TECHNICAL FIELD The present application generally relates to a low cost, portable and drift corrected semi-conducting metal oxide gas sensor device and process for domestic and industrial applications, and more particularly, to compensation of drift in a semiconductor gas sensor with slope and standard deviation variation.
BACKGROUND
It has been observed that base line drift of semiconducting metal oxide (SMO) based sensing elements poses serious problem towards gas detection. Several factors, including but not limited to, change in ambient humidity, surface poisoning of the sensing element, change in morphology etc may be responsible for the drift in base resistance which eventually poses problems such as multiple recalibration of the sensing system, false alarm, misclassification error during pattern recognition etc.
To the best of the knowledge and belief of the present inventors it remains as an open challenge to design and fabricate a drift-free sensing module for an automated SMO based gas detection system. A typical response transient for an ‘n’ type SMO sensor towards reducing gas sensing is shown in Figure 1. As shown in the Figure, the slope and standard deviation of the resistance transient prior to the gas exposure (marked I) is much smaller than the slope and standard deviation of the resistance transient during gas sensing (marked II).
In the present invention it has been demonstrated that the problems associated with the base line drift may be averted through the continuous monitoring of the slope or standard deviation of the response transient during gas sensing. A micro-controller based electronic circuit module has been developed to record the resistance (or voltage) transient as well as the slope of the transient and it is demonstrated how, by continuously monitoring the slope and standard deviation, the problem of resistance (or voltage) drift may be averted.
SMO based chemi-resistors type sensing elements are potentially attractive to fabricate gas detection systems required as for example in, environmental monitoring, LPG bottling plant, and in aeronautical, mining, food and beverage, health-care

industries etc. For these applications, it remains challenging to develop an economic drift-free sensing system. Several approaches have been undertaken to address the drift issue. For example, suitable algorithm has been developed that combines the ability of principal component analysis with that of wavelet decomposition method to improve the sensitivity of detecting sensor drift.
Although the developed algorithm is claimed to perform well in a mixed gas environment as well, however, it is suspected that due to different sensitivity of each sensor (in a sensor array) towards different gases, a linear PCA may not yield effective drift correction, as disclosed in the prior-art docuemt “Ding Hui, Liu Jun-hua and Shen Zhong-ru, Sensors and Actuators B, 96 (2003) 354–363”. Also a machine learning approach, namely an ensemble of classifiers, has been utilized to address the drift issue over extended periods of time with high accuracy rates. The proposed ensemble method is based on a support vector machines that uses a weighted combination of classifiers trained at different points of time. It was illustrated that the ensemble of classifiers is able to cope well with sensor drift and performs better than the baseline competing methods, as disclosed in the prior-art document “Alexander Vergaraa, Shankar Vembua,Tuba Ayhanb, Margaret A. Ryanc, Margie L. Homerc and Ramon Huertaa, Sensors and Actuators B 166– 167 (2012) 320– 329”. Alternatively, an evolutionary based adaptive drift-correction method has been designed to work with various classification systems. This approach exploits an evolutionary strategy to iteratively tweak the coefficients of a linear transformation which can mitigate the negative effects of the drift. It is claimed that the said method learns the optimal correction strategy without the use of models or other hypotheses on the behavior of the chemical sensors, as disclosed in the prior-art document “S. Di Carlo, M. Falasconi, E. Sanchez, A. Scionti, G. Squillero and A. Tonda, Pattern Recognition Letters 32 (2011) 1594–1603”. Also, orthogonal signal correction (OSC) has been proposed to be effective for drift compensation in chemical sensor arrays. The performance of OSC is also compared with component correction (CC). A simple classification algorithm has been utilized for assessing the performance of the algorithms on a dataset composed by measurements of multiple analytes using an array of conductive polymer gas sensors over a ten month period, as disclosed in the prior-art document “M. Padilla, A. Perera, I. Montoliu, A.

