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Fast characterization of foodstuff by headspace mass spectrometry (HS-MS)
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Trends in Analytical Chemistry, Vol. 22, No. 11, 2003
Fast characterization of foodstuff by headspace mass spectrometry (HS-MS) Christophe Pe´re`s, Fre´de´ric Begnaud, Luc Eveleigh, Jean-Louis Berdague´ The field of the rapid characterization of products by HS-MS is reviewed. The general principle of HS-MS systems consists of introducing volatile components present in the HS of a sample without prior chromatographic separation into the ionization chamber of a mass spectrometer. The spectrum resulting from simultaneous ionization and fragmentation of the mixture of molecules introduced constitutes a ‘‘fingerprint’’ that is characteristic of the product being analyzed. Exploitation of this spectral information allows one determine the composition of the sample. # 2003 Published by Elsevier B.V. Keywords: Electronic nose; Foodstuff; MS-based sensor; Multivariate analysis; Quality control
1. Introduction Christophe Pe´re`s*, Fre´de´ric Begnaud, Luc Eveleigh, JeanLouis Berdague´a Laboratoire Flaveur, INRA de Theix, F-63122 Saint-Gene`sChampanelle, France
*Corresponding author. Tel.: +32 5620 4031; Fax: +32 5620 4859; E-mail: christophe.peres@ richrom.com
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With the objective of achieving an ever increasing level of surveillance and quality control, there exists at present a strong demand for research in the domain of the rapid characterization of raw materials and food products. However, even if sensory analysis and physical chemistry techniques remain powerful and recognized tools for resolving this type of problem, setting up such methods nevertheless requires a considerable amount of work and investment. Hence, manufacturers of analytical instruments and a number of research laboratories have become interested in MS as a potential means of obtaining more rapid and less expensive solutions. Experiments have shown that direct injection of the volatile fraction of a product (pre-concentrated or not) into the ionization chamber of a mass spectrometer allows one to obtain a characteristic spectrum called the ‘‘signature’’ or ‘‘spectral ¢ngerprint’’ (Fig. 1). These signatures can then be used for classi¢cation, prediction
of sensory properties, or estimation of technological parameters. To date, direct coupling of a mass spectrometer to techniques, such as static HS (SHS-MS) [1^6] or dynamic HS extraction (DHS-MS) [2,7,8], or solid-phase microextraction (SPME-MS) [2,9,10], has led to the development of fast, economical methods of characterization (Table 1). These new systems for rapid analysis by HS-MS are currently subject to growing interest and are starting to ¢nd a commercial outlet in the gas-sensors market. These instruments are known as ‘‘mass sensors’’ or sometimes ‘‘new generation electronic noses’’. The state of the art on the recent technical and methodological advances in the ¢eld of rapid characterization of food stu¡ by HS-MS is covered in this article. Instrumental aspects, data analysis and method development are investigated.
2. Instrumental aspects The general principle of HS-MS systems consists of introducing volatile components present in the HS of a sample without prior chromatographic separation into the ionization chamber of a mass spectrometer. The spectrum resulting from simultaneous ionization and fragmentation of the mixture of molecules introduced constitutes a ‘‘¢ngerprint’’ that is characteristic of the product being analyzed. Exploitation of this spectral information allows one determine the composition of the sample. HS-MS systems comprise a module for extraction and injection of the volatile
0165-9936/03/$ - see front matter # 2003 Published by Elsevier B.V. doi:10.1016/S0165-9936(03)01206-8
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Figure 1. Signal produced by an HS-MS system. Sample: chocolate liqueur. (a) Total ion current as a function of time. (b) Spectral fingerprint of sample. The spectrum is the mean abundance values of all the mass fragments recorded between 1 and 3 minutes. m/z=43 uma: most common ions in organic mass spectroscopy. m/z=122 uma: specific ion for trimethylpyrazine.
Table 1. Main applications of systems for rapid characterization by HS-MS Type of discrimination Study Classification
Detection of defects Control Follow-up of processing
System
Reference
Geographical origin of essential oils of rose SHS-MS [3] Tomato sauces according to concentration of ‘‘garlic’’ aromas SHS-MS [5] Grapefruit juices according to concentration of vanillin – identification of vanillin SHS-ion-trap-MS [6] ‘‘Camembert-like’’ cheeses according to heat treatment of milk and stage of ripening DHS-MS [8] ‘‘Camembert-like’’ cheeses according to heat treatment of milk and stage of ripening SPME-MS [10] Off-odors in milk SPME-MS [9] ‘‘Use by’’ date of milk SPME-MS [44] Freshness of cloves SHS-MS [4] Sterilization of meat meals SHS-MS [1] Ripening of cheese SHS-MS [2] P&T-MS SPME-MS
components, directly linked through a transfer line to a mass spectrometer. Fig. 2 summarizes the main components for operating a HS-MS system. 2.1. The extraction-injection module 2.1.1. Extraction of volatile components without preconcentration: static HS systems. The static HS method consists of placing the sample in a hermetically sealed vial and then, once equilibrium has been established between the matrix and the gaseous phase, sampling the HS. Frequently used for the analysis of milk products [11^14], this technique o¡ers the advantage of being very simple to use; the sample temperature, equilibration time and size of the vial are the main parameters to optimize [15]. However, because there is no pre-concentration, the sensitivity of this type of instrumentation can prove insu⁄cient for certain applications. Five main types of extraction-injection module have been used for foodstu¡ and their characteristics are listed in Table 2.
2.1.1.1. Systems using gas syringes. An aliquot is taken from the HS and injected by means of a syringe. This method was used to classify Swiss Emmental cheeses according to their stage of ripening [2,16]. 2.1.1.2. Systems using a stream of inert gas. An aliquot of the HS is transferred by the carrier gas into the ionization chamber of the mass spectrometer for a short period of time. Instruments equipped with this type of extraction-injection have been employed in the context of studies using Time of Flight [17] and Ion Trap mass spectrometers [6]. As the particularities of these systems are linked more to the technical characteristics of the spectrometers, the latter two studies will be detailed later in the review. 2.1.1.3. Balanced pressure sampling systems. The vial is pressurized by the carrier gas to a pressure equal to the carrier-gas inlet pressure of the transfer line. Next, the carrier-gas supply is interrupted by closing a valve http://www.elsevier.com/locate/trac
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Figure 2. Main HS-MS systems. (a) Static HS-MS. (b) Dynamic HS-MS. (c) SPME-MS.
