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 (GC-MS/MS). Optimization of the HS-SPME conditions, temperature (T, °C) and extra-ction time (t, minutes), was carried out using a 2 2 factorial central composite rota- tional 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 F calculated was 2.23 higher than F tabulated. 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.
Volatile compounds directly contribute to wine aroma which is a fundamental charact- eristic of identity, quality and acceptance by the consumer market. These compounds form a matrix capable of stimulating a response by the sensory human olfactory system [
Due to the complex chemical composition of wines, matrix where the aroma comp- ounds are present, a efficient method of extraction is needed to isolate the target analytes as well as serving as a tool for pre-concentration increasing sensitivity of the analytical system used. Several extraction methods for the analysis of volatile compounds in wines, techniques of distillation, solvent extraction and solid phase extraction (SPE) have been reported in the literature [
Introduced by Arthur and Pawliszyn in early 1990’s [
Considering that SPME technique is an equilibrium technique with the maximum sensitivity obtained in an equilibrium point instead of an exhaustive one, during devel- opment of a SPME method some parameters must be optimized. Usually, the param- eters monitored are the type of fiber coating, sampling mode (direct immersion or headspace), agitation, time, temperature, ionic strength, pH, volume of sample, type of vial used, volume of headspace, conditions of desorption [
Traditionally, gas chromatography coupled to mass spectrometry (GC-MS) is the most used technique for the analysis of volatile compounds in wine [
The aim of this work is to optimize a extraction method of volatile compounds in Chardonnay wine using solid phase micro extraction in headspace mode (HS-SPME) and analysis by gas chromatography coupled to tandem mass spectrometry (GC-MS/ MS). Temperature and extraction time were optimized using multivariate statistical analysis with a 22 factorial central compound rotational design (CCRD) and response surface methodology for determining the optimum condition of extraction.
Analytical standards used were 1-hexanol, 3-methyl-1-butanol, 2-phenylethanol, isoamyl acetate, hexyl acetate, ethyl lactate, diethyl succinate, ethyl butanoate, ethyl hexanoate, ethyl octanoate, ethyl decanoate, hexanoic acid, octanoic acid, decanoic acid, nerol, linalool, α-terpineol, α-ionone and β-ionone, purchased from Sigma- Aldrich (Saint Louis, MO, USA), with purity ≥ 99%. A synthetic model wine was prepared with water previously purified in a Milli-Q® system (Millipore, Bedford, MA, USA), 12% (v/v) of ethanol HPLC grade (JT Baker, Xalostoc, México) and 2 g∙L−1 of tartaric acid (Merck, Darmstadt, Germany). The pH was adjusted to 3.2 using sodium hydroxide (NaOH) 1M. Sodium chloride (NaCl) was purchased from Vetec (Rio de Janeiro, Brasil).
Samples of Chardonnay wine were obtained in local market in Campinas, São Paulo, Brazil, and four bottles of a same production lot were used in experiments. Wines were produced in Andradas, Minas Gerais, Brasil (22˚04'04''S 46˚34'08''W) in 2011 vintage. For analysis, 10 mL aliquots of wine were pipetted into a 40 mL SPME vial, 3.0 g of sodium chloride was added and complete with screw-top caps and PTFE/silicon septa (Supelco Inc., Bellefonte, PA, EUA). During the sampling time, sample was constantly stirred with a small magnetic stirring bar. SPME fiber (Supelco Inc., Bellefonte, PA, EUA) used in this study was 50/30 µm with divinylbenzene/carboxen/ polydimethylsi- loxane (DVB/CAR/PDMS) coating conditioned before use according to the manufac- turer’s instructions. DVB/CAR/PDMS fibers were chosen according to the range of polarity and different functionalities of the mixture of molecules analyzed in this study: alcohols, esters, fatty acids, C13-norisoprenoids and monoterpenes. Fiber was exposed to the sample headspace after equilibrium time of 10 minutes. The factors optimized were time of fiber exposure and temperature of sample, due to their influence in equilibrium system. After extraction, fiber was introduced into gas chromatography injector for desorption of the analytes at a temperature of 270˚C, in splitless mode for 15 minutes.
