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Shotgun lipidomics strategy for fast analysis of phospholipids in fisheries waste and its potential in species differentiation.

JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY(2012)

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摘要
An efficient shotgun lipidomics strategy was established and optimized for fast phospholipid profiling of viscera from three fish species: Lateolabrax japonicas, Ctenopharyngodon idellus, and Carassius auratus. This strategy relies on direct infusion of total lipid extracts into a tandem mass spectrometer without additional separation of the individual molecular species. Four classes of phospholipids, including phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylinositol (PI), and phosphatidylserine (PS), were analyzed, and at least 81 molecular species of phospholipids were identified, including 34 species of PC, 24 species of PE, 12 species of PS, and 11 species of PI, in both positive- and negative-ion electrospray ionization mode. The results show that fish viscera, which are traditionally discarded as fisheries wastes, are nutritional in phospholipids with total contents of the four detected phospholipid classes ranging from 1.52 to 3.29 mg/g in the three tested fish species. Regardless of the tested fish species, PC and PE are the dominant phospholipid classes, followed by PI and PS. Furthermore, principal component analysis (PCA) was applied to normalize the relative amounts of the identified phospholipid species. The results demonstrate that PS 18:0/22:6, PI 18:0/20:4, and PI 18:0/20:5 were the main contributors of cumulative value and could be used as an indicator for fish species differentiation. This shotgun lipidomics method was >10 times faster than traditional methods, because no chromatographic separation was needed. The successful application of this strategy paves the way for full utilization of traditionally discarded fisheries wastes and provides an alternative means for fish species differentiation.
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关键词
phospholipids,shotgun lipidomics,fish viscera,multidimensional mass spectrometry,principal component analysis
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