







































ECQ. Heng et al. /Future Sustainability                                                                               November 2025| Volume 03 | Issue 04 | Pages 
32-46 

32 

 

 

 

Article 

Regression analysis and classification of 

temperature modulated metal oxide 

semiconductor gas sensors responses on flue gas 
Elvina Chu Qing Heng, Nikko Leo, Ting Soon Ling, Hong Siang Chua, Hui En Lee* 

Swinburne University of Technology Sarawak Campus 

               A R T I C L E   I N F O 
 

Article history: 
Received 01 June 2025  
Received in revised form 
10 July 2025 
Accepted 20 July 2025 
 
Keywords:  
Air quality monitoring, Electronic nose (eNose), 
Metal oxide semiconductor (MOS) sensors, 
Regression analysis, Gas sensors,  
Flue gas detection 
 
*Corresponding author 
Email address: 
helee@swinburne.edu.my 
 
  
DOI: 10.55670/fpll.fusus.3.4.4 
 

A B S T R A C T 
 

Industrial emissions, particularly from flue gases, pose significant risks to 

environmental sustainability and public health. Conventional air quality 

monitoring systems often suffer from high costs, delayed reporting, and limited 

detection capabilities. This study presents a cost-effective, real-time air quality 

monitoring solution using an electronic nose (eNose) system integrated with 

Metal Oxide Semiconductor (MOS) gas sensors. These sensors target key 

pollutants, such as carbon monoxide (CO) and carbon dioxide (CO2), which also 

serve as indicators of transformer faults in industrial settings. The eNose 

system leverages machine learning for both regression and classification tasks, 

enabling accurate quantification of pollutant levels and categorization of air 

quality into defined categories. Principal Component Analysis (PCA) is 

employed to optimize feature extraction, enhancing model precision and 

efficiency. Notably, the system integrates digitally controlled buck converters 

for automatic temperature regulation, reducing sampling time from 390 to 130 

seconds. Additionally, a redesigned airtight sensor chamber and optimized 

airflow design, along with the use of Tedlar bags, improve sample integrity and 

minimize interference. Hardware development involved prototyping on 

breadboards using LM2575, LM2576, and LM2574 ICs, followed by the creation 

of a compact 10 cm × 10 cm PCB for efficient power management. Multimeter 

testing verified reliable electrical connections. Experimental validation showed 

the system achieved over 91% accuracy in distinguishing between "good" and 

"bad" air quality levels. Strong correlations between sensor output and 

pollutant concentrations confirm system reliability. This research 

demonstrates a scalable, efficient tool for real-time air quality monitoring and 

fault detection in industrial environments. 

1. Introduction 

Air pollution poses a critical global challenge, 

significantly affecting public health and environmental 

sustainability. Industrial processes—especially the 

combustion of fossil fuels—emit flue gases containing 

harmful pollutants, including nitrogen oxides (NOx), sulfur 

oxides (SOx), carbon monoxide (CO), carbon dioxide (CO₂), 

volatile organic compounds (VOCs), and particulate matter. 

These pollutants degrade air quality, contribute to climate 

change, and lead to severe health risks [1]. The World Health 

Organization (WHO) estimates that outdoor air pollution is 

linked to approximately 4.2 million deaths annually, 

underscoring the urgent need for accurate, real-time air 

quality monitoring systems [2]. In response to these 

challenges, this study employs the Metal Oxide 

Semiconductor (MOS) gas sensor module previously 

developed in our earlier work [3–5]. In that study, 

temperature modulation was applied to the MOS sensors by 

varying the heater voltage to influence each sensor's gas 

selectivity, also referred to as its sensitivity profile. This 

technique significantly increased the dimensionality of sensor 

data. By exploring the multi-dimensional nature of the 

generated data, this study aims to identify the optimal heater 

voltage setting that enables precise selectivity toward specific 

Future Sustainability 

Open Access Journal 

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November 2025| Volume 03 | Issue 04 | Pages 32-46 

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ISSN 2995-0473 

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ECQ. Heng et al. /Future Sustainability                                                                               November 2025| Volume 03 | Issue 04 | Pages 32-46 

33 

 

flue gas components. Flue gas, also referred to as exhaust gas, 

is the byproduct of burning fossil fuels like coal, oil, or gas in 

industrial applications such as boilers and engines [6]. These 

gases, mainly composed of CO₂ and CO, are generated from 

sectors such as energy, transportation, and manufacturing 

[2]. While these activities are essential for industrial 

operations, they contribute substantially to air pollution and 

climate change. In Malaysia, this issue has become 

increasingly serious, especially in urban areas with intense 

industrial activity and heavy traffic. Rapid urbanization has 

worsened the situation by increasing industrial production 

and vehicle emissions, further deteriorating air quality. In 

addition to air pollution, transformer faults are a growing 

concern. Faults such as overheating and insulation 

breakdowns can lead to the release of CO₂ and CO into 

transformer oil. These issues can result in expensive 

equipment damage, unplanned operational downtime, and 

disruptions to the power supply. Effective monitoring of these 

gases is crucial for early fault detection in transformers and 

to control pollution levels in urban areas. Traditional gas 

monitoring technologies like Continuous Emission 

Monitoring Systems (CEMS), Gas Chromatography (GC), and 

Mass Spectrometry (MS) provide precise results but are often 

expensive, complex, and unsuitable for large-scale 

deployment due to their reliance on specialized equipment 

and trained personnel. Furthermore, these methods typically 

lack real-time capabilities, making them inefficient for 

continuous air quality assessment [7]. 

