Quantitative structure-property relationship analysis for the retention index of fragrance-like compounds on a polar stationary phase

被引:32
作者
Rojas, Cristian [1 ,2 ]
Duchowicz, Pablo R. [1 ]
Tripaldi, Piercosimo [3 ]
Pis Diez, Reinaldo [4 ]
机构
[1] UNLP, CONICET, CCT La Plata, Inst Invest Fisicoquim Teor & Aplicadas INIFTA, RA-1900 La Plata, Buenos Aires, Argentina
[2] Univ Azuay, Decanato Gen Invest, Cuenca, Ecuador
[3] Univ Azuay, Fac Ciencia & Tecnol, Lab Quim Fis Alimentos, Cuenca, Ecuador
[4] UNLP, Fac Ciencias Exactas, CONICET, CEQUINOR Ctr Quim Inorgan,Dept Quim, RA-1900 La Plata, Buenos Aires, Argentina
关键词
Fragrance; Carbowax 20M column; QSPR theory; Dragon software; Replacement method; k-Means cluster analysis; GAS-CHROMATOGRAPHY; LIQUID-CHROMATOGRAPHY; FLAVOR COMPOUNDS; QSAR MODELS; PREDICTION; ALKYLBENZENES; APPLICABILITY; SELECTION; HYDROCARBONS; DESCRIPTORS;
D O I
10.1016/j.chroma.2015.10.028
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
A quantitative structure-property relationship (QSPR) was developed for modeling the retention index of 1184 flavor and fragrance compounds measured using a Carbowax 20M glass capillary gas chromatography column. The 4885 molecular descriptors were calculated using Dragon software, and then were simultaneously analyzed through multivariable linear regression analysis using the replacement method (RM) variable subset selection technique. We proceeded in three steps, the first one by considering all descriptor blocks, the second one by excluding conformational descriptor blocks, and the last one by analyzing only 3D-descriptor families. The models were validated through an external test set of compounds. Cross-validation methods such as leave-one-out and leave-many-out were applied, together with Y-randomization and applicability domain analysis. The developed model was used to estimate the I of a set of 22 molecules. The results clearly suggest that 3D-descriptors do not offer relevant information for modeling the retention index, while a topological index such as the Randic-like index from reciprocal squared distance matrix has a high relevance for this purpose. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:277 / 288
页数:12
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