Classification of Volatile Compounds with Morphological Analysis of
e-nose Response
Rita Alves
, Jo
ao Rodrigues
, Efthymia Ramou
, Susana I. C. J. Palma
, Ana C. A. Roque
and Hugo Gamboa
LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics),
Faculdade de Ci
encias e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal
Associate Laboratory i4HB- Institute for Health and Bioeconomy, School of Science and Technology,
NOVA University Lisbon, 2819-516 Caparica, Portugal
UCIBIO – Applied Molecular Biosciences Unit, Department of Chemistry, School of Science and Technology,
NOVA University Lisbon, 2819-516 Caparica, Portugal
Electronic Nose, Volatile Organic Compounds, Euclidean Distance, Morphology, Classification.
Electronic noses (e-noses) mimic human olfaction, by identifying Volatile Organic Compounds (VOCs). This
work presents a novel approach that successfully classifies 11 known VOCs using the signals generated by
sensing gels in an in-house developed e-nose. The proposed signals’ analysis methodology is based on the
generated signals’ morphology for each VOC since different sensing gels produce signals with different shapes
when exposed to the same VOC. For this study, two different gel formulations were considered, and an average
f1-score of 84% and 71% was obtained, respectively. Moreover, a standard method in time series classification
was used to compare the performances. Even though this comparison reveals that the morphological approach
is not as good as the 1-nearest neighbour with euclidean distance, it shows the possibility of using descriptive
sentences with text mining techniques to perform VOC classification.
Electronic noses mimic the biological olfaction pro-
cess through an array of sensors that have different re-
sponses when in contact with Volatile Organic Com-
pounds (VOCs). These devices can be trained to de-
tect the presence of individual VOCs or the presence
of VOCs mixtures without identifying the individual
VOCs that compose the mixture. E-noses were devel-
oped and first mentioned by Persaud and Dodd (Per-
saud and Dodd, 1982) in 1982. With technology’s
development, electronic noses equipped with artificial
intelligence, are widely used for VOCs’ pattern recog-
nition (Bos et al., 2013), having promising applica-
tions in distinguishing odours in fields such as envi-
ronment monitoring (Chandler et al., 2015; Wilson
and Baietto, 2011; Lee et al., 2003), medical diagnos-
tics (Fens et al., 2009; Di Natale et al., 2003; Coronel
Teixeira et al., 2017; Bruins et al., 2013; Pavlou et al.,
2004; Hockstein et al., 2004; Hockstein et al., 2005;
Saidi et al., 2018), public security affairs (Hu et al.,
2018), agricultural production (Karakaya et al., 2020;
Chen et al., 2018), and food industry (Santos et al.,
2004; Chen et al., 2018; Chandler et al., 2015; Lee
et al., 2003).
In e-noses, the classification of VOCs is per-
formed with the analysis of differences between the
signals generated for each VOC. This analysis can
be categorised as shape-based, and structure-based
(Keogh et al., 2004). Shape-based methods perform
local comparisons between time series, being exam-
ples distance measures such as the Euclidean distance
(ED) or the Dynamic Time Warping (DTW) distance
(Lin et al., 2012). Both methods are a standard and
have been extensively used in this problematic, per-
forming well in short time series. ED and DTW are
usually combined with a 1-Nearest Neighbour (NN)
classifier (Sch
afer, 2015).
Structure-based methods rely on broader charac-
teristics of time series, such as the presence of specific
morphological structures or patterns, being more ade-
quate for longer signals (Sch
afer, 2015). Dictionary-
based methods are one subcategory of structure-based
methods and have recently been used with good per-
Alves, R., Rodrigues, J., Ramou, E., Palma, S., Roque, A. and Gamboa, H.
Classification of Volatile Compounds with Morphological Analysis of e-nose Response.
DOI: 10.5220/0010827200003123
In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 4: BIOSIGNALS, pages 31-39
ISBN: 978-989-758-552-4; ISSN: 2184-4305
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
formances (Sch
afer, 2015). These techniques rely on
a transformation of the time series into a sequence of
symbols by means of methods such as the Symbolic
Aggregate approXimation (SAX) (Lin et al., 2007).