To address these limitations, this study explores the 

development of an improved Metal Oxide Semiconductor 

(MOS)-based gas sensor system, commonly referred to as an 

electronic nose (eNose). These sensors mimic the human 

olfactory system, offering a cost-effective, compact, and 

scalable solution for detecting gas compositions, such as CO 

and CO₂, in both flue gases and transformer environments. 

Integrated with regression analysis, Principal Component 

Analysis (PCA), and machine learning classification 

algorithms, the eNose can accurately quantify pollutant 

concentrations and classify air quality levels in real time [8,9]. 

Despite their potential, MOS sensors face challenges, 

particularly with manual temperature modulation. This 

process involves adjusting the sensor’s temperature to 

improve selectivity and sensitivity. However, manual control 

often results in delays between temperature adjustments and 

sensor response, reducing measurement reliability [10,11]. 

Inconsistent readings due to air leaks in non-airtight sensor 

chambers and time-consuming sample collection methods 

further compromise accuracy and efficiency. Automating the 

temperature modulation process can enhance sensor 

precision and consistency, enabling more reliable gas 

differentiation in complex mixtures like flue gases. 

Additionally, improving air sampling methods to reduce 

collection time and ensure airtight conditions will further 

streamline the system’s performance. Ultimately, this study 

aims to design a MOS-based gas sensor system that delivers 

accurate, efficient, and real-time detection of CO and CO₂ for 

both environmental monitoring and transformer fault 

detection. Through advanced regression analysis and 

classification techniques applied to eNose sensor data, the 

initiative seeks to improve the accuracy and responsiveness 

of air quality assessments. By addressing the current 

limitations of sensor-based technologies, this system 

presents a scalable and practical alternative to conventional 

monitoring methods, enabling more effective air quality 

control and transformer fault diagnostics. 

2. Methodology 

2.1 Sensor case design for gas sampling 

Figure 1 illustrates that the sensor chamber case has 

been thoughtfully designed to ensure accurate and reliable 

gas sampling. It features an airtight seal and a controlled gas 

inlet system, which allows only the intended gas mixture to 

enter the chamber, thus preventing contamination from 

outside air and enhancing the precision of the sensor 

readings. To minimize the risk of leaks, silicone gaskets are 

used, ensuring that the results remain consistent and 

accurate. Additionally, the chamber case includes a manual 

valve, as shown in Figure 2, along with a Tedlar bag, providing 

precise control over the flow rate and composition of the gas 

sample, which gives greater confidence in the collected data. 

Furthermore, the chamber's compact and lightweight design, 

made possible through 3D printing, makes it cost-effective 

and easy to use in a variety of settings. 

 

Figure 1. SolidWorks 3D model of the chamber case with central inlet 

for controlled gas flow 

 

 

Figure 2. Designed and printed gas sampling chamber case for gas 

detection 



ECQ. Heng et al. /Future Sustainability                                                                               November 2025| Volume 03 | Issue 04 | Pages 32-46 

34 

 

2.2 Digital temperature modulation design by using a 

buck converter 

Figure 3 represents the design process for digital 

temperature modulation. Before proceeding to full-scale 

testing and PCB design, each buck converter was first 

assembled on a breadboard, as shown in Figure 4, to ensure 

proper functionality. This approach allowed for easy 

adjustments and troubleshooting within a flexible setup. For 

the 1A temperature control system, we utilized the LM2575 

IC in conjunction with a digital potentiometer to adjust the 

output voltage, which ranged from 4.0V to 5.0V. The 3A 

converter, designed for the pneumatic system and based on 

the LM2576 IC, was tested to confirm a stable 5V output 

under a 3A load, with an oscilloscope monitoring for any 

voltage fluctuations. Finally, the 500mA sensor array 

converter, using the LM2574 IC, provided a consistent 5V 

output, with careful observation of voltage stability and 

thermal performance. This prototyping phase confirmed that 

all converters met the required specifications prior to moving 

forward with the PCB design. 

 
Figure 3. Process of designing the digital temperature modulation 

 

 

 
Figure 4. Buck converter breadboard prototyping with the LM2574 

500mA IC for troubleshooting 

 

 

After successfully testing individual buck converters on 

a breadboard, the next step was to combine all three circuits 

into a single PCB design. Figure 5 displays the backend PCB 

layout for combined buck converters. This design aimed to 

efficiently manage power for the MOS sensor module, 

pneumatic system, and sensor array. The PCB design process 

involved converting the schematic into a compact and 

functional layout. Key considerations included footprint 

design, component placement, and routing. Accurate pad 

layouts were created to ensure proper alignment of 

components and to minimize assembly errors. Components 

were strategically arranged to reduce wiring complexity 

while maintaining adequate spacing for airflow and heat 

dissipation. Routing was done with care, using 1 mm traces 

for power lines and a copper-filled ground zone to enhance 

current flow and cooling. The final PCB design measures 

10cm x 10cm, a standard size that facilitates easy 

manufacturing, testing, and future modifications. 

 
Figure 5. Backend PCB layout for combined buck converters 

 

 

Once the PCB is prepared, the next step is to solder the 

components. Figure 6 demonstrates the circuit board with 

polarized capacitors (highlighted) that require careful 

orientation during assembly. First, gather the necessary tools: 

a soldering iron, solder wire, flux, and tweezers. Clean the PCB 

pads to ensure a smooth soldering process, and then apply 

flux to help the solder adhere. Carefully place the components 

in their correct positions, paying special attention to 

polarized parts like capacitors and diodes. 

