modulation frequency classification. g, Accuracies for binary
modulation frequency classification. h, Class-balanced accuracies for binary
modulation mode classification (corr. vs. anti-corr.). i, Subset of g for IA-
EB, for mouse performance comparison (described in detail in Ackels et
al.[25]). j, Subset of h for IA-EB, for mouse performance comparison. Panels
a-e show representative trials only. For f-j, electronic nose accuracy mean
and SD (clipped at 1.0) arise from repeated training and testing with
different random seeds.

In the presence of multiple odours, detecting whether the odour encounters are
correlated or not can help to infer whether they come from the same source or
from separate locations [19]. Further, information about the encounter
frequency can give rise to spatial source information [21]. It has been shown
that mice can distinguish between correlated and anti-correlated odour pairs
reliably up to correlation frequencies of $40\text{\,}\mathrm{Hz}$ [25]; a
feat that has not yet been matched in robotic systems. Considering performance
metrics based on similar tasks, here we explored the ability of the electronic
nose to resolve temporal structure of odour stimuli. Rapidly alternating odour
pairs were presented at frequencies between $2\text{\,}\mathrm{Hz}$ and
$60\text{\,}\mathrm{Hz}$ for a duration of one second. We discriminated
between two odour pulse trains being either in phase (correlated) or shifted
by half a cycle (anti-correlated) (Fig. 4a). The resulting odour patterns
follow the pulses rapidly (PID recordings in Fig. 4b).

While heater modulations lend themselves for extracting phase-locked data
features and thus allowing for efficient odour classification, maintaining the
sensor heater temperature constant instead allows for analysing the data in
continuous time. Particularly when observing repeating patterns or complex
temporal dynamics of a stimulus this may be advantageous, as the sensor
response can be regarded in its frequency domain. Thus, for the following
experiments, we operated four MOx sensors of the electronic nose under a
constant hotplate temperature of $400\text{\,}\mathrm{\SIUnitSymbolCelsius}$.
The sensors responded to the stimuli by dropping their resistance at the
pulse-train onset, with the stimulus modulation visually embedded in the
response (Fig. 4c). We extracted data features by differentiating and
logarithmically scaling the raw sensor response, followed by a discrete
Fourier transform (DFT) (Fig. 4d). For each sensor, where the maximal
magnitude is found, the frequency, the magnitude and the phase were extracted.
This yielded a 12-dimensional data feature (Fig. 4e). A visual comparison of
the features reveals distinct differences between correlated and anti-
correlated pulse trains (top vs. middle), as well as between different
frequencies (middle vs. bottom).

On those features, ensembles of Random Forest classifiers were trained for
three tasks: 1. Decoding the modulation frequency of two odour pulse trains
from a set of frequencies, 2. predicting the modulation frequency of two odour
pulse trains from pairs of frequencies, and 3. decoding if two odours pulse
trains are either correlated or anti-correlated. For the latter two tasks and
a subset of the odours, a comparison with the mouse performance as detailed in
Ackels et al. [25] is provided.

The test accuracies for the three tasks and all gas combinations are shown in
Fig. 4f-h. For task 1, the data recorded with the electronic nose enables
nearly perfect frequency classification (Fig. 4f) for modulation frequencies
up to $5\text{\,}\mathrm{Hz}$, then on average decreasing to $(0.91\pm 0.20)$
and $(0.90\pm 0.15)$ for $10\text{\,}\mathrm{Hz}$ and $20\text{\,}\mathrm{Hz}$
respectively, and finally dropping to $(0.61\pm 0.26)$ and $(0.72\pm 0.25)$
for $40\text{\,}\mathrm{Hz}$ and $60\text{\,}\mathrm{Hz}$ respectively. For
the pair-wise frequency classification (task 2, Fig. 4g), classification
performance is perfect for modulation frequency pairs $2\text{\,}\mathrm{Hz}$
vs $20\text{\,}\mathrm{Hz}$ and $4\text{\,}\mathrm{Hz}$ vs
$20\text{\,}\mathrm{Hz}$, decreasing to accuracies of $(0.97\pm 0.07)$,
$(0.90\pm 0.13)$ and $(0.90\pm 0.13)$ for the pairs $10\text{\,}\mathrm{Hz}$
vs $20\text{\,}\mathrm{Hz}$, $40\text{\,}\mathrm{Hz}$ vs
$20\text{\,}\mathrm{Hz}$ and $60\text{\,}\mathrm{Hz}$ vs
$20\text{\,}\mathrm{Hz}$, respectively. For discriminating correlated vs.
anti-correlated pulse trains (task 3, Fig. 4h), it appears that the electronic
nose, on average, enables high prediction scores of $(0.95\pm 0.08)$,
$(0.79\pm 0.20)$, $(0.72\pm 0.19)$ and $(0.70\pm 0.14)$ for modulation
frequencies of $2\text{\,}\mathrm{Hz}$, $5\text{\,}\mathrm{Hz}$,
$10\text{\,}\mathrm{Hz}$ and $20\text{\,}\mathrm{Hz}$, respectively. This
drops to $(0.54\pm 0.06)$ for $40\text{\,}\mathrm{Hz}$ and finally to
$(0.47\pm 0.05)$ for $60\text{\,}\mathrm{Hz}$.