The first approach proposed for Time Series Classi-
fication (TSC) with symbolic representations was the
Bag of Patterns (BoP). This method was inspired by
the Bag of Words model from the text mining sce-
nario, using SAX as the symbolic transformer (Lin
et al., 2012). Further, proposed methods were concep-
tually inspired on the BoP, using the same reasoning.
Other techniques are found, such as Bag of SFA Sym-
bols (BOSS) and Word ExtrAction for time SEries
cLassification (WEASEL) (Sch
afer, 2015; Sch
and Leser, 2017).
Recently, a new class of gas sensors was de-
veloped and is being explored for classification of
individual VOCs in an in-house built e-nose (Hus-
sain et al., 2017; Esteves et al., 2019; Fraz
ao et al.,
2021). The chemical changes that take place in the e-
nose sensors are responsible for the generated signal.
These sensors change their properties when exposed
to VOCs, and the resulting response of that change is
converted to an electrical signal. The resulting sig-
nal from the interaction with the VOCs is produced
using unique sensing materials that change their opti-
cal properties according to the VOC they are exposed
to (Santos et al., 2019). The sensors are composed
of sensing materials that constitute a new class of hy-
brid gels for gas sensing, composed of molecules of
Liquid Crystal (LC) and Ionic Liquid (IL), forming
LC-IL droplets. The configuration of the LC droplets
change when exposed to a VOC, creating different op-
tical patterns for different compounds (Hussain et al.,
2017; Santos et al., 2019; Esteves et al., 2019).
The optical e-nose explores the optical properties
of the sensing films. A schematic of the e-nose is
presented in Figure 1, as well as its fundamental sys-
tems. The delivery system, responsible for leading
the gas sample towards the sensor array, has two air
pumps, the relays, and the chamber where the sample
is stored. The existing pumps in the delivery system
are intended to manage the sensors’ exposure to the
target samples. The exposure pump is responsible for
carrying the air containing VOCs into the detection
chamber. The recovery pump re-establishes the ini-
tial conditions in the detection chamber. The control
of both generates the VOC exposure/recovery cycles
adua et al., 2018).
The fact that the generated signals could vary their
morphology according to the VOC they are associated
with can be an advantage in identifying compounds.
Thus, in this work, an e-nose is used with two differ-
ent sensing gels to test the ability of two classifiers
to correctly label the VOC at which the e-nose is ex-
posed. Multiple experiments have been acquired for
both sensing materials, as the purpose is to label a
VOC based on a classifier trained with a database of
past experiments. In addition to this, a novel method
is proposed. This method falls in the category of
dictionary-based methods and relies on the signals’
morphology described by a set of ordered patterns.
This novel method will be compared with the standard
1-nearest neighbour euclidean distance classifier. The
main objective of this work is to find if the gel formu-
lations are good enough to correctly label VOCs and
evaluate the performance of the proposed method. We
observed that the methodology developed was not as
precise in identifying VOCs as the standard one, hav-
ing, however, shown quite satisfactory results, indi-
cating that there is room for improvement.
The data set comprises signals from 11 known VOCs
(acetone, acetonitrile, chloroform, dichloromethane,
diethyl ether, ethanol, ethyl acetate, heptane, hex-
ane, methanol, and toluene) acquired with sensing
gels with different chemical formulations. The gels
are composed of a polymer (the bovine gelatin) and
molecules of LC and IL.
Two sensor formulations were tested,
namely, containing bovine gelatin and: (1) the
IL [BMIM][DCA] and the LC 4-cyano-4’-n-
octylbiphenyl (8CB); and (2) the IL [BMIM][DCA]
and the LC 4-cyano-4’-pentylbiphenyl (5CB). These
were the chosen sensing gels so that different types of
morphology could exist and enrich the data set. For
the first formulation, 3 experiments were made, which
resulted in 444 analyzed cycles (= 37/VOC). For the
second formulation, 8 experiments were acquired,
and 1444 cycles were generated (= 120/VOC).
The acquisition conditions were the same for all
experiments, regardless of the sensor. An example
of the acquired cycles is presented in Figure 2. The
rows indicate the VOC to which each formulation
was exposed to.
