Prediction of the GC-MS Retention Indices for a Diverse Set of Terpenes as Constituent Components of Camu-camu (Myrciaria dubia (HBK) Mc Vaugh) Volatile Oil, Using Particle Swarm Optimization-Multiple Linear Regression (PSO-MLR)

الموضوعات :

Majid Mohammadhosseini ¹

1 - Islamic Azad University, Shahrood Branch, Shahrood, Iran

تاريخ الإرسال : 19 الأحد , رجب, 1435 تاريخ التأكيد : 20 الإثنين , صفر, 1440 تاريخ الإصدار : 20 الإثنين , صفر, 1440

الکلمات المفتاحية: Cross validation, Particle swarm Optimization-multiple linear regression (PSO-MLR), Quantitative structure-retention relationship (QSRR), Retention indices (RIs), Prediction Essential oil, External validation GC GC-MS,

ملخص المقالة :

A reliable quantitative structure retention relationship (QSRR) study has been evaluated to predict the retention indices (RIs) of a broad spectrum of compounds, namely 118 non-linear, cyclic and heterocyclic terpenoids (both saturated and unsaturated), on an HP-5MS fused silica column. A principal component analysis showed that seven compounds lay outside of the main cluster. After elimination of the outliers, the data set was divided into training and test sets involving 80 and 28 compounds. The method was tested by application of the particle swarm optimization (PSO) method to find the most effective molecular descriptors, followed by multiple linear regressions (MLR). The PSO-MLR model was further confirmed through “leave one out cross validation” (LOO-CV) and “leave group out cross validation” (LGO-CV), as well as external validations. The promising statistical figures of merit associated with the proposed model (R2train=0.936, Q2LOO=0.928, Q2LGO=0.921, F=376.4) confirm its high ability to predict RIs with negligible relative errors of predictions (REP train=4.8%, REP test=6.0%).

المصادر:

