Print Email Facebook Twitter Outlier detection in UV/Vis spectrophotometric data Title Outlier detection in UV/Vis spectrophotometric data Author Lepot, M.J. (TU Delft Sanitary Engineering) Aubin, Jean Baptiste (INSA Lyon) Clemens, F.H.L.R. (TU Delft Sanitary Engineering; Deltares) Mašić, Alma (Swiss Federal Institute of Aquatic Science and Technology) Date 2017 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. In order to build robust calibration data sets, several spectra must be recorded per sample. This study compares two approaches – principal component analysis and data depth theory – to identify outliers and select the most representative spectrum (MRS) among the repetitively recorded spectra. Detection of samples that contain outliers is consistent between the methods in more than 70% of the samples. Identification of spectra as outliers is consistent in more than 95% of the cases. The identification of MRS differs depending on the approach used. In their current form, both of the proposed approaches can be used for outlier detection and identification. Further studies are suggested to combine the methods and develop an automated ranking and sorting system. Subject calibrationidentificationoutliersampleUV/Vis spectrophotometer To reference this document use: http://resolver.tudelft.nl/uuid:a1338560-041e-45a0-8b72-1fd1b2751a46 DOI https://doi.org/10.1080/1573062X.2017.1280515 ISSN 1573-062X Source Urban Water Journal, 14 (9), 908-921 Part of collection Institutional Repository Document type journal article Rights © 2017 M.J. Lepot, Jean Baptiste Aubin, F.H.L.R. Clemens, Alma Mašić Files PDF Lepot_et_al_3_.pdf 1.1 MB Close viewer /islandora/object/uuid:a1338560-041e-45a0-8b72-1fd1b2751a46/datastream/OBJ/view