Start by soldering one pin of each component to hold it in 

place, and then secure the remaining pins. After soldering, 

inspect the joints to ensure they are clean and solid. Use a 

multimeter to test the circuit. Finally, clean off any excess flux 

and add any finishing touches, such as heatsinks, to complete 

the process. This ensures everything is securely connected 

and ready for testing. 

 

2.3 Troubleshooting and resolving inductor overload in 

the buck converter circuit 

Inductors have a maximum current rating, and when this 

limit is exceeded, they can overheat and fail. In this case, the 

original inductor was unable to handle the 3A current, leading 

to its failure. Figure 7 illustrates the replacement process of 

the 100 μH inductor in the circuit to address the overload 



ECQ. Heng et al. /Future Sustainability                                                                               November 2025| Volume 03 | Issue 04 | Pages 32-46 

35 

 

issue. After replacing it with a 100μH inductor rated for 3A, 

the system functioned correctly again. This experience 

underscores the importance of selecting components that are 

properly rated for the system’s current demands to ensure 

reliable performance and prevent damage. 

 
Figure 6. Circuit board with polarized capacitors (highlighted) that 

require careful orientation during assembly 

 

 

 
Figure 7. Replace 100μH inductor rated for 3a in the buck converter 

circuit 

2.4 Air sampling process 

Flue gas samples were collected using a redesigned 

Metal Oxide Semiconductor (MOS) gas sensor, a 5V motor 

pump, and Tedlar bags. Figure 8 illustrates the collection of 

75 samples distributed across three air quality categories: 

good, moderate, and poor. The samples were sourced from 

clean forested areas, vehicle exhaust points, and gardens with 

vehicle presence. The motor pump ensured consistent 

sampling, while the Tedlar bags helped maintain gas purity. 

Table 1 summarises the distribution of air samples collected 

across three air quality categories: bad, moderate, and good, 

with 25 samples gathered for each category, totalling 75 

samples. For the bad air quality category, samples were 

directly collected from the exhausts of four different vehicles: 

Alza, Bezza, Van, and Myvi, to represent highly polluted 

environments. The moderate category involved sampling air 

from four separate areas within the Malaysia-China 

Friendship Park, a location with a mix of natural and urban 

influences. For the good category, air was sampled from four 

locations along the 7th Mile Haji Baki Bamboo Trail, a clean 

forested area with minimal pollution. All samples were 

properly labeled and stored for no more than 24 hours to 

maintain their integrity before analysis. 

 
Figure 8. Sample collection using Tedlar bags with a total of 75 air 

samples 

 
Table 1. Air sampling locations and sample distribution 

 

 

Table 2 outlines the timeline and actions involved in the 

sampling procedure for data collection. Before collecting air 

samples, the gas sensors are allowed a 30-minute warm-up 

period to stabilize. During this time, the heater voltage is set 

to 4.0V, and the system is calibrated to ensure accurate 

readings. This process is both safe and non-invasive, while the 

temperature control further enhances sensor sensitivity. The 

sensors operate with a built-in heater powered by a 5V 

supply, starting at 4.0V and increasing by 0.1V every 10 

seconds until reaching 5.0V at 120 seconds. After that, the 

voltage drops back to 4.0V, which helps the sensors respond 

accurately to the air samples. The updated sampling 

procedure has streamlined the process, reducing the total 

sample time from 390 seconds to 130 seconds through fully 

automated voltage adjustments. This improvement enhances 

efficiency, allowing for more samples to be collected in less 

time. The 10-step voltage change creates multiple data points, 

Air Quality 

Category 

Sampling 

Location 

Number of 

Samples 

Store 

Time 

Bad Directly collect 

air from 4 

different car 

exhausts (Alza, 

Bezza, Van, and 

Myvi). 

25 Not over 

24 hours 

Good 7th Mile Haji 

Baki Bamboo 

Trail (Jungle), 

from 4 areas. 

25 

Moderate Malaysia China 

Friendship Park, 

from 4 areas. 

25 



ECQ. Heng et al. /Future Sustainability                                                                               November 2025| Volume 03 | Issue 04 | Pages 32-46 

36 

 

providing richer and more detailed information, which also 

improves sensor sensitivity for more accurate detection of 

pollutant levels. 

Table 2. Sampling procedure timeline and actions for data collection 

Time Actions 

Connecting Sample Connect the tube from the Tedlar bag to the 

chamber. Open the valve from the Tedlar 

bag first, not the valve from the chamber. 

Pre-sampling  The heater automatically sets to 4.0V. 

Switch the calibration button to "open." The 

system adjusts the sensors until their 

output stabilizes at 1.00 ± 0.02V. Once 

stable, switch the calibration button "off." 

0 – 10s Ensure the output voltage of the sensors is 

at a steady state at the baseline value, which 

is around 1.00 ± 0.05V, for 10s. 

11s – 20s Start from 11s, open the chamber valve and 

gently press the Tedlar bag to let air into the 

chamber for 10 seconds. Close the chamber 

valve after 10 seconds. 

21s – 120s The heater voltage automatically steps up 

to 4.1V at 21 seconds, increasing by 0.1V 

every 10 seconds until reaching 5.0V at 120 

seconds. 

121s – 130s  At 121 seconds, the heater voltage 

automatically steps back down to 4.0V. 

Post – sampling  Start the purging process to clear the 

chamber. Turn on the vacuum valves and 

air pump until the sensor output stabilizes 

at 1.00 ± 0.05V. Perform a final check to 

ensure a steady reading around 1.00 ± 

0.02V and get ready for the next sample. 

