Linear and Nonlinear Quantitative Structure Linear Retention Indices Relationship Models for Essential Oils
 
More details
Hide details
1
Chemometrics Lab, Department of Chemistry, Faculty of Science, Islamic Azad University, Ilam Branch, Iran
CORRESPONDING AUTHOR
Hadi Noorizadeh   

Chemometrics Lab, Department of Chemistry, Faculty of Science, Islamic Azad University, Ilam Branch, Iran
Publication date: 2017-10-24
 
Eurasian J Anal Chem 2013;8(2):50–63
 
KEYWORDS
ABSTRACT
Genetic algorithm and multiple linear regression (GA-MLR), partial least square (GA-PLS) and kernel PLS (GA-KPLS) techniques were used to investigate the correlation between linear retention indices (LRI) and descriptors for 101 diverse compounds in essential oils of six Stachys species which obtained by gas chromatography/electron impact mass spectrum (GC-EIMS). The correlation coefficient LGO-CV (Q2) between experimental and predicted LRI for training and test sets by GAMLR, GA-PLS and GA-KPLS was 0.936, 0.942 and 0.967 (for 80 compounds), 0.860, 0.871 and 0.919 (for 21 compounds), respectively. This indicates that GA-KPLS can be used as an alternative modeling tool for quantitative structure–retention relationship (QSRR) studies.
 
REFERENCES (35)
1.
Heath HB (1978) In flavor technology. Westport, Connecticut: AVI Publishing company, Inc.
 
2.
Kim NS, Lee DD (2002) Comparison of different extraction method for the analysis of fragrance from Lavandula species by gas chromatography-mass spectrometry, J. Chromatogr. A. 982: 31.
 
3.
Guner A, Ozhatay N, Ekim T, Baser KHC (2000) Flora of Turkey and the East Aegean Islands, Edinburgh: Edinburgh University Press.
 
4.
Güner A, Özhatay N, Ekim T, K.H.C. Baser, Flora of Turkey and the East Aegean Islands, Edinburgh: Edinburgh University Press, 2000.
 
5.
Topçu G, Tan N, Kökdil G (1997) A. Ulubelen, Phytochemistry, Terpenoids from Salvia glutinosa, Phytochemistry 45: 1293.
 
6.
[6] Topçu G, Ertas A, Kolak U, Öztürk M (2007) Antioxidant activity tests on novel triterpenoids from Salvia macrochlamys, Arkivoc 7: 195.
 
7.
Ulubelen A, Topçu G, Bozok-Johansson B (1997) Cardioactive and antibacterial terpenoids from some Salvia species, J. Nat. Prod 60: 1275.
 
8.
Peters R, Tonoli D, van Duin M, Mommers J, Mengerink Y, Wilbers ATM, van Benthem R, Koster CHD, Schoenmakers PJ, derWal SJV (2008) Low-molecularweight model study of peroxide cross-linking of ethylene-propylene (-diene) rubber using gas chromatography and mass spectrometry I. Combination reactions of alkanes, J. Chromatogr. A 1201: 141.
 
9.
Jennings W, Shibamoto T (1980) Quantitative Analysis of Flavor and Fragrance Volatile by Glass Capillary Column Gas Chromatography, Academic Press, New York.
 
10.
Ong VS, Hites RS (1991) Relationship between gas chromatographic retention indexes and computer calculated physical properties of four compound classes, Anal. Chem. 63: 2829.
 
11.
Peng CT, Ding SF, Hua RL, Yang WC (1988) Prediction of retention indexes: I. structure-retention index relationship on apolar column, J. Chromatogr 436: 137.
 
12.
Kaliszan R (1997) Structure and Retention in Chromatography, Harwood, Amsterdam.
 
13.
Qin LT, Liu SHSH, Liu HL, Tong J (2009) Comparative multiple quantitative structure–retention relationships modeling of gas chromatographic retention time of essential oils using multiple linear regression, principal component regression, and partial least squares techniques, J. Chromatogr A 1216: 5302.
 
