ELECTRONIC NOSES APPLICATIONS AND TECHNOLOGIES
Feature extraction methods for the analysis of an electronic nose response
Lamagna1, A; Rodríguez1, D.F.; Scoccola1,2, N. N
1Departamento
de Física, Comisión Nacional de Energía Atómica,
Avda. Libertador 8250, (1425) Buenos Aires, Argentina.
2Universidad
Favaloro, Solís 453 (1078), Buenos Aires, Argentina.
Reich, S.L.3*,
3Escuela de Ciencia y Tecnología, Universidad de San Martín, Alem 3901, (1653) Buenos Aires Argentina, Fax: (54) 11 6772 7121, *E-Mail:silreich@cnea.gov.ar
Received November 05, 2004. In final form May 23, 2005
Abstract
This work deals
with different methods for the analysis of the response of an
electronic nose. For this purpose we employed data obtained with a prototype
of electronic nose, developed in our laboratories, applied to the analysis
of a basic ingredient of beer, hop. We aimed to fulfill the two main requirements
of the local brewing industry, namely, deterioration of hop’s quality,
due to aging or poor storage and quick discrimination of the various similar
types of pellets usually employed. Different methods for the analysis of the
response of each sensor have been studied. A comparison between discrimination
obtained with a linear dimensional reduction via Principal Component Analysis
and a non linear reduction obtained with Self Organized Maps has also been
made.
Resumen
Este
trabajo estudia diferentes métodos para la identificación de las
características de una muestra medida con una nariz electrónica.
Para ello se han utilizado mediciones realizadas con un prototipo de nariz electrónica,
desarrollado en nuestros laboratorios, para el análisis del lúpulo
que es un ingrediente esencial de la cerveza. El objetivo de este trabajo es
la obtención de una identificación de características del
lúpulo que sea robusta y orientada a satisfacer dos requerimientos de
la industria cervecera local: detección del deterioro de su calidad por
envejecimiento o almacenamiento defectuoso y la obtención de una rápida
discriminación entre variedades. Se compara la discriminación
obtenida mediante una reducción dimensional lineal como es el Análisis
de Componentes Principales y la reducción no lineal obtenida mediante
mapas autoorganizados.
Introduction
Hops, the female inflorescences of the hop plant (Humulus lupulus L.), are used
in the brewing industry to add bitterness and aroma to beer. Their flowers aroma
and bouquet derive from their essential oils that represent among 0.5 - 3% of
the dry weight of the cone. Approximately 300 different chemical components
[1] characterize these oils. However, due to volatilization during the elaboration
process, only a small proportion of them is present in beer.
Hop suffers a continuous process of deterioration after the crop due to oxidative
processes when exposed to ambient air. Thus, an early alarm of the onset of
oxidative processes is an important parameter for the brewing industry. The
essential oils in hops are currently analyzed with gas chromatography coupled
to gas spectroscopy [2], although these methods are costly and time consuming.
The use of electronic noses is an appealing possibility to overcome these problems
In fact, electronic noses have been widely employed in the food industry as
a rather costless and efficient method specially aimed to screening techniques
for, among others, quality control purposes [3].
Following requirements of the local beer industries, which demand rigorous techniques
and yet not very expensive for quality control in order to successfully compete
within the beer market, we studied the feasibility of electronic noses to perform
some of the tasks currently undertaken by conventional gas chromatography.
A serious obstacle for the use of an electronic nose for beer control is the
difficulty in the detection of some odors masked by the presence of alcohol.
Thus as a first step in the feasibility study, we considered the analysis of
hops as a raw material, either as flowers or preprocessed pellets.
The two main requirements of the local brewing industry are deterioration of
hop’s quality, due to aging or poor storage and quick discrimination of
the various similar types of pellets usually employed. Besides, it is desirable
to allocate future measurements to already defined categories.
Discrimination and classification are multivariate techniques concerned with
the separations of distinct sets of objects (or measurements) and with the allocation
of new objects to previously defined groups. More specifically, the goal of
discrimination is to try to describe, either graphically or algebraically, the
differential features of the data, finding the best discriminants so as to maximize
the separation between classes. On the other hand the goal of classification
is to derive a rule that can be used to optimally assign new measurements to
the already labeled classes. Although not always possible, the method employed
to discriminate may serve as an allocator. It should be mentioned that although
the ability of electronic noses to deal with food characterization have been
successfully verified elsewhere [4], the implementation of a classification
system faces problems arising from false classifications [5] environmental disturbances
[6] and correct feature extraction methods [7].
