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Application of multiple headspace-solid-phase microextraction followed by gas chromatography–mass spectrometry to quantitative analysis of tomato aroma components
Journal of Chromatography A, 1216 (2009) 127–133
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Application of multiple headspace-solid-phase microextraction followed by gas chromatography–mass spectrometry to quantitative analysis of tomato aroma components E. Serrano, J. Beltrán ∗ , F. Hernández Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat, 12071 Castellón, Spain
a r t i c l e
i n f o
Article history: Received 11 July 2008 Received in revised form 3 November 2008 Accepted 12 November 2008 Available online 18 November 2008 Keywords: Multiple Headspace-solid-phase microextraction MHS-SPME Tomato Aroma Volatile compounds GC–MS Quantitation
a b s t r a c t The objective of this paper is to investigate the potential of multiple headspace-solid-phase microextraction (MHS-SPME) for the determination of volatile compounds in complex matrix samples. A method based on MHS-SPME for the determination of around 20 volatile compounds, responsible of tomato flavour and aroma has been developed and validated, using gas chromatography with mass spectrometry (ion trap analyser) for analysis. For this purpose, the experimental ˇ parameter, resulting from the MHS-SPME theoretical development, has been obtained from real sample analysis (in triplicate) for each identified compound, carrying out up to 5 consecutive extractions. Later, this parameter is used to perform quantitation of real samples after just a single HS-SPME extraction. Precision, expressed as repeatability, has been evaluated by analysing six replicates of a real sample, showing relative standard deviations between 4 and 20%. For accuracy study, quantitative results have been compared with those obtained by means of standard additions on replicate samples, and no statistically significant differences between the two methods were observed. Since MHS-SPME uses the estimated total area corresponding to the complete extraction of compounds (obtained from the ˇ parameter), quantitation can be carried out by external calibration using standards in solvent and splitless injection, instead of by SPME. Linearity, tested in the range 0.05–15 g/mL, showed satisfactory values, with coefficients of correlation between 0.995 and 0.999. Limits of detection were in the range of 0.25–5 ng/g. MHS-SPME has been proved to be an adequate technique to avoid matrix effects in complex samples quantitation. Its applicability to the determination of volatile tomato components, together with its limitations, is discussed in this article. © 2008 Elsevier B.V. All rights reserved.
1. Introduction Volatile components contributing to fruits and vegetables aroma and flavour have been widely studied, especially aromas. Although more than 400 volatile compounds have been identified, only a small number is essential and seems to contribute really to aroma profiles. Analytical methodology for determination of volatile compounds in vegetable matrices is continuously improving due to the important role of these compounds in organoleptic quality of food [1–3]. Aromas present in food samples belong to different chemical families, including esters, ketones, aldehydes, alcohols, terpens, phenols, and its derivatives. As a result, optimisation of multicomponent sample preparation procedures is difficult. Moreover, there are significant differences in partition coefficients between real
∗ Corresponding author. Tel.: +34 964 38 73 60; fax: +34 964 387368. E-mail address: [email protected] (J. Beltrán). 0021-9673/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.chroma.2008.11.026
samples and standard solutions and the use of surrogates and/or internal standards is required for adequate quantitation. In spite of analytical efforts, quantitation of aromas is rather problematic, and in most cases, not fully satisfactory results are obtained. Direct headspace (HS) injection, purge and trap (P&T), HSsolid phase microextraction (SPME) or HS-solid-phase dynamic extraction (SPDE) are extraction techniques widely applied for the determination of aroma profile or volatile fraction composition of many different samples (fruits, vegetables, plants, beverages. . .) [2,4]. Other techniques, like liquid-phase microextraction (LPME) or sorptive extraction (either by direct immersion or by headspace sampling) have been recently applied to the determination of volatile compounds in different matrices and are under study [5–8]. SPME, introduced by Pawliszyn in the earlier 90’s [9,10], is a well known sample extraction technique widely applied in food and environmental analysis, as it is simple, rapid, solvent-less sample preparation technique that can perform sampling, clean-up, and concentration in the same step [11–13]. HS-SPME is widely used for determination of volatile compounds typically followed
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Table 1 Accuracy and precision study for the MHS-SPME-GC–MS method developed. Compound
ˇ
Z-3-hexenal Hexanal E,E-2,4-hexadienal 6-Methyl-5-hepten-2-one 6-Methyl-5-hepten-2-ol E,E-2,4-heptadienal R-limonene 2-Isobutylthiazole Guaiacol E-2-octenal Linalool 2-Phenylethanol Methyl salicylate ␣-Terpineol -Ciclocytral Z-citral E-citral E,E-2,4-decadienal Diphenyl ether Geranylacetone -Ionone
0b 0.13 0.76 0.27 0b 0.33 0.50 0b 0.29 0.13 0.38 0b 0.11 0.16 0.44 0.70 0.65 0.12 0.44 0.72 0.34
Precision
LOD (ng/g)
RSD (%) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 a b
Accuracy (ng/g)a MHS-SPME
18 14 20 11 4 20 19 14 16 12 19 6 9 9 16 9 11 19 13 14 12
20 20 10 1.5 2 2 2 1.5 1 2 3 1 1 1 0.4 2.5 2.5 1 0.5 5 0.25
140 570 1060 250 6.6 5.3 15 28 5.5 9.8 18 98 2.9 3.5 7.2 26.8 107 9.3 3.4 340 13.9
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
6 90 150 40 0.3 0.7 3 4 0.5 1.4 3 12 0.5 0.6 1.4 1.8 19 1.4 0.6 70 2.4
Std. add 124 566 1132 265 7.1 4.4 17.4 34.0 5 9.2 18.1 100.4 3.2 3.9 7.2 32.0 118.0 12.0 3.5 437 16.8
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
4 9 23 3 0.7 0.4 0.3 0.6 0.9 0.5 0.7 1.3 0.5 0.3 0.4 1.0 1.7 0.9 0.6 6 0.4
Sample concentration and confidence interval, calculated for n = 6 in MHS-SPME method and from the regression data in standard addition method. Analyte exhaustively isolated in the first extraction.
by gas chromatography (GC) analysis. Due to its higher confirmatory potential mass spectrometry (MS) detection is commonly applied, working in full scan or SIM modes typically using ion trap or quadrupole detectors [14,15]. It is well known that SPME is a non-quantitative extraction technique, so the amount of analyte isolated is not the total present in sample, thus being necessary to calibrate using extracted spiked blank samples. Unfortunately, this is not easy when determining the aroma profile in vegetables as no blank samples can be obtained. A stepped procedure called multiple headspace extraction (MHE), whose theoretical bases were established in the earlier 80’s [16,17], has been proposed as an alternative to overcome some difficulties, as matrix effects, typically observed in quantitation of volatile compounds in solid samples. SPME can also be performed in a multiple mode (M-SPME). The goal of the process is the estimation of the response for total analyte, which is directly related to the total amount of analyte present in sample. Thus, it can allow a better quantitation of the analytes in complex matrices and, even more, an external calibration may be used. As described in the literature, MHS-SPME has been applied to the determination of volatile compounds in different type of matrices including packaging materials, soils, nylon, oils or wines [18–27]. Recently, an emerging technique, SDME, has also been combined with multiple headspace extraction [28]. M-SPME considers that n1 , defined as the amount of analyte sorbed into fibre after the first extraction, is proportional to the initial amount on the sample (n0 ):
If one assumes that there is a linear relationship between the amount of analyte in the fibre and the instrumental response of analyte (peak area, A), Eq. (3) could be written as:
n1 = a · n0
Once obtained ˇ, only a single extraction is carried out in samples, and AT is estimated by means of Eq. (5) from the response obtained in this first extraction (A1 ) and from the ˇ parameter obtained experimentally. As previously reported [29], a compound presenting a ˇ value below 0.4 would be exhaustively isolated in the first extractions, as a result of the significant reduction of the analyte amount in sample from one extraction to the next. On the contrary, a high ˇ value (over 0.95) means that even after several consecutive extractions on the same sample the amount of analyte in sample remains virtually unaffected (amount extracted is negligible when compared to the total analyte amount). Thus, only when the ˇ values are in the range of 0.4–0.95 application of MHS-SPME is feasible, as a
(1)
˛ can be defined as: ˛=
Kfs · Vf Kfs · Vf + Khs · Vh + Vs
(2)
where Kfs and Khs are the fibre coating-sample and headspacesample distribution constants, and Vf , Vh , Vs are the coating, headspace and sample volumes. It can be demonstrated [18] that the amount of analyte in the fibre coating after the ith extraction (ni ) can be expressed as follows: ni = n1 · (1 − ˛)(i−1)
(3)
Ai = A1 · (1 − ˛)(i−1)
(4)
Assuming that the sum of peak areas (from i = 1 to ∞) corresponding to consecutive extractions (which follow a geometric progression), corresponds to the theoretical total area (AT ) of analyte, the later could be determined by means of a single extraction of the sample and the application of a mathematical approach, described in the following equation: AT =
A1 1−ˇ
(5)
where ˇ is equal to (1 − ˛) and A1 the peak area of the first extraction. From an experimental point of view, MHS-SPME is a stepped process where the objective is the empirical determination of ˇ parameter, which is characteristic for each analyte and depends on sample matrix and on the extraction conditions selected. For this purpose, several consecutive HS-SPME extractions on the same sample (similar to the real samples that will be analyzed) are carried out, and ˇ is obtained from the slope of the linear plot described in Eq. (6), which derives from Eq. (4): log Ai = log A1 + (i − 1) · log ˇ
(6)
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detectable reduction in analyte amount in sample is produced from one extraction to the next one. In the present study, the potential of multiple headspace solid-phase microextraction for the quantitative determination of volatile compounds in complex matrix samples (tomato) using external solvent calibration has been investigated. Besides, the possibility of quantitation of samples above the linear range has been tested. To our knowledge, this is the first application of MHS-SPME to quantitative determination of aroma components of tomato samples. More than 20 volatile compounds had been identified in samples characterized in previous works, and these compounds have been studied and determined in current work. 2. Experimental
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1.03 min (total chromatographic analysis time of 45 min). MS analysis was performed in full scan mode (m/z scan range of 60–200 Da), with electron impact ionization energy of 70 eV in the positiveion mode and internal ionisation configuration. Transfer line, trap and manifold temperatures were set at 275 ◦ C, 200 ◦ C and 60 ◦ C, respectively. 3. Results and discussion The experimental design of the research has been directed towards the optimization of the MHS-SPME and experimental calculation of ˇ parameters. Once obtained the ˇ parameter and the optimum SPME conditions, the procedure has been fully validated with real samples. Results obtained are shown in the following sections, giving special attention to accuracy, which was stimated by comparison with standard addition method quantitation.
