外文摘要：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.
外文关键词：machine learning;neural network;Lipidomics;Lamb;Food authenticity;Linear discriminant model
作者：Liu, Chongxin;Zhang, Dequan;Li, Shaobo;Dunne, Peter;Brunton, Nigel Patrick;Grasso, Simona;Liu, Chunyou;Zheng, Xiaochun;Li, Cheng;Chen, Li
作者单位：Chinese Acad Agr Sci;Univ Coll Dublin;Guangxi Univ Sci & Technol
期刊名称：FOOD CHEMISTRY
期刊影响因子：0.0
出版年份：2024
出版刊次：437
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台