In this work, hops are analyzed through detection of their aroma by an electronic
nose prototype (PampaNose 1) [8], developed in our laboratories, based on thin
film metal oxide sensors. Several feature extraction methods were applied to
the time dependent relative signal of each sensor. In the first method we simply
extract the maximum, steady state response that implies just a number per sensor
while in the others we implement feature extraction procedures that take into
account not only the steady state but also the transient regime of the sensor
response. This last step was implemented in two ways, considering the relative
signal and its derivative as well as the Fourier transform. The extracted features
were then fed into the discrimination procedure. We employed two basic discrimination
schemes: linear transformation of the data, such as the popular Principal Components
algorithm (PCA) and the non algorithmic transformations of Neural Networks,
specifically we used the unsupervised self organized neural networks (SOM) [9].
The PCA is based on a linear transformation which projects the original N-dimensional
space into one of lower (two in our case) dimension. On the other hand, the
SOM might be understood as a non-linear projection of the probability density
of a high dimensionality set of points into a two dimensional array of nodes
(neurons). For both approaches, we checked the success of correctly discriminating
and classifying by visualizing categories in a two dimensional plane.
We have also studied noisy corruption of the response. This was done, within
each designed experiment, blurring the measured sensors signals with noise.
In this way we were able to enlarge the data set by the artificially generated
new noisy data and, therefore, to study the robustness of the analysis against
disturbances.
Experimental
The
electronic nose prototype (Pampa Nose 1.[5] ) employed was developed in our
laboratories. It has six metal oxide sensors (MOS) of tin dioxide whose resistivity
changes according to the reducing properties of the sensed gaseous sample. Distinct
electric behavior of the corresponding sensible film is achieved through doping
some of these sensors with Au or with tetra t-Butyl Zinc Phthalocyanine. Two
commercial sensors, for temperature and relative humidity control, are included
inside the sensor chamber, to keep track of these important parameters during
the measurements.
Four different cultivars of hops were selected for this work. They correspond
to the usual Argentine varieties employed by the local breweries. For the analysis,
7 grams of each sample were put inside 250 cm3 glass bottles. The
bottles were sealed and held at room temperature (20°C±2°C) for
15 minutes in order to reach a stable headspace composition.
The headspace was afterwards fluxed into the e-nose sensor chamber at a velocity
of 50 ml/min. The nose response was registered for a time interval of approximately
15 minutes each 0.5 minutes and after that, a 30 minutes purge was done. Ambient
air was used as carrier gas. The sampled headspace and the carrier air were
alternately switched by a three-way valve according to this time schedule.
In what follows, relative time signals are considered. That is to say, for all
points of the sensor time response, the ambient air response after the transient
time (R0) is subtracted and the corresponding difference is
divided by R0 .
Results
and discussion
We have analyzed the aging and further discrimination for four varieties of
hops, preprocessed as pellets, two of them (VA1 and VA2) corresponding to local
varieties (Traful and Mapuche) and two types of Cascade pellets (C1 and C2).
The first proposed task is the detection of the aging of the pellets through
the analysis of its aroma. Hop's pellets suffer a continuos oxidation process
after crop. For industrial purposes it is important that this deterioration
is noticed in advance since it affects the final quality of beer. We report
the results for one variety of pellets (VA1 or Traful type). For this purpose,
samples of this fresh hop's pellets were kept inside a freezer at a temperature
of -20° C in closed recipients. They were taken from their recipients and
kept in ambient air for a given period of time before the measurement. Four
periods of time were considered: very short (up to 8 hours), short (up to 5
days), medium (up to 10 days) and long (more than 15 days). This categories
are labeled SS, S, M and L. This process was repeated several times.
As mentioned in the introduction, three alternative feature extraction methods
were employed for the sensor signal:
f1) maximum steady peak value of the relative signal response vector,
f2) maximum steady peak value of the relative signal response vector and maximum
absolute value of its first derivative,
f3) coefficients of the discrete Fourier transform of the signal obtained by
the fast algorithm (FFT).
We discard other methods that would give more than three significant figures
for the typical sensor signal that has been sampled at 32 points.
After the feature extraction, the information was reduced using both the PCA
algorithm and the SOM method. The PCA algorithm was performed using the covariant
matrix and taking into account the first two principal components. For the SOM
analysis a 10x13 rectangular network with a Gaussian neighboring function was
used. In both cases the input consists of one number per sensor in the f1 case,
two per sensor for method f2 and three for method f3 since for the Fourier transform,
only three frequencies appear with non negligible amplitude.