In Ackels et al. [25], the odour pair isoamyl acetate — ethyl butyrate (IA-EB)
has been used to test the discrimination power of fast odour dynamics in mice.
In the following, we consider the corresponding subset of the electronic nose
recordings and compare them to the named study. For the pair-wise frequency
classification (task 2, Fig. 4i), the electronic nose classification
performance is perfect for all the tested modulation frequency pairs, from
$2\text{\,}\mathrm{Hz}$ vs $20\text{\,}\mathrm{Hz}$ up to
$60\text{\,}\mathrm{Hz}$ vs $20\text{\,}\mathrm{Hz}$. Here, the mouse
performed significantly worse — for the pair $2\text{\,}\mathrm{Hz}$ vs
$20\text{\,}\mathrm{Hz}$, the mouse accuracy score was $(0.83\pm 0.05)$ and
then progressively dropped down to $(0.53\pm 0.03)$ for
$10\text{\,}\mathrm{Hz}$ vs $20\text{\,}\mathrm{Hz}$. Finally, considering the
results from the phase prediction task (task 3, Fig. 4j), it appears that the
electronic nose enables perfect prediction scores up to modulation frequencies
of $10\text{\,}\mathrm{Hz}$, which then steeply drops to an accuracy of
$(0.76\pm 0.05)$ for $20\text{\,}\mathrm{Hz}$, $(0.60\pm 0.00)$ for
$40\text{\,}\mathrm{Hz}$, and finally to chance level for
$60\text{\,}\mathrm{Hz}$. In comparison, the mouse scores $(0.78\pm 0.11)$ at
a modulation frequency of $2\text{\,}\mathrm{Hz}$, linearly decaying in
accuracy down to chance level at around $80\text{\,}\mathrm{Hz}$.

In order to validate if the observed performance can be attributed to the
odour signal, and not to potential artefacts caused by potential hotplate
temperature variations (which may be caused by unnoticed flow fluctuations),
we repeated the experiment using the hotplate temperature signal (see Fig.
S3i), as well as the PID responses (see Fig. S3m). In both cases, the
analogous feature extraction and classification pipeline was performed,
resulting in classification performances as displayed in Fig. S3j-l for the
hotplate temperature, and Fig. S3n-p for the PID responses. The analysis
confirmed that there was not enough information in the hotplate temperature
response alone to classify the odourants with above chance performance.
Further, using the PID response, which should be unaffected by potential flow
fluctuations and is commonly used as a ground-truth measurement, nearly
perfect accuracy scores were achieved for most gas combinations across the
tested tasks.

## Discussion

For many tasks and applications in robotics, natural and turbulent
environments pose the challenge of highly dynamic and rapidly changing odour
concentrations, which demands high temporal resolution odour sampling and
processing. Intrigued by the exceptional speed at which animals process and
respond to odours, we challenged the limits of artificial olfaction by
introducing and evaluating a portable and low-power high-speed electronic
nose. For this, we coupled an integrated design of MEMS-based MOx sensors and
fast sampling periphery with a set of highly optimised algorithms for control,
sensing, and signal processing. To assess its capabilities, the electronic
nose was subjected to odour stimuli delivered through a high-bandwidth system.
Odours were presented in the form of square pulses with varying durations and
concentrations, or as pairs of pulse trains at varying frequencies and phase.

We demonstrated that the electronic nose can successfully infer the odour
identity of single-odour pulses down to durations of $10\text{\,}\mathrm{ms}$,
albeit being trained on $1\text{\,}\mathrm{s}$ odour pulses only. This was
achieved through modulating the sensor temperature with cycle periods of
$50\text{\,}\mathrm{ms}$, extracting and pre-processing the corresponding
sensor response, then training and evaluating a classifier. Further, we
demonstrate the electronic nose’s ability to predict whether two-odour pulse
trains were correlated or anti-correlated up to switching frequencies of
$40\text{\,}\mathrm{Hz}$; matching or exceeding mice on the equivalent task.
For tasks involving determining the odour switching frequency (multi-class and
binary), we demonstrate a high performance up to $60\text{\,}\mathrm{Hz}$,
outperforming mice on equivalent tasks. For this, the sensor heaters were set
to provide a constant temperature, which allowed an analysis of the data in
the frequency domain.

Some valuable insights on task-specific sensing modes and sampling can be
gained from those results. First, data windows that are phase-locked with
ultra-short sensor temperature cycles appear to be a sensible choice when
given the task of odour classification. The high sampling rate allows for a
multi-dimensional data feature (here, 8 sensors times 50 samples per feature),
which — together with the pre-stimulus normalisation procedure — successfully
captures the odour-specific sensor response. In mammalian sensory
neuroscience, the analogy would be the phase-coupling of spike-trains to the
inhalation cycle, allowing the spike-timings to encode information about the
odour identity, thus suggesting one "sniff" as the unit of olfactory
processing [66]. Conversely, if the task is not classification but decoding
temporal information about the odour stimuli, such as frequency or
correlations, integrating the information across an artificial time window
would limit the temporal resolution, hence recording continuously and without
heat modulation might be the better choice. The ability of mammals to access
temporal stimulus information at sub-sniff resolution has been demonstrated
[25, 59], and shown to be relevant for behavioural tasks [25]. Those findings
may suggest future experiments in which both modes are active simultaneously
on separate sensor instances — continuously-sampled constant-temperature and
time-integrated temperature-modulated — which could allow for extracting
information about the temporal profile and the identity in parallel. Research
on insect have suggested dual-pathway olfactory systems [67], which may
facilitate the simultaneous extraction of odour identity and concentration
information [68]. Such an approach might suggest elegant solutions to the
olfactory cocktail party problem [69].

