Scalable and Easy-to-use System Architecture for Electronic Noses

Ana P

adua

, Daniel Os

orio

2,∗

, Jo

ao Rodrigues

2,∗

, Gonc¸alo Santos

, Ana Porteira

, Susana Palma

Ana Cec

ılia Roque

and Hugo Gamboa

UCIBIO, REQUIMTE, Departamento de Qu

ımica, Faculdade de Ci

encias e Tecnologia da Universidade NOVA de Lisboa,

2829-516 Caparica, Portugal

Laborat

orio de Instrumentac¸

ao Engenharia Biom

edica e F

ısica da Radiac¸

ao (LIBPhys-UNL), Departamento de F

ısica,

Faculdade de Ci

encias e Tecnologia da Universidade NOVA de Lisboa, Portugal

Keywords:

E-nose, System Integration, Scalability, VOCs.

Abstract:

The purpose of this work was the development of a scalable and easy-to-use electronic noses (E-noses) system

architecture for volatile organic compounds sensing, towards the ﬁnal goal of using several E-noses acquiring

large datasets at the same time. In order to accomplish this, each E-nose system is comprised by a delivery

system, a detection system and a data acquisition and control system. In order to increase the scalability, the

data is stored in a database common to all E-noses. Furthermore, the system was designed so it would only

require ﬁve simple steps to setup a new E-nose if needed, since the only parameter that needs to be changed

is the ID of the new E-nose. The user interacts with a node using an interface, allowing for the control and

visualization of the experiment. At this stage, there are three different E-nose prototypes working with this

architecture in a laboratory environment.

1 INTRODUCTION

Electronic noses (E-noses) are devices that detect and

discriminate different odors. An E-nose is generally

deﬁned as an instrument which comprises an array

of electrochemical sensors with partial speciﬁcity and

an appropriate pattern recognition system, capable of

recognizing individual components or mixtures of va-

pors (Gardner and Bartlett, 1994).

E-noses architecture’s includes four elements: (i)

a sample delivery system, that switches the tested air

with the reference air; (ii) a detection chamber, where

the odors originate electrical signals; (iii) a data ac-

quisition and control system, that acquire and send

the signals generated in the detection chamber to a

computer; and (iv) software for feature extraction and

pattern recognition algorithms (Gutirrez and Horrillo,

2014). The way E-noses sense odorant molecules re-

sembles the human olfactory system. The analyte in-

teracts with the sensor array, that consists in a set

of broadly-tuned responsive materials. Once stim-

ulated, the sensors give characteristic signature re-

sponses to the odors detected. Subsequently, the soft-

ware processes the raw signals, extracts and selects

useful information and performs pattern recognition

Authors divided the work equally

(Yan et al., 2015). Therefore, a database can be cre-

ated from the patterns of known odors, and used to

train a pattern recognition system so that new odors

detected can be then classiﬁed and identiﬁed.

The most commonly used sensing materials are:

metal oxides, conducting polymers and composites.

Moreover, surface acoustic wave sensors, gas sensi-

tive ﬁeld effect transistors, quartz micro-balance sen-

sors, and micro-electro-mechanical systems associ-

ated with nanomaterials can also be used for gas sens-

ing (Loutﬁ et al., 2015). Recently, the term E-nose has

been used by some companies referring to systems

based on ultra-fast gas chromatography (e.g. zNose

from Electronic Sensor Technology), or ion mobility

spectrometry (e.g. Lonestar gas sensor from Owl-

stone), as well.

E-noses can be optimized by reducing the de-

vices’ price, minimizing the number of sensors re-

quired for discrimination, becoming more portable

through miniaturization, and using user-friendly and

faster signal processing tools.

The expression ’scale-up of an E-nose’ refers to

the process of producing the devices at higher num-

ber. In particular, a scalable acquisition and control

system is an important achievement to acquire and vi-

sualize more data in less time. It is useful for research

Pádua, A., Osório, D., Rodrigues, J., Santos, G., Porteira, A., Palma, S., Roque, A. and Gamboa, H.

Scalable and Easy-to-use System Architecture for Electronic Noses.