Chaudry, K. Persaud and S. Marco Chemometrics and Intelligent Laboratory Systems 100 (2010) 28–35”.
All these methods need high computation efficient processor for data analyses. Also the data acquisition and subsequent analyses is time consuming and use of computation intensive processor would make the sensing system expensive for a portable gas detection system.
A prior-art patent reference EP0738405B1 discloses a ndir gas sensor measuring the variable (voltage) at a time when its value should equal the extrinsically-known value. The differences are plotted versus time, and a best-fitting straight line is determined which indicates the drift. Throughout the next cycle as the variable is continuously sensed, the drift determined from the best-fitting straight line is continuously applied to correct the sensed value.
Another prior-art patent reference EP1642121B1 discloses that in order to prevent the sensor signal drift, the control and evaluation unit applies a bias voltage to the electrode pattern, the bias voltage value being adjustable according to the sensor characteristic and/or the charge thereof.
Another prior-art patent reference EP1350116 discloses method comparing a component concentration corresponding to the quiescent period with one or more additional component concentrations relating to different quiescent periods and calculates an estimated background gas concentration for a predetermined number of time periods. The estimated background concentration is then compared to a preset, or expected, background concentration, and a correction value is calculated. For baseline drift, a correction value is determined to be the difference between the estimated background concentration and the preset (expected) background concentration. For span drift, a correction value is determined to be the ratio of the estimated background concentration to the preset (expected) background concentration. Measured concentrations by the gas sensor are then adjusted by the correction value.
Another prior-art patent reference US 6237394 discloses a method of correcting drift in a sensor includes the step of identifying a calibration command. A nominal zero pressure sensor drift correction factor for the sensor is identified by relying upon a calibration voltage value and a calibration temperature value secured at a

nominal zero pressure condition. Sensor output is subsequently adjusted according to the nominal zero pressure sensor drift correction factor.
Another prior-art patent reference US 7669455 discloses an automatic zero point correction device that includes a pressure sensor, wherein output from the sensor is outputted and the sensor output is inputted to a time-varying zero point drift correction means of the sensor; a sensor output judgment means of the time-varying zero point drift correction means, wherein the sensor output judgment means operates to make a judgment determining whether the sensor output is larger than a set value; and operating condition judgment means of the time-varying zero point drift correction means, wherein the operating condition judgment means judges operating conditions of the sensor, wherein the time-varying zero point drift correction means operates to cancel time-varying zero point drift of the sensor when the sensor output judgment means determines sensor output is larger than the set value and the operating condition judgment means determines operating conditions of the sensor are within previously set operating conditions.
SUMMARY
This summary is provided to introduce concepts related to a gas sensing device and process thereof for domestic and industrial applications, and the concepts are further described below in the detailed description. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.
The above-described problems are addressed and a solution is achieved in the present invention by providing a gas sensing device and process thereof for domestic and industrial applications.
It is therefore a primary object of the present invention to provide a low cost, portable and drift corrected semi-conducting metal oxide gas sensing device.
It is another object of the present invention is to provide a low cost, portable and drift corrected semi-conducting metal oxide gas sensing method.
It is still another object of the present invention to avert the several recalibration of baseline in automatic gas analyzer system.

