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May 2011

Metabolomics in Food Science

GC–MS Analysis of High-Molecular-Weight Phthalates in Sediments

Vitamin D Analysis with Multiplexed LC–MS and On-Line SPE

Evaluating S/N as a Measure of MS Performance

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6 Current Trends in Mass Spectrometry May 2011 www.spec t roscopyonl ine .com

Single Multipoint Calibration Curve for Discovery Bioanalysis 8

Benjamin Begley and Michael Koleto

A single calibration curve run with staggered calibrants bracketing the unknowns is compared to running complete duplicate calibration curves, one at the beginning and one at the end of unknown sample analysis in an effort to accelerate discovery bioanalysis.

High-Throughput Quantitative Analysis of Vitamin D Using a Multiple Parallel 12LC–MS System Combined with Integrated On-Line SPE

Adrian M. Taylor and Michael J. Y. Jarvis

A newly developed high-throughput method for the quantitation of vitamin D using both multiplexed LC and on-line SPE is discussed.

A New Path to High-Resolution HPLC–TOF-MS — Survey, Targeted, and Trace Analysis 18Applications of TOF-MS in the Analysis of Complex Biochemical Matrices

Jeffrey S. Patrick, Kevin Siek, Joe Binkley, Viatcheslav Artaev, and Michael Mason

The impact of speed of analysis and selectivity to the depth of coverage and accuracy of the analyses are discussed.

Why Use Signal-To-Noise As a Measure of MS Performance When It Is Often Meaningless? 28

Greg Wells, Harry Prest, and Charles William Russ IV

Signal-to-noise of a chromatographic peak from a single measurement has been used determine the performance of two different MS systems, but this parameter can no longer be universally applied and often fails to provide meaningful estimates of the instrument detection limits (IDL).

Food Metabolomics: Fact or Fiction? 34

Leon Coulier, Albert Tas, and Uwe Thissen

The different aspects of food metabolomics are described using tomato taste as an example.

Determining High-Molecular-Weight Phthalates in Sediments Using GC–APCI-TOF-MS 42

Frank David, Pat Sandra, and Peter Hancock

Gas chromatography combined with atmospheric-pressure chemical ionization (APCI) was used to analyze high-molecular-weight phthalates.

Preview of the 59th Annual ASMS Conference 46

Megan Evans

A preview of the this year’s ASMS Conference, taking place June 5–9 in Denver, Colorado.

May 2011

DepartmentsProduct Resources 48

Ad Index 50

Cover image courtesy of Getty Images/Indeed.

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www.spec t roscopyonl ine .com8 Current Trends in Mass Spectrometry May 2011

Benjamin Begley and Michael Koleto

Compensation for divergence during an analytical batch is a primary motive for including duplicate calibration curves that bracket the unknown samples being measured. The drawback is that this adds time to the analysis. A single calibration curve run with staggered calibrants bracketing the unknowns was compared to running complete duplicate calibration curves, one at the beginning and one at the end of unknown sample analysis in an effort to accelerate discovery bioanalysis. The data, in terms of correlation coefficient (r2) and the weighted residuals plot, show that a single cali-bration curve, with staggered calibrants bracketing the unknown samples, can be used successfully to accelerate discovery (nonregulated) bioanalysis.

Single Multipoint Calibration Curve for Discovery Bioanalysis

Designing a robust and reproducible generic liquid chro-matography–tandem mass spectrometry (LC–MS-MS) method that can support discovery (nonregulated) bio-

analysis of different molecules on a daily basis requires balancing scientific rigor with adequacy. Data integrity remains the critical objective. The only assumptions made in the discovery environ-ment are in regard to extraction efficiency, stability, and response function. In essence, the generic method has to be good, or “ad-equate,” enough such that reliable information can be quickly re-turned to facilitate the decision making process.

The minimum acceptance criteria are a measure of systematic bias, or accuracy, and a measure of random error, or precision, the combination of which is a measure of total assay error (1). In addi-tion to these requirements, reproducibility, sensitivity, and selectiv-ity are the other key factors needed to cover the typical 1–10,000 ng/mL range commonly required in discovery pharmacokinetic (PK) bioanalysis. These factors are constrained by typical chro-matographic run times of 2 min or less and a minimal sample cleanup step that is usually protein precipitation–based for in-vivo applications. All of this adds to the challenge of developing robust generic LC–MS-MS methods.

The Calibration Curve — Regression Analysis ConsiderationsThe main role of LC–MS-MS bioanalysis PK studies is to provide drug concentration data for an accurate indication of the new chemical entity (NCE) exposure time profiles. The standard curve serves as a reference line against which the concentrations of the unknown samples are calculated. The standard curve itself is a result of regression analysis that takes into consideration the mean values of all x (concentration) and Y (area ratio) and the line of best fit through each data point to attempt to account for random error.