2.1. Chemicals, reagents and materials 3.1. MHS-SPME optimisation Reference standards of volatile compounds studied (Z-3hexenal, hexanal, E,E-2,4-hexadienal, 6-methyl-5-hepten-2-one, 6methyl-5-hepten-2-ol, E,E-2,4-heptadienal, R-limonene, 2-isobutylthiazole, guaiacol, E-2-octenal, linalool, 2-phenylethanol, methyl salicylate, ␣-terpineol, -ciclocytral, Z-citral, E-citral, E,E-2,4-decadienal, diphenyl ether, geranylacetone, -ionone, phenylacetaldehyde, benzaldehyde) were supplied by Supelco (Sigma–Aldrich and Fluka; Barcelona, Spain) as pure compounds (92–99.5%). Stock solutions (500 mg/L) of the individual volatile standards were prepared in acetone, and working solutions were prepared by volume dilution with n-hexane. All standard solutions were stored at −18 ◦ C in sealed glass vials completely filled (without headspace) to avoid analyte losses and to ensure reproducibility, as recommended in references dealing with volatile compounds storage [30,31]. Acetone and n-hexane GC grade were obtained from Scharlab (Barcelona, Spain). Tomato samples used for method optimisation and validation were obtained from commercial sources. SPME carboxen/polydimethylsiloxane (CAR/PDMS) fibre coating was supplied by Supelco (Sigma–Aldrich; Barcelona, Spain). 2.2. MHS-SPME procedure Fresh tomato samples (stored at 4 ◦ C) were triturated and homogenized using a blender (UltraTurrax) for 1 min at ambient temperature, and then placed in 10 mL-vials and frozen at −18 ◦ C without any headspace until analysis. SPME fibre coating used for this work was CAR/PDMS which seemed to be the most adequate for the determination of small and polar volatile molecules related to tomato aroma, as we found in previous works [2] on agreement with the literature [32–34]. HS-SPME was carried out as follows: 0.1 g of tomato sample were weighted in a 10 mL-vial, sealed and extracted at 70 ◦ C for 50 min (with 10 min of pre-heating) using a CAR/PDMS fibre. After absorption process, SPME fibre was immediately desorbed at 300 ◦ C for 5 min on GC injection port, maintaining the fibre for an additional 5 min into the injector to avoid carry-over effect. 2.3. GC–MS conditions A Varian CP-3800 gas chromatograph coupled to a Varian 4000 MS mass spectrometer (ion trap analyser) was used to identify and quantify volatiles isolated from tomato samples. Analytes were separated on a 30 m × 0.25 mm VF-5MS (0.25 m film thickness) capillary column, using helium at a constant flow of 1 mL/min as carrier gas. The column temperature program was: 40 ◦ C for 5 min; then temperature was increased to 141 ◦ C at 3 ◦ C/min and then increased to 300 ◦ C at 30 ◦ C/min with a final isothermal stage of
From our previous works [2], SPME fibre coating (CAR/PDMS) was pre-selected and total extraction time was set to 60 min (10 min pre-heating and 50 min extraction). However, sample mass and extraction temperature were fully optimised in the present work. As the ˇ parameter is procedure-dependent and related to matrixeffect, it must be estimated from a real sample, considering the effect of extraction conditions on this parameter during optimisation step. In a first approach, several consecutive extractions (until most compounds were not detected in the chromatogram corresponding to the last extraction) were carried out using different sample masses: 0.1 g, 0.5 g, 1 g and 2 g. SPME extractions were performed at 40 ◦ C for 50 min (with a pre-heating time of 10 min). In order to fit experimental data to Eq. (6) and to obtain experimental ˇ values, a difference of at least 5% between two consecutive extractions must be achieved, i.e. each single extraction must reduce significantly the analyte amount in sample. Results obtained when extracting sample amounts of 1 g and 2 g, showed no significant differences on peak areas for successive extractions. This observation would imply that even if the sample/HS were at equilibrium, some analytes were so concentrated in HS phase that the effect of the amount extracted onto the fibre was negligible. Thus, determination of ˇ was not possible under these conditions and, consequently, MHS-SPME procedure could not be applied. Due to the high concentration of several analytes present in sample (major components of aroma and flavour), the decrease of sensitivity when using smaller sample amounts, i.e. 0.1 g and 0.5 g was negligible. In these experiments, ˇ ranged from 0.40 (capronaldehyde) to 0.81 (geranylacetone) using 0.5 g sample, and from 0.23 (Z-3-hexenal) to 0.83 (E,E-2,4-heptadienal) when using 0.1 g. In both cases, ˇ could not be obtained for a few compounds that presented a non-linear correlation between extractions. Moreover none of the studied compounds was exhaustively isolated with a single extraction at 40 ◦ C, which makes the application of MHSSPME for these analytes attractive. Finally, 0.1 g was selected for further experiments, in order to minimize the HS phase saturation. In order to optimise extraction temperature, up to five consecutive extractions using sample mass of 0.1 g were carried out at each of the following temperatures: 40 ◦ C, 60 ◦ C, 70 ◦ C and 80 ◦ C. For most analytes tested, it was found that at low extraction temperatures (40 ◦ C and 60 ◦ C), the first extraction deviated from the rest, existing only a correct linear relationship between the second and the following extractions. This process could be explained by the slow diffusion of analytes from sample to headspace phase at
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Table 2 Linearity study for the MHS-SPME-GC–MS developed method. Compound
(ng/L) 1 2 3 4 5 6 7 8 9 10 11 12 13
Z-3-hexenal Hexanal E,E-2,4-hexadienal 6-Methyl-5-hepten-2-one 6-Methyl-5-hepten-2-ol E,E-2,4-heptadienal R-limonene 2-Isobutylthiazole Guaiacol E-2-octenal Linalool 2-Phenylethanol Methyl salicylate
14 15 16 17 18 19 20 21
␣-Terpineol -Ciclocitral Z-citral E-citral E,E-2,4-decadienal Diphenyl ether Geranylacetone -Ionone
a b c
Regression parametersa
Linear range
0.200–5 0.004–5.5 0.006–6 0.020–10 0.022–5.5 0.005–10 0.022–6 0.006–11 0.005–5.5 0.05–5 0.004–5 0.030–7.5 0.020–0.200 0.200–10 0.009–8.5 0.010–5 0.028–11 0.004–14 0.050–10 0.007–15 0.055–5.5 0.012–0.230 0.230–12
c
(ng/g)
2–50 0.04–55 0.06–60 0.20–100 0.22–55 0.05–100 0.22–60 0.06–110 0.05–55 0.50–50 0.04–50 0.30–75 0.20–2 2–100 0.09–85 0.10–50 0.28–110 0.04–140 0.50–100 0.07–150 0.55–55 0.12–2.30 2.30–120
a
(51 ± 2) × 103
(52 ± 6) × 102 (58 ± 6) × 102 (53 ± 1) × 102
(60 ± 3) × 103 (23 ± 1) × 103
(58 ± 2) × 102
R2 b
b
c
(160 ± 2) × 102 (188 ± 3) × 102 (218 ± 5) × 103 (172 ± 1) × 103 (70 ± 2) × 103 (259 ± 2) × 103 (184 ± 1) × 103 (364 ± 4) × 103 (163 ± 2) × 103 (63 ± 6) × 103 (15 ± 4) × 104 (240 ± 9) × 103 (291 ± 1) × 102 (13 ± 1) × 104 (141 ± 1) × 103 (900 ± 9) × 102 (96 ± 1) × 103 (15 ± 5) × 103 (68 ± 1) × 102 (443 ± 3) × 103 (45 ± 6) × 103 (89 ± 2) × 103 (53 ± 2) × 104
(40 ± 5) × 10 (52 ± 7) (78 ± 3) × 102 (75 ± 5) × 102 (33 ± 2) × 102 (89 ± 6) × 102 (19 ± 5) × 103 (29 ± 1) × 103 (15 ± 5) × 103 (61 ± 5) × 102 (38 ± 2) × 102 (28 ± 1) × 10 (14 ± 3) × 10 (42 ± 1) × 102 (71 ± 9) × 102 (32 ± 3) × 102 (17 ± 5) × 102 (41 ± 1) × 10 (83 ± 1) × 102 (97 ± 1) × 102 (89 ± 5) × 10 (58 ± 3) × 10 (16 ± 4) × 103
0.998 0.999 0.998 0.998 0.997 0.998 0.999 0.999 0.999 0.998 0.998 0.999 0.995 0.998 0.998 0.997 0.999 0.998 0.997 0.999 0.998 0.998 0.997
Experimental data were fitted to either y = ax2 + bx + c or y = bx + c models. Correlation coefficients. Linear range corresponding to real samples (0.1 g of tomato).
these temperatures, which affects specially to the first extraction where an excess of analyte is present to be sorbed on the SPME fibre. For the following extractions, compound amount in vapour and solid phases had to reach equilibrium again. This process differs for each compound depending on polarity of analytes, possible interaction with matrix components or different volatility. When 80 ◦ C was tested as extraction temperature, we observed an important decrease in the response for most analytes, possibly due to the displacement of equilibrium between fibre and headspace to the vapour phase, decreasing compounds retention on the fibre surface. This fact has already been observed in previous HS-SPME studies [35,36]. This led to an unfavourable increase in the ˇ parameters with values ranging from 0.54 (for eugenol) to 0.82 (for E-citral). Besides, several thermal degradation compounds as furanderivates or methoxyphenyl oximes were identified at high concentration levels using the mass spectra library included in the MS software (NIST Mass Spectral library 2.0). These degradation products might interfere in aroma determination modifying the true aroma profile.
Finally, 70 ◦ C was selected as a compromise. Data obtained at this temperature showed a linear relationship for most of the compounds with no significant decrease in their response, so this temperature was selected as optimum. Under these conditions, ˇ parameters were obtained by triplicate from the slope of the linear plot of peak area logarithms vs. (i – 1) using Eq. (6), where i is the number of consecutive extractions. Three consecutive extractions were carried out for this purpose. ˇ-Values were in the range of 0.11–0.76, except for Z-3-hexenal, 6-methyl-5-hepten-2-ol, 2-isobutylthiazole and 2-phenylethanol which were exhaustively isolated after the first extraction (ˇ = 0) (Table 1). Determination of ˇ parameter for phenylacetaldehyde and benzaldehyde was not possible, since no significant differences were observed on peak areas corresponding to successive extractions (ˇ > 0.95). As stated before, this could be due to the high concentration of these compounds in samples that overloads the vapour phase even after consecutive extractions. Fig. 1 shows extracted ion chromatograms for four selected compounds resulting from three consecutive headspace extractions
Fig. 1. Illustrative GC–MS extracted ion chromatograms (quantitation m/z ion) for four selected volatile compounds corresponding to three repeated SPME extractions of a real tomato sample (0.1 g) at 70 ◦ C for 50 min.
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Fig. 2. Lineal plots used to determine ˇ values (numerical data shown in Table 3).
under optimum conditions and ˇ values obtained for these four compounds. Experimental data (peak areas vs. number of repeated extractions) are plotted and the fitting parameters are also shown in Fig. 2 for the four compounds. Once ˇ values for the selected compounds are determined, Eq. (5) is used to calculate the theoretical area (AT ) corresponding to the total amount of analyte present in the sample. In these conditions it should be possible to calculate the concentration of the sample by using an external calibration curve injected in the GC–MS system using standards prepared in solvent. In order to evaluate the feasibility of quantitation using external solvent calibration, ˇ parameters corresponding to several volatile compounds were obtained extracting spiked water samples. Values obtained ranged between 0.12 (hexanal) and 0.88 (for Z-citral). For this purpose, several consecutive headspace SPME extractions were carried out on a 100 L LC-grade water aliquot spiked at 10 ng/mL for each studied compound. Then, total peak areas AT for each compound were calculated following the multiple SPME theoretical bases (Eq. (5)) using single extraction peak areas and the ˇ parameters previously calculated. Quantitation was then carried out by interpolation of the calculated AT in a calibration curve obtained injecting 1 L of pure-solvent standards. Recoveries obtained in this
way were in the range 76–113%, showing the feasibility of applying headspace microextraction in the multiple mode but using external solvent calibration with direct liquid injection. Validation of the method should demonstrate the applicability of the described procedure to complex matrix samples, like tomato. 3.2. Method validation The MHS-SPME-GC–MS method developed has been validated in terms of linearity, precision, accuracy and limit of detection. Linearity was studied by directly injecting (by triplicate) hexane standard solutions at ten concentration levels in the range from 0.002 g/mL to 15 g/mL. Calibration curves showed adequate coefficients of correlation (R2 ), higher than 0.995 and residuals lower than 13% with no tendency. Precision of the overall analytical procedure has been evaluated as repeatability, by means of replicate real sample analysis (n = 6), and it was expressed as RSD (%) of the calculated concentrations. The method has been found to have satisfactory precision, with coefficients of variation between 4 and 20% for all compounds studied (Table 2).
Fig. 3. GC–MS full scan chromatogram (60–200 m/z) for the first extraction corresponding to the real tomato sample used in accuracy study.
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Fig. 4. GC–MS extracted ion chromatograms (quantitation m/z ion) for the first extraction corresponding to the real tomato sample used in accuracy study.
On the other hand, for accuracy study in tomato matrix, we selected the standard additions methodology as a reference because of recovery assays could not be applied as blank tomato samples cannot be obtained. For standard additions assays (four addition points), between 1 and 100 ng of each analyte (depending on the amount previously found), were added to 0.1 g tomato sample and it was analysed by HS-SPME-GC–MS under extraction conditions optimised previously for the multiple headspace mode. Four of the studied compounds (geranylacetone, E,E-2,4hexadienal, 6-methyl-5-hepten-2-one and hexanal) showed total peak areas (AT ) clearly above the studied linear range, which could led to quantitation errors. Nevertheless, considering the MHS theoretical bases, the fact that AT is a theoretical that will never be experimentally obtained in the GC system, might allow extending the linear range of the calibration curve to quantify these high concentrations in samples without relevant errors. Thus, quantitation above linear range might be considered acceptable (as guidance results). Geranylacetone, E,E-2,4-hexadienal, 6-methyl-5-hepten-
2-one or hexanal, levels of which in tomato were around 350, 900, 250 and 560 ng/g, respectively, are examples of this situation. Quantitative results obtained applying both methodologies (MHS-SPME and HS-SPME with standard additions) to individual samples (see Table 2) were statistically compared by means of a regression line (Table 3). A coefficient of correlation (R2 ) of 0.992 was obtained. As it can be seen, the slope and intercept values did not differ significantly from 1 and 0, respectively. The method showed satisfactory accuracy, and no statistical differences were observed for the compounds at concentrations ≤ 100 ng/g. For Table 3 Results obtained from statistical analysis for comparing the analytical methodologies studied.