It is still another object of the present invention to implement the method into a microprocessor for real time data collection and transmission for environmental monitoring with minimum drift effect in a harsh environment.
In one aspect the present invention demonstrates the procedure for compensation of drift in a semiconductor gas sensor with slope and standard deviation variation. Such method of the present invention can be easily programmed and automated in a microcontroller embedded with gas sensor, LCD and interfacing unit for real time data collection and transmission. The slope and/ or standard deviation change can be utilized for concentration mapping and an important input features to different pattern recognition analysis for gas and its concentration prediction. The idea can also be extended for other types of sensors to remove drift effect and analyze estimation as well.
Accordingly, in one embodiment of the present invention, a gas sensing device (100) for monitoring gas to minimize a drift effect in a harsh gas environment is disclosed. The gas sensing device (100) comprises of at least one sensor (102), a microprocessor (104), and a feedback circuitry (106). The at least one sensor (102) is exposed to the harsh gas environment containing the gas to which the gas sensing device is sensitive, to generate at least on signal. The microprocessor (104) having the at least one sensor embedded, wherein the microprocessor is configured to monitor a baseline of the at least one sensor based on the at least on signal for feature extraction. The microprocessor (104) further estimates a slope of the baseline and a standard deviation of the baseline. Furthermore, the microprocessor (104) measures a voltage transient in a predefined time-span window. The feedback circuitry (106) in the microprocessor (104) is configured to provide an alert based on any deviation in the baseline of the at least one sensor (102).
In another embodiment of the present invention, a process for monitoring gas to minimize a drift effect in a harsh gas environment using a gas sensing device (100) is disclosed. The process comprises the steps of sensing gas using at least one sensor (102) exposed to the harsh gas environment containing the gas, to generate at least on signal; monitoring a baseline of the at least one sensor (102) based on the at least on signal for feature extraction by using a microprocessor (104); estimating a slope of the baseline

and a standard deviation of the baseline based on the at least on signal for feature extraction; measuring a voltage transient in a predefined time-span window based on the at least on signal for feature extraction; and displaying an alert based on any deviation in the baseline of the at least one sensor (102) using a feedback circuitry (106) in the microprocessor (104).
These and other features and advantages will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to refer like features and components.
Corresponding reference characters indicate corresponding parts throughout the several views. The exemplification set out herein illustrates preferred embodiments of the invention, in one form, and such exemplification is not to be construed as limiting the scope of the invention in any manner.
Figure 1 illustrates, a resistance transient of a semiconducting metal oxide (SMO) based gas sensor as disclosed in the prior-art is shown, in accordance with an embodiment of the present subject matter.
Figure 2 illustrates, a gas sensing device (100) and its components for monitoring gas to minimize a drift effect in a harsh gas environment is shown, in accordance with an embodiment of the present subject matter.
Figure 3 illustrates, a process for monitoring gas to minimize a drift effect in a harsh gas environment is shown, in accordance with an embodiment of the present subject matter.
Figure 4 illustrates a moving window for slope or standard deviation calculation is shown, in accordance with an embodiment of the present subject matter.
Figure 5 illustrates comparison of baseline and response slope or standard deviation is shown, in accordance with an embodiment of the present subject matter.

Figure 6 illustrates a flow chart of the method of the present invention is shown, in accordance with an embodiment of the present subject matter.
Figure 7 illustrates a real time data and slope calculation by microprocessor is shown, in accordance with an embodiment of the present subject matter.
Figure 8 illustrates a drift minimized output used for feature extraction strategy for qualitative and quantitative classification of gases is shown, in accordance with an embodiment of the present subject matter.
Figure 9 illustrates the slope or standard deviation dependence on concentration is shown, in accordance with an embodiment of the present subject matter.
Figure 10(a) and 10(b) illustrate a developed prototype gas analyzer system and sketch of the measurement set up respectively is shown, in accordance with an embodiment of the present subject matter.
Figure 11 illustrates a drifted baseline correction in real time for VOCs at (100-300 ppm) is shown, in accordance with an embodiment of the present subject matter.
Figure 12 illustrates a calibration for concentration estimation after drift compensation is shown, in accordance with an embodiment of the present subject matter.
It is to be understood that the attached drawings are for purposes of illustrating the concepts of the invention and may not be to scale.
DETAILED DESCRIPTION
In order to make the aforementioned objectives, technical solutions and advantages of the present application more comprehensible, embodiments are described below with accompanying figures.
The objects, advantages and other novel features of the present invention will be apparent to those skilled in the art from the following detailed description when read in conjunction with the appended claims and accompanying drawings.
Now, preferred embodiments of the present invention will be described in detail with reference to the annexed drawings. In the drawings, the same or similar

