Nicolas Morati

Thierry Contaret
email: thierry.contaret@im2np.fr

Sami Gomri

Tomas Fiorido

Jean-Luc Seguin

Marc Bendahan

Noise spectroscopy data analysis-based gas identification with a single MOX sensor

Keywords: MOX gas sensor, noise spectroscopy, multivariate analysis, gas discrimination

Air quality monitoring and analysis have become a major issue in recent years. Metal oxide (MOX) gas sensors are very sensitive due to high variability of their resistivity in presence of gas. However, they are not selective, i.e. it is not possible to determine the nature and concentration of the gas using only variations in the resistance of the sensor. Noise spectroscopy is one of the solutions to improve selectivity. In this paper, we evaluate recent noise spectroscopy-based gases identifications methods, to distinguish the nature of different gases using a single MOX sensor. From noise measurements performed on MOX gas sensors with tungsten trioxide (WO3) sensing layer, under several nitrogen dioxide (NO2), ozone (O3) and carbon monoxide (CO) concentrations in dry air, we have created a database. This database is increased by extracting the spectral attributes of noise responses, and then reduced by using principal component analysis to extract only useful information. The results obtained in this study demonstrated that it is possible to distinguish three air quality gases with only one single WO3 sensor.

Introduction

Nowadays, there is a growing need for low-cost, low-power, miniaturized gas sensors mainly due to the connectivity of sensing devices on global network (internet of things) enabling immediate sharing of information in a variety of fields such as portable and connected devices for domestic and industrial air quality control, security and defence, food quality control …

In the case of air quality where health, social and political issues have become important to the population, there is a strong demand for air pollution monitoring systems. Indeed, air pollution is characterized by the presence of a set of important pollutants of chemical origin (VOC, fine particles, ozone, nitrogen dioxide, carbon monoxide and sulphur dioxide) and biological (molds, mites). As this variety of pollutants is large and the concentrations harmful to health are very low and permanent in ambient air, it has become imperative to design detection systems that are sufficiently sensitive to detect and discriminate between several gases in polluted ambient air. In this context, a great deal of research and development work is being carried out to design small and cheap gas sensors with high sensitivity, selectivity and stability, with respect to a given application. Metal oxide (MOX) gas sensors are readily available and widely used in portable and low cost gas monitoring devices because of their high sensitivity, stability, and attractive life time. However, this type of gas sensor suffers an inherent lack of selectivity, because the gas detection mechanism is rather unspecific and more or less any type of reducing or oxidizing gas is detected.

Due to this poor selectivity, MOX gas sensors are more often assembled into a multi sensor array that forms the core of an electronic nose. Electronic nose is a complex system used in the identification of gas mixtures. It consists of a multi sensor array, an information-processing unit, software with digital pattern-recognition algorithms and reference-library databases [START_REF] Gutiérrez | Advances in artificial olfaction: sensors and applications[END_REF][START_REF] Capelli | Electronic Noses for Environmental Monitoring Applications[END_REF]. The sensor array is composed of different sensors chosen to respond to a wide range of chemical classes. The outputs of individual sensors are collectively assembled and integrated to generate a distinct digital response pattern. Identification and classification of an analyte mixture is accomplished through recognition of this unique chemical signature (electronic fingerprint) of collective sensor responses. The challenge in miniaturizing devices and lowering power consumption is to minimize the number of sensors required for a given application. To do this, we must try to increase the amount of information provided by one sensor using advanced measurements, like temperature modulation or fluctuation enhanced sensing (FES). [START_REF] Yan | Electronic Nose Feature Extraction Methods: A Review[END_REF][START_REF] Smulko | New approaches for improving selectivity and sensitivity of resistive gas sensors: a review[END_REF].

The FES principle uses the fluctuations of the gas sensors' response as an information source. This experimental technique is based on noise spectroscopy: the measurement and the analysis of the power spectral density (PSD) of the fluctuations measured at the terminals of sensors in the presence of one or more gases. Measuring these fluctuations caused by adsorption-desorption and diffusion noise provides enhanced selectivity and sensitivity.

Several studies have shown that noise spectroscopy is a relevant signal-processing tool able to extract selective informations on multiple gases with a single sensor [START_REF] Smulko | New approaches for improving selectivity and sensitivity of resistive gas sensors: a review[END_REF][START_REF] Kish | Extracting information from noise spectra of chemical sensors: Single sensor electronic noses and tongues[END_REF][START_REF] Solis | Identifying natural and artificial odours through noise analysis with a sampling-and-hold electronic nose[END_REF].

In earlier work [START_REF] Gomri | A New Gases Identifying Method with MOX Gas Sensors using Noise Spectroscopy[END_REF], we developed a model of adsorption-desorption (A-D) noise based on the physical origin of noise in MOX sensors. From this model, we developed three gases identifications methods able to discriminate several gases characterizing air quality, as well as their concentration, with a single MOX sensor, under real operating conditions, i.e. in the permanent presence of a gas concentration.

The capability of discriminating gases was evaluated using Principal Component Analysis (PCA). The content is organized as follows:

General methodology

Enhancement of the MOX gas sensors selectivity has been the subject of many studies for decades. Different measurement methodologies (dynamic response by thermal modulation, response in pulsed thermal mode, sensors arrays, noise spectroscopy…) associated with signal processing algorithms (Fast Fourier Transform, PCA, neural networks…) have been developed

and have shown their possibilities [START_REF] Ortega | An intelligent detector based on temperature modulation of a gas sensor with a digital signal processor[END_REF][START_REF] Amamoto | Development of pulse-drive semiconductor gas sensor[END_REF][START_REF] Pardo | Data analysis for a hybrid sensor array[END_REF][START_REF] Göpel | SnO2 sensors: current status and future prospects[END_REF]. The gas detection and identification technique that we are developing is focused on low frequency noise spectroscopy.

The interest of measuring the noise response of a gas sensor comes from observing the change in spectral density of fluctuations in the resistance of the sensor in the presence of several gases. The advantage of this measurement technique is to acquire the response of the sensor in steady mode, ie after the response time of sensor. Furthermore, this technique does not depend on the characteristics of the dilution system of an experimental measurement bench and makes it possible to approach detection in the permanent presence of a gas mixture. The statistical study of microscopic fluctuations generating sensor resistance fluctuations constitutes an important source of information on the sensor itself. This high sensitivity is based on the fact that disturbances of microscopic fluctuations require only very low energy.

The statistical distribution functions characterizing these fluctuations are data tables, which can contain a quantity of information greater by several orders of magnitude than that provided by a simple quantity, such as the variation of the resistance of the sensor used in classical measurement. In addition, the noise parameters that can be extracted from noise responses have a physical origin in the adsorption-desorption process of the gas molecules on the surface of the sensitive layer of the sensor.

Used methodology for our study is summarized in Fig. 1. First, a noise response database is created by measuring the power spectrum of the amplified current fluctuations across the sensor when the concentration of the target gas is constant. The noise measurement system for gas sensors is described in section 3. Then, the database is increased by extracting the spectral attributes of the noise responses using the characterization methods presented in section 4, and then reduced using principal component analysis to extract only useful information. Gas discrimination is achieved by the online projection of measurements in the principal space consisting of the two main components. PCA is a robust and unsupervised model recognition approach commonly used for multivariate data analysis. It is a statistical procedure that converts a set of observations of possibly correlated variables into a new set of values called principal components. PCA has proven its effectiveness in many areas of application [START_REF] Djeziri | Hybrid method for remaining useful life prediction in wind turbine systems[END_REF][START_REF] Nguyen | Fault prognosis for batch production based on percentile measure and gamma process: Application to semiconductor manufacturing[END_REF][START_REF] Nguyen | Health Indicator Extraction for Fault Prognosis in Discrate Manufacturing Processes[END_REF], including electronic noses [START_REF] Hu | Electronic Noses: From Advanced Materials to Sensors Aided with Data Processing[END_REF] Fig. 1. Summary of the methodology used