Intercept Slope
Coefficients
SD
Lower 95%
Upper 95%
1.031 1.025
0.714 0.025
−0.511 0.970
2.573 1.079
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those compounds present at very high levels it was not feasible to carry out an adequate statistical comparison, but concentration levels obtained by both approaches (MHS-SPME with external standard calibration and standard additions) were similar, differing in less than 15% in most cases. Limits of detection (LODs), defined as the lowest concentration that the analytical process can differentiate from background levels, were estimated for a signal-to-noise ratio (S/N) of 3 from the chromatograms corresponding to the first extraction of real tomato samples. The method allowed reaching limits of detection between 0.25 ng/g and 5 ng/g, with exception of E,E-2,4-hexadienal with LOD of 10 ng/g, and Z-3-hexenal and hexanal, which presented LOD of 20 ng/g. Fig. 3, showing a chromatogram corresponding to a real sample analysis in the full scan mode, offers an overall view of the volatile profile studied. Typical extracted ion chromatograms corresponding to the real sample of tomato analysed by MHS-SPME-GC–MS and used in method validation are shown in Fig. 4. Based on multiple SPME theory and on experimental determination of ˇ parameters from real samples, satisfactory quantitation of the total amount of analyte in samples can be carried out, as this approach already takes into account the potential matrix effects. In this way, SPME, which is an equilibrium technique with severe quantitative limitations, becomes a quantitative technique. On the other hand, HS sampling-based methodologies usually involve the use of surrogate standards [21,25,37] to correct physical behaviour of analytes in order to improve quantitation, but using MHS-SPME approach, this it is not required. Total analysis time (around 1 h per sample including extraction and GC determination) and limits of detection of the developed methodology are in the range usually reported for SPME-based methodologies [30–32]. Although experimental calculation of ˇ parameters is a time-consuming step, it can be easily assumed because of the increase in accuracy achieved, and the satisfactory subsequent use of external solvent calibration, which reduces procedure complexity, and facilitates quantification in samples. 4. Conclusions A method based on multiple headspace-solid-phase microextraction (MHS-SPME) followed by gas chromatography–mass spectrometry analysis has been developed for the determination of around 20 volatile aroma compounds responsible of tomato flavour and aroma. The method has been validated and quality parameters have been studied, showing satisfactory values. It has been proved to be accurate, as no statistical differences in quantitative results obtained by applying both standard addition and MHSSPME methodologies for tomato aroma compounds have been found. By means of determining ˇ parameters corresponding to each compound in selected extraction conditions, MHS-SPME has been found to be an adequate technique to avoid matrix effects in complex samples like tomato. Since total area of compounds can be estimated, quantitation of analytes using solvent external standards and direct injection in the GC–MS can be carried out. Another important advantage of this technique is that no surrogates are necessary to correct the extraction process and for adequate quantification, unlike other SPME-based methods.
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Quantitation of analytes present at levels of several hundred of ng/g has been feasible even using theoretical peak areas above the linearity of the method. In some cases, quantitative data were satisfactory but in others (e.g. highly concentrated volatiles) the application of MHS-SPME was unfeasible because the ˇ parameter could not be calculated. This matter should be treated in more detail in further works centred mainly on concentrated analytes. Acknowledgements E. Serrano acknowledges Universitat Jaume I of Castellón for her grant (Beca per a la Formació de Personal Investigador). The authors thank their colleague R. Serrano for his helpful comments. The purchase of the Varian GC–MS system was financed by Generalitat Valenciana (INFRAEXTRUCTURA04/022). References [1] B. Baccouri, S. Ben Temime, E. Campeol, P.L. Cioni, D. Daoud, M. Zarrouk, Food. Chem. 102 (2007) 850. [2] J. Beltrán, E. Serrano, F.J. López, A. Peruga, M. Valcárcel, S. Roselló, Anal. Bioanal. Chem. 385 (2006) 1255. [3] R. Bozalongo, J. Carrillo, M.A.F. Torroba, M. Tena, J. Chromatogr. A 1173 (2007) 10. [4] C. Bicchi, C. Cordero, E. Liberto, P. Rubiolo, B. Sgorbini, J. Chromatogr. A 1024 (2004) 217. [5] M. Kaykhaii, M. Rahmani, J. Sep. Sci. 30 (2007) 573. [6] A. Przyjazny, J.M. Kokosa, J. Chromatogr. A 977 (2002) 143. [7] G. Wang, R. Zhang, Y. Sun, K. Xie, C. Ma, Chromatographia 65 (2007) 363. [8] D.J. Caven-Quantrill, A.J. Buglass, J. Chromatogr. A 1117 (2006) 121. [9] C.L. Arthur, L.M. Killam, K.D. Buchholz, J. Pawliszyn, J.R. Berg, Anal. Chem. 64 (1992 1960). [10] K.D. Buchholz, J. Pawliszyn, Anal. Chem. 66 (1994) 160. [11] P. Tolgyessy, J. Hrivnak, J. Chromatogr. A 1127 (2006) 295. [12] A. Zalacain, J. Marín, J.L. Alonso, M.R. Salinas, Talanta 71 (2007) 1610. [13] D.J. Caven-Quantrill, A.J. Buglass, Flavour Fragr. J. 22 (2007) 206. [14] S. Guillot, L. Peytavi, S. Bureau, R. Boulanger, J.P. Lepoutre, J. Crouzet, S. SchorrGalindo, Food Chem. 96 (2006) 147. [15] E. Alissandrakis, P.A. Tarantilis, P.C. Harizanis, M. Polissiou, Food Chem. 100 (2007) 396. [16] B. Kolb, P. Pospisil, Chromatographia 10 (1977) 705. [17] B. Kolb, Chromatographia 15 (1982) 587. [18] O. Ezquerro, B. Pons, M.T. Tena, J. Chromatogr. A 999 (2003) 155. [19] O. Ezquerro, B. Pons, M.T. Tena, J. Chromatogr. A 1020 (2003) 189. [20] O. Ezquerro, G. Ortiz, B. Pons, M.T. Tena, J. Chromatogr. A 1035 (2004) 17. [21] O. Ezquerro, M.T. Tena, J. Chromatogr. A 1068 (2005) 201. [22] M. Groning, M. Hakkarainen, J. Chromatogr. A 1052 (2004) 61. [23] A. Martínez-Urunuela, J.M. González-Saiz, C. Pizarro, J. Chromatogr. A 1089 (2005) 31. [24] J.D. Carrillo, M.T. Tena, Anal. Bioanal. Chem. 385 (2006) 937. [25] C. Pizarro, N. Perez-del-Notario, J.M. Gonzalez-Saiz, J. Chromatogr. A 1143 (2007) 176. [26] C. Pizarro, N. Perez-del-Notario, J.M. Gonzalez-Saiz, J. Chromatogr. A 1166 (2007) 1. [27] J.L. Gomez-Ariza, T. Garcia-Barrera, J. Anal. At. Spectrom. 21 (2006) 884. [28] E. Hansson, M. Hakkarainen, J. Chromatogr. A 1102 (2006) 91. [29] M.T. Tena, J.D. Carrillo, Trends Anal. Chem. 26 (2007) 206. [30] K. Kozlowska, Z. Polkowska, A. Przyjazny, J. Namiesnik, Trends Anal. Chem. 25 (2006) 609. [31] R. van Willige, D. Schoolmeester, A. van Ooij, J. Linssen, A. Voragen, J. Food Sci. 67 (2002 2023). [32] R. Azodanlou, C. Darbellay, J.L. Luisier, J.C. Villettaz, R. Amado, J. Agric. Food Chem. 51 (2003) 715. [33] O. Gonzalez-Rios, M.L. Suarez-Quiroz, R. Boulanger, M. Barel, B. Guyot, J.P. Guiraud, S. Schorr-Galindo, J. Food Comp. Anal. 20 (2007) 289. [34] E.E. Stashenko, J.R. Martinez, J. Biochem. Biophys. Methods 70 (2007) 235. [35] A.R. Ghiasvand, L. Setkova, J. Pawliszyn, Flavour Fragr. J. 22 (2007) 377. [36] E. Carasek, E. Cudjoe, J. Pawliszyn, J. Chromatogr. A 1138 (2007) 10. [37] M. Aznar, T. Arroyo, J. Chromatogr. A 1165 (2007) 151.
