Autoregressive modeling of analytical sensor data can yield classifiers in the predictor coefficient parameter space

Melissa D. Krebs, Robert D. Tingley, Julie E. Zeskind, Joung Mo Kang, Maria E. Holmboe, Cristina E Davis

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

Summary: The analysis of chromatographic data resulting from complex chemical mixtures is challenging. Components may co-elute, causing their signals to overlap. An algorithm that will increase the signal-to-noise ratio so compounds present in low abundance can be better distinguished from noise is useful in this type of analysis. The autoregressive (AR) filter offers the advantage of smoothing chromatograms to increase this ratio, while also offering data compression and increased resolution. Furthermore, this filter can be useful for classification, as the roots of the predictor coefficient vectors represent features present in the data and can therefore be used for pattern recognition. In this paper, we present a novel method for applying AR filtering to chromatogram data. We show that the AR filter outperforms the Savitzky-Golay filter for smoothing noise while retaining important information within chromatograms, and also that AR correlation coefficients have the potential to be used to classify chromatogram data into groups.

Original languageEnglish (US)
Pages (from-to)1325-1331
Number of pages7
JournalBioinformatics
Volume21
Issue number8
DOIs
StatePublished - Apr 15 2005
Externally publishedYes

ASJC Scopus subject areas

  • Clinical Biochemistry
  • Computer Science Applications
  • Computational Theory and Mathematics

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