A Research on Detection and Identification of Harmful Gas Utilizing Cataluminescence-Based Sensor Array

2013 ◽  
Vol 288 ◽  
pp. 109-113
Author(s):  
Li Shan Zhou
Keyword(s):  
Metal Oxides ◽  
Static Analysis ◽  
Sensor Array ◽  
Working Temperature ◽  
Analysis Methods ◽  
Detection And Identification ◽  
Nanosized Catalysts ◽  
Promising Perspective

A novel cataluminescence(CTL)-based sensor array consisting of 9 types of catalytic nanomaterials was developed for the determination and identification of harmful gas. The sensing nanomaterials, including nano-sized metal oxides, carbonates and decorated nanoparticles, have been selected carefully. A 3 x 3 array was integrated by depositing these nanosized catalysts onto the ceramic chip. Dynamic and static analysis methods were utilized to characterize the performance of the sensor array to 4 kinds of harmful gas. Each compound gives its unique CTL pattern after interact with the sensor array, which can be employed to recognize ether, acetone, chloroform, and toluene. PCA was conducted to classify the harmful gas and the plots showed that the groups were well classified. In addition, the patterns obtained at different working temperature and the analytical characteristics of array were investigated. The CTL-based sensor array shows promising perspective for the recognition and discrimination of harmful gas.

A portable embedded explosion gas detection and identification device based on intelligent electronic nose system

Sensor Review ◽  
2016 ◽  
Vol 36 (1) ◽  
pp. 57-63 ◽  
Author(s):  
Gu Gong ◽  
Hua Zhu
Keyword(s):  
Electronic Nose ◽  
Chemical Sensors ◽  
Olfactory System ◽  
Sensor Array ◽  
Detection System ◽  
Gas Detection ◽  
Content Type ◽  
Detection And Identification ◽  
Before And After ◽  
Promising Perspective

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.

Identification of accelerants, fuels and post-combustion residues using a colorimetric sensor array

The Analyst ◽  
2015 ◽  
Vol 140 (17) ◽  
pp. 5929-5935 ◽  
Author(s):  
Zheng Li ◽  
Minseok Jang ◽  
Jon R. Askim ◽  
Kenneth S. Suslick
Keyword(s):  
Sensor Array ◽  
Colorimetric Sensor ◽  
Colorimetric Sensor Array ◽  
Detection And Identification ◽  
Combustion Residues

A linear (1 × 36) colorimetric sensor array has been integrated with a pre-oxidation technique for detection and identification of a variety of fuels and post-combustion residues.

A Hyperspectral Library of Foliar Diseases of Wheat

2021 ◽  
Author(s):  
David Bohnenkamp ◽  
Jan Behmann ◽  
Stefan Paulus ◽  
Ulrike Steiner ◽  
Anne-Katrin Mahlein
Keyword(s):  
Developmental Stages ◽  
Leaf Tissue ◽  
Plant Diseases ◽  
Identification System ◽  
Spectral Library ◽  
Foliar Diseases ◽  
Robust Detection ◽  
Analysis Methods ◽  
Detection And Identification ◽  
Biotrophic Fungi

This work established a hyperspectral library of important foliar diseases of wheat in time series to detect spectral changes from infection to symptom appearance induced by different pathogens. The data was generated under controlled conditions at the leaf-scale. The transition from healthy to diseased leaf tissue was assessed, spectral shifts were identified and used in combination with histological investigations to define developmental stages in pathogenesis for each disease. The spectral signatures of each plant disease that are indicative of a certain developmental stage during pathogenesis - defined as turning points - were combined into a spectral library. Different machine learning analysis methods were applied and compared to test the potential of this library for the detection and quantification of foliar diseases in hyperspectral images. All evaluated classifiers provided a high accuracy for the detection and identification for both the biotrophic fungi and the necrotrophic fungi of up to 99%. The potential of applying spectral analysis methods, in combination with a spectral library for the detection and identification of plant diseases is demonstrated. Further evaluation and development of these algorithms should contribute to a robust detection and identification system for plant diseases at different developmental stages and the promotion and development of site-specific management techniques of plant diseases under field conditions.

A New Dynamic Protocol Analysis Model

2013 ◽  
Vol 765-767 ◽  
pp. 1761-1765
Author(s):  
Fu Lin Li ◽  
Jie Yang ◽  
Hong Wei Zhou ◽  
Ying Liu
Keyword(s):  
Static Analysis ◽  
Theorem Proving ◽  
Protocol Analysis ◽  
Experimental Results ◽  
Analysis Model ◽  
Formal Validation ◽  
Analysis Methods

Traditional static analysis methods such as formal validation and theorem proving were used to analyze protocols security previously. These methods can not measure and evaluate actual security of protocols accurately for the setting and suppose are far from the actual conditions. This paper proposes a new dynamic protocol analysis model. The system based on the model can be used to active test in actual running conditions, analyze known protocols security, integrity, robustness, and analyze unknown protocols online, provide support for protocol designer. The systems structure, working flow and implementation of key modules are described. The experimental results validate the validity of the models design.

scholarly journals Performance Analysis of MAU-9 Electronic-Nose MOS Sensor Array Components and ANN Classification Methods for Discrimination of Herb and Fruit Essential Oils

Chemosensors ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 243
Author(s):  
Mansour Rasekh ◽  
Hamed Karami ◽  
Alphus Dan Wilson ◽  
Marek Gancarz
Keyword(s):  
Performance Analysis ◽  
Essential Oils ◽  
Electronic Nose ◽  
Sensor Array ◽  
Oxide Semiconductor ◽  
Regulatory Quality ◽  
Analysis Methods ◽  
Artificial Neural Network Ann ◽  
Mos Sensor ◽  
Ann Classification

The recent development of MAU-9 electronic sensory methods, based on artificial olfaction detection of volatile emissions using an experimental metal oxide semiconductor (MOS)-type electronic-nose (e-nose) device, have provided novel means for the effective discovery of adulterated and counterfeit essential oil-based plant products sold in worldwide commercial markets. These new methods have the potential of facilitating enforcement of regulatory quality assurance (QA) for authentication of plant product genuineness and quality through rapid evaluation by volatile (aroma) emissions. The MAU-9 e-nose system was further evaluated using performance-analysis methods to determine ways for improving on overall system operation and effectiveness in discriminating and classifying volatile essential oils derived from fruit and herbal edible plants. Individual MOS-sensor components in the e-nose sensor array were performance tested for their effectiveness in contributing to discriminations of volatile organic compounds (VOCs) analyzed in headspace from purified essential oils using artificial neural network (ANN) classification. Two additional statistical data-analysis methods, including principal regression (PR) and partial least squares (PLS), were also compared. All statistical methods tested effectively classified essential oils with high accuracy. Aroma classification with PLS method using 2 optimal MOS sensors yielded much higher accuracy than using all nine sensors. The accuracy of 2-group and 6-group classifications of essentials oils by ANN was 100% and 98.9%, respectively.

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