The proposed technology and its evaluation hold promise for tackling many
real-world challenges that require rapid odour sensing. In particular, any
instance of olfactory robotic solutions might currently be cut short in terms
of performance; as for both UGVs and UAVs, the sensor response time dictates
the maximum speed at which the agent can move while still obtaining spatially
resolved measurements [54]. Thus, such applications may directly benefit from
using the proposed sensor modalities, allowing for faster identification and
localisation of odour sources. For instance, a recent work proposed swarms of
nano quadcopters performing gas source localisation in indoor environments
[70], and evaluated different search strategies. A decreased latency in
detection and classification may not only assist in more efficient source
localisation, but also in expanding the use case to multiple odours and more
complex outdoor environments. Another recent study proposes odour sensing on
drones for wildfire monitoring [56]. For detecting smoke; vision and gas
sensors are fused, however the gas sensor update frequency is just
$1\text{\,}\mathrm{Hz}$. Given the intermittent and fine-structured nature of
odour plumes, an improvement on sensing timescales could reduce false-
negatives and aid in gaining critical time in localising the fire.

Beyond robotics, most applications in security still use static and relatively
slow sensing platforms, for e.g. the odour-based detection of explosives [71]
at airports. At checkpoints, fast and portable electronic noses could replace
random spot checks with exhaustive controls, and thus minimise risk further.
Further, recent investigations on mammalian olfactory-guided behaviour use
head-mounted MOx sensors as control recordings [72]. Using a high-resolution
data acquisition system — particularly one that matches the temporal
capabilities of the subject — would allow for better data quality and hence
could improve the resulting models.

In a related vein, neuromorphic information processing [73, 74] has seen much
traction in recent years, where in particular the reduced latency, power
consumption and data bandwidth have enabled highly optimised vision and
auditory sensors [75, 76]. We suggest that the information embedded in
millisecond odour packets, together with the sparse and intermittent nature of
odour plume encounters, makes the sense of olfaction an ideal candidate for
neuromorphic sensing. We foresee that revealing the rapid nature of the
sensors will further stimulate this field of research, motivating event-driven
and asynchronous odour sampling [77], for MOx sensors [78, 79] and beyond [80,
81].

In conclusion, our study marks a groundbreaking advancement in electronic
olfaction systems, demonstrating the ability to discern odours and decode
odour patterns with unprecedented temporal precision in miniaturised low-power
settings. Our findings unlock new possibilities for developing robotic systems
capable of rapidly and precisely tracking odour plumes in compact and low-
power environments, with the potential to transform electronic nose designs
and their applications across various domains.

### Data and code availability

The dataset and analysis code will be made publicly available upon
publication, but can be provided earlier on request.

## Methods

### Electronic nose

#### Circuit board design

The electronic nose uses readily available components and is illustrated in
Figure Fig. S1a. It features a Raspberry Pi Pico microcontroller and
incorporates eight MEMS-fabricated MOx gas sensors of four different types,
grouped into four packages.

The sensor packages comprise two SGX Sensortech MiCs-6814 sensors (sensors 1
and 5) and two ScioSense CCS801 sensors (sensors 2,3,4 and 6,7,8) , capable of
detecting a wide range of reducing and oxidising gases, including volatile
odour compounds (VOCs), hydrocarbons, carbon monoxide, hydrogen, nitrogen
oxides, and ammonia. Figure Fig. 1c displays an optical microscopy image of
the sensor structure. For sensing, the DC resistance across the sensing
element is measured. The integrated micro-hotplates allow for operation at
temperatures of up to $500\text{\,}\mathrm{\SIUnitSymbolCelsius}$.

For controlling the micro-hotplates, eight operational amplifiers (2x
STMicroelectronics TS924) are employed, and the sensor heater voltage is
configured using two Digital-to-Analogue Converters (DACs), specifically the
Texas Instrument DAC60004, which offer four channels each at 12 bits and 1
kHz. Additionally, two Analogue-to-Digital Converters (ADCs), the Texas
Instrument ADS131M08, are used to read sensor and heater resistances. These
are differential, simultaneous-sampling ADCs which read out the eight gas
channels and the eight temperature channels in lockstep at 24 bits and 1 kHz
(the two ADCs share the same clock). To monitor environmental conditions, a
digital pressure-humidity-temperature sensor, the TE Connectivity MS8607, is
included, which samples data at 24/16/24 bits and 50 Hz. Real-time data
logging is facilitated through the inclusion of a microSD card. The device’s
power needs, ranging from $1.2\text{\,}\mathrm{W}$ to
$1.5\text{\,}\mathrm{W}$, allow for multiple days of continuous operation on a
pocket-sized battery pack, making it suitable for extended field recordings or
robotic environments.

#### Sensor heater modulation

To implement controlled heater modulation, continuous measurement of the
hotplate temperature and regulation of power delivered to the resistive
heating element are essential. Each heater voltage $V_{\text{heat}}$ was
adjusted using a DAC and an associated amplifier, while the resulting current
$I_{\text{heat}}$ was monitored using an ADC in conjunction with a fixed-value
sense resistor $R_{\text{sense}}$. From these two quantities one can compute
the heater resistance $R_{\text{heat}}=V_{\text{heat}}/I_{\text{heat}}$ and
dissipated power $P_{\text{heat}}=V_{\text{heat}}I_{\text{heat}}$. Because the
device did not directly measure $V_{\text{heat}}$, the resistance calculated
by substituting the known control and sense voltages
$V_{\text{dac}}-V_{\text{sense}}\approx V_{\text{heat}}$ was subject to
errors, of which transient errors caused by lag and settling time in the DAC
and amplifier were deemed the most significant. These affected the sample
acquired immediately after a change in control voltage; therefore we used a
Kalman filter [82] to estimate $R_{\text{heat}}$, setting the measurement
uncertainty proportionally to the rate of change of the control voltage
$V_{\text{dac}}$.

