Multivariate and predictive modelling of arsenic and cadmium in rice: Influence of origin, grain type, and processing.

IF 7.3 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Environmental Pollution Pub Date : 2025-11-01 Epub Date: 2025-08-05 DOI:10.1016/j.envpol.2025.126927

Mohammad Mahmudur Rahman, Md Imran Ullah Sarkar, Zarah Anderson, Md Tofail Hosain, Abhishek Sharma, Srinivasulu Asadi, Ravi Naidu

{"title":"Multivariate and predictive modelling of arsenic and cadmium in rice: Influence of origin, grain type, and processing.","authors":"Mohammad Mahmudur Rahman, Md Imran Ullah Sarkar, Zarah Anderson, Md Tofail Hosain, Abhishek Sharma, Srinivasulu Asadi, Ravi Naidu","doi":"10.1016/j.envpol.2025.126927","DOIUrl":null,"url":null,"abstract":"<p><p>Rice is a global dietary staple and understanding the accumulation of toxic and essential elements in rice grains is vital for public health, particularly in high-consumption regions. While prior studies have assessed elemental contamination, applying advanced machine learning (ML) to predict and analyze geographic patterns remains limited. This study analyzed arsenic (As), cadmium (Cd), and six essential elements (Zn, Fe, Cu, Mn, Se, and Mo) in 46 rice samples sold in Sydney, Australia, using ICP-MS. Multivariate statistics and various ML models were used to assess the influences of origin, rice type, and grain type on elemental profiles. As and Cd levels showed limited variation between groups, but notable differences within groups. PCA indicated moderate geographical clustering of As and Cd. Inorganic As predominated, especially in rice from Thailand, India, Pakistan, and others. Brown and purple rice had higher elemental concentrations. This combined profiling and ML approach supports improved food safety surveillance and origin-specific risk mitigation.</p>","PeriodicalId":311,"journal":{"name":"Environmental Pollution","volume":" ","pages":"126927"},"PeriodicalIF":7.3000,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Pollution","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1016/j.envpol.2025.126927","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/8/5 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}

引用次数: 0

Abstract

Rice is a global dietary staple and understanding the accumulation of toxic and essential elements in rice grains is vital for public health, particularly in high-consumption regions. While prior studies have assessed elemental contamination, applying advanced machine learning (ML) to predict and analyze geographic patterns remains limited. This study analyzed arsenic (As), cadmium (Cd), and six essential elements (Zn, Fe, Cu, Mn, Se, and Mo) in 46 rice samples sold in Sydney, Australia, using ICP-MS. Multivariate statistics and various ML models were used to assess the influences of origin, rice type, and grain type on elemental profiles. As and Cd levels showed limited variation between groups, but notable differences within groups. PCA indicated moderate geographical clustering of As and Cd. Inorganic As predominated, especially in rice from Thailand, India, Pakistan, and others. Brown and purple rice had higher elemental concentrations. This combined profiling and ML approach supports improved food safety surveillance and origin-specific risk mitigation.

Abstract Image

查看原文本刊更多论文

水稻中砷和镉的多元预测模型：来源、籽粒类型和加工的影响。

稻米是全球的主食，了解稻米中有毒元素和必需元素的积累对公共卫生至关重要，特别是在高消费地区。虽然之前的研究已经评估了元素污染，但应用先进的机器学习（ML）来预测和分析地理模式仍然有限。本研究采用ICP-MS对澳大利亚悉尼销售的46份大米样品中的砷、镉和6种必需元素进行了分析。采用多元统计和各种ML模型来评估产地、水稻类型和籽粒类型对元素剖面的影响。As和Cd水平组间差异有限，组内差异显著。主成分分析表明砷和镉的地理聚类较为温和，无机砷在泰国、印度、巴基斯坦等地的水稻中占主导地位。糙米和紫米的元素含量较高。这种综合分析和ML方法支持改进的食品安全监测和特定来源的风险缓解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Environmental Pollution 环境科学-环境科学

CiteScore

16.00

自引率

6.70%

发文量

2082

审稿时长

2.9 months

期刊介绍： Environmental Pollution is an international peer-reviewed journal that publishes high-quality research papers and review articles covering all aspects of environmental pollution and its impacts on ecosystems and human health. Subject areas include, but are not limited to: • Sources and occurrences of pollutants that are clearly defined and measured in environmental compartments, food and food-related items, and human bodies; • Interlinks between contaminant exposure and biological, ecological, and human health effects, including those of climate change; • Contaminants of emerging concerns (including but not limited to antibiotic resistant microorganisms or genes, microplastics/nanoplastics, electronic wastes, light, and noise) and/or their biological, ecological, or human health effects; • Laboratory and field studies on the remediation/mitigation of environmental pollution via new techniques and with clear links to biological, ecological, or human health effects; • Modeling of pollution processes, patterns, or trends that is of clear environmental and/or human health interest; • New techniques that measure and examine environmental occurrences, transport, behavior, and effects of pollutants within the environment or the laboratory, provided that they can be clearly used to address problems within regional or global environmental compartments.