从数据到洞察力:通过文献挖掘和机器学习技术,探索不同食品组中的污染物
Zita E Martins1,2, Helena Ramos1, Ana Margarida Araújo1
1LAQV/REQUIMTE, Departamento de Ciências Químicas, Laboratório de Bromatologia e Hidrologia, Faculdade de Farmácia, Universidade do Porto, Rua de Jorge Viterbo Ferreira n.° 228, 4050-313 Porto, Portugal.
Current research in food science
|August 21, 2023
概括
这项研究使用机器学习分析了来自254篇论文的11723个食品污染数据点. 它绘制了72种食品中96种污染物的全球分布模式,包括重金属和农药.
科学领域:
- 食品安全和毒理学
- 环境化学环境化学
- 在食品分析中的数据科学.
背景情况:
- 食物是人类接触非故意化学污染物的主要途径.
- 现有的科学文献包含有价值的量化数据,但手动总结是不切实际的.
- 全球粮食大宗商品在污染物方面受到严格监管.
研究的目的:
- 系统地收集和分析72种食品中96种污染物的量化数据.
- 通过文献挖掘和机器学习识别食品污染物的全球分布模式和趋势.
- 通过探索各种数据集中的联系,提供对食品污染的全面概述.
主要方法:
- 采用了文献挖掘和机器学习技术.
- 收集了过去二十年发表的254篇论文的数据,产生了11,723个数据点.
- 分析的重点是96种污染物,包括重金属,PAH,农药,真菌毒素和HAA.
主要成果:
- 在全球范围内,金属是研究最多的污染物,其次是PAHs,真菌毒素和农药.
- 研究的地理分布不均,欧洲和亚洲处于领先地位.
- 所有食物组都含有金属;PAH,真菌毒素和农药的分布有所不同,而HAA主要存在于鱼类和海鲜中.
结论:
- 该研究成功地为许多化学污染物绘制了全球食品污染模式的地图.
- 调查结果突出了污染物流行和研究重点的区域和食品组特异性变化.
- 综合数据方法为了解和管理食品污染风险提供了宝贵的资源.
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