粮食可追溯性中的同位素:机理,应用和与机器学习的整合
Nan Qiao1, Qian Zhang2, Guilin Han3
1State Key Laboratory of Geomicrobiology and Environmental Changes, Institute of Earth Sciences, China University of Geosciences (Beijing), Beijing 100083, China.
Food chemistry
|February 28, 2026
概括
(Sr) 同位素有效地追踪食品的来源和真实性. 将同位素与机器学习 (ML) 结合起来,可显著提高全球认证的食品可追溯系统.
科学领域:
- 食品科学和分析化学.
- 地质化学和同位素分析.
- 食品认证中的计算方法.
背景情况:
- 食品来源对于全球供应链的真实性,安全性和质量至关重要.
- (Sr) 同位素为食品的可追溯性提供了稳定且与地质相关的标记物.
- 现有的方法面临复杂的数据集和重叠的同位素范围的挑战.
研究的目的:
- 审查在不同类别的食品中应用Sr同位素比率.
- 突出Sr同位素在原料和加工食品中的稳定性和歧视力.
- 强调将Sr同位素与机器学习 (ML) 集成,以改善食品真实性.
主要方法:
- 系统审查关于同位素在食品中的应用的科学文献.
- 在各种食物矩阵中分析 Sr 同位素稳定性和歧视力.
- 将Sr同位素数据与ML算法 (PCA,LDA,SVM,HCA) 的整合.
主要成果:
- 的同位素比表明在初级作物,牲畜和加工食品中具有稳定性和歧视力.
- 在与 Sr 同位素数据相结合时,ML 算法显著提高了分类准确性.
- 同位素和ML的协同使用为食品可追溯性提供了一个强大的框架.
结论:
- 同位素是食品来源和认证的强大工具.
- 将同位素与ML算法集成,提高了食品追溯系统的可靠性.
- 这种方法促进了全球食品认证的科学理解和实际应用.
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