This chapter presents the overall pipeline to per-
form the analysis and classification. As mentioned
in the introduction, the purpose is first to search for
a method that is able to perform the correct identi-
fication of a VOC with the knowledge of past ex-
periments. A standard methodology for this type of
BIOSIGNALS 2022 - 15th International Conference on Bio-inspired Systems and Signal Processing
Figure 1: Schematic of the e-nose and its systems. Adapted from (Santos et al., 2019).
Figure 2: Representation of overlapped cycles obtained
with the optical sensing gels containing bovine gelatin
with the IL [BMIM][DCA] and LC 8CB, and the IL
[BMIM][DCA] and the LC 5CB for all VOCs.
problems is using a 1 NN-euclidean classifier, which
works well with short signals, being also very quick.
In addition to this method, this work proposes a novel
methodology that intends to perform time series clas-
sification based on higher level structures with a lin-
guistic representation of the signals. The performance
of the latter will be compared with the standard 1 NN-
euclidean method.
3.1 Pre-processing
The analysis starts with a pre-processing stage, which
comprehends three main steps: (1) noise reduction,
(2) cycle segmentation and (3) outlier removal. The
first step will be filtering the signals by applying a
median filter and a smoothing function, to ensure high
frequency noise and high fluctuations are attenuated.
The median filter has as input the signal and the size
of the median filter window, returning a signal with
the same size as the original containing the median
filtered result. The smooth function uses a window
with 1 second.
The dataset for each experiment has a square sig-
nal that indicates the moments in which the pumps
are working, which are the exposure (pump signal =
1) and recovery (pump signal = 0). This information
was used to split the signals into individual cycles.
Cycles with a signal-to-noise ratio inferior to 3 are re-
moved, as well as outliers, identified by calculating
the euclidean distance of a cycle to the mean wave of
the experiment.
3.2 Time Series: Word Vector Classifier
The proposed methodology is inspired by the reason-
ing from text data mining for text classification. This
pipeline uses the well known Bag of Words (BoW)
to generate a feature matrix with vectors that repre-
sent the frequency of words found for each document.
Each vector is a representation of a document. From
this matrix, a simple classifier such as a naive bayes
model or a linear Support Vector Machine (SVM) can
be used (HaCohen-Kerner et al., 2020). Besides, the
BoW matrix can be converted to a term-frequency in-
verse frequency (Tf-idf ) matrix as well. In order to
use this reasoning, the time series needs to be con-
verted to text, adding a sentence generation layer to
the process. In this case, the conversion to text is per-
formed with SSTS, which applies a conversion of the
time series to text based on (1) a pre-processing step
and (2) a connotation step. Patterns are then found
in a third step (3) the search. For this, a regular ex-
pression is used in this symbolic representation, and
words are generated for each of the patterns found,
building a representation with written sentences.
The overall process is depicted in Figure 3.
Classification of Volatile Compounds with Morphological Analysis of e-nose Response
Figure 3: Steps for the vectorization of the set of time series to be classified. Step 1: Convert the time series into sentences;
Step 2: Convert the sentences into a vectorized representation (BoW); Step 3: Transform the BoW into the Tf-idf.
3.2.1 Pattern Search and Sentence Generation
As presented in Figure 3, the process starts by con-
verting the signal into a symbolic representation.
Each step of SSTS is selected by the user to opti-
mize the search of the desired pattern. For example,
when searching for moments when the signal is ris-
ing, the user selects (1) the pre-processing that best
can prepare the signal for this search, (2) the connota-
tion that corresponds to the first derivative, converting
each sample of the signal into a character for when it
is rising (p), falling (n) or flat (z). The rising mo-
ments of the signal are then searched with a regular
expression, such as ”p+”. To this pattern, the word
”Rise” is attributed. This process is applied for a pre-
defined group of patterns and for each of these, a word
is given. The words are then ordered and the sentence
is build, as showed in step 1 of Figure 3.
The connotation methods used for this analysis
and the possible characters generated during this step
are presented in Table 1.
The list of patterns used for this analysis is pre-
sented in Table 2.
