Fast Characterization of Cheeses by Dynamic Headspace-Mass

Anal. Chem. 2002, 74, 1386-1392

Fast Characterization of Cheeses by Dynamic Headspace-Mass Spectrometry Christophe Pe´re`s,* Christian Denoyer, Pascal Tournayre, and Jean-Louis Berdague´

Laboratoire Flaveur, INRA de Theix, F-63122 Saint-Gene` s-Champanelle, France

This study describes a rapid method to characterize cheeses by analysis of their volatile fraction using dynamic headspace-mass spectrometry. Major factors governing the extraction and concentration of the volatile components were first studied. These components were extracted from the headspace of the cheeses in a stream of helium and concentrated on a Tenax TA trap. They were then desorbed by heating and injected directly into the source of a mass spectrometer via a short deactivated silica transfer line. The mass spectra of the mixture of volatile components were considered as fingerprints of the analyzed substances. Forward stepwise factorial discriminant analysis afforded a limited number of characteristic mass fragments that allowed a good classification of the batches of cheeses studied. The characterization of raw materials and foods is of prime strategic importance to the agrifood industry. Recent research has shown that the rapid analysis of the volatile fraction of foods by mass spectrometry can be effective. Various methods can be employed to collect the volatile components. Direct coupling to a mass spectrometer using, for example, static headspace (SHS-MS),1-6 dynamic headspace (DHS-MS)5,7-9 or solid-phase microextraction (SPME-MS)5,10-12 methods yields “fingerprints” of the analyzed substances with or without preconcentration of the volatile fraction. Owing to their rapidity, these nonseparative methods can be used to classify cheeses and to monitor their quality. Within the framework of a study about the on-line QC of dairy products, the objective of this work was to develop a DHS-MS method for the rapid characterization of cheeses. To this end, a * Corresponding author. E-mail: [email protected]. (1) Dittmann, B.; Nitz, S.; Horner, G. Adv. Food Sci. 1998, 20, 115-121. (2) Shiers, V.; Squibb, A. D. In 5th symposium on olfaction and electronic nose; Hunt Valley, Baltimore, MD, 1998. (3) Dittmann, B.; Nitz, S. Sens. Actuators 2000, 69, 253-257. (4) Dittmann, B.; Zimmermann, B.; Engelen, C.; Jany, G.; Nitz, S. J. Agric. Food Chem. 2000, 48, 2887-2892. (5) Schaller, E.; Zenha¨usern, S.; Zesiger, T.; Bosset, J. O.; Escher, F. Analusis 2000, 28, 743-749. (6) Goodner, K. L.; Rouseff, R. L. J. Agric. Food Chem. 2001, 49, 250-253. (7) Berdague´, J. L.; Vernat, G.; Rossi, V. Viandes Prod. Carne´ s 1993, 14 (5), 135-138. (8) Berdague´, J. L.; Viallon, C.; Kondjoyan, N.; Denoyer, C.; Thonat, C. Viandes Prod. Carne´ s 1998, 19 (1), 78-80. (9) Vernat, G.; Berdague´, J. L. In Bioflavour 95 symposium, Dijon, 1995; pp 5962. (10) Marsili, R. T. J. Agric. Food Chem. 1999, 47, 648-654. (11) Marsili, R. T. J. Agric. Food Chem. 2000, 48, 3470-3475. (12) Pe´re`s, C.; Viallon, C.; Berdague´, J. L. Anal. Chem. 2001, 73, 1030-1036.

1386 Analytical Chemistry, Vol. 74, No. 6, March 15, 2002

Table 1. Principal Characteristics of the Camembert-Type Cheeses Analyzed code

manufacturer

milk treatment

ripening stage (days)