 

 

2.5 Data collection 

The data collection process involves acquiring raw 

sensor data from diverse environments with varying air 

quality conditions. The dataset is systematically divided into 

two subsets: a training set for model development and a 

validation set for performance evaluation. 

To create the training datasets, a total of 60 air samples were 

collected and categorized as follows: 

• 20 samples of Bad Air, 

• 20 samples of Good Air, and 

• 20 samples of Moderate Air. 

For model validation, an additional 15 air samples were 

collected, distributed evenly among the categories: 

• 5 samples of Bad Air, 

• 5 samples of Good Air, and 

• 5 samples of Moderate Air. 

The electronic nose (eNose) system equipped with Metal 

Oxide Semiconductor (MOS) sensors was used to capture 

sensor output voltages over specific time intervals. For 

regression analysis, a commercial carbon dioxide (CO2) 

detector is used to measure and collect CO2 concentrations, in 

parts per million (ppm), with five readings at each sampling 

point. 

2.6 Data extraction 

Table 3 presents the MOS sensors used in this study, 

along with their respective targeted gases. Sensor data is 

recorded continuously from 0 to 130 seconds. However, for 

analysis, sensor data is extracted from the time intervals 

between 25 seconds and 115 seconds, in 10-second 

increments, as shown in Figure 9. This approach selects 

sensor readings at ten key intervals, capturing critical 

temporal variations in the sensor's response to different gas 

concentrations. The extracted data from these intervals is 

then fed into MATLAB to train and validate the classification 

models. 

Table 3. MOS sensors and targeted gas 

 

 

 
 

Figure 9. Sensors' instantaneous response extracted at 10-time 

intervals of the sampling process 

 

 

2.7 Data analysis 

The machine learning classification process, as 

illustrated in Figure 10 (a), involves a series of steps designed 

to accurately categorize air quality levels based on sensor 

data collected from the eNose system. This project will utilize 

the MATLAB Classification Learner application, which offers 

30 classification models. The classification models and their 

respective model categories are tabulated in Table 4. The 

process begins with Principal Component Analysis (PCA), 

which reduces the dimensionality of the raw sensor data 

while retaining critical features. This step enhances 

Types of MOS sensors Targeted gas 

TGS2600 
Hydrogen (H2), Carbon Monoxide 

(CO), Methane (CH4) 

TGS2602 
VOCs, Ammonia (NH3), Hydrogen 

Sulfide (H2S) 

TGS2620 Alcohol, VOCs 

TGS2611 Methane (CH4) 



ECQ. Heng et al. /Future Sustainability                                                                               November 2025| Volume 03 | Issue 04 | Pages 32-46 

37 

 

computational efficiency and improves the overall 

performance of the classification models. The reduced dataset 

is utilized to train machine learning models using MATLAB’s 

Classification Learner application, where algorithms such as 

decision trees, support vector machines, and ensemble 

methods are assessed to determine the most effective model. 

𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 (%) = (
𝐶𝑜𝑟𝑟𝑒𝑐𝑡 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛𝑠

𝑇𝑜𝑡𝑎𝑙 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛𝑠
) × 100                               (1) 

Once the models are trained, data validation is 

performed using a separate validation dataset to ensure the 

generalizability and robustness of the models when applied 

to new, unseen data. The accuracy of each model is then 

computed using metrics such as training and validation 

accuracy. Equation (1) is used to compute the accuracy of 

each model. Following this, the fine-tuning process begins, 

where hyperparameters are optimized, and the best-

performing models are recalibrated to further enhance their 

performance. Finally, the optimized models are evaluated to 

confirm their reliability and readiness for deployment in real-

world air quality monitoring applications. 

 

(a)                                                     (b) 

Figure 10. (a) Workflow for the machine learning classification 

process (b) Workflow for the regression analysis process 

The regression analysis workflow, depicted in Figure 10 

(b), outlines the steps involved in quantifying pollutant 

concentrations based on sensor output voltages. The process 

begins by plotting sensor output voltage (Vout) against 

commercial reference pollutant concentrations (in ppm). 

Next, the data is cleaned to remove outliers and 

inconsistencies, ensuring the integrity of the analysis. A line 

of best fit is then generated using linear regression to model 

the relationship between Vout and pollutant concentrations. 

Finally, the best-performing regression plots are evaluated 

for accuracy and reliability, with metrics such as R-squared 

values used to validate model performance. 

Table 4. Classification models and model categories 

 

3. Results and discussion 

3.1 Digital temperature modulation output voltage 

testing 

After integrating the buck converters onto the PCB, 

voltage regulation testing was conducted to verify their 

output voltages. The voltage regulation test of LM2575 Buck 

converter on PCB was setup as shown in Figure 11. LM2576 

provided a stable 5.07V, suitable for high-current 

applications, while LM2574 delivered 5.05V for low-current 

applications. The LM2575, controlled by an Arduino, adjusted 

its output from 4.0V to 5.0V in 0.1V increments, 

Decision 

Tree 

1.Linear 

SVM 

4.Cosine 

KNN 

1.Narrow 

Neural Network 

1. Fine Tree 2.Quadratic 

SVM 

5. Cubic KNN 2.Medium 

Neural Network 

2.Medium 

Tree 

3.Cubic 

SVM 

6.Weighted 

KNN 

3.Wide Neural 

Network 

3. Coarse Tree 4.Fine 

Gaussian 

SVM 

Ensemble 

Classifiers 

4.Bilayered 

Neural Network 

Discriminant 

Analysis 

5.Medium 

Gaussian 

SVM 

1.Boosted 

Trees 

5.Trilayered 

Neural Network 

1.Linear 

Discriminant 

6.Coarse 

Gaussian 

SVM 

2.Bagged 

Trees 

Kernel 

Approximation 

Classifiers 

Naïve Bayes 

Classifier 

Nearest 

Neighbour 

Classifiers 

3.Subspace 

Discriminant 

1. SVM Kernel 

1.Gaussian 

Naïve Bayes 

1. Fine KNN 4.Subspace 

KNN 

2.Logistic 

Regression 

Kernel 

2.Kernel 

Naïve Bayes 

2.Medium 

KNN 

5.RUS 

Boosted 

Trees 

 

