A Noise Spectroscopy-Based Features Extraction Method to Detect Two Gases Using One Single MOX Sensor

Gomri, Sami; Bedoui, Souhir; Morati, Nicolas; Fiorido, Tomas; Contaret, T.; Seguin, Jean-Luc; Kachouri, Abdennaceur; Masmoudi, Mohamed

doi:10.1109/jsen.2019.2923699

Cited by 9 publications

(5 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The AC noise that came from the power supply of the measurement equipment were 60 Hz and 120 Hz and they would be shifted from the measurement due to the surface reaction of the molecule and gas sensor. Similarly, Gomri et al [54] analyzed the power spectral density (PSD) of the noise measured at gas sensor terminals during the sensor interaction with target analytes. Features including the derivative and the max power of noise PSD were proved effectively discriminate two different gases.…”

Section: Manual Feature Extractionmentioning

confidence: 99%

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

Liu

2021

Sensors

View full text Add to dashboard Cite

Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.

show abstract

Section: Manual Feature Extractionmentioning

confidence: 99%

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

Liu

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Figure 5. The PDS of the bacterium isolate measured by the Taguchi sensor [30][31][32][33][34][35][36][37] shown by the blue line. For reference spectrum the PDS was measured in laboratory air, shown by the red line.…”

Section: Bitsmentioning

confidence: 99%

Ternary Fingerprints with Reference Odor for Fluctuation-Enhanced Sensing

Kish

Seguin

King

2020

Preprint

Self Cite

View full text Add to dashboard Cite

An improved method for Fluctuation Enhanced Sensing (FES) is introduced. We enhanced the old binary fingerprinting method, where the fingerprint bit values were ± 1, by introducing ternary fingerprints utilizing a reference odor. In the ternary method, the fingerprint bit values are -1, 0, and +1 where the 0 value stands for the situation where the slope of the spectrum is identical to that of the reference odor. The application of the reference odor spectrum makes the fingerprint relative to the reference. This feature increases the information entropy of the fingerprints. The method is briefly illustrated by sensing bacterial odor in cow manure isolates. A= -1 B=1 C= -1 D=1 E=1Bit values:

show abstract

“…The gas sensor array functions as an olfactory epithelium to sense the ambient atmosphere and generate gas-sensitive information. This gas-sensitive information is then filtered by feature extraction to find useful features. , Then, these features are analyzed by machine learning to determine the type and concentration of the gas. It is discovered through the artificial olfaction perception process that increasing the number and type of sensors in the array will improve the performance of artificial olfaction.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Assisted Volatile Organic Compound Gas Classification Based on Polarized Mixed-Potential Gas Sensors

Wang

Zhang

Wang

et al. 2023

ACS Appl. Mater. Interfaces

View full text Add to dashboard Cite

The performance of electrochemical gas sensors depends on the reactions at the three-phase boundary. In this work, a mixed-potential gas sensor containing a counter electrode, a reference electrode, and a sensitive electrode was constructed. By applying a bias voltage to the counter electrode, the three-phase boundary can be polarized. The polarization state of the threephase boundary determined the gas-sensitive performance. Taking 100 ppm ethanol vapor as an example, by regulating the polarization state of the three-phase boundary, the response value of the sensor can be adjusted from −170 to 40 mV, and the sensitivity can be controlled from −126.4 to 42.6 mV/decade. The working temperature of the sensor can be reduced after polarizing the three-phase boundary, lowering the power consumption from 1.14 to 0.625 W. The sensor also showed good stability and short response−recovery time (3 s). Based on this sensor, the Random Forest algorithm reached 99% accuracy in identifying the kind of VOC vapors. This accuracy was made possible by the ability to generate several signals concurrently. The above gas-sensitive performance improvements were due to the polarized three-phase boundary.

show abstract

A Noise Spectroscopy-Based Features Extraction Method to Detect Two Gases Using One Single MOX Sensor

Cited by 9 publications

References 27 publications

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

Ternary Fingerprints with Reference Odor for Fluctuation-Enhanced Sensing

Machine Learning-Assisted Volatile Organic Compound Gas Classification Based on Polarized Mixed-Potential Gas Sensors

Contact Info

Product

Resources

About