Vaughan S. Langford

Aroma is an important characteristic in the acceptance of beef by consumers, and preference is often culturally dependent. Traditional grading of aroma quality has been carried out using sensory analysis of very limited numbers of samples, due to the inherent costs and the lack of appropriate technologies to replace the human olfactory system. Since meat aroma is derived from various volatile organic compounds (VOCs) that impart favorable or unfavorable qualities, there is potential to apply selected ion flow tube mass spectrometry (SIFT

MS) to facilitate wider scale, economic grading of carcasses. SIFT

MS is a direct analysis technique that can provide both high sample throughput and selective analysis of the chemically diverse volatiles that contribute to the aroma. This paper describes an investigation of the applicability of SIFT

MS to beef grading. Classifications of beef aroma were provided by a trained sensory panel. SIFT

MS was used to analyze the same samples for aroma volatiles. Application of multivariate statistical analysis to the combined data set demonstrated that SIFT

MS discriminates premium quality beef from eight sensory defects, and, therefore, could be applied as an instrumental grading tool obviating sensory panel grading.

	There are several factors that contribute to the acceptability of a beef cut to the consumer, but most important is its flavor. Certain volatile organic compounds (VOCs) impart favorable or unfavorable characteristics to the flavor, and these can be detected using a variety of analytical techniques. Beef flavor is made up from a significant number of volatile compounds, largely arising from the cooking process. These flavor compounds are generated in the Maillard reaction, lipid oxidation, and interactions between them during the cooking process (1–7), and from vitamin degradation (1,8). A wide range of volatile flavor compounds are produced during the cooking of beef, and, if an understanding of the flavor compounds is to be found, many different volatile compounds should be monitored. Further, the aroma of cooked beef can be complex, and many hundreds of volatile flavor compounds have been identified (2,3,6,9-13). For example, a list of 90 VOCs have been monitored as flavor compounds in cooked beef, using the technique of gas chromatography–olfactometry (GC/O) (2,14). These compounds include aldehydes, ketones, alcohols, hydrocarbons, and pyrazines. Many of the traditional techniques for monitoring beef flavors are based on gas chromatography-mass spectrometry (GC–MS), often in conjunction with olfactometry. However, analysis using gas chromatographic techniques are slow and require expert operation, so they are impractical for process applications.

	Recently, direct mass spectrometric methods have been developed that eliminate time-consuming chromatographic analysis. In this paper, we apply one of these methods: Selected Ion Flow Tube Mass Spectrometry (SIFT–MS), a rapid, highly sensitive analyzer of whole air, to the detection of VOCs from various New Zealand beef samples. The rapid analysis provided by SIFT-MS has the potential to quickly identify premium quality carcasses early in the production process.

	SIFT–MS (15,16) is a real-time analytical technique for direct, comprehensive gas analysis to ultra-trace levels (17). Data obtained by SIFT–MS instruments compare well with the leading chromatographic method for volatile organic compound (VOC) analysis, GC–MS (18).

	SIFT–MS uses soft, precisely controlled chemical ionization, coupled with mass spectrometric detection (Figure 1), to rapidly quantify VOCs to low part-per-trillion concentrations by volume (pptv). Eight chemical ionization agents (reagent ions) are now available in commercial SIFT–MS instruments: H

 (19). These reagent ions react with VOCs and inorganic gases in well controlled ion-molecule reactions, but they do not react with the major components of air (N

, and Ar). This enables SIFT–MS to analyze air at trace and ultra-trace levels without pre-concentration.


	Figure 1: Schematic diagram of SIFT-MS – a direct chemical-ionization analytical technique.

	Rapid switching between the eight reagent ions provides very high selectivity. The key benefit of the additional ions is not primarily in the number of reagents ions, but in the multiple reaction mechanisms that provide additional independent measurements of each compound, delivering unparalleled selectivity and detection of an extremely broad range of compounds in real time.

