Use of Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry and Metabonomic Profiling To Differentiate between Normally Slaughtered and Dead on Arrival Poultry Meat.
Kate L SidwickAmy E JohnsonCraig D AdamLuisa PereiraDavid F ThompsonPublished in: Analytical chemistry (2017)
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.
Keyphrases
- liquid chromatography
- mass spectrometry
- tandem mass spectrometry
- high resolution mass spectrometry
- simultaneous determination
- ultra high performance liquid chromatography
- quality control
- high performance liquid chromatography
- ms ms
- gas chromatography
- solid phase extraction
- liquid chromatography tandem mass spectrometry
- human health
- small molecule
- skeletal muscle
- loop mediated isothermal amplification
- single cell
- electronic health record
- mental health
- social media
- atrial fibrillation
- emergency department
- high throughput
- machine learning
- big data
- data analysis
- antimicrobial resistance
- deep learning