and injection is performed by expansion of the gaseous mixture into the transfer line. In this way, di¡erent tomato sauces were discriminated according to their aromatic characteristics by Dittmann et al. [5]. 2.1.1.4. Pressure/loop systems. The sample vial is pressurized by an inert gas to a pre-set value. Next, the vial is opened temporarily toward a sample loop of a gas-sampling valve and not directly to the transfer line. Finally, purging of the sample loop with the carrier gas injects the volatile components into the mass spectrometer. Using this type of instrument, a study conducted by Berdague¤ et al. [1] enabled the development of a protocol for a posteriori control of the heat treatment of meat meals by SHS-MS. By contrast, work carried out
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with the same instrument has shown that this system is not su⁄ciently sensitive for rapid characterization of cheeses [18]. 2.1.1.5. HS membrane introduction (HS-MI). The volatile compounds that desorb from the sample pass through a speci¢c membrane by pervaporation and are then introduced directly into the ionization chamber by opening a valve. HS-MI has been hyphenated with MS by Mende's et al. [19] for rapid analysis of traces of volatile organic compounds (VOCs) in solid matrixes. This technique has been applied to soil samples, but it can equally well be employed for analyzing food products. However, the hydrophobic nature of the membrane (generally a silicone-based polymer) favors the
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Table 2. Main characteristics of HS-MS systems used to date to characterize products by rapid analysis of their volatile fraction Name
Manufacturer
Mass analyzer
Extraction system
Chemsensor MS-Sensor Smart Nose
Agilent Technologies HKR Sensorsysteme GmbH Smart Nose
Quadrupole (HP5973, Agilent) Quadrupole (TurboMass, Perkin Elmer) Quadrupole (Balzers)
Pressure/loop SHSa (HP 7694, Agilent) Balanced-pressure SHS (HS40XL, Perkin Elmer) Gas syringe SHS and SPMEb (CTC CombiPal) Purge-and-Trap (Tekmar LCS 2000)
SPME-MS
Home made [9,10] Quadrupole (Saturn, Varian) (MD800, Fisons) INRA-SRV/Flaveur, France Home made [6]
DHS-MS Ion-trap-MS TOF MS AP+ Nose HS-MIMS
SPME (75 mm Carboxen/PDMS)
Micromass
Quadrupole (MD800, Fisons) Ion-trap (Finnigan GCQ Plus, Thermoquest) Time of flight (LCT, Micromass )
Home made [19]
Quadrupole (Balzers)
DHSc (Compact Desorber-1, INRA) SHS (Systems using a stream of inert gas, Home made) [6] SHS (Systems using a stream of inert gas), Interface APCI+ SHS (HS-MI, Home made) [19]
a
Static HS; Solid Phase MicroExtraction; c Dynamic HS. b
permeation of apolar molecules and limits that of polar compounds [20,21]. 2.1.2. Extraction of the volatile components with pre-concentration. In order to increase amounts of material injected, di¡erent methods of pre-concentration have been proposed. Pre-concentration improves the sensitivity of the system but introduces a supplementary step, which can prove limiting from a temporal point of view and/or generate analytical artefacts (memory e¡ects, bleeding, irreversible adsorption). In this respect, the pre-concentration medium must be chosen with care, as pointed out below. 2.1.2.1. Purge and trap and dynamic HS systems. The purge and trap (P&T) and dynamic HS (DHS) techniques are classical methods of pre-concentration of volatile compounds used in a variety of applications [22]. In both systems, the volatile components are purged by a stream of inert gas and trapped onto an adsorbent. The constant depletion of the HS leads to a displacement of the equilibrium in favor of the desorption of these molecules from the matrix. The trapped molecules are subsequently desorbed by heating and injected into the ionization chamber of the mass spectrometer. Apart from the choice of trap, the main parameters to optimize are the temperature of the sample, the equilibration time, the £ow rate of the extractor gas and the purge time of the HS. In the case of P&T, the gas £ow is injected through the sample (preferably in liquid or powder form), whereas, in the case of DHS, only the HS is purged with the gas. The di¡erent traps that exist, listed and described by Nun‹ez et al. [23] and Harper [24], are made of activated or graphite carbon or porous polymers. The activated or graphite carbon traps have a large speci¢c surface area and a high capacity of adsorption for more polar
compounds. This adsorption can sometimes be irreversible and may be followed by the retention of large amounts of water. Conversely, the porous polymer traps (e.g. Tenax, Chromosorb) have a lower adsorption capacity but present the advantage of having less a⁄nity for water. The Tenax TA trap is currently an adsorbent of choice for the analysis of volatile compounds on account of its low bleeding, thermal stability and weak a⁄nity for water. Lastly, traps combining several types of speci¢c adsorbent have also met with great success, as they allow one to increase the retention capacities for molecules belonging to di¡erent chemical families [25]. 2.1.2.2. SPE. Whereas the P&T and DHS techniques require complex equipment (gas extractor, numerous valves, double gas circuit), SPME is a user-friendly preconcentration method. The principle involves exposing a silica ¢ber covered with a thin layer of adsorbent in the HS of the sample in order to trap the volatile components onto the ¢ber [26]. The adsorbed compounds are then desorbed by heating and injected into the ionization chamber of the mass spectrometer. Apart from the nature of the adsorbent deposited on the ¢ber, the main parameters to optimize are the equilibration time, the sample temperature and the duration of extraction. Because of their good performance, notably for the extraction of highly volatile molecules, these ¢bers are very popular in the ¢elds of agricultural foods, biology and environment [27^31]. In the context of non-separative analyses, SPME has been used for rapid classi¢cation of batches of cheeses [10] or the characterization of o¡odors in milk [9]. 2.1.2.3. New pre-concentration extraction methods. Some new extraction techniques derived from SPME also display a certain potential for direct coupling to a mass spectrometer. http://www.elsevier.com/locate/trac
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Zhang and Pawliszyn [32] have proposed a system comprising a SPME ¢ber combined with a gas extraction syringe. On withdrawal of the SPME ¢ber, a volume of gas is sampled from the HS, thus enabling the analysis of compounds having low a⁄nity for the ¢ber. A modi¢ed version, where the adsorbent phase is deposited on the internal side of the syringe, is known as solid-phase dynamic extraction (SPDE). HS sorptive extraction (HSSE) was developed with the objective of improving the sensitivity limits of SPME because of the small quantity of adsorbent present on the ¢ber. As a general rule, this quantity is about 0.5 mL for a classical SPME ¢ber, whereas it is currently 50^200 mL for HSSE. This extraction system takes the form of a magnetic rod coated with poly(dimethyl siloxane) (PDMS). This device is easy to use; the extraction step involves placing the bar in the HS of the sample to be analyzed. Next, the bar is placed in a thermodesorption unit to release the volatile compounds into the mass spectrometer. When applied to the analysis of the volatile molecules present in the HS of diverse matrixes, such as medicinal plants, bananas or co¡ee, HSSE permitted detection limits 2^3 orders of magnitude lower than those of SPME [33].
compact dimensions and lower price as compared to other instruments. Apart from the case of the TOF analyzer mentioned above, only one laboratory-designed instrument uses an ion trap. Detection of the ions is ensured by either electron multipliers or photon multipliers, with comparable sensitivities. Electron multipliers are presently most widely employed because of their attractive price, but their gradual loss of sensitivity constitutes a major inconvenience (average lifespan of one year for 40 h use per week) [35]. By contrast, photomultipliers are inherently less susceptible to £uctuations of the vacuum and risks of chemical contamination and hence display stability and longevity almost 10 times superior to those of the electron multipliers. The choice of the type of secondary pump (di¡usion or turbomolecular) is similarly particularly important, in so far as the quantities of volatile compounds injected can be large and the time interval too short to allow a return to equilibrium between two samples. Because of their high pumping capacities, turbomolecular pumps have a distinct advantage in this respect over the di¡usion pumps, but they require careful maintenance.
2.2. The transfer line The transfer line ensures coupling of the extractioninjection module to the mass spectrometer. This line must be designed to maintain a high-quality vacuum in the spectrometer source while allowing rapid transfer of the extracted molecules between the two modules. The length is in the range 0.5^5 m, according to the internal diameter (0.05^0.20 mm). Heating is necessary to prevent recondensation of the compounds. In addition, the internal side, whether fused silica or silica coated steel, must be inactivated in order to limit the risks of pollution in the course of analyses (memory e¡ects or loss of molecules through reversible or irreversible adsorption) and to avoid the catalytic degradation of certain compounds.
