coffee analysis with an electronic nose

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1. INTRODUCTION
ELECTRONIC Noses (EN), in the broadest meaning, are instruments that
analyze gaseous mixtures for discriminating between different (but similar) mixtures and, in
the case of simple mixtures, quantify the concentration of the constituents. ENs consists of a
sampling system (for a reproducible collection of the mixture), an array of chemical sensors,
Electronic circuitry and data analysis software. Chemical sensors, which are the heart of the
system, can be divided into three categories according to the type of sensitive material used:
inorganic crystalline materials (e.g. semiconductors, as in MOSFET structures, and metal
oxides); organic materials and polymers; biologically derived materials.
The use of ENs for food quality analysis tasks is twofold. ENs is normally
used to discriminate different classes of similar odour-emitting products. In particular ENs
already served to distinguish between different coffee blends and between different coffee
roasting levels. On the other hand, ENs can also be used to predict sensorial descriptors of
food quality as determined by a panel (often one generically speaks of correlating EN and
sensory data). ENs can therefore represent a valid help for routine food analysis.

The combination of gas chromatography and mass spectroscopy (GC-MS) is
by far the most popular technique for the identification of volatile compounds in foods and
beverages. This is because the separation achieved by the gas chromatographic technique is
complemented by the high sensitivity of mass spectroscopy and its ability to identify the
molecules eluting from the column on the basis of their fragmentation patterns. Detection
limits as low as 1 ppb (parts per billion) are frequently reached. The main drawbacks of the
approach are, however, the cost and complexity of the instrumentation and the time required
to fully analyze each sample (around one hour for a complete chromatogram).
Comparatively, ENs are simpler, cheaper devices. They recognize a fingerprint, that is global
information, of the samples to be classified. For food products, the sensory characteristics
determined by a panel are important for quality assessment. While man still is the most
efficient instrument for sensorial evaluation, the formation of a panel of trained judges
involves considerable expenses.
Commercial coffees are blends, which, for economic reasons, contain
(monovarietal) coffees of various origins. For the producers the availability of analysis and
control techniques is of great importance. There exists a rich literature on the characterization
of coffee using the chemical profile of one of its fractions, such as the headspace of green or
roasted beans or the phenolic fraction. In the literature up to 700 diverse molecules have been

identified in the headspace. Their relative abundance depends on the type, provenance and
manufacturing of the coffee. It is to be noticed that none of these molecules can alone be
identified as a marker. On the contrary one has to consider the whole spectrum, as for
instance the gas chromatographic profile.

2. COMPARISION OF ELECTRONIC NOSE WITH
BIOLOGICAL NOSE
Each and every part of the electronic nose is similar to human nose. The
function of inhaling is done by the pump which leads the gas to the sensors. The gas inhaled
by the pump is filtered which in the human is the mucus membrane. Next comes the sensing
of the filtered gas, which will be done by the sensors i.e., olfactory epithelium in human nose.
Now in electronic nose the chemical retain occurs which in human body is enzymal reaction.
After this the cell membrane gets depolarised which is similar to the electric signals in the
electronic nose. This gets transferred as nerve impulse through neurons i.e., neural network
and electronic circuitries.

3.DIFFERENT TYPES OF SENSORS
There are different types of electronic noses which can be selected according
to requirements. Some of the sensors available are calorimetric, conducting, piezoelectric etc.
Conducting type sensors can again be sub divided into metal oxide and polymers. In this type
of sensors the functioning is according to the change in resistance. The sensor absorbs the gas
emitted from the test element and this results in the change of resistance correspondingly.
According to the Resistance-Voltage relation V=I*R. Here V is the voltage drop, R is the
resistance of the sensor and I is the current through it. By this relation as resistance changes
the voltage drop across the sensor also change. This voltage is measured and is given to the
circuit for further processes. The voltage range for using metal oxide sensor in from 200 C to
400 C. The working principle of polymer sensor is same as that of metal oxide sensor The
only change is in the temperature range i.e., the room temperature.
Piezoelectric sensors are sub-divided into quartz crystal microbalances and
surface acoustic wave. In quartz crystal the surface absorbs the gas molecules. This results in
the change of mass, which causes a change in the resonant frequency of the quartz crystal.
This change in frequency is proportional to the concentration of the test material. The change
in frequency also results a change in the phase. In surface acoustic wave we measure the
change in phase of the resonant frequency.
Calorimetric sensors are preferable only for combustible species of test
materials. Here the sensors measure the concentration of combustibles species by detecting
the temperature rise resulting from the oxidation process on a catalytic element.

