Study on signal process of gas sensor array with non-linear principal component analysis

Guangfen Wei, Zhenan Tang, Jun Yu, Philip Ching Ho Chan

Research output: Journal article publicationConference articleAcademic researchpeer-review


Gas sensor array is a useful device to enhance the selectivity of gas detectors and to identify the components of gas mixture. The key step for processing signal from a gas sensor array is to extract the signal feature and make pre-classification for tested gases. Conventional Principal Component Analysis (PCA) is widely used for this purpose. However, conventional PCA is a linear and variance-covariance matrix based technique and it is therefore not strictly applicable for processing the gas sensor array signals that exhibit significant non-linear behavior. Thus, in this paper, non-linear PCA (NPCA) algorithm is introduced to process the gas sensor array signals to adapt to the non-linear characteristics. The signals we processed are the responses of a micro-hotplate (MHP) based integrated gas sensor array to a CO and NO2binary gas mixture. The gas sensor array, consisted of four SnO2thin-film sensing elements, was fabricated with integrated circuit (IC) technology and micromachining on silicon substrate. The recognition results of NPCA and conventional PCA are compared in this paper.
Original languageEnglish
Pages (from-to)181-185
Number of pages5
JournalProceedings of SPIE - The International Society for Optical Engineering
Publication statusPublished - 1 Dec 2001
Externally publishedYes
EventMicromachining and Microfabrication Process Technology and Devices - Nanjing, China
Duration: 7 Nov 20019 Nov 2001


  • Gas sensor array
  • Non-linear principal component analysis (NPCA)
  • Signal process

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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