Hi Gyu Moon Youngmo Jung Beomju Shin Donggeun Lee Kayoung Kim Deok Ha Woo Seok Lee Sooyeon Kim Chong-Yun Kang Taikjin Lee Chulki Kim 2024-01-19T13:00:22Z 2024-01-19T13:00:22Z 2022-02-17 2022-02 1424-8220 https://pubs.kist.re.kr/handle/201004/115772 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 acqui-sition 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. ？ 2022 by the authors. Licensee MDPI, Basel, Switzerland. English Multidisciplinary Digital Publishing Institute (MDPI) Identification of Chemical Vapor Mixture Assisted by Artificially Extended Database for Environmental Monitoring Article 10.3390/s22031169 1 Sensors, v.22, no.3 Sensors 22 3 Y scie scopus 000760146300001 2-s2.0-85123873968 Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering Instruments & Instrumentation Article FIELD-EFFECT TRANSISTOR ELECTRONIC-NOSE THIN-FILM GAS NANOWIRE RECOGNITION SENSORS BIOGAS Chemiresistive sensor array Identification of gas mixture Machine learning Principal component analysis (PCA) Support vector machine (SVM)