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在食鱼中探索与重金属相关的潜在人类健康风险:利用数据驱动和计算建模方法

Pritom Bhowmik Akash¹, Sazal Kumar², Md Saikoth Jahan³

¹Department of Civil Engineering, Chittagong University of Engineering & Technology, Chatttogram, 4349, Bangladesh.

Biological trace element research

|September 11, 2024

概括

孟加拉国鱼类中的重金属 (化物) 污染对健康构成风险. 草食鱼提供了更安全的消费限制,而盐水和食肉鱼则具有更高的风险,引导消费者选择更安全的饮食选择.

科学领域:

环境科学环境科学
食品安全食品安全
毒理学毒理学毒理学

背景情况:

孟加拉国饮食鱼类是金属化物污染的重要来源.
人们对与食用受污染的鱼类相关的人类健康风险存在担忧.

研究的目的:

为了评估来自孟加拉国的鱼类中的重金属 (化物) 污染.
利用已公布的数据 (2000-2022) 评估相关的人体健康风险.
通过计算建模和人工神经网络 (ANN) 来确定安全的鱼类消费限制.

主要方法:

对鱼类中金属化物污染的已公布数据进行系统审查 (2000-2022年).
使用目标危险系数 (THQ) 的健康风险评估.
通过计算建模和ANN.通过安全鱼类消费限制的估计.

主要成果:

盐水和食肉鱼具有更高的非癌症风险;淡水和食肉鱼具有更高的癌症风险.
(As), (Cr), (Hg) 和 (Ni) 是主要的风险因素.
在确定安全消费限值方面,ANN和决策树回归 (DTR) 实现了94.7%的准确性.

结论:

草食鱼的污染程度较低,安全消费限值较高 (175.09g/天).
安全消费率根据鱼类不同:食草动物 (175.09g/天),食肉动物 (168.63g/天),食肉动物 (153.05g/天),盐水动物 (156.51g/天),淡水动物 (180.59g/天) 和欧类动物 (182.17g/天).
这些发现有助于消费者选择更安全的鱼类,并减少金属化物暴露带来的健康风险.

关键词:

人工神经网络的人工神经网络孟加拉国孟加拉国孟加拉国饮食安全极限是饮食安全极限.鱼类鱼类鱼类鱼类鱼类重金属 (重金属) 是一种类型的物质.危害人类健康的风险

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