Support 

Vector 

Machines 

(SVM) 

3.Coarse 

KNN 

Neural 

Network 

Classifiers 

 



ECQ. Heng et al. /Future Sustainability                                                                               November 2025| Volume 03 | Issue 04 | Pages 32-46 

38 

 

demonstrating precise voltage control for temperature 

modulation. Table 5 summarises the output voltage testing 

results of the LM2576, LM2574, and Arduino-controlled 

LM2575 buck converters. These results confirm the 

successful integration and reliable performance of all three 

converters. 

 
Figure 11. Voltage regulation test of the LM2575 buck converter on 
the PCB setup 

 
Table 5. Output voltage testing results of LM2576, LM2574, and 
arduino controlled LM2575 buck converters 

3.2 Evaluating the digital temperature modulation 

sensor response in a graph 

This test evaluated the performance of the Digital 

Temperature Control system, which is designed to improve 

sensor accuracy, consistency, and efficiency. The 

performance of this system is presented in Figure 12. During 

the test, the heater voltage was gradually increased by 0.1V 

every 10 seconds, starting at 4.0V and reaching a peak of 5.0V, 

before smoothly returning to 4.0V. The response curve 

indicated that the sensors (TGS2600, TGS2602, TGS2620, and 

TGS2611) produced stable and consistent outputs 

throughout the process. The updated process improved 

sampling consistency and reduced sampling time. While the 

previous design took 390 seconds to complete each sample, 

the automated system now accomplishes this in just 130 

seconds. This threefold reduction not only saves time but also 

allows researchers to collect more samples, ultimately 

increasing overall efficiency and productivity. The gradual 

voltage adjustments ensured that the sensors responded 

steadily, resulting in accurate and uninterrupted data 

collection. Furthermore, the system’s capability to adjust 

voltage without delays or fluctuations underscores its 

reliability. These results demonstrate that the Automatic 

Temperature Control system effectively enhances sampling 

performance while making the process faster and more 

efficient. 

 
Figure 12. Performance of the digital temperature control system 

3.3 Sensor performance and stability in different air 

quality 

The response curves offer valuable insights into how the 

Metal Oxide Semiconductor (MOS) gas sensors react to 

different air quality conditions, highlighting their 

effectiveness in identifying and classifying pollutant levels. 
Figures 13 and Figure 14 show the response curves for bad 

air quality samples, where sensors (TGS2600, TGS2620, and 

TGS2611) react strongly to high concentrations of CO₂ and 

CO. The voltage rises rapidly, reaching between 3V and 5V 

within the first 10 seconds after flue gas is introduced into the 

chamber. TGS2600 and TGS2620 maintain steady 

performance throughout the sampling period. However, 

TGS2611 shows less reliable behavior after 20 seconds, with 

its voltage dropping to a range of 2.5V to 4V by 120 seconds. 

This decline is likely due to changes in air saturation and 

temperature variations, which impact pollutant levels and 

sensor accuracy. 

 

 

Buck Converter 

IC 

Input 

Voltage (V) 

Time (s) Output 

Voltage (V) 

3A Current 

LM2576 

12 - 5.07 

500mA Current 

LM2574 

12 - 5.05 

1A Current 

LM2575 

12 0-19 4.02 

20-29 4.11 

30-39 4.21 

40-49 4.31 

50-59 4.41 

60-69 4.50 

70-79 4.60 

80-89 4.74 

90-99 4.82 

100-109 4.94 

110-119 5.03 

120-129 4.02 



ECQ. Heng et al. /Future Sustainability                                                                               November 2025| Volume 03 | Issue 04 | Pages 32-46 

39 

 

  
 

 

 

 

 

 

 

 

 

For good air quality samples, as shown in Figure 15, the 

sensors maintain stable voltage levels below 2.5V, 

reflecting low concentrations of CO₂ and CO in the clean 

air. This steady performance indicates the sensors’ 

reliability in detecting minimal pollutant levels. In Figure 

16, moderate air quality samples also exhibit steady 

voltage responses, like good air quality. The voltages 

remain below 2.5V, placing all moderate samples in the 

clean air category. A clear distinction emerges when 

comparing bad air samples to good and moderate ones. 

Bad air samples produce significantly higher voltage 

responses due to the higher concentrations of CO₂ and 

CO, while good and moderate samples yield much lower 

and more stable outputs.  

 

 

 

 

 

 

 

 

 

 

 

These findings confirm the sensors’ ability to reliably 

differentiate between varying air quality conditions, 

providing consistent and accurate responses for classifying 

pollutants. 

3.4 Sensor accuracy analysis 

Table 6 presents the accuracy of four gas sensors: 

TGS2600, TGS2602, TGS2620, and TGS2611, in detecting 

gases at various time intervals ranging from 25 to 115 

seconds. The results indicate that optimal performance 

occurred between 45 and 65 seconds, where all sensors 

achieved their highest accuracy levels. TGS2602 recorded the 

highest accuracy of 65.33% at both 45 and 65 seconds, 

followed closely by TGS2600, which peaked at 62.67% at 55 

and 65 seconds.  