Rm1 Rm2 Rm3 Htm cou

A B B C D

raw raw raw heat-treated heat-treated

30 30 42 30 30

system for the dynamic extraction of volatile components, with preconcentration and flash desorption of the adsorbent, was coupled to a mass spectrometer. The products studied were five batches of different “Camembert”-type cheeses. The extraction conditions that afforded the most intense DHS-MS signals associated with minimal introduction of air and water into the mass spectrometer source were defined. For better interpretation of the information contained in the spectra obtained by DHS-MS, the volatile compounds desorbed from the cheeses were identified by DHS-GC/MS. MATERIALS AND METHODS Nature and Preparation of the Products Analyzed. The cheeses studied were commercial products of the Camembert type (Table 1). They were cut up into equal-sized portions weighing 25 g, wrapped in aluminum foil, vacuum-packed in polyethylene bags, and stored at -25 °C. Sample Preparation. The packed portions were thawed at ambient temperature before analysis. To generate the sample headspace, 5 g of cheese was placed in a 20-mL vial (Interchim, Montluc¸ on, France), which was immediately sealed with a butyl Teflon septum cap and a crimped aluminum closure (Interchim). Dynamic Headspace-Mass Spectrometry. The apparatus for the extraction and concentration of the volatile components consisted of an automated dynamic headspace system (Compact Desorber 1, SRV, INRA Theix, France) coupled to an autosampler (HP19395A, Hewlett-Packard, Palo Alto, CA) with a capacity of 21 vials, configured in “dynamic extraction” mode. The system worked sequentially vial by vial: (i) Headspace Stabilization. Stabilization of the headspace in the vial was accomplished by equilibration for 20 min at 30 °C. Prior tests at this temperature showed that artifacts arising from the injection of water vapor in the spectrometer source were limited. (ii) Extraction/Trapping. To extract and trap the volatile components, the septum was pierced with a double syringe. One 10.1021/ac011053h CCC: $22.00

© 2002 American Chemical Society Published on Web 02/07/2002

Figure 1. (a) Desorption peak of a sample of Camembert cheese (Rm3) obtained by DHS-MS. The extraction was performed by purging the headspace for 2 min at 120 mL‚min-1. (b) Average spectrum from 0.3 to 0.8 min.

of the two syringes was then injected helium (quality U, purity 99.995%, Air Liquide) into the vial while the other syringe sent the headspace through an adsorbent trap (stainless steel, length 18 cm, inside diameter 2.5 mm, Tenax TA 0.1 g; Interchim). (iii) Desorption/Injection. After the extraction and trapping step, the trap was heated ohmically to 220 °C for 3 min under a stream of helium carrier gas (He N55, purity 99.9995%, Air Liquide) corresponding to a pressure applied to the trap head of 0.8 bar. The substances thereby desorbed were transferred in splitless mode to the source of a mass spectrometer (MD800, Fisons Instruments) via a deactivated silica transfer line (length 0.5 m, inside diameter 0.15 mm; SGE) heated to 180 °C in a chromatograph oven (GC 8060, Fisons Instruments). The range of m/z covered was 15-150 atomic mass units (amu). The total ion current obtained under these conditions took the form of an asymmetrical peak of width ∼0.5 min (Figure 1). The mean abundance values of the mass fragments recorded between 0.3 and 0.8 min were used in calculations. (iv) Cooling of the trap (return to ambient temperature) was performed before the following sample was added (2 min). A series of pretrials was run to define the conditions for rapid analysis that limited the introduction of air and water in the mass spectrometer source. The main factors were found to be “extractor gas flow rate” and “purge time”. Five flow rates (20, 40, 60, 80, and 120 mL‚min-1) and three purge times (2, 5, and 10 min) were tested according to an experimental design in complete blocks with three repeats. The discriminant power of the DHS-MS method was evaluated from five different batches of cheeses (analyzed 10 times each, Table 1). The “purge time” and the “gas flow rate” were set at 2 min and 120 mL‚min-1, respectively. In all, 50 cheese samples were analyzed in three series of 16, 17, and 17 samples. The order in which the cheeses were analyzed was randomized within each

