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Liu C, Zhang D, Li S, Dunne P, Patrick Brunton N, Grasso S, Liu C, Zheng X, Li C, Chen L. Combined quantitative lipidomics and back-propagation neural network approach to discriminate the breed and part source of lamb. Food Chem 2024; 437:137940. [PMID: 37976785 DOI: 10.1016/j.foodchem.2023.137940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 09/18/2023] [Accepted: 11/04/2023] [Indexed: 11/19/2023]
Abstract
The study successfully utilized an analytical approach that combined quantitative lipidomics with back-propagation neural networks to identify breed and part source of lamb using small-scale samples. 1230 molecules across 29 lipid classes were identified in longissimus dorsi and knuckle meat of both Tan sheep and Bahan crossbreed sheep. Applying multivariate statistical methods, 12 and 7 lipid molecules were identified as potential markers for breed and part identification, respectively. Stepwise linear discriminant analysis was applied to select 3 and 4 lipid molecules, respectively, for discriminating lamb breed and part sources, achieving correct rates of discrimination of 100 % and 95 %. Additionally, back-propagation neural network proved to be a superior method for identifying sources of lamb meat compared to other machine learning approaches. These findings indicate that integrating lipidomics with back-propagation neural network approach can provide an effective strategy to trace and certify lamb products, ensuring their quality and protecting consumer rights.
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Affiliation(s)
- Chongxin Liu
- Institute 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
| | - Dequan Zhang
- Institute 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
| | - Shaobo Li
- Institute 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
| | - Peter Dunne
- School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - Nigel Patrick Brunton
- School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - Simona Grasso
- School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - Chunyou Liu
- Institute 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 Biological and Chemical Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China
| | - Xiaochun Zheng
- Institute 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
| | - Cheng Li
- Institute 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
| | - Li Chen
- Institute 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.
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