Measurement methods and device

The specific noise spectroscopy system to measure the sensor noise response in air, nitrogen dioxide, carbon monoxide and ozone is the same one used in our recent paper [START_REF] Gomri | A Noise Spectroscopy based Selective Gas Sensing with MOX Gas Sensors[END_REF]. It is presented in Fig. 2. The power spectrum of the amplified current fluctuations across the sensor is measured using low noise current preamplifier and FFT spectrum analyser. The sensors are placed in a stainless steel measurement chamber where mixed gases can be admitted and evacuated via distribution valves. To obtain a linear flow and homogeneous mixture, the incoming gases are mixed by a diffuser after electronic gas flow controllers. The gas chamber, the preamplifier, the sensors biasing and the power supply for sensor local heating are incorporated in a Faraday cage. The studied MOX gas sensors are based on tungsten trioxide (WO3). WO3 thin films were prepared by reactive radio frequency (13.56 Mhz) magnetron sputtering, using a 99.9% pure tungsten target. The thickness of the WO3 film is about 40 nm. The films were sputtered on SiO2/Si substrates with platinum interdigitated electrodes, in a reactive atmosphere of oxygen-argon mixture. The microsensor doesn't have an integrated heating device. It was placed on a heated plate and biased by a voltage of 1V with a specific system of microtips. For more details concerning the preparation of the WO3 based sensing layer, one can see the descriptions given in [START_REF] Gomri | A Noise Spectroscopy based Selective Gas Sensing with MOX Gas Sensors[END_REF]. As the resistance of the sensing layer is very high (MΩ range), and for best measurement conditions, the sensor is biased by a voltage source, and the fluctuation of the current crossing the sensing layer is amplified by a low noise current amplifier Stanford Research SR570 (LNA on figure 1). The power spectrum density (PSD) of the outputted voltage is recorded using a SR785 FFT Signal Analyzer, in the 0.1Hz-100kHz range, at a sampling rate of 256 000 samples/s. The build in high pass filter of the SR570 amplifier was set to a cutoff frequency of 0.03Hz with a -12dB/octave slope. The PSD of the current fluctuations was recorded when the gas microsensor was exposed to the target gas diluted in dry air and the sensor resistance was constant (steady state operating). The measurement time is about 10 mn, mostly depending on the number of measurement points in the lowest frequency decades.

The gas microsensor was exposed to 4 concentrations of three different gases characteristic of the air quality (see Table I), diluted in dry air. These are ozone and nitrogen dioxide, which are oxidizing gases, and carbon monoxide, which is a reducing gas. For each gas, the acquisition of the noise spectrum begins when the response of the sensor is stable (steady regime). Figure 3 shows an example of sensor response for an ozone concentration of 80 ppb for the complete measurement duration.

Noise Spectroscopy-based Gases Identifications Methods

In this section, we first present the main noise sources present in MOX sensors. Then, we briefly describe the noise spectroscopy-based gases identifications methods developed in previous work.

Noise sources in MOX gas sensors

In MOX gas sensors, we observe intrinsic noise sources similar to those found in semiconductors, and additional noise sources related to the chemical processes on the surface of the sensitive layer. Intrinsic noise sources are:

(i)

The shot noise due to fluctuations of the carriers that create the conduction current. At low frequencies, the spectral density of conduction current is constant and is written:

cond I I q S cond   = 2
G T k S th I    = 4 (2)
where k is the Boltzmann constant, T is the thermodynamic temperature and G is the conductance of semiconductor. The thermal noise is thus a white noise;

(iii) The generation-recombination (G-R) noise caused by the carriers' number fluctuations associated with the processes of generation and recombination. It's an excess noise in addition to fundamental noise sources (shot noise and thermal noise). The spectral density of current fluctuations associated with the capture and emission of carriers by a single trap in the bulk semiconductor is given by the relation:

2 2 2 4 1   f K S LF I R G + = - (3)
where LF K is the current noise spectral density at low frequencies proportional to the G-R induced variation on the current, and τ the average time associated with a trap. When the generation-recombination involves interface traps, the phenomenon is called Random Telegraph Signal (RTS). If the fluctuations come from a large number of generation-recombination process, the spectral density approximately varies as 1/f in the low frequencies band [START_REF] Wong | Low-frequency noise study in electron devices: review and update[END_REF].

The 1/f noise or flicker noise is predominant over the thermal noise or shot noise.

It involves various energy traps. It is located in the bulk [18] or at the surface of semiconductor, or at the interface between semiconductor and electrical contact [START_REF] Mcwhorter | Semiconductor Surface Physics[END_REF]. It is known that the characterization of excess noise in microelectronics devices is a measure of performance and quality. It provides relevant information on the traps density, type and location [START_REF] Contaret | Low frequency noise characterization and modeling in ultrathin oxide MOSFETs[END_REF].

The additional noise sources in the metal-oxide gas sensors are related to the properties of the metal-oxide sensitive layer and the chemical environment. In metal oxides, the noise depends strongly on the oxygen stoichiometry and displacement of oxygen atoms. The electronic charge transport is associated with the presence of oxygen at the surface and in the crystal lattice sites of the metal-oxide. The adsorption-desorption process of oxygen atoms and the presence of inhomogeneities, stresses and grain boundaries in the metal oxide cause fluctuations of the oxygen density and, thus fluctuations of the electrical conductance [START_REF] Raychaudhuri | Measurement of 1/f noise and its application in materials science[END_REF].

In a gaseous environment, conductance fluctuations of microsensors due to free carrier's number and mobility fluctuations are thus related to concentration and distribution fluctuations of chemical species. The measured noise spectra result from the superposition of three noise sources contributions:

(i)
The adsorption-desorption (A-D) noise due to the adsorption-desorption of gas molecules. It is similar to the G-R noise in intrinsic semiconductor;

(ii) The diffusion noise related to the diffusion of molecules adsorbed on the surface;

(iii) The shot noise due to the current through the potential barriers at grain boundaries in the sensitive layer.

The dominant noise source in gas microsensors is the adsorption-desorption noise [START_REF] Contaret | Noise spectroscopy measurements in metallic oxide gas microsensors[END_REF]. The modelling of the spectral behavior of A-D noise source is based on Wolkenstein's theory [START_REF] Wolkenstein | Electronic Processes on Semiconductor Surfaces During Chemisorption[END_REF].

Electrical conductivity fluctuations due to free carrier number and mobility fluctuations are proportional to the density fluctuations of adsorbed molecules:

) ( ) ( 2 0 f S f S N    = (4) where ) ( f S 
is the power spectral density (PSD) of electrical conductivity fluctuations. 0  depends on free carrier density and mobility at adsorption-desorption equilibrium.

) ( f S N 
is the PDS of adsorbed molecules density fluctuation which can be written [START_REF] Gomri | A mobility and free carriers density fluctuations based model of adsorption-desorption noise gas sensor[END_REF][START_REF] Gomri | Noise Modeling in MOX Gas Sensors[END_REF]:

2 0 1 1 ) (         + = c N f f H f S  (
 . The time constant  is 1 d d d d -               -       = eq eq N a N d  (7)

Noise current spectral decomposition

As the dominant noise source in gas sensors is A-D noise, low frequency noise spectra are characterized by a sum of several Lorentzian. The power density spectrum (PDS) of fluctuations of the total sensing layer resistance Rsensor  is written [START_REF] Gomri | Noise Modeling in MOX Gas Sensors[END_REF]:

 =         + = g i ci i Rsensor f f S f S 1 2 1 1 ) (  ( 8
)
where g is the number of most prevalent grain sizes involved in the sensing layer. i S and ci f are, respectively, the low frequency noise level and the cut-off frequency of the Lorentzian number i. Their expressions are given in [START_REF] Gomri | Noise Modeling in MOX Gas Sensors[END_REF] and they depend on the nature of the detected gas, and on the grain size. If the gas sensor resistance Rsensor is biased by a voltage V0, the measured noise is converted into a current noise. It thus takes into account the contribution of A-D noise generated in the gas sensor resistance and converted into a current noise, and the contribution of thermal noise generated by the sensing layer resistance. The PSD of the fluctuations of the total terminal current across the gas sensor resistance can be written [START_REF] Gomri | Noise Modeling in MOX Gas Sensors[END_REF]:

sensor g i ci i sensor I R kT f f S R V f S sensor 4 1 1 ) ( 1 4 2 0 2 +         + =  =  (9)
At low frequencies the A-D noise will dominate and at high frequencies its will be dominated by a white noise due to the thermal noise induced by the sensing layer resistance. As the Lorenzian parameters depend on the nature and the concentration of the gas, the spectral decomposition of each spectrum enables to extract the noise parameters for each gas.

First derivative of noise current spectral density

In our recent work [START_REF] Gomri | A New Gases Identifying Method with MOX Gas Sensors using Noise Spectroscopy[END_REF], we calculated the theoretical expression of the first derivative of the PSD of the gas sensor noise, and showed that it admitted a minimum which depends on the nature of the detected gas.

