优先次序
卷积神经网络
生物信息学
环境科学
水生毒理学
采样(信号处理)
深度学习
污染
鉴定(生物学)
人工智能
计算机科学
环境化学
机器学习
化学
毒性
生物
生态学
工程类
管理科学
计算机视觉
基因
生物化学
有机化学
滤波器(信号处理)
作者
Varvara Nikolopoulou,Reza Aalizadeh,Maria‐Christina Nika,Nikolaos S. Thomaidis
标识
DOI:10.1016/j.jhazmat.2021.128194
摘要
Peak prioritization is one of the key steps in non-target screening of environmental samples to direct the identification efforts to relevant and important features. Occurrence of chemicals is sometimes a function of time and their presence in consecutive days (trend) reveals important aspects such as discharges from agricultural, industrial or domestic activities. This study presents a validated computational framework based on deep learning conventional neural network to classify trends of chemicals over 30 consecutive days of sampling in two sampling sites (upstream and downstream of a river). From trend analysis and factor analysis, the chemicals could be classified into periodic, spill, increasing, decreasing and false trend. The developed method was validated with list of 42 reference standards (target screening) and applied to samples. 25 compounds were selected by the deep learning and identified via non-target screening. Three classes of surfactants were identified for the first time in river water and two of them were never reported in the literature. Overall, 21 new homologous series of the newly identified surfactants were tentatively identified. The aquatic toxicity of the identified compounds was estimated by in silico tools and a few compounds along with their homologous series showed potential risk to aquatic environment.
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