Identification of Chemical Vapor Mixture Assisted by Artificially Extended Database for Environmental Monitoring

Title
Identification of Chemical Vapor Mixture Assisted by Artificially Extended Database for Environmental Monitoring
Authors
우덕하이석강종윤이택진김철기김가영신범주이동근정영모문희규김수연
Keywords
chemiresistive sensor array; identification of gas mixture; machine learning
Issue Date
2022-02
Publisher
Sensors
Citation
VOL 22, NO 1169-13
Abstract
A fully integrated sensor array assisted by pattern recognition algorithm has been a primary candidate for the assessment of complex vapor mixtures based on their chemical fingerprints. Diverse prototypes of electronic nose systems consisting of a multisensory device and a post processing engine have been developed. However, their precision and validity in recognizing chemical vapors are often limited by the collected database and applied classifiers. Here, we present a novel way of preparing the database and distinguishing chemical vapor mixtures with small data acquisition for chemical vapors and their mixtures of interest. The database for individual vapor analytes is expanded and the one for their mixtures is prepared in the first­order approximation. Recognition of individual target vapors of NO2, HCHO, and NH3 and their mixtures was evaluated by applying the support vector machine (SVM) classifier in different conditions of temperature and humidity. The suggested method demonstrated the recognition accuracy of 95.24%. The suggested method can pave a way to analyze gas mixtures in a variety of industrial and safety applications.
URI
https://pubs.kist.re.kr/handle/201004/74709
ISSN
1424-8220
Appears in Collections:
KIST Publication > Article
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE