Combined chemometric analysis of 1H NMR, 13C NMR and stable isotope data to differentiate organic and conventional milk. (1st December 2015)

• Record Type: Journal Article
• Authors: Erich, Sarah; Schill, Sandra; Annweiler, Eva; Waiblinger, Hans-Ulrich; Kuballa, Thomas; Lachenmeier, Dirk W.; Monakhova, Yulia B.
• Is Part Of: Food chemistry. Volume 188(2015)
• Page Start: 1
• Page End: 7
• Publication Date: 2015-12-01
• Subjects: NMR spectroscopy -- Organic milk -- Stable isotope-ratio mass spectrometry (IRMS) -- Chemometrics -- Data fusion; Linolenic acid (PubChem CID: 5280934); Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2015.04.118
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Milk protein was isolated from 85 milk samples to determine the carbon isotope ratio. Milk fat was extracted to determine the carbon isotope ratio, α-linolenic acid content and 1 H and 13 C NMR spectra. Combined chemometric analysis of 1 H NMR, 13 C NMR and stable isotope data to differentiate organic and conventional milk. A classification rate of 95% compared to 81% for NMR and 90% for IRMS was achieved. Abstract: The increased sales of organically produced food create a strong need for analytical methods, which could authenticate organic and conventional products. Combined chemometric analysis of 1 H NMR-, 13 C NMR-spectroscopy data, stable-isotope data (IRMS) and α-linolenic acid content (gas chromatography) was used to differentiate organic and conventional milk. In total 85 raw, pasteurized and ultra-heat treated (UHT) milk samples (52 organic and 33 conventional) were collected between August 2013 and May 2014. The carbon isotope ratios of milk protein and milk fat as well as the α-linolenic acid content of these samples were determined. Additionally, the milk fat was analyzed by 1 H and 13 C NMR spectroscopy. The chemometric analysis of combined data (IRMS, GC, NMR) resulted in more precise authentication of German raw and retail milk with a considerably increased classification rate of 95% compared to 81% for NMR and 90% for IRMS using linear discriminate analysis.