PMID- 36473641
OWN - NLM
STAT- MEDLINE
DCOM- 20230105
LR  - 20230111
IS  - 1873-6424 (Electronic)
IS  - 0269-7491 (Linking)
VI  - 317
DP  - 2023 Jan 15
TI  - Application of gas chromatographic data and 2D molecular descriptors for accurate 
      global mobility potential prediction.
PG  - 120816
LID - S0269-7491(22)02031-0 [pii]
LID - 10.1016/j.envpol.2022.120816 [doi]
AB  - Mobility is a key feature affecting the environmental fate, which is of 
      particular importance in the case of persistent organic pollutants (POPs) and 
      emerging pollutants (EPs). In this study, the global mobility classification 
      artificial neural networks-based models employing GC retention times (RT) and 2D 
      molecular descriptors were constructed and validated. The high usability of RT 
      was confirmed based on the feature selection step performed using the 
      multivariate adaptive regression splines (MARS) tool. Although RT was found to be 
      the most important, according to Kruskal-Wallis ANOVA analysis, it is 
      insufficient to build a robust model, which justifies the need to expand the 
      input layer with 2D descriptors. Therefore the following molecular descriptors: 
      MPC10, WTPT-2, AATS8s, minaaCH, GATS7c, RotBtFrac, ATSC7v and ATSC1p, which were 
      characterized by a high predicting potential were used to improve the 
      classification performance. As a result of machine learning procedure ten of the 
      most accurate neural networks were selected. The external validation showed that 
      the final models are characterized by a high general accuracy score 
      (85.71-96.43%). The high predicting abilities were also confirmed by the 
      micro-averaged Matthews correlation coefficient (MAMCC) (0.73-0.88). To evaluate 
      the applicability of the models, new retention times of selected POPs and EPs 
      including pesticides, polycyclic aromatic hydrocarbons, pharmaceuticals, 
      fragrances and personal care products were measured and used for mobility 
      prediction. Further, the classifiers were used for photodegradation and 
      chlorination products of two popular sunscreen agents, 
      2-ethyl-hexyl-4-methoxycinnamate and 2-ethylhexyl 4-(dimethylamino)benzoate.
CI  - Copyright (c) 2022 Elsevier Ltd. All rights reserved.
FAU - Studzinski, Waldemar
AU  - Studzinski W
AD  - Faculty of Chemical Technology and Engineering, Bydgoszcz University of Science 
      and Technology, Seminaryjna 3, 85-326, Bydgoszcz, Poland.
FAU - Przybylek, Maciej
AU  - Przybylek M
AD  - Department of Physical Chemistry, Pharmacy Faculty, Collegium Medicum of 
      Bydgoszcz, Nicolaus Copernicus University in Torun, Kurpinskiego 5, 85-950, 
      Bydgoszcz, Poland. Electronic address: m.przybylek@cm.umk.pl.
FAU - Gackowska, Alicja
AU  - Gackowska A
AD  - Faculty of Chemical Technology and Engineering, Bydgoszcz University of Science 
      and Technology, Seminaryjna 3, 85-326, Bydgoszcz, Poland.
LA  - eng
PT  - Journal Article
DEP - 20221203
PL  - England
TA  - Environ Pollut
JT  - Environmental pollution (Barking, Essex : 1987)
JID - 8804476
RN  - 0 (Environmental Pollutants)
SB  - IM
MH  - *Neural Networks, Computer
MH  - Chromatography, Gas/methods
MH  - *Environmental Pollutants
OTO - NOTNLM
OT  - Artificial neural networks
OT  - Emerging pollutants
OT  - Gas chromatography
OT  - Global mobility
OT  - Persistent organic pollutants
OT  - Retention times
COIS- Declaration of competing interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2022/12/07 06:00
MHDA- 2023/01/06 06:00
CRDT- 2022/12/06 19:25
PHST- 2022/09/09 00:00 [received]
PHST- 2022/11/15 00:00 [revised]
PHST- 2022/12/02 00:00 [accepted]
PHST- 2022/12/07 06:00 [pubmed]
PHST- 2023/01/06 06:00 [medline]
PHST- 2022/12/06 19:25 [entrez]
AID - S0269-7491(22)02031-0 [pii]
AID - 10.1016/j.envpol.2022.120816 [doi]
PST - ppublish
SO  - Environ Pollut. 2023 Jan 15;317:120816. doi: 10.1016/j.envpol.2022.120816. Epub 
      2022 Dec 3.