Detection and identification of odorants using an electronic nose

Purpose – The purpose of this study satisfied the need for rapid, sensitive and highly portable identification of an explosion gas. In our study, a battery-operated, low-cost and portable gas detection system consisting of a cataluminescence-based sensor array was developed for the detection and identification of explosion gas. This device shows how the discriminatory capacity of sensor arrays utilizing pattern recognition operate in environments. Design/methodology/approach – A total of 25 sensor units, including common metal oxides and decorated materials, have been carefully selected as sensing elements of 5 × 5 sensor array. Dynamic and static analysis methods were utilized to characterize the performance of the explosion gas detection system to five kinds of explosion gases. The device collects images of chemical sensors before and after exposing to the target gas and then processes those images to extract the unique characteristic for each gas. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used to analyze the image patterns. Findings – Our study demonstrated that the portable gas detection device shows promising perspective for the recognition and discrimination of explosion gas. It can be used for the olfactory system of robot made by integrating the electronic nose and computer together. Originality/value – The device collects images of chemical sensors before and after exposing to the target gas and then processes those images to extract the unique characteristic for each gas. HCA and (PCA were used to analyze the image patterns. Our study demonstrated that the portable gas detection device shows promising perspective for the recognition and discrimination of explosion gas. It can be used for olfactory system of robot made by integrating the electronic nose and computer together.

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Classification and Identification of Industrial Gases Based on Electronic Nose Technology

Sensors ◽

10.3390/s19225033 ◽

2019 ◽

Vol 19 (22) ◽

pp. 5033 ◽

Cited By ~ 2

Author(s):

Li ◽

Luo ◽

Sun ◽

GholamHosseini

Keyword(s):

Discriminant Analysis ◽

Electronic Nose ◽

Recognition Rate ◽

Principal Component ◽

Kernel Discriminant Analysis ◽

Linear Discriminant ◽

Detection And Identification ◽

Industrial Gases ◽

Gas Identification ◽

The One

Rapid detection and identification of industrial gases is a challenging problem. They have a complex composition and different specifications. This paper presents a method based on the kernel discriminant analysis (KDA) algorithm to identify industrial gases. The smell prints of four typical industrial gases were collected by an electronic nose. The extracted features of the collected gases were employed for gas identification using different classification algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), PCA + LDA, and KDA. In order to obtain better classification results, we reduced the dimensions of the original high-dimensional data, and chose a good classifier. The KDA algorithm provided a high classification accuracy of 100% by selecting the offset of the kernel function c = 10 and the degree of freedom d = 5. It was found that this accuracy was 4.17% higher than the one obtained using PCA. In the case of standard deviation, the KDA algorithm has the highest recognition rate and the least time consumption.

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Identification of hepatitis a virus in cell cultures by solid phase immune electron microscopy

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100142815 ◽

1986 ◽

Vol 44 ◽

pp. 238-239

Author(s):

C.D. Humphrey ◽

T.L. Cromeans ◽

E.H. Cook ◽

D.W. Bradley

Keyword(s):

Electron Microscopy ◽

Liquid Phase ◽

Cell Cultures ◽

Rapid Detection ◽

Hepatitis A ◽

Hepatitis A Virus ◽

Solid Phase ◽

Immune Electron Microscopy ◽

Detection And Identification

There is a variety of methods available for the rapid detection and identification of viruses by electron microscopy as described in several reviews. The predominant techniques are classified as direct electron microscopy (DEM), immune electron microscopy (IEM), liquid phase immune electron microscopy (LPIEM) and solid phase immune electron microscopy (SPIEM). Each technique has inherent strengths and weaknesses. However, in recent years, the most progress for identifying viruses has been realized by the utilization of SPIEM.

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