综合性脂组学概况和可解释的机器学习,用于特定物种的肉类和骨粉的鉴定
Bing Gao1, Zhichao Yang2, Jiabin Zheng2
1College of Information Engineering, Jiangsu Vocational College of Agriculture and Forestry, Jurong, Jiangsu 212400, China.
Journal of chromatography. A
|March 14, 2026
概括
准确的物种识别肉骨粉 (MBM) 对于预防疾病传播至关重要. 这项研究使用脂管学和机器学习以100%的准确性识别MBM中的物种,确保料安全.
科学领域:
- 分析化学 分析化学
- 生物化学 生物化学
- 食品安全 食品安全
背景情况:
- 准确的物种识别肉和骨面粉 (MBM) 是一个监管的必要性,以防止物种内部回收.
- 这种做法可以促进通过受污染的动物料传播牛形脑病变 (BSE).
研究的目的:
- 从四种不同物种的MBM样本中全面描述脂质组成概况.
- 开发一种可靠的方法,使用非目标脂管学和机器学习在MBM中精确识别物种.
主要方法:
- 应用非标脂质组学来系统分析76个MBM样本的脂质资料.
- 在43个子类中确定了4180个脂质分子.
- 用严格的统计标准选特定物种的脂质生物标志物 (P<0.05,VIP>1.00,FC>1.50或FC<0.67).
- 机器学习算法,包括支持矢量机 (SVM),用于构建物种识别模型.
- 使用夏普利添加式扩展 (SHAP) 分析来解释模型预测.
主要成果:
- 总共有46种高度物种特异性的脂质生物标志物被确定.
- 在分类MBM物种方面,SVM模型实现了100%的准确性.
- 脂质分子TG{44:0},TG{49:0}和Cer{t41:1}被确定为对物种分化的关键贡献者.
结论:
- 该研究建立了一个分析框架,将脂管学和可解释的机器学习结合起来,以精确识别MBM物种.
- 这种方法为监管合规和提高动物料安全提供了科学基础.
- 开发的方法提供了一个高度准确和可靠的解决方案,用于检测MBM中的物种.
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