	In this paper, a Voice200 SIFT–MS instrument (Syft Technologies, Christchurch, New Zealand; 

) was utilized in selected ion mode (SIM). The compounds targeted together with the masses used for quantitation are summarized in Table 1. The three standard positively charged reagent ions were utilized to achieve selective analysis.

	To create a range of flavor profiles, samples were sourced either from different types of cattle, or by modifying processing and storage conditions for vacuum packet primal cuts:

		normal production of prime grass-fed cattle

		high pH beef from both prime and bull carcasses

		manipulation of product by additional aging/chilling at higher temperatures to develop undesirable storage-related flavors

		sourcing product with abnormal flavors from both bulls and cows.

	Previous studies have shown that beef from older bull and cow animals can be associated with "barnyard" and "milk" notes, respectively. Beef with an elevated final pH (high pH) relates to carcasses that have insufficient glycogen at the point of slaughter to enable the normal post mortem glycolytic cycle to progress. This condition has been associated with "sour" or metallic-like notes. Therefore, samples with these attributes were included in the sample selection provided by Carne Technologies (Table 2).

	Samples were collected and stored frozen prior to sensory analysis, then thawed and minced prior to cooking and serving. Sensory panelists trained by Carne Technologies to recognize and assess aromas and flavors were used to evaluate each sample. The attributes used by the sensory panelists consisted of meaty, beefy, barnyard, sour/acidic, milky, grassy/pasture, sweetness, offal, spoilage, rancid, storage, other/foreign (such as garlic or pepper), and aftertaste. Each attribute was scored on a 9-point scale where 0 is absent and 9 is intense/extreme. All samples, except "prime beef," exhibited different sensory defects.

	For SIFT–MS analysis, frozen samples were thawed at room temperature and finely diced. Samples of uncooked beef (20 grams) were placed in one-liter Schott bottles and capped with pierceable septa. Samples were incubated at 60 °C for one hour prior to analysis, using a SIFT-MS instrument equipped with a high-performance inlet (HPI) that is both passivated and heated (120 °C). The HPI provides a direct path from the sample headspace to the flow tube, minimizing loss of flavor volatiles.

	The Selected Ion Mode (SIM) data from SIFT–MS were treated using multivariate statistical analysis to determine the ability of SIFT–MS to discriminate between the premium samples labelled "prime beef" and samples deemed defective by the sensory panel.

	The multivariate statistical methodology applied in this work was Soft Independent Modeling by Class Analogy (SIMCA), which was developed by Wold in the 1970s (20). SIMCA applies principal component analysis (PCA) to the whole dataset and to each of the classes with the goal of creating a model that discriminates each class from the others. The Infometrix Inc. (Bothell, WA) implementation of the SIMCA algorithm in the Pirouette software package was employed here.

	Three types of output from the SIMCA analysis are presented in this report:

 These three-dimensional plots show how each sample falls with respect to the three most important principal components derived from PCA on the entire data set. Each user-defined class shows the sample with the same color and a "cloud" representing the calculated space in which all samples of the class are expected to lie. Better class separations lead to more confident assignment of unknown samples to a predefined class, if a suitable one exists.

These are a measure of the separation between classes, a value of three usually being considered acceptable for class separation (21). Sometimes, the class separability indicated by these distances is not apparent in the three-dimensional class projection plot.

 This parameter helps identify variables that provide the most discrimination between the classes. A variable with larger discriminating power has greater influence on separating the classes than one with a small discriminating power. There does not appear to be a set threshold value above which this variable is considered good, because these values vary strongly with interclass distance.