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Journal of Chromatography A journal homepage: www.elsevier.com/locate/chroma
Application of multiple headspace-solid-phase microextraction followed by gas chromatography–mass spectrometry to quantitative analysis of tomato aroma components E. Serrano, J. Beltrán ∗ , F. Hernández Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat, 12071 Castellón, Spain
a r t i c l e
i n f o
Article history: Received 11 July 2008 Received in revised form 3 November 2008 Accepted 12 November 2008 Available online 18 November 2008 Keywords: Multiple Headspace-solid-phase microextraction MHS-SPME Tomato Aroma Volatile compounds GC–MS Quantitation
a b s t r a c t The objective of this paper is to investigate the potential of multiple headspace-solid-phase microextraction (MHS-SPME) for the determination of volatile compounds in complex matrix samples. A method based on MHS-SPME for the determination of around 20 volatile compounds, responsible of tomato flavour and aroma has been developed and validated, using gas chromatography with mass spectrometry (ion trap analyser) for analysis. For this purpose, the experimental ˇ parameter, resulting from the MHS-SPME theoretical development, has been obtained from real sample analysis (in triplicate) for each identified compound, carrying out up to 5 consecutive extractions. Later, this parameter is used to perform quantitation of real samples after just a single HS-SPME extraction. Precision, expressed as repeatability, has been evaluated by analysing six replicates of a real sample, showing relative standard deviations between 4 and 20%. For accuracy study, quantitative results have been compared with those obtained by means of standard additions on replicate samples, and no statistically significant differences between the two methods were observed. Since MHS-SPME uses the estimated total area corresponding to the complete extraction of compounds (obtained from the ˇ parameter), quantitation can be carried out by external calibration using standards in solvent and splitless injection, instead of by SPME. Linearity, tested in the range 0.05–15 g/mL, showed satisfactory values, with coefficients of correlation between 0.995 and 0.999. Limits of detection were in the range of 0.25–5 ng/g. MHS-SPME has been proved to be an adequate technique to avoid matrix effects in complex samples quantitation. Its applicability to the determination of volatile tomato components, together with its limitations, is discussed in this article. © 2008 Elsevier B.V. All rights reserved.
1. Introduction Volatile components contributing to fruits and vegetables aroma and flavour have been widely studied, especially aromas. Although more than 400 volatile compounds have been identified, only a small number is essential and seems to contribute really to aroma profiles. Analytical methodology for determination of volatile compounds in vegetable matrices is continuously improving due to the important role of these compounds in organoleptic quality of food [1–3]. Aromas present in food samples belong to different chemical families, including esters, ketones, aldehydes, alcohols, terpens, phenols, and its derivatives. As a result, optimisation of multicomponent sample preparation procedures is difficult. Moreover, there are significant differences in partition coefficients between real
∗ Corresponding author. Tel.: +34 964 38 73 60; fax: +34 964 387368. E-mail address: [email protected] (J. Beltrán). 0021-9673/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.chroma.2008.11.026
samples and standard solutions and the use of surrogates and/or internal standards is required for adequate quantitation. In spite of analytical efforts, quantitation of aromas is rather problematic, and in most cases, not fully satisfactory results are obtained. Direct headspace (HS) injection, purge and trap (P&T), HSsolid phase microextraction (SPME) or HS-solid-phase dynamic extraction (SPDE) are extraction techniques widely applied for the determination of aroma profile or volatile fraction composition of many different samples (fruits, vegetables, plants, beverages. . .) [2,4]. Other techniques, like liquid-phase microextraction (LPME) or sorptive extraction (either by direct immersion or by headspace sampling) have been recently applied to the determination of volatile compounds in different matrices and are under study [5–8]. SPME, introduced by Pawliszyn in the earlier 90’s [9,10], is a well known sample extraction technique widely applied in food and environmental analysis, as it is simple, rapid, solvent-less sample preparation technique that can perform sampling, clean-up, and concentration in the same step [11–13]. HS-SPME is widely used for determination of volatile compounds typically followed
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Table 1 Accuracy and precision study for the MHS-SPME-GC–MS method developed. Compound
ˇ
Z-3-hexenal Hexanal E,E-2,4-hexadienal 6-Methyl-5-hepten-2-one 6-Methyl-5-hepten-2-ol E,E-2,4-heptadienal R-limonene 2-Isobutylthiazole Guaiacol E-2-octenal Linalool 2-Phenylethanol Methyl salicylate ␣-Terpineol -Ciclocytral Z-citral E-citral E,E-2,4-decadienal Diphenyl ether Geranylacetone -Ionone
0b 0.13 0.76 0.27 0b 0.33 0.50 0b 0.29 0.13 0.38 0b 0.11 0.16 0.44 0.70 0.65 0.12 0.44 0.72 0.34
Precision
LOD (ng/g)
RSD (%) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 a b
Accuracy (ng/g)a MHS-SPME
18 14 20 11 4 20 19 14 16 12 19 6 9 9 16 9 11 19 13 14 12
20 20 10 1.5 2 2 2 1.5 1 2 3 1 1 1 0.4 2.5 2.5 1 0.5 5 0.25
140 570 1060 250 6.6 5.3 15 28 5.5 9.8 18 98 2.9 3.5 7.2 26.8 107 9.3 3.4 340 13.9
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
6 90 150 40 0.3 0.7 3 4 0.5 1.4 3 12 0.5 0.6 1.4 1.8 19 1.4 0.6 70 2.4
Std. add 124 566 1132 265 7.1 4.4 17.4 34.0 5 9.2 18.1 100.4 3.2 3.9 7.2 32.0 118.0 12.0 3.5 437 16.8
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
4 9 23 3 0.7 0.4 0.3 0.6 0.9 0.5 0.7 1.3 0.5 0.3 0.4 1.0 1.7 0.9 0.6 6 0.4
Sample concentration and confidence interval, calculated for n = 6 in MHS-SPME method and from the regression data in standard addition method. Analyte exhaustively isolated in the first extraction.
by gas chromatography (GC) analysis. Due to its higher confirmatory potential mass spectrometry (MS) detection is commonly applied, working in full scan or SIM modes typically using ion trap or quadrupole detectors [14,15]. It is well known that SPME is a non-quantitative extraction technique, so the amount of analyte isolated is not the total present in sample, thus being necessary to calibrate using extracted spiked blank samples. Unfortunately, this is not easy when determining the aroma profile in vegetables as no blank samples can be obtained. A stepped procedure called multiple headspace extraction (MHE), whose theoretical bases were established in the earlier 80’s [16,17], has been proposed as an alternative to overcome some difficulties, as matrix effects, typically observed in quantitation of volatile compounds in solid samples. SPME can also be performed in a multiple mode (M-SPME). The goal of the process is the estimation of the response for total analyte, which is directly related to the total amount of analyte present in sample. Thus, it can allow a better quantitation of the analytes in complex matrices and, even more, an external calibration may be used. As described in the literature, MHS-SPME has been applied to the determination of volatile compounds in different type of matrices including packaging materials, soils, nylon, oils or wines [18–27]. Recently, an emerging technique, SDME, has also been combined with multiple headspace extraction [28]. M-SPME considers that n1 , defined as the amount of analyte sorbed into fibre after the first extraction, is proportional to the initial amount on the sample (n0 ):
If one assumes that there is a linear relationship between the amount of analyte in the fibre and the instrumental response of analyte (peak area, A), Eq. (3) could be written as:
n1 = a · n0
Once obtained ˇ, only a single extraction is carried out in samples, and AT is estimated by means of Eq. (5) from the response obtained in this first extraction (A1 ) and from the ˇ parameter obtained experimentally. As previously reported [29], a compound presenting a ˇ value below 0.4 would be exhaustively isolated in the first extractions, as a result of the significant reduction of the analyte amount in sample from one extraction to the next. On the contrary, a high ˇ value (over 0.95) means that even after several consecutive extractions on the same sample the amount of analyte in sample remains virtually unaffected (amount extracted is negligible when compared to the total analyte amount). Thus, only when the ˇ values are in the range of 0.4–0.95 application of MHS-SPME is feasible, as a
(1)
˛ can be defined as: ˛=
Kfs · Vf Kfs · Vf + Khs · Vh + Vs
(2)
where Kfs and Khs are the fibre coating-sample and headspacesample distribution constants, and Vf , Vh , Vs are the coating, headspace and sample volumes. It can be demonstrated [18] that the amount of analyte in the fibre coating after the ith extraction (ni ) can be expressed as follows: ni = n1 · (1 − ˛)(i−1)
(3)
Ai = A1 · (1 − ˛)(i−1)
(4)
Assuming that the sum of peak areas (from i = 1 to ∞) corresponding to consecutive extractions (which follow a geometric progression), corresponds to the theoretical total area (AT ) of analyte, the later could be determined by means of a single extraction of the sample and the application of a mathematical approach, described in the following equation: AT =
A1 1−ˇ
(5)
where ˇ is equal to (1 − ˛) and A1 the peak area of the first extraction. From an experimental point of view, MHS-SPME is a stepped process where the objective is the empirical determination of ˇ parameter, which is characteristic for each analyte and depends on sample matrix and on the extraction conditions selected. For this purpose, several consecutive HS-SPME extractions on the same sample (similar to the real samples that will be analyzed) are carried out, and ˇ is obtained from the slope of the linear plot described in Eq. (6), which derives from Eq. (4): log Ai = log A1 + (i − 1) · log ˇ
(6)
E. Serrano et al. / J. Chromatogr. A 1216 (2009) 127–133
detectable reduction in analyte amount in sample is produced from one extraction to the next one. In the present study, the potential of multiple headspace solid-phase microextraction for the quantitative determination of volatile compounds in complex matrix samples (tomato) using external solvent calibration has been investigated. Besides, the possibility of quantitation of samples above the linear range has been tested. To our knowledge, this is the first application of MHS-SPME to quantitative determination of aroma components of tomato samples. More than 20 volatile compounds had been identified in samples characterized in previous works, and these compounds have been studied and determined in current work. 2. Experimental
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1.03 min (total chromatographic analysis time of 45 min). MS analysis was performed in full scan mode (m/z scan range of 60–200 Da), with electron impact ionization energy of 70 eV in the positiveion mode and internal ionisation configuration. Transfer line, trap and manifold temperatures were set at 275 ◦ C, 200 ◦ C and 60 ◦ C, respectively. 3. Results and discussion The experimental design of the research has been directed towards the optimization of the MHS-SPME and experimental calculation of ˇ parameters. Once obtained the ˇ parameter and the optimum SPME conditions, the procedure has been fully validated with real samples. Results obtained are shown in the following sections, giving special attention to accuracy, which was stimated by comparison with standard addition method quantitation.