4. EXPERIMENTAL SET-UP
4.1 The Pico-1 Electronic nose

Five semiconductors, SnO2 based thin films sensors were utilised. Two are
pure SnO2 sensors; one is catalysed with gold, one with palladium and one with platinum.
They were grown by sputtering with the RGTO technique. RGTO technique is a technique
for growing SnO2 thin films with high surface area. The surface of the film after thermal
oxidation step of the RGTO technique presents porous, nano-sized agglomerates which are
known to be well suited for gas absorption. A thin layer of noble metals was deposited as
catalyst on three sensors to improve sensitivity and selectivity. Thin film sensor produced by
sputtering is comparatively stable and sensitive. Furthermore, since the growing conditions
are controllable, they can be taylored towards the particular application. Even if catalysed the
sensors are not selective and therefore sensor arrays together with multivariate pattern
recognition techniques are used.
The odour sampling system depends on the type of sample and on its
preparation. For a simple gas mixtures one uses automated gas mixing stations consisting of
certified gas bottles, switches and mass flow controllers. In the case of complex odours like
food odours, the volatile fraction (the so-called headspace) is formed inside a vial where a
certain amount of odour-emitting sample is put. The vapour can then be collected either by
flushing a carrier inside the vial (dynamic headspace scheme) or extracted with a syringe and
injected, at constant velocity, in the air flow which is used as carrier (static headspace
scheme).
There are two different design considerations of designing the sensors. Those
are first design consideration i.e., linear and the second design consideration i.e., parallel
design. The first is comparatively less costlier than the second one. At the same time it has

certain disadvantages that the distribution of the sample into each sensor element is uneven,
but second consideration have this advantage. The construction of second type is much
complex when compared to first.
The basic schematic diagram of an electronic nose is shown in the below figure.
Fig.1 Process in electronic nose
Sample
Vapour
Array of
Sensors
Array
of
Signals
Result
Pattern Recognition

Fig 2. Lab arrangement of electronic nose for coffee analysis

1. An auto sampler (Hs 850 CE Instruments). This device is a standard
component of chromatographs; its utility is a high sample throughput and a high
reproducibility due to the automation of the measurement process. It consists of a sample
carousel, where the vials containing the odour-emitting sample are held; an oven, where the
sample is pre-conditioned; a movable mechanic arm with syringe (A).
The electro-mechanical part of the EN used in this experiment consists of (see
a scheme in fig. 2):
2. A mass flow controller (B) to set the flow of the carrier gas.
3. A stained steel chamber © which can contain up to five chemical sensors plus a
humidity sensor.
4. Control electronics (D) permitting to steer the system (auto sampler, mass flow
controllers and sensors) via PC.
The typical measurement consists of the exposure of the sensors to a
concentration step, that is a change of odour concentration from zero to c (each component of
the vector stands for a gas component) and back to zero again, and of the recording of the
subsequent change in resistance. The classical feature extracted from the response curve is
the relative change in resistance.

A set of Mat lab functions (toolbox) has been developed for analyzing the
data. The toolbox permits to perform the following tasks.
Data cleaning (median filter for spikes removal, possible noise averaging) and
plotting (for gaining a first impression of the response curves). Software for drift
compensation is currently under study.
Exploratory analysis (visual). First various plots of the response curves and of the
features can be drawn for each sensor separately (univariate analysis). The most
important multivariate tool for exploratory analysis is Principal Component Analysis
(PCA) (score and loading plots). PCA is implemented with a simple user interface
giving the possibility of selecting the sensors and classes to be displayed and of
grouping classes together. PCA also serves for feature reduction before the use of
Multilayer Perceptrons (MLP).
Learning with MLP. The inputs to the MLP are the projections of the data on the first
m principal components (the so called PCA scores). The number of inputs m (PCA
dimensions) is then a variable to be optimized. To prevent over fitting early stopping
(ES) or weight decay regularization can be used. Both a division in two sets (training
and testing) or in three sets for ES (training set is subdivided in training and validation
sets) is possible. The error function is minimized with the Levenberg Marquardt
algorithm. Ten network initialization are usually performed and the net with the best
result on the test set is held.

Decomposition of the global learning tasks in successive classification subtasks
(hierarchical classification). First the classification between the more istinct clusters is
performed, then the finer differences are determined in subsequent steps. This is
particularly useful when dealing with a big number of classes and a small number of
data. Ensembles of MLPs based on output coding decomposition have also been
studied. Work is in progress on the topics of boosting and bagging for increased
classification accuracy.

Fig 3. Internal view of electronic nose circuit

4.2 The measurements
Measurements were done on ground coffee. Two groups of coffees were
analyzed. The first one consists of 6 single varieties (SV) and the blend Italian Certified
Espresso (ICE) for reference (this group will be called SV) and the second one of 7 blends,
including the ICE, see tables I, II. The fourth row of the tables contains a brief
characterization of the coffees, where the commercial value is indicated with + and -.Two
grams of ground coffee are introduced into a vial with a volume of 20cm3 which is crimped
with seal and septa. The vial is then left in an incubation oven at 50C for 30 minutes in order
to generate the aroma. Ten vials for every coffee type of the first group and 12 vials for every
coffee type of the second group were prepared. Three successive extractions were performed
from the same vial. All together there are 10 7 3 = 210 measurements for the first group
and 12 7 3 = 252 measurements for the second group. While the data set is not big for
machine learning standards, where it is usual to have hundreds of examples for each class,
this is a considerable dataset to be collected with an E-Nose, where complete datasets
normally don t exceed 100-200 examples (while it is rather common to have less then 10
instances for each class).
Table I. First group of coffee: Single varieties + ICE

