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NMR‐based differentiation of conventionally from organically produced chicken eggs in Germany
Author(s) -
Ackermann Svenja M.,
Lachenmeier Dirk W.,
Kuballa Thomas,
Schütz Birk,
Spraul Manfred,
Bunzel Mirko
Publication year - 2019
Publication title -
magnetic resonance in chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.483
H-Index - 72
eISSN - 1097-458X
pISSN - 0749-1581
DOI - 10.1002/mrc.4838
Subject(s) - principal component analysis , barn , chemistry , linear discriminant analysis , organic product , yolk , animal welfare , sample (material) , food science , statistics , chromatography , mathematics , biology , ecology , civil engineering , engineering , agriculture
Both the German and European organic food markets are growing fast, and there is also a rising demand for organic chicken eggs. Consumers are willing to pay higher prices for organic eggs produced in an animal‐appropriate environment considering animal welfare. Strict labelling requirements do not prevent chicken eggs from being a subject of food fraud. Conventionally produced (barn/free‐range) eggs can easily be mislabeled as organic eggs. Especially because the demand for organically produced chicken eggs is likely to exceed supply in the future, mislabeling appears to be a realistic scenario. Therefore, there is a need for analytical methods that are suitable to classify eggs as being either conventionally or organically produced. Nuclear magnetic resonance (NMR) spectroscopy in combination with multivariate data analysis is a suitable tool to screen eggs according to the different systems of husbandry. Sample preparation is based on a fat extraction method, which was optimised for application to freeze‐dried egg yolk. Samples were analysed using typical q‐NMR parameters. A nontargeted approach was used for the analysis of the 1 H NMR data. Principal component analysis (PCA) was applied followed by a linear discriminant analysis (PCA‐LDA) and Monte Carlo cross‐validation. In total, 344 chicken eggs (214 barn/free‐range eggs and 130 eggs from organic farms), most of them originating from Germany, were used to build and validate the prediction model. The results showed that the prediction model allowed for the correct classification of about 93% of the organic eggs.