elements are denoted by the same reference numerals even though they are depicted in different drawings. In the following description, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention unclear.
Referring now to figure 2, a gas sensing device (100) for monitoring gas to minimize a drift effect in a harsh gas environment in one embodiment of the present invention, is disclosed. The gas sensing device (100) comprises of at least one sensor (102), a microprocessor (104), and a feedback circuitry (106). The at least one sensor (102) is exposed to the harsh gas environment containing the gas to which the gas sensing device is sensitive, to generate at least on signal. The microprocessor (104) having the at least one sensor embedded, wherein the microprocessor is configured to monitor a baseline of the at least one sensor based on the at least on signal for feature extraction. The microprocessor (104) further estimates a slope of the baseline and a standard deviation of the baseline. Furthermore, the microprocessor (104) measures a voltage transient in a predefined time-span window. The feedback circuitry (106) in the microprocessor (104) is configured to provide an alert based on any deviation in the baseline of the at least one sensor (102).
In one implementation, the extracted voltage transient is normalized and transferred into frequency domain and magnitude of lower frequency harmonics are used to extract features from the at least on signal.
In one implementation, the slope variation, the standard deviation variation, and other features are utilized to calculate a principle component analysis (PCA) scores. The PCA score is provided as an input to an artificial neural network for classification of the gas qualitatively and quantitatively. The other feature is selected from a group of feature comprising a temperature, humidity, and any combinations thereof.
In one implementation, the slope and/ or standard deviation change is utilized for concentration mapping and an important input features to different pattern recognition analysis for gas and its concentration prediction.
In one implementation, the sensor (102) includes thin film of binary semiconductor layer comprising a material selected from the group consisting of silicon

carbide, diamond, Group III nitrides, alloys of Group III nitrides, zinc oxide, and any combinations thereof.
In one implementation, the data related to any deviation in the baseline of the at least one sensor (102) is collected from microcontroller (104) using a RS 232 to USB converter of a portable device.
In one implementation, the sensors (s) (102) are connected with at least one power supply and at least one load resistance.
In one implementation, the voltage drop across the load resistance is fed to input of the microprocessor.
In one implementation, the alerts are provided on a display, wherein the display is connected to a feedback circuitry (106) in the microprocessor (104).
While illustrative embodiments of the present invention are described below, it will be appreciated that the present invention may be practiced without the specified details, and that numerous implementation-specific decisions may be made to the invention described herein to achieve the developer's specific goals, such as compliance with system-related and business-related constraints, which will vary from one system to other system such a development effort might be complex and time-consuming, it would nevertheless be a routine undertaking for those of ordinary skill in the art having the benefit of this disclosure. For example, selected aspects are shown in block diagram form, rather than in detail, in order to avoid obscuring or unduly limiting the present invention. Such descriptions and representations are used by those skilled in the art to describe and convey the substance of their work to others skilled in the art. The present invention will now be described with reference to the drawings described below.
While aspects of described for a gas sensing device and process thereof for domestic and industrial applications are disclosed, may be implemented in any number of different computing systems, environments, and/or configurations, the embodiments are described in the context of the following exemplary system.
Referring now to figure 3, process for monitoring gas to minimize a drift effect in a harsh gas environment using a gas sensing device (100) is disclosed, in an embodiment of the present invention. The process comprises the steps of sensing gas using at least one sensor (102) exposed to the harsh gas environment containing the gas,