Figure 13. Shape of sensor response curve showing instability and voltage drop over time in bad air quality (sample 2) 

Figure 14. Shape of sensor response curve showing instability and voltage drop over time in bad air quality (sample 8) 



ECQ. Heng et al. /Future Sustainability                                                                               November 2025| Volume 03 | Issue 04 | Pages 32-46 

40 

 

 

 

 

 

 

TGS2620 reached its maximum of 60.00% at 65 seconds, 

while TGS2611 performed best at 45 seconds with an 

accuracy of 58.67%. After 75 seconds, the accuracy of most 

sensors either declined or stabilized, with TGS2602 notably 

dropping to a consistent 52.00% from 85 seconds onward. 

These findings suggest that the 45- to 65-second window is 

the most effective period for capturing accurate gas detection 

data, underscoring the importance of timing in optimizing 

sensor performance. Table 7 presents the refined analysis of 

gas sensor accuracy for four sensor models: TGS2600, 

TGS2602, TGS2620, and TGS2611, measured across time 

intervals from 25 to 115 seconds. The results show a notable 

improvement in detection performance compared to the 

initial analysis. TGS2600 maintained a high and consistent 

accuracy of 92.86% from 25 to 55 seconds before stabilizing 

at 91.43% for the remainder of the test.  

 

 

 

 

 

 

TGS2602 improved to 91.43% from 45 seconds onward, 

while TGS2620 demonstrated consistent accuracy, peaking at 

92.86% at 25 seconds and maintaining 91.43% throughout 

most subsequent intervals. TGS2611 maintained an accuracy 

of 91.43% across nearly all time points, with a slight increase 

to 92.86% at 105 seconds. Overall, all four sensors exhibited 

strong and stable performance with minimal fluctuation after 

45 seconds, indicating improved system stability and sensor 

reliability under refined testing conditions. At 25 seconds, 

TGS2600 and TGS2620 recorded the highest accuracy of 

92.86%, effectively detecting and classifying air quality as 

good or bad. TGS2602 and TGS2611 followed closely with 

accuracies of 90.00% and 91.43%, respectively. Over time, all 

sensors stabilized at 91.43%, demonstrating reliable and 

consistent detection capability. These findings confirm the 

sensors' ability to accurately differentiate between air quality 

conditions. TGS2600 and TGS2620 showed superior early 

Figure 15. Shape of sensor response curve showing stable voltage over time in good air quality (sample 24) 

Figure 16. Shape of sensor response curve showing stable voltage over time in moderate air quality (sample 56) 



ECQ. Heng et al. /Future Sustainability                                                                               November 2025| Volume 03 | Issue 04 | Pages 32-46 

41 

 

detection performance, making them particularly well-suited 

for both short-term and long-term air quality monitoring in 

real-world applications. 

Table 6. The accuracy of the gas sensors in correctly detecting gases 

(initial analysis) 

 TGS2600 TGS2602 TGS2620 TGS2611 

Time 

(s) 

Accuracy (%) 

25 53.33 60.00 50.67 52.00 

35 50.67 61.33 48.00 49.33 

45 54.67 65.33 54.67 58.67 

55 62.67 64.00 57.33 56.00 

65 62.67 65.33 60.00 52.00 

75 58.67 53.33 58.67 49.33 

85 56.00 53.33 54.67 52.00 

95 54.67 52.00 54.67 54.67 

105 57.33 52.00 56.00 54.67 

115 61.33 52.00 56.00 52.00 

 

Table 7. The accuracy of gas sensors in correctly detecting gases 

(refined analysis) 

 TGS2600 TGS2602 TGS2620 TGS2611 

Time 

(s) 

Accuracy (%) 

25 92.86 90.00 92.86 91.43 

35 92.86 90.00 91.43 91.43 

45 92.86 91.43 91.43 91.43 

55 92.86 91.43 91.43 91.43 

65 91.43 91.43 91.43 91.43 

75 91.43 91.43 91.43 91.43 

85 91.43 91.43 91.43 91.43 

95 91.43 91.43 91.43 91.43 

105 91.43 91.43 91.43 92.86 

115 91.43 91.43 90.00 91.43 

 

The sensor response to different air quality samples 

categorized as "bad," "good," and "moderate" is illustrated in 

Figure 13, Figure 14, Figure 15, and Figure 16, respectively. 

These graphs correspond to Sample 2, Sample 8, Sample 24, 

and Sample 56. Each graph plots the sensor output voltage 

over time (in seconds) for the four Metal Oxide 

Semiconductor (MOS) sensors used in the study. For the bad 

air sample (Sample 2 & 8), the sensor output voltage rises 

sharply and stabilizes at high levels, reflecting strong 

detection of high pollutant concentrations typical of poor air 

quality. For the good air sample (Sample 24), voltage readings 

are significantly lower, stabilizing at low levels, indicating 

reduced pollutants and cleaner air. For the moderate air 

sample (Sample 56), voltage readings fall between the bad 

and good samples, showing a moderate rise and stabilization, 

effectively capturing intermediate air quality conditions. 

These results confirm the sensors’ ability to differentiate 

between varying pollution levels. 

 

3.5 Classification results 

The best-performing classification models and their 

corresponding validation and classification accuracies are 

summarized in Table 8. The table highlights the top-

performing classification models: Bagged Trees, Tri-layer 

Neural Network, SVM Kernel, and Logistic Regression Kernel. 

