Predicting odor mixture's responses on machine olfaction sensors

Phaisangittisagul, Ekachai; Nagle, H. Troy

doi:10.1016/j.snb.2010.12.049

Cited by 24 publications

(18 citation statements)

References 24 publications

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“…Each odorant presented to the sensor array produces a signature or characteristic pattern of the odorant (Baldwin et al, 2011;Huan et al, 2011;Colea et al, 2011;Im et al, 2011 Phaisangittisagul andNagle, 2011).…”

Section: Methodsmentioning

confidence: 99%

Inheritance based Intelligent Technique Employing Nested-XOR with Recursion for Recognition and Classification of Odors using Multi-Sensor Nose System

Iskandarani¹

2011

American Journal of Applied Sciences

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Problem statement: Odor originality and fraud is an important issue in today's world. In addition knowing the original material that an odor belongs to even after being mixed with others is also of vital importance. In addition measuring quality of mixed odors in terms of their affinity and belonging to a specific category or is critical. Approach: Design, testing and analysis of multi-sensor odor system for the objectives of odor recognition, classification and correlation are carried out.Results: Various mixtures are tested and checked for originality through inheritance using The built multi-sensor electronic nose. The testing devices and interpretation using Nested-XOR based algorithm with recursion showed clearly some odors to be related to others or derived from the same source. Conclusion/Recommendations: Clear evidence of ability to trace components of an odor mixture and to determine its originality and quality.

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Section: Methodsmentioning

confidence: 99%

Inheritance based Intelligent Technique Employing Nested-XOR with Recursion for Recognition and Classification of Odors using Multi-Sensor Nose System

Iskandarani¹

2011

American Journal of Applied Sciences

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“…16 Details of operation of the odor recorder can be found elsewhere. 15,17 Using the above method, apple flavor was reconstructed. 16 The recipe contains basic components, including butyl isobutyrate, 1-butanol, 1-hexanol, propionic acid, and butyl propionate.…”

Section: Active Odor Sensingmentioning

confidence: 99%

“…17 The sniffer is designed to digitize the smell to preserve its informative features (the odor recipe) of the original odor input. 17 In this manner an e-nose can be used as the sniffer.…”

Section: Odor Digitizationmentioning

confidence: 99%

“…17 The sniffer is designed to digitize the smell to preserve its informative features (the odor recipe) of the original odor input. 17 In this manner an e-nose can be used as the sniffer. 17 Once the odor recipe of the target odor is determined, it can be transmitted through the network and then be reproduced anytime using the odor blender contained in the whiffer.…”

Section: Odor Digitizationmentioning

confidence: 99%

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Methods of Testing in Odor Analysis

Wypych¹

2013

Handbook of Odors in Materials

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“…Electronic nose (EN) has been rapidly developed as a novel gas detection technology in the past two decades. It is widely implicated in toxic/harmful gas detection [1], food safety [2], environmental monitoring [3], medical industry [4], and other fields [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations

Sun

Luo

et al. 2018

Journal of Electrical and Computer Engineering

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Gas sensors have been widely reported for industrial gas detection and monitoring. However, the rapid detection and identification of industrial gases are still a challenge. In this work, we measure four typical industrial gases including CO 2 , CH 4 , NH 3 , and volatile organic compounds (VOCs) based on electronic nose (EN) at different concentrations. To solve the problem of effective classification and identification of different industrial gases, we propose an algorithm based on the selective local linear embedding (SLLE) to reduce the dimensionality and extract the features of high-dimensional data. Combining the Euclidean distance (ED) formula with the proposed algorithm, we can achieve better classification and identification of four kinds of gases. We compared the classification and recognition results of classical principal component analysis (PCA), linear discriminate analysis (LDA), and PCA + LDA algorithms with the proposed SLLE algorithm after selecting the original data and performing feature extraction. The experimental results show that the recognition accuracy rate of the SLLE reaches 91.36%, which is better than the other three algorithms. In addition, the SLLE algorithm provides more efficient and accurate responses to high-dimensional industrial gas data. It can be used in real-time industrial gas detection and monitoring combined with gas sensor networks.

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Predicting odor mixture's responses on machine olfaction sensors

Cited by 24 publications

References 24 publications

Inheritance based Intelligent Technique Employing Nested-XOR with Recursion for Recognition and Classification of Odors using Multi-Sensor Nose System

Inheritance based Intelligent Technique Employing Nested-XOR with Recursion for Recognition and Classification of Odors using Multi-Sensor Nose System

Methods of Testing in Odor Analysis

Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations

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