# coffee Name Type Quality (+/-)
1. ICE Blend, +
2. Brazil Arabic natural, +
3. Ethiopia Arabic washed, +
4. Rio Minas Arabic natural with defects, -
5. Guatemala Arabic washed, +
6. Peru Arabic natural,-
7. Cameron Arabic,-
TABLE II. The second group of coffees: blends.
# coffee Name Note, Quality
1. ICE Reference, +
2. ICE, more toasted Strong, +
3. ICE, without natural Study, +
4. Robusta Bad,
5. ICE def#1 Unripe, -
6. ICE def#2 Rancid, -
7. Commercial Arabic + Robusta +-
Experimental parameters like samples conditioning temperature and fluxes
were optimised to reduce the sensor stress and to increase the measurement rate while still
reaching sensor s steady state conditions (which are believed to be more reproducible). The
time interval between the extractions sufficient for the headspace to reach equilibrium
conditions was found to be 40 min.

Fig. 4
An external view of an Electronic Nose interfaced with PC is shown in the
above figure.
As for the sensorial analysis, the panels (formed respectively by 18 and 14
judges) judged the final product (cups of espresso coffee) according to 10 quantitative
descriptors (colour intensity, cream texture, olfactory intensity, roasted, body, acidity,
bitterness, astringency, global positive odour and global negative odour) and 4 qualitative
descriptors (attractiveness, finesse, balance and richness). Each descriptor is given a mark
from one to nine. One sample for every coffee type (plus a random repetition per group) is
tasted. In the quantitative analysis the panel is given a reference for adjusting its judgements,
while this is not the case for the qualitative analysis which should provide a personal,
hedonic impression. Since the qualitative values are not calibrated, their spread is

considerable. Therefore, for every coffee type, the mean over the 4 qualitative descriptors and
over the panellists is considered as a reliable global parameter characterizing the sensorial
appeal of a coffee. This quantity is pictorially termed Hedonic Index (HI). The two averages
help to reduce the uncertainty (standard deviation) by a factor vN, where N is the number of
sensorial measurements, i.e. N = judges qualitative descriptors. For the SV group the
standard deviation of the HI is s mean = 0.2). The detailed procedures adopted for testing the
Espresso in this study are described in.

5.ADVANTAGES OVER HUMAN SNIFFERS
The human sniffers are costly when compared to electronic nose. It is because these
people have to be trained. This is a time consuming that a construction of an electronic nose.
Now for the confirmation of the values obtained from a sniffer the result obtained from the
sniffer has to be compared with some other sniffer s value. And here there are great chances
of difference in the values got by each individual. Detection of hazardous or poisonous gas is
not possible with a human sniffer. Thus taking into consideration all these cases we can say
that electronic nose is highly efficient than human sniffer.

6. OTHER APPLICATIONS OF ELECTRONIC NOSES
There are various applications in which an electronic nose may be used. For
example, to monitor the characteristic odour generated by a manufactured product (e.g. drink,
food, tobacco, soaps). The electronic nose research group has considerable experience in the
analysis of coffee odours (e.g. roasting level and bean type), lager beer odours (lager type and
malodours) as well as having analysed tobaccos, spirits, wines, transformer oils, plastics and
drinking water. More recent work is on the use of e-noses for medical diagnostics and
biotechnology. It is also used in automotives. The use of this over here is to analyse quantity
of smoke that is given out and the quantity of the desired gas. One another application is for
environmental monitoring especially to test the presence of poisonous material in the
environment. It is also used for military application for the very same purpose. It is also used
in medical field.
7. CONCLUSIONS

In this contribution I presented a description of E-Nose the advantage of which
consists in the sensor type and in the data analysis software. Thin film semiconductor sensors
are stable and sensitive, while the Mat lab toolbox permits to reliably analyze small datasets.
Two groups of measurements on coffee samples were analyzed. Classification figures of over
90% for both groups have been obtained with PCA and multilayer perceptrons. More
importantly, EN data have been correlated with panel test judgments. As far as we know, the
prediction of a global sensorial parameter, as the coffee s hedonic index, using just EN data
has been never reported. These results show that it is possible to use the Electronic Nose for
routine work in food quality analysis.
Researches are still going on to make electronic nose much more compact than
the present one to make it more compact and to make electronic nose I.C.s. In future we
might be able to manufacture olfactory nerves.
8. REFERENCE

1. IEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENTS,
2002
2. IEE SPECTRUM, 1996
3. WW.IIT.EDU
4.101seminarstopics.com