	The data obtained from SIFT–MS analysis of the headspace of New Zealand beef samples are summarized in Table 3. For each class of sample, the replicate measurements have been averaged and the extent of variation indicated using two standard deviations of the mean. For most compounds, measurements show satisfactory repeatability given that analysis was carried out manually (since undertaking this study, application of automation to SIFT–MS has demonstrated significant repeatability improvements compared to manual analysis [22]). Limits of quantitation are typically below one part-per-billion by volume (ppbv) for headspace analysis using SIFT–MS. Direct analysis using soft chemical ionization means that all compounds were analyzed in a single, two-minute run for each sample. SIFT–MS requires no preconcentration or sample dilution, due to its wide dynamic range and its high robustness to moisture.

	The top-rated prime beef samples generally lie in the mid-range of concentration values for the compounds targeted here, except for ethyl acetate, indole, and methanol, which are at the upper end. The repeatability observed for these premium steaks is particularly pleasing, because it illustrates that there is great consistency across the 40 animals that they were obtained from.

	Defective beef samples tend to deviate significantly for several target compounds compared to the premium cuts. For example, the samples labeled "over-aged" and two of the "high pH" samples have elevated ammonia concentrations. The over-aged sample has elevated hydrogen sulfide and methyl mercaptan, both of which are extremely pungent; the latter is also detected in the "High pH 1" sample. This means that there is some potential for detecting particular defects by applying thresholds for a handful of markers.


	Figure 2: Class projections obtained from SIMCA multivariate analysis of the headspace data that are presented in averaged form in Table III. Each colored point in the class projections graph represents a replicate measurement. For clarity, the more congested region is expanded and rotated at right.

	However, certain defects (such as those found in the "Cow 1" and "Cow 2" samples) are not so readily distinguished from premium steaks based on thresholds of several marker compounds. Application of multivariate statistical analysis to the concentration data can be applied to achieve this. Figure 2 shows the class projections obtained when the full data set is processed using the SIMCA algorithm. Each colored point in this plot represents a replicate measurement. For clarity, the more congested region is expanded on the right-hand side of Figure 2. Table 4 summarizes the interclass distances obtained from the SIMCA analysis: all beef classes in this study are separable since the distances are all greater than three (21), including those that look marginal in the class projections plot ("Norm pH", "Cow 1", "Cow 2").

	Table 5 summarizes the relative significance of various target compounds in discrimination of the beef samples: the larger the number, the greater the contribution. Ethanol dominates discrimination. Given that the flavor impact of ethanol is lower than the other target compounds, and that ethanol was the key discriminating volatile between grass versus pellet fed cattle (from a parallel, unpublished study), the data was reprocessed using the SIMCA algorithm with both ethanol and methanol removed (methanol was the second most significant discriminator in the feed study). The right-hand column of Table 5 lists the discriminating powers with this revised compound list, while Table 6 gives the interclass distances. The prime beef samples remain well separated from the defective samples, as do most of the defects from each other. However, the two "cow" samples ("Cow 1" and "Cow 2") are no longer completely separable with an interclass distance of 2.3, and "Cow 1" is not entirely distinguishable from the sample labelled "Norm pH".

	The results presented here demonstrate that the SIFT-MS technique can effectively discriminate between prime and defective beef flavors when coupled with multivariate statistical analysis. The laboratory-based approach used here enables throughputs of about 30 samples/hour to be achieved. Even in its present form, SIFT–MS shows promise for wider scale flavor quality testing off-line compared to the traditional sensory approach.

	The goal, however, is to take this demonstrated flavor-screening potential to an in-line or near-line application. To achieve this, sample throughput needs to be enhanced twentyfold, to meet the needs of modern beef processing lines. This requires both optimization of the SIFT-MS analytical method and rapid heating of the beef sample to facilitate faster release of flavor compounds. A possible rapid sampling approach could utilize the i-knife, which was invented for surgical applications, but has recently been evaluated for meat (31).

	Nicola Simmons, Clyde Daly, and Vaughan Langford designed the experiments. The late Tracey Cummings coordinated sensory analysis and selected samples for instrumental analysis. Langford conducted the instrumental analysis and processed the data. Langford and Murray McEwan wrote this article.

