A chemiresistive sensor array based on polyaniline nanocomposites and machine learning classification

被引:5
作者
Kroutil, Jiri [1 ]
Laposa, Alexandr [1 ]
Ahmad, Ali [1 ]
Voves, Jan [1 ]
Povolny, Vojtech [1 ]
Klimsa, Ladislav [2 ]
Davydova, Marina [2 ]
Husak, Miroslav [1 ]
机构
[1] Czech Tech Univ, Dept Microelect, Tech 2, Prague 16627, Czech Republic
[2] Czech Acad Sci, FZU Inst Phys, Slovance 1999-2, Prague 18221, Czech Republic
关键词
feature extraction; gas sensor; pattern recognition; sensor array; RAMAN-SPECTROSCOPY; GAS SENSORS; AMMONIA GAS; RECOGNITION; POLYMER; NOSE;
D O I
10.3762/bjnano.13.34
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
The selective detection of ammonia (NH3), nitrogen dioxide (NO2), carbon oxides (CO2 and CO), acetone ((CH3)(2)CO), and toluene (C6H5CH3) is investigated by means of a gas sensor array based on polyaniline nanocomposites. The array composed by seven different conductive sensors with composite sensing layers are measured and analyzed using machine learning. Statistical tools, such as principal component analysis and linear discriminant analysis, are used as dimensionality reduction methods. Five different classification methods, namely k-nearest neighbors algorithm, support vector machine, random forest, decision tree classifier, and Gaussian process classification (GPC) are compared to evaluate the accuracy of target gas determination. We found the Gaussian process classification model trained on features extracted from the data by principal component analysis to be a highly accurate method reach to 99% of the classification of six different gases.
引用
收藏
页码:411 / 423
页数:13
相关论文
共 40 条
  • [1] Ahluwalia A., 2001, ENCY MAT SCI TECHNOL, P344, DOI DOI 10.1016/B0-08-043152-6/00071-1
  • [2] AROMA DISCRIMINATION BY PATTERN-RECOGNITION ANALYSIS OF RESPONSES FROM SEMICONDUCTOR GAS SENSOR ARRAY
    AISHIMA, T
    [J]. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY, 1991, 39 (04) : 752 - 756
  • [3] High-temperature ceramic gas sensors: A review
    Akbar, Sheikh
    Dutta, Prabir
    Lee, Chonghoon
    [J]. INTERNATIONAL JOURNAL OF APPLIED CERAMIC TECHNOLOGY, 2006, 3 (04) : 302 - 311
  • [4] Mint treatment day prediction using a multi-sensors system and machine learning algorithms
    Amkor, Ali
    Maaider, Kamal
    El Barbri, Noureddine
    [J]. SENSORS AND ACTUATORS A-PHYSICAL, 2021, 328
  • [5] Polyaniline/Biopolymer Composite Systems for Humidity Sensor Applications: A Review
    Anisimov, Yuriy A.
    Evitts, Richard W.
    Cree, Duncan E.
    Wilson, Lee D.
    [J]. POLYMERS, 2021, 13 (16)
  • [6] Bermak A., 2006, Encyclopedia of Sensors, VX, P1
  • [7] Boser B. E., 1992, Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, P144, DOI 10.1145/130385.130401
  • [8] Gas identification using density models
    Brahim-Belhouari, S
    Bermak, A
    [J]. PATTERN RECOGNITION LETTERS, 2005, 26 (06) : 699 - 706
  • [9] Brahim-Belhouari S, 2003, PROCEEDINGS OF THE 3RD IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY, P138
  • [10] Polyaniline based impedance humidity sensors
    Chani, M. Tariq Saeed
    Karimov, Kh. S.
    Khalid, F. Ahmad
    Moiz, S. Abdul
    [J]. SOLID STATE SCIENCES, 2013, 18 : 78 - 82