to generate at least on signal at step 202; monitoring a baseline of the at least one sensor (102) based on the at least on signal for feature extraction by using a microprocessor (104) at step 204; estimating a slope of the baseline and a standard deviation of the baseline based on the at least on signal for feature extraction at step 206; measuring a voltage transient in a predefined time-span window based on the at least on signal for feature extraction at step 208; and displaying an alert based on any deviation in the baseline of the at least one sensor (102) using a feedback circuitry (106) in the microprocessor (104) at step 208.
In one implementation, the process may further include the step of normalizing the extracted voltage transient; and transferring the normalized voltage transient into frequency domain; and extracting features using magnitude of lower frequency harmonics from the at least on signal.
In one implementation, the process may further include the step of calculating a principle component analysis (PCA) score utilizing the slope variation, the standard deviation variation, and other features.
In one implementation, the process may further include the step of classifying the gas qualitatively and quantitatively by providing the PCA scores to an artificial neural network.
In one implementation, the other feature is selected from a group of feature comprising a temperature, humidity, and any combinations thereof.
The detailed working, considering the various components, calculation, and formula used, output generated, and the overall process of the proposed device is explained below. This is an exemplary embodiment of the present invention and should not limit the scope of the invention in any way.
As outlined earlier, various factors including the change in humidity, sensor poisoning etc. contribute to the sensor drift. Base line drift in turn poses serious problem towards gas detection using SMO based sensors. In the present invention continuously monitoring the slope as well as standard deviation of the response transient the drift compensation can successfully be made. For a practical gas detection system, the sensors are initially trained from the pattern of various gas mixtures. Due to the sensor drift during prolonged uses, often such training becomes obliterated causing the so

called misclassification error among the different gases. For a simple detector based circuit for any reducing gas (such as CO, CH4 etc), the input voltage from sensors are fed to the comparator and an alarm starts to buzz when a gaseous species is adsorbed on the sensors surface and causes a change of base resistance value to reach a cut off limit. This works well as long as the baseline of the sensor becomes stable but if the base line is drifted up on prolonged use then it can trigger a false alarm. It remains challenging to fabricate a drift-free gas sensing system.
In the present invention, the problem of drift as well as misclassification error have been addressed by continuous monitoring of the slope and standard deviation of the voltage transient measured in a predefined time-span window as shown in figure 4. The estimated slopes and standard deviation of the baseline, as well as during response and recovery are also shown in figure 4. An ATMEGA microcontroller has been used to monitor the baseline, its slope and standard deviation. As shown in figure 4, in a base line drifted voltage transient, the estimated slopes and standard deviation, both in the base line as well as during response are marginally drifted. As illustrated in figure 5, during gas sensing, the estimated standard deviation and slope changes abruptly as compared to its counterpart estimated when the sensor was exposed in air ambient. The time window for the estimation of these standard deviation and slopes as well as their standard deviation is preset according to the response (or recovery) time of the sensor in use. The logical flow-chart of the microprocessor is illustrated in figure 6. After an initial warm up period the microprocessor starts to acquire the voltage signal and estimate the slope as well as its standard deviation up to a preset time window (say 100s).
The slope or standard deviation of the baseline is observed very low as compared to response transient. From the data obtained from different SMO based sensors in our laboratory, it has been found that the magnitude of drifted baseline slope differs from -2 to +2 and the entire response and recovery slope or standard deviation is greater than this limit as shown in figure 7. So the cut off value is set for slope as -2 to 2. The program continuously monitors this range whether it crosses beyond this limit. During the gas exposure on the sensor surface, response slope or standard deviation starts to increase and feedback circuit gives the alarm via microprocessor.