DOI: 10.5220/0006597101790186

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 1: BIODEVICES, pages 179-186

ISBN: 978-989-758-277-6

179

purposes, allowing the production of large amounts

of data, that can be then analysed and processed. One

of the most important issues is to replicate the sen-

sor array production uniformly at large scale. For

example, for graphene-based chemical sensors, dif-

ferences in carbon nanotubes (CNTs) structure and

chirality causes variability in the response of differ-

ent devices, which may compromise the reliability of

the results. Another challenge is to grow graphene

at large scale with uniform thickness (Zhang et al.,

2009). On the contrary, MOS sensors are manufac-

tured applying easily controllable processes, therefore

can be produced quickly on a large scale with guaran-

tee of sensor uniformity (Fine et al., 2010).

The delivery system and the data acquisition and

control system can also be adapted for acquiring

larger datasets. This article describes the scale-up

process of a data acquisition and control system cre-

ated for research purposes in laboratorial environ-

ment. A database was created to organize large

datasets acquired from three in-house assembled E-

noses.

2 METHODOLOGY

2.1 Requirements

Our research group is developing E-noses using new

sensing materials(Hussain et al., 2017). This de-

vice should be easy-to-use (independently of the user

background/ﬁeld of study), easy to assemble and re-

pair, scalable and reliable.

To facilitate the use of the E-nose, the user should

have an interface to insert the following parameters

that describe the experiment in an unambiguous way:

sensors identiﬁers (sensors IDs), session conﬁgura-

tion, VOC speciﬁcations, and user ID.

Figure 1 presents an overview of the E-nose sys-

tem and, in the following sections ,the hardware and

the system architectures will be described.

2.2 Hardware

Each E-nose is composed by three hardware systems:

a delivery system, a detection system, and a data ac-

quisition and control system. The delivery system

is composed by two air pumps, tubes and connec-

tors that transfer the VOCs to be analysed from the

headspace of the sample chamber (in the exposure pe-

riods) or the environmental air (in the recovery peri-

ods) to the detection chamber. A two relays module

switches the pump state in each period. The exposure

Figure 1: Architecture of an E-nose plus the network layer.

Each device is composed by multiple systems, namely the

acquisition and control system, the delivery system, and the

detection system. The network layer establishes the inter-

action between the device, the user and the database.

pump is ON in the exposure periods and OFF in the

recovery ones, and the recovery pump vice-versa.

The detection chamber consists in a 3-D printed

black protective box. Inside, there is a set of opti-

cal sensors that change their properties while inter-

acting with VOCs. The optical detection system in-

cludes two PCBs: one with an emission circuit, and

the other with a detection circuit. The sensing mate-

rials are placed in an adequate support, between the

PCBs.

The acquisition and control system includes a mi-

crocontroller (Arduino Due), and an embedded sys-

tem (Raspberry Pi). The output signals given by the

sensors placed in the detection chamber are connected

to the analog input pins of the microcontroller. The

Arduino selected is a 32-bit ARM based microcon-

troller development board, with 12 analog input pins,

a 84 MHz clock, able to run at 3.3 V and has a 12-

bit analog-to-digital converter (ADC), which converts

the analog values into digital values ranging from 0 to

4095. The high number of analog ports and the low

voltage power input, coupled with the ease of pro-

graming make this development board an ideal choice

for this project.

The embedded system chosen for this project was

the Raspberry Pi, since it offers more than enough

power for the needs of this project and has a big active

on-line community. Each one of the E-noses devel-

oped is connected to a Raspberry Pi. The ﬁrst and the

second assembled E-noses use Raspberry Pi 2, and

the last one already has Raspberry Pi 3.

Table 1 gives a comparison between the processor

and RAM values for each Raspberry Pi version.

Both Raspberry Pi version 2 and 3 have powered

USB ports, and version 3 also has on-board Wi-Fi and

bluetooth version 4. Due to disparity of performance

in the project embedded system’s, all the code and

architecture was designed to run on the performance

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lagging device, the version 2. This was accomplished

by making the tasks performed by the Raspberry Pi

as lean as possible. In the purposed architecture, the

Raspberry Pi is only responsible for saving the data

and controlling the Arduino.