2.1. Chemicals, reagents and materials 3.1. MHS-SPME optimisation Reference standards of volatile compounds studied (Z-3hexenal, hexanal, E,E-2,4-hexadienal, 6-methyl-5-hepten-2-one, 6methyl-5-hepten-2-ol, E,E-2,4-heptadienal, R-limonene, 2-isobutylthiazole, guaiacol, E-2-octenal, linalool, 2-phenylethanol, methyl salicylate, ␣-terpineol, -ciclocytral, Z-citral, E-citral, E,E-2,4-decadienal, diphenyl ether, geranylacetone, -ionone, phenylacetaldehyde, benzaldehyde) were supplied by Supelco (Sigma–Aldrich and Fluka; Barcelona, Spain) as pure compounds (92–99.5%). Stock solutions (500 mg/L) of the individual volatile standards were prepared in acetone, and working solutions were prepared by volume dilution with n-hexane. All standard solutions were stored at −18 ◦ C in sealed glass vials completely filled (without headspace) to avoid analyte losses and to ensure reproducibility, as recommended in references dealing with volatile compounds storage [30,31]. Acetone and n-hexane GC grade were obtained from Scharlab (Barcelona, Spain). Tomato samples used for method optimisation and validation were obtained from commercial sources. SPME carboxen/polydimethylsiloxane (CAR/PDMS) fibre coating was supplied by Supelco (Sigma–Aldrich; Barcelona, Spain). 2.2. MHS-SPME procedure Fresh tomato samples (stored at 4 ◦ C) were triturated and homogenized using a blender (UltraTurrax) for 1 min at ambient temperature, and then placed in 10 mL-vials and frozen at −18 ◦ C without any headspace until analysis. SPME fibre coating used for this work was CAR/PDMS which seemed to be the most adequate for the determination of small and polar volatile molecules related to tomato aroma, as we found in previous works [2] on agreement with the literature [32–34]. HS-SPME was carried out as follows: 0.1 g of tomato sample were weighted in a 10 mL-vial, sealed and extracted at 70 ◦ C for 50 min (with 10 min of pre-heating) using a CAR/PDMS fibre. After absorption process, SPME fibre was immediately desorbed at 300 ◦ C for 5 min on GC injection port, maintaining the fibre for an additional 5 min into the injector to avoid carry-over effect. 2.3. GC–MS conditions A Varian CP-3800 gas chromatograph coupled to a Varian 4000 MS mass spectrometer (ion trap analyser) was used to identify and quantify volatiles isolated from tomato samples. Analytes were separated on a 30 m × 0.25 mm VF-5MS (0.25 m film thickness) capillary column, using helium at a constant flow of 1 mL/min as carrier gas. The column temperature program was: 40 ◦ C for 5 min; then temperature was increased to 141 ◦ C at 3 ◦ C/min and then increased to 300 ◦ C at 30 ◦ C/min with a final isothermal stage of
From our previous works [2], SPME fibre coating (CAR/PDMS) was pre-selected and total extraction time was set to 60 min (10 min pre-heating and 50 min extraction). However, sample mass and extraction temperature were fully optimised in the present work. As the ˇ parameter is procedure-dependent and related to matrixeffect, it must be estimated from a real sample, considering the effect of extraction conditions on this parameter during optimisation step. In a first approach, several consecutive extractions (until most compounds were not detected in the chromatogram corresponding to the last extraction) were carried out using different sample masses: 0.1 g, 0.5 g, 1 g and 2 g. SPME extractions were performed at 40 ◦ C for 50 min (with a pre-heating time of 10 min). In order to fit experimental data to Eq. (6) and to obtain experimental ˇ values, a difference of at least 5% between two consecutive extractions must be achieved, i.e. each single extraction must reduce significantly the analyte amount in sample. Results obtained when extracting sample amounts of 1 g and 2 g, showed no significant differences on peak areas for successive extractions. This observation would imply that even if the sample/HS were at equilibrium, some analytes were so concentrated in HS phase that the effect of the amount extracted onto the fibre was negligible. Thus, determination of ˇ was not possible under these conditions and, consequently, MHS-SPME procedure could not be applied. Due to the high concentration of several analytes present in sample (major components of aroma and flavour), the decrease of sensitivity when using smaller sample amounts, i.e. 0.1 g and 0.5 g was negligible. In these experiments, ˇ ranged from 0.40 (capronaldehyde) to 0.81 (geranylacetone) using 0.5 g sample, and from 0.23 (Z-3-hexenal) to 0.83 (E,E-2,4-heptadienal) when using 0.1 g. In both cases, ˇ could not be obtained for a few compounds that presented a non-linear correlation between extractions. Moreover none of the studied compounds was exhaustively isolated with a single extraction at 40 ◦ C, which makes the application of MHSSPME for these analytes attractive. Finally, 0.1 g was selected for further experiments, in order to minimize the HS phase saturation. In order to optimise extraction temperature, up to five consecutive extractions using sample mass of 0.1 g were carried out at each of the following temperatures: 40 ◦ C, 60 ◦ C, 70 ◦ C and 80 ◦ C. For most analytes tested, it was found that at low extraction temperatures (40 ◦ C and 60 ◦ C), the first extraction deviated from the rest, existing only a correct linear relationship between the second and the following extractions. This process could be explained by the slow diffusion of analytes from sample to headspace phase at
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Table 2 Linearity study for the MHS-SPME-GC–MS developed method. Compound
(ng/L) 1 2 3 4 5 6 7 8 9 10 11 12 13
Z-3-hexenal Hexanal E,E-2,4-hexadienal 6-Methyl-5-hepten-2-one 6-Methyl-5-hepten-2-ol E,E-2,4-heptadienal R-limonene 2-Isobutylthiazole Guaiacol E-2-octenal Linalool 2-Phenylethanol Methyl salicylate
14 15 16 17 18 19 20 21
␣-Terpineol -Ciclocitral Z-citral E-citral E,E-2,4-decadienal Diphenyl ether Geranylacetone -Ionone
a b c
Regression parametersa
Linear range
0.200–5 0.004–5.5 0.006–6 0.020–10 0.022–5.5 0.005–10 0.022–6 0.006–11 0.005–5.5 0.05–5 0.004–5 0.030–7.5 0.020–0.200 0.200–10 0.009–8.5 0.010–5 0.028–11 0.004–14 0.050–10 0.007–15 0.055–5.5 0.012–0.230 0.230–12
c
(ng/g)
2–50 0.04–55 0.06–60 0.20–100 0.22–55 0.05–100 0.22–60 0.06–110 0.05–55 0.50–50 0.04–50 0.30–75 0.20–2 2–100 0.09–85 0.10–50 0.28–110 0.04–140 0.50–100 0.07–150 0.55–55 0.12–2.30 2.30–120
a
(51 ± 2) × 103
(52 ± 6) × 102 (58 ± 6) × 102 (53 ± 1) × 102
(60 ± 3) × 103 (23 ± 1) × 103
(58 ± 2) × 102
R2 b
b
c
(160 ± 2) × 102 (188 ± 3) × 102 (218 ± 5) × 103 (172 ± 1) × 103 (70 ± 2) × 103 (259 ± 2) × 103 (184 ± 1) × 103 (364 ± 4) × 103 (163 ± 2) × 103 (63 ± 6) × 103 (15 ± 4) × 104 (240 ± 9) × 103 (291 ± 1) × 102 (13 ± 1) × 104 (141 ± 1) × 103 (900 ± 9) × 102 (96 ± 1) × 103 (15 ± 5) × 103 (68 ± 1) × 102 (443 ± 3) × 103 (45 ± 6) × 103 (89 ± 2) × 103 (53 ± 2) × 104
(40 ± 5) × 10 (52 ± 7) (78 ± 3) × 102 (75 ± 5) × 102 (33 ± 2) × 102 (89 ± 6) × 102 (19 ± 5) × 103 (29 ± 1) × 103 (15 ± 5) × 103 (61 ± 5) × 102 (38 ± 2) × 102 (28 ± 1) × 10 (14 ± 3) × 10 (42 ± 1) × 102 (71 ± 9) × 102 (32 ± 3) × 102 (17 ± 5) × 102 (41 ± 1) × 10 (83 ± 1) × 102 (97 ± 1) × 102 (89 ± 5) × 10 (58 ± 3) × 10 (16 ± 4) × 103
0.998 0.999 0.998 0.998 0.997 0.998 0.999 0.999 0.999 0.998 0.998 0.999 0.995 0.998 0.998 0.997 0.999 0.998 0.997 0.999 0.998 0.998 0.997
Experimental data were fitted to either y = ax2 + bx + c or y = bx + c models. Correlation coefficients. Linear range corresponding to real samples (0.1 g of tomato).
