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Preliminary evidences of colour differences in European sea bass reared under organic protocols
Institution:1. Department of Animal Production, Veterinary Faculty, University of León, Campus de Vegazana s/n, 24071 Le, Spain.
Abstract:Product diversification, among which organic farming, is an important issue in modern aquaculture activities. Discriminating organic vs. conventional products is complex, but appearance may help in tracing different batches of produce. To test this fact, sea basses were fed a commercial or an organic diet, and fishes of each different group were photographed before and during the experiment. Body landmarks were digitized on each colour-calibrated (using the TPS-3D algorithm) image; on the basis of landmarks configuration, the RGB matrices were warped using a geometric morphometrics procedure. The calibrated colour matrix of each warped individual (195 × 135,225) was analyzed with a 50–50 MANOVA, followed by a partial least squares discriminant analysis. Finally, a cluster analysis on the diet/time groups was performed. Growth and changes in condition factor over time are not dependent on the rearing method. Colour (as represented by the pixel vector) does depend on time and on rearing method, based on the MANOVA method used. Standard length and condition factor were not good predictors of colour. The partial least square discriminant analysis was highly effective in detecting colour differences on the basis of the fish diet. The 9-group dendrogram showed that the wild sample and the organic fish cluster together. The head, darker in fishes raised conventionally, is the part showing the greatest difference; the longer the life spent under the 2 regimens, the stronger the differences. In conclusion, these preliminary results demonstrate that a colorimetric analysis is able to distinguish 2 batches of fishes fed different diets in different environmental conditions and – in the present instance – to certify the organically grown sea basses.
Keywords:TPS-3D  Aquaculture  Geometric morphometrics  Fish aspect  PLSDA
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