3. Development of a HS-MS analysis method
2.3. The mass spectrometer Ionization of the molecules introduced into the source of the mass detector is mainly carried out by electron impact at 70 eV, a method that yields ‘‘¢ngerprints’’ of high discriminating power for a large number of agrifood products [34]. Although marginal at present, an interface permitting ‘‘soft’’ ionization of the molecules at atmospheric pressure has been proposed (Table 2). In association with a time-of-£ight (TOF) analyzer, this technology o¡ers the advantage of providing more speci¢c information by allowing one to trace the molecular origin of the ions (mass ¢ngerprinting) [17]. The majority of the mass spectrometers used for HS-MS are equipped with quadrupole ¢lters. These spectrometers owe their success to their high performance,
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3.1. Selection of conditions limiting the introduction of contaminants The operating conditions chosen should not lead to dysfunction or rapid contamination of the instrument. In the case of a mass spectrometer, maintenance of a good quality vacuum necessitates limiting the quantities of material injected, while introduction of oxygen is similarly known to a¡ect the lifespan of the ¢lament quickly. Purging the HS with an inert gas before the equilibration step represents a simple solution to minimize the latter risk. Moreover, agri-food matrixes are frequently characterized by signi¢cant water content that can give rise to numerous analytical problems. Strong adsorption of water on the traps can in£uence both the reproducibility of the measurements and the lifespan of the adsorbents [10]. In addition, injected water vapor can strongly perturb the ionization of the molecules and modify the mass spectra (diminution of the abundance of certain ions, formation of adducts) [36]. Finally, the introduction of large quantities of water may in the long term cause deterioration of the mass detector [37]. A minimal introduction of water into the system is thus a parameter of prime importance to take into account in the analysis of moist food products. Sample analysis at ambient temperature or with moderate heating is the simplest solution. Several other techniques may also be used to limit water injection into the analytical system:
Trends in Analytical Chemistry, Vol. 22, No. 11, 2003 incorporation of hygroscopic salts into the matrix [38]; removal of a part of the water retained onto the trap by purging with a dry gas [39]; use of a cold trap (usually maintained at ^10 C to ^15 C) placed between the sample and the transfer line (system without pre-concentration) or between the sample and the adsorbent (system with pre-concentration) [40]; or, permeation, which makes use of the selective di¡usion of water molecules through the wall of a speci¢c tube [41^43]. However, these methods may lead to a partial or total loss of chemical families, notably of the more polar and more volatile molecules [10,40^42]. In any case, incorporation of hygroscopic salts and purging of the trap with a dry gas are simple to use. 3.2. Correction of signal drift: a necessity for database set-up The use of rapid characterization systems based on ¢ngerprint recognition relies heavily on the availability of robust, permanently valid databases. Robustness strongly depends on the experimenter, whereas the permanence of databases is often undermined by the instability of instrument performance. Although a necessity, any procedure for calibration or correction of the signal should nevertheless not be too rigid or generate important supplementary costs. A large number of studies have been devoted to this problem in the ¢eld of gas detectors, but the results remain fairly limited and speci¢c to this technology. The question of the temporal stability of the signal also arises in the case of MS characterization systems. Progressive pollution of the source, maintenance operations, or a vacuum quality a¡ected by simultaneous introduction of large amounts of material can lead to signal drifts. Thus, although various tuning procedures exist, if the signal goes uncorrected during a programme of analyses, the resulting data may be weakened or even valueless. Accordingly, it is important to have procedures to monitor the state of the mass detectors and help correct drift. In this way, internal or external normalization is a simple operation, routinely employed in the context of data analysis to eliminate e¡ects linked to variations in the intensity of the signal (sensitivity, quantities injected). 3.2.1. Internal normalization. The internal normalization of a given mass spectrum involves expressing the abundance of each mass fragment with respect to the sum of the fragments or with respect to a reference fragment derived either from the product or from an internal standard. Normalization with respect to the sum of the mass fragments is frequently employed.
Trends However, in cases where the more abundant fragments display stronger drift than the others, this correction can prove insu⁄cient because of the linkage existing between all the fragments. Use of an internal standard involves adding to each sample a precisely known quantity of one or more molecules of which the fragments (and in particular the fragment chosen as reference) do not interfere with the ions of the products to be analyzed. In a recent study of prediction of the ‘‘use by’’ date of milk by SPME-MS, Marsili [44] was able to compensate for the signal drift resulting from important instrumental problems (use of several SPME ¢bers, replacement of a turbomolecular pump and of the electron multiplier) through addition of chlorobenzene as an internal standard. However, this type of method is applicable to liquid samples only and, for solid products, it is di⁄cult to imagine this option without a solubilization step. 3.2.2. External normalization. This method involves analyzing reference samples, solutions or gases in order to determine and correct the drift by comparison with the signals obtained for these references. The abundance of each mass fragment is expressed with respect to that of a reference fragment derived from the chosen external standard. Begnaud et al. [45] have proposed a correction algorithm to rectify the drift observed with a SHS-MS system. In practice, this method is achieved by inserting reference samples at regular intervals during the analysis sequence. The drift of each fragment was thus corrected, but the procedure entails carrying out more analyses. In [45], 3 reference samples for 8^10 samples were necessary ^ which corresponded to an increase of 30% in cost of analysis per sample. Furthermore, this method requires management of the reference samples (choice of a reference sample, physical and chemical stability, storage), which is sometimes di⁄cult, especially in the agri-food sector. Normalization of the data may similarly be carried out with respect to an ion naturally present in all the spectra and displaying a variance independent of the products analyzed. The mass fragment m/z=40 (argon) is frequently chosen [46]. Although this choice may have the advantage of being economical, it is unlikely to be reliable, as the level of argon in a laboratory is far from constituting a stable reference and varies with geographical location. Finally, Pe¤re's et al. [47] have recently developed a new method of correcting temporal drift through addition of a reference gas (Standard Gas Addition (SGA)). The general principle involves continuously introducing a very small quantity of xenon into the source of the mass spectrometer. The abundance of each mass fragment in the spectrum of the product analyzed is then normalized with respect to the abundance of http://www.elsevier.com/locate/trac
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xenon measured during the analysis. This technique o¡ers the advantage of taking into account the real ionization conditions existing in the source of the spectrometer during introduction of the molecules desorbed and has been successfully employed for the rapid characterization of cheeses by DHS-MS [8]. Fig. 3 demonstrates the advantage of using SGA instead of
Euclidian internal normalization. However, it is usually better to use internal normalization than no signal pretreatment. 3.3. Selection of the relevant mass fragments and model set-up If the spectral ‘‘¢ngerprints’’ reveal large numbers of mass fragments, part of this signal is often non-informative, so it is necessary to select the fragments relevant to the problem in question. It is possible to envisage at least two strategies, as follows.
1. Prior knowledge of the product often allows one to determine the molecules of interest, the speci¢c mass fragments of which will be informative in the context of a rapid analysis. This step may require use of separative methods. In the case of analysis of the volatile fraction of food products, association of GC with MS (GC-MS) or olfactory detection (GC-O) [48,49] has proved of value [50]. In this way, it is possible to focus the mass spectrometer on the detection of the fragments of the molecule of interest and increase its sensitivity, an approach which is particularly interesting for HS-MS systems without a module for pre-concentrating the molecules. In a study of the characterization of cloves [4], a series of separative analyses by HS-GC-MS showed that the abundance of three molecules permitted discrimination of the samples according to their freshness, and HS-MS could be performed rapidly using three corresponding ions. 2. Statistical methods may similarly be employed to select the characteristic fragments. Two situations can be envisaged, depending on the nature of the study:
Figure 3. Effect of signal correction on signal analysis. PCA plot of ‘‘Camembert-like’’ cheeses analyzed by SPME-MS. *: Coulommiers; &: raw milk, brand 1; ~: raw milk, brand 2. (a) Projection of the raw data on PC1 and PC 2. (b) Projection of normalized data. (c) Projection of data after SGA correction.
