Methods to identify outliers in repetitions of UV/Vis spectra

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
Pages145-146
Publication statusPublished - 2016
Event8th International Conference on Sewer Processes and Networks - SS Rotterdam, Rotterdam, Netherlands
Duration: 31 Aug 20162 Sep 2016
Conference number: 8
http://www.spn8.nl/

Conference

Conference8th International Conference on Sewer Processes and Networks
Abbreviated titleSPN8
CountryNetherlands
CityRotterdam
Period31/08/162/09/16
Internet address

Keywords

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

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