14.
Riahi S, Pourbasheer E, Ganjali MR, 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, J. Hazard. Mater 166: 853-859.
 
15.
Bombarda I, Dupuy N, Le Van Da JP, Gaydou EP (2008) Comparative chemometric analyses of geographic origins and compositions of lavandin var. Grosso essential oils by mid infrared spectroscopy and gas chromatography, Analytica Chimica Acta 613: 31.
 
16.
Olivero J, Gracia T, Payares P, Vivas R, Diaz D, Daza E, Geerlings P (1997) Molecular structure and gas chromatographic retention behavior of the components of Ylang-Ylang oil, J. Pharm. Sci 86: 625.
 
17.
Kaliszan R, (1993) Quantitative structure-retention relationships applied to reversedphase high-performance liquid chromatography, J. Chromatogr. A 656: 417.
 
18.
Scholkopf B, Smola AJ, Muller KR (1998) Nonlinear component analysis as a kernel eigenvalue problem, Neural Comput 10: 1299.
 
19.
Rosipal R, Trejo LJ (2001) Kernel partial least squares regression in reproducing kernel Hilbert space, J. Mach. Learning Res 2: 97.
 
20.
Haykin S (1999) Neural Networks, Prentice-Hall, New Jersey.
 
21.
Kelen M, Tepe B (2008) Chemical composition, antioxidant and antimicrobial properties of the essential oils of three Salvia species from Turkish flora, Bioresour. Technol 99: 4096.
 
22.
Todeschini R, Consonni V, Mauri A, Pavan P (2003) DRAGON-Software for the calculation of molecular descriptors; Version 3.0 for Windows.
 
23.
Cai W, Xia B, Shao X, Guo Q, Maigret B, Pan Z (2001) Molecular interactions of - cyclodextrin inclusion complexes using a genetic algorithm, J. Mol. Struct. (Theochem.) 535:115.
 
24.
Goldberg DE (2000) Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley–Longman, Reading, MA, USA.
 
25.
Depczynski U, Frost VJ, Molt K (2000) Genetic algorithms applied to the selection of factors in principal component regression, Anal. Chim. Acta 420: 217.
 
26.
Citra M (1999) Estimating the pKa of phenols, carboxylic acids and alcohols from semi-empirical quantum chemical methods, Chemosphere 38: 191.
 
27.
Booth TD, Azzaoui K, Wainer IW (1997) Prediction of Chiral Chromatographic Separations Using Combined Multivariate Regression and Neural Networks, Anal. Chem 69: 3879.
 
28.
Wold S, Sjostrom M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics, Chemom. Intell. Lab. Syst 58: 109.
 
29.
Geladi P, Kowalski BR (1986) Partial least-squares regression: a tutorial, Anal. Chim. Acta. 185: 1.
 
30.
Rosipal R, Trejo LJ (2001) Kernel partial least squares regression in reproducing kernel Hilbert space, J. Mach. Learning Res. 2: 97.
 
31.
Kim K, Lee JM, Lee IB (2005) A novel multivariate regression approach based on kernel partial least squares with orthogonal signal correction, Chemom. Intell. Lab. Syst. 79: 22.
 
32.
Wold S (1991) validation of QSARs, Quant. Struct-Act. Relat. 10: 191. 33. Golbraikh A, Tropsha A (2002) Beware of q2, J. Mol. Graph. Model. 20: 269.
 
33.
Booth TD, Azzaoui K, Wainer IW (1997) Prediction of Chiral Chromatographic Separations Using Combined Multivariate Regression and Neural Networks, Anal. Chem. 69: 3879.
 
34.
Azzaoui K, Morin-Allory L (1996) Comparison and quantification of chromatographic retention mechanisms on three stationary phases using structure-retention relationships, Chromatographia. 42: 389.
 
35.
Todeschini R, Consonni V (2000) Handbook of molecular descriptors, Wiley-VCH, Weinheim.
 
eISSN:1306-3057