The most important consideration for bioanalysis spanning a broad range (1–10,000 ng/mL) is the choice of weighting factor. This is because LC–MS-MS bioanalytical data in almost all cases is heteroscadastic, a statistical term used for data in which the stan-dard deviation changes within the range of measurements. In other words, the error is more or less proportional to concentration, and without proper weighting, the regression is inherently biased by the highest concentration (2). This is especially relevant in discovery bioanalysis where the high point on the curve can be as great as four orders of magnitude away, inherently introducing bias at the critical low end, near the limit of quantitation (LOQ), of the curve. Additionally, the coefficient of determination, or r2 (regression coef-ficient), actually can be misleading and often is a poor measure of curve fit quality because it fundamentally assumes constant error at all concentrations, which is never the case in bioanalysis (3).

It is important to determine the best weighting factor, espe-cially when covering the wide dynamic range typical in discovery bioanalysis. Statistically, the 1/x2 weighting provides the best fit because it most correctly approximates the variance at the low end of the curve, and by doing so, normalizes the error across the range. This effectively allows for the best fit (2). Because unknown samples are bracketed between two standard curves (one run at the front and one run at the back), the weighting factor chosen also has to account for divergence. The divergence is especially impor-tant in LC–MS-MS as factors affecting response are continually changing. At this stage, stability of the NCE is still undetermined, column performance can degrade over time, and ion-source ef-ficiency can be affected as the source becomes coated with sample components. All of these are typical considerations in discovery bioanalysis.

Running two curves to test for divergence, commonly referred to as standard technique (Figure 1b), takes time — and time is an

www.spec t roscopyonl ine .com May 2011 Current Trends in Mass Spectrometry 9

important factor in discovery bioanalysis. Another approach to estimate divergence is to run calibrants across the same range in a scattered manner at the beginning and the end of the sample analysis, referred to as staggered technique (Figure 1a). In this case, calibrants at concentrations 1, 5, 50, 500, and 5000 ng/mL are run at the beginning and then 1, 2.5, 10, 100, 1000, and 10,000 ng/mL are run at the end. The 1-ng/mL calibrant is run twice because it

is most susceptible to divergence (exceed-ing the ±20% CV criteria at the LOQ). This article attempts to correlate regression data obtained from the use of a single staggered calibration curve and compare it to the data obtained from the standard approach of running two full calibration curves, one at the beginning and one at the end of sam-ple analysis. The goal of the experiment is to accelerate discovery bioanalysis without compromising data integrity.

ExperimentalDiphenhydramine (C17H21NO, FW: 256.2) was used as the test compound, with reserpine (C33H40N2O9, FW: 609.3; 10 ng/mL) as the analog inter-nal standard (IS). Calibrants were di-luted using K2-EDTA rat plasma, and the calibration standards were seri-ally diluted using a Hamilton Starlet Liquid Handler (Hamilton Co., Reno, Nevada).

Figure 1: (a) Staggered multipoint calibration curve set-up. Calibrants are prepared from a single stock solution and no QCs are used. The 1 ng/mL

sample is repeated twice to ensure the LOQ criteria are met. (b) Standard method using duplicate standard (STD) curves and quality control samples

(QCs). The QCs and STDs are from two independent stock solutions to ensure the integrity of the regression.

(a) (b)


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Figure 3: The plot of the weighted residuals for the (a) staggered and (b) standard techniques. This plot is perhaps most useful in determining divergence as a result of the impact of sensitivity. Note the scale on the y-axis for the LOQ. Based on these data, a single calibration curve using the staggered technique provides comparable data and can be used in discovery bioanalysis.

Figure 2: Correlation coefficient (r2) results for the (a) staggered (r2 = 0.998) and (b) standard (r2 = 0.999) multipoint calibration curves, respectively. No difference is observed between these two regression techniques. Note that r2 can be a misleading indicator as it assumes a constant bias for each weighted point across the bioanalytical dynamic range, which is seldom the case.

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Plate 1 (Figure 1a) contained the multi-point staggered calibrants. Calibrants at 1, 5, 50, 500, and 5000 ng/mL were placed in the front of the plate, plasma blanks were placed in the middle, and calibrants 1, 2.5, 10, 100, 1000, and 10,000 ng/mL were placed at the end. Plate 2 (Figure 1b) contained duplicate sets of the calibrants in a low-to-high series, bracketing 50 plasma blanks in the middle, with interspersed quality control samples (QCs) at low, mid, and high concentra-tions. Both plates were repeated 10 times and the calibration curves were compared for divergence.

A model 1100 LC pump (Agilent Tech-nologies, Santa Clara, California) was used to perform the chromatography with a CTC PAL autosampler (CTC, Basel, Switzerland). Mobile phase A was water (0.2% formic acid) and mobile phase B was acetonitrile (0.2% formic acid). A gradient was structured as follows, with starting conditions set at 5% B at 0 min and 45% B in 0.01 min with a flow rate of 0.8 mL/min. The % B was ramped to 95% in 1.4 min; the flow rate was increased to 1.3 mL/min. After holding these conditions for 0.2 min, the gradient was ramped back down to the starting conditions. The total run time was 1.8 min. The column used was an 30 mm × 2.1 mm, 3-µm Atlantis C18 col-

umn (Waters Corp, Milford, Massachusetts). Analysis was performed on a TSQ Quantum Ultra mass spectrometer using atmospheric-pressure chemical ionization (APCI, Thermo Fisher Scientific, San Jose, California) in the MRM mode using unit resolution in Q1 and Q3. The scan time for both transitions was 0.2 s (resperine, m/z 609→195 and diphen-hydramine, m/z 257→167).