these temperatures, which affects specially to the first extraction where an excess of analyte is present to be sorbed on the SPME fibre. For the following extractions, compound amount in vapour and solid phases had to reach equilibrium again. This process differs for each compound depending on polarity of analytes, possible interaction with matrix components or different volatility. When 80 ◦ C was tested as extraction temperature, we observed an important decrease in the response for most analytes, possibly due to the displacement of equilibrium between fibre and headspace to the vapour phase, decreasing compounds retention on the fibre surface. This fact has already been observed in previous HS-SPME studies [35,36]. This led to an unfavourable increase in the ˇ parameters with values ranging from 0.54 (for eugenol) to 0.82 (for E-citral). Besides, several thermal degradation compounds as furanderivates or methoxyphenyl oximes were identified at high concentration levels using the mass spectra library included in the MS software (NIST Mass Spectral library 2.0). These degradation products might interfere in aroma determination modifying the true aroma profile.
Finally, 70 ◦ C was selected as a compromise. Data obtained at this temperature showed a linear relationship for most of the compounds with no significant decrease in their response, so this temperature was selected as optimum. Under these conditions, ˇ parameters were obtained by triplicate from the slope of the linear plot of peak area logarithms vs. (i – 1) using Eq. (6), where i is the number of consecutive extractions. Three consecutive extractions were carried out for this purpose. ˇ-Values were in the range of 0.11–0.76, except for Z-3-hexenal, 6-methyl-5-hepten-2-ol, 2-isobutylthiazole and 2-phenylethanol which were exhaustively isolated after the first extraction (ˇ = 0) (Table 1). Determination of ˇ parameter for phenylacetaldehyde and benzaldehyde was not possible, since no significant differences were observed on peak areas corresponding to successive extractions (ˇ > 0.95). As stated before, this could be due to the high concentration of these compounds in samples that overloads the vapour phase even after consecutive extractions. Fig. 1 shows extracted ion chromatograms for four selected compounds resulting from three consecutive headspace extractions
Fig. 1. Illustrative GC–MS extracted ion chromatograms (quantitation m/z ion) for four selected volatile compounds corresponding to three repeated SPME extractions of a real tomato sample (0.1 g) at 70 ◦ C for 50 min.
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Fig. 2. Lineal plots used to determine ˇ values (numerical data shown in Table 3).
under optimum conditions and ˇ values obtained for these four compounds. Experimental data (peak areas vs. number of repeated extractions) are plotted and the fitting parameters are also shown in Fig. 2 for the four compounds. Once ˇ values for the selected compounds are determined, Eq. (5) is used to calculate the theoretical area (AT ) corresponding to the total amount of analyte present in the sample. In these conditions it should be possible to calculate the concentration of the sample by using an external calibration curve injected in the GC–MS system using standards prepared in solvent. In order to evaluate the feasibility of quantitation using external solvent calibration, ˇ parameters corresponding to several volatile compounds were obtained extracting spiked water samples. Values obtained ranged between 0.12 (hexanal) and 0.88 (for Z-citral). For this purpose, several consecutive headspace SPME extractions were carried out on a 100 L LC-grade water aliquot spiked at 10 ng/mL for each studied compound. Then, total peak areas AT for each compound were calculated following the multiple SPME theoretical bases (Eq. (5)) using single extraction peak areas and the ˇ parameters previously calculated. Quantitation was then carried out by interpolation of the calculated AT in a calibration curve obtained injecting 1 L of pure-solvent standards. Recoveries obtained in this
way were in the range 76–113%, showing the feasibility of applying headspace microextraction in the multiple mode but using external solvent calibration with direct liquid injection. Validation of the method should demonstrate the applicability of the described procedure to complex matrix samples, like tomato. 3.2. Method validation The MHS-SPME-GC–MS method developed has been validated in terms of linearity, precision, accuracy and limit of detection. Linearity was studied by directly injecting (by triplicate) hexane standard solutions at ten concentration levels in the range from 0.002 g/mL to 15 g/mL. Calibration curves showed adequate coefficients of correlation (R2 ), higher than 0.995 and residuals lower than 13% with no tendency. Precision of the overall analytical procedure has been evaluated as repeatability, by means of replicate real sample analysis (n = 6), and it was expressed as RSD (%) of the calculated concentrations. The method has been found to have satisfactory precision, with coefficients of variation between 4 and 20% for all compounds studied (Table 2).
Fig. 3. GC–MS full scan chromatogram (60–200 m/z) for the first extraction corresponding to the real tomato sample used in accuracy study.
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Fig. 4. GC–MS extracted ion chromatograms (quantitation m/z ion) for the first extraction corresponding to the real tomato sample used in accuracy study.
On the other hand, for accuracy study in tomato matrix, we selected the standard additions methodology as a reference because of recovery assays could not be applied as blank tomato samples cannot be obtained. For standard additions assays (four addition points), between 1 and 100 ng of each analyte (depending on the amount previously found), were added to 0.1 g tomato sample and it was analysed by HS-SPME-GC–MS under extraction conditions optimised previously for the multiple headspace mode. Four of the studied compounds (geranylacetone, E,E-2,4hexadienal, 6-methyl-5-hepten-2-one and hexanal) showed total peak areas (AT ) clearly above the studied linear range, which could led to quantitation errors. Nevertheless, considering the MHS theoretical bases, the fact that AT is a theoretical that will never be experimentally obtained in the GC system, might allow extending the linear range of the calibration curve to quantify these high concentrations in samples without relevant errors. Thus, quantitation above linear range might be considered acceptable (as guidance results). Geranylacetone, E,E-2,4-hexadienal, 6-methyl-5-hepten-
2-one or hexanal, levels of which in tomato were around 350, 900, 250 and 560 ng/g, respectively, are examples of this situation. Quantitative results obtained applying both methodologies (MHS-SPME and HS-SPME with standard additions) to individual samples (see Table 2) were statistically compared by means of a regression line (Table 3). A coefficient of correlation (R2 ) of 0.992 was obtained. As it can be seen, the slope and intercept values did not differ significantly from 1 and 0, respectively. The method showed satisfactory accuracy, and no statistical differences were observed for the compounds at concentrations ≤ 100 ng/g. For Table 3 Results obtained from statistical analysis for comparing the analytical methodologies studied.