series. Under these conditions, a rapid analysis throughput, namely, one sample every 7 min (extraction/trapping 2 min; desorption/injection 3 min; trap cooling 2 min) is possible from the second sample (the analysis of the first sample takes 27 min because it includes the 20-min headspace equilibration step. For the subsequent samples, the automated system equilibrates several vials simultaneously). Dynamic Headspace-Gas-Phase Chromatography/Mass Spectrometry. The extraction and trapping (helium flow rate of 120 mL‚min-1 for 2 min at 30 °C) and the desorption and injection of the volatile components were carried out under the same conditions as above. The desorbed compounds were then separated by high-resolution gas-phase chromatography (capillary column SPB-5, length 60 m, diameter 0.32 mm, film thickness 1 µm; Supelco, Bellefonte, PA). The oven temperature was programmed as follows: 5 min at 40 °C, then rise at 3 °C‚min-1 to 200 °C, and hold for 5 min at 200 °C. The carrier gas was helium N55 (purity 99.9995%; Air Liquide). The pressure applied to the column head was 0.6 bar, and the split ratio was 9:1. Detection was by mass spectrometry on the total ion current obtained by electron impact at 70 eV (MD800, Fisons Instruments, Milan, Italy). The range of m/z values scanned was 15-230 amu. Identifications were made by comparison of experimental spectra with those of the NIST’98 data bank (NIST/EPA/NIH Mass Spectral Library, version 1.6) and by comparison of retention indices with those of the data bank of Kondjoyan and Berdague´.13 According to the resolution of the peaks, their area was either calculated from the total ion current or estimated from integrations carried out in “selected ion monitoring” mode. The resulting peak areas were expressed in arbitrary units of area (aua). (13) Kondjoyan, N.; Berdague´, J. L. A compilation of relative retention indices for the analysis of aromatic compounds; Laboratoire Flaveur, INRA.; ClermontFerrand, France, 1996.

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Figure 2. Influence of extractor gas flow rate and purge time on abundance of (A) ions of 45 < m/z < 150, (B) ion 18 (water), and (C) ions 28 and 32 (nitrogen and oxygen). Values of abundance are in arbitrary units of abundance (aua), three repeats per measurement, and mean ( σ.

Data Analysis. For the classification operations, the mass fragments lighter than 45 amu were not taken into account because of their multiple origins (ambient air and the carrier gas for the most abundant ones). In addition, only mass fragments above the detection threshold (evaluated at 2500 aua according to Begnaud and Berdague´)14 were considered as meaningful information that could be used later. To build a classification model, the five most relevant variables (p < 0.05 on introduction and elimination of variables) were selected by stepwise discriminant analysis (SAS software).15 The discriminating power of the DHS-MS was evaluated from the percentages of items well classified in their home group (by “leave-one-out” cross-validation) and by the sum of the Mahalanobis distances, which characterize the ratio of between- to within-group distances. RESULTS AND DISCUSSION Choice of Operating Conditions. The influence of “extractor gas flow rate” and “purge time” on the signal obtained by DHS(14) Begnaud, F.; Berdague´, J. L., submitted for publication. (15) Sas Institute Inc. SAS/STAT User’s Guide; 1988; pp 902-922.

1388 Analytical Chemistry, Vol. 74, No. 6, March 15, 2002

MS is depicted in Figure 2. For all extraction flow rates, the area of the desorption peak or total ion count (TIC) increased with purge time (Figure 2A). The TIC abundance maximums were reached logically later when the purge time was shortened. For a purge time of 10 min, a TIC maximum abundance was obtained with an extractor gas flow rate of 60 mL‚min-1, but for higher flow rates, a breakthrough of volatile compounds occurred as seen by a fall in the TIC. Detailed analysis of the fragments specific to air and water showed that the quantities of water vapor and air introduced into the mass spectrometer fell as the extractor gas flow rate and the headspace purge time increased (Figure 2B and C). The influence of the extractor gas flow rate on the quantities of trapped water and air was especially noticeable for a purge time of 2 min, when at low flow rates (20 or 40 mL‚min-1) the headspace in the vial (∼15 mL), and so the gas flowing through the trap, were always rich in air and water vapor. For higher extractor gas flow rates for longer purge times, or for both, the air and water vapor were swept out of the vial and the Tenax trap, on which both are only weakly retained. Because the presence of water is liable to

Figure 3. (a) Chromatogram of a sample of Camembert cheese Rm3 obtained by DHS-GC/MS (5 g of cheese, headspace purge of 2 min at 120 mL‚min-1). (b) Average mass spectrum from 0 to 55 min.