Results and DiscussionFigures 2a and 2b show the results for the staggered and standard multi-point calibra-tion curves, respectively. Both techniques use linear r2 (0.998 vs. 0.999) regression coefficients (coefficients of determination) and thus can be considered comparable. Al-though reporting r2 is conventional practice, one should take into consideration that the regression coefficient assumes that error is fairly constant across the bioanalytical range, which is seldom the case in bioanaly-sis. A better way to compare the techniques is to look at the plot of the weighted residu-als (Figures 3a and 3b), which is useful in determining divergence as a direct conse-quence of analytical sensitivity. Based on these graphs, the single calibration curve run in a staggered manner is comparable to duplicate calibration curves. The data

thus indicate that the information content required for rigorous quantitation is rela-tively unaffected and that running a single calibration curve in a staggered setup can be used in discovery bioanalysis without com-promising data integrity. One can almost argue that using a single calibration curve in a staggered manner is perhaps more rig-orous in terms of adhering to guidelines that require simple statistical models (4). The larger question remains whether running a duplicate curve at the end of the sample analysis adds any significant value to the experimental end-point, which often de-termines bioavailability for an NCE.

ConclusionStatistically, the 1/x2 weighting provides the best fit because it most correctly approxi-mates the variance at the low end of the curve and by doing so, normalizes the error at the high, low, and mid levels. No practical difference in the divergence was observed using the staggered technique in terms of r2 (0.998 vs. 0.999), slope (1.089 vs. 1.077), or during the comparison of the weighted residuals versus the concentrations (Figures 3a and 3b). This indicates that running a single calibration curve using the staggered technique may be a viable alternative to running duplicate calibration curves.

AcknowledgmentsThe authors would like to thank Rohan A. Thakur for his contribution to this article. The authors also wish to thank Richard Le-lacheur, Vice President, PharmaNet USA for his input and discussions.

References(1) S. Bansal and A. Destefano, AAPS 9(1),

E109–E114 (2007).

(2) A.M. Almeida et al., J. Chrom. 215–222

(2002).

(3) M. Kiser and J.W. Dolan, LCGC Europe

17(3), 138–143 (2004).

(4) “Guidance for Industry: Bioanalytical

Method Validation,” US FDA (2001).

Benjamin Begley is Analytical Chemist

and Michael Koleto is Associate Director

with PharmaNet in Princeton, New Jersey. ◾

For more information on this topic, please visit our homepage at: www.spectroscopyonline.com

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High-Throughput Quantitative Analysis of Vitamin D Using a Multiple Parallel LC–MS System Combined with Integrated On-Line SPE

Adrian M. Taylor and Michael J. Y. Jarvis

Recently, liquid chromatography–tandem mass spectrometry (LC–MS-MS) has emerged as a reliable method to perform 25-OH-vitamin D3 and 25-OH-vitamin D2 analysis. Because sample preparation and LC separa-tion are usually the most time consuming steps in a typical quantitative LC–MS-MS analysis, we have developed a high-throughput method for the quantitation of vitamin D using both multiplexed LC and on-line SPE. The overlapping LC runs and targeted data acquisition, combined with online sample preparation, result in a higher overall throughput for the system. The method described here is for the routine analysis of 25-OH-vitamin D3 and D2 (25-hydroxycholecalciferol and 25-hydroxyergocalciferol) in human serum and plasma samples. Calibration is performed using a lyophilized, multilevel plasma calibrator set of known concentration. Lyophilized plasma control samples at clinically relevant low and high concentrations serve to establish the target analyte range. The method uses deuterated 25-OH-vitamin D3 as an internal standard to correct for sample and instrument variability. Samples are analyzed using the atmospheric-pressure chemical ionization (APCI) mode for maximum sensitivity.

Vitamin D comprises a group of fat-soluble vitamins involved in the regulation of calcium and phos-phorus levels in the body (1). Research has shown

that vitamin D deficiencies are linked to colon and breast

cancers (2), impaired bone mineralization, rickets in children, and possibly osteoporosis in adults (3). The two major forms are vitamin D3 (cholecalciferol) and vitamin D2 (ergocalciferol). Vitamin D3 is produced in


Degasser Degasser

Pump B1

Pump A1+ CBM

Pump B2

Pump A2+ CBM

Columnoven

Loadingpump

Figure 1: Integrated multiplex LC–MS-MS system configuration and module arrangement used in

the analysis of 25-OH-vitamin D3 and 25-OH-vitamin D2.

the skin upon exposure to ultraviolet B radiation from sunlight, or may be obtained from nutritional sources, especially fatty fish. Vitamin D2 is not synthesized by the human body and must be obtained from plant and other nutritional sources. Vitamin D3 is converted to 25-OH-vitamin D3 in the liver and then further hy-droxylated to 1,25-OH-vitamin D3, the major biologically active hor-mone, in the kidneys. Vitamin D is stored in the body as 25-OH-vitamin D, which is the most commonly mea-sured metabolite for determining an individual’s vitamin D levels.