Using "( 9)", the expression of the first derivative of the PSD of the gas sensor noise can be written as follows:

 =       + - = g i ci ci i sensor I f f f f S R V df f dS sensor 1 2 2 2 2 4 2 0 1 2 ) (  (10)
The first derivative of each single Lorentzian has a minimum at the frequency 3 / ci f that depends on the nature of the detected gas.We have demonstrated that the complete expression of the first derivative of the PSD of the gas sensor noise has a minimum at a frequency between 3 / 1 c f and 3 / cg f [START_REF] Gomri | Noise Modeling in MOX Gas Sensors[END_REF]. The extraction of this minimum thus enables to characterize the nature of the detected gas.

Product of the noise current spectral density by the frequency

Another gas identification method is based on the product ( )

) ( ) ( f S f S f Th I sensor -  
where f is the frequency,

) ( f S sensor I 
is the PSD of gas sensor noise current [START_REF] Amamoto | Development of pulse-drive semiconductor gas sensor[END_REF] and

sensor th R kT f S 4 ) ( = .
This product is written as follows:

( )  =         + = -  g i ci i sensor Th I f f f S R V f S f S f sensor 1 4 2 0 2 1 ) ( ) (  (11)
The product of frequency by the PSD of the gas sensing layer resistance fluctuations often has a maximum which is characteristic of the gas and, that is the combination of maxima of each term [START_REF] Gomri | A Noise Spectroscopy based Selective Gas Sensing with MOX Gas Sensors[END_REF]. The extraction of the maximum of the product also enables to characterize the nature of the gas detected.

Results

In this section, we first present the results of the extraction of the frequency attributes from the spectra measured under the three target gases. Then, the results of the PCA on the created database are given and discussed.

Spectral decomposition of the noise responses under different gases

The developed gas identification methods are based on precise spectral decomposition of measured noise responses. To extract useful information from the measured noise across the sensor in the presence of gas, we take into account the characteristics of low noise amplifier by: All measured spectra clearly show Lorentzian components according to adsorption-desorption noise theory. Indeed, the noise generated due to the adsorption of a gas on one type of adsorption site, with a given adsorption energy, has a Lorentzian spectrum. For the three studied gases, we observe that two Lorentzians dominate the low frequency spectrum. Over 1 kHz we observe white noise mainly composed of the thermal noise due to sensitive layer resistance and the thermal noise of the amplifier due to the feedback resistor. Details of spectral decomposition are also plotted in Fig. 4. Measured noise spectra is higher by one or more orders of magnitude compared to amplifier noise level (curve 6). The extracted noise parameters of spectral decomposition of all measured spectra are reported in Table II, according to notations of equation [START_REF] Amamoto | Development of pulse-drive semiconductor gas sensor[END_REF]. The values and the variation ranges of spectral parameters are specific for each gas. For all studied gas concentrations, the evolution of thermal noise is consistent with the evolution of the resistance of the sensitive layer in the presence of each of the three studied gases.

First derivative of the PSD of the noise responses under different gases

The first derivative of noise current spectral density of the measured gas sensor noise is calculated as (2) using extracted parameters of TABLE II. In Fig. 5 we present the plots of the first derivative of the PSD of the gas sensor noise response under four concentrations of nitrogen dioxide. We have similar curves for ozone and carbon monoxide. For the three gases, we clearly observe that the first derivative of the measured gas sensor noise spectrum presents a negative minimum at a frequency between 3 / 1 c f and 3 / 2 c f , according to the developed theory in [START_REF] Gomri | A New Gases Identifying Method with MOX Gas Sensors using Noise Spectroscopy[END_REF]. These negative minimums are reported in TABLE III and have specific value ranges for each gas. Such result shows the consistency of this gases identification method. The minimum of first derivative of PSD is rather sharp and depends on the nature of the detected gas.

Product of the PSD of the noise responses by the frequency

In Fig. 6, we plot the product ( )

) ( ) ( f S f S f Th I sensor -  
in the case of ozone, nitrogen dioxide and carbon monoxide using the extracted parameters in TABLE II. Over 100 Hz, we observe a 1/f slope as predicted in [START_REF] Göpel | SnO2 sensors: current status and future prospects[END_REF] for all concentrations of studied gases. On the other hand, at low frequencies, the behavior is different for each gas. For NO2, the curves of the product of noise current spectral density by the frequency show two maximums of approximately equal values while the maximum at lower frequency is higher for CO. The curves for ozone have a particular behavior because of the higher sensitivity of tungsten trioxide to this gas. This high sensitivity to ozone is characterized by a higher low frequency noise level compared to other gases. So, the quantity ( )

) ( ) ( f S f S f Th I sensor -  
, has a maximum which is characteristic of the nature of the detected gas. Extracted maximums are reported in TABLE IV and have specific value ranges for each gas. Thus, detecting the maximum of the ( )

) ( ) ( f S f S f Th I sensor -   is a
sensitive method to identify a gas. This result confirms that the choice of this parameter seems interesting for the identification of the detected gas compared to other parameters such as the average slope of the product ) ( f S f sensor I   [START_REF] Ayhan | Fluctuation enhanced sensing (FES) with a nanostructured, semiconducting metal oxide film for gas detection and classification[END_REF] or the characteristic frequency of the maximum of this same product [START_REF] Rumyantsev | Selective Sensing of Individual Gases Using Graphene Devices[END_REF].

Data analysis for gas identification

For each of the three gas identification methods, we have shown that the extracted parameters were different for ozone, nitrogen dioxide and carbon monoxide. The ability to discriminate several gases using our different noise spectroscopy-based gas identifying methods has been evaluated by the principal component analysis (PCA). Our noise database consists of Tables II, II and IV. The PCA enable us to convert the set of observation of correlated or uncorrelated variables of our database into a new set of values (principal components).

Then, the score plots of PCA show the relations between these analyzed variables (different concentrations of the three gases in our studies). PCA has been performed using the Pearson matrix calculation. The PCA variables are the all extracted parameters of the spectral decomposition (TABLE II), the minimum of first derivative of noise current spectral density (TABLE III) and the maximum of the product of the PSD of noise current spectral density by the frequency (TABLE IV). The observations are the set of twelve concentrations of the studied gases.

The calculation of the correlation matrix shows that several variables are correlated and others uncorrelated. For our study, the first low frequency magnitude S1 of spectral decomposition is correlated with the minimum of first derivative of PSD and the maximum of ( )

) ( ) ( f S f S f Th I sensor -  
. Conversely, values of thermal noise are uncorrelated with values of cutoff frequency of the second lorentzian. About the variability of the PCA eigenvalues, the majority of the inertia is carried by two axes PC1 and PC2 (about 91% cumulated) which are therefore the two principal components. Table V presents the coordinates of the PCA variables according to PC1 and PC2. H1 and Fc1 are the low frequency magnitude and cut-off frequency of first lorentzian, respectively. H2 and Fc2 are the low frequency magnitude and cut-off frequency of second lorentzian, respectively. TH is the thermal noise. Min_Si' is the minimum of first derivative of PSD. F0 is the frequency at this minimum and Max_fSi is the maximum of product of PSD by the frequency. All PCA variables have a significant length in the PC1 / PC2 plane. The PC1 axis is rather related to H1 and Max_fSi while the PC2 axis is rather related to the Fc2 parameter. The scores plot of the first two principal components is given in Fig. 7. We clearly observe three groups of data.

Each group corresponds to one gas. A first separation can be made according to the PC1 These results were obtained with three gases diluted in dry air. Under actual conditions for measuring pollutants in the atmosphere, humidity is present in proportions ranging from 15%

to almost 100%, and it is known that the response of MOX sensors is affected by the presence of humidity, which leads to a decrease of the sensor response. However, as explained in Section 2, noise spectroscopy provides a quantity of information greater by several orders of magnitude than that provided by a simple measurement of the resistance of the sensor. So, we may think that the spectral attributes studied in this article, associated with powerful classification algorithms such as Support Vector Machines, k Nearest Neighbors or Neural Networks, will allow the identification of target gases to continue, even in the presence of humidity.

Conclusion

In this paper, we have presented new developments in gases identification methods using noise spectroscopy. These methods are based on precise spectral decomposition of measured noise responses of micro sensor under gases. For each method, we studied the ability to discriminate the nature of detected gas. In particular, the minimum of the first derivative of noise current spectral density is different for each studied gases. Similarly, we have observed that the maximum of the product of noise current spectral density by the frequency is characteristic of the nature of the detected gas. Finally, the PCA multivariable analysis method has been applied to all extracted spectral data and has shown that it is possible to identify the nature of three gas using noise spectroscopy-based methods applied to one single MOX gas sensor.

After these promising results, more work is in progress in order to:

increase the database by adding more gases concentrations and mixing the gases with each other and with humidity;

test the detection of other gases representative of air quality, such as BTEX (benzene, toluene, ethyl benzene, xylene);

study whether the number of spectral attributes used for gases identification can be reduced;

experiment classification algorithms such as Support Vector Machines, k Nearest Neighbors or Neural Networks.