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1. The data may display a coherent, exploitable structure; in other words the variations of a characteristic studied in the samples induce in the mass spectra modi¢cations that are visible on direct examination or that can be visualized by means of unsupervised statistical tools (Principal Component Analysis (PCA), classi¢cation). 2. The di¡erences between spectra exist but are weaker than those described above. The data should then be treated by supervised methods (Functional Data Analysis (FDA), Partial Least Squares (PLS), neuromimetic networks) that select under constraint the fragments or combinations of fragments relevant to the characteristics of interest.
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Figure 4. PCA of the spectral signatures of cork wine stoppers. *: Spanish cork; &: Moroccan cork; ^: Portuguese cork. (a) Whole data set: spectral range 50–180 uma (131 variables). (b) Reduced data set: only 31 significant variables were kept. Variables were chosen using PLS for the prediction of the geographical origin of the cork.
Fig. 4 shows the PCA for a whole data set and the PCA for same data, after removing the variables found to be the least signi¢cant in a PLS analysis. However, it is desirable to obtain supporting chemical evidence, such as chromatography, to validate the selection of variables using a statistical approach and the pertinence of the fragments selected should indeed be assessed by the user on the basis of knowledge of the product. In fact, even if it is di⁄cult categorically to relate a mass fragment of a spectral ‘‘¢ngerprint’’ to a speci¢c volatile component, it is nevertheless possible to deduce the main origin of certain ions (sulfur compounds, esters, ketones) selected by statistical analysis. In a study of the rapid characterization of cheeses by DHS-MS, Pe¤re's et al. [8] showed that, among the ¢ve fragments selected by an ascending step-by-step procedure, three were major fragments of sulfur compounds identi¢ed by GC-MS. The mass fragments chosen by one of these two strategies can subsequently be used to develop models appropriate for the task to be performed. The statistical tools employed are methods of visualization, such as PCA, classi¢cation/recognition (e.g. FDA, ascending hierarchic classi¢cation) or prediction/quanti¢cation (e.g. regression, neuromimetic networks). In general, PCA and FDA are the most frequently employed [3^6,9]. PLS was used by Marsili [44] to predict the ‘‘use by’’ date of milk, while Berdague¤ et al. [1] used the potential of neuromimetic networks to estimate the sterilization temperature of meat meals. Finally, to complete the modeling procedure, it is imperative to confront the models developed with test data, in other words data derived from products that were not used for their construction. All too often neglected, this step is essential to test the reliability of the estimations or classi¢cations performed. Such validation allows one to avoid solutions biased by over¢tting or by e¡ects caused by chemical interference
(even if the fragments were selected using the ¢rst of the above strategies).
4. Conclusions HS-MS techniques represent a new approach to the rapid characterization of agri-food products. These systems enable one to obtain the spectral ‘‘¢ngerprint’’ of a sample within a few minutes by analysis of its volatile fraction. Di¡erent methods of extraction and injection of the volatile components make it possible to account for the speci¢cities of the matrixes studied. Moreover, transfer of this type of technology to the industrial sector for the purposes of classi¢cation or quality control is now entirely feasible, in that solutions have been found to resolve the problems of instrumental drift, so it is possible to create and manage permanent databases. In terms of technical evolution, use of ‘‘soft’’ ionization methods (ionization at atmospheric pressure, chemical ionization) in association with ‘‘high-resolution’’ mass analyzers (TOF, ion trap) now permits one to get information concerning the molecular origin of the ions and to deduce the identity of certain compounds present in the HS. Such ‘‘¢ngerprints’’ enable not only characterization of a given product but also detection of molecules directly responsible for odors or aromas. These advances open new perspectives for the rapid characterization of products by MS in a wide variety of domains of application.
References [1] J.L. Berdague¤, C. Viallon, F. Begnaud, J.P. Frencia, Viandes Prod. Carne¤s 21 (2000) 3. [2] E. Schaller, S. Zenha«usern, T. Zesiger, J.O. Bosset, R. Escher, Analusis 28 (2000) 743. [3] B. Dittmann, S. Nitz, G. Horner, Adv. Food Sci. 20 (1998) 115.
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[4] B. Dittmann, S. Nitz, Sens. Actuators 69 (2000) 253. [5] B. Dittmann, B. Zimmermann, C. Engelen, G. Jany, S. Nitz, Agric. Food Chem. 48 (2000) 2887. [6] K.L. Goodner, R.L. Rouse¡, J. Agric. Food Chem. 49 (2001) 250. [7] J.L. Berdague¤, G. Vernat, V. Rossi, Viandes Prod. Carne¤s 14 (1993) 135. [8] C. Pe¤re's, C. Denoyer, P. Tournayre, J.L. Berdague¤, Anal. Chem. 74 (2002) 1386. [9] R.T. Marsili, J. Agric. Food Chem. 47 (1999) 648. [10] C. Pe¤re's, C. Viallon, J.L. Berdague¤, Anal. Chem. 73 (2001) 1030. [11] E. Fernandez-Garcia, J. Agric. Food Chem. 44 (1996) 1833. [12] F. Ulberth, J. Assoc. O¡. Anal. Chem. 74 (1991) 630. [13] K.R. Christensen, G.A. Reineccius, J. Dairy Sci. 75 (1992) 2098. [14] J. Kubickova, W. Grosch, Int. Dairy J. 7 (1997) 65. [15] B. Kolb, L.S. Ettre, Static headspace-gas chromatography. Theory and practice, Wiley-VCH, New York, USA, 1997. [16] E. Schaller, J.O. Bosset, F. Escher, Analusis 28 (2000) 217. [17] J. Pugh, Q.Y. Yang, W.H. Zhu, , Micromass Appl. Brief AB213 (1999). [18] C. Pe¤re's, F. Begnaud, J.L. Berdague¤, Sens. Actuators B 87 (2002) 49. [19] M.A. Mendes, R. Sparrapan, M.N. Eberlin, Anal. Chem. 72 (2000) 2166. [20] T. Kotiaho, R.A. Ketola, M. Ojala, T. Mansikka, R. Kostiainen, Am. Environ. Lab. 3 (1997) 19. [21] F.R. Lauritsen, T. Kotiaho, Rev. Anal. Chem. 15 (1996) 237. [22] L. Pillonel, J.O. Bosset, R. Tabacchi, Lebensmittel-Wissenschaft Technol. 35 (2002) 1. [23] A.J. Nu¤n‹ez, L.F. Gonzalez, J. Janak, J. Chromatogr. 300 (1984) 127. [24] M. Harper, J. Chromatogr. A 885 (2000) 129. [25] I. Laye, D. Karleskind, C.V. Morr, Milchwissenschaft 50 (1995) 268. [26] Z. Zhang, J. Pawliszyn, Anal. Chem. 65 (1993) 1843. [27] D. Page, G. Lacroix, J. Chromatogr. A 873 (2000) 79. [28] P.G. Hill, R.M. Smith, J. Chromatogr. A 872 (2000) 203. [29] X.-P. Lee, T. Kumazawa, K. Kondo, K. Sato, O. Suzuki, J. Chromatogr. B 734 (1999) 155. [30] M. Abalos, J.M. Bayona, J. Pawliszyn, J. Chromatogr. A 873 (2000) 107. [31] F. Augusto, A.L.P. Valente, E.D. Tada, S.R. Rivellino, J. Chromatogr. A 873 (2000) 117. [32] Z. Zhang, J. Pawliszyn, J. High Resolut. Chromatogr. 19 (1996) 155. [33] B. Tienpont, F. David, C. Bicchi, P. Sandra, 23rd International Symposium on Capillary Chromatography, Riva del Garda, Italy, IOPMS, Kortrijk, Belgium, 2000. [34] F. Begnaud, J.L. Berdague¤, Sens. Actuators B 81 (2002) 351. [35] Burle, Electron multiplier handbook for mass spectrometry applications, Burle, 2002.