Tr ad it iona l l abor ator y-ba sed testing for 25-OH-vitamin D3 and 25-OH-vitamin D2 is per formed using radioimmunoassay (R IA), chemiluminescence immunoassay, or high performance liquid chro-matography (HPLC)–UV. Recently, l iqu id chromatog raphy–ta ndem mass spectrometry (LC–MS-MS) has emerged as a reliable method to perform 25-OH-vitamin D3 and

25-OH-vitamin D2 analysis (4). To improve t he t hroughput of LC–MS-MS analysis, we have developed a rapid method for the quantifica-tion of these analytes using a sys-tem that has been specif ically de-

signed to significantly increase the throughput of routine assays by ac-celerating both the sample prepara-tion and the LC analysis. The system synchronizes autosampler injection ports , LC pumps, selector va lve,

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Figure 2: Plumbing diagrams and solvent flow paths at typical states of a multiplex run. Upper left: loading stream 1, eluting stream 2, acquiring

stream 2; upper right: loading stream 2, eluting stream 1, acquiring stream 1; lower left: eluting stream 1, eluting stream 2, acquiring stream 1; lower

right: eluting stream 1, eluting stream 2, acquiring stream 2.

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Figure 3: Method pane of the MPX Driver software.

and a mass spectrometer, enabling the autosampler to inject samples into two parallel LC streams using a system-defined timing schedule. The mass spectrometer acquires

data throughout a specified retention time window in each LC sample run. The system also controls a series of switching valves, which are used for on-line solid-phase extraction (SPE)

and the subsequent liquid chroma-tography analysis. The overlapping LC runs and targeted data acquisi-tion, combined with online sample preparation, result in a higher over-all throughput for the system. This application demonstrates how the multiplex system with integrated on-line sample cleanup can be used to double the throughput of the vitamin D analysis by LC–MS-MS.

ExperimentalAn MPX-2 SP High-Throughput Sys-tem (AB Sciex, Foster City, Califor-nia) with on-line SPE was used for the analysis of 25-OH-vitamin D3 and D2 . The integrated multiplex LC–MS-MS system consisted of a 4000 QTR AP mass spectrometer (AB Sciex), two Prominence XR LC systems (Shimadzu, Kyoto, Japan), a CTC PAL autosampler (CTC Ana-lytics, Zwingen, Switzerland) with DLW option, a pump containing a four-solvent selection valve for sam-ple loading and cleanup, and f ive switching valves for f low path con-trol (Figure 1). The two chromato-


Loading(60 s)

Retention window

Safe time (5 s)

Ready to start the nextrun in stream 1

Ready to start the nextrun in stream 2

Loading(60 s)

Equilibrating(12 s)

Equilibrating(12 s)

A B C F

D E G I J

H

Figure 4: Sequence of timed events during a typical multiplex run.

Figure 5: Representative chromatograms on stream 1 and 2. Left: Chromsystems calibrator level

1 at 19.2 ng/mL (47.8 nmol/L) 25-OH-vitamin D3 and 15.5 ng/mL (37.5 nmol/L) 25-OH-vitamin

D2. Right: Chromsystems control level 1 at 16.7 ng/mL (41.7 nmol/L) 25-OH-vitamin D3 and 17.4

ng/mL (42.1 nmol/L) 25-OH-vitamin D2.

graphic channels in the multiplex system share a single high-pressure loading pump, which provides ad-ditional f lexibility for injection and wash solvent composition. All hard-ware modules are controlled by An-alyst 1.5.1 software (AB Sciex) with the MPX Driver Software 1.1 (AB Sciex). Throughout the analysis, pre-cise timing for the switching valves allowed each LC stream to perform interleaved injection, on-line sample cleanup, and LC gradient elution. Targeted MS data acquisition was en-abled by the selection of a retention time window around the chromato-graphic peaks of interest using the MPX Method Editor. Figure 2 shows common f low paths for a typical on-line sample cleanup and LC–MS-MS analysis.

Sample PreparationLyophilized calibrators and controls were obtained from Chromsystems (Munich, Germany), and were recon-stituted in distilled water. A 100-µL volume of each sample-calibrator-control was pipetted into a 1.5-mL reaction vial. Next, 25 µL of a pre-cipation reagent and 200 µL of an in-ternal standard solution were added, and the samples were mixed using a vortex mixer for at least 20 s to pre-cipitate the proteins. Samples were then incubated for 10 min at 4 °C in a water–ice bath, and centrifuged at 15,000g for 10 min. The clean, pre-cipitate-free supernatant was trans-ferred to an HPLC via l equipped with a 200-µL insert for analysis by LC–MS-MS.

