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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 α-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

journal of the serbian chemical society

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