Multivariate Optimization of Volatile Compounds Extraction in Chardonnay Wine by Headspace-Solid Phase Micro Extraction and Gas Chromatography Coupled with Tandem Mass Spectrometry

A method for optimization of extraction of volatile compounds in Chardonnay wine was developed using headspace-solid phase microextraction (HS-SPME) and gas chromatography coupled with triple quadrupole tandem mass spectrometry (GCMS/MS). Optimization of the HS-SPME conditions, temperature (T, ̊C) and extraction time (t, minutes), was carried out using a 2 factorial central composite rotational design (CCRD). Total area of chromatographic peaks of nineteen compounds was monitored in order to identify the best response and the data was collected on multiple reaction monitoring (MRM) mode. The mathematical model that describes the response surface for the CCRD was validated using the analysis of variance (ANO VA) with 95% of confidence level. This model showed a lack of fit based on mean square pure error ratios for each response, in which Fcalculated was 2.23 higher than Ftabulated. Even though the models cannot be rigorously used to make quantitative predictions, the coefficients of the model, especially the linear ones, are useful for understanding systematic behaviour of the response values as a function of the factor levels. Multivariate statistical design can be used in optimization of HS-SPME extraction parameters with reduced number of experiments and can be useful in sampling method of volatile compounds of Chardonnay wines analysis by CG-MS/MS. The optimal condition achieved in this method was 30 ̊C and 45 minutes of extraction. How to cite this paper: de Bona Sartor, S., Sganzerla, M., Filho, J.T. and Godoy, H.T. (2016) Multivariate Optimization of Volatile Compounds Extraction in Chardonnay Wine by Headspace-Solid Phase Micro Extraction and Gas Chromatography Coupled with Tandem Mass Spectrometry. American Journal of Analytical Chemistry, 7, 712723. http://dx.doi.org/10.4236/ajac.2016.710064 Received: August 3, 2016 Accepted: October 21, 2016 Published: October 25, 2016 Copyright © 2016 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access S. de Bona Sartor et al.