HPLCChromatographic separation was achieved using the analytical col-umn included in the Chromsys-tems MassChrom reagent k it for the LC–MS-MS analysis of 25-OH-vitamin D3/D2 in serum or plasma. Mobile phases A and B were used as provided in the kit. The injected sample was initially loaded onto a trap column and rinsed for 1 min using mobile phase A. After 1 min, the sample was eluted from the trap column onto the analytical column,

Table I: Accuracy and precision for analysis of 25-OH-vitamin D3

Sample Name%CV (n = 6) Accuracy %

Stream 1 Stream 2 Stream 1 Stream 2

Cal 1 2.8 2.4 99.5 104.3

Cal 2 3.0 3.7 103.0 99.2

Cal 3 2.9 3.5 96.1 93.2

Cal 4 2.5 2.1 107.6 102.4

Cal 5 2.1 2.0 97.2 101.2

QC 1 3.9 6.0 105.2 100.0

QC 2 1.8 5.6 105.4 85.9


using mobile phase B. The duration of the entire run was 5 min.

MS-MS

MS detection was carried out using the integrated multiplex LC–MS-MS system operating in multiple reaction monitoring (MRM) mode. Atmo-spheric-pressure chemical ionization (APCI) was used, in positive polar-ity mode, for maximum sensitivity.

The MRM transitions 383.4/211.3 and 395.3/269.3 were used to detect 25-OH-vitamin D3 and 25-OH-vita-min D2, respectively. Both Q1 and Q3 were operated at unit resolution.

Results and Discussion

A multiplex LC method was created using the hardware module control software, which synchronizes sample injections in staggered LC runs and en-

ables targeted MS data acquisition in a predetermined retention time window on both of the parallel LC streams. The retention time window to be used for MS data acquisition was specified by highlighting the chromatographic re-gion of interest (Figure 3).

A conventional singleplex LC–MS-MS analysis of 25-OH-vitamin D3 and 25-OH-vitamin D2 was performed, consisting of 92 consecutive injections, and the run time was then compared to that of the equivalent multiplex LC–MS-MS analysis of 92 samples using the integrated multiplex LC–MS-MS system. The sequence of timed events during the multiplex run is illustrated in Figure 4. The use of Cliquid software (AB Sciex) enabled automated sample analysis, processing of acquired data, and reporting of results. The total run time for the singleplex analysis was 8 h 26 min. In comparison, the run time for the multiplex analysis was 4 h 19 min, thus saving 4 h 17 min and increasing overall throughput 1.9 times.

Stream 1

Stream 2

25-OH Vitamin D3, 19.2 – 69.7 µg/L

(R=0.9961)

2010_04_08_174914_0001.rdb (25-OH-vitamin D3): “Linear” Regression (1”/X” weighting): y = 0.882 x + 0.0236 (r=0.9961)

1.25

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An

aly

te

Are

a / IS

Are

a

25-OH Vitamin D3, 19.2 – 69.7 µg/L

(R=0.9973)


1.3

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aly

te

Are

a / IS

Are

a

25-OH Vitamin D2, 15.5 – 60.4 µg/L

(R=0.9987)


1.19

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aly

te

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a / IS

Are

a

25-OH Vitamin D2, 15.5 – 60.4 µg/L

(R=0.9992)


1.25

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An

aly

te

Are

a / IS

Are

a

Figure 6: Standard calibration curves for 25-OH-vitamin D2 and 25-OH-vitamin D

3.

Table II: Accuracy and precision for analysis of 25-OH-vitamin D2

Sample Name%CV (n = 6) Accuracy %

Stream 1 Stream 2 Stream 1 Stream 2

Cal 1 2.2 5.3 103.0 97.9

Cal 2 3.6 1.7 99.6 106.3

Cal 3 3.7 4.1 97.4 92.7

Cal 4 2.8 2.3 103.4 104.0

Cal 5 4.0 3.0 98.2 99.8

QC 1 2.8 4.2 104.5 96.6

QC 2 4.0 3.8 107.6 97.2



The quality of the results was in no way compromised when the analysis was performed on parallel channels using the integrated multiplex LC–MS-MS system. Rep-resentative chromatograms for Calibrator 1 and Control 1 are shown in Figure 5, and the %CVs and accuracies obtained for the calibrators and controls (analyzed on both streams) are summarized in Tables I and II. The coefficient of variation obtained for the analysis of the calibrators and controls, using on-line sample clean-up, were all less than 10%, and the accuracies ranged from 85.9% to 107.6% for both 25-OH vitamin D3 and 25-OH vitamin D2, on both stream 1 and stream 2.

The calibration curves in Figure 6 were generated using the Chromsystems plasma calibrators, with the low- and high-level controls. The calibration curves were produced on both LC streams, for both 25-OH vitamin D3 and 25-OH vitamin D2, and demonstrate the range and linearity obtained for the assay. These results dem-onstrate that the data integrity was maintained when adapting the singleplex on-line SPE LC–MS-MS method to the integrated multiplex LC–MS-MS system.

