The chemical composition of the volatile fraction obtained by head-space
solid phase microextraction (HS-SPME), single drop microextraction (SDME) and
the essential oil obtained by cold-press from the peels of C. sinensis cv.
valencia were analyzed employing gas chromatography-flame ionization detector
(GC-FID) and gas chromatography-mass spectrometry (GC-MS). The main
components were limonene (61.34 %, 68.27 %, 90.50 %), myrcene (17.55 %, 12.35
%, 2.50 %), sabinene (6.50 %, 7.62 %, 0.5 %) and ?-pinene (0 %, 6.65 %, 1.4
%) respectively obtained by HS-SPME, SDME and cold-press. Then a quantitative
structure-retention relationship (QSRR) study for the prediction of retention
indices (RI) of the compounds was developed by application of structural
descriptors and the multiple linear regression (MLR) method. Principal
components analysis was used to select the training set. A simple model with
low standard errors and high correlation coefficients was obtained. The
results illustrated that linear techniques such as MLR combined with a
successful variable selection procedure are capable of generating an
efficient QSRR model for prediction of the retention indices of different
compounds. This model, with high statistical significance (R2 train = 0.983,
R2 test = 0.970, Q2 LOO = 0.962, Q2 LGO = 0.936, REP(%) = 3.00), could be
used adequately for the prediction and description of the retention indices
of the volatile compounds.
Chemical composition of the essential oils of Citrus sinensis cv. valencia and a quantitative structure-retention relationship study for the prediction of retention indices by multiple linear regression