2022 journal article
Assessment of artificial neural network to identify compositional differences in ultrahigh-resolution mass spectra acquired from coal mine affected soils
Talanta.
author keywords: Natural organic matter; Weight; FT-ICR MS; Neural network; Soil
MeSH headings : Coal / analysis; Environmental Monitoring; Mass Spectrometry; Neural Networks, Computer; Soil / chemistry; Soil Pollutants / analysis
UN Sustainable Development Goal Categories
13. Climate Action
(Web of Science)
Source: ORCID
Added: June 4, 2022
This study assessed the applicability of artificial neural networks (ANNs) as a tool to identify compounds contributing to compositional differences in coal-contaminated soils. An artificial neural network model was constructed from laser desorption ionization ultrahigh-resolution mass spectra obtained from coal contaminated soils. A good correlation (R2 = 1.00 for model and R2 = 0.99 for test) was observed between the measured and predicted values, thus validating the constructed model. To identify chemicals contributing to the coal contents of the soils, the weight values of the constructed model were evaluated. Condensed hydrocarbon and low oxygen containing compounds were found to have larger weight values and hence they were the main contributors to the coal contents of soils. In contrast, compounds identified as lignin did not contribute to the coal contents of soils. These findings were consistent with the conventional knowledge on coal and results from the conventional partial least square method. Therefore, we concluded that the weight interpretation following ANN analysis presented herein can be used to identify compounds that contribute to the compositional differences of natural organic matter (NOM) samples.