The pre-processing step is not presented in Table
1 as it is the same for all signals and made during the
pre-processing stage. The connotation and the search
step are showed and the corresponding word assigned
to the pattern as well. The groups of rows represent
the words that are used to build a sentence. As there
are 7 groups, in general, each signal is characterized
by 7 sentences. The search pattern is a regular ex-
pression that depends in the translation made by the
connotation method.
Table 1: Connotation (Con) methods and their meaning for
each single characters (Char) in which the samples of the
time series are translated.
Con Char Description
p positive slope
n negative slope
z zero slope
r positive slope with low in-
R positive slope with high in-
f negative slope with low in-
F negative slope with high in-
R quick positive slope
r slow positive slope
F quick negative slope
f slow negative slope
0 lower than a threshold
1 higher than a threshold
D Concave
C Convex
3.2.2 Signal Vectorization
From the generated sentences, it is possible to use
natural language processing (NLP) techniques to per-
form a feature analysis and classification. Typically,
the process involves using a BoW or a Tf-idf repre-
sentation, which are defined by evaluating the num-
ber of occurrences of words in a document. For each
signal, a document and a corresponding vector, with
word occurrences (tf ), is generated for each signal.
This vector can be compared with the other vectors
BIOSIGNALS 2022 - 15th International Conference on Bio-inspired Systems and Signal Processing
Table 2: The connotation variables, search regular ex-
pressions and corresponding words assigned to the pattern
searched. The parameter m indicates the size, in samples, of
the difference between a peak or a plateau. For this work,
m=20 samples.
Connotation Search Word
p+ Rising
n+ Falling
z+ Flat
p+z{,m}n+ Peak
n+z{,m}p+ Valley
p+z{m,}n+ posPlateau
n+z{m,}p+ negPlateau
r+ smallRise
R+ highRise
f+ smallFall
F+ highFall
r+z*F+ smallRisehighFall
R+z*f+ highRisesmallFall
f+z*r+ smallFallsmallRise
F+z*R+ highFallhighRise
r+z*f+ smallRisesmallFall
R+z*F+ highRisehighFall
f+z*R+ smallFallhighRise
F+z*r+ highFallsmallRise
R+ quickRise
r+ slowRise
F+ quickFall
r+ slowFall
z+ Straight
(0p)+(0z)*(0n)+ lowPeak
(1p)+(1z)*(1n)+ highPeak
(0n)+(0z)*(0p)+ lowValley
(1n)+(1z)*(1p)+ highValley
tive + 1st
(Dp)+ concaveRising
(Dn)+ concaveFalling
(Cp)+ convexRising
(Cn)+ convexFalling
to evaluate the similarity between signals. The BoW
vectors are made with the following formula:
t f
being t the word that exists in all documents, d the
document, t’ the term that belongs to document d.
As presented in Figure 3, in the step 2, the BoW is
built with the possibility of gaining context over what
surrounds the words in a sentence, for instance, if the
sequence ”Flat Rise” is common in one of the classes,
it might be an important feature, more than the indi-
vidual counterparts, ”Flat” and ”Rise”. In that sense,
an N-gram was given to build the BoW. In the exam-
ple presented, an N-gram of size 2 is used and the
final example vector is generated from the sentences
of the document. From sentence ”Flat Valley Flat”,
the words ”Flat”, ”Valley”, ”Flat Valley” and ”Valley
Flat” are represented. For this work, an N-gram value
of 5 was used.
3.2.3 The Tf-idf Representation
Opposed to the BoW model, the Tf-idf model max-
imizes differences between documents by means of
including the inverse document frequency term (idf),
represented by the following equations:
id f (t, D) = log
|d D : t d|
being D, the set of documents and N the total number
of documents. The final equation of the Tf-idf model
is the following:
t f id f (t, d, D) = t f (t, d) · id f (t, D) (3)
This matrix was the chosen representation, as the
literature emphasises that better results are typically
achieved and other methods that use symbolic repre-
sentation of time series use Tf-idf by default (Sch
2015; Lin et al., 2012). This model will be used with
a SVM with a linear kernel (linearSVC) for the VOC
classification. The sklearn package from Python was
used to perform both vectorization and classification
The classification of VOCs was performed with both
a 1-NN-euclidean classifier and a novel proposed
method TSWordVectorizer. The results are presented
in Figures 4 and 5, respectively. These Figures show
the averaged confusion matrices for both methods
over using each experiment as a testing set. In ad-
dition, Table 3 show the overall performance of both
methods for both sensing formulations.