Intercept Slope
Coefficients
SD
Lower 95%
Upper 95%
1.031 1.025
0.714 0.025
−0.511 0.970
2.573 1.079
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those compounds present at very high levels it was not feasible to carry out an adequate statistical comparison, but concentration levels obtained by both approaches (MHS-SPME with external standard calibration and standard additions) were similar, differing in less than 15% in most cases. Limits of detection (LODs), defined as the lowest concentration that the analytical process can differentiate from background levels, were estimated for a signal-to-noise ratio (S/N) of 3 from the chromatograms corresponding to the first extraction of real tomato samples. The method allowed reaching limits of detection between 0.25 ng/g and 5 ng/g, with exception of E,E-2,4-hexadienal with LOD of 10 ng/g, and Z-3-hexenal and hexanal, which presented LOD of 20 ng/g. Fig. 3, showing a chromatogram corresponding to a real sample analysis in the full scan mode, offers an overall view of the volatile profile studied. Typical extracted ion chromatograms corresponding to the real sample of tomato analysed by MHS-SPME-GC–MS and used in method validation are shown in Fig. 4. Based on multiple SPME theory and on experimental determination of ˇ parameters from real samples, satisfactory quantitation of the total amount of analyte in samples can be carried out, as this approach already takes into account the potential matrix effects. In this way, SPME, which is an equilibrium technique with severe quantitative limitations, becomes a quantitative technique. On the other hand, HS sampling-based methodologies usually involve the use of surrogate standards [21,25,37] to correct physical behaviour of analytes in order to improve quantitation, but using MHS-SPME approach, this it is not required. Total analysis time (around 1 h per sample including extraction and GC determination) and limits of detection of the developed methodology are in the range usually reported for SPME-based methodologies [30–32]. Although experimental calculation of ˇ parameters is a time-consuming step, it can be easily assumed because of the increase in accuracy achieved, and the satisfactory subsequent use of external solvent calibration, which reduces procedure complexity, and facilitates quantification in samples. 4. Conclusions A method based on multiple headspace-solid-phase microextraction (MHS-SPME) followed by gas chromatography–mass spectrometry analysis has been developed for the determination of around 20 volatile aroma compounds responsible of tomato flavour and aroma. The method has been validated and quality parameters have been studied, showing satisfactory values. It has been proved to be accurate, as no statistical differences in quantitative results obtained by applying both standard addition and MHSSPME methodologies for tomato aroma compounds have been found. By means of determining ˇ parameters corresponding to each compound in selected extraction conditions, MHS-SPME has been found to be an adequate technique to avoid matrix effects in complex samples like tomato. Since total area of compounds can be estimated, quantitation of analytes using solvent external standards and direct injection in the GC–MS can be carried out. Another important advantage of this technique is that no surrogates are necessary to correct the extraction process and for adequate quantification, unlike other SPME-based methods.
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Quantitation of analytes present at levels of several hundred of ng/g has been feasible even using theoretical peak areas above the linearity of the method. In some cases, quantitative data were satisfactory but in others (e.g. highly concentrated volatiles) the application of MHS-SPME was unfeasible because the ˇ parameter could not be calculated. This matter should be treated in more detail in further works centred mainly on concentrated analytes. Acknowledgements E. Serrano acknowledges Universitat Jaume I of Castellón for her grant (Beca per a la Formació de Personal Investigador). The authors thank their colleague R. Serrano for his helpful comments. The purchase of the Varian GC–MS system was financed by Generalitat Valenciana (INFRAEXTRUCTURA04/022). References [1] B. Baccouri, S. Ben Temime, E. Campeol, P.L. Cioni, D. Daoud, M. Zarrouk, Food. Chem. 102 (2007) 850. [2] J. Beltrán, E. Serrano, F.J. López, A. Peruga, M. Valcárcel, S. Roselló, Anal. Bioanal. Chem. 385 (2006) 1255. [3] R. Bozalongo, J. Carrillo, M.A.F. Torroba, M. Tena, J. Chromatogr. A 1173 (2007) 10. [4] C. Bicchi, C. Cordero, E. Liberto, P. Rubiolo, B. Sgorbini, J. Chromatogr. A 1024 (2004) 217. [5] M. Kaykhaii, M. Rahmani, J. Sep. Sci. 30 (2007) 573. [6] A. Przyjazny, J.M. Kokosa, J. Chromatogr. A 977 (2002) 143. [7] G. Wang, R. Zhang, Y. Sun, K. Xie, C. Ma, Chromatographia 65 (2007) 363. [8] D.J. Caven-Quantrill, A.J. Buglass, J. Chromatogr. A 1117 (2006) 121. [9] C.L. Arthur, L.M. Killam, K.D. Buchholz, J. Pawliszyn, J.R. Berg, Anal. Chem. 64 (1992 1960). [10] K.D. Buchholz, J. Pawliszyn, Anal. Chem. 66 (1994) 160. [11] P. Tolgyessy, J. Hrivnak, J. Chromatogr. A 1127 (2006) 295. [12] A. Zalacain, J. Marín, J.L. Alonso, M.R. Salinas, Talanta 71 (2007) 1610. [13] D.J. Caven-Quantrill, A.J. Buglass, Flavour Fragr. J. 22 (2007) 206. [14] S. Guillot, L. Peytavi, S. Bureau, R. Boulanger, J.P. Lepoutre, J. Crouzet, S. SchorrGalindo, Food Chem. 96 (2006) 147. [15] E. Alissandrakis, P.A. Tarantilis, P.C. Harizanis, M. Polissiou, Food Chem. 100 (2007) 396. [16] B. Kolb, P. Pospisil, Chromatographia 10 (1977) 705. [17] B. Kolb, Chromatographia 15 (1982) 587. [18] O. Ezquerro, B. Pons, M.T. Tena, J. Chromatogr. A 999 (2003) 155. [19] O. Ezquerro, B. Pons, M.T. Tena, J. Chromatogr. A 1020 (2003) 189. [20] O. Ezquerro, G. Ortiz, B. Pons, M.T. Tena, J. Chromatogr. A 1035 (2004) 17. [21] O. Ezquerro, M.T. Tena, J. Chromatogr. A 1068 (2005) 201. [22] M. Groning, M. Hakkarainen, J. Chromatogr. A 1052 (2004) 61. [23] A. Martínez-Urunuela, J.M. González-Saiz, C. Pizarro, J. Chromatogr. A 1089 (2005) 31. [24] J.D. Carrillo, M.T. Tena, Anal. Bioanal. Chem. 385 (2006) 937. [25] C. Pizarro, N. Perez-del-Notario, J.M. Gonzalez-Saiz, J. Chromatogr. A 1143 (2007) 176. [26] C. Pizarro, N. Perez-del-Notario, J.M. Gonzalez-Saiz, J. Chromatogr. A 1166 (2007) 1. [27] J.L. Gomez-Ariza, T. Garcia-Barrera, J. Anal. At. Spectrom. 21 (2006) 884. [28] E. Hansson, M. Hakkarainen, J. Chromatogr. A 1102 (2006) 91. [29] M.T. Tena, J.D. Carrillo, Trends Anal. Chem. 26 (2007) 206. [30] K. Kozlowska, Z. Polkowska, A. Przyjazny, J. Namiesnik, Trends Anal. Chem. 25 (2006) 609. [31] R. van Willige, D. Schoolmeester, A. van Ooij, J. Linssen, A. Voragen, J. Food Sci. 67 (2002 2023). [32] R. Azodanlou, C. Darbellay, J.L. Luisier, J.C. Villettaz, R. Amado, J. Agric. Food Chem. 51 (2003) 715. [33] O. Gonzalez-Rios, M.L. Suarez-Quiroz, R. Boulanger, M. Barel, B. Guyot, J.P. Guiraud, S. Schorr-Galindo, J. Food Comp. Anal. 20 (2007) 289. [34] E.E. Stashenko, J.R. Martinez, J. Biochem. Biophys. Methods 70 (2007) 235. [35] A.R. Ghiasvand, L. Setkova, J. Pawliszyn, Flavour Fragr. J. 22 (2007) 377. [36] E. Carasek, E. Cudjoe, J. Pawliszyn, J. Chromatogr. A 1138 (2007) 10. [37] M. Aznar, T. Arroyo, J. Chromatogr. A 1165 (2007) 151.