Our publication “TrendProbe: Time profile analysis of emerging contaminants by LC-HRMS non-target screening and deep learning convolutional neural network” by Varvara Nikolopoulou, Reza Aalizadeh, Maria-Christina Nika and Nikolaos S. Thomaidis, is available in the Journal of Hazardous Materials.
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 Asopos river). From trend analysis and factor analysis, the chemicals could be classified into periodic, spill, increasing, decreasing and false trend. Three classes of surfactants were identified for the first time in river water and two of them were never reported in the literature. 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.