Benchmark of outlier detection methods for spectral data
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.
Free to watch
Sessions are free to watch. Please login to view this session or create an account.
Speakers
![Mathieu Lepot](/assets/file_store/elearning_files/29/speakers/54/thumbnails/100w_Mathieu_Lepot.jpg)
Mathieu Lepot (TU Delft)
Delft University of Technology · Department of Water management Netherlands · Delft
Digital Edition
AET 28.2 April/May 2024
May 2024
Business News - Teledyne Marine expands with the acquisition of Valeport - Signal partners with gas analysis experts in Korea Air Monitoring - Continuous Fine Particulate Emission Monitor...
View all digital editions
Events
Jul 10 2024 Birmingham, UK
Jul 21 2024 Cape Town, South Africa
Australasian Waste & Recycling Expo
Jul 24 2024 Sydney, Australia
Jul 30 2024 Jakarta, Indonesia
China Energy Summit & Exhibition
Jul 31 2024 Beijing, China