The data is stored in a data storage and backup

system, normally called Network Attached Storage

(NAS) for a convenient and organized storage of the

information that is acquired in each E-nose. The NAS

used in this project is a QNAP TS-453A. All E-nose

systems share the same folder, composed by a com-

mon excel ﬁle (where the conﬁguration of each exper-

iment session will be stored), and a speciﬁc directory

for each E-nose.

2.3 System Architecture

Figure 1 shows every layer of each E-nose and how

the elements interact and communicate with each

other. Each experiment is launched by the user after

setting the necessary parameters. The process runs in

the Raspberry Pi and at the same time, a process is

launched in the personal computer of the user to en-

able real-time visualization. This data is also saved in

the NAS.

This section will thoroughly explain how the var-

ious layers interact in the process of control, acqui-

sition and visualization, showing the ﬂowchart of the

entire experiment session, explaining as well how the

Arduino actions are controlled with a python script

and how the interface is used for monitoring and vi-

sualization.

2.3.1 Acquisition and Control

In each E-nose system, the Arduino board is responsi-

ble for controlling the pump relays module, acquiring

the data from the photo receptors, and read the room

temperature and humidity values.

The Arduino has been ﬂashed with ﬁrmata plus

ﬁrmware in order to be controlled by a python script

which runs in the Raspberry Pi. This is accomplished

by means of a python library that enables the interac-

tion with the Arduino ﬁrmware - pymataIO.

Each of the six photo receptors is connected to an

analog input channel of the Arduino, while the hu-

midity and temperature sensor is connected using the

C protocol.

Table 1: CPU and RAM values for each version of the Rasp-

berry Pi.

Raspberry Pi version CPU type CPU frequency RAM

1B+ Single 32-bit 700 MHz 500 Mb

2 Quadcore 32-bit 900 MHz 1 Gb

3 Quadcore 64-bit 1200 MHz 1 Gb

Due to the need of using I

C, the pymataIO library

was chosen, since between the two most used libraries

used to control Arduinos ﬂashed with the ﬁrmata plus

ﬁrmware, pymataIO provides a simpler method to im-

plement the protocol.

The control and acquisition script runs in the

Raspberry Pi. In the ﬁrst place, the user sets the con-

ditions in which the experiment session will be con-

ducted by specifying the parameters that proﬁle the

sample: the sensors’ labels, the room’s conditions,

the total duration of the experiment session, the ex-

posure and recovery duration, and the sampling rates

at which the values from both the photo receptor’s ar-

ray and the temperature and humidity sensors are ac-

quired.

Figure 2: Flowchart that explains how the main process oc-

curs, which demonstrates how the Arduino is controlled.

Figure 2 depicts the ﬂowchart of the process that

controls the Arduino board. After setting the main pa-

rameters, the main cycle starts and a ﬁrst recovery cy-

cle is performed to clean the detection chamber con-

ditions to a reference state. Further, the acquisition

cycle (main cycle) starts and will last for the duration

speciﬁed initially.

As shown in Figure 2, in each iteration of the main

cycle the period of the current activated pump, and the

time for acquiring the room temperature and humidity

are checked. When the period of the pump cycle has

Scalable and Easy-to-use System Architecture for Electronic Noses

181

elapsed, the pump mode (exposition or recovery) is

changed and the pump timer is reset. The temperature

and humidity values are acquired and saved at deﬁned

periods. This process is repeated in each iteration,

ending when the duration of the experiment session

has elapsed.

2.3.2 Network Layer

In order to make the system as scalable as possible,

each acquisition module must be able to easily con-

nect to the NAS and to the user.

First, the NAS, Raspberry Pi systems, and the user

must be connected to the same network, in this case

the internal LAN of the laboratory. By being con-

nected to the same LAN, it makes it easier for all the

devices to see and be seen by the rest of the hardware

components.

With all the components connected to the same

LAN, the IP of both the NAS and the Raspberry Pi

systems must be set to a static IP in order to make the

connection easier. In the case of the Raspberry Pi’s,

the static IP also gives a unique ID to each of them.