1. http://en.wikipedia.org/wiki/Essential oil.
2. Akhlaghi H., Nekoei M., Mohammadhosseini M., Motavalizadehkakhky A., 2012. Chemical composition of the volatile oils from the flowers, stems and leaves of Prangos latiloba Korov. using the head space solid phase microextraction method prior to analysis by gas chromatography-mass spectrometry. Journal of Essential Oil Bearing Plants. 15, 328-335.
3. Eminagaoglu O., Tepe B., Yumrutas O., Akpulat H. A., Daferera D., Polissiou M., Sokmen A., 2007. The in vitro antioxidative properties of the essential oils and methanol extracts of Satureja spicigera (K. Koch.) Boiss. and Satureja cuneifolia ten. Food Chemistry. 100 (1): 339-343.
4. Pino J.A., Quijano C.E.C., 2008. Volatile constituents of Camu-camu (Myrciaria dubia (HBK) McVaugh) leaves. Journal of Essential Oil Research. 20 (3): 205-207.
5. Heberger K., 2007. Quantitative structure-(chromatographic) retention relationships. Journal of Chromatography A. 1158 (1-2): 273-305.
6. Goudarzi N., Goodarzi M., Mohammadhosseini M. M., Nekooei M., 2009. QSPR models for prediction of half wave potentials of some chlorinated organic compounds using SR-PLS and GA-PLS methods. Molecular Physics. 107 (17): 1739-1744.
7. Hansch C., Leo A. 1995. Exploring QSAR Fundamentals: Applications in Chemistry and Biology. American Chemical Society. Washington DC.
8. Khajeh A., Modarress H., 2012. Quantitative structure-property relationship prediction of liquid heat capacity at 298.15 K for organic compounds. Industrial & Engineering Chemistry Research. 51 (17): 6251-6255.
9. Mirkhani S.A., Gharagheizi F., 2012. Predictive quantitative structure-property relationship model for the estimation of ionic liquid viscosity. Industrial & Engineering Chemistry Research. 51 (5): 2470-2477.
10. Mohammadhosseini M., Nekoei M., 2012. Quantitative structure-electrochemistry relationship (QSER) study for prediction of half-wave reduction potentials of some chlorinated organic compounds by GA-MLR. Asian Journal of Chemistry. 25, 349-352.
11. Nekoei M., Salimi M., Dolatabadi M., Mohammadhosseini M., 2011. Prediction of antileukemia activity of berbamine derivatives by genetic algorithm-multiple linear regression. Monatshefte fur Chemie. 142 (9): 943-948.
12. Nekoei M., Salimi M., Dolatabadi M., Mohammadhosseini M., 2011. A quantitative structure-activity relationship study of tetrabutylphosphonium bromide analogs as muscarinic acetylcholine receptors agonists. Journal of the Serbian Chemical Society. 76 (8): 1117-1127.
13. Senior S.A., Nassar A.M., 2012. Determination of pKa for substituted benzoic acids in mixed solvent using density functional theory and QSPR. International Journal of Quantum Chemistry. 112, 683-694.
14. Toropova A.P., Toropov A.A., Benfenati E., Gini G., 2012. QSAR models for toxicity of organic substances to daphnia magna built up by using the CORAL freeware. Chemical Biology & Drug Design, 79 (3): 332-338.
15. Fragkaki A.G., Tsantili-Kakoulidou A., Angelis Y.S., Koupparis M., Georgakopoulos C., 2009. Gas chromatographic quantitative structure-retention relationships of trimethylsilylated anabolic androgenic steroids by multiple linear regression and partial least squares. Journal of Chromatography A, 1216 (47): 8404-8420.
16. Ghasemi J., Asadpour S., Abdolmaleki A., 2007. Prediction of gas chromatography/electron capture detector retention times of chlorinated pesticides, herbicides, and organohalides by multivariate chemometrics methods. Analytica Chimica Acta. 588 (2): 200-206.
17. Mohammadhosseini M., Deeb O., Alavi- Gharabagh A., Nekoei M., 2012. Exploring novel QSRRs for simulation of gas chromatographic retention indices of diverse sets of terpenoids in Pistacia lentiscus L. essential oil using stepwise and genetic algorithm multiple linear regressions. Analytical Chemistry Letters. 2, 80-102.
18. Mohammadhosseini M., Zamani H.A., Akhlaghi H., Nekoei M., 2011. Hydrodistilled volatile oil constituents of the aerial parts of Prangos serpentinica (Rech.f., Aell. Esfand). Hernnstadt and Heyn from Iran and quantitative structure-retention relationship simulation. Journal of Essential Oil Bearing Plants. 14 (5): 559-573.
19. Chen H.F., 2008. Quantitative predictions of gas chromatography retention indexes with support vector machines, radial basis neural networks and multiple linear regression. Analytica Chimica Acta. 609 (1): 24-36.
20. Mohammadhosseini M., 2012. Chemical profile and antibacterial activity in hydrodistilled oil from aerial parts of Prangos ferulacea (L.) Lindl. and prediction of gas chromatographic retention indices by using genetic algorithm multiple linear regressions. Asian Journal of Chemistry. 24, 3814-3820.
21. Noorizadeh H., Farmany A., 2010. qsrr models to predict retention indices of cyclic compounds of essential oils. Chromatographia. 72 (5-6):563-569.
22. Riahi S., Ganjali M. R., Pourbasheer E., Norouzi P., 2008. QSRR study of GC retention indices of essential-oil compounds by multiple linear regression with a genetic algorithm. Chromatographia. 67 (11-12): 917-922.
23. Riahi S., Pourbasheer E., Ganjali M. R., Norouzi P., 2009. Investigation of different linear and nonlinear chemometric methods for modeling of retention index of essential oil components: Concerns to support vector machine. Journal of Hazardous Materials. 166 (2-3): 853-859.
24. Parsopoulos K.E., Vrahatis M.N. 2010. Particle Swarm Optimization and Intelligence: Advances and Applications. Information Science Publishing (IGI Global)
25. Mohammadhosseini M., 2013. Novel PSO-MLR algorithm to predict the chromatographic retention behaviors of natural compounds. Analytical Chemistry Letters. 3 (4): 226-248.
26. Dewar M.J.S., Zeoblish E.G., Healy E.F., Stewart J. J., 1985. Development and use of quantum mechanical molecular models.76. AM1: a new general purpose quantum mechanical molecular model. Journal of the American Chemical Society. 107, 3902-3909.
27. Todeschini R., Consonni V. 2000. Handbook of Molecular Descriptors. Wiley-VCH. Weinheim, Germany.
28. Olivero J., Garcia T., Payares P., Vivas R., Diaz D., Daza E., Geerliger P., 1997. Molecular structure and gas chromatographic retention behavior of the components of ylangââ‚‌“ylang oil. Journal of Pharmaceutical Sciences. 86, 625-630.
29. Heberger K., 1999. Evaluation of polarity indicators and stationary phases by principal component analysis in gas-liquid chromatography. Chemometrics and Intelligent Laboratory Systems. 47, 41-49.
30. Wold S., Esbensen K., Geladi P., 1987. Principal component analysis. Chemometrics and Intelligent Laboratory Systems. 2, 37-52.
31. Heberger K., Milczewska K., Voelkel A., 2005. Principal component analysis of polymer-solvent and filler-solvent interactions by inverse gas chromatography. Colloids and Surfaces a-Physicochemical and Engineering Aspects. 260 (1-3): 29-37.
32. Heberger K., Gorgeny M., 1999. Principal component analysis of Kovats indices for carbonyl compounds in capillary gas chromatography. Journal of Chromatography A. 845, 21-31.
33. Eberhart R.C., Kennedy J., presented in part at the Proceedings of the 6th Symposium on Micro Machine and Human Science, Nagoya, Japan, 1995.
34. Castillo O., Melin P., 2012. Optimization of type-2 fuzzy systems based on bio-inspired methods:A concise review. Information Sciences. 205, 1-19.
35. Tropsha A., Gramatica P., Gombar V.K., 2003. The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models. QSAR & Combinatorial Science. 22 (1): 69-77.
36. Noorizadeh H., Farmany A., Khosravi A., 2010. Investigation of retention behaviors of essential oils by using QSRR. Journal of the Chinese Chemical Society. 57 (5A): 982-991.
37. Skoog D., Holler F.J., Nieman T.A. 1998. Principles of Instrumental Analysis, Fifth Ed.
38. Cazes J. 2004. Encyclopedia of Chromatography, Update Supplement.
39. Todeschini R., Gramatica P., 1997. SD-Modeling and prediction by WHIM descriptors. Part 5: Theory development and chemical meaning of the WHIM descriptors. Molecular Informatics. 16, 113-119.
40. Consonni V., Todeschini R., Pavan M., 2002. Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel 3D molecular descriptors. Journal of Chemical Information and Computer Sciences. 42 (3): 682-692.