# SPME Method Optimized by Box-Behnken Design for Impact Odorants in Reduced Alcohol Wines
## 4 Conclusions  
A total of 23 compounds extracted using a DVB/CAR/PDMS SPME fiber were successfully quantified using GC-MS. The optimum HS-SPME conditions for the extraction of 23 target impact odorants in reduced alcohol red and white wine that contributed to the regression models using a Box-Behnken experimental design were determined in order to study the effect of the SPME conditions, namely, extraction time, extraction temperature, and sample volume. The three-factor Box-Behnken analysis showed significant (\(p<0.05\)) relationships between the SPME variables and the component headspace concentrations, with the extraction temperature resulting in the most critical factor when studying the equilibrium of reduced alcohol wine's impact odorants.  
The concentration of several analytes decreased with dealcoholization, with ethyl hexanoate and ethyl octanoate being the most affected. It was also shown that the concentration of many volatile compounds remained significantly decreased in the headspace of reconstituted wines (i.e., 5% \(v/v\) reduced alcohol wines adjusted back to their natural ethanol concentration) compared to that of 5% \(v/v\) reduced alcohol samples, confirming losses of these compounds from the wine during the ethanol removal process and revealing that the final composition of the reduced alcohol wine's headspace ismainly due to the combination of two factors: (i) the type of the dealcoholization treatment and (ii) the ethanol content reduction.  
A negative relationship between the ethanol concentration and most analyte responses during the SPME sampling process is apparent and the confounding impact of competition for SPME binding sites between ethanol and other volatile compounds requires a rigorous analytical approach to enable comparisons between wines of varying ethanol concentrations. Sample dilution to a constant ethanol concentration will alleviate the inconsistent competition between ethanol and analyte binding on the SPME fiber but will also alter the partitioning of compounds from the liquid to headspace, thereby confounding the final comparisons of different wines. Researchers should consider that variations in sample conditions such as the ethanol content may confound comparisons of different wines, particularly those between natural and dealcoholized treatments. For the most accurate assessment of the concentration of volatile compounds, analytical conditions that account for varying ethanol concentrations must be used. Invariably, this will require analytical calibrations at each important ethanol concentration for headspace analysis.  
The following are available online at [[http://www.mdpi.com/2304-8158/7/8/127/s1](http://www.mdpi.com/2304-8158/7/8/127/s1)]([http://www.mdpi.com/2304-8158/7/8/127/s1](http://www.mdpi.com/2304-8158/7/8/127/s1)), Table S1: Design matrix for the optimization experiment using experimental conditions; Table S2: Coded design matrix where experimental factors are coded \(-1,0,1\) at each level; Table S3: Optimized SPME conditions for target aroma compounds for the 5% \(v/v\) treatment; Table S4: Optimized SPME conditions for target aroma compounds for the 8% \(v/v\) treatment; Table S5: Optimized SPME conditions for target aroma compounds for the 13% \(v/v\) treatment; Table S6: Target compounds identification and calibration parameters for three different levels of ethanol content of wine.  
[PERSON], [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data. All authors contributed to the preparation of the manuscript.  
[PERSON] would like to acknowledge a Charles Sturt University Faculty of Science scholarship for funding this study.  
The authors declare no conflict of interest.