用机器学习模型驱动的比较代谢学来对Panax quinquefolius进行培养差异化和起源追踪
Rongrong Zhou1,2, Yikun Wang3, Lanping Zhen4
1Hunan Provincial Hospital of Integrated Traditional Chinese and Western Medicine, Hunan Academy of Traditional Chinese Medicine, Changsha 410208, China.
Foods (Basel, Switzerland)
|April 26, 2025
概括
研究人员开发了一种人工智能驱动的方法,使用代谢学来区分野生和栽培的美国人参. 这种方法可以准确地识别人参的来源和质量,这对这种有价值的药用植物至关重要.
科学领域:
- 代谢学 代谢学 代谢学
- 化学信息学 化学信息学
- 药理学是指药理学,即药理学是指药理学.
背景情况:
- 美国人参 (Panax quinquefolius L.) 是一种有价值的药用植物.
- 种植和原产地显著影响人参的质量和有效性.
- 野生人参和栽培人参之间的代谢差异不太清楚.
研究的目的:
- 开发一种可靠的方法来识别野生与栽培的美国人参.
- 追踪地理来源并评估美国人参的质量.
- 了解美国人参种植的代谢变异.
主要方法:
- 使用超高性能液态色谱与飞行时间质谱学 (UHPLC-Q/TOF-MS) 相结合.
- 应用比较代谢学和机器学习 (ML) 策略.
- 采用主要组件分析 (PCA) 和层次集群分析 (HCA).
主要成果:
- 在野生 ginseng 和栽培 ginseng 之间观察到不同的代谢表型.
- 在ESI+模式中确定了八种差异性代谢物,在ESI-模式中确定了三种差异性代谢物,其中包括七种人参化物.
- 一个随机森林ML模型在区分人参种类方面实现了100%的准确性.
结论:
- 人工智能驱动的比较代谢学可以准确地识别人参种类和来源.
- 提供了对栽培人参中代谢变化的分子基础的见解.
- 提供了一个新的工具,用于质量控制和美国人参的地理追踪.
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