Application of HS-SPME, SDME and Cold-Press Coupled to GC/MS to Analysis the Essential Oils of Citrus sinensis CV. Thomson Navel and QSRR Study for Prediction of Retention Indices by Stepwise and Genetic Algorithm-Multiple Linear Regression Approaches

Abstract

Volatile fractions in the essential oils (EOs) obtained from the peels of Citrus sinensis CV. Thomson Navel by head-space solid phase micro-extraction (HS-SPME), single drop micro-extraction (SDME) and cold-press (CP) techniques were analyzed by means of gas chromatography-mass spectrometry (GC-MS). Fifteen, nine and fourteen compounds were identified by HS-SPME, SDME and CP methods, respectively representing 99.75 %, 99.45 % and 98.5 % of the chemical profiles in the peels of C. sinensis. The main components were found to be limonene (72.69 %, 71.96 %, 90.40 %), myrcene (9.65 %, 9.80 %, 1.10 %), sabinene (2.63 %, 9.35 %, 0.90 %) and α-pinene (2.00 %, 7.05 %, 1.60 %) by HS-SPME, SDME and CP, respectively. In addition, a quantitative structure-retention relationship (QSRR) study has been developed for prediction of retention indices (RIs) of the occurred natural compounds. The suitable set of molecular descriptors was calculated and the important descriptors were selected by stepwise (SW) and genetic algorithm (GA) variable selection features combined with multiple linear regression (MLR). A comparison between the attained results indicated the superiority of the genetic algorithm over the stepwise in the feature-selection step. The predictive quality of the QSRR models was tested for an external set of five compounds, randomly chosen out of 27 compounds. The genetic algorithm-multiple linear regression model (GA-MLR) with two selected descriptors gave rise in the best predicted results. The accuracy of the proposed GA-MLR model is illustrated using cross-validation, validation through an external test set and Y-randomization evaluation techniques.