使用纳米电子喷雾电离质谱学结合机器学习的快速分析和验证中国
Ruiyue Wang1, Liangliang Qu2, Yiran Wang1
1Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Food chemistry
|March 14, 2024
概括
这项研究引入了一种快速的nanoESI-MS方法来分类. 机器学习使用代谢物指纹准确地根据气候区域和颜色区分.
科学领域:
- 分析化学 分析化学
- 食品科学 食品科学 食品科学
- 代谢学 代谢学 代谢学
背景情况:
- 的分析对于质量控制至关重要.
- 现有的类分类方法可能耗时.
- 了解基于原产地和外观的 прополис组成的变化是很重要的.
研究的目的:
- 开发一种简单,快速,高吞吐率的方法来分析和分类.
- 研究水提取物 (WEP) 和乙醇提取物 (EEP) 之间的差异.
- 根据气候区和颜色对样品进行分类.
主要方法:
- 利用纳米ESI-MS (负离子模式) 来分析37个中国样品.
- 综合多变量分析以探索光谱变异.
- 使用机器学习模型进行分类和预测.
主要成果:
- 获得了样品的指纹特征光谱.
- 在不同气候区之间确定了10种不同的代谢物.
- 在不同颜色之间确定了11种差异性代谢物.
- 使用机器学习实现了95.6%的气候区分类精度和85.6%的颜色分类精度.
结论:
- 纳米ESI-MS与机器学习方法相结合,为精确的类分类提供了一个强大的工具.
- 这种方法对食品的质量控制和认证有很大的潜力.
- 这项研究为进一步研究的代谢学和地理来源提供了基础.
相关概念视频
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