Improving the Sensory and Nutritional Quality of Fresh Meat

, J.P. Kerry, D. Ledwood, Eds. (Woodhead Publishing, Cambridge, UK, 2009), pp. 111–146.

	(2) H. Van Ba, I. Hwang, D. Jeong, and A. Touseef, 

	(5) R. Tansawat, C.A.J. Maughan, R.E. Ward, S. Martini, and D.P. Cornforth, 

	(6) J.S. Elmore, D.S. Mottram, M. Enser, and J.D.J. Wood, 

 (Taylor & Francis, Abingdon, UK, 2nd Ed., 2006).

	(11) D. Machiels, L. Istasse, and S.M. van Ruth, 

	(13) H. Van Ba, M. Oliveros, K.S. Ryu, and L. Hwang, J. Food Science of Animal Resource 

	(14) Y. Takakura, T. Sakamoto, S. Hirai, T. Masuzawa, H. Wakabayashi, and T. Nishimura, 

	(17) B.J. Prince, D.B. Milligan, and M.J. McEwan, 

	(18) V.S. Langford, I. Graves, and M.J. McEwan, 

	(19) D. Hera, V.S. Langford, M.J. McEwan, T.I. McKellar, and D.B. Milligan, 

	(21) O.M. Kvalheim and T.V. Karstang, in 

Multivariate Pattern Recognition in Chemometrics, Illustrated by Case Studies

, R.G. Brereton, Ed. (Elsevier: Amsterdam, 1992), pp. 209–238.

	(22) M.J. Perkins, V.S. Langford, and M.J. McEwan, 

	(28) G.J. Francis, D.B. Milligan, and M.J. McEwan, J

	(29) T.L. Williams, N.G. Adams, and L.M. Babcock, 

	(31) J. Balog, D. Perenyi, C. Guallar-Hoyas, A. Egri, S.D. Pringle, S.Stead, O.P. Chevallier, C.T. Elliot, and Z. Takats, 

 is with Syft Technologies, Ltd. in Christchurch, New Zealand. 

 is with Syft Technologies, Ltd. in Christchurch, New Zealand and the Department of Chemistry at the University of Canterbury in Christchurch, New Zealand. 

 are with Carne Technologies Ltd. in Cambridge, New Zealand. Direct correspondence to 

Articles

Application of multivariate statistical analysis to a combined data set demonstrated that SIFT-MS discriminates premium quality beef from eight sensory defects, and, therefore, could be applied as an instrumental grading tool, obviating sensory panel grading.

Rapid Classification of Beef Aroma Quality Using SIFT-MS

Since its introduction in 1995, selected ion flow tube mass spectrometry (SIFT-MS) has found many applications in research and industry, together with substantial peer-reviewed literature. In this article, we describe a new application of SIFT-MS that is suited to the contract laboratory, where the direct analysis provided by SIFT-MS may have significant benefits because of its accelerated sample throughput. Using the environmentally significant BTEX compounds (benzene, toluene, ethylbenzene, and the xylenes) as a case study, we demonstrate high-throughput analysis from several matrices (air, water, and soil). For the BTEX compounds, detection limits in the single-digit parts-per-billion concentration range (by volume) are readily achievable within seconds using SIFT-MS, because sample analysis is achieved without chromatography, preconcentration, or drying. For the first time with SIFT-MS, we also present a calibration approach that enables speciation of ethylbenzene from the xylenes in real time. 

	A number of different technologies have been employed to monitor the aromatic hydrocarbons benzene, toluene, ethylbenzene, and xylenes (collectively known as BTEX) for different applications. Benzene, toluene, and ethylbenzene have been linked with cancers (1–6), with a common source of exposure being land contaminated by oil spillage (7,8). The importance of analyzing these species in environmental matrices, including air, water, and soil, is high and various reference methods have been developed (9–12).

