# SPME Method Optimized by Box-Behnken Design for Impact Odorants in Reduced Alcohol Wines
## 1 Introduction  
Increasing medical and social behavior issues associated with immmoderate alcohol consumption have led the World Health Organization (WHO) to introduce, in 2010, a global strategy to reduce the alcohol intake in the population [1]. This has directed many governments to enforce pricing and taxation policies on alcohol production, with the wine sector being financially impacted [2]. Alcohol isalso the main source of caloric content in wine and nutritional mandatory labeling occurs in many countries such as South Africa, France, and Germany [3]. Although warning health labeling in some countries such as the United States is currently voluntary, for large chain restaurants nutritional labeling is likely to be mandatory for all foods and beverages served. This development is likely to impact the export market with analytical information as well as a reason to reduce the alcohol content in wine to be competitive with other products. All these considerations have stimulated a greater interest among researchers in order to implement new technologies for reducing the alcohol content in wine [4; 5; 6]. Several techniques have been experimented to reduce the wine alcohol content, but dealcoholization (i.e., removal of alcohol from wine) using membrane-based devices remains the most employed technique at a commercial scale. From the literature, dealcoholization is also one of the most studied techniques in the field, in part because ethanol removal causes significant losses of important impact odorants, which researchers are committed to minimizing [7]. Impact odorants such as ethyl esters, acetates of higher alcohols, and terpen, are of primary importance for understanding the consumer acceptability of wines, due to their contribution to desirable aroma attributes at very small concentrations (\(10^{-4}\)-\(10^{-12}\) g/L) [8; 9]. Several extraction techniques such as distillation, solvent extraction, and solid-phase have been used prior to the analysis of these volatile compounds; however, headspace solid-phase extraction (HS-SPME) is currently the most used for dealcoholized wine trials [7]. Sampling headspace vapors by SPME essentially includes two operating steps: (i) partitioning of analytes between the fiber coating and the headspace gas phase, and (ii) desorption of the concentrated analytes into an analytical instrument such as a gas chromatograph-mass spectrometer (GC-MS). Compared to conventional extraction techniques, SPME is relatively simple, fast, and cost-efficient. Moreover, SPME requires no-solvent and a limited manipulation of the sample [10; 11]. Despite the advantages of SPME, comparisons of HS-SPME-GC-MS results between two or more treatments (e.g., natural and dealcoholized wines) may be confounded by varying experimental conditions, such as the fiber exposure time, sample temperature and volume, and the type and uniformity of the sample matrix including the ethanol content [12; 13; 14]. In particular, ethanol, the main component of the alcohol content of wine, has a great impact on the partitioning coefficient of other volatile compounds and, thus, on their concentration in the wine headspace [15]. In addition, the competition between ethanol and other analytes for binding sites on the SPME fiber during headspace sampling will be altered with varying sample ethanol concentrations, which may confound characterization studies and may lead to the misinterpretation of results [16]. Optimization of experimental conditions using multivariate statistical approaches such as full three-level factorial Box-Behnken designs, a response surface methodology, helps to overcome these problems [17]. Nevertheless, Box-Behnken designs have not been widely used for the optimization of SPME methods [18]. The aim of this study was to optimize an SPME-GC-MS method to identify and quantify important volatile compounds in red and white reduced alcohol wines.