In figure 8 the shaded section are sensor signal processing and drift minimization (slope or standard deviation calculation), feature extraction (FFT) from normalized sensor output on the basis of slope or standard deviation value and PCA score of all features and variables to artificial neural network for the gas quality and quantity determination.
In figure 7 a drifted alcohol sensing data taken by the composite sensing materials as sensor and a microcontroller (MC) based data acquisition unit interfaced with laptop has been shown. The corresponding slope and standard deviation information during the measurement is also calculated by MC unit and is shown in figure 7. One can easily see although there is a severe base line drift during repeated response and recovery cycles, yet the slope or standard deviation during response and recovery are marginally affected. After drift compensation of the sensors data the extracted time domain voltage transient during the gas exposure is normalized and transferred into frequency domain and magnitude of lower frequency harmonics. This is a well-known feature extraction technique. In addition the slope standard deviation variation during the gas exposure has also been included. Other factors like ambient humidity and temperature is also included as variables. All these factors are utilized and PCA score is calculated to reduce the dimension keeping maximum variance in first two components which is directed to the input of artificial neural network for classification of the gas(s) qualitatively and quantitatively figure 8.
A low cost microprocessor like ATMEGA (Atmel) is utilized for this purpose which performs these tasks. The 10 bit resolution voltage is taken through its in built ADC and window size is maintained by declaring a one dimensional array in the main function. The LCD is used for simultaneous display of voltage and the corresponding slope and standard deviation value calculated from window. The data are transferred to laptop via RS 232 port of the laptop. The other mathematical task like FFT, PCA or ANN computation is started when slope or standard deviation is abruptly changes and crosses a predefined cut off value. It has been found that all the drifts and noise effect reside within +2 to -2 figure 7. The programs are written and tested by C language with AVR studio software and ATMEGA microcontroller.

Features of the present invention:
• The drift effect is successfully compensated in order to avoid baseline change, false alarming and misclassification error.
• The transient analysis is started when slope or standard deviation level exceeds a cut off value and even if baseline changes largely it produces no harm because transient input is processed only for large slope or standard deviation value which occurs due to sudden abrupt change of base line in presence of gas.
• The relative change in different concentration (ppm) is also found maximum different slope variation.
• The calculation of slope is done by fitting the particular time frame data through the straight line equation Y= m X + c. This is performed in a continuous mode like a (water pipe line). The procedure is simple and fast to calculate. Similarly the standard deviation (SN) formula is used to find variance within the time frame data.
• Where X1, X2, X3, values are the consecutive value of the voltage obtained from sensor in the time frame window and is the mean value of these observations and N is the size of the time frame window. During the gas exposure, SN inside the time frame increases and for a drifted baseline its magnitude becomes lower which is similar to slope calculation.
• The information of the slope and standard deviation along with frequency domain data and other factors like ambient humidity and temperature is utilized for formation of data registry in the form of 2D matrix and several repetitions is done for training of the network.
• The procedure can be easily programmed and burnt into microprocessor for real time data collection and transmission for environmental monitoring with minimum drift effect in a harsh environment.
Testing:

Measurement in the real time with the developed microprocessor based gas sensing data acquisition set up along with said facility was performed. Different sensors including thin film of binary semiconductor like tungsten oxide, zinc oxide and composite materials both in dynamic gas flow control unit and static environment were tested. The thin film sensors are developed in our lab by a sol gel spin coating method and are tested for various gases like methane, carbon mono-oxide, hydrogen and several volatile organic components (VOCs) like methanol, butanol etc. The heating system is fabricated by a homemade heater. The drifts are observed as usual for different sensors and gases. The data are collected from microcontroller via RS 232 to USB converter of a laptop. Sensors are connected with power supply and load resistance. The voltage drop across the load resistance is fed to input of ATMEGA microprocessor (PORT A).
Response and recovery of several VOCs with thin film sensor in static chamber as shown in figure 10 were tested. It was found that in spite of change of baseline there was no effect in response and baseline slope and standard deviation as shown in figure 11.
It is also to be noted that maximum slope or standard deviation change for a particular concentration is proportional to amount of gas concentration (ppm). This can be useful for calibration of concentration estimation with slope or standard deviation change figure 12.
Advantages of the present invention:
• Low cost, easy to fabricate and a good compensation of long term drift arises from different environmental situation.
• Rapid prototyping of the proposed sensing module and feasible industrial packaging process is possible.
• The protocol can be generalized and utilized to other types of sensors facing severe drift problem.
Finally, it should be understood that the above embodiments are only used to explain, but not to limit the technical solution of the present application. Despite the detailed description of the present application with reference to above preferred embodiments, it should be understood that various modifications, changes or equivalent

replacements can be made by those skilled in the art without departing from the scope of the present application and covered in the claims of the present application.