	Chromatographic techniques have been the traditional choice-in particular, gas chromatography (GC) with various detectors (most notably flame ionization detection [FID], photoionization detection [PID], and mass spectrometry [MS]). However, GC is usually slow, because of the time-consuming chromatographic separation of analytes, so direct analysis techniques are frequently being evaluated. Among these, direct mass spectrometry (DMS) techniques have come to prominence because of their ability to analyze samples much more rapidly than GC, and with better selectivity than spectroscopic techniques. However, DMS techniques have struggled to distinguish ethylbenzene from the xylene isomers, so their measurement has been reported as a total concentration of "ethylbenzene plus xylenes." Where speciation is required, methods using chromatographic separation must be retained, which means off-line analysis. For example, in the European Union-unlike the United States-chromatography would have been essential because regulators have imposed different occupational exposure limits (OELs) for ethylbenzene and the xylenes (time-weighted averages [TWAs] of 100 and 50 ppm, respectively) (13). Likewise, recent vehicle interior air quality (VIAQ) regulations from Japan and Korea impose quite different permissible emission limits on these compounds-albeit the inverse of the EU workplace regulations (Table I).

	With the increasing need to rapidly speciate these isomers-whether in the laboratory or in-process monitoring-enhanced performance of DMS is required, rather than chromatography. Clearly, higher-resolution MS is not a solution, because the elemental composition is the same (C

). The need is to utilize an ionization approach that yields different product ions. With a portfolio of eight standard rapidly switchable reagent ions, selected ion flow tube mass spectrometry (SIFT-MS) is the ideal technique with which to seek a solution to distinguishing ethylbenzene from xylenes in real time.

	In this article, we describe a new approach to resolving ethylbenzene from the xylene isomers, by utilizing their different reactivities with the O

 reagent ion of SIFT-MS. Based on calibration, this methodology enables the SIFT-MS technique to achieve direct, real-time speciation of the xylenes from ethylbenzene. We applied this comprehensive approach to BTEX analysis to direct measurement of ambient air, water headspace, and methanolic extraction from soil. We find that this method is successful for air and water, whereas for methanolic extraction only a total can be reported due to methanol residues consuming the O

	SIFT-MS (14,15) is a real-time analytical technique for direct, comprehensive gas analysis to ultratrace levels (16). Data obtained by SIFT-MS instruments compare well with those obtained using the accepted chromatographic method for volatile organic compound (VOC) analysis (17).

	SIFT-MS uses soft, precisely controlled chemical ionization coupled with MS detection to rapidly quantify VOCs to low parts-per-trillion concentrations by volume (pptv). Eight chemical ionization agents (reagent ions) are now available in commercial SIFT-MS instruments: H

 (18). These reagent ions react with VOCs and inorganic gases in well-controlled ion–molecule reactions, but they do not react with the major components of air (N

, and Ar). This ionization approach enables SIFT-MS to analyze air at trace and ultratrace levels without preconcentration.

	Rapid switching between the eight reagent ions provides very high selectivity. The key benefit of the additional ions is not primarily in the number of reagent ions, but in the multiple reaction mechanisms that provide additional independent measurements of each compound, delivering unparalleled selectivity and detection of an extremely broad range of compounds in real time.

 SIFT-MS instrument (Syft Technologies) was used.

	Autosampler integration enables the direct gas analysis provided by SIFT-MS to be applied for rapid analysis of discrete samples. Autosamplers have been integrated most commonly with chromatographic analytical techniques, where rapid injection is required to achieve good chromatographic separation. However, SIFT-MS analyzes samples continuously (since chromatography is eliminated), so it requires steady sample injection of the gas sample for the duration of the analysis.

	The data presented here were obtained using an integrated Gerstel Multipurpose Sampler (MPS) autosampler, which has proved the best-suited commercial autosampler system for integration with the SIFT-MS technique. Headspace analysis was carried out from 20-mL sample vials on a standard Gerstel vial rack. Gerstel's Maestro control software has the ability to overlap parts of the preparation and injection sequence, leading to highly efficient sample scheduling and increased sample throughput.