Conclusions

The abi l ity to multiplex two LC systems, synchro-nized to a single tandem mass spectrometer, a l lows laboratories to increase throughput to handle increas-ing numbers of samples. The dual channel multiplex LC–MS-MS system, with integrated and automated on-line sample cleanup, achieved a twofold increase in throughput for the analysis of 25-OH-vitamin D2 and 25-OH-vitamin D3 when compared to the same ana lysis performed on a conventiona l LC–MS-MS system with on-line SPE. This allows for considerable savings in both time and cost. No compromise in data quality, accuracy, or reproducibility was observed when the identical analysis was transferred from a singleplex to a multiplex system.

References

(1) Dietary Supplement Fact Sheet: Vitamin D. National

Institutes of Health. Office of Dietary Supplements.

http://dietary-supplements.info.nih.gov/factsheets/

vitamind.asp

(2) M.F. Holick, Am. J. Clin. Nutr. 80(6), 1678–1688 (2004).

(3) K. Rajakumar, Pediatrics 112(2), 132–135 (2003).

(4) Z. Maunsell, D.J. Wright, and S.J. Rainbow, Clin. Chem. 51(9),

1683–1690 (2005).

Adrian M. Taylor and Michael J. Y. Jarvis are with AB

Sciex, 71 Four Valley Drive, Concord, Canada L4K 4V8. ◾

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A New Path to High-Resolution HPLC–TOF-MS — Survey, Targeted, and Trace Analysis Applications of TOF-MS in the Analysis of Complex Biochemical Matrices

Demands on modern analytical chemistry are driven by enhanced information content, faster analyses, faster data processing, and higher throughput.

Tools in separation science continue to increase the speed and information content of traditional analyses as dem-onstrated by the growing popularity of ultrahigh-pressure liquid chromatography (UHPLC) (1), fast gas chromatog-raphy (GC) (2), and GC×GC (3). This creates demands for

faster data acquisition and higher fidelity data, which in mass spectrometry (MS) translate to mass accuracy and resolution. To address these demands, the mass resolu-tion, mass accuracy, and data acquisition speeds of mass spectrometry have been pushed to improve instrument capabilities. A historical overview of mass analyzers is provided in Table I with the representative performance attributes provided.

High performance time-of-flight mass spectrometry (TOF-MS) is applied to the analysis and characterization of complex biological samples. High mass accuracy, high resolu-tion, and accurate relative isotope abundance are all applied to the determination of analytes covering a range of concentrations in complex matrices including plasma and urine. Qualitative and quantitative evaluations are provided and include demonstrations of the impact of high-performance MS on sensitivity and selectivity. The ability to lever-age high-performance MS in conjunction with a broad dynamic range in rapid ultrahigh-pressure liquid chromatography (UHPLC) analyses to identify unknowns and to propose putative metabolic biomarkers is demonstrated. The impact of speed of analysis and selectivity to the depth of coverage and accuracy of the analyses are discussed.

Jeffrey S. Patrick, Kevin Siek, Joe Binkley, Viatcheslav Artaev, and Michael Mason


The utility and value of high-resolu-tion, high mass accuracy MS has been emphasized in numerous recently re-ported applications of the technology (4–9). High-resolution MS has been the domain of Fourier-transform (FT) MS and magnetic-sector instruments for decades. The paradigm is shifting away from just high-resolution MS to high-performance MS that includes mass re-solving power, mass accuracy, isotope abundance, and acquisition speed in its considerations. New developments in high-performance mass spectrom-eters have shown significant impact. Time-of-f light (TOF) instruments with higher resolution and enhanced performance are a significant portion of the efforts. Recent advances in TOF mass spectrometers have enabled these instruments to provide high resolution and mass accuracy with speed, simplic-ity, and convenience. The measurement of mass-to-charge ratio (m/z) in a TOF analyzer is described by the following equation:

t = D(m/2z)1/2

in which t is time, D is the f light distance, m is the mass, and z is the charge on the ion. This provides the fundamental relationship that t is pro-portional to D, and in turn, resolution (defined as [m/δm] or [t/2*δt]) is also a function of D and t. This has been one of the challenges to TOF — to achieve long flight paths in reasonable physical space and provide high performance. A number of key advances have oc-curred over the past two decades that have improved the performance of TOF mass analyzers and have included orthogonal acceleration (10), high-

speed electronics, delayed extraction (10), and pulsed ionization (11), which permit the most effective utilization of the f light path available. In spite of these advances, TOF mass analyz-ers are highly sensitive to the initial conditions of the ions as they enter the mass analyzer. This includes distribu-tions in velocity, time, and space dur-ing ion generation and at entry to the mass analyzer. To provide an extended f light path and address initial ion dis-persion, electrostatic time-focusing elements such as reflectrons (12) have been implemented, improving resolu-tion significantly.

