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« Previous AbstractPlant bioassay to assess the effects of allelochemicals on the metabolome of the target species Aegilops geniculata by an NMR-based approach    Next AbstractOptimization of a Solid-Phase Microextraction method for the Gas Chromatography-Mass Spectrometry analysis of blackberry (Rubus ulmifolius Schott) fruit volatiles »

Talanta


Title:Statistical analysis for improving data precision in the SPME GC-MS analysis of blackberry (Rubus ulmifolius Schott) volatiles
Author(s):D'Agostino MF; Sanz J; Martinez-Castro I; Giuffre AM; Sicari V; Soria AC;
Address:"Universita degli Studi Mediterranea di Reggio Calabria - Dipartimento AGRARIA, Contrada Melissari, 89124 Reggio Calabria, Italy. Instituto de Quimica Organica General (CSIC), Juan de la Cierva 3, 28006 Madrid, Spain. Instituto de Quimica Organica General (CSIC), Juan de la Cierva 3, 28006 Madrid, Spain. Electronic address: acsoria@iqog.csic.es"
Journal Title:Talanta
Year:2014
Volume:20140302
Issue:
Page Number:248 - 256
DOI: 10.1016/j.talanta.2014.02.058
ISSN/ISBN:1873-3573 (Electronic) 0039-9140 (Linking)
Abstract:"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"
Keywords:"Data Interpretation, Statistical *Food Analysis *Gas Chromatography-Mass Spectrometry Principal Component Analysis Reference Values Reproducibility of Results Rubus/*chemistry *Solid Phase Microextraction Volatile Organic Compounds/*analysis Blackberry (R;"
Notes:"MedlineD'Agostino, M F Sanz, J Martinez-Castro, I Giuffre, A M Sicari, V Soria, A C eng Research Support, Non-U.S. Gov't Netherlands 2014/05/21 Talanta. 2014 Jul; 125:248-56. doi: 10.1016/j.talanta.2014.02.058. Epub 2014 Mar 2"

 
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