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Evaluation of sample preparation methods for NMR-based metabolomics of cow milk. | LitMetric

Evaluation of sample preparation methods for NMR-based metabolomics of cow milk.

Heliyon

Toxalim, Research Centre in Food Toxicology, Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, F-31027, Toulouse, France.

Published: October 2018

The quality of milk metabolome analyzed by nuclear magnetic resonance (NMR) is greatly influenced by the way samples are prepared. Although this analytical method is increasingly used to study milk metabolites, a thorough examination of available sample preparation protocols for milk has not been reported yet. We evaluated the performance of eight milk preparation methods namely (1) raw milk without any processing; (2) skimmed milk; (3) ultrafiltered milk; (4) skimming followed by ultrafiltration; (5) ultracentrifuged milk; (6) methanol; (7) dichloromethane; and (8) methanol/dichloromethane, in terms of spectra quality, repeatability, signal-to-noise ratio, extraction efficiency and yield criteria. A pooled sample of milk was used for all protocols. Skimming, ultracentrifugation and unprocessed milk protocols showed poor NMR spectra quality. Protocols involving multiple steps, namely methanol/dichloromethane extraction, and skimming followed by ultrafiltration produced inadequate results for signal-to-noise ratio parameter. Methanol and skimming associated to ultrafiltration provided good repeatability results compared to the other protocols. Chemical-based sample preparation protocols, particularly methanol, showed more efficient metabolite extraction compared to physical preparation methods. When considering all evaluation parameters, the methanol extraction protocol proved to be the best method. As a proof of utility, methanol protocol was then applied to milk samples from dairy cows fed a diet with or without a feed additive, showing a clear separation between the two groups of cows.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6197446PMC
http://dx.doi.org/10.1016/j.heliyon.2018.e00856DOI Listing

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