Benchmark of outlier detection methods for spectral data

Mathieu Lepot, Alma Mašić, Jean Baptiste Aubin, Francois Clemens

Research output: Contribution to conferenceAbstractScientific

Abstract

UV/Vis spectrophotometers have been used to monitor water quality since the early 2000s. Calibration of these devices requires sampling campaigns to elaborate relations between recorded spectra and measured concentrations. Recent sensor improvements allow recordings of a spectrum in as little as 15 seconds, making it possible to record several spectra for the same sample. Spectrum repetitions provide new opportunities to detect outliers – a task that is difficult in non-repetitive spectra recordings. A well-executed outlier detection can e.g. result in a more accurate calibration of the spectrophotometer or an improved construction of a regression model. In this work, two methods are presented and tested to detect outliers in repetitions of spectral data: one based on data depth theory (DDT) and one on principal component analysis (PCA). Results show that the two methods are generally consistent in identifying outliers, with only small differences between the methods.
Original languageEnglish
Number of pages2
Publication statusPublished - 2016
Event3rd New Developments in IT and Water Conference - Telford, United Kingdom
Duration: 1 Nov 20163 Nov 2016
Conference number: 3
https://www.ilmexhibitions.com/wwem/

Conference

Conference3rd New Developments in IT and Water Conference
Abbreviated titleWWEM 2016
CountryUnited Kingdom
CityTelford
Period1/11/163/11/16
Internet address

Keywords

  • DDT
  • detection
  • outlier
  • PCA
  • spectra
  • UV/Vis

Fingerprint Dive into the research topics of 'Benchmark of outlier detection methods for spectral data'. Together they form a unique fingerprint.

  • Cite this

    Lepot, M., Mašić, A., Aubin, J. B., & Clemens, F. (2016). Benchmark of outlier detection methods for spectral data. Abstract from 3rd New Developments in IT and Water Conference, Telford, United Kingdom.