Fig. 2 .

 2 Fig. 2. General diagram of measurements setup used to characterize microsensors noise responses.

Fig. 3 .

 3 Fig. 3. Example of a response of studied WO3 gas sensor under 80ppb ozone (O3) concentration.

  Thermal noise due to thermal agitation of the charge carriers in the semiconductor which causes voltage fluctuations. The noise current spectral density related to these voltage fluctuations is written:

  square value of the adsorbed molecules density fluctuation N

2 N

 2 where a and d are the number of adsorbed and desorbed molecules per unit area, respectively. Whether using Langmuir's theory or Wolkenstein's theory, calculating the time constant shows dependence with the gas parameters (mass of adsorbed molecule, adsorption energy, gas partial pressure …). So, 0 H and c f are function of gas species. Details of the calculation of  and can be found in Reference [15]. In earlier work [26], we presented a model of adsorption-desorption (A-D) noise in MOX gas sensors, developing the idea that the fluctuation of the gas sensor resistance is, among other noise sources, due to the fluctuation of the density of gas molecules on the surface of the sensing film. The modeling was developed by taking into account the polycrystalline structure of the sensing layer and the effect of the adsorbed molecule's density fluctuation on the grain boundary barrier height. From this model based on the physical origin of noise in MOX sensors, we developed three gases identifications methods. The first is based on the spectral decomposition of the noise responses. The second method uses the calculation of the first derivative of noise current spectral density, and the third, the product of the noise current spectral density by the frequency.

  is the intrinsic current spectral density of sensor, andm easured V S is the measured noise voltage spectral density.

  is the measured frequency response of the gain of the SR570 low noise current amplifier. Fig.4gives an example of current spectral density of the sensor under 10ppm of CO (curve1).

Fig. 4 .

 4 Fig. 4. Spectral decomposition of a noise response under CO (10 ppm): measurements (curve 1), total noise (curve 2), adsorption-desorption noise modeled by lorentzians ( curve 3 and curve 4), white noise proportional to the resistance of the sensor (curve 5) and extracted noise model of low-noise current preamplifier (curve 6).

Fig. 5 .

 5 Fig. 5. Plots of the first derivtive of noise current spectral density for the four concentrations of nitrogen dioxide.

Fig. 6 .

 6 Fig. 6. Plots of the quantity

  component. The corresponding ozone data has a strongly positive PC1 values due to a higher value of the H1 and Max_fSi extracted parameters, while the NO2 and the CO have negative PC1 values. The component PC2 makes it possible to separate these two gases. Indeed, the values for NO2 have a negative value of PC2 and are therefore rather related to the parameter TH and H2. Conversely, the values for CO have a positive value of PC2. CO is further characterized by Fc2 parameters. PCA results show that a clear discrimination is possible between the three studied gases.

Fig. 7 .

 7 Fig. 7. PCA scores plot

TABLE I

 I

	.	VARIOUS GASES CONCENTRATION USED IN EXPERIMENTAL SET UP
	Gases	C1	Concentrations C2 C3	C4
	NO2	1 ppm	2 ppm	5 ppm	10 ppm
	O3	80 ppb	110 ppb	160 ppb	240 ppb
	CO	5 ppm	10 ppm	20 ppm	40 ppm

TABLE II .

 II EXTRACTED NOISE PARAMETERS OF SPECTRAL DECOMPOSITION OF ALL MEASURED SPECTRA

TABLE III .

 III MINIMUM OF THE FIRST DERAVATIVE OF THE PSD OF NOISE CURRENT SPECTRAL DENSITY (A 2 /HZ 2 )

	Gas	C1	Concentration C2 C3	C4
	O3	-4 10 -20	-5,2 10 -20	-6,2 10 -20	-7,8 10 -20
	NO2	-9,8 10 -23	-6,5 10 -23	-5 10 -23	-3,2 10 -23
	CO	-3,9 10 -22	-4,2 10 -22	-4,9 10 -22	-6,5 10 -22

TABLE IV .

 IV MAXIMUM OF THE PRODUCT OF THE PSD OF NOISE CURENT SPECTRAL DENSITY BY THE FREQUENCY (A 2 )

	Gas	C1	Concentration C2 C3	C4
	O3	3,3 10 -22	4 10 -22	5 10 -22	6 10 -22
	NO2	3 10 -24	2 10 -24	1,3 10 -24	1,2 10 -24
	CO	1,1 10 -23	1,2 10 -23	1,3 10 -23	2 10 -23

TABLE V .

 V

TABLE OF CORRELATIONS

 OF BETWEEN VARIABLES AND THE TWO PRINCIPAL COMPONENTS.

	PCA Variable	PC1	PC2
	H1	0,992	-0,059
	Fc1	-0,988	0,051
	H2	-0,509	-0,578
	Fc2	-0,445	0,873
	TH	-0,720	-0,510
	Min_Si '	-0,988	0,051
	F0	-0,987	0,065
	Max_fSi	0,990	-0,049

Acknowledgement

Nicolas MORATI would like to thank Région Sud of France and Nanoz SAS for financial support.


domain: phys.cond chim phys

text:
Miha Gunde
email: miha.gunde@gmail.com

Nicolas Salles
email: nsalles33@gmail.com

Anne Hémeryck
email: anne.hemeryck@laas.fr

Layla Martin-Samos

Iterative Rotations and Assignments (IRA): a shape matching algorithm for atomic structures

Keywords: shape matching, structural superposition, linear assignment problem, pattern recognition, off-lattice kinetic Monte Carlo

IRA is a Fortran library that solves the shape matching problem for atomic structures, stored as sets of points representing the atomic positions. In the case of exact-and near-congruence, IRA provides the optimal rigid transformation between the structures, given by the atomic assignments, the rotation/reflection matrix, and the translation vector. IRA is also able to operate on structures containing a non-equal number of atoms, i.e. matching a structure to any of its fragments. Any application that requires the solution of a shape matching problem could benefit from IRA.

Introduction

The shape matching problem is very well known in the computer vision community, where it is generally referred to as the point-set registry problem [1,2]. When applied to atomic structures, it is sometimes referred to as the alignment problem [3,4], structural superposition [5,6], or similar. The algorithm Iterative Rotations and Assignments (IRA) has been developed [7,[START_REF] Gunde | Development of IRA: a shape matching algorithm, its implementation, and utility in a general off-lattice kMC kernel[END_REF] to solve the shape matching problem, specifically for application in the field of materials modeling at atomic scale. The problem can be described as finding a rigid transformation between two structures, such that the two structures match as best as possible when overlaid one on the other.

Our IRA algorithm accurately and efficiently resolves the shape matching problem between two generic atomic structures, including structures with different numbers of atoms. The solution is given in the form of a rotation matrix, a translation vector, and a permutation matrix. Performance of the algorithm in the sense of matching success rate, and benchmarking against two other software, is reported in Refs. [7,[START_REF] Gunde | Development of IRA: a shape matching algorithm, its implementation, and utility in a general off-lattice kMC kernel[END_REF].

The development repository of the code is accessible at https://github.com/mammasmias/IterativeRot ationsAssignments.

Problem statement

In general terms, the shape matching problem can be stated as follows. Two structures A and B are considered congruent, or equivalent, when they are related by a transformation that preserves distances, such as rigid rotation, reflection, translation, and permutation of indistinguishable points (atoms). The congruence relation can be written as:

P B B = RA + t ( 1
)
where R is a rotation/reflection matrix, t is a translation vector, and P B is permutation. The problem is to find the transformations corresponding to R, t, and P B that best match A to B. This can be achieved by optimizing a distance D between the two structures, which is variant on R and t, and invariant on P B :

arg min R,t D(RA + t, B) . ( 2
)
The approach we have developed (see Refs. [7,[START_REF] Gunde | Development of IRA: a shape matching algorithm, its implementation, and utility in a general off-lattice kMC kernel[END_REF]) returns the approximate rotation/reflection and translation, R apx and t apx , and the optimal permutation P B (which is strictly from Iterative Rotations and Assignments, IRA). The optimal transformations R and t which satisfy Eq. ( 1) are then found as small corrections to R apx and t apx , by applying the standard Singular Value Decomposition (SVD) [START_REF] Arun | [END_REF] to structures A ′ and B ′ (where A ′ = R apx A + t apx , and B ′ = P B B.). This correction is only required when the structures A and B are not exactly congruent, but exhibit some small distortions.