generate artifacts during the ionization of the volatile compounds, and because oxygen in the air erodes the mass spectrometer filament, an extraction time of 2 min and helium flow rate of 120 mL‚min-1 were used for the discrimination tests. Under these conditions, a rapid analysis throughput of one sample every 7 min was possible. The memory effect of the system, evaluated by the analysis of empty vials after analysis of the cheese samples, was estimated at ∼4% of the total desorption peak area. Thus, heating the trap to 220 °C allows an efficient desorption of the volatile components from the trap. Volatile compounds extracted in the experimental conditions set previously were identified by DHS-GC/MS separative analysis (Figure 3a). Nearly 100 substances (alkanes, esters, methyl ketones, carboxylic acids, etc.) of widely ranging polarity and molecular weight were identified. Among these substances (Table 2), the presence of 14 sulfur-containing compounds, whose

contribution to the aroma of Camembert-type cheeses is known,16-19 can be noted. The average spectrum of the chromatogram obtained by DHS-GC/MS (Figure 3b) can be considered as a reconstitution of an average spectrum obtained by DHS-MS (Figure 1b). Comparison of these two spectra shows strong similarities. In both fingerprints, relatively abundant signals typical of sulfur-containing compounds can be seen in similar proportions. The fragments of mass 47, 62, 79, and 94 are characteristic of compounds such as methanethiol, dimethyl sulfide, dimethyl disulfide. or dimethyl trisulfide. The fragmentation of separated volatile compounds (DHS-GC/SM analysis) was therefore globally very similar to that observed when the compounds were intro(16) Jaillais, B.; Bertrand, V.; Auger, J. Talanta 1999, 48, 747-753. (17) Kubickova, J.; Grosch, W. Int. Dairy J. 1997, 7, 65-70. (18) Sable´, S.; Cottenceau, G. J. Agric. Food Chem. 1999, 47, 4825-4836. (19) Dumont, J. P.; Roger, S.; Adda, J. Lait 1976, 559-560, 595-599.


1389

Table 2. Compounds Identified in DHS-GC/MS Analysis of the Volatile Fraction of a Sample of Camembert Cheese (Rm3) compound methanethiol sulfur dioxide ethanol acetone propan-2-ol penta-1,3-diene dimethyl sulfide methylene chloride carbon disulfide propanal, 2-methyl propanol acetic acid butan-2,3-dione butan-2-one hexane butan-2-ol 3-buten-2-ol, 2-methyl ethyl acetate trichloromethane propanol, 2-methyl tetrahydrofuran trichloroethane butanal, 3-methyl benzene butanol butanal, 2-methyl propan-2-ol, 1-methoxy propionic acid pentan-2-one pentanal pentan-2-ol methyl thioacetate heptane acetoine ethyl propanoate butanol, 3-methyl butanol, 2-methyl disulfide dimethyl toluene butanoic acid hexan-2-one propane, 2-methyl,2-methylthio ethyl butanoate octane tetrachloroethylene dimethyl sulfoxide butanoic acid, 2-methyl m-xylene hexanol cyclohexanone heptan-4-one p-xylene butan-1-ol, 3-methyl acetate butan-1-ol, 2-methyl acetate heptan-3-one heptan-2-one 2,4-dithiapentane styrene propyl butanoate heptan-2-ol o-xylene nonane heptanal propanal, 3-methylthio dimethyl sulfone R-pinene benzaldehyde benzenesulfonic acid, 4-hydroxy trisulfide dimethyl 1-octen-3-ol octan-3-one β-myrcene octan-2-one 1390

flavor notea cooked cabbage alcohol, mild ethereal, fruity slightly buttery taste boiled cabbage malty alcohol, sweet vinegar, pungent buttery acetone, ethery

solvent, pineapple, fruity alcohol, penetrating green, malty sweet, fruity vinegar, pungent fruity, acetone, sweet mild green, fusel oil cooked cauliflower pineapple, sweet, solvent fruity, alcohol cauliflower, garlic rancid, cheesy, sweaty floral, fruity pineapple, sweet, banana

blue cheese, spicy, musty garlic sweet, fruity earthy, oily, sweetish

bitter almond, aromatic, sweet alliaceous, meaty mushroom mushroom fruity, spicy fruity, musty, floral, green


