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Use of Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry and Metabonomic Profiling To Differentiate between Normally Slaughtered and Dead on Arrival Poultry Meat
Author(s) -
Kate L. Sidwick,
Amy E. M. Johnson,
Craig D. Adam,
L. Pereira,
David F. Thompson
Publication year - 2017
Publication title -
analytical chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.117
H-Index - 332
eISSN - 1520-6882
pISSN - 0003-2700
DOI - 10.1021/acs.analchem.7b02749
Subject(s) - quadrupole time of flight , chemistry , mass spectrometry , chromatography , workflow , profiling (computer programming) , poultry meat , time of flight mass spectrometry , lipidomics , sample (material) , computational biology , food science , tandem mass spectrometry , computer science , database , biochemistry , biology , ion , organic chemistry , ionization , operating system
Metabonomic profiling techniques, with established quality control methods, have been used to detect subtle metabolic differences in tissue that could aid in the discovery of fraud within the food industry. Liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS) was utilized to acquire metabolic profiles of muscle, heart, and liver tissue from normally slaughtered and dead on arrival chickens. A workflow including XCMS Online for data processing and robust confirmatory statistics was used in order to differentiate between the two sample types. It was found that normally slaughtered and dead on arrival chicken can be differentiated based on the metabolic profile and multivariate analysis. Markers were found to be significantly different between the two sample types in all samples. With the use of the METLIN database and MS/MS analysis of chemical standards, sphingosine was identified as a marker in the muscle tissue samples which may offer potential for the detection of fraudulently processed chicken meat. The approach taken in this work has shown that it is possible to apply the described workflows to food fraud problems, with an objective of identifying key markers that could be investigated further to determine their usefulness for fraud detection.

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