To compute the optimal permutations, i.e. to solve the Linear Assignment Problem (LAP), we have developed an algorithm called Constrained Shortest Distance Assignments (CShDA). The atomic assignment from CShDA is used to compute the Hausdorff distance as distance function D in Eq. (2). By imposing the one-to-one assignment constraint of CShDA, we exploit the non-commutativity of the Hausdorff distance to generalize the IRA algorithm to structures containing different number of atoms. Further details can be found in Refs. [7,[START_REF] Gunde | Development of IRA: a shape matching algorithm, its implementation, and utility in a general off-lattice kMC kernel[END_REF].

Software design and functionality

The software implementing our IRA algorithm is written as a collection of Fortran routines, gathered and compiled into a library. As such, an application calling IRA routines can be compiled by simply statically linking the library. The input atomic structures are saved quantities with intent (in) attribute, and are therefore never modified globally by IRA routines.

The low-level routine is the cshda routine, which computes the atomic assignments P B , and constrains the computation of Hausdorff distance. It is called from a higher-level routine get gamma m, which implements the search on the discretized space of rotations, and returns the approximate rotation R apx and translation t apx . The setup and the main call to this routine are encapsulated in the driver routines ira equal, and ira nonequal, for the cases of equal, and non-equal numbers of atoms, respectively. The input arguments to the driver routines are the two atomic structures A and B, and their output are the following quantities: the 3 × 3 rotation matrix R apx , the 3-dimensional translation vector t apx , and the permutation P B in the form of an N -dimensional array of integers.

The SVD rotation is in svdrot m routine, and returns the optimal R and t, corresponding to Eq. ( 1). The difference in the cases when the structures contain equal, or different numbers of atoms, lies in the choice of the origin point, or central atom. In the case of equal numbers of atoms, the origin point is chosen by default as the geometrical center of each structure. This choice can be modified by selecting any point common to both structures, such as any known common atom. For a custom choice, the driver routine ira equal must be modified. In the case of a different numbers of atoms, the origin point is an arbitrarily chosen central atom from the structure A. The search over structure B is then performed over all candidate origin points, or central atoms. Similarly to the case of an equal number of atoms, the origin point of structure A can be modified in the driver routine ira nonequal. As discussed in Refs. [7,[START_REF] Gunde | Development of IRA: a shape matching algorithm, its implementation, and utility in a general off-lattice kMC kernel[END_REF], the speed and accuracy of the algorithm can in this case be greatly improved by reducing the set of candidate central atoms in structure B, which can be done by choosing the central atom of A based on some additional criterion. This additional criterion is the subject of a pre-processing done on the structures, which can be simply based on the chemical types (choosing an atom of the type which is least present in the structure), or can be more involved (finding atoms whose immediate local environment is equivalent).

Impact overview

The IRA algorithm provides a reliable and efficient way of solving the shape matching problem, for congruent, and near-congruent structures, including structures with different numbers of atoms. Any application that requires the solution of a shape matching problem could benefit from IRA as a whole, or any of its parts.

Limitations

The implementation of the library is for now serial. As such, the matching of structures with several hundreds of atoms could become a significant computational bottleneck.

When the structures are far from congruence, the transformation and distance that are returned from our approach are not unambiguous. It is therefore not recommended to use IRA for defining "absolute" metrics between structures.

Future applications

There are a number of applications of IRA within our ideas of the future work. The most immediate application is already being developed and implemented (see Ref. [START_REF] Gunde | Development of IRA: a shape matching algorithm, its implementation, and utility in a general off-lattice kMC kernel[END_REF]), which is the application of IRA in a general off-lattice kinetic Monte-Carlo (kMC) and in the generation of a catalogue of unique structures.

In particular, the kMC algorithm generates a stochastic but statistically correct sequence of events, where each event has a certain probability. As such, a single step of the kMC consists of choosing a single event among a set of possible events. When applying the kMC algorithm to a simulation of phenomena at the atomic scale, an event represents a change in the atomic structure, and the set of possible events is given by a catalogue of events. Each event in the catalogue is defined by two structures, the structure before, and the structure after the event. The system of simulation is expected to contain structures that are locally equivalent (exactly-, or near-congruent) to the structures given in the catalogue of events. However their relative orientation, translation, and permutation of the atoms are generally unknown, but are needed for a correct execution of a chosen event. This is the problem where our IRA shape matching algorithm comes into action. As it has been shown to be a fully reliable algorithm in finding the correct rigid transformations in the cases of exactly-and near-congruent structures [7,[START_REF] Gunde | Development of IRA: a shape matching algorithm, its implementation, and utility in a general off-lattice kMC kernel[END_REF], it is an ideal algorithm for the described situation within a kMC. The solution of shape matching given by IRA in this situation is used to construct a relative transformation between the structure given by the event catalogue and the structure within the system of simulation. This transformation makes it possible to correctly execute the event chosen by the kMC algorithm in our system of simulation. Moreover, as IRA does not make any assumptions about the structures, i.e. it does not assume an underlying rigid lattice, and it does not even assume an equal number of atoms present in the two structures, the kMC simulation becomes unlimited by these assumptions, and can be generalized to any type of structure: bulk, surface, interface, grain boundary, single defects, complex defects, extended and non-spherical structures, and amorphous phases.

Moreover, the catalogue of events on which kMC relies, is generated with the help of IRA. More concretely, a separate application would scan the input structures of an event, and extract the most important and relevant atoms participating in an event. Then, IRA is used to determine whether an event with equivalent structures is already present in the catalogue, if not, then add current event into the catalogue. At the same time, a modified version of IRA can be used to determine the possible different directions of execution of the same event, which means finding all the unique symmetry transformations of a local structure (see Ref. [START_REF] Gunde | Development of IRA: a shape matching algorithm, its implementation, and utility in a general off-lattice kMC kernel[END_REF]). This approach reduces the number of structures needed in an event catalogue, as the different directions of the same event are stored in a single event entry, as opposed to each direction being a separate entry.

Acknowledgements

The authors are active members of the Multiscale And Multi-Model Approach for MaterialS In Applied Science consortium (MAMMASMIAS consortium), and acknowledge the efforts of the consortium in fostering scientific collaboration. This work was partially funded from the European Union's Horizon 2020 research and innovation program under grant agreement No. 871813 MUNDFAB, and by the European Union's Horizon 2020 research and innovation program under grant agreement No 899285 MAGNELIQ.

description C1 Current code version v1.0.0 C2 Permanent link to code/repository used for this code version https:// github.com/mammasmias/ Iter ativeRotations Assignments/ tree/master/ IRA C3 Permanent link to Reproducible Capsule https:// codeocean.com/ capsule/ 11 61 76 8/ tree/v1 // github.com/mammasmias/ Iter ativeRotations Assignments/ blob/master/ IRA/ README.


Sara Boumali
email: boumali.sara_meriem@umc.edu.dz

Mohamed Taoufik Benhabiles

Ahmed Bouziane

Fouad Kerrour

Khalifa Aguir

Mohamed Taoufik Benhabiles

Acetone discriminator and concentration estimator for diabetes monitoring in human breath

Keywords: Acetone, electronic nose, feature extraction, gas sensor, support vector machine

teaching and research institutions in France or abroad, or from public or private research centers.

Introduction

Diabetes mellitus is a chronic disease that affects 1 in 11 adults according to the International Diabetes Federation [START_REF] Zheng | Global aetiology and epidemiology of type 2 diabetes mellitus and its Complications[END_REF], it is caused either by a lack of secretion of insulin, a use default by the body or in some cases both [START_REF]American Diabetes Association, Classification and diagnosis of diabetes: Standards of Medical Care in Diabetes-2019[END_REF]. This serious illness affects the lifestyle of the patients because of the complications related to the abnormal glucose level that circulates in the blood, for example, it damages the eyes, the kidneys, the nervous system, the heart and the blood vessels over time and it can even cause death [START_REF]American Diabetes Association, Classification and diagnosis of diabetes: Standards of Medical Care in Diabetes-2019[END_REF], [START_REF] Diabetes | The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus[END_REF].

In fact, nowadays diabetes is considered as being the ninth major cause of death around the world [START_REF] Zheng | Global aetiology and epidemiology of type 2 diabetes mellitus and its Complications[END_REF] which makes the prevention and the monitoring of the disease progression a primordial step in the diagnosis, control and the monitoring of the patient's health condition by himself or clinicians [START_REF] Bruce | Prevalence of complications of diabetes among patients with diabetes mellitus attending a tertiary care centre in Tamil[END_REF], [START_REF] Hadar | Noninvasive, continuous, real-time glucose measurements compared to reference laboratory venous plasma glucose values[END_REF].