All models achieved a classification accuracy of 100%, 

correctly classifying all training data without 

misclassification. For validation accuracy, each model 

recorded 93.33%, indicating strong generalization and 

consistent reliability when applied to unseen data. The 

confusion matrices for the four best-performing models 

(Bagged Trees, Tri-layer Neural Network, SVM Kernel, and 

Logistic Regression Kernel) are shown in Table 9. These 

matrices provide a detailed view of each model’s ability to 

classify air quality samples into “Bad,” “Good,” and 

“Moderate” categories. Among the models, Bagged Trees 

demonstrated the best performance, with minimal 

misclassifications. Specifically, only one “Good” sample was 

classified as “Moderate,” and one “Moderate” sample was 

classified as “Good,” reflecting high reliability and robustness. 

The Tri-layer Neural Network also achieved strong results, 

but it exhibited slightly more errors, misclassifying three 

“Bad” samples as “Good” and one “Moderate” sample as 

“Good,” suggesting reduced sensitivity in identifying poor air 

quality. The SVM Kernel model performed consistently, with 

balanced classification across categories. However, it 

struggled with borderline cases, misclassifying two “Bad” 

samples as “Moderate” and one “Good” sample as “Moderate.” 

Logistic Regression Kernel showed the highest 

misclassification rates, with two “Bad” samples classified as 

“Good” and one “Moderate” sample classified as “Good,” 

indicating reduced robustness when differentiating between 

similar air quality categories. Despite all models achieving 

100% classification accuracy during training, their validation 

accuracy – 93.33% for each model – reveals differences in 

their ability to generalize to unseen data. Bagged Trees stood 

out as the most reliable model for distinguishing air quality 

levels, followed closely by the Tri-layer Neural Network and 

SVM Kernel. Logistic Regression Kernel, while effective, 

exhibited more challenges in handling nuanced distinctions 

between air quality categories. These results confirm Bagged 

Trees as the most effective and robust model for this 

classification task. 

Table 8. Best performing classification models 

 

 

 

Types of 

Classification 

Models 

Best Performing Models 

Validation 

accuracy (%) 

Classification 

accuracy (%) 

Bagged Trees 93.33 100 

Trilayer Neural 

Network 
93.33 100 

SVM Kernel 93.33 100 

Logistic Regression 

Kernel 
93.33 100 



ECQ. Heng et al. /Future Sustainability                                                                               November 2025| Volume 03 | Issue 04 | Pages 32-46 

42 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 9. Confusion matrices of best-performing models 

Confusion Matrix 

Bagged Trees 

 

SVM Kernel 

 

Tri-layer Neural Network 

 

Logistic Regression Kernel 

 
 

Table 1. CO2 Concentrations at Kampung Haji Baki 

 Collection Points 

Kampung A Kampung B Kampung C Kampung D 

CO2 concentrations 

(ppm) 

4525 3735 3100 1930 

4045 3670 3155 1885 

4430 3880 1880 1745 

4310 4115 1835 1580 

4270 3845 1855 1359 

Average (ppm) 4316 3849 2365 1699.8 

 

 

 

 

 



ECQ. Heng et al. /Future Sustainability                                                                               November 2025| Volume 03 | Issue 04 | Pages 32-46 

43 

 

3.6 Linear regression results 

For air samples with CO₂ concentrations exceeding the 

5000 ppm detection limit of standard sensors (e.g., car 

exhaust), measurements were capped at 5000 ppm for 

regression modelling. This simplifies analysis by 

standardizing maximum values while acknowledging the 

sensor’s limitations. Although variability above 5000 ppm is 

not captured, this approach ensures meaningful insights 

without introducing bias from non-detectable values. The 

regression analysis was performed to quantify CO₂ 

concentration levels (in ppm) across different collection 

points using sensor output voltages. The analysis focused on 

two key locations: Kampung Haji Baki and the Malaysia-China 

Friendship Park, with the results summarized in Table 10 and 

Table 11, respectively. Table 10 shows CO₂ concentrations at 

Kampung Haji Baki, categorized as “good air,” with averages 

ranging from 1,699.8 ppm to 4,316 ppm, reflecting low 

pollutant levels. Table 11 presents CO₂ concentrations at 

Malaysia-China Friendship Park, categorized as “moderate 

air,” with averages between 459 ppm and 565 ppm, indicating 

slightly higher pollutant levels. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The sensor output voltage was plotted against 

commercial PPM values across 10 time intervals (25s to 

115s). The R-squared values, indicating the goodness of fit for 

the linear trendline, were calculated for each sensor 

(TGS2600, TGS2602, TGS2620, TGS2611). Higher R-squared 

values (closer to 1) signify better consistency and reliability 

of the data. Outliers were removed by identifying significant 

deviations from mean values and overlapping points to 

prevent skewed regression analysis. This process improved 

regression accuracy, increased R-squared values, and 

enhanced the predictive reliability of the models. As shown in 

Table 12, outlier removal significantly improved the R-

squared value, indicating better consistency in the data. For 

further analysis, three intervals – 25s, 45s, and 75s – were 

selected due to their relatively high R-squared values. These 

intervals demonstrated strong linear relationships across all 

sensors, ensuring robust and reliable trends for interpreting 

results and discussion. TGS2602 sensor consistently achieved 

the highest R-squared values across the selected time 

intervals (25s, 45s, 75s), as shown in Table 13.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 11. CO2 concentrations at Malaysia–China Friendship Park 

 Collection Points 

China A China B China C China D 

CO2 concentrations 

(ppm) 

440 595 410 470 

430 575 410 505 

420 550 680 525 

455 555 465 545 

550 550 415 540 

Average (ppm) 459 565 476 517 

 