WE CLAIM:
1. A gas sensing device (100) for monitoring gas to minimize a drift effect in a
harsh gas environment, the gas sensing device (100) comprising:
at least one sensor (102) exposed to the harsh gas environment containing the gas to which the gas sensing device is sensitive, to generate at least on signal;
a microprocessor (104) having the at least one sensor embedded, wherein the microprocessor is configured to monitor a baseline of the at least one sensor based on the at least on signal for feature extraction, thereby
estimating a slope of the baseline and a standard deviation of the baseline;
measuring a voltage transient in a predefined time-span window; and a feedback circuitry (106) in the microprocessor (104) configured to provide an alert based on any deviation in the baseline of the at least one sensor (102).
2. The gas sensing device (100) according to claim 1, wherein the extracted voltage transient is normalized and transferred into frequency domain and magnitude of lower frequency harmonics are used to extract features from the at least on signal.
3. The gas sensing device (100) according to claim 1, the slope variation, the standard deviation variation, and other features are utilized to calculate a principle component analysis (PCA) score.
4. The gas sensing device (100) according to claim 3, wherein the PCA score is provided as an input to an artificial neural network for classification of the gas qualitatively and quantitatively.
5. The gas sensing device (100) according to claim 3, wherein the other feature is selected from a group of feature comprising a temperature, humidity, and any combinations thereof.

6. The gas sensing device (100) according to claim 1, wherein the slope and/ or standard deviation change is utilized for concentration mapping and an important input features to different pattern recognition analysis for gas and its concentration prediction.
7. The gas sensing device (100) according to claim 1, wherein the sensor (102) includes thin film of binary semiconductor layer comprising a material selected from the group consisting of silicon carbide, diamond, Group III nitrides, alloys of Group III nitrides, zinc oxide, and any combinations thereof.
8. The gas sensing device (100) according to claim 1, wherein the data related to any deviation in the baseline of the at least one sensor (102) is collected from microcontroller (104) using a RS 232 to USB converter of a portable device.
9. The gas sensing device (100) according to claim 1, wherein the sensor (s) (102)
are connected with at least one power supply and at least one load resistance.
10. The gas sensing device (100) according to claim 9, wherein the voltage drop across the load resistance is fed to input of the microprocessor.
11. The gas sensing device (100) according to claim 1, wherein the alerts are provided on a display, wherein the display is connected to a feedback circuitry (106) in the microprocessor (104).
12. A process for monitoring gas to minimize a drift effect in a harsh gas environment using a gas sensing device (100), the process comprising the steps of:
sensing gas using at least one sensor (102) exposed to the harsh gas environment containing the gas, to generate at least on signal;
monitoring a baseline of the at least one sensor (102) based on the at least on signal for feature extraction by using a microprocessor (104);
estimating a slope of the baseline and a standard deviation of the baseline based on the at least on signal for feature extraction;

measuring a voltage transient in a predefined time-span window based on the at least on signal for feature extraction; and
displaying an alert based on any deviation in the baseline of the at least one sensor (102) using a feedback circuitry (106) in the microprocessor (104).
13. The process according to claim 11, further comprises of
normalizing the extracted voltage transient; and
transferring the normalized voltage transient into frequency domain; and extracting features using magnitude of lower frequency harmonics from the at least on signal.
14. The process according to claim 11, further comprises of calculating a principle component analysis (PCA) score utilizing the slope variation, the standard deviation variation, and other features.
15. The process according to claim 11 and 13, further comprises of classifying the gas qualitatively and quantitatively by providing the PCA scores to an artificial neural network.
16. The process according to claim 13, wherein the other feature is selected from a group of feature comprising a temperature, a humidity, and any combinations thereof.