Blood glucose monitoring devices that are on the market offer only a limited number of measurements per day while a diabetic needs continuous real time blood glucose monitoring CGM to be informed in the case of severe hyperglycemia or hypoglycemia especially for patients who are dependent on insulin [START_REF] Hirsch | Clinical application of emerging sensor technologies in diabetes management: consensus guidelines for continuous glucose monitoring (CGM)[END_REF], [START_REF] Schrangl | Limits to the Evaluation of the Accuracy of Continuous Glucose Monitoring Systems by Clinical Trials[END_REF]. In addition to this, conventional glucometers require that patients prick their finger each time to get the amount of blood needed for the sensor operation, and it is so painful that there are some people who reduce the number of measurements just to avoid the pain [START_REF] Todd | Towards Non-Invasive Extraction and Determination of Blood Glucose Levels[END_REF]. So, the CGM ideal device must be non-invasive, portable, accurate, low-cost, easy to use and does not require much calibration [START_REF] Schrangl | Limits to the Evaluation of the Accuracy of Continuous Glucose Monitoring Systems by Clinical Trials[END_REF], [START_REF] Sanchez | Use of electronic noses for diagnosis of digestive and respiratory diseases through the breath[END_REF], [START_REF] Oliver | Glucose sensors: a review of current and emerging technology[END_REF], [START_REF] Avari | Is it possible to constantly and accurately monitor blood sugar levels, in people with type 1 diabetes, with a discrete device (non-invasive or invasive)?[END_REF].

In recent years, studies have shown that urine, tears, saliva, sweat and exhaled breath can be used to measure changes in glucose concentration indirectly instead of blood [START_REF] Lekha | A Method for Detection and Classification of Diabetes Noninvasively[END_REF]. In this work, we are interested in the use of exhaled breath analysis for non-invasive blood glucose monitoring, a method that offers the advantage of being a mixture that is not as complex as urine and blood and that the collection of samples is not a complicated process [START_REF] Khodkumbhe | Metal Oxide Semiconductor-based gas sensor for Acetone sensing[END_REF]. In fact, a single human breath contains about 500 volatile organic compounds (VOC), many of them are considered as biomarkers that can indicate the state of many diseases non-invasively [START_REF] Lekha | A Method for Detection and Classification of Diabetes Noninvasively[END_REF], [START_REF] Wilson | Advances in electronic-nose technologies for the detection of volatile biomarker metabolites in the human breath[END_REF]. The body of a diabetic use fat for energy, this conversion causes the production of acetone that is extracted in the breath and urine. It has been proven that the concentration of exhaled acetone is well correlated with the blood glucose concentration [START_REF] Lekha | A Method for Detection and Classification of Diabetes Noninvasively[END_REF], [START_REF] Saasa | Sensing Technologies for Detection of Acetone in Human Breath for Diabetes Diagnosis and Monitoring[END_REF], [START_REF] Saasa | Effect of varying ethanol and water compositions on the acetone sensing properties of WO3 for application in diabetes mellitus monitoring[END_REF] this is the reason why we are focusing on the development of a system that makes it possible to measure very low acetone concentrations.

One of the most powerful techniques for VOC detection is the use of an electronic nose (e-nose) which is able to detect, quantify and even identify in some cases the desired gas molecules in a gas mixture sample [START_REF] Sanchez | Use of electronic noses for diagnosis of digestive and respiratory diseases through the breath[END_REF], [START_REF] Leonardi | Development of electronic nose technologies for biomedical applications[END_REF]. E-nose is an intelligent artificial system that mimics the human olfactory system, it consists of a gas sensor matrix coupled to a data acquisition system. The obtained data will be processed through a classification program in order to identify and quantify the examined gas [START_REF] Leonardi | Development of electronic nose technologies for biomedical applications[END_REF], [START_REF] Esfahani | Non-Invasive Diagnosis of Diabetes by Volatile Organic Compounds in Urine Using FAIMS and Fox4000 Electronic Nose[END_REF], [START_REF] Boumali | Can WO3 And SnO2 be used as acetone gas sensors in exhaled human breath for noninvasive blood glucose monitoring?[END_REF], [START_REF] Faleh | Recognition of O3 concentration using WO3 gas sensor and principal component analysis[END_REF].

One of the most challenging parts of the development of an e-nose is the design of the sensor array which must be both selective and sensitive especially because the acetone is in the breath with a small concentration which varies between 0.2 ppm and 0.8 ppm for a normal person and exceeds 1.8 ppm for a diabetic person [START_REF] Khodkumbhe | Metal Oxide Semiconductor-based gas sensor for Acetone sensing[END_REF], considering that those people are not fasting, or under ketogenic dieting or in a workout [START_REF] Ruzsányi | [END_REF].

Among the best types of sensors that can be chosen are the semiconductor oxide metal-based sensors, these sensors are widely used because of their low cost, miniaturization and their high sensitivity compared to other types [START_REF] Saasa | Sensing Technologies for Detection of Acetone in Human Breath for Diabetes Diagnosis and Monitoring[END_REF] [START_REF] Usman | A review of biosensors for non-invasive diabetes monitoring and screening in human exhaled breath[END_REF]. For this purpose several materials have been used as a sensitive layer, in this paper we are interested in three of the most used materials in this subject: tungsten trioxide (WO 3 ), tin dioxide (SnO 2 ) and zinc oxide (ZnO).

The first one is based on WO 3 , the second on SnO 2 and the third on ZnO. The sensors were tested in the presence of acetone, ethanol and water vapor. On the other hand, we developed a pattern recognition algorithm that was tested with the database consisting of sensors responses.

In fact, pattern recognition in the field of gas analysis can be defined as the process that allows the identification of the odors present in a gas mixture [START_REF] Di Natale | Pattern recognition in gas sensing: well-stated techniques and advances[END_REF], [START_REF] Gardner | Pattern recognition in odour sensing[END_REF].

The feature extraction is of paramount importance in pattern recognition, its aim is to extract the useful and nonredundant information in the original signal [START_REF] Distante | On the study of feature extraction methods for an electronic nose[END_REF], [START_REF] Vergara | Chemical gas sensor drift compensation using classifier ensembles[END_REF]. It is already proven that the right selection of features increases the performance of the e-nose system [START_REF] Faleh | Enhancing WO3 gas sensor selectivity using a set of pollutant detection classifiers[END_REF].

In gas analysis for e-nose applications, features are either extracted from the steady state of sensor response or from the transient response or some signal transforms such as fast Fourier transform (FFT) and discrete wavelet transform (DWT) [START_REF] Faleh | Enhancing WO3 gas sensor selectivity using a set of pollutant detection classifiers[END_REF].

The proposed method in this study is based on the extraction of six features: two steady-state (fractional difference and difference), two transient (integral and first derivative) and from transform domain (FFT and DWT) with the selection of the best relevant features among them.

In the present paper, authors use support vector machine (SVM) based method for data classification. SVM is among the classifiers that have proven their efficiency in recent years since its appearance in 2000 by Vapnik [START_REF] Paluszek | An Overview of Machine Learning[END_REF].

Since SVM is also a supervised learning method that allows to predict the target values after creating a model based on the training data [START_REF] Suykens | support vector machines[END_REF], [START_REF] Pardo | Comparing the performance of different features in sensor arrays[END_REF], here we discuss the use of Least Squares Support Vector Machine (LS-SVM) for estimating acetone and ethanol concentration.

The paper is organized as follows. In Section 2 we describe our experimental setup; the next section presents the proposed method. Section 4 is focused on the results and discussion. At the end, the conclusions are illustrated in Section 5.

Experimental Setup

For the sensors realization the first step was the deposition of the metal oxide sensitive layers (WO 3 , SnO 2 or ZnO) by magnetron cathode sputtering RF on silicon substrates containing platinum contact electrodes. Experimental set-up

After nano-particle film deposition, the sensors go through thermal annealing in dry air for 2 hours at 500°C in order to stabilize the oxide layer. Actually, each resulting active film has a thickness of 50 nm.

The block diagram of the experimental set up used for sensor's measurements is shown in Fig. 1.

The responses of WO 3 , ZnO and SnO 2 transducers to different concentrations of acetone and ethanol in the presence of water vapor were recorded separately. In our study, both water vapor and ethanol are considered as interfering elements.

As shown in the Fig. 1 during the experiments, the sensor is placed in a homemade test chamber made of stainless steel, the stainless steel avoid VOC adsorption.