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[36] D.A. Heatherbell, R.E. Wrolstad, L.M. Libbey, J. Agric. Food Chem. 19 (1971) 1069. [37] J.V. Hinshaw, LC.GC Int. 3 (1990) 22. [38] D. Canac-Arteaga, F. Begnaud, C. Viallon, J.L. Berdague¤, Analusis 28 (2000) 973. [39] D. Canac-Arteaga, C. Viallon, J.L. Berdague¤, Analusis 27 (1999) 780. [40] D. Canac-Arteaga, C. Viallon, J.L. Berdague¤, Analusis 27 (1999) 864. [41] T. Noij, A. Van Es, C. Cramers, J. Rijks, J. High Resolut. Chromatogr. 10 (1987) 60. [42] J.F. Pankow, Environ. Sci. Technol. 25 (1991) 123. [43] P.G. Simmonds, J. Chromatogr. 289 (1984) 117. [44] R.T. Marsili, J. Agric. Food Chem. 48 (2000) 3470. [45] F. Begnaud, C. Pe¤re's, J.L. Berdague¤, 6th Food Authenticity and Safety International Symposium, Nantes, France, 2001. [46] R. Feldho¡, C.A. Saby, P. Bernadet, Flavour Fragrance J. 15 (2000) 215. [47] C. Pe¤re's, F. Begnaud, J.L. Berdague¤, Anal. Chem. 74 (2002) 2279. [48] R.J. Stevenson, X.D. Chen, O.E. Mills, Food Res. Int. 29 (1996) 265. [49] G. Arora, F. Cormier, B. Lee, J. Agric. Food Chem. 43 (1995) 748. [50] T.P. Wampler, in: R. Marsili, Techniques for Analyzing Food Aroma, Marcel Dekker, Inc., New York, USA, 1997, pp. 27^58. Chistophe Peres was an engineer in the Flavour Laboratory, INRA, Clermont-Ferrand-Theix, France. He was awarded the degree of engineer at Institut National Agronomique Paris-Grignon (INA P-G, Paris, France) in 1999 and was awarded the degree of Ph.D. in 2002. His work is focused on the new ways to characterize and authenticate foodstu¡s origin by MS. In 2003, he joined the team of the Professor Dr. P. Sandra (Research Institute for Chromatography, Kortrijk, Belgium). Fre¤de¤ric Begnaud is an engineer in the Flavour Laboratory, INRA, Clermont-Ferrand-Theix, France. He was awarded the degree of engineer at ENSCR (Rennes, France) in 1997, and is in the last year of his doctoral studies. His work is focused on new ways of characterizing and authenticating the origin of foodstu¡s. Luc Eveleigh studied analytical chemistry at the Institut National Agronomique Paris-Grignon (INA P-G, Paris, France) and received his Ph.D. in 1994 for a work about GC-MS. Since 1995, he has been a senior lecturer at the INA P-G. He is involved in the development and applications of fast methods for food analysis, including near infrared spectroscopy and e-noses. Jean-Louis Berdague¤ is the scienti¢c manager of the Flavour Laboratory, INRA, Clermont-Ferrand-Theix, France. He was awarded the degree of engineer at ENSBANA (Dijon, France) in 1979 and a doctorate in food science and nutrition in 1982. His main topics of interest are £avor analysis of food products and olfactometry.
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Fast characterization of foodstuff by headspace mass spectrometry (HS-MS) Christophe Pe´re`s, Fre´de´ric Begnaud, Luc Eveleigh, Jean-Louis Berdague´ The field of the rapid characterization of products by HS-MS is reviewed. The general principle of HS-MS systems consists of introducing volatile components present in the HS of a sample without prior chromatographic separation into the ionization chamber of a mass spectrometer. The spectrum resulting from simultaneous ionization and fragmentation of the mixture of molecules introduced constitutes a ‘‘fingerprint’’ that is characteristic of the product being analyzed. Exploitation of this spectral information allows one determine the composition of the sample. # 2003 Published by Elsevier B.V. Keywords: Electronic nose; Foodstuff; MS-based sensor; Multivariate analysis; Quality control
1. Introduction Christophe Pe´re`s*, Fre´de´ric Begnaud, Luc Eveleigh, JeanLouis Berdague´a Laboratoire Flaveur, INRA de Theix, F-63122 Saint-Gene`sChampanelle, France
*Corresponding author. Tel.: +32 5620 4031; Fax: +32 5620 4859; E-mail: christophe.peres@ richrom.com
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With the objective of achieving an ever increasing level of surveillance and quality control, there exists at present a strong demand for research in the domain of the rapid characterization of raw materials and food products. However, even if sensory analysis and physical chemistry techniques remain powerful and recognized tools for resolving this type of problem, setting up such methods nevertheless requires a considerable amount of work and investment. Hence, manufacturers of analytical instruments and a number of research laboratories have become interested in MS as a potential means of obtaining more rapid and less expensive solutions. Experiments have shown that direct injection of the volatile fraction of a product (pre-concentrated or not) into the ionization chamber of a mass spectrometer allows one to obtain a characteristic spectrum called the ‘‘signature’’ or ‘‘spectral ¢ngerprint’’ (Fig. 1). These signatures can then be used for classi¢cation, prediction
of sensory properties, or estimation of technological parameters. To date, direct coupling of a mass spectrometer to techniques, such as static HS (SHS-MS) [1^6] or dynamic HS extraction (DHS-MS) [2,7,8], or solid-phase microextraction (SPME-MS) [2,9,10], has led to the development of fast, economical methods of characterization (Table 1). These new systems for rapid analysis by HS-MS are currently subject to growing interest and are starting to ¢nd a commercial outlet in the gas-sensors market. These instruments are known as ‘‘mass sensors’’ or sometimes ‘‘new generation electronic noses’’. The state of the art on the recent technical and methodological advances in the ¢eld of rapid characterization of food stu¡ by HS-MS is covered in this article. Instrumental aspects, data analysis and method development are investigated.
2. Instrumental aspects The general principle of HS-MS systems consists of introducing volatile components present in the HS of a sample without prior chromatographic separation into the ionization chamber of a mass spectrometer. The spectrum resulting from simultaneous ionization and fragmentation of the mixture of molecules introduced constitutes a ‘‘¢ngerprint’’ that is characteristic of the product being analyzed. Exploitation of this spectral information allows one determine the composition of the sample. HS-MS systems comprise a module for extraction and injection of the volatile
0165-9936/03/$ - see front matter # 2003 Published by Elsevier B.V. doi:10.1016/S0165-9936(03)01206-8
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Figure 1. Signal produced by an HS-MS system. Sample: chocolate liqueur. (a) Total ion current as a function of time. (b) Spectral fingerprint of sample. The spectrum is the mean abundance values of all the mass fragments recorded between 1 and 3 minutes. m/z=43 uma: most common ions in organic mass spectroscopy. m/z=122 uma: specific ion for trimethylpyrazine.
