数字农科院2.0

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

作者： Liu, Chongxin;Zhang, Dequan;Li, Shaobo;Dunne, Peter;Brunton, Nigel Patrick;Grasso, Simona;Liu, Chunyou;Zheng, Xiaochun;Li, Cheng;Chen, Li

作者机构：

关键词： Lipidomics;Lamb;Food authenticity;Linear discriminant model;Machine learning;Neural network

期刊名称： FOOD CHEMISTRY

ISSN： 0308-8146

年卷期： 2024 年 437 卷

页码：

收录情况： SCIE（2024版） ; ; EI（2024版）

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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