In order to access the NAS, each Raspberry Pi

auto-mounts the folders in the shared folders direc-

tory via the network ﬁle system (NFS) protocol. Each

Raspberry Pi connects to a particular folder in the

NAS to save the data acquired and to a common folder

containing the Excel ﬁle. Each Raspberry Pi folder is

named using its own IP, e.g., eNose108 is the NAS

folder where the Raspberry Pi with the 108 ending IP

connects to. The user also mounts the NAS folders in

his/hers computer in order to access the ﬁles for set-

ting up, controlling and visualizing the experiments.

To communicate with the Raspberry Pi ’s via a PC,

the secure shell (SSH) protocol was used. In order to

use this protocol in python, the paramiko library was

used. This library implements the SSHv2 protocol.

This library is used to run the data acquisition script,

query the Raspberry Pi if there is any script running

and to stop the script if the user deems necessary.

2.3.3 Interface, Control and Visualization

In order to ease the management of the experiment,

an user interface has been built using a python GUI

library, TKinter. The interface runs in the personal

computer of the user and coordinates all the processes

that run during the experiment, namely the main pro-

cess that controls the Arduino and runs on the Rasp-

berry Pi, and the visualization process which also runs

in the personal computer of the user. This interaction

is also possible because the interface is connected to

the Raspberry Pi by SSH.

As mentioned in the previous section, each E-nose

system is associated to its corresponding Raspberry Pi

ID. The interface will also be linked to that speciﬁc ID

in order to check, every time it is started, if any exper-

iment session is already running in the corresponding

Raspberry Pi. If so, the main page of the interface will

be frame B (see Figure 4), where all the characteris-

tics of the experiment are shown as disabled, whereas

in Figure 3, is represented the main page of the inter-

face, in which would appear enabled for the user to

interact with.

The main page enables the user to conﬁgure the

conditions in which the experiment will be conducted.

The ﬁrst appearance shows a default conﬁguration,

which can be changed according to the needs of the

user. Each entry is veriﬁed in order to guarantee that

the input text type is valid.

After the conﬁguration is done, the user can press

the button ”Run” to start the experiment session. This

action triggers a new thread which sends a command

via SSH to the Raspberry Pi that runs the python script

that activates the Arduino and runs as demonstrated

in Figure 2. All the remaining buttons and entries are

disabled, except the button ”Stop” which is enabled

and can be pressed by the user to stop the experiment

session if necessary.

The visualization starts automatically when the

”Run” button is pressed by calling a subprocess that

runs the plotting script in the user’s computer. This

part of the software has been developed in Bokeh,

which is a web based python interactive plotting plat-

form.

As the access to data from the NAS can be slow,

the data has been saved in multiple temporary ﬁles

with 100 samples, which are accessed and read by the

plotting process and erased afterwards.

During the experiment the data is acquired and

plotted. When the experiment ﬁnishes, all the pro-

cesses are stopped.

The interface and the processes that are launched

are completely independent. If the interface is closed,

the main process that controls the Arduino will still

be active, as well as the visualization subprocess. This

procedure guarantees that the experiment does not fail

by means of any unexpected occurrence.

This independence is also relevant for the scala-

bility of the system, since all the scripts are separated

into speciﬁc blocks, it will be easier to use what is

necessary for any future improvement and upgrade of

the system.

2.3.4 Database and Data File Structure

The name of the ﬁles where the data from the exper-

iments are stored has itself information about the ex-

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Figure 3: Representation of the frame A of the interface, which shows the data that has to be set by the user and the buttons

that enable the interaction.

Figure 4: Flowchart of the interface code. In this chart

is shown that the interface connects via SSH to the corre-

sponding Raspberry Pi and starts the main process that con-

trols the Arduino and runs in the Raspberry Pi, represented

by the letter A and the ﬂowchart of Fig. 2. The process

C represents the visualization task that runs in the personal

computer of the user.

periment. It is a string with the following parameters:

’data’ + ’experiment session date and hour’ + ’E-nose

version’ + ’last three digits of Raspberry Pi’s E-nose

IP’. The ﬁles contain a header that describes all the

experiment conditions, and a body with data from the

experiment.

