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Journal article
Targeted lipidomics coupled with machine learning for authenticating the provenance of chicken eggs
Food chemistry, Vol.410, 135366
2023
PMID: 36641906
Abstract
•A simple lipid extraction of chicken egg yolks was developed for LC-MS/MS analysis.
•937 lipid species from 20 major lipid subclasses were characterized in egg yolk.
•Statistical modeling was used to classify the types of conventional chicken eggs.
•Cage, barn, and free-range eggs can be differentiated based on lipid profile.
•Eggs from caged birds can be accurately predicted based on the lipidomic signature.
Free-range eggs are ethically desirable but as with all high-value commercial products, the establishment of provenance can be problematic. Here, we compared a simple one-step isopropanol method to a two-step methyl-tert-butyl ether method for extracting lipid species in chicken egg yolks before liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. The isopropanol method extracted 937 lipid species from 20 major lipid subclasses with high reproducibility (CV < 30 %). Machine learning techniques could differentiate conventional cage, barn, and free-range eggs using an external test dataset with an accuracy of 0.94, 0.82, and 0.82, respectively. Lipid species that differentiated cage eggs were predominantly phosphocholines and phosphoethanolamines whilst the free-range egg lipidomes were dominated by acylglycerides with up to three fatty acids. The lipid profiles were found to be characteristic of the cage, barns, and free-range eggs. The lipidomic analysis together with the statistical modeling approach thus provides an efficient tool for verifying the provenance of conventional chicken eggs.
Details
- Title
- Targeted lipidomics coupled with machine learning for authenticating the provenance of chicken eggs
- Authors/Creators
- S-T Chin - Murdoch University, School of Medical, Molecular and Forensic SciencesG. Hoerlendsberger - Discipline of Information Technology, Murdoch University, 90 South Street, Perth, WA 6150, AustraliaK. W. Wong - Discipline of Information Technology, Murdoch University, 90 South Street, Perth, WA 6150, AustraliaS. Li - Murdoch University, School of Information TechnologyS-H Bong - Australian National Phenome Centre, Health Futures Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, AustraliaL. Whiley - Australian National Phenome Centre, Health Futures Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, AustraliaJ. Wist - Murdoch University, Centre for Computational and Systems MedicineR. Masuda - Murdoch UniversityJ. Greeff - Department of Primary Industries and Regional DevelopmentE. Holmes - Murdoch University, Centre for Computational and Systems MedicineJ. K. Nicholson - Murdoch University, Health Futures InstituteR. L. Loo - Murdoch University, Centre for Computational and Systems Medicine
- Publication Details
- Food chemistry, Vol.410, 135366
- Publisher
- Elsevier Ltd
- Identifiers
- 991005546870107891
- Copyright
- © 2023 The Authors.
- Murdoch Affiliation
- School of Information Technology; Centre for Water, Energy and Waste; School of Medical, Molecular and Forensic Sciences; Health Futures Institute; Centre for Computational and Systems Medicine
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
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- Citation topics
- 2 Chemistry
- 2.211 Mass Spectrometry
- 2.211.990 Metabolomics
- Web Of Science research areas
- Chemistry, Applied
- Food Science & Technology
- Nutrition & Dietetics
- ESI research areas
- Agricultural Sciences