The kind of resistive heating element present in our design exhibits a quasi-
linear relationship between hotplate resistance and temperature [83], so we
used a linear model to map the heater resistance to a calculated hotplate
temperature. The parameters of that linear model were set before recording the
data by measuring the heater response to a series of power steps, and matching
it with calibration data from the manufacturer’s datasheet, namely the nominal
hotplate temperature delta above ambient air temperature at nominal heating
power. This calibration procedure was repeated on an approximately weekly
basis to account for the possible ageing of the sensors.

Achieving short temperature steps of a duration not much greater than the
thermal time constant of the hotplate presented a number of challenges.
Because the shortest steps consisted of only 25 samples
($25\text{\,}\mathrm{ms}$ at $1\text{\,}\mathrm{kHz}$), we employed a
combination of open-loop and closed-loop control. In that scheme, we operated
the heater at a constant voltage for the duration of each step. That voltage
was selected based on a learned mapping between a desired temperature change,
and the control voltage required to achieve it. We adjusted the mapping after
every step to compensate for the effects of airflow and ambient temperature
fluctuations, using a proportional controller with a relatively slow
adaptation rate
($0.1\text{\,}\mathrm{V}\text{\,}{\mathrm{\SIUnitSymbolCelsius}}^{-1}\text{\,}{\mathrm{s}}^{-1}$).
With this, fast and repeatable temperature modulation patterns can be obtained
without introducing artefacts due to e.g. control loop oscillations.

For experiments with a constant heater temperature, achieving fast temperature
changes was not an issue, but care was taken to avoid the artefacts caused by
the DAC’s 12-bit quantisation of applied heater voltage. These quantisation
steps of about $0.7\text{\,}\mathrm{mV}$ led to small but measurable
transients in the recorded sensor signal. Since these transients were in the
same frequency band as the signals of interest, we decided to also keep the
heater voltage constant during each stimulus. We adjusted it to eliminate the
temperature error after each stimulus, which was sufficient since the thermal
environment of the sensors changed only slowly.

#### Sensor responses to ambient air

The resistance of MOx sensors depends not only on the presence of gases, but
also on the operating temperature. In heater cycle mode and odourless air (see
Fig. 2b, left), the sensors exhibited nearly-exponential relationships between
the recorded sensor resistances and the hotplate temperatures in the range of
$200\text{\,}\mathrm{\SIUnitSymbolCelsius}$ and
$400\text{\,}\mathrm{\SIUnitSymbolCelsius}$, with deviations at lower
temperatures (see Fig. S1c). A small deviation can be observed between the
trajectories corresponding to heating and cooling, however this may be
attributed to uncertainties in estimating the temperature of the sensor, which
is close to but not necessarily equal to that of the hotplate. The resistances
returned to their initial values after a completed cycle, without significant
hysteresis.

### Odour delivery setup

#### Reagents

All odourants were obtained in their pure liquid form from Sigma-Aldrich, and
contained in 15-ml glass vials (27160-U, Sigma-Aldrich). The odorants ethyl
butyrate, isoamyl acetate and cineol were diluted 1:5 with mineral oil, while
2-heptanone was diluted 1:20 with mineral oil. 2-heptanone was diluted to a
lower concentration as a 1:5 dilution saturated some of the gas sensors.

#### Olfactometer

Odours were presented using a custom made olfactometer capable of constructing
temporally complex stimuli with temporal bandwidths of up to
$500\text{\,}\mathrm{H}\mathrm{z}$ (Fig. 1d). This temporal olfactory delivery
device (TODD) has been outlined previously[25, 59]. The device consisted of 8
independent channels which contained either odour (diluted with mineral oil)
or pure mineral oil. These 8 channels were grouped into two sets of 4. Each
set of four consisted of an odour manifold, which contained odours or pure
mineral oil in glass vials which were fed by a common air flow. Each channel
in this odour manifold was fed into its own high speed valve on a separate
valve manifold. Each high speed valve could be opened and closed at
frequencies of up to $500\text{\,}\mathrm{H}\mathrm{z}$. On each valve
manifold, one of the channels containing pure mineral oil was set to remain
open indefinitely, acting as a ’carrier’ valve (grey valves in Fig. 1d). When
a trial was triggered, this carrier valve flow was reduced in accordance with
the amount of additional airflow generated by the other valves on the
manifold, therefore maintaining a continuous rate of air flow through the
system. In some cases, the carrier valve was not simply reduced, but was used
to generate temporally complex airflow to compensate for the temporal patterns
generated using the other valves in the system. Signals to the valves were
convolved with a high frequency $500\text{\,}\mathrm{H}\mathrm{z}$ continuous
signal, referred to as ’shattering’. This shattering was included as it has
been previously found to improve the temporal fidelity of the resultant odour
signal. The airflow to the TODD was maintained at a rate of
$1\text{\,}\mathrm{L}\mathrm{/}\min$ using a custom closed loop-feedback flow
controller.

