应用非定向液体染色学-质谱法用于常规分析食品使用三维桶和机器学习
Jule Hansen1, Christof Kunert2, Hella Münstermann1
1Institute of Food Chemistry, Hamburg School of Food Science, University of Hamburg, Grindelallee 117, 20146, Hamburg, Germany.
Scientific reports
|July 18, 2024
概括
一种新方法允许分别处理来自不同来源的非目标液态染色学-高分辨率质谱学 (LC-HRMS) 数据. 这种方法可以准确地确定食品原产地,并持续学习模型,以加强食品欺诈的检测.
科学领域:
- 分析化学 分析化学
- 食品科学 食品科学 食品科学
- 化学测量 化学测量 化学测量
背景情况:
- 敏感和可重复的分析方法对于检测食品改至关重要.
- 液体染色学与高分辨率质谱学 (LC-HRMS) 结合,提供了全面的分析指纹.
- 目前不同仪器和时间之间的数据可比性的局限性阻碍了对整个指纹的分析.
研究的目的:
- 开发和优化一种处理非目标LC-HRMS数据的方法,独立于设备和时间.
- 为了实现分析指纹的全面分析,以改善食品欺诈的检测.
- 为食品认证创建一个持续学习的分类模型.
主要方法:
- 开发和优化用于非定向LC-HRMS的数据处理方法.
- 用随机森林模型来确定蜂蜜的地理来源.
- 使用835个蜂蜜样本的大数据集对方法的验证.
主要成果:
- 成功确定蜂蜜样本的地理来源.
- 证明该方法适用于开发持续学习分类模型的应用性.
- 对于来自6个不同国家的126个测试样本,获得了94%的分类准确度.
结论:
- 开发的方法可以对非目标LC-HRMS数据进行强有力的分析,克服设备间和时间间的变化.
- 该方法支持全面的指纹分析,这对于保持领先于不断发展的食品改技术至关重要.
- 这种方法有助于创建适应性,高精度的分类模型,用于食品认证和地理原产地确定.
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