通过多数据融合和机器学习方法追踪Lithocarpus litseifolius的来源和种植实践
Yifan Tang1,2, Ping Yu3,4,5, Feng Xiong3,5
1Academy of Pharmacy, Xi'an-Jiaotong Liverpool University, Suzhou, China. yifan.tang@163.com.
NPJ science of food
|February 13, 2026
概括
丽托卡普斯 (lithocarpus litseifolius) 或甜茶,质量不一致. 这项研究使用了机器学习的多元素和化合物分析来准确识别其起源并改进种植策略.
科学领域:
- 植物化学 植物化学
- 化学测量 化学测量 化学测量
- 农业科学 农业科学
背景情况:
- 甜茶 (Lithocarpus litseifolius) 是一种有价值的药用和食用植物,具有潜在的健康益处.
- 目前的种植做法导致质量不一致,原材料监管不善.
- 地理来源和种植方法显著影响甜茶的植物化学特征.
研究的目的:
- 开发一种可靠的方法来区分Lithocarpus litseifolius的地理来源.
- 识别关键的化学和元素标记,表明原产地和种植实践.
- 提供对质量形成机制的见解,并优化种植策略.
主要方法:
- 从7个区域的163个样本中分析了22种功能性化合物,4种稳定同位素比和49种多元素.
- 在预测模型中应用直角局部最小平方差分分析 (OPLS-DA).
- 整合8个机器学习算法与多层次数据融合和软投票组合学习.
主要成果:
- OPLS-DA成功生成了用于区分种植地区的预测模型.
- 六个关键变量 (咖啡因,Rb,Ce,δ15N,Sr,3"-O-乙佛洛里津) 被确定为关键的歧视因素.
- 一个最佳的起源分类器使用集体学习实现了100.00%的准确性.
- 确定了影响甜茶质量的七个主要环境因素.
结论:
- 建立了一个非常准确的方法来确定Lithocarpus litseifolius的地理来源.
- 关键的化学和元素标记器为质量变化提供了有价值的见解.
- 这些发现支持制定标准化的种植策略,以保持一致的甜茶质量.
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