4.1 Ability to Classify VOCs
The results presented by Figures 4 and 5 show that the
e-nose is able to produce different signals for different
VOCs. The 5CB formulation was better in doing so,
providing better results for both methods. Regarding
the signals from the 5CB formulation, it is relevant to
mention that experiments acquired in different days
are able to be reproduced over time. Considering that
experimental conditions may vary due to slight differ-
ences in the VOC concentration, or in the preparation
of the formulation, the 5CB sensor is able to provide
signals that account for this variability. This was not
Classification of Volatile Compounds with Morphological Analysis of e-nose Response
Table 3: Overall results of the classification of VOCs for both used methods and both sensor formulations.
TSWordVectorizer 1 NN-euclidean
P R F1 P R F1 P R F1 P R F1
Acetone 0.76 0.94 0.84 0.83 0.78 0.80 0.77 0.83 0.80 0.95 0.93 0.94
0.84 0.82 0.83 0.81 0.81 0.81 0.65 1.00 0.79 1.00 0.88 0.94
Chloroform 0.94 0.90 0.92 0.89 0.82 0.85 1.00 1.00 1.00 1.00 0.99 0.99
Dicholoromethane 0.89 0.61 0.72 0.91 0.82 0.86 0.78 0.54 0.64 0.95 0.88 0.91
Diethyl Ether 0.87 0.84 0.86 0.86 0.82 0.84 0.62 0.83 0.71 0.91 1.00 0.95
Ethanol 0.61 0.60 0.61 0.82 0.91 0.86 0.85 0.79 0.81 0.92 0.95 0.93
Ethyl Acetate 0.85 0.67 0.75 0.81 0.83 0.82 1.00 0.75 0.80 0.98 0.93 0.95
Heptane 0.53 0.60 0.56 0.81 0.83 0.82 0.89 0.62 0.73 0.93 0.94 0.94
Hexane 0.51 0.40 0.45 0.86 0.89 0.87 0.80 0.73 0.76 0.94 0.93 0.94
Methanol 0.65 0.61 0.63 0.85 0.81 0.83 0.77 0.83 0.80 0.83 0.92 0.87
Toluene 0.66 0.91 0.77 0.80 0.93 0.86 1.00 1.00 1.00 0.94 1.00 0.97
Total 0.74 0.72 0.72 0.84 0.84 0.84 0.83 0.81 0.80 0.94 0.94 0.94
P - Precision; R - Recall; F1 - f1-score.
Figure 4: Confusion matrix of the classification of VOCs with the 5CB formulation for both methods. An average f1-score of
84.0% and 94.0% was achieved for TSWordVectorizer and 1-NN-Euclidean methods, respectively. The analyzed VOCs are
labelled from 0 to 10 in the following order: acetone, acetonitrile, chloroform, dichloromethane, diethyl ether, ethanol, ethyl
acetate, heptane, hexane, methanol, and toluene.
met with the 8CB sensor. In this case, the signals gen-
erated are richer in their morphological changes, but
a higher variability in the shape of the signal is found
in different experiments. Even so, the fact that less
experiments were used with the 8CB sensor can in-
dicate that more experiments are needed to make the
database more robust.
Overall, the 5CB formulation was able to provide
better results than 8CB with both classification meth-
4.2 Comparison between Methods
The TSWordVectorizer model shows to be promis-
ing in performing this type of tasks. Although rely-
ing solely in a higher structural level, describing the
morphological sequence based on the ordered pres-
ence of patterns, this method was able to mostly cor-
rectly classify each VOCs, with an average f1-score
of 84% for the 5CB formulation and 72% for the
8CB formulation. This method had more difficulties
in classifying VOCs that had very similar morphol-
ogy. For instance, the 8CB sensor exhibited a very
similar response to Ethanol and Methanol, as well
as to Heptane and Hexane (Figure 2). In that sense,
more mistakes are made between these compounds,
which is also verified with the euclidean method but
with less impact in the overall performance. The 1-
NN-euclidean method achieved an average f1-score
of 94.0% and 81.4% for the 5CB and 8CB formula-
tions, respectively.
