Optimization of a Solid-Phase Microextraction method for the Gas Chromatography-Mass Spectrometry analysis of blackberry (Rubus ulmifolius Schott) fruit volatiles.

A Solid-Phase Microextraction method for the Gas Chromatography-Mass Spectrometry analysis of blackberry (Rubus sp.) volatiles has been fully optimized by means of a Box-Behnken experimental design. The optimized operating conditions (Carboxen/Polydimethylsiloxane fiber coating, 66°C, 20 min equilibrium time and 16 min extraction time) have been applied to the characterization for the first time of the volatile composition of Rubus ulmifolius Schott blackberries collected in Italy and Spain. A total of 74 volatiles of different functionality were identified; esters and aliphatic alcohols were the predominant classes in both sample types. Methylbutanal (2.02-25.70%), ethanol (9.84-68.21%), 2,3-butanedione (2.31-14.71%), trans-2-hexenal (0.49-17.49%), 3-hydroxy-2-butanone (0.08-7.39%), 1-hexanol (0.56-16.39%), 1-octanol (0.49-10.86%) and methylbutanoic acid (0.53-21.48%) were the major compounds in most blackberries analyzed. Stepwise multiple regression analysis of semiquantitative data showed that only two variables (ethyl decanoate and ethyl acetate) were necessary for a successful differentiation of blackberries according to their harvest location.

Statistical analysis for improving data precision in the SPME GC-MS analysis of blackberry (Rubus ulmifolius Schott) volatiles.

Statistical analysis has been used for the first time to evaluate the dispersion of quantitative data in the solid-phase microextraction (SPME) followed by gas chromatography-mass spectrometry (GC-MS) analysis of blackberry (Rubus ulmifolius Schott) volatiles with the aim of improving their precision. Experimental and randomly simulated data were compared using different statistical parameters (correlation coefficients, Principal Component Analysis loadings and eigenvalues). Non-random factors were shown to significantly contribute to total dispersion; groups of volatile compounds could be associated with these factors. A significant improvement of precision was achieved when considering percent concentration ratios, rather than percent values, among those blackberry volatiles with a similar dispersion behavior. As novelty over previous references, and to complement this main objective, the presence of non-random dispersion trends in data from simple blackberry model systems was evidenced. Although the influence of the type of matrix on data precision was proved, the possibility of a better understanding of the dispersion patterns in real samples was not possible from model systems. The approach here used was validated for the first time through the multicomponent characterization of Italian blackberries from different harvest years.