However, these approaches are lim-ited by dispersions and higher order aberrations, as well as f light lengths on the order of a few meters. Other efforts have attempted to extend the f light path using cyclotron and tor-roidal analyzers (13). Duty cycle and effective mass range are typical com-promises to high levels of mass mea-surement performance. With these advances, TOF has achieved a posi-tion in the realm of mass analyzers in which it provides sensitivity, mass accuracy and resolution that surpass some magnetic sector systems and encroach on the analyses historically reserved for FTMS systems. The prin-ciple advantage of a TOF system is the absence of scanning. This provides a mass measurement system well suited to analyte surveys rich in information content and high in duty cycle. These advances have opened the world of high resolution accurate mass analy-sis, historically the domain of FTMS and magnetic sectors, to TOF mass analyzers with the added benefit of simplicity and speed. The significant

advances of TOF mass analyzers to-ward high-performance mass spec-trometers continue in the approach discussed below.

The New TechnologyIn one of the most recent advances in TOF-MS, Verentchikov and colleagues (14,15) have introduced the multire-f lecting time-of-f light analyzer that uses a Folded Flight Path (FFP). This is depicted in Figure 1, which shows how the ions effectively pass between each of two planar ion mirrors and through an Einzel lens array. A major challenge in the multireflecting systems is poor transmission because of defocusing of ion trajectories. In the FFP configu-ration, transmission is improved by using nonlinear electrostatic fields in the gridless mirrors. The ions are constantly refocused as they traverse the flight path and create the extended f light path needed to enhance the res-olution. The gridless design minimizes ion loss during the f light. The mass analyzer offers three effective f light paths of approximately 2-, 20-, and 40- m lengths with operations in the same planar flight “tube.” These operational options are depicted in Figure 2. The number of passes or reflections deter-mines the effective pathlength, and by inference, the available resolving power. The performance capabilities of this system are provided in Table I.

This design is applied to the analy-sis of analytes in complex matrices in the form of UHPLC coupled to atmospheric pressure ionizat ion with TOF-MS. The matrices include plasma and urine, and the analytes include compounds of pharmaceuti-cal interest and naturally occurring

Table I: Overview of high-performance mass spectrometers

Platform Resolving Power Mass Accuracy Acquisition RateIsotopic

AbundanceMass Range

Magnetic sector >50,000 <100 ppb <10 spectra/s Excellent Limited

Fourier transform (includes both magnetic and electrostatic traps)

>106<10 ppb (trade-off

with acquisition rate and magnet size)

Trade-off with other attributes

including resolving power

Poor (space charge)

Limited by dura-tion, field strength,

and experiment

Time of flight (includes TOF and Q-TOF configu-

rations)

>104 (new systems approaching 105)

1–5 ppm (lower values achievable with special

consideration)

Typically trade-off with resolving

power

Good (limited by dynamic

range)

Limited by pulse frequency and ana-

lyzer attributes


Gridless Mirror

Gridless Mirror

Detector

lon Source

Periodic lon Lenses

Figure 1: Depiction of multireflecting TOF or folded flight path design.

Figure 2: The three modes of operation of the MRT/HRT system. The FFP design facilitates

multiple flight distances for ions, and as such, the potential for multiple levels of resolving power.

Ions pass between electrostatic mirrors and are refocused by central lenses for the length of the

analyzer. Two full passes across the analyzer provide a 40-m flight path (ultrahigh resolution) and

100,00 resolving power, one pass through the entire analyzer provides a 20-m flight path (high

resolution) and 50,000 resolving power, and a partial pass through the analyzer provides a 2-m

path (nominal resolution) and 2500 resolving power. (Note: A = accelerator and D = detector.)

Ultrahigh Resolution High Resolution Nominal Resolution

R = 100,000

706 706.2 706.4 706.6 706.8 707

m/z

707.2 707.4 707.6 707.8 708 706 706.2 706.4 706.6 706.8 707

m/z m/z

707.2 707.4 707.6 707.8 708 690 700 710 720 730 740

4:1 mass rangeR = 50,000

m/z = 50–2500R = 2500

m/z = 50–2500

A

D

A

D

A

D

biological analytes associated with disease states. The applications dis-cussed include the identification and relative quantitation of metabolites in plasma from three strains of Zucker rats, an example of the study of ani-mal models of disease (16), and the analysis for metabolites of common cold medications in human urine.

ExperimentalThe following represent the basic con-ditions used in the analyses described in the results and discussion which fol-low. Chromatographic separation was achieved using an Agilent Technologies (Santa Clara, California) 1290 UHPLC system with mobile phases consisting of water with 0.1% (v/v) formic acid (A)

and acetonitrile with 0.1% (v/v) formic acid (B). Mobile phase was delivered at 0.1–0.2 mL/min and with gradients covering 0–80% B between 3 and 30 min. Injection volumes varied between 1 and 5 µL. The column used was a 50 mm × 1 mm Hypersil Gold C18 AQ (Thermo Fisher Scientific, West Palm Beach, Florida). MS was achieved on a LECO Citius LC-HRT system equipped with a LECO electrospray ionization (ESI) source (LECO Corporation, St. Joseph, Michigan) and operated in high resolution mode (R = 50,000 [FWHM]). Data were processed using Chroma-TOF-HRT software (LECO). Acquisi-tion rates of 2–40 spectra/s were used both with and without in-source colli-sion-induced dissociation. Calibration was achieved using external calibration with Agilent Tune Mix.