The header has 22 commented lines, starting with

a ”#”. The ﬁrst 6 lines present the sensors’ IDs. The

line indicates the VOC or type of sample that is in-

side the sample chamber. Lines number 8, 9, 10 and

11 refer to the parameters of the sample proﬁle (quan-

tity, day, label and temperature). The atmospheric

pressure is registered at line number 12. The exposure

and recovery period times, the experiment duration,

and the sampling rate are shown in lines 13, 14 and

15, respectively. Line 16 refers to the periods of ac-

quisition from the temperature and humidity sensors.

The remaining lines are more descriptive, including

the researcher(s) responsible name(s), the experiment

goal, the script name, the E-nose version and the last

three digits of the Raspberry Pi IP’s.

The body is composed by 10 columns. The 1

refers to the time since the beginning of the exper-

iment session (in seconds). The 2

mentions the

pump state (”True” if the recovery pump is work-

ing, and ”False” if rather the exposure pump is ON).

Columns 3 up to 8 present the values given by each

one of the 6 photo detectors. And, columns 9 and 10

contain the data from the temperature and humidity

sensor, respectively. Each column entry is separated

by the next by a ”;”.

The database is composed by four tables: the sen-

sors table, the pictures table, and the two experiments

tables. The sensors table should be written by the user

prior to the experiment. The pictures table contains

the sensing ﬁlm images, visualized under 90-degree

crossed polarizing ﬁlters at the microscope (magniﬁ-

cation: 5x), before and after the experiment. Lastly,

the experiments tables ﬁelds are automatically written

by the script, while the experiment is running.

Scalable and Easy-to-use System Architecture for Electronic Noses

183

For the sensing ﬁlms table the following ﬁelds

are obligatory and therefore should be ﬁlled: ”ID”,

”Label”, ”Production date”, ”IL”, ”IL quantity (µL)”,

”LC”, ”LC quantity (µL)”, ”Polymers”, ”Poly-

mers quantity (mg)”, ”Water quantity (µL)”, ”Cross-

linking”, ”Spread technique”, ”Quantity deposited

(µL)”. Other ﬁelds are optional: ”Thickness (µm)”

and ”SC parameters”.

There are two experiments tables: one that con-

templates all the experiments performed (including

even the aborted experiments), and another that just

contains the completed experiments.

Both the experiments tables have the following

columns: ”experiment ID”, ”Date and hour”, ”E-nose

ID”, ”ID sensor 1”, ”ID sensor 2”, ”ID sensor 3”,

”ID sensor 4”, ”ID sensor 5”, ”ID sensor 6”, ”Sam-

ple type”, ”Sample day”, ”Sample label”, ”Sample

quantity (mL or g)”, ”Sample temperature ( C)”, ”At-

mospheric pressure (hPa)”, ”Exposure time (s)”, ”Re-

covery time (s)”, ”Duration (min)”, ”Sampling rate

(Hz)”, ”Responsible person(s)”, ”Goal”, and ”Tem-

perature and humidity acquisition period (min)”. The

”ID sensor” columns are ﬁlled with the IDs associated

to the sensor used, which were previously attributed

in the sensors Table.

3 SCALABILITY AND RESULTS

3.1 Scalability

One of the main objectives of this work is make a

scalable experimental system. Currently there are 3

E-noses, but in the future a 20 E-nose system is in the

development pipeline. Figure 5 represents how mul-

tiple E-noses systems would interact.

Figure 5: Demonstration of the arrangement of a scaled sys-

tem that comprises multiple E-noses connected to the same

database.

The current system is designed to be scalable. In

order to add a new node, 5 steps are needed, which

for a 20 node system is manageable.

Since the IP of the Raspberry Pi is needed on the

interface script in order to create a SSH session to

communicate with it, the ﬁle name contains the end

number of the IP address. By using the name of the

ﬁle to get the IP of the Raspberry Pi, there is no need

to edit the ﬁle itself.

Also, the directory name and structure created in

fourth point on the list above is common to all Rasp-

berry Pi’s. The common structures allows the data

acquisition script to be device ”agnostic”, where the

hard-coded paths don’t need to be changed. These

hard-coded paths include the excel ﬁle location and

the folder where the data will be saved.