Rtb 5.58 5.91 5.92 6.57 6.65 6.96 7.26 7.58 7.81 8.28 8.29 9.18 9.29 9.64 9.74 9.84 10.28 10.43 10.63 11.04 11.19 12.06 12.38 12.89 12.90 12.91 13.66 14.06 14.14 14.72 14.83 14.85 14.90 15.45 15.77 17.06 17.29 17.90 19.37 19.94 20.61 21.24 21.34 21.39 22.32 22.95 24.14 25.81 26.10 26.10 26.20 26.33 26.41 26.56 27.11 27.36 27.68 27.78 27.81 27.84 27.98 28.08 28.11 28.40 29.06 30.81 31.32 33.10 33.24 33.25 33.84 34.03 34.25

Ric

TIC/iond

areae

507 517 528 534 549 549 582 586 593 600 603 609 615 618 623 627 645 650 661 661 661 671 678 681 695 698 699 700 708 712 732 735 747 769 773 787 797 799 800 813 825 845 865 873 873 875 877 878 880 889 890 892 894 895 895 897 900 901 910 921 938 957 974 977 977 985 989 991

47 64 TIC TIC TIC 67 TIC 84 76 72 TIC 60 86 TIC 57 TIC 71 TIC 83 43 72 97 TIC 78 56 Tic 45 74 TIC 58 55 Tic 71 88 61 TIC TIC TIC 91 60 58 104 71 85 TIC TIC TIC 91 56 TIC 71 91 70 70 TIC TIC TIC 104 89 55 91 128 81 104 94 TIC TIC TIC TIC 72 TIC TIC 58

4589865 14775 87534040 16762248 44536292 234134 22700602 391014 1241474 701871 4553864 2716052 156456 359812 359812 289689 49731 104290 87771 1261478 135106 9477 1082112 17868 651363 463258 30996 202853 1416742 232254 443765 1779379 101090 854944 86926 3581119 1286983 139985136 94446 11246 447928 164496 801594 365156 2153 1919 1748784 41045 37799 160698 17789 53999 38116 62170 12170 1125795 1298970 25323 106650 193891 20604 146766 142489 207984 1679 73182 27289 669785 2512118 43935 254055 105840 15534

Table 2. (Continued) compound ethyl hexanoate octan-3-ol benzene,1,3,5-trimethyl benzene,1,2,4-trimethyl octanal 3-carene sylvestrene limonene phenylacetaldehyde 3-methylbutyl butanoate acetophenone 8-nonen-2-one nonan-2-one nonanal benzene, 1-ethyl,4-methoxy benzenamine, N-ethyl ethyl octanoate decan-2-one decanal benzothiazole 2-phenylethyl acetate undecan-2-one undecanal no. of identified peaks

flavor notea pineapple, banana, apple

orange blossom, floral, sweet fruity, musty, floral floral, citrus, orange, rose, fatty apricot, wine, floral fruity, musty quinoleine, rubbery floral, rose

Rtb

Ric

TIC/iond

areae

34.27 34.38 34.57 34.66 34.67 35.69 36.54 36.78 37.53 37.78 39.00 39.50 40.03 40.55 41.73 42.66 45.85 46.18 46.45 48.65 49.53 51.17 51.89

991 994 1000 1001 1001 1018 1032 1036 1049 1054 1068 1082 1091 1107 1118 1141 1190 1198 1202 1245 1252 1279 1300

88 59 105 105 84 TIC 93 93 TIC TIC TIC TIC TIC TIC TIC TIC 88 58 TIC TIC TIC TIC TIC

232413 11488 67925 17542 83929 15976 23958 30396 123591 68002 41298 48429 1078512 281138 12493 2761439 44296 33181 1199638 123044 78406 146970 63212 96

a Sensory properties of the most characteristic substances.18 b Retention time (min). c Retention index on SPB-5. d When the resolution was not perfect or if single ion mode sensitivity was necessary, chromatographic peak areas were not calculated from the TIC but were estimated from integrations performed on the specific indicated ions. e Mean calculated on three samples (arbitrary units of area).

