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Published on: September 20, 2016
Geographical origin authentication of lamb: Advancing label control through 4D-DIA quantitative proteomics and
Chongxin Liu1, Simona Grasso2, Nigel Patrick Brunton2
1Institute of Food Science and Technology, Chinese Academy of Agriculture Sciences, Key Laboratory of Agro-Products Quality and Safety Control in Storage and Transport Process, Ministry of Agriculture and Rural Affairs, Beijing 100193, China; School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland.
This study used proteomics to identify protein markers in Tan lamb, enabling accurate verification of lamb
Area of Science:
- Proteomics
- Food Science
- Animal Science
Background:
- Consumer trust in food origin labelling is paramount.
- Tan lamb is a valuable agricultural product with regional variations.
- Accurate origin verification is essential for market integrity.
Purpose of the Study:
- To identify protein markers for distinguishing Tan lamb from different regions.
- To develop a predictive model for authenticating lamb origin.
- To enhance food traceability in lamb products.
Main Methods:
- 4D-DIA quantitative proteomics was used to analyze Tan lamb protein profiles.
- Least Absolute Shrinkage and Selection Operator (LASSO) identified potential protein markers.
- Six machine learning algorithms were trained and validated for predictive modeling.
Main Results:
- Significant regional differences in lamb protein composition were identified.
- Fourteen protein markers, selected using λmin, showed high predictive accuracy.
- Logistic regression emerged as the optimal model for origin prediction.
- Specific proteins (W5PF65, W5PQE5, W5Q501) were highlighted as key origin markers.
Conclusions:
- Proteomic analysis combined with machine learning can accurately verify Tan lamb origin.
- The identified protein markers and predictive model enhance food traceability.
- This approach supports authentic food origin labelling and consumer confidence.