The GC-MS analysis were performed on a Agilent 7890A gas chromatograph (Agilent Technologies, Palo Alto, CA, EUA) equipped with a Agilent 7000 Triple Quad mass detector (Agilent Technologies, Palo Alto, CA). Liner used was specific for SPME analysis purchase from Sigma Aldrich (Saint Louis, MO, USA), with 0.75 mm of internal diameter. Chromatographic separation was achieved using a capillary column Supelcowax® 10 (100% polyethyleneglycol) (Supelco Inc., Bellefonte, PA, EUA.) with following dimensions: 30 m × 0.25 mm × 0.25 µm. Carrier gas was high purity Helium at a constant flow of 1.0 mL∙min−1 in splitless injection mode. The injector temperature was 270˚C and oven temperature program initialize with 30˚C, was held for 2 minutes and then increasing 4˚C min−1 to 130˚C (2 minutes) followed to increase 8˚C min−1 to 250˚C (5 minutes) [
Mass spectras were obtained by using electron impact (EI) as ionization mode and −70 eV as electron energy. Temperatures of interface, source and quadrupoles (Q1, Q2 and Q3) were 250˚C, 260˚C and 150˚C, respectively. Nitrogenium and Helium were used in collision cell (Q2) at 2.25 mL∙min−1 and 1.5 mL∙min−1 flows, respectively. Acquisition was performed in multiple reaction monitoring (MRM) mode. Precursor ions were used as qualifiers and product ions were as identifiers (
Compound | Precursor ion selected (m/z) | Product ion selected (m/z) | Energy Colision (V) | Retention time (min) |
---|---|---|---|---|
Alcohols | ||||
1-hexanol | 69 | 43 | 40 | 21.76 |
3-methyl-1-butanol | 77 | 55 | 20 | 29.39 |
2-phenyl ethanol | 91 | 65 | 40 | 34.61 |
Esters | ||||
Hexyl acetate | 84 | 56 | 25 | 27.78 |
Isoamyl acetate | 87 | 70 | 25 | 37.16 |
Ethyl lactate | 75 | 45 | 25 | 16.03 |
Diethyl succinate | 129 | 101 | 25 | 29.02 |
Ethyl butanoate | 101 | 29 | 25 | 21.65 |
Ethyl hexanoate | 115 | 27 | 25 | 21.71 |
Ethyl octanoate | 143 | 73 | 35 | 27.78 |
Ethyl decanoate | 155 | 101 | 35 | 30.93 |
Fatty acids | ||||
Decanoic acid | 129 | 57 | 30 | 21.66 |
Hexanoic acid | 99 | 55 | 30 | 14.68 |
Octanoic acid | 115 | 85 | 30 | 37.14 |
C13-norisoprenoids | ||||
α-ionone | 136 | 109 | 40 | 35.40 |
β-ionone | 177 | 135 | 40 | 24.29 |
Monoterpenes | ||||
Linalool | 121 | 80 | 35 | 28.78 |
α-terpineol | 136 | 59 | 35 | 19.89 |
Nerol | 139 | 84 | 35 | 27.14 |
Data were acquired and processes using Agilent Mass Hunter software (version B.05.00, Agilent Technologies). The compounds identification was achieved by com- paring the retention time and mass spectra obtained from sample with standards compounds presented in a model synthetic wine injected at same conditions. Qualifier and identifier ions were considered positive when they showed similarity of at least 75% with the standards prepared and analyzed as well as comparing the MS fragmentation with the mass spectras present in the National Institute of Standards Mass Spectral Library (NIST 2011).
Optimization of the HS-SPME conditions was carried out using a 22 factorial central composite rotational design (CCRD) with four axial points (α = 1.4142) and tree central points [
Volatiles compounds monitored in this study were chosen because they represent the major chemical classes of aroma compounds in wines: alcohols, esters, fatty acids, monoterpenes and C13-norisoprenoids [
Experiment | Factors† | Response‡ | |||
---|---|---|---|---|---|
T (˚C) | Extraction temperature | t (min) | Extraction time | ||
1 | −1 | 33 | -1 | 35 | 2.50E+07 |
2 | 1 | 48 | -1 | 35 | 2.44E+07 |
3 | −1 | 34 | 1 | 55 | 2.49E+07 |
4 | 1 | 48 | 1 | 55 | 2.43E+07 |
5 | −1.41 | 30 | 0 | 45 | 2.54E+07 |
6 | 1.41 | 50 | 0 | 45 | 2.43E+07 |
7 | 0 | 40 | 1.41 | 59 | 2.50E+07 |
8 | 0 | 40 | −1.41 | 30 | 2.49E+07 |
9§ | 0 | 40 | 0 | 45 | 2.48E+07 |
10§ | 0 | 40 | 0 | 45 | 2.49E+07 |
11§ | 0 | 40 | 0 | 45 | 2.49E+07 |
12§ | 0 | 40 | 0 | 45 | 2.49E+07 |
†: with α = 1.4142; ‡: expressed in arbitrary units; §: central point repetition.
