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QSRR prediction of gas chromatography retention indices of essential oil components

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Abstract

A comprehensive and largest (to the best of our knowledge) database of 791 essential oil components (EOCs) with corresponding gas chromatographic retention properties has been built. With this data set, Quantitative structure–retention relationship (QSRR) models for the prediction of the Kováts retention indices (RIs) on the non-polar DB-5 stationary phase have been built using the DRAGON molecular descriptors and the regression methods: multiple linear regression (MLR) and artificial neural networks (ANN). The obtained models demonstrate good performance, evidenced by the satisfactory statistical parameters for the best MLR (R 2 = 96.75% and \(Q_{\text{ext}}^{2}\) = 98.0%) and ANN (R 2 = 97.18% and \(Q_{\text{ext}}^{2}\) = 98.4%) models, respectively. In addition, the built models provide information on the factors that influence the retention of EOCs over the DB-5 stationary phase. Comparisons of the statistical parameters for the QSRR models in the present study with those reported in the literature demonstrate comparable to superior performance for the former. The obtained models constitute valuable tools for the prediction of RIs for new EOCs whose experimental data are undetermined.

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Acknowledgements

YM-P gives thanks to support from USFQ with partial finance of Project ID5400 “Chancellor Grant 2016”.

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Authors and Affiliations

  1. Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Traslacional (MeM&T), Edificio de Especialidades Médicas, Av. Interoceánica Km 12 ½ -Cumbayá, Quito, 170157, Ecuador

    Yovani Marrero-Ponce

  2. Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, 17-1200-841, Quito, Ecuador

    Yovani Marrero-Ponce

  3. Grupo de Investigación Ambiental (GIA), Programas Ambientales, Facultad de Ingenierías, Fundación Universitaria Tecnológico de Comfenalco (COMFENALCO), Programa de Ingeniería de Procesos, Bolívar, Cartagena de Indias, Bolívar, Colombia

    Yovani Marrero-Ponce

  4. Department of Chemistry, Federal University of Lavras, Lavras, Brazil

    Stephen J. Barigye

  5. Facultad de Medicina, Universidad de Las Américas, Quito, Pichincha, Ecuador

    Stephen J. Barigye

  6. Department of Pharmacy, Faculty of Chemistry-Pharmacy, Central University of Las Villas, Villa Clara, 54830, Santa Clara, Cuba

    María E. Jorge-Rodríguez & Trang Tran-Thi-Thu

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  1. Yovani Marrero-Ponce
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  2. Stephen J. Barigye
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  3. María E. Jorge-Rodríguez
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Correspondence to Stephen J. Barigye.

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Marrero-Ponce, Y., Barigye, S.J., Jorge-Rodríguez, M.E. et al. QSRR prediction of gas chromatography retention indices of essential oil components. Chem. Pap. 72, 57–69 (2018). https://doi.org/10.1007/s11696-017-0257-x

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