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Quantitative structure-property relationship (QSPR) study to predict retention time of polycyclic aromatic hydrocarbons using the random forest and artificial neural network methods

Crossref DOI link: https://doi.org/10.1007/s11224-019-01476-w

Published Online: 2020-01-29

Published Print: 2020-08

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Emrarian, Moona

Sohrabi, Mahmoud Reza

Goudarzi, Nasser

Tadayon, Fariba
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Text and Data Mining valid from 2020-01-29

Version of Record valid from 2020-01-29
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Article History

Received: 30 October 2019

Accepted: 9 December 2019

First Online: 29 January 2020

Compliance with ethical standards

:

: Moona Emrarian, Mahmoud Reza Sohrabi, Nasser Goudarzi, Fariba Tadayon declare that they have no conflict of interest.

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