	BTEX and chloroform primary standard solutions were prepared at 1000 ppm in methanol. Working standards were then prepared, via serial dilution, in 10 mL of deionized water in 20-mL headspace vials. Standard solutions were incubated at 60 °C for 15 min. The headspace was sampled using a 2.5-mL headspace syringe, and then injected into the SIFT-MS instrument at a flow rate of 50 µL/s.

	A methanolic extraction technique was adapted from GC–MS for determination of BTEX in soil (7).

	In the first step, linear detection of BTEX in the presence of high methanol concentrations was demonstrated over a range of BTEX concentrations (5–2000 ppbv in solution). Saturated sodium chloride solution (10 mL) was placed in 20-mL headspace vials. Standard solutions were prepared in methanol and 250-µL volumes of these were spiked into the aqueous solutions in sealed vials. Samples were incubated for 15 min at 60 °C. Then 2.5 mL of headspace was injected at 50 µL/s. Standard solution dilutions and spiking was carried out by the autosampler. Linearities as determined from 

 values-were greater than 0.997 for all compounds in the stated range and recoveries were 77–108% for 20–2000 ppbv solutions.

	Next, soil samples were prepared as follows. Soil samples (2 g) were spiked with known amounts of primary standard and then sonicated with 6 mL of methanol (Merck, LiChrosolv grade), as extraction solvent, in 10-mL headspace vials using a Gerstel QuickMix shaker for 2 min. They were then centrifuged at 4500 rpm for 5 min using an Anatune CF200 centrifuge. A 250-µL aliquot of methanol was then taken and spiked into a 10-mL volume of saturated sodium chloride solution in a 20-mL headspace vial. Incubation and analysis by SIFT-MS were carried out using the procedure described above. Following the addition of the extraction solvent, the entire sample preparation and analysis was fully automated.

	Parameters for detection of benzene, toluene, ethylbenzene, and the total xylene isomers using the positively charged reagent ions (H

) of SIFT-MS are summarized in Table II. Rate coefficients are not shown, but these occur at or near collision rate for all reagent ions (19).

	For ethylbenzene and the xylene isomers, the between-compound variability is less than 10% (that is, within experimental uncertainty), so that for H

 the rate coefficients provide a valid sum of all C

 reagent ion was used to quantify the total concentration of ethylbenzene and the three xylene isomers. To differentiate ethylbenzene from the total xylene isomers, the O

 reagent ion was used because of the different product ion branching behaviors. Separation of the isomers was achieved through calibration of the 91 to 106 ratios using 20:80, 50:50, and 80:20 ethylbenzene–xylene mixtures carried out at two nominal concentrations (150 ppbv and 10 ppmv). The calibration results are summarized in Figure 1, where the two points for a given ratio arise from ratio determinations at the two concentrations, showing excellent agreement. Notice the linear response and the 

-axis intercepts for 0% and 100% agree very well with the branching ratios shown in Table II for xylene and ethylbenzene, respectively (19).

 Calibration of relative abundance ratios (expressed as percentages) for the 

 reacts with different mixtures of ethylbenzene and the xylenes. More details are given in the text.

	Continuous air analysis demonstrates the speed of BTEX analysis using SIFT-MS. For a semicontrolled environment, the Anatune Limited laboratory proved ideal. This laboratory is used actively for demonstration and application development activities, both for conventional chromatographic methods as well as SIFT-MS, so the air is a relatively complex matrix of trace analytes (typical concentrations in the tens to hundreds of ppbv).

	With the SIFT-MS instrument analyzing the laboratory air continuously (a measurement for each compound being acquired approximately every 3 s), various combinations of 

-xylene and ethylbenzene (pure samples of each, and a mixture) were introduced from 20-mL headspace vials. Vials were shaken and then uncapped and recapped after a few minutes. After a few minutes, the laboratory windows were opened to ventilate the room before the next sample was opened.