Figure 4. Classification of cheeses from data selected by factorial discriminant analysis (m/z ) 58, 89, 83, 63, 47). The extraction was carried out with a purge time of 2 min and an extractor gas flow rate of 120 mL‚min-1. The ellipses represent the 95% confidence limit for each group.

duced as a mixture (DHS-MS analysis). This observation indicates that recombination of ionic species from different volatile compounds was limited in the DHS-MS analysis. This is an important finding because, in the presence of strong interactions between ionic species, rapid characterization by DHS-MS would be much less reliable or be bound to fail. Rapid Characterization of Cheeses. Forward stepwise factorial discriminant analysis performed from DHS-MS spectra selected the five mass fragments m/z ) 58, 89, 83, 63, and 47, in that order, as the best variables to calculate the discriminant canonical variables. The molecular origin of these mass fragments

can be found in several compounds identified in the headspace by DHS-GC/MS. Fragment 58 is preponderant in the spectra of acetone, 3-methylbutanal, pentanal, and hexan-2-one. Ion 89 is characteristic of esters such as ethyl butanoate or propyl butanoate. Fragment 83 is present in the spectra of chloro derivatives such as dichloromethane or tetrachloroethylene. Ion 63 is typical of dimethyl sulfoxide but is also present in the spectra of other sulfur-containing compounds such as dimethyl sulfide or 2,4dithiapentane. Last, ion 47 is present in high proportions in the spectra of methanethiol, dimethyl sulfide, and dimethyl disulfide, which are abundant in the headspace of cheeses. Inspection of Analytical Chemistry, Vol. 74, No. 6, March 15, 2002

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the chromatograms obtained by DHS-GC/MS also indicates that these compounds were present in widely ranging amounts in the headspaces of the five batches of cheese studied. The results of the discrimination tests show that 100% of the cheeses were well classified after the cross-validation procedure. A more detailed study of the distribution of the groups (Figure 4) indicates that the raw milk cheeses can be distinguished from the heat-treated ones by fragments characteristic of sulfurcontaining compounds that are 15 times more abundant. No signal drift or “series” effect over successive analyses was observed, and no pretreatment (e.g., normalization) was necessary to improve the discriminating power of the data. In earlier work, rapid characterization of the same cheeses by SPME-MS12,20 yielded less satisfactory discrimination results than those obtained by DHSMS (wider within-group dispersion for cheeses characterized by a sum of Mahalanobis distances of 147 in SPME-MS analysis against 182 by DHS-MS). The better performance of the DHSMS method is mainly due to the absence of signal drift (the aging of the SPME fibers caused drift)12 and to the automation of the injection of the extract into the mass spectrometer source. In addition, the extraction of the volatile compounds was more (20) Pe´re`s, C.; Viallon, C.; Berdague´, J. L. In 15th International Mass Spectrometry Conference; Barcelona, Spain, 2000.

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effective here by DHS than by SPME. A greater number of volatile compounds were identified by DHS (96 substances identified in analysis by DHS against 70 by SPME) and signals at least 10 times more abundant than by SPME were observed. Thus, the information contained in the DHS-MS fingerprints seems to be richer than that contained in the SPME-MS fingerprints. CONCLUSION The dynamic headspace-mass spectrometry method “coupled” to multivariate analysis offers a rapid, simple, and effective method for characterizing cheeses by analysis of their volatile fraction. The method described makes it possible to distinguish between different cheeses according to their origin, manufacturing process, or ripening stage in ∼7 min. The data analysis is simple and needs no statistical pretreatment. Also, the protocol chosen for the analysis by DHS-MS is meant to help reduce thermal, mechanical, and chemical modification of the sample, thereby saving time and reducing the risk of analytical artifacts.

Received for review October 2, 2001. Accepted November 29, 2001. AC011053H

Fast Characterization of Cheeses by Dynamic Headspace-Mass

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