和在中的多变量和预测建模:来源,谷物类型和加工的影响
Mohammad Mahmudur Rahman1, Md Imran Ullah Sarkar2, Zarah Anderson3
1Global Centre for Environmental Remediation (GCER), College of Engineering, Science and Environment, The University of Newcastle, Callaghan, NSW, 2308, Australia; crc for Contamination Assessment and Remediation of the Environment (crcCARE), The University of Newcastle, Callaghan, NSW, 2308, Australia.
Environmental pollution (Barking, Essex : 1987)
|August 4, 2025
概括
这项研究分析了大米中的有毒和必需元素,发现原产地和谷物类型影响水平. 机器学习识别了地理模式,有助于食品安全和对和等元素的风险评估.
科学领域:
- 食品科学 食品科学 食品科学
- 环境科学 环境科学
- 公共卫生 公共卫生
背景情况:
- 米是全球的主食,这使得元素积累成为公共卫生问题.
- 了解有毒元素 (,) 和基本元素的分布至关重要,尤其是在高消耗地区.
- 应用先进机器学习 (ML) 来分析大米元素污染的地理模式的研究有限.
研究的目的:
- 在大米样本中分析 (As), (Cd) 和基本元素 (Zn,Fe,Cu,Mn,Se,Mo).
- 采用多变量统计和ML模型来评估原产地,大米类型和谷物类型对元素概况的影响.
- 确定大米中元素污染的地理模式和潜在来源.
主要方法:
- 在澳大利亚悉尼销售的46个大米样本的分析,使用诱导合等离子体质谱法 (ICP-MS).
- 应用多变量统计技术,包括主要组件分析 (PCA).
- 利用各种机器学习模型来预测和分析元素配置文件和地理模式.
主要成果:
- (As) 和 (Cd) 含量在原产地,大米和谷物类型之间变化有限,但在组内存在显著差异.
- 主要组件分析 (PCA) 显示了As和Cd的温和地理集群.
- 无机As占主导地位,特别是在来自泰国,印度和巴基斯坦的大米中. 棕和紫品种的元素度更高.
结论:
- 一种结合分析和机器学习的方法有效地支持食品安全监督.
- 确定原产地特定的元素概况有助于制定有针对性的风险减轻策略.
- 该研究强调了在管理元素暴露时考虑大米原产地和谷物类型的重要性.
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