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The authentication of Yanchi tan lamb based on lipidomic combined with particle swarm optimization-back propagation neural network

文献类型： 外文期刊

作者： Yang, Qi 1 ; Zhang, Dequan 1 ; Liu, Chongxin 1 ; Xu, Le 1 ; Li, Shaobo 1 ; Zheng, Xiaochun 1 ; Chen, Li 1 ;

作者机构： 1.Chinese Acad Agr Sci, Inst Food Sci & Technol, Key Lab Agroprod Qual & Safety Control Storage & T, Minist Agr & Rural Affairs, Beijing 100193, Peoples R China

关键词： Tan lamb; Lipidomic; Food authenticity; Geographical indication; Chemometrics; Machine learning

期刊名称：FOOD CHEMISTRY-X （ 影响因子：8.2； 五年影响因子：8.2 ）

ISSN： 2590-1575

年卷期： 2024 年 24 卷

页码：

收录情况： SCI

摘要： This study successfully combined widely targeted lipidomic with a back propagation (BP) neural network optimized based on a particle swarm algorithm to identify the authenticity of Yanchi Tan lamb. An electronic nose and gas chromatography-olfactometry-mass spectrometry (GC-O-MS) were used to explore the flavor differences in Tan lamb from various regions. Among the 17 identified volatile compounds, 16 showed significant regional differences (p < 0.05). Lipidomic identified 1080 molecules across 41 lipid classes, with 11 lipids, including Carnitine 15:0, Carnitine 17:1, and Carnitine C8:1-OH, serving as potential markers for Yanchi Tan lamb. In addition, a stepwise linear discriminant model and three types of BP neural networks were used to identify the origin of Tan lamb. The results showed that particle swarm optimization-back propagation (PSO-BP) neural network had the best prediction effect, with 100 % prediction accuracy in both the training and test sets. The established PSO-BP model was able to achieve effective discrimination between Yanchi and non-Yanchi Tan lamb. These results provide a comprehensive perspective on the discrimination of Yanchi Tan lambs and improve the understanding of Tan lamb flavor and lipid composition in relation to origin.

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