#### Calibration

To ensure a continuous total airflow whilst maintaining a high signal
fidelity, prior to the electronic nose recording session, the output of the
olfactometer was measured with both a PID (200B miniPID, Aurora Scientific)
and flowmeter (AWM5101VN, Honeywell). The PID was positioned a short distance
away from the output of the olfactometer (> 2cm) and calibration trials were
presented. These were selected in a way to making sure to cover all the
different valve combinations of the experiment. The PID response to presented
odours was measured and the fidelity estimated. If the fidelity was found to
be too low, the rate of flow into each channel was tuned to increase the
fidelity. Next, the PID was replaced with the flowmeter, and the same selected
calibration trials were presented. If the rate of flow varied during the trial
presentation, the compensatory flow or carrier flow was modulated to return
the net flow back to pre-trial levels. The flow through the odour valve was
kept constant so as to not alter the odour signal fidelity. Airflow was
modulated by altering the duty-cycle of the valve shattering. Once there was
no visual change in the rate of flow between the trial and pre-trial levels
the flowmeter was removed and the olfactometer was deemed to be calibrated.

#### Fidelity Calculations

For quantifying the olfactometers’ temporal fidelity after calibration, we
deployed single-odour pulse trains of different frequencies and obtained
simultaneous PID and flow meter recordings. Here, the odourant Ethyl Butyrate
was used, as its ionisation energy is well-suited for the used PID. In
particular, at $t=$0\text{\,}\mathrm{s}$$ the carrier flow valves opened while
odour valves remained closed, for a duration of $10\text{\,}\mathrm{s}$. At
$t=$10\text{\,}\mathrm{s}$$ the odour valve and odourless compensation valve
deployed anti-correlated pulse trains of various frequencies, for
$2\text{\,}\mathrm{s}$ (see Fig. S1a (left) for the $10\text{\,}\mathrm{Hz}$
example). The fidelity for each square pulse was calculated as the value of
peak to trough, normalised to the peak to the baseline value. The fidelities
shown in Fig. S1a (right) were computed as the mean and standard-deviation
across all square pulses fidelities of a particular modulation frequency.

### Experimental protocol

#### Electronic nose placement

The electronic nose was attached to a movable arm and fixed in place
downstream of the olfactometer outlet, with a distance of approximately
$3\text{\,}\mathrm{c}\mathrm{m}$ from outlet nozzle to the gas sensors. To
ensure that the gas flow reached all the sensors on the board, we fine-tuned
the alignment of the electronic nose with respect to the nozzle by trial-and-
error until a strong response was obtained on all channels.

#### Heater modulation and odour delivery protocol

Three experiments with different sensor heater conditions were performed (see
Fig. S1d for the conditions over time):

  1. 1.

Sensor 1-8: $50\text{\,}\mathrm{ms}$ cycles between
$150\text{\,}\mathrm{\SIUnitSymbolCelsius}$ and
$400\text{\,}\mathrm{\SIUnitSymbolCelsius}$.

  2. 2.

Sensor 1-4: constant temperature of
$400\text{\,}\mathrm{\SIUnitSymbolCelsius}$, Sensor 5-8:
$50\text{\,}\mathrm{ms}$ cycles between
$150\text{\,}\mathrm{\SIUnitSymbolCelsius}$ and
$400\text{\,}\mathrm{\SIUnitSymbolCelsius}$.

  3. 3.

Sensor 1-4: constant temperature of
$400\text{\,}\mathrm{\SIUnitSymbolCelsius}$, Sensor 5-8:
$200\text{\,}\mathrm{ms}$ cycles between
$150\text{\,}\mathrm{\SIUnitSymbolCelsius}$ and
$400\text{\,}\mathrm{\SIUnitSymbolCelsius}$ (not used).

For each heater condition, odour stimuli of different pulse widths and
concentrations (controlled by adjusting the shattering duty cycle of odour and
mineral oil valves) were presented. After each odour stimulus, there was a
$30\text{\,}\mathrm{s}$ recovery phase before the next stimulus onset. The set
of different stimuli included:

  * •

4 odours + two control (’blank’) vials, 50 repetitions $1\text{\,}\mathrm{s}$
odour pulses, at 100% concentration.

  * •

4 odours, 20 x $1\text{\,}\mathrm{s}$ odour pulses, at concentrations of 20%,
40%, 60%, and 80%.

  * •

4 odours, 5 repetitions of shorter odour pulses in the range [10, 20, 50, 100,
200, 500] $\text{\,}\mathrm{ms}$, at 100% concentration.

  * •

2x6 odour pairs, 5 repetitions of $1\text{\,}\mathrm{s}$ anti-correlated pulse
trains, at frequencies in the range [1, 2, 5, 10, 20, 40, 60]
$\text{\,}\mathrm{Hz}$, at 100% concentration.

  * •

6 odour pairs, 5 repetitions of $1\text{\,}\mathrm{s}$ correlated pulse
trains, at frequencies in the range [1, 2, 5, 10, 20, 40, 60]
$\text{\,}\mathrm{Hz}$, at 100% concentration.

Within each experimental run, all the stimuli were presented in a fully
randomised order. Fig. S1e shows the distribution of odours over time, binned
in $1\text{\,}\mathrm{h}$ time intervals. A statistical ${\chi}^{2}$ test was
performed, confirming that the null hypothesis can’t be rejected ($p=0.364$),
i.e. that the trials are in fact randomised. Importantly, the odour delivery
protocol has not been synchronised with the sensor heater modulation phase.