 

Table 12. Comparison of Vout vs. commercial (ppm) at 25-th second interval for TGS2602 sensor 

Before Outlier Removal: After Outlier Removal: 

  
 



ECQ. Heng et al. /Future Sustainability                                                                               November 2025| Volume 03 | Issue 04 | Pages 32-46 

44 

 

These intervals exhibit the strongest linear relationships 

between Vout and commercial PPM values, establishing 

TGS2602 as the most reliable sensor for regression analysis 

in this study. Furthermore, while commercial CO₂ sensors are 

limited to a maximum concentration of 5000 ppm, the MOS 

gas sensors used in this study demonstrate the capability to 

measure beyond this range, highlighting their extended 

detection potential. For moderate and bad samples, TGS2602 

and TGS2620 recorded the highest R-squared values among 

the four sensors at the selected time intervals (25s, 45s, 75s), 

as shown in Table 14. To highlight the most reliable data, this 

paper presents the Vout vs. PPM plots for TGS2620 at the 25s 

interval and TGS2602 at the 45s and 75s intervals. These 

intervals demonstrate the strongest linear relationships, 

making them ideal for regression analysis. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Referring to Table 10 and Table 11, CO₂ concentrations 

differ between "good air" and "moderate air" quality levels. At 

Kampung Haji Baki (Table 10), average CO₂ concentrations 

range from 1699.8 ppm to 4316 ppm, categorized as good air. 

In contrast, Malaysia-China Friendship Park (Table 11) shows 

averages from 459 ppm to 565 ppm, reflecting moderate air 

quality. These variations highlight the impact of location on 

CO₂ levels. Forests like Kampung Haji Baki have higher CO₂ 

due to soil respiration, microbial activity, and restricted 

airflow from dense vegetation. Urban parks, such as Malaysia-

China Friendship Park, benefit from photosynthesis, better 

airflow, and aquatic CO₂ absorption, resulting in lower CO₂ 

levels (400–700 ppm). 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 13. Plot of Vout vs. commercial (ppm) for good and bad samples 

at 25s, 45s and 75s 

Time 

interval 
Plot of Vout vs Commercial (ppm) 

25s 

 

45s 

 

75s 

 

 

 

Table 14. Plot of Vout vs. commercial (ppm) for moderate and bad 

samples at 25s, 45s and 75s 

Time 

interval 
Plot of Vout vs Commercial (ppm) 

25s 

 

45s 

 

75s 

 

 

 



ECQ. Heng et al. /Future Sustainability                                                                               November 2025| Volume 03 | Issue 04 | Pages 32-46 

45 

 

4. Conclusions 

This research demonstrated the development and 

effectiveness of an advanced eNose system, equipped with 

Metal Oxide Semiconductor (MOS) sensors, for real-time air 

quality and flue gas monitoring. By addressing key limitations 

of traditional gas sensing systems, the study introduced a 

more efficient, accurate, and cost-effective solution for 

detecting pollutants such as carbon monoxide (CO) and 

carbon dioxide (CO₂). Key advancements included precise 

temperature control, improved sensor chamber design, and 

optimized sampling methods, all of which enhanced the 

performance and stability of the MOS sensors. The integration 

of automatic digital buck converters significantly reduced the 

sampling time from 390 seconds to just 130 seconds, while 

maintaining consistent and reliable operation. The 

redesigned sensor chamber ensured improved airflow, 

maintained airtight conditions, and minimized external 

interference, resulting in more accurate gas detection. The 

system successfully quantified pollutant concentrations and 

classified air quality into "good," "moderate," and "bad" 

categories with over 91% classification accuracy. Machine 

learning models, such as Bagged Trees and SVM Kernel, 

further improved classification reliability, while linear 

regression models demonstrated strong correlations, 

confirming the accuracy of sensor outputs. Analysis of sensor 

behavior revealed that poor air quality was characterized by 

rapid voltage increases (3V–5V), while good to moderate air 

conditions showed stable voltages below 2.5V. Notably, 45-

second sampling intervals provided an optimal balance 

between sensor response stability and classification accuracy. 

Additionally, outlier removal significantly improved data 

quality and model performance. Effective strategies, such as 

the use of Tedlar bags for sample preservation and automated 

voltage adjustments, enhanced the consistency of air sample 

collection. Looking ahead, future research should explore the 

eNose’s performance under varied environmental conditions, 

including fluctuations in temperature and humidity, to ensure 

greater robustness. Refining sampling methodologies for 

moderate and high-pollution scenarios, incorporating 

advanced sensors with extended detection ranges, and 

conducting long-term field deployments will further align the 

system with conventional air monitoring standards. In 

conclusion, the eNose system represents a scalable, portable, 

and reliable tool for environmental monitoring and industrial 

fault detection. Its high efficiency, adaptability, and real-time 

capabilities make it a valuable asset for modern air pollution 

management strategies. 

Acknowledgements 
The author would like to express sincere gratitude to Ir. 
A/Prof. Dr. Chua Hong Siang and Dr. Lee Hui En for their 
exceptional mentorship, guidance, and unwavering support 
throughout the project. Their expertise and insightful 
feedback were instrumental in shaping the research and 
enhancing its overall quality. Appreciation is also extended to 
Swinburne University of Technology Sarawak for providing 
the essential resources—funding, facilities, and software—
that made this project possible. 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 

manipulation of figures, competing interests, and compliance 
with policies on research ethics. The authors adhere to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 

be available upon request from the corresponding author. 

Conflict of interest 

The authors declare no potential conflict of interest. 

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