Suppression of Strong Background Interference on E-Nose Sensors in an Open Country Environment

Tian, Fengchun; Zhang, Jian; Yang, Simon X.; Zhao, Zhenzhen; Liang, Zhenlin; Liu, Yan; Wang, Di

doi:10.3390/s16020233

Cited by 19 publications

(11 citation statements)

References 20 publications

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“…However, gas response as well as baseline sensor resistance depend on ambient temperature and humidity. [531,532] For reliable electronic noses, the temperature and humidity dependence of gas sensing characteristics should be reduced to a negligible level.…”

Section: Reliable Sensing Materials For Artificial Olfactionmentioning

confidence: 99%

Rational Design of Semiconductor‐Based Chemiresistors and their Libraries for Next‐Generation Artificial Olfaction

2020

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With rapid progress in device integration and computing power, the realization of highly miniaturized one-chip artificial olfaction with superior performance is close and should be further supported by the rational design of chemical sensors and chemical sensing materials. The number of gas sensors tends to increase in order to sniff more complex odors with the progress of civilization. This explains the evolution from a single chemical sensor to complicated artificial olfaction using sensor arrays. At the advent of oxide chemiresistive gas sensors in the 1960s and 1970s, a single gas sensor was widely used to detect toxic, explosive, dangerous, and harmful gases, such as CO, C 3 H 8 , CH 4 , H 2 S, and NO 2 , in a selective manner. [26,27] However, a single sensor is often insufficient for recognizing most of the natural odors encountered in daily life, which consist of hundreds to thousands of different chemicals. [28] In the mammalian olfactory system, odorants are initially detected by olfactory receptors (ORs) covering the surface of the cilia projected from olfactory sensory neurons (OSNs), which are located in the olfactory epithelium lining the nasal cavity. These signals are transmitted to the glomeruli in the olfactory bulb, where a high degree of signal integration happens (Figure 1a). [29] Finally, the signals are sent through mitral cells to a higher brain region (olfactory cortex), and the signals are subsequently combined or modified in a variety of ways by parallel processing for analysis and interpretation (Figure 1b). [30] Each OR can detect various kinds of odorants, and similarly, each odorant can also be recognized by a number of ORs. That is, the olfactory system determines multiple odorants from various combinations of ORs (Figure 1c). [31] For better olfaction, both OSNs and ORs should be abundant. A larger number of OSNs is advantageous for detecting trace concentrations of analytes and ligands. [32] For instance, dogs with more OSNs are better than humans at detecting explosives, searching and rescuing, diagnosing disease, and assessing agricultural products. [33] If a mammal can detect larger numbers of faint gas components within odors, he can perceive and identify the odors more accurately. From this perspective, gas sensors with lower detection limits would be assembled to the stronger artificial olfaction. Kinds of ORs are also important. Mammals are known to have ≈1000 different kinds of ORs, and combinations of dissimilar ORs enable the discrimination of a myriad of odorants. [29] To mimic this, sensor arrays consisting of small number (5-20) of sensors that show Artificial olfaction based on gas sensor arrays aims to substitute for, support, and surpass human olfaction. Like mammalian olfaction, a larger number of sensors and more signal processing are crucial for strengthening artificial olfaction. Due to rapid progress in computing capabilities and machinelearning algorithms, on-demand high-performance artificial olfaction that can eclipse human olfaction becomes inevitable onc...

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Section: Reliable Sensing Materials For Artificial Olfactionmentioning

confidence: 99%

Rational Design of Semiconductor‐Based Chemiresistors and their Libraries for Next‐Generation Artificial Olfaction

2020

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“…In order to improve the precision of automatic control, it is necessary to separate and suppress the environmental interference. PCA and ICA were used to achieve this goal, and combined with some a priori knowledge, two low-pass filters were used to suppress the white noise and background interference (the smell caused by burning coal) [ 28 ]. The steps of the proposed method were as follows: firstly, a low-pass filter was used to filter out the white noise; secondly, PCA was used to reduce dimension and noise; thirdly, the outputs of PCA were used as inputs of ICA; finally, the second low-pass filtering was used to filter the outputs of ICA.…”

Section: Methods For Suppressing the Interference Caused By Changementioning

confidence: 99%

Study on Interference Suppression Algorithms for Electronic Noses: A Review

Liang

Tian

Yang

et al. 2018

Sensors

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Electronic noses (e-nose) are composed of an appropriate pattern recognition system and a gas sensor array with a certain degree of specificity and broad spectrum characteristics. The gas sensors have their own shortcomings of being highly sensitive to interferences which has an impact on the detection of target gases. When there are interferences, the performance of the e-nose will deteriorate. Therefore, it is urgent to study interference suppression techniques for e-noses. This paper summarizes the sources of interferences and reviews the advances made in recent years in interference suppression for e-noses. According to the factors which cause interference, interferences can be classified into two types: interference caused by changes of operating conditions and interference caused by hardware failures. The existing suppression methods were summarized and analyzed from these two aspects. Since the interferences of e-noses are uncertain and unstable, it can be found that some nonlinear methods have good effects for interference suppression, such as methods based on transfer learning, adaptive methods, etc.

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“…Despite their advantages, those E-noses still have limitations when they are applied on a bus such as being affected by interference in complex environments. As an interference, the temperature could affect the response values of MOS sensors [24] which can be overcome in two common ways: constant temperature control [23,25] and temperature compensation [26][27][28][29][30]. As far as we know, no electronic nose has been reported for detecting flammable liquids on buses.…”

Section: Introductionmentioning

confidence: 99%

Development of Electronic Nose for Qualitative and Quantitative Monitoring of Volatile Flammable Liquids

Wang

Zheng

et al. 2020

Sensors

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A real-time electric nose (E-nose) with a metal oxide sensor (MOS) array was developed to monitor 5 highly flammable liquids (ethanol, tetrahydrofuran, turpentine, lacquer thinner, and gasoline) in this work. We found that temperature had a significant impact on the test results and temperature control could efficiently improve the performance of our E-nose. The results of our qualitative analysis showed that principal component analysis (PCA) could not efficiently distinguish these samples compared to a back-propagation artificial neural network (BP-ANN) which had a 100% accuracy rate on the test samples. Quantitative analysis was performed by regression analysis and the average errors were 9.1%–18.4%. In addition, through anti-interference training, the E-nose could filter out the potential false alarm caused by mosquito repellent, perfume and hair jelly.

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Suppression of Strong Background Interference on E-Nose Sensors in an Open Country Environment

Cited by 19 publications

References 20 publications

Rational Design of Semiconductor‐Based Chemiresistors and their Libraries for Next‐Generation Artificial Olfaction

Rational Design of Semiconductor‐Based Chemiresistors and their Libraries for Next‐Generation Artificial Olfaction

Study on Interference Suppression Algorithms for Electronic Noses: A Review

Development of Electronic Nose for Qualitative and Quantitative Monitoring of Volatile Flammable Liquids

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