#### PID recordings and odour onset/offset determination

A shortened version of the odour delivery protocol was deployed and recorded
with the PID. Fig. S1f displays a PID response to a $1\text{\,}\mathrm{s}$
isoamyl acetate pulse. For all the odours, the mean and standard deviation of
the pre-stimuls baseline were computed, and a threshold of 4 times the
standard deviation ($4\sigma$) defined. This was used to estimate an upper
bound for the time from theoretical stimulus onset to odour exposure at the
sensing site, and a lower bound from theoretical stimulus offset to the
purging of the sensor site. Fig. S1g displays all the extracted onset and
offset values, indicating that the odour may reach the sensor as rapidly as in
$10\text{\,}\mathrm{ms}$, while the purging may take several hundreds of
milliseconds. While PIDs are extremely fast, they too have a finite and odour
dependent response time, thus the actual times may be shorter than this.

### Pulse classification analysis

#### Feature extraction and validation

For evaluating what data features may be most suitable for the rapid
classification of short odour pulses, we used experiment B, where sensor 1-4
were operated at a constant temperature of
$400\text{\,}\mathrm{\SIUnitSymbolCelsius}$, and sensor 5-8 employed heater
cycles of $50\text{\,}\mathrm{ms}$ in the range of
$150\text{\,}\mathrm{\SIUnitSymbolCelsius}$ and
$400\text{\,}\mathrm{\SIUnitSymbolCelsius}$.

Different sensor data features were used and evaluated: Data windows of
$50\text{\,}\mathrm{ms}$ starting at a given time $t$ relative to the simulus
onset at $t_{onset}=$0\text{\,}\mathrm{s}$$ were used to extract 1\. raw data
from constant heater sensors, 2\. pre-stimulus
($t_{pre}=-$5\text{\,}\mathrm{s}$$) baseline subtracted data from constant
heater sensors, 3\. raw data from cycled heater sensors, and 4\. pre-stimulus
($t_{pre}=-$5\text{\,}\mathrm{s}$$) baseline normalised data from cycled
heater sensors. For the normalisation in the latter, the procedure is
illustrated in Fig. S2c and described in the following. The extraction of the
feature $G(\mathcal{D}_{s},t)$ can be summarised as applying a chain of a
sensor-wise logarithmic transformation and a maximum scaling, to both a
$50\text{\,}\mathrm{ms}$ baseline data snippet before stimulus onset (here,
$t_{pre}=-$5\text{\,}\mathrm{s}$$) and to a snippet at time $t$, and then
computing their vector difference:

$\centering
G(\mathcal{D}_{s},t)=\frac{\log(H(\mathcal{D}_{s},t))}{\max(\log(H(\mathcal{D}_{s},t)))}-\frac{\log(H(\mathcal{D}_{s},t_{pre}))}{\max(\log(H(\mathcal{D}_{s},t_{pre})))}\@add@centering$
(1)

Here, $H(\mathcal{D}_{s},t)$ describes a kernel extracting data from the
sensor recordings $\mathcal{D}_{s}$, starting at time $t$ and stopping after
one full heater cycle (e.g. 50 measurements). The sensor index is denoted as
$s$.

The data was split into one set for training & validation, and one set for
testing — with a ratio of 60% to 40% (see Fig. S2a). The former was used to
train and validate a k-NN classifier using different features via cross-
validation. The latter was used to evaluate the performance with the different
features used.

Classifier training was performed on data features from sensor responses
between $500\text{\,}\mathrm{ms}$ and $1000\text{\,}\mathrm{ms}$ after
stimulus onset, where the stimuli were $1000\text{\,}\mathrm{ms}$ odour pulses
of the gases 2H, EB, IA, Eu and blank, at 100% concentration. Testing was
performed on equivalent data features, however now the stimuli concentration
was sampled in the range [20, 40, 60, 80, 100]%, and the blank class was
omitted. Fig. S2d displays the achieved performance using the different data
features. The normalised cycled-heater data feature $G(\mathcal{D}_{s},t)$
outperforms the other tested features, both in accuracy at 100% concentration,
as well as for the reduced concentrations. For clarity, Fig. 2e shows a subset
of Fig. S2d.

#### Dynamic pulse classification

For the dynamic classification of short odour pulses, experiment A was used
with all 8 sensors modulated on a $50\text{\,}\mathrm{ms}$ period betweeen
$150\text{\,}\mathrm{\SIUnitSymbolCelsius}$ and
$400\text{\,}\mathrm{\SIUnitSymbolCelsius}$. Again, a 60% vs. 40% split for
training & validation vs. testing was performed, where the former was used to
determine a suitable classifier and its hyper-parameters via cross-validation,
while the latter served to evaluate the performance of the classifier (see
Fig. S2b). Data features were extracted as described in Eq. Eq. 1. For
training, the underlying data for the features are the sensor responses for
the subsequent $2000\text{\,}\mathrm{ms}$ after the onset of
$1000\text{\,}\mathrm{ms}$ odour stimuli, for concentrations in the range [20,
40, 60, 80, 100]%. The data features were labelled according to their time $t$
as follows:

Algorithm 1 Training data labelling procedure

if $t_{onset}\leq t<t_{offset}-\tau+d$ then

Feature is fully within measurable odour pulse.

if $\mathcal{O}_{stimulus}\text{ is not 'blank'}$ then

$y=\mathcal{O}_{stimulus}$

else

$y=\text{'blank'}$

end if

else if $t_{offset}+d\leq t$ then

Feature is fully after measurable odour pulse.

$y=\text{'blank'}$

else

Feature timing is ambiguous; exclude data feature from training set.

