caa a22 4500 463157575 CHVBK 20180406164738.0 cr unu---uuuuu 170326e20070301xx s 000 0 eng 10.1007/s00217-006-0314-0 doi (NATIONALLICENCE)springer-10.1007/s00217-006-0314-0 Identification of organically farmed Atlantic salmon by analysis of stable isotopes and fatty acids [Elektronische Daten] [Joachim Molkentin, Hans Meisel, Ines Lehmann, Hartmut Rehbein] Using isotope ratio mass spectrometry (IRMS), the ratios of carbon (δ 13C) and nitrogen (δ 15N) stable isotopes were investigated in raw fillets of differently grown Atlantic salmon (Salmo salar) in order to develop a method for the identification of organically farmed salmon. IRMS allowed to distinguish organically farmed salmon (OS) from wild salmon (WS), with δ 15N-values being higher in OS, but not from conventionally farmed salmon (CS). The gas chromatographic analysis of fatty acids differentiated WS from CS by stearic acid as well as WS from CS and OS by either linoleic acid or α-linolenic acid, but not OS from CS. The combined data were subjected to analysis using an artificial neural network (ANN). The ANN yielded several combinations of input data that allowed to assign all 100 samples from Ireland and Norway correctly to the three different classes. Although the complete assignment could already be achieved using fatty acid data only, it appeared to be more robust with a combination of fatty acid and IRMS data, i.e. with two independent analytical methods. This is also favourable with respect to a possible manipulation using suitable feed components. A good differentiation was established even without an ANN by the δ 15N-value and the content of linoleic acid. The general applicability in the context of consumer protection should be checked with further samples, particularly regarding the variability of feed composition and possible changes in smoked salmon. Springer-Verlag, 2006 Stable isotopes nationallicence Fatty acids nationallicence Identification nationallicence Organically farmed salmon nationallicence Conventionally farmed salmon nationallicence Wild salmon nationallicence Artificial neural network nationallicence Molkentin Joachim Institute of Dairy Chemistry and Technology, Federal Research Centre for Nutrition and Food, Hermann-Weigmann-Str. 1, 24103, Kiel, Germany aut Meisel Hans Institute of Dairy Chemistry and Technology, Federal Research Centre for Nutrition and Food, Hermann-Weigmann-Str. 1, 24103, Kiel, Germany aut Lehmann Ines Department of Fish Quality, Federal Research Centre for Nutrition and Food, Palmaille 9, 22767, Hamburg, Germany aut Rehbein Hartmut Department of Fish Quality, Federal Research Centre for Nutrition and Food, Palmaille 9, 22767, Hamburg, Germany aut European Food Research and Technology Springer-Verlag 224/5(2007-03-01), 535-543 1438-2377 224:5<535 2007 224 217 https://doi.org/10.1007/s00217-006-0314-0 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00217-006-0314-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Molkentin Joachim Institute of Dairy Chemistry and Technology, Federal Research Centre for Nutrition and Food, Hermann-Weigmann-Str. 1, 24103, Kiel, Germany aut NATIONALLICENCE 700 1- Meisel Hans Institute of Dairy Chemistry and Technology, Federal Research Centre for Nutrition and Food, Hermann-Weigmann-Str. 1, 24103, Kiel, Germany aut NATIONALLICENCE 700 1- Lehmann Ines Department of Fish Quality, Federal Research Centre for Nutrition and Food, Palmaille 9, 22767, Hamburg, Germany aut NATIONALLICENCE 700 1- Rehbein Hartmut Department of Fish Quality, Federal Research Centre for Nutrition and Food, Palmaille 9, 22767, Hamburg, Germany aut NATIONALLICENCE 773 0- European Food Research and Technology Springer-Verlag 224/5(2007-03-01), 535-543 1438-2377 224:5<535 2007 224 217 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer