Combined quantitative lipidomics and back-propagation neural network approach to discriminate the breed and part source of lamb
文献类型: 外文期刊
第一作者: Liu, Chongxin
作者: Liu, Chongxin;Zhang, Dequan;Li, Shaobo;Liu, Chunyou;Zheng, Xiaochun;Li, Cheng;Chen, Li;Liu, Chongxin;Dunne, Peter;Brunton, Nigel Patrick;Grasso, Simona;Liu, Chunyou
作者机构:
关键词: Lipidomics; Lamb; Food authenticity; Linear discriminant model; Machine learning; Neural network
期刊名称:FOOD CHEMISTRY ( 影响因子:8.8; 五年影响因子:8.6 )
ISSN: 0308-8146
年卷期: 2024 年 437 卷
页码:
收录情况: SCI
摘要: The study successfully utilized an analytical approach that combined quantitative lipidomics with backpropagation 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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