$y=\text{'rejected'}$

end if

Here, $t_{onset}=$0\text{\,}\mathrm{ms}$$ and
$t_{offset}=$1000\text{\,}\mathrm{ms}$$ are the stimulus onset and offset
respectively, $\tau=$50\text{\,}\mathrm{ms}$$ is the feature duration,
$d=$10\text{\,}\mathrm{ms}$$ is the upper bound stimulus delay as computed
earlier, $\mathcal{O}_{stimulus}$ is the stimulus odour of the corresponding
trial, and $y$ is the prescribed label of the data feature in question. This
procedure is illustrated in Fig. 3a.

For the normalised data features, several classification algorithms were
trained and validated via five-fold stratified cross-validation. The best
performing algorithm with corresponding hyper-parameters was selected, which
here was a Support Vector Machine (SVM) classifier with radial basis function
kernel [84] ($C=1e3$, $\gamma=1e-4$, balanced class weight). Ultimately, an
ensemble classifier was composed from the five SVMs trained on each split.

For testing how well the trained classifier performs on shorter odour pulses,
the features were extracted from sensor response data for the subsequent
$2000\text{\,}\mathrm{ms}$ after the onset of odour stimuli of different
durations, at 100% concentration. The stimulus durations fall within the set
{10, 20, 50, 100, 200, 500, 1000}$\text{\,}\mathrm{ms}$. For each data
feature, the classifier predicted the odour, which is illustrated as a raster
plot in Fig. 3b. The predicted odours $y$ were compared against the actual
stimulus odour $\mathcal{O}_{stimulus}$ and divided in predicting ’correct
odour’, ’wrong odour’ and ’no odour’, resulting in Fig. 3c. To extract the
accuracy for each pulse duration, a confusion matrix was composed by — for
each trial — comparing the most predicted non-blank class against
$\mathcal{O}_{stimulus}$, across multiple trials. The on- and offset times
correspond to the elapsed time from odour onset to first non-blank prediction,
and from odour offset to first ’blank’ prediction, respectively.

An analogous procedure was followed for testing the trained classifier on
anti-correlated patterns of odour pairs, resulting in predictions over time,
as shown in Fig. S2e & Fig. S2f.

### Temporal structure analysis

For the temporal structure analysis, i.e. the determination of the frequency
and the phase-shift of the two-odour pulse trains, the constant heater sensor
data (i.e. sensors 1-4) of experiment B and C were used. In particular,
experiment B was used for training and validation (i.e. finding and evaluating
a suitable data feature and classification algorithm), where experiment C was
used for testing, see Fig. S3a for an illustration.

#### Feature extraction

For each data trial, we extracted sensor data $\mathcal{D}_{s}$ from
$t=t_{onset}$ to $t=t_{offset}+b$, where $b=$100\text{\,}\mathrm{ms}$$ to
account for the stimulus delay and potential sensor lag. The data was then
log-transformed and differentiated before applying a discrete Fourier
transformation $\mathcal{F}(.)$, using the fast Fourier Transformation
algorithm [85]:

$L(\mathcal{D}_{s})=\mathcal{F}\left(\frac{d}{dt}\log(\mathcal{D}_{s})\right)$
(2)

All triplets [frequency, magnitude, phase] were extracted, and sorted
according to the magnitude. For each of the four sensors, the triplet with the
highest magnitude was selected, collectively composing a 12-dimensional data
feature.

#### Temporal structure classification

The data features and potential classifiers were evaluated on a 10-fold cross-
validation using the training & validation data (i.e. experiment B) and the
three different tasks described earlier. We decided on utilising a Random
Decision Forest (RDF) classifier [86] (balanced class weight, $N_{tree}=100$),
and on using the same 12-dimensional data feature for all tasks. See Fig.
S3b-d for an evaluation of the data features. For each cross-validation
training split we trained a RDF classifier, then combined them to form an
ensemble classifier for each task, which was finally evaluated on the testing
data (i.e. experiment C). For the validation using hotplate temperature and
PID data, the analogous pipeline was used, except that we omitted the log-
transformation.

### Comparing electronic nose performance with mouse performance

Performance analysis of the electronic nose to discriminate odour correlation
structure was carried out in the style of a previously published experimental
dataset (See Ackels et al. 2021 [25]). This allowed for a direct comparison of
the electronic nose performance with that of mice during an operant
conditioning task. A complete description of the experimental conditions and
data analysis can be found in the original paper. In brief, two cohorts of up
to 25 mice were housed in a common home cage system [87] that is used as an
automated operant conditioning setup. Mice were trained to discriminate
perfectly correlated from perfectly anti-correlated odour stimuli switching at
frequencies ranging from $2\text{\,}\mathrm{Hz}$ to $81\text{\,}\mathrm{Hz}$.
Task frequency was randomized from trial to trial. Odours were presented with
a multi-channel high-speed odour delivery device similar to the one used in
this manuscript. During a go/no-go task animal performance was rated based on
their lick responses to $S+$ (rewarded) and $S-$ (unrewarded) stimuli. For
roughly half of all mice, the correlated pattern was $S+$ and the anti-
correlated pattern was $S-$ . In the other half of the group this reward
valence was reversed. All stimuli were 2 s long. A water reward could be
gained by licking so that licking was detected for at least 10% of the
stimulus time during an $S+$ presentation (a ‘Hit’). Licking for the same
amount of time during $S-$ presentation resulted in a timeout interval of
$7\text{\,}\mathrm{s}$. In all other response cases, the inter-trial interval
was $3\text{\,}\mathrm{s}$ and no water reward was delivered. All behavioural
performance within a specified trial bin was calculated as a weighted average
of $S+$ versus $S-$ performance:

$\text{Performance}=\frac{(Hit/S+)+(CR/S-)}{2}$ (3)

in which $S+$ is the total number of rewarded trials, $S-$ is the total number
of unrewarded trials, $Hit$ is the total number of rewarded trials in which a
lick response was detected, and $CR$ (correct rejection) is the total number
of unrewarded trials in which no lick response was detected.