The operating temperature of the detectors is fixed at 250°C for the three types; it is reached using an external heater. For the production of the concentrations of the chosen gases we used a water-containing bubbler where we insert the ethanol or acetone in liquid form using a micro needle, the liquids are diluted with distilled water and the amount of liquid introduced is calculated using the Dubowsky formula [START_REF] Dubowski | Response of Breath-Alcohol Analyzers to Acetone: Further Studies[END_REF] in order to have the chosen concentrations. In facts, all sensors were exposed to 0, 1, 2, 5 and 10 ppm of each gas, a flow rate controller (0254, Brooks Instrument thermal mass flow) was used to control the flow of gas.

Keithley 6430 (Tektronix company) measures the voltage across via resistance and a computer controls the data acquisition via an HP-VEE Program.

Resistance response

Fig. 2, illustrates the typical resistance response curves of SnO 2 , ZnO and WO 3 gas sensors in the presence of different gases with 1 ppm at the operating temperature of 250°C that we considered as the optimal temperature for our transducers. At the beginning of the experiment, the sensors are powered for 30 minutes before starting the measurement. This step is very important for the stabilization of the sensitive film of each sensor for the reproducibility enhancement. The resistance of gas sensors decreased quickly when exposed to acetone or ethanol due to the gases reducing nature. We notice that the response and the recovery speeds of the SnO 2 sensor in the presence of acetone were faster than those of the ZnO and WO 3 sensors. Furthermore, the response and the recovery speeds of all the sensors were quite comparable to each other in the presence of ethanol. We also observe that 1 minute response time is adequate for the sensors to achieve a steady state, especially for the SnO 2 sensor. For the recovery time, 15 minute was applied for all sensors to completely return to their original baseline. The same concentrations were selected for the both gases: 0, 1, 2, 5 and 10 parts per million (ppm). We repeat the operation of gas injection twice for each gas and for each concentration, which gives us 20 measurements for each sensor. Response of SnO2 sensor exposed to 1ppm and 5 ppm acetone at 250°C.

Gas sensing behavior

The response of SnO 2 sensor for 1 ppm and 5 ppm acetone exposure were shown in Fig. 3 for two exposure cycles of each concentration at the operating temperature of 250°C. For practical use in different applications, a good gas sensor must have the ability to produce the same pattern for the same conditions. Perfect repeatability is a very important feature to make a commercial sensor. As we observe, the SnO 2 sensor has very short response and recovery time that are almost stable for variant exposures and an agreeable repeatability was seen.

Signal Processing

The most important signal processing elements of an Enose are its feature extraction and classification techniques, which use the sensor's response to extract and select the most relevant features and implement the pattern recognition methods in order to classify the different odors.

Feature Extraction

The goal of feature extraction is to extract relevant information from the e-nose response with less redundancy, to obtain an optimal recognition result. However, it is still a great challenge to select features to improve the pattern recognition accuracy [START_REF] Pardo | Comparing the performance of different features in sensor arrays[END_REF].

According to the studies of feature extraction in the past twenty years, there are many feature extraction techniques which have been applied in e-nose applications. In general, the features employed in the area of e-nose can be categorized into two main sections: Features extracted from the steady-state part, and features extracted from the transient part of the sensor response. Another approach to extract features for e-noses is based on some transforms and then the coefficients of these transforms will be used as new features to separate classes. For this study, we have selected six features as characteristic parameters. We have first used two main conventional features as steady-state descriptors [START_REF] Yan | Electronic Nose Feature Extraction Methods: A Review[END_REF], fractional difference and difference (see Fig. 4), whose expressions are shown by the following equations ( 1) and ( 2):

(

(2)

• : the initial resistance of a sensor calculated as the final value of its resistance before the injection gas phase.

• the steady-state resistance calculated by averaging the last five seconds of the injection gas phase.

Fractional difference effectively serves to reduce the effect of the temperature on the sensors, in addition the fractional difference is helpful to linearize the process that generates a concentration dependence in metal oxide chemiresistor sensors.

On the other side, the difference feature is generally used to eliminate the additive errors, which are added both to the steady-state and baseline response.

It has been proven that features extracted from the transient response are more informative than the steady-state features [START_REF] Distante | On the study of feature extraction methods for an electronic nose[END_REF]. For this reason, we have used two characteristics as transient features, integral and derivative.

Integral may reflect the accumulative total of the reaction degree change and derivatives may reflect the sensor reaction rate to the odor. The integral and derivative features have been investigated for the response and recovery time [START_REF] Distante | On the study of feature extraction methods for an electronic nose[END_REF]. The derivative feature presents better performance considering only the gas injection phase. On the other hand, the integral feature presents better performance considering the whole curve analysis (gas injection phase + cleaning phase). In this study, instead of using the whole cleaning phase with 15 minutes, only 5 minutes will be used as a recovery period, since after 5 minutes the response curve is almost stable, see Fig. 4.

As features extracted from transform domain, we have used the most frequently transforms in signal processing step of an e-nose, which are Fast Fourier transform (FFT) [START_REF] Deng | Enhancing the discrimination ability of a gas sensor array based on a novel feature selection and fusion framework[END_REF] and the discrete wavelet transform (DWT) [START_REF] Feng | A background elimination method based on wavelet transform in wound infection detection by electronic nose[END_REF]. The Fourier transform, which used the sine and cosine as the basis functions, decomposes the response signal of the gas sensor into the superposition of the DC component and different harmonic components, and the amplitude of each component can be selected as a feature. In this study for the FFT feature we have used the amplitudes of the DC component.

Wavelet transformation [START_REF] Feng | A background elimination method based on wavelet transform in wound infection detection by electronic nose[END_REF] is an extension of Fourier transform. It maps the sensor response signals into a new space with basis functions precisely localizable in time and frequency space. The DWT divides the signal into the approximation (low frequency) and details (high frequency) at each scale or level. The coefficients are extracted by convolving the mother wavelet and the signal, which means that they represent the correlation degree. In this study, to guarantee the orthogonal requirement of feature extraction, we have used the Daubechies family (DB1) as the mother wavelet.

Like the integral and derivative features, the DWT has been also investigated in three time intervals, response time, recovery time and the complete curve (i.e. from the response time to the next when a new odor is present). The DWT feature presents better performance considering the whole curve analysis (gas injection phase + cleaning phase) [START_REF] Distante | On the study of feature extraction methods for an electronic nose[END_REF]. In this study, for both FFT and DWT we have used the whole curve to calculate the FFT and the DWT coefficients and only 5min will be used as a recovery period, since after 5 min the response curve is almost stable as we said earlier.

Feature selection

To choose, precisely, the most relevant features in order to make a separation of the two classes of gas, we used the ReliefF algorithm [START_REF] Liu | Weighting and Local methods[END_REF], which is one of the most effective strategies in the feature selection. This algorithm is known for his robustness, and can work perfectly with datasets of highly interdependent features, even for incomplete and noisy data. Another suitable characteristic for this algorithm, it can be used in multi-class problems [START_REF] Kononenko | Weighting and local methods non-myopic feature quality evaluation with (R)ReliefF[END_REF].

For the majority applications of e-nose, it is a challenge to choose the best classification method for gas identification. Depending on different odors and employed sensors, we can select different classifier for each combination. The recent methods are turning to statistical learning techniques; the most widespread of them is the SVM (support vector machine). SVM is a supervised learning technique widely used for both classification and regression problems [START_REF] Marco | Signal and Data Processing for Machine Olfaction and Chemical Sensing: A Review[END_REF], with the possibility to work with large data. This technique deals well with nonlinear discrimination problems by plunging the data into a larger space in which the classification problem is linearized. The goal of this technique is to find a separating hyper-plane, with the largest margin possible between classes. For this reason we chose to work with Lib-SVM (A Matlab Library for SVM) [START_REF] Chang | LIBSVM : a library for support vector machines[END_REF]. This library helps users to easily use SVM as a tool.

Quantitative Analysis

After the classification step and the identification of the target gas, the following process will be the concentration estimation for quantitative analysis of the analyte. In this study, we chose the least squares version of the SVM (LS-SVM) in the regression mode to estimate the gas concentration. This version can perform calculations faster than the original version. The LS-SVM method provides a high degree of accuracy for concentration prediction, particularly for data sets for which a considerable nonlinearity can be expected.