Analysis of variance (ANOVA) with 95% of confidence was used to determine which factors significantly affect the response of the HS-SPME procedure and validate the mathematical model that describes the response surface of DCCR.
The statistical significance of regression given by the quadratic means of the residues (MQR/MQr) or Fcalculated was 395.97. When comparing, at the level of 95%, the values of Fcalculated and Ftabulated (5%, 6%, 95%) which value is 4.39 can be observed that Fcalculated > Ftabulated about 90.2 times, indicating that the correlation between variables can be considered adequate to this model.
Sourcesofvariation | Sum ofsquares (SS) | Degreesoffreedom (df) | Mean of the squares (MS) | Fcal† | Ftab‡ | Fcal/Ftab | ||
---|---|---|---|---|---|---|---|---|
Regression | 8.27E+11 | 5 | 1.65E+11 | 395.97 | 4.39 | 90.2 | ||
Residues | 1.63E+11 | 6 | 2.71E+10 | |||||
Lack of fit | 1.55E+11 | 3 | 5.17E+10 | 20.68 | 9.28 | 2.23 | ||
Pure error | 7.50E+09 | 3 | 2.50E+09 | |||||
Total | 1.14E+12 | 11 | ||||||
R2 | 0.720 | |||||||
†: Fcalculated; ‡: Ftabulated..
Based on the obtained quadratic model was generated response surface to the experiment (
The process of extraction by HS-SPME involves the partition of the analytes in matrix, in headspace and fiber coating. In the equilibrium, amount of extracted sample is proportional to the partition coefficient and concentration of the analytes in the headspace. Extraction can be considered optimal when the concentration of the analytes reaches equilibrium distribution between the extraction phase (fiber coating) and headspace [
In HS-SPME method for extraction of volatile compounds, temperature has great influence on efficiency of the process. The kinetics of the extraction process is directly affected by temperature, as it acts in determining the vapor pressure of the analytes in the matrix [
Extraction time, or fiber exposure to the sample headspace, influences the equilibr- ium between the phases involved and thus the extraction efficiency. Compounds with a lower partition coefficient, the time required to reach equilibrium must be increased. Compounds with higher partition coefficient require less time to reach equilibrium [
The exposure of the fiber for shorter periods of time, or before reach the equilibrium, can make the concentration of the extracted compounds become underestimated. In
addition to exposure of the fiber for very long periods of time makes the compounds starts to compete for the active site in the fiber and also affects the final concentration [
Analyzing the lack of fit of the model generated, based on the values of quadratic mean and pure error of each response where Fcalculated was 2.23 times Ftabulated. However, for there to be considered a good fit of the model, Fcalculated < Ftabulated [
After the HS-SPME extraction process, volatile compound were separated and ident- ified using gas chromatography coupled to tandem mass spectrometry (GC-MS/MS). Confirmation of identity of each analyte was performed comparing the spectra obtai- ned by the injection of analytical standards and between the analytes present in the sample. Use of GC-MS/MS provides a high degree of selectivity, sensitivity and security in identification of compounds [
HS-SPME as extraction method of volatile compounds in wines have been widely used, nevertheless during development of the SPME method, parameters which affects the response must be optimized. In this study, multivariate statistical design was used in optimization of HS-SPME extraction parameters (time and temperature of extraction) with reduced number of experiments. Furthermore, the statistical design provide results to achieve an optimum extraction point of volatile compounds in Chardonnay wine, with temperature in 30˚C and time of 45 minutes. The combined use of techniques of HS-SPME and GC-MS/MS was suitable for the analysis of volatile compounds in Chardonnay wine.
Authors would like to acknowledge the financial support of Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) (grant number: 2011/17094-2), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Dr. Rodrigo Ramos Catharino (School of Medical Sciences, University of Campinas/UNICAMP) for providing the equipment to carry out chromatographic analysis.
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 Ex- traction and Gas Chromatography Coupled with Tandem Mass Spectrometry. Ameri- can Journal of Analytical Chemistry, 7, 712- 723. http://dx.doi.org/10.4236/ajac.2016.710064