Table 1. Main applications of systems for rapid characterization by HS-MS Type of discrimination Study Classification
Detection of defects Control Follow-up of processing
System
Reference
Geographical origin of essential oils of rose SHS-MS [3] Tomato sauces according to concentration of ‘‘garlic’’ aromas SHS-MS [5] Grapefruit juices according to concentration of vanillin – identification of vanillin SHS-ion-trap-MS [6] ‘‘Camembert-like’’ cheeses according to heat treatment of milk and stage of ripening DHS-MS [8] ‘‘Camembert-like’’ cheeses according to heat treatment of milk and stage of ripening SPME-MS [10] Off-odors in milk SPME-MS [9] ‘‘Use by’’ date of milk SPME-MS [44] Freshness of cloves SHS-MS [4] Sterilization of meat meals SHS-MS [1] Ripening of cheese SHS-MS [2] P&T-MS SPME-MS
components, directly linked through a transfer line to a mass spectrometer. Fig. 2 summarizes the main components for operating a HS-MS system. 2.1. The extraction-injection module 2.1.1. Extraction of volatile components without preconcentration: static HS systems. The static HS method consists of placing the sample in a hermetically sealed vial and then, once equilibrium has been established between the matrix and the gaseous phase, sampling the HS. Frequently used for the analysis of milk products [11^14], this technique o¡ers the advantage of being very simple to use; the sample temperature, equilibration time and size of the vial are the main parameters to optimize [15]. However, because there is no pre-concentration, the sensitivity of this type of instrumentation can prove insu⁄cient for certain applications. Five main types of extraction-injection module have been used for foodstu¡ and their characteristics are listed in Table 2.
2.1.1.1. Systems using gas syringes. An aliquot is taken from the HS and injected by means of a syringe. This method was used to classify Swiss Emmental cheeses according to their stage of ripening [2,16]. 2.1.1.2. Systems using a stream of inert gas. An aliquot of the HS is transferred by the carrier gas into the ionization chamber of the mass spectrometer for a short period of time. Instruments equipped with this type of extraction-injection have been employed in the context of studies using Time of Flight [17] and Ion Trap mass spectrometers [6]. As the particularities of these systems are linked more to the technical characteristics of the spectrometers, the latter two studies will be detailed later in the review. 2.1.1.3. Balanced pressure sampling systems. The vial is pressurized by the carrier gas to a pressure equal to the carrier-gas inlet pressure of the transfer line. Next, the carrier-gas supply is interrupted by closing a valve http://www.elsevier.com/locate/trac
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Figure 2. Main HS-MS systems. (a) Static HS-MS. (b) Dynamic HS-MS. (c) SPME-MS.
and injection is performed by expansion of the gaseous mixture into the transfer line. In this way, di¡erent tomato sauces were discriminated according to their aromatic characteristics by Dittmann et al. [5]. 2.1.1.4. Pressure/loop systems. The sample vial is pressurized by an inert gas to a pre-set value. Next, the vial is opened temporarily toward a sample loop of a gas-sampling valve and not directly to the transfer line. Finally, purging of the sample loop with the carrier gas injects the volatile components into the mass spectrometer. Using this type of instrument, a study conducted by Berdague¤ et al. [1] enabled the development of a protocol for a posteriori control of the heat treatment of meat meals by SHS-MS. By contrast, work carried out
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with the same instrument has shown that this system is not su⁄ciently sensitive for rapid characterization of cheeses [18]. 2.1.1.5. HS membrane introduction (HS-MI). The volatile compounds that desorb from the sample pass through a speci¢c membrane by pervaporation and are then introduced directly into the ionization chamber by opening a valve. HS-MI has been hyphenated with MS by Mende's et al. [19] for rapid analysis of traces of volatile organic compounds (VOCs) in solid matrixes. This technique has been applied to soil samples, but it can equally well be employed for analyzing food products. However, the hydrophobic nature of the membrane (generally a silicone-based polymer) favors the
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Table 2. Main characteristics of HS-MS systems used to date to characterize products by rapid analysis of their volatile fraction Name
Manufacturer
Mass analyzer
Extraction system
Chemsensor MS-Sensor Smart Nose
Agilent Technologies HKR Sensorsysteme GmbH Smart Nose
Quadrupole (HP5973, Agilent) Quadrupole (TurboMass, Perkin Elmer) Quadrupole (Balzers)
Pressure/loop SHSa (HP 7694, Agilent) Balanced-pressure SHS (HS40XL, Perkin Elmer) Gas syringe SHS and SPMEb (CTC CombiPal) Purge-and-Trap (Tekmar LCS 2000)
SPME-MS
Home made [9,10] Quadrupole (Saturn, Varian) (MD800, Fisons) INRA-SRV/Flaveur, France Home made [6]
DHS-MS Ion-trap-MS TOF MS AP+ Nose HS-MIMS
SPME (75 mm Carboxen/PDMS)
Micromass
Quadrupole (MD800, Fisons) Ion-trap (Finnigan GCQ Plus, Thermoquest) Time of flight (LCT, Micromass )
Home made [19]
Quadrupole (Balzers)
DHSc (Compact Desorber-1, INRA) SHS (Systems using a stream of inert gas, Home made) [6] SHS (Systems using a stream of inert gas), Interface APCI+ SHS (HS-MI, Home made) [19]
a
Static HS; Solid Phase MicroExtraction; c Dynamic HS. b
permeation of apolar molecules and limits that of polar compounds [20,21]. 2.1.2. Extraction of the volatile components with pre-concentration. In order to increase amounts of material injected, di¡erent methods of pre-concentration have been proposed. Pre-concentration improves the sensitivity of the system but introduces a supplementary step, which can prove limiting from a temporal point of view and/or generate analytical artefacts (memory e¡ects, bleeding, irreversible adsorption). In this respect, the pre-concentration medium must be chosen with care, as pointed out below. 2.1.2.1. Purge and trap and dynamic HS systems. The purge and trap (P&T) and dynamic HS (DHS) techniques are classical methods of pre-concentration of volatile compounds used in a variety of applications [22]. In both systems, the volatile components are purged by a stream of inert gas and trapped onto an adsorbent. The constant depletion of the HS leads to a displacement of the equilibrium in favor of the desorption of these molecules from the matrix. The trapped molecules are subsequently desorbed by heating and injected into the ionization chamber of the mass spectrometer. Apart from the choice of trap, the main parameters to optimize are the temperature of the sample, the equilibration time, the £ow rate of the extractor gas and the purge time of the HS. In the case of P&T, the gas £ow is injected through the sample (preferably in liquid or powder form), whereas, in the case of DHS, only the HS is purged with the gas. The di¡erent traps that exist, listed and described by Nun‹ez et al. [23] and Harper [24], are made of activated or graphite carbon or porous polymers. The activated or graphite carbon traps have a large speci¢c surface area and a high capacity of adsorption for more polar
compounds. This adsorption can sometimes be irreversible and may be followed by the retention of large amounts of water. Conversely, the porous polymer traps (e.g. Tenax, Chromosorb) have a lower adsorption capacity but present the advantage of having less a⁄nity for water. The Tenax TA trap is currently an adsorbent of choice for the analysis of volatile compounds on account of its low bleeding, thermal stability and weak a⁄nity for water. Lastly, traps combining several types of speci¢c adsorbent have also met with great success, as they allow one to increase the retention capacities for molecules belonging to di¡erent chemical families [25]. 2.1.2.2. SPE. Whereas the P&T and DHS techniques require complex equipment (gas extractor, numerous valves, double gas circuit), SPME is a user-friendly preconcentration method. The principle involves exposing a silica ¢ber covered with a thin layer of adsorbent in the HS of the sample in order to trap the volatile components onto the ¢ber [26]. The adsorbed compounds are then desorbed by heating and injected into the ionization chamber of the mass spectrometer. Apart from the nature of the adsorbent deposited on the ¢ber, the main parameters to optimize are the equilibration time, the sample temperature and the duration of extraction. Because of their good performance, notably for the extraction of highly volatile molecules, these ¢bers are very popular in the ¢elds of agricultural foods, biology and environment [27^31]. In the context of non-separative analyses, SPME has been used for rapid classi¢cation of batches of cheeses [10] or the characterization of o¡odors in milk [9]. 2.1.2.3. New pre-concentration extraction methods. Some new extraction techniques derived from SPME also display a certain potential for direct coupling to a mass spectrometer. http://www.elsevier.com/locate/trac
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Zhang and Pawliszyn [32] have proposed a system comprising a SPME ¢ber combined with a gas extraction syringe. On withdrawal of the SPME ¢ber, a volume of gas is sampled from the HS, thus enabling the analysis of compounds having low a⁄nity for the ¢ber. A modi¢ed version, where the adsorbent phase is deposited on the internal side of the syringe, is known as solid-phase dynamic extraction (SPDE). HS sorptive extraction (HSSE) was developed with the objective of improving the sensitivity limits of SPME because of the small quantity of adsorbent present on the ¢ber. As a general rule, this quantity is about 0.5 mL for a classical SPME ¢ber, whereas it is currently 50^200 mL for HSSE. This extraction system takes the form of a magnetic rod coated with poly(dimethyl siloxane) (PDMS). This device is easy to use; the extraction step involves placing the bar in the HS of the sample to be analyzed. Next, the bar is placed in a thermodesorption unit to release the volatile compounds into the mass spectrometer. When applied to the analysis of the volatile molecules present in the HS of diverse matrixes, such as medicinal plants, bananas or co¡ee, HSSE permitted detection limits 2^3 orders of magnitude lower than those of SPME [33].