In our study we use the regression mode of the LS-SVMLab toolbox v1.8 in Matlab [START_REF] Brabanter | LS-SVMlab Toolbox User's Guide version 1.8[END_REF]. Using this toolbox, in the experimental step we can create a mathematical model in order to predict the gas concentrations. Concerning the kernel function of the model, LS-SVM works with four types, which are linear, polynomial, radial basis function (RBF) and multi-layer perceptron (MLP). After choosing the kernel function, the most important next step is the selection of the model parameters to get a good estimation of gas concentration [START_REF] Nguyen | Optimal feature selection for support vector machines[END_REF][START_REF] Aydin | A multi-objective artificial immune algorithm for parameter optimization in support vector machine[END_REF]. In our empirical study, these parameters were found by using some optimization algorithms. First, suitable starting points are determined by coupled simulated annealing method (CSA). Next, from the starting points, the final parameters are calculated by multidimensional unconstrained non-linear optimization. The obtained parameters are the regularization parameter γ and the squared kernel parameter (σ²).

Results and Discussion

Feature matrix

Let "I" be the feature matrix, which will constitute the database. In this work, the rows, which represent the measurement; are the various combinations of different classes of gas and concentrations at the operating temperature of 250 °C. Whereas the columns, which represent the variables; are the selected six features extracted from the response of every sensor. The feature matrix

In our case, every line or measurement is represented by eighteen features (three sensors and six features). The organization of all features is represented in the following matrix (fig. 6): whereas , , , , , represent the fractional difference, difference, derivative, integral, FFT and wavelet features, respectively, for each class "c", exposure cycle "e", concentration "i" and sensor "j". c = 1, 2; is the class number, they correspond to acetone and ethanol, respectively. e = 1, 2; is the exposure cycle, they correspond to the first and the second sample of the repeatability, respectively. i = 0, 1, 2, 5, 10; gas concentration (ppm). j = 1, 2, 3; the sensor number, they correspond to the SnO 2 , WO 3 and ZnO, respectively. All features of the matrix are then normalized, which means that each column is normalized between 0 and 1.

ReliefF algorithm for feature selection

The resulting six feature vectors were used with target values for the selection of the best relevant features that have the ability to separate the two types of gas (Acetone and Ethanol). We calculated the weights of six features. To better observe the values, we have multiplied all the values by 100, so we will get (-100 ≤ W[i] ≤ 100), while W[i] ≤ 0 means that this feature i has no relevance. All results are presented in the next table I. Regarding the results of this table, we observe that some features are more relevant than the others, and we can easily see the difference. The Derivative has the most pertinent feature for gas identification, followed by the Wavelet coefficients, which confirms the good results of gas identification based on transient response and transform domain.

Classification with LIB-SVM

After the feature extraction step with the ReliefF algorithm, the best two features (Derivative and Wavelet coefficients) are selected to learn the classifier in order to identify the tested gases. The performance of the classifier was evaluated by the accuracy. In his study, we used the LIB-SVM-3.23 package for the multi-class classification phase, with the optimal regularization parameter C=1. The training and testing data were acquired by dividing the raw data in two groups, 50% of the data were used for training and the rest 50% were used for testing.

A set of SVM methods, based on different kernel types, were evaluated in order to select the best kernel type. Efficient results were obtained with linear kernel function, LIB-SVM achieve the classification accuracy of 100%. The other methods based on polynomial, radial basis function and sigmoid give only 50% for the accuracy.

Estimating Gas Concentration Using LS-SVM

As in the classification stage; the same 50% of the training data are employed to create a mathematical model for the LS-SVM, and the rest 50% are used to test the accuracy of the model. After comparing the results of different kernel function, we choose the RBF kernel, which work very well with such type of data, as considered also in other studies [START_REF] Barman | Investigation of noise-induced instabilities in quantitative biological spectroscopy and its implications for noninvasive glucose monitoring[END_REF][START_REF] Chauchard | Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes[END_REF].

For the evaluation of the proposed method, we use the root mean square error (RMSE) to compare between the actual concentrations and the predicted values. RMSE expression is shown by the following equation ( 5):

(5)

Where , , n are actual concentration, predicted concentration, and the number of data, respectively.

For the input data of the LS-SVM, instead of working with the standard method which uses the sensor resistance ratio between and as input to determine the gas concentration, we propose a new method which uses, for each sensor, a single feature that gives the best result for the concentration estimation. So, the input data of the LS-SVM will be the combination of the best three features plus a vector of the target gas concentration. As result, for the acetone the best three features of SnO 2 , WO 3 and ZnO are difference, derivative and integral, respectively. For the ethanol the best three features of SnO 2 , WO 3 and ZnO are fractional difference, Fourier and derivative, respectively. Fig. 7 shows the regression plot to estimate the concentration of the acetone and ethanol, where the vertical axis is the estimated concentration using LS-SVM and the horizontal axis is the real concentration. Perfect estimation leads to find points in the diagonal, which means that the estimated values of the gas concentration match the real values. (i.e., x=y). As can be seen in Fig. 7, with the exception of the first point for ethanol, all the estimated concentrations are predicted very well, since all the values are very close to the diagonal. For the quantitative analysis, the general performance is evaluated by the RMSE.

Table II show the RMSE results between the real and the estimated concentrations of the standard method using the sensor resistance ratio for each sensor, the combination of the three ratios and the proposed method.

As can be seen from the RMSE values, our method presents the best result to estimate the concentration for both acetone and ethanol with an error of 0.2236 and 0.6639 respectively. By comparing this with the conventional method used to estimate the concentration even in gas sensors sold on the market we can clearly see that our method is much better.

The presented procedure and the use of the LS-SVM algorithm helped to define which of the working conditions, applied to the investigated gas sensing layer, was most informative for gas detection. So, based on the combination of the best three features of diverse sensors using LS-SVM, we can determine with high precision the estimated gas concentration especially for the acetone which will be used for acetone detection in the breath of diabetes mellitus patients.

CONCLUSION

In this study, a sensor array made of three different semiconductor gas sensors is applied to classify and predict acetone and ethanol concentrations using an SVM classification algorithm.

In this approach, six different features were selected from the sensors response, the selection of the most effective features based on the ReliefF algorithm shows that the use of transient features and features extracted from transform domain is the best method for gas recognition. Classification accuracy of 100% was achieved using the LIB-SVM classifier based on a linear kernel function. Also, a new method based on the implementation of only the best feature for each sensor in the LS-SVM algorithm is proposed for the estimation of the gas concentration. The quantitative prediction of acetone and ethanol concentration is obtained successfully with a root mean square error of 0.2236 and 0.6639 respectively.

The results of this work confirm that the good selection of the extracted features and classifier leads to a better performance of the electronic nose in both the fields of gas identification and gas concentration prediction.

Further studies aim to use the proposed technique to design and develop an effective portable electronic nose device for continuous non-invasive blood glucose monitoring by using our three homemade sensors accompanied by a humidity sensor and a temperature sensor plus the algorithm described in this manuscript and test the system on human breath samples then do some clinical in vivo studies to validate the obtained results.

Fig. 1 .

 1 Fig. 1.Experimental set-up

Fig. 2 .

 2 Fig. 2. Response curve of ZnO, SnO2 and WO3 gas sensors in presence of: (a) Acetone, (b) Ethanol.

Fig. 3 .

 3 Fig. 3.Response of SnO2 sensor exposed to 1ppm and 5 ppm acetone at 250°C.

Fig. 4 .

 4 Fig. 4. Example of response Curve of SnO2 in presence of Acetone with 10 ppm

  The integral and derivative expressions are shown by the following equations (3) and (4)[START_REF] Yan | Feature Extraction from Sensor Data for Detection of Wound Pathogen Based on Electronic Nose[END_REF] :(3) with f(t) is the sensor response value, a represents the start response time of the gas injection phase, b is the end of the recovery time of the cleaning phase, and t represents a time point between a and b.[START_REF] Bruce | Prevalence of complications of diabetes among patients with diabetes mellitus attending a tertiary care centre in Tamil[END_REF] with is the time point from a to the start time of the cleaning phase, and and represent the sensor response value at and time points, respectively.

Fig. 5 .

 5 Fig. 5.Flowchart of the proposed e-nose.

Fig. 6 .

 6 Fig.6.The feature matrix

Fig. 7 .

 7 Fig. 7.

Table I -

 I ReliefF algorithm results

	FEATURES	WEIGHTS RANKING
	FRACTIONAL DIFFERENCE	4.6338	5
	DIFFERENCE	3.9008	6
	DERIVATIVE	6.5427	1
	INTEGRAL	5.3418	4
	FFT	6.3716	3
	WAVELET	6.5426	2

Table II Root

 II Mean Squared Error (RMSE) results for different method.

			GAS
	INPUT OF LS-SVM
		ACETONE	ETHANOL
	(SnO 2 )	266.0717	0.9459
	(WO 3 )	236.3854	1.6644
	(ZnO)	2.2222	1.211
	(SnO 2 , WO 3 , ZnO)	1.1985	0.7533
	OUR METHOD	0.2236	0.6639