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130th General Meeting of Korean Chemical Society Artificial Intelligence in Environmental Analysis

Submission Date :
8 / 2 / 2022 , 11 : 41 : 36
Abstract Number :
130080200807
Presenting Type:
Oral Presentation
Presenting Area :
Analytical Chemistry - Oral Presentation of Young Analytical ChemistsⅠ
Authors :
Han Bin Oh
Department of Chemistry, Sogang University, Korea
Assigned Code :
TBA
Presenting Time :
TBA
Last decade, we witnessed the expansion of the artificial intelligence approach in many scientific areas, including environmental analysis. In the environmental analysis, identifying small molecules using liquid chromatography-mass spectrometry (LC-MS or LC-MS/MS) is often formidable, due to the lack of a database. Since the chemical space is very extensive, it is often necessary to narrow down the chemicals into a few subgroups. Along this direction, an approach to label chemicals can be used; for example, dansylation that is selectively conjugated at phenolic -OH or amine group. Using this approach, only the subgroups of chemicals, which have a phenol or amine functional group can be selected and identified in LC-MS. However, this dansylation approach has a weakness in that MS/MS of the dansylated chemicals cannot produce characteristic fragments upon collisional activation, thus with MS/MS not being used for molecular identification. In this situation, the retention time of LC can be a piece of valuable information. Thus, we have attempted to construct an artificial neural network (ANN) model for the dansylated compounds. Furthermore, a standalone software, equipped with a graphic user interface, is constructed to aid the identification of compounds under examination. In the symposium, the details of the constructed software and its applications will be shown.