The most time expensive and scalability costly is

the python related tasks, more speciﬁcally the up-

grade stage, taking up to 20 minutes.

3.2 Results

The outcomes of this work refer to the structured ar-

chitecture that has been developed and evaluate if it

goes towards the purpose of creating a scalable, easy

to use and low-cost system. In order to present some

of the results, the interaction between the individual

elements of the E-nose system will be shown with a

demonstration of an experiment session.

In Figure 3, the entries have been ﬁlled in the cor-

responding ﬁelds. This experiment was to test 5 mL

of acetone at 24 °C as VOC. The ﬁlms were exposed

during 20 seconds, following a recovery of 20 sec-

onds as well. The experiment lasted for 6 minutes

(360 seconds). During this time, the data from the

photo receptors have been acquired at a sampling rate

of 90 Hz, while the temperature and humidity values

were acquired in intervals of 5 minutes.

This conﬁguration is saved in the main Excel ﬁle

that is located in the database and saves every conﬁg-

uration of all E-noses, as can be seen in the line 7 of

the Excel depicted in Figure 6.

In Figure 7, the plots of this experiment can be

visualized.

Figure 7 shows the signal plots visualized in real-

time using bokeh. Six optical signals were obtained,

when the optical sensors were cyclically exposed to

acetone (exposure time: 20 s) and then recovered

(making atmospheric air pass through them for 20 s).

Different signals were generated, due to the use of

sensing materials with different compositions in the

sensing ﬁlms unit. The response timing demonstrated

that the multiple elements of the system are synchro-

nized and correctly interconnected.

Nonetheless, this interconnection is only assured

while the elements are connected to the same net-

work. If not, the elements for plotting and control

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184

Figure 6: Print-screen of the Excel ﬁle that stores all the conﬁgurations. In this is depicted that the conﬁguration from the

current example has been correctly saved and the ﬁelds correctly ﬁlled.

Figure 7: Representation of the data acquired along the experiment conﬁgured with the parameters of Figure 3. The ﬁrst six

plots are the values acquired from the photo receptor array in volts. The bottom plots show the room temperature in Celsius

degrees (left) and the percentage of relative humidity (right).

will not work and the Arduino will have to be acti-

vated manually in the Raspberry Pi.

One important remark involves the latency of the

database in updating the temporary ﬁles that are used

for plotting. By reducing the size of the ﬁles to 100

samples per ﬁle, we force the NAS to update the

folder thereby reducing the latency. Nevertheless,

there is still a perceived latency to delay the ”real-

time” plotting of the acquired data, which in the worst

cases can be up to 10 seconds.

4 CONCLUSION AND FUTURE

WORK

The main outcome of this work was the development

of a scalable and reliable data acquisition architecture

for an E-nose device prototype. This system is able

to be easily scaled up to a 20 E-nose system, taking

5 steps to setup a new node. The system also has a

ease-to-use interface, that allows the user to conﬁg-

ure all the parameters of an acquisition. A real-time

visualization solution is also provided, allowing for

an interactive and succinct experiment. Theses ex-

periments can run in all the current iteration of the

E-nose and up to a sampling rate of 100 Hz. This

means that the same experiment can run in any one

of the in-house developed E-noses, even if some have

the Raspberry Pi 2 and others the Raspberry Pi 3. Due

to the reasons mentioned above, the primary level of

this research has been achieved successfully.

An improvement could be the introduction of a

scheduler, a table where the user can schedule an ex-

periment. Using this alternative, the interface would

be used to ﬁll the schedule table. This would allow

for the experiment to be programmed outside the lab

if, e.g., an on-line spreadsheet service was used. This

Scalable and Easy-to-use System Architecture for Electronic Noses

185

solution would need a script to be always running on

each Raspberry Pi that checks the scheduler for a new

job.

Finally, to mitigate against software problems or

emergency situations, a physical button connected ei-

ther to the Raspberry Pi or the Arduino could be in-

corporated into the system architecture in order to

abruptly stop the experiment.

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