A sparse QSRR model for predicting retention indices of essential oils based on robust screening approach-Reference-Cited by-同舟云学术

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A sparse QSRR model for predicting retention indices of essential oils based on robust screening approach

Published:2017-08-03 Issue:8 Volume:28 Page:691-703
ISSN:1062-936X
Container-title:SAR and QSAR in Environmental Research
language:en
Short-container-title:SAR and QSAR in Environmental Research

Author:

Al-Fakih A. M.¹²,Algamal Z. Y.³,Lee M. H.⁴^ORCID,Aziz M.¹⁵

Affiliation:

1. Department of Chemistry, Universiti Teknologi Malaysia, Johor, Malaysia

2. Department of Chemistry, Faculty of Science, Sana’a University, Sana’a, Yemen

3. Department of Statistics and Informatics, University of Mosul, Mosul, Iraq

4. Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, Johor, Malaysia

5. Advanced Membrane Technology Centre, Universiti Teknologi Malaysia, Johor, Malaysia

Funder

Ministry of Higher Education

Ministry of Higher Education of Malaysia

Universiti Teknologi Malaysia

Publisher

Informa UK Limited

Subject

Drug Discovery,Molecular Medicine,General Medicine,Bioengineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/1062936X.2017.1375010

Reference35 articles.

1. Synthesis and in vitro evaluation of peracetyl and deacetyl glycosides of eugenol, isoeugenol and dihydroeugenol acting against food-contaminating bacteria

2. Essential oil composition, total phenolic, flavonoid contents, and antioxidant activity of Thymus species collected from different regions of Iran

3. Chemometric model for predicting retention indices of constituents of essential oils

4. Comparative evaluation of the antibacterial activities of the essential oils of Rosmarinus officinalis L. obtained by hydrodistillation and solvent free microwave extraction methods

5. QSRR Study of GC Retention Indices of Essential-Oil Compounds by Multiple Linear Regression with a Genetic Algorithm

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1. Quantitative structure-property relationship modelling for predicting retention indices of essential oils based on an improved horse herd optimization algorithm;SAR and QSAR in Environmental Research;2023-10-03

2. Quantitative Structure Retention-Relationship Modeling: Towards an Innovative General-Purpose Strategy;Molecules;2023-02-10

3. QSRR models for predicting the retention indices of VOCs in different datasets using an efficient variable selection method coupled with artificial neural network modeling: ANN-based QSPR modeling;Journal of the Iranian Chemical Society;2022-04-12

4. Least absolute deviation estimator‐bridge variable selection and estimation for quantitative structure–activity relationship model;Journal of Chemometrics;2019-06-02

5. Quantitative structure-retention relationship model for predicting retention indices of constituents of essential oils of Thymus vulgaris (Lamiaceae);Journal of the Serbian Chemical Society;2019

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