基于LC-MS/MS的代谢学与机器学习相结合,用于选培根中的候选生物标志物
Xinyi Huang1, Yingmin Tian1, Yufei Yang1
1School of Biological Science and Engineering, Shaanxi University of Technology, 723001 Hanzhong, China.
Food chemistry: X
|February 12, 2026
概括
本研究介绍了使用代谢学和机器学习进行培根认证的客观方法. 它确定了关键代谢物以区分培根类型,改善了肉制品的质量控制和生物标志物发现.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 计算生物学 计算生物学
背景情况:
- 传统的培根认证方法是主观的.
- 客观认证是质量控制和消费者信任所需要的.
研究的目的:
- 为培根认真制定一个客观的歧视框架.
- 整合代谢学和机器学习来识别歧视性生物标志物.
主要方法:
- 液体染色学-并联质谱学 (LC-MS/MS) 用于代谢分析.
- 机器学习算法 (随机森林,KNN,SVM,FNN) 用于特征选择和模型验证.
- 统计分析 (克鲁斯卡尔-瓦利斯H测试) 以确定重要的代谢物.
主要成果:
- 在猪肚 (LRW) 和猪 (LRT) 培根之间确定了100种不同的代谢物.
- 使用特征选择技术将其减少到22个关键代谢物.
- 突出了四个候选的歧视性代谢物:6-氧甲甲尼丁 (LRT),二甲基乙醇胺,山丁和乙烯烟酸 (LRW).
- 通过使用机器学习模型进行双向验证,实现了强大的歧视性性能.
结论:
- 开发的框架为培根认证提供了一个客观的方法.
- 鉴定的特定代谢物可以作为培根起源的生物标志物.
- 这种数据驱动的方法可用于在其他肉制品中发现生物标志物.
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