BIOSIGNALS 2022 - 15th International Conference on Bio-inspired Systems and Signal Processing
Figure 5: Confusion matrix of the classification of VOCs with the 8CB formulation for both methods. An average f1-score of
72.0% and 81.4% was achieved for TSWordVectorizer and 1-NN-Euclidean methods, respectively. The analyzed VOCs are
labelled from 0 to 10 in the following order: acetone, acetonitrile, chloroform, dichloromethane, diethyl ether, ethanol, ethyl
acetate, heptane, hexane, methanol, and toluene.
Both methods follow the same tendency for each
VOC: the precision is higher or lower for the same
VOCs. Nevertheless, the TSWordVectorizer was not
as good as the simpler and quicker 1-NN-euclidean
method, which means that improvements have to be
made. In the literature, dictionary-based methods,
such as this one, are recommended for longer time
series, with higher structural differences. In order to
be applied successfully on short time series, other de-
scriptions have to be designed, reflecting the presence
of other patterns that can highlight other properties
of the signal. In addition, several classification mis-
takes were made because the shape differences are not
at the overall shape level, but rather in the properties
of the shape itself. For instance, regarding Methanol
and Ethanol of formulation 8CB, the shape is exactly
the same, but differences in how the signal rises and
falls are what enable the distinction made by the eu-
clidean method. In that sense, another layer of analy-
sis could be added regarding the properties of the ex-
isting shapes, enabling the differentiation of signals
with the same overall shape. Moreover, a grid search
over the variables of the method should be performed
to optimize the performance, namely for the n-gram
value and peak size (m parameter in Table 2).
The main purpose of this work was to discover if it
was possible to predict the label of a VOC by means
of a previous database acquired with the same e-nose
and sensors but with past samples of the same type
of VOC. This was achieved with excellent accuracy
for the 5CB formulation and medium accuracy for the
8CB formulation, using two different time series anal-
ysis methods. More data should be acquired to build
a robust database for the differentiation of such com-
pounds. The possibility of combining multiple for-
mulations in the same e-nose is also promising and
would definitely improve the performance.
The usage of a simple and standard method as the
1 NN-euclidean was good enough to perform a clear
identification of the correct VOCs, which is promis-
ing, since the process is simple and quick. In the
other hand, the proposed method was not as good, but
shows promising results for this type of task. More
improvements should be made, namely in perform-
ing a differentiation at the feature level of the patterns
used to describe the signals. Additionally, more pat-
terns can be defined to highlight other dynamics of the
signals. Besides, this method could be used with other
classifiers in an ensemble learning pipeline, since it
gives a different look over the signals.
Finally, the proposed methodology has the poten-
tial to deliver an explainability and interpretability
over the differences between classes, namely by us-
ing the Tf-idf weight values for each pattern (Senin
and Malinchik, 2013).
In this work, we have demonstrated the applica-
bility of TSWordVectorizer to VOC-sensing signals
in an innovative signal analysis pipeline that shows
potential for further improvements and expansion to
real-world VOC samples classification.
Classification of Volatile Compounds with Morphological Analysis of e-nose Response
This project has received funding from the European
Research Council (ERC) under the EU Horizon 2020
research and innovation programme [grant reference
SCENT-ERC-2014-STG-639123, (2015-2022)] and
by national funds from FCT - Fundac¸
ao para a
encia e a Tecnologia, I.P., in the scope of the
project UIDP/04378/2020 and UIDB/04378/2020 of
the Research Unit on Applied Molecular Biosciences
UCIBIO and the project LA/P/0140/2020 of the As-
sociate Laboratory Institute for Health and Bioecon-
omy - i4HB, which is financed by national funds from
financed by FCT/MEC (UID/Multi/04378/2019).
This work was also partly supported by Fundac¸
para a Ci
encia e Tecnologia, under PhD grant
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