A Novel Multisensor Detection System Design for Low Concentrations of Volatile Organic Compounds

Qian, Junhui; Tian, Fengchun; Luo, Yu; Lu, Meng-Chen; Zhang, Ailing

doi:10.1109/tie.2021.3080218

Cited by 16 publications

(2 citation statements)

References 47 publications

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“…Another approach for electronic nose measurements of small volumes of odors or odors of weak concentration may be an application of pre-concentration units [ 61 , 62 , 63 , 64 , 65 , 66 , 67 ].…”

Section: Discussionmentioning

confidence: 99%

Development of a Low-Cost Electronic Nose with an Open Sensor Chamber: Application to Detection of Ciboria batschiana

Borowik

Grzywacz

Tarakowski

et al. 2023

Sensors

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In the construction of electronic nose devices, two groups of measurement setups could be distinguished when we take into account the design of electronic nose chambers. The simpler one consists of placing the sensors directly in the environment of the measured gas, which has an important advantage, in that the composition of the gas is not changed as the gas is not diluted. However, that has an important drawback in that it is difficult to clean sensors between measurement cycles. The second, more advanced construction, contains a pneumatic system transporting the gas inside a specially designed sensor chamber. A new design of an electronic nose gas sensor chamber is proposed, which consists of a sensor chamber with a sliding chamber shutter, equipped with a simple pneumatic system for cleaning the air. The proposal combines the advantages of both approaches to the sensor chamber designs. The sensors can be effectively cleared by the flow of clean air, while the measurements are performed in the open state when the sensors are directly exposed to the measured gas. Airflow simulations were performed to confirm the efficiency of clean air transport used for sensors’ cleaning. The demonstrated electronic nose applies eight Figaro Co. MOS TGS series sensors, in which a transient response caused by a change of the exposition to measured gas, and change of heater voltage, was collected. The new electronic nose was tested as applied to the differentiation between the samples of Ciboria batschiana fungi, which is one of the most harmful pathogens of stored acorns. The samples with various coverage, thus various concentrations of the studied odor, were measured. The tested device demonstrated low noise and a good level of repetition of the measurements, with stable results during several hours of repetitive measurements during an experiment lasting five consecutive days. The obtained data allowed complete differentiation between healthy and infected samples.

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Section: Discussionmentioning

confidence: 99%

Development of a Low-Cost Electronic Nose with an Open Sensor Chamber: Application to Detection of Ciboria batschiana

Borowik

Grzywacz

Tarakowski

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The algorithm creates multiple bootstrapped samples from the original dataset with replacement and trains a decision tree for each sample, selecting a random subset of features for each split. The final prediction is obtained by majority voting for classification tasks or averaging for regression tasks ( Qian et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Using machine learning models to predict the quality of plant-based foods

Tachie

Aryee

2023

Current Research in Food Science

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Mixed gas concentration inversion based on the hierarchical feature fusion convolutional neural network

Lü

Zhao

et al. 2023

Optics Communications

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A Novel Multisensor Detection System Design for Low Concentrations of Volatile Organic Compounds

Cited by 16 publications

References 47 publications

Development of a Low-Cost Electronic Nose with an Open Sensor Chamber: Application to Detection of Ciboria batschiana

Development of a Low-Cost Electronic Nose with an Open Sensor Chamber: Application to Detection of Ciboria batschiana

Using machine learning models to predict the quality of plant-based foods

Mixed gas concentration inversion based on the hierarchical feature fusion convolutional neural network

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