	Figure 2 shows the results obtained for ethylbenzene and 

-xylene. The total concentration of ethylbenzene and 

 reagent ion, whereas speciated ethylbenzene and xylenes concentrations were obtained using O

. It is clear that the first exposure was xylenes, followed by ethylbenzene and then a 50:50 mixture of the two. Note that an additional 11 compounds (including benzene and toluene) were monitored concurrently, but these are not shown here for clarity.

 Separation of ethylbenzene and xylenes concentrations using O

 with the total concentration (ethylbenzene + xylenes) superimposed. 

	The high robustness of SIFT-MS analysis to humidity enables the technique to be applied to direct headspace analysis of BTEX and other hydrophobic volatiles (for example, chloroform) without any need for water stripping (that is, no purging, trapping, and drying).

	Linear detection of BTEX and chloroform in the headspace of standard aqueous solutions (2.5–1000 ppbv in solution) is shown in Figure 3. Note that these data were generated without using an internal standard and exhibit excellent repeatability (Table III), with relative standard deviations (RSDs) better than 4% for solutions prepared at 250-nL/L (250 ppbv) concentrations. For this method, the limit of detection (LOD) is 0.3 nL/L and the limit of quantitation (LOQ) is 1 nL/L. These values are equivalent to an LOD of 0.26 µg/L for BTEX and 0.45 µg/L chloroform and an LOQ of 0.87 µg/L for BTEX and 1.5 µg/L chloroform.

 Linear detection of benzene, toluene, ethylbenzene, 

-xylene, and chloroform from 2.5 to 1000 ppbv by volume in solution.

	Ethylbenzene and the xylenes are very effectively differentiated in these headspace measurements (Figure 3). Notice that the ethylbenzene headspace concentrations are slightly higher than those of 

-xylene, despite the standard being a 50:50 mixture. The higher benzene headspace concentrations result from a combination of water solubility and boiling point (benzene: 1.8 g/L, bp = 80 °C; toluene: 0.5 g/L, bp = 110 °C; ethylbenzene: 0.15 g/L, bp = 136 °C; 

-xylene: 0.16 g/L, bp = 139 °C; chloroform: 8 g/L, bp = 60 °C). The order of BTEX compounds is therefore exactly as predicted on the basis of their physicochemical properties, whereas the position of chloroform arises because of its higher water affinity, despite its lower boiling point.

	With current automation technology, which was optimized for GC–MS, we see a 3.5-fold increase in sample throughput when compared to the standard headspace GC–MS methods.

	Significant levels of BTEX compounds can be found in soil because of pollution events-gasoline spills and leakage of underground storage tanks are some common causes. Various extraction approaches have been tried in the past, such as vapor partitioning. However, methanolic extractions are far more robust for extraction and recovery of VOCs from soil (7).

	Methanolic extraction can also be applied with SIFT-MS when the NO

 reagent ion can be used for detection of target compounds, because of the slow reaction rate for the NO

 + methanol reaction. This constraint arises because H

 both react rapidly with methanol, whereas NO

 reacts much more slowly (over 100-fold slower). All of the BTEX compounds are sensitively detected with NO

 (see Table II); benzene has two product ions with NO

, whereas toluene and the sum of ethylbenzene and the xylenes have one product ion. Because O

 cannot be used with methanolic extraction, only a total concentration of ethylbenzene plus xylenes can be achieved.

	Results for SIFT-MS headspace analysis following automated methanolic extraction of BTEX from spiked soil samples are shown in Figure 4. Linear detection is achieved using SIFT-MS, even in the presence of over 1000 ppmv of methanol in the headspace. Note that, for clarity, Figure 4 is plotted in terms of the spiked individual concentrations of ethylbenzene and 

-xylene, but only the total is measured here because of residual methanol overwhelming the O

 reagent ion signal, so speciation cannot be achieved.