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## Acknowledgements

The authors would like to thank Y. Zhang for assisting with the odourant
samples, as well as M. Psarrou, S. Sutton, Y. Bethi, N. Ralph and P. Hurley
for fruitful discussions.

### Funding

Part of this work was funded by an NSF/MRC award under the Next Generation
Networks for Neuroscience initiative (NeuroNex Odor to action, NSF #2014217,
MRC #MR/T046759/1). Part of this work was supported by a Boehringer Ingelheim
Fonds PhD fellowship to CDC.

### Author contributions statement

MS, AS, ND, DD, TW, SR and AvS conceptualised the study. DD designed the
electronic nose hardware. DD and ND developed the electronic nose software.
ND, DD, TW, SR and CDC conducted the investigations and experiments. ND and DD
performed formal analysis. ND created the visualisations. MS, AS and AvS
acquired funding. ND drafted the original writing. Everyone participated in
the review and editing process.

### Competing interests

The authors declare no competing interests.

## Supplementary Materials

Figure S1: Supplementary figure for experimental setup. a, Electronic nose
design, displaying how the microcontroller unit (MCU) sets and reads out the
sensor heaters in a closed loop, while reading out the analyte dependent
sensor resistances. Further, the MCU connects to an environmental sensor (PHT)
and a micro SD card. b, R-T curve of a $50\text{\,}\mathrm{ms}$ temperature
cycle between $150\text{\,}\mathrm{\SIUnitSymbolCelsius}$ and
$400\text{\,}\mathrm{\SIUnitSymbolCelsius}$ without external stimulus,
displaying how the sensor response closely follows the hotplate temperature.
c, Different sensor hotplate settings over time. For each experiments, all the
stimuli were presented in randomised order. d, Heatmap depicting the
distribution of odour presentations over a set of 1 hour time intervals. A
${\chi}^{2}$ test was performed to assess the randomness of class distribution
over time intervals, with the computed p-value indicated as ’$p$’. e, PID
response to a $1\text{\,}\mathrm{s}$ isoamyl acetate pulse. Grey-dotted and
red dotted lines denote mean of pre-stimulus baseline and 4 standard
deviations threshold respectively. Where the response crosses the threshold
upwards (downwards), the odour onset (offset) is registered. f, Extracted
odour onsets (w.r.t. $t=$0\text{\,}\mathrm{ms}$$) and offsets (w.r.t.
$t=$1000\text{\,}\mathrm{ms}$$) for $1000\text{\,}\mathrm{ms}$ pulses of
different odours. For all experiments, the odourants are abbreviated as
follows. IA: isoamyl acetate; EB: ethyl butyrate; Eu: cineol; 2H: 2-heptanone;
blank: odourless control.  Figure S2: Supplementary figure for fast odour
classification. a, Data splitting for robustness analysis of the rapid heater
modulation data features (see Fig. 2e). b, Data splitting for evaluating the
dynamic classification of millisecond odour pulses (see Fig. 3). c,
Normalisation procedure for the heater modulation data feature. Time shifted
by cycle phase $\rho$ w.r.t. odour onset, for visual guidance only. d,
Accuracy scores for a k-nearest neighbours (k-NN) classifier trained on
$50\text{\,}\mathrm{ms}$ data features from $1000\text{\,}\mathrm{ms}$ odour
pulses at full concentration, and tested on $50\text{\,}\mathrm{ms}$ features
from $1000\text{\,}\mathrm{ms}$ odour pulses at different concentration levels
(tuned by adjusting the duty cycle of the micro-valves). Features are compared
for constant heater sensor readings (raw and baseline-normalised) and cycled
heater sensor readings (raw and normalised, as described in c)) e, Odour
stimulus classification for anti-correlated odour patterns over time. An RBF-
kernel SVM classifier was trained on $50\text{\,}\mathrm{ms}$ features from
$1000\text{\,}\mathrm{ms}$ odour pulses, and tested on odour anti-correlated
odour patterns of various frequencies. Shown here is is a
$2\text{\,}\mathrm{Hz}$ pattern. f, Classification correctness over time
(evaluated via the true odour presence), for anti-correlated odour patterns of
different switching frequencies.  Figure S3:  Supplementary figure for
temporal pattern discrimination. a, Data splitting for evaluating the temporal
pattern discrimination performance (see Fig. 4). b-d, Validation accuracy
plots for different extracted DFT-spectrogram peak features using the MOx gas
sensor resistances. b, Modulation frequency classification, c, pairwise
modulation frequency classification, and d, correlated vs anti-correlated
modulation discrimination. e, MOx heater temperature values for different
odour modulations. Here shown are data for the odour pair IA (isoamyl
acetate)- EB (ethyl butyrate), 5 trials each for 5 Hz correlated, 5 Hz anti-
correlated, 20 Hz correlated and 20 Hz anti-correlated respectively. f-h, Test
accuracy plots for different odour pair modulations, using the MOx heater
temperature values. i, Photoionisation Detector (PID) responses for different
odour modulations (odour stimuli as in e). j-l,, Test accuracy plots for
different odour pair modulations, using the Photoionisation Detector (PID)
responses. For all the classification tasks, an ensemble of Random Forest
Classifiers was used. The mean and error estimations arise from repeating
training and testing with different random seeds.