Multispectral Image Analysis for Robust Prediction of Astaxanthin Coating

Martin Georg Ljungqvist, Stina Frosch, Michael Engelbrecht Nielsen, Bjarne Kjær Ersbøll

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The aim of this study was to investigate the possibility of predicting the type and concentration level of astaxanthin coating of aquaculture feed pellets using multispectral image analysis. We used both natural and synthetic astaxanthin, and we used several different concentration levels of synthetic astaxanthin in combination with four different recipes of feed pellets. We used a VideometerLab with 20 spectral bands in the range of 385-1050 nm. We used linear discriminant analysis and sparse linear discriminant analysis for classification and variable selection. We used partial least squares regression (PLSR) for prediction of the concentration level. The results show that it is possible to predict the level of synthetic astaxanthin coating using PLSR on either the same recipe, or when calibrating on all recipes. The concentration prediction is adequate for screening for all recipes. Moreover, it shows that it is possible to predict the type of astaxanthin used in the coating using only ten spectral bands. Finally, the most selected spectral bands for astaxanthin prediction are in the visible range of the spectrum.
Original languageEnglish
JournalApplied Spectroscopy
Issue number7
Pages (from-to)738-746
Publication statusPublished - 2013

Bibliographical note

This paper was published in Applied Spectroscopy and is made available as an electronic reprint with the permission of OSA. The paper can be found at the following URL on the OSA website: Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under law.


  • Multispectral
  • Image analysis
  • Spectral imaging
  • NIR
  • Astaxanthin
  • Fish feed
  • Coating


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