同位素和REEs指纹与机器学习相结合,用于谷物的可追溯性
Qian Zhang1, Min Li1, Guilin Han2
1Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Food research international (Ottawa, Ont.)
|March 12, 2026
概括
同位素和稀土元素有效地留下了中国的指纹. 机器学习模型准确地识别了地理来源,增强了食品真实性的工具.
科学领域:
- 地质化学 地质化学
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
背景情况:
- 地理来源对于食品的真实性至关重要,但对于等产品缺乏可追溯性工具.
- 开发可靠的方法来验证的来源对于消费者信任和防止欺诈至关重要.
研究的目的:
- 评估同位素 (87Sr/86Sr) 和稀土元素 (REEs) 作为中国谷物的地球化学指纹.
- 用元素和同位素分析区分四种代表性中国的地理来源.
主要方法:
- 在四个中国样中确定了87Sr/86Sr比率和REE度.
- 应用多变量化学分析,包括主要成分分析 (PCA),线性差异分析 (LDA) 和随机森林 (RF).
主要成果:
- 87Sr/86Sr的比率因而异,反映了当地地质,只有一个例外表明非本地材料.
- REE含量与Sr含量和87Sr/86Sr比率相关,表明共享的来源控制.
- LDA实现了100%的分类准确性,RF在识别来源方面实现了88.89%的准确性.
结论:
- 综合的87Sr/86Sr,REEs和机器学习为验证的地理来源提供了一个强大的策略.
- 这种多元地质化学方法增强了食品真实性和可追溯性系统.
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