compact dimensions and lower price as compared to other instruments. Apart from the case of the TOF analyzer mentioned above, only one laboratory-designed instrument uses an ion trap. Detection of the ions is ensured by either electron multipliers or photon multipliers, with comparable sensitivities. Electron multipliers are presently most widely employed because of their attractive price, but their gradual loss of sensitivity constitutes a major inconvenience (average lifespan of one year for 40 h use per week) [35]. By contrast, photomultipliers are inherently less susceptible to £uctuations of the vacuum and risks of chemical contamination and hence display stability and longevity almost 10 times superior to those of the electron multipliers. The choice of the type of secondary pump (di¡usion or turbomolecular) is similarly particularly important, in so far as the quantities of volatile compounds injected can be large and the time interval too short to allow a return to equilibrium between two samples. Because of their high pumping capacities, turbomolecular pumps have a distinct advantage in this respect over the di¡usion pumps, but they require careful maintenance.
2.2. The transfer line The transfer line ensures coupling of the extractioninjection module to the mass spectrometer. This line must be designed to maintain a high-quality vacuum in the spectrometer source while allowing rapid transfer of the extracted molecules between the two modules. The length is in the range 0.5^5 m, according to the internal diameter (0.05^0.20 mm). Heating is necessary to prevent recondensation of the compounds. In addition, the internal side, whether fused silica or silica coated steel, must be inactivated in order to limit the risks of pollution in the course of analyses (memory e¡ects or loss of molecules through reversible or irreversible adsorption) and to avoid the catalytic degradation of certain compounds.
3. Development of a HS-MS analysis method
2.3. The mass spectrometer Ionization of the molecules introduced into the source of the mass detector is mainly carried out by electron impact at 70 eV, a method that yields ‘‘¢ngerprints’’ of high discriminating power for a large number of agrifood products [34]. Although marginal at present, an interface permitting ‘‘soft’’ ionization of the molecules at atmospheric pressure has been proposed (Table 2). In association with a time-of-£ight (TOF) analyzer, this technology o¡ers the advantage of providing more speci¢c information by allowing one to trace the molecular origin of the ions (mass ¢ngerprinting) [17]. The majority of the mass spectrometers used for HS-MS are equipped with quadrupole ¢lters. These spectrometers owe their success to their high performance,
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3.1. Selection of conditions limiting the introduction of contaminants The operating conditions chosen should not lead to dysfunction or rapid contamination of the instrument. In the case of a mass spectrometer, maintenance of a good quality vacuum necessitates limiting the quantities of material injected, while introduction of oxygen is similarly known to a¡ect the lifespan of the ¢lament quickly. Purging the HS with an inert gas before the equilibration step represents a simple solution to minimize the latter risk. Moreover, agri-food matrixes are frequently characterized by signi¢cant water content that can give rise to numerous analytical problems. Strong adsorption of water on the traps can in£uence both the reproducibility of the measurements and the lifespan of the adsorbents [10]. In addition, injected water vapor can strongly perturb the ionization of the molecules and modify the mass spectra (diminution of the abundance of certain ions, formation of adducts) [36]. Finally, the introduction of large quantities of water may in the long term cause deterioration of the mass detector [37]. A minimal introduction of water into the system is thus a parameter of prime importance to take into account in the analysis of moist food products. Sample analysis at ambient temperature or with moderate heating is the simplest solution. Several other techniques may also be used to limit water injection into the analytical system:
Trends in Analytical Chemistry, Vol. 22, No. 11, 2003 incorporation of hygroscopic salts into the matrix [38]; removal of a part of the water retained onto the trap by purging with a dry gas [39]; use of a cold trap (usually maintained at ^10 C to ^15 C) placed between the sample and the transfer line (system without pre-concentration) or between the sample and the adsorbent (system with pre-concentration) [40]; or, permeation, which makes use of the selective di¡usion of water molecules through the wall of a speci¢c tube [41^43]. However, these methods may lead to a partial or total loss of chemical families, notably of the more polar and more volatile molecules [10,40^42]. In any case, incorporation of hygroscopic salts and purging of the trap with a dry gas are simple to use. 3.2. Correction of signal drift: a necessity for database set-up The use of rapid characterization systems based on ¢ngerprint recognition relies heavily on the availability of robust, permanently valid databases. Robustness strongly depends on the experimenter, whereas the permanence of databases is often undermined by the instability of instrument performance. Although a necessity, any procedure for calibration or correction of the signal should nevertheless not be too rigid or generate important supplementary costs. A large number of studies have been devoted to this problem in the ¢eld of gas detectors, but the results remain fairly limited and speci¢c to this technology. The question of the temporal stability of the signal also arises in the case of MS characterization systems. Progressive pollution of the source, maintenance operations, or a vacuum quality a¡ected by simultaneous introduction of large amounts of material can lead to signal drifts. Thus, although various tuning procedures exist, if the signal goes uncorrected during a programme of analyses, the resulting data may be weakened or even valueless. Accordingly, it is important to have procedures to monitor the state of the mass detectors and help correct drift. In this way, internal or external normalization is a simple operation, routinely employed in the context of data analysis to eliminate e¡ects linked to variations in the intensity of the signal (sensitivity, quantities injected). 3.2.1. Internal normalization. The internal normalization of a given mass spectrum involves expressing the abundance of each mass fragment with respect to the sum of the fragments or with respect to a reference fragment derived either from the product or from an internal standard. Normalization with respect to the sum of the mass fragments is frequently employed.