Results for the feature extraction possibilities f1, f2 and f3 and for the two
above mentioned dimensional reductions are displayed in Figures
1a to 1e. In each case the left panel corresponds to the PCA analysis and
the right panel to the SOM method, where only the winner neurons are marked
in the plot. Figures 1a and 1b, indicate
that the use of the maximum amplitude of the signal, either with PCA or SOM,
allows to separate the aging of the hop's pellets in three categories. The first
comprises short and very short periods, that is, there is no visible differentiation
of hops aroma after 5 days of storage in ambient air. The second region corresponds
to medium exposure to ambient air. Finally, a third region corresponding to
those samples that were exposed more than 15 days to ambient air can be discriminated.
Either for PCA or for SOM projection it is verified that increasing aging of
the sample, corresponds to increasing distances in the two-dimensional plane
from the fresh hop's locus. The second feature extraction method f2, includes
information about the maximum of the first derivative of the signal, a parameter
measuring the rise time. Figures 1c and 1d
show that, although not very significant, this second method allows a somewhat
better discrimination for the PCA projection and almost the same result for
the SOM reduction. In any case, the f2 extraction procedure shows a smaller
dispersion among the same class while the distance between classes is slightly
increased. Figures 1e and 1f show
the limitation of using, in this case, the Fourier transform as a feature extraction
method from the signal. Probably the approximation of the signal function by
its Fourier decomposition introduces uncertainties that blurred the final discrimination.
Figure
1: Different days of storage: square symbols correspond to very short (SS),
rhomboids to short (S), triangles to medium (M) and circle to large (L) storage
time. Score plot for PCA analysis of the nose response for feature extraction
methods f1 (a), f2 (c), and f3
(e). SOM network with the winner neurons for feature extraction methods
f1 (b), f2 (d), and f3
(f).
Figure
2: Score plot for PCA analysis of the nose response for different hop's
varieties. Open symbols correspond to measured data while solid represent their
noise corruption. Almost 94% of the total variance in the first two Principal
Components.
The second
study carried out concerns the discrimination among the four pellets' varieties
considered: two of them (VA1 and VA2) corresponding to local varieties (Traful
and Mapuche) and two types of Cascade pellets (C1 and C2). For each of them,
three different samples were measured in different days. We do not report here
consecutive measurements of the same sample, since they show no significant
variations among themselves.
Based on the results of the aging analysis discussed above, in this part we
have only applied method f1 for the feature extraction from the signal and the
linear dimensional reduction PCA as discrimination method. The use of other
methods (i.e. SOM as discrimation method) is not expected to lead to significantly
different results.
Results corresponding to the measured data are indicated with open symbols in
Figure 2 with 94% of the total covariance taken into account
with the first two principal components. Once the projection transformation
was defined, new data was mathematically generated and then projected onto this
space. They were generated adding a random noise (of about 15%) to the already
registered signals. The results are plotted with the same convention but with
solid symbols. This was done to analyze the possibility to define regions in
the (PC1-PC2) plane that can be assigned to a given variety of pellets. It can
be seen from Figure 2 that there can be establish two different
regions. One for VA1 and VA2 varieties, the local cultivars, while the two Cascade
type pellets can be grouped into another region. This discrimination is also
supported by gas chromatography studies performed by the laboratories of one
local brewery.
Conclusions
The
results presented in this work indicate that the use of the Pampa Nose provides
an adequate tool to carry out several tasks concerning the analysis of hop properties
by measuring their aromas in ambient air conditions. The aging process was studied
via three different alternatives for feature extraction of the sensors signals
and two discriminating methods (PCA and SOM). We were able to verify that with
both methods it is possible to associate increasing aging of the hops with increasing
distance from the fresh aroma locus in a two dimensional plane and thus it is
possible to introduce a "cut-off" for undesired hops rather quickly
and costless.
Another point to be mentioned is that taking into account the rise time properties
together with the maximum relative signal, the discrimination is slightly improved.
The use of the Fourier spectrum of the signal implies that at least components
for three frequencies are required but in spite of the apparently better information
about the relative signal, no achievement above the simpler method is obtained
and the same behaviour is observed either with SOM or PCA. We were also able
to discriminate between different varieties of hops with a PCA analysis of the
nose response and, introducing mathematically noisy corruptions of the signals,
we checked the ability of a previously defined (PC1-PC2) plane to allocate different
varieties to the corresponding class.
We expect that the present study will allow to implement an adequate and rather
simple e-nose analysis to be operated by the brewery industry for hop's quality
control.
Acknowledgments
The authors wish to acknowledge financial support of J.P.A. S.A. (Javier Picco
y Asoc.) through PID131-2001 of the National Agency for the Promotion of Science
and Technology (ANPCYT) of Argentina.
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