The first attempt at non-linear in silico prediction of sampling rates for polar organic chemical integrative samplers (POCIS)
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Published version
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Author(s)
Type
Journal Article
Abstract
Modeling and prediction of polar organic chemical integrative sampler (POCIS) sampling rates (Rs) for 73 compounds using artificial neural networks (ANNs) is presented for the first time. Two models were constructed: the first was developed ab initio using a genetic algorithm (GSD-model) to shortlist 24 descriptors covering constitutional, topological, geometrical and physicochemical properties and the second model was adapted for Rs prediction from a previous chromatographic retention model (RTD-model). Mechanistic evaluation of descriptors showed that models did not require comprehensive a priori information to predict Rs. Average predicted errors for the verification and blind test sets were 0.03 ± 0.02 L d-1 (RTD-model) and 0.03 ± 0.03 L d-1 (GSD-model) relative to experimentally determined Rs. Prediction variability in replicated models was the same or less than for measured Rs. Networks were externally validated using a measured Rs data set of six benzodiazepines. The RTD-model performed best in comparison to the GSD-model for these compounds (average absolute errors of 0.0145 ± 0.008 L d-1 and 0.0437 ± 0.02 L d-1, respectively). Improvements to generalizability of modeling approaches will be reliant on the need for standardized guidelines for Rs measurement. The use of in silico tools for Rs determination represents a more economical approach than laboratory calibrations.
Date Issued
2016-07-01
Date Acceptance
2016-07-01
Citation
Environmental science & technology, 2016, 50 (15), pp.7973-7981
ISSN
0013-936X
Start Page
7973
End Page
7981
Journal / Book Title
Environmental science & technology
Volume
50
Issue
15
Copyright Statement
© 2016 American Chemical Society. This is an open access article published under a Creative Commons Attribution (CC-BY)
License, which permits unrestricted use, distribution and reproduction in any medium,
provided the author and source are cited.
License, which permits unrestricted use, distribution and reproduction in any medium,
provided the author and source are cited.
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Identifier
http://www.scopus.com/inward/record.url?scp=84980438867&partnerID=8YFLogxK
Subjects
Calibration
Environmental Monitoring
Organic Chemicals
Water Pollutants, Chemical
Organic Chemicals
Water Pollutants, Chemical
Calibration
Environmental Monitoring
Environmental Sciences
Notes
10.1021/acs.est.6b01407 Modeling and prediction of polar organic chemical integrative sampler (POCIS) sampling rates (Rs) for 73 compounds using artificial neural networks (ANNs) is presented for the first time. Two models were constructed: the first was developed ab initio using a genetic algorithm (GSD-model) to shortlist 24 descriptors covering constitutional, topological, geometrical and physicochemical properties and the second model was adapted for Rs prediction from a previous chromatographic retention model (RTD-model). Mechanistic evaluation of descriptors showed that models did not require comprehensive a priori information to predict Rs. Average predicted errors for the verification and blind test sets were 0.03 ± 0.02 L d-1 (RTD-model) and 0.03 ± 0.03 L d-1 (GSD-model) relative to experimentally determined Rs. Prediction variability in replicated models was the same or less than for measured Rs. Networks were externally validated using a measured Rs data set of six benzodiazepines. The RTD-model performed best in comparison to the GSD-model for these compounds (average absolute errors of 0.0145 ± 0.008 L d-1 and 0.0437 ± 0.02 L d-1, respectively). Improvements to generalizability of modeling approaches will be reliant on the need for standardized guidelines for Rs measurement. The use of in silico tools for Rs determination represents a more economical approach than laboratory calibrations.
Publication Status
Published
Date Publish Online
2016-07-18