Trends However, in cases where the more abundant fragments display stronger drift than the others, this correction can prove insu⁄cient because of the linkage existing between all the fragments. Use of an internal standard involves adding to each sample a precisely known quantity of one or more molecules of which the fragments (and in particular the fragment chosen as reference) do not interfere with the ions of the products to be analyzed. In a recent study of prediction of the ‘‘use by’’ date of milk by SPME-MS, Marsili [44] was able to compensate for the signal drift resulting from important instrumental problems (use of several SPME ¢bers, replacement of a turbomolecular pump and of the electron multiplier) through addition of chlorobenzene as an internal standard. However, this type of method is applicable to liquid samples only and, for solid products, it is di⁄cult to imagine this option without a solubilization step. 3.2.2. External normalization. This method involves analyzing reference samples, solutions or gases in order to determine and correct the drift by comparison with the signals obtained for these references. The abundance of each mass fragment is expressed with respect to that of a reference fragment derived from the chosen external standard. Begnaud et al. [45] have proposed a correction algorithm to rectify the drift observed with a SHS-MS system. In practice, this method is achieved by inserting reference samples at regular intervals during the analysis sequence. The drift of each fragment was thus corrected, but the procedure entails carrying out more analyses. In [45], 3 reference samples for 8^10 samples were necessary ^ which corresponded to an increase of 30% in cost of analysis per sample. Furthermore, this method requires management of the reference samples (choice of a reference sample, physical and chemical stability, storage), which is sometimes di⁄cult, especially in the agri-food sector. Normalization of the data may similarly be carried out with respect to an ion naturally present in all the spectra and displaying a variance independent of the products analyzed. The mass fragment m/z=40 (argon) is frequently chosen [46]. Although this choice may have the advantage of being economical, it is unlikely to be reliable, as the level of argon in a laboratory is far from constituting a stable reference and varies with geographical location. Finally, Pe¤re's et al. [47] have recently developed a new method of correcting temporal drift through addition of a reference gas (Standard Gas Addition (SGA)). The general principle involves continuously introducing a very small quantity of xenon into the source of the mass spectrometer. The abundance of each mass fragment in the spectrum of the product analyzed is then normalized with respect to the abundance of http://www.elsevier.com/locate/trac
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xenon measured during the analysis. This technique o¡ers the advantage of taking into account the real ionization conditions existing in the source of the spectrometer during introduction of the molecules desorbed and has been successfully employed for the rapid characterization of cheeses by DHS-MS [8]. Fig. 3 demonstrates the advantage of using SGA instead of
Euclidian internal normalization. However, it is usually better to use internal normalization than no signal pretreatment. 3.3. Selection of the relevant mass fragments and model set-up If the spectral ‘‘¢ngerprints’’ reveal large numbers of mass fragments, part of this signal is often non-informative, so it is necessary to select the fragments relevant to the problem in question. It is possible to envisage at least two strategies, as follows.
1. Prior knowledge of the product often allows one to determine the molecules of interest, the speci¢c mass fragments of which will be informative in the context of a rapid analysis. This step may require use of separative methods. In the case of analysis of the volatile fraction of food products, association of GC with MS (GC-MS) or olfactory detection (GC-O) [48,49] has proved of value [50]. In this way, it is possible to focus the mass spectrometer on the detection of the fragments of the molecule of interest and increase its sensitivity, an approach which is particularly interesting for HS-MS systems without a module for pre-concentrating the molecules. In a study of the characterization of cloves [4], a series of separative analyses by HS-GC-MS showed that the abundance of three molecules permitted discrimination of the samples according to their freshness, and HS-MS could be performed rapidly using three corresponding ions. 2. Statistical methods may similarly be employed to select the characteristic fragments. Two situations can be envisaged, depending on the nature of the study:
Figure 3. Effect of signal correction on signal analysis. PCA plot of ‘‘Camembert-like’’ cheeses analyzed by SPME-MS. *: Coulommiers; &: raw milk, brand 1; ~: raw milk, brand 2. (a) Projection of the raw data on PC1 and PC 2. (b) Projection of normalized data. (c) Projection of data after SGA correction.
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1. The data may display a coherent, exploitable structure; in other words the variations of a characteristic studied in the samples induce in the mass spectra modi¢cations that are visible on direct examination or that can be visualized by means of unsupervised statistical tools (Principal Component Analysis (PCA), classi¢cation). 2. The di¡erences between spectra exist but are weaker than those described above. The data should then be treated by supervised methods (Functional Data Analysis (FDA), Partial Least Squares (PLS), neuromimetic networks) that select under constraint the fragments or combinations of fragments relevant to the characteristics of interest.
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Figure 4. PCA of the spectral signatures of cork wine stoppers. *: Spanish cork; &: Moroccan cork; ^: Portuguese cork. (a) Whole data set: spectral range 50–180 uma (131 variables). (b) Reduced data set: only 31 significant variables were kept. Variables were chosen using PLS for the prediction of the geographical origin of the cork.
Fig. 4 shows the PCA for a whole data set and the PCA for same data, after removing the variables found to be the least signi¢cant in a PLS analysis. However, it is desirable to obtain supporting chemical evidence, such as chromatography, to validate the selection of variables using a statistical approach and the pertinence of the fragments selected should indeed be assessed by the user on the basis of knowledge of the product. In fact, even if it is di⁄cult categorically to relate a mass fragment of a spectral ‘‘¢ngerprint’’ to a speci¢c volatile component, it is nevertheless possible to deduce the main origin of certain ions (sulfur compounds, esters, ketones) selected by statistical analysis. In a study of the rapid characterization of cheeses by DHS-MS, Pe¤re's et al. [8] showed that, among the ¢ve fragments selected by an ascending step-by-step procedure, three were major fragments of sulfur compounds identi¢ed by GC-MS. The mass fragments chosen by one of these two strategies can subsequently be used to develop models appropriate for the task to be performed. The statistical tools employed are methods of visualization, such as PCA, classi¢cation/recognition (e.g. FDA, ascending hierarchic classi¢cation) or prediction/quanti¢cation (e.g. regression, neuromimetic networks). In general, PCA and FDA are the most frequently employed [3^6,9]. PLS was used by Marsili [44] to predict the ‘‘use by’’ date of milk, while Berdague¤ et al. [1] used the potential of neuromimetic networks to estimate the sterilization temperature of meat meals. Finally, to complete the modeling procedure, it is imperative to confront the models developed with test data, in other words data derived from products that were not used for their construction. All too often neglected, this step is essential to test the reliability of the estimations or classi¢cations performed. Such validation allows one to avoid solutions biased by over¢tting or by e¡ects caused by chemical interference
(even if the fragments were selected using the ¢rst of the above strategies).
4. Conclusions HS-MS techniques represent a new approach to the rapid characterization of agri-food products. These systems enable one to obtain the spectral ‘‘¢ngerprint’’ of a sample within a few minutes by analysis of its volatile fraction. Di¡erent methods of extraction and injection of the volatile components make it possible to account for the speci¢cities of the matrixes studied. Moreover, transfer of this type of technology to the industrial sector for the purposes of classi¢cation or quality control is now entirely feasible, in that solutions have been found to resolve the problems of instrumental drift, so it is possible to create and manage permanent databases. In terms of technical evolution, use of ‘‘soft’’ ionization methods (ionization at atmospheric pressure, chemical ionization) in association with ‘‘high-resolution’’ mass analyzers (TOF, ion trap) now permits one to get information concerning the molecular origin of the ions and to deduce the identity of certain compounds present in the HS. Such ‘‘¢ngerprints’’ enable not only characterization of a given product but also detection of molecules directly responsible for odors or aromas. These advances open new perspectives for the rapid characterization of products by MS in a wide variety of domains of application.
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