 Linear detection of benzene, toluene, and the sum of ethylbenzene and the xylenes in spiked soil over the range 16-340 ppb following methanolic extraction. See the text for more details.

	Table IV shows the results obtained for calibration standards and the various spiked soil samples in the sequence that they were run. Spike levels in parts per billion are applicable to all samples; the indicative mass per gram is calculated for each compound. RSDs for the calibrations are excellent and recoveries of 75–111% are all acceptable. For the experimental conditions used here, the LOQ and LOD are 40 ng/g and 12 ng/g, respectively. There is potential to achieve lower LOQs and LODs through better optimized extraction conditions.

	This study has achieved a twofold increase in sample throughput for SIFT-MS when compared with GC–MS analysis using the current sequential sample preparation and analysis method. It may be possible to further increase this by using the multiple positions available with the autosampler modules and by batch-preparing the samples.

	Monitoring isomers of aromatic hydrocarbons at low concentrations has traditionally been the domain of chromatographic methods to first achieve separation between the isomers followed by flame ionization or mass spectrometric detection. We have shown in this work that the DMS technique of SIFT-MS has enabled excellent differentiation of the ethylbenzene isomer from the xylene isomers using known O

 reagent ion chemistry for both air and water. This approach can be applied in real time and in high-throughput applications both in testing and contract laboratories through to continuous process monitoring applications.

	Future work will include fully validation of air, aqueous, and soil methods over the full linear range accessible to the SIFT-MS technique. It will also investigate multiple headspace extraction (MHE) as a means to directly analyze volatiles from soil without solvent extraction, thus overcoming the current impediment to resolving ethylbenzene and xylenes: methanol residue consuming the reagent ion.

	(2) C.S. Chen, Y.C. Hseu, S.H. Liang, J.-Y. Kuo, and S.C. Chen, 

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	(8) C.-W. Tsao, H.-G. Song, and R. Bartha, 

	(9) United States Environmental Protection Agency. Compendium of methods for the determination of toxic organic compounds in ambient air using active sampling on to sorbent tubes. Compendium method TO-17. 1999. US Environmental Protection Agency, Cincinnati, Ohio.

	(10) United States Environmental Protection Agency. Compendium of methods for the determination of toxic organic compounds in ambient air. 2nd edition. Compendium method TO-15. 1999. Cincinnati, Ohio.

	(11) United States Environmental Protection Agency. Volatile organic compounds in water by purge and trap capillary column gas chromatography with photoionization and electrolytic conductivity detectors in series. Method 502.2. 1999. Munch, J.W. (Ed). Cincinnati, Ohio.

	(12) T. Nilsson, F. Pelusio, L. Montanarella, B. Larsen, S. Facchetti, and J.O. Madsen, 

	(13) See the NIOSH International Chemical Safety Cards (

https://www.cdc.gov/niosh/ipcsneng/nengnameA.html

	(16) B.J. Prince, D.B. Milligan, and M.J. McEwan, Rapid Commun. 

	(17) V.S. Langford, I. Graves, and M.J. McEwan, 

	(18) D. Hera, V.S. Langford, M.J. McEwan, and T.I. McKellar, 

 is with Anatune Limited, Cambridge, United Kingdom. 

 is with Syft Technologies Limited, Christchurch, New Zealand. 

 is with Syft Technologies Limited, Christchurch, New Zealand and the Department of Chemistry at the University of Canterbury, Christchurch, New Zealand. Direct correspondence about this article to: 

For the BTEX compounds, detection limits in the single-digit parts-per-billion concentration range (by volume) are readily achievable within seconds using selected ion flow tube mass spectrometry (SIFT-MS), because sample analysis is achieved without
chromatography, preconcentration, or drying.

Vaughan S. Langford

Articles

Rapid Classification of Beef Aroma Quality Using SIFT-MS

High-Throughput Analysis of Volatile Compounds in Air, Water, and Soil Using SIFT-MS