Rat plasma samples were obtained from Bioreclamation (Hicksville, New York) and were from lean, fatty, and obese Zucker animals. The plasma was from terminal bleeds with the rats being 7–9 weeks of age. The plasma samples were deproteinated using Mi-crocon (Billerica, Massachusetts) cen-trifugal devices with a 5K cutoff. After protein removal, samples were diluted 5× into aqueous 0.2% heptafluorobu-tyric acid before UHPLC analysis.

Urine was obtained from a healthy male volunteer before and after a single dose of cold medicine containing 6.5 mg doxylamine, 15 mg dextrometho-rphan, and unspecified amounts of polyethyleneglycol (PEG) and other excipients. To remove salt and protein from each sample, a 0.15-mL aliquot was diluted with 1.05 mL methanol and centrifuged. A 1.0-mL aliquot of the clear supernatant was evaporated, reconstituted with 0.025 mL methanol and 0.10 mL water with 0.1% formic acid, and analyzed by UHPLC–TOF-MS under high resolution conditions. All other chemicals used were obtained from Fisher Scientific (Fairlawn, New Jersey) or Sigma-Aldrich (St. Louis, Missouri).

Results and DiscussionRat Plasma Metabolomics: Plasma samples from lean (n = 10), fatty (n = 9) and obese (n = 10) Zucker rats

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Figure 3: Relative response ratios for select analytes in plasma from Zucker lean, fatty, and obese

rats. (a) Full-scale plot; (b) zoomed plot.

7

(a) (b)

2.5

2

1.5

1

0.5

0

6

5

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3

Fatty:Lean

Resp

on

se r

ati

o

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on

se r

ati

o

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Obese:Lean

Fatty:Lean

Fatty:Obese

Obese:Lean

2

1

0

Analyte Analyte

3-O

H-anthranilate

ADMA

Cytidine

Hippurate

Leucine/lsoleucine

Kynurenine

Pantothenate

Phenylalanine

Tryptophan

Tyrosine

Urate

3-O

H-anthranilate

ADMA

Cytidine

Hippurate

Leucine/lsoleucine

Kynurenine

Pantothenate

Phenylalanine

Tryptophan

Tyrosine

Urate

Figure 4: Extracted ion chromatogram of 232.1554 ± 0.01 from selected rat samples.

were analyzed by LC–MS using high-performance TOF-MS. The analysis of the data was achieved by means of two mechanisms. In one, a list of es-tablished analytes was used to search the data for these analytes using an accurate mass targeted approach. In these instances, the exact m/z value was searched against the existing data to within 0.001 m/z unit. In the other, the accurate m/z values obtained from the analyses were used to deter-mine molecular formulas, and the formulas or the accurate m/z values were searched in ChemSpider (17) or

KEGG (18) or other similar metabo-lite databases to facilitate the identi-fication of the analyte in question. In both of these instances, the ratio of area responses in the samples from the different phenotypes were then averaged and compared to each other for differences as three ratios. Spe-cifically, the Lean:Fatty, Lean:Obese, and Fatty:Obese response ratios were generated, and the averages for each phenotype are provided graphically in Figure 3 for representative ana-lytes. The plot shows several selected or readily identified analytes that show

positive changes in the Lean:Fatty and Lean:Obese ratios relative to the Fatty:Obese ratio. These include leu-cine/isoleucine, hippurate, dimethyl-arginine, kynurenine, and urate. The nearly sixfold change in leucine/iso-leucine is the most striking and largest proportional positive change for the “fatty” phenotype, while the change in kynurenine is the highest magnitude change negatively correlated with the “fatty” state. Both kynurenine and leu-cine/isoleucine have been previously associated with diabetes and obesity (19–23).

At the beginning of this study, standards were not available for con-firmation of identity. As such, experi-ments including the fragmentation of “precursor” ions before the mass analyzer (so-called in source CID) with accurate mass measurement of fragment ions and the use of rela-tive isotope abundance were used to clarify or support identification. One analyte identif ied in these studies was butyryl carnitine. In this case, an ion at m/z = 232.1554 was observed to change in intensity between lean and fatty/obese states. The formula search for this m/z provided two for-mulas within 5 ppm of the target m/z: C11H21N1O4 (0.77 ppm error) and C12H17N5 (1.3 ppm error). To provide additional information on the struc-tures of the analyte, the fragment ions were extracted into a separate data channel and evaluated. Figure 4 shows an extracted ion chromato-gram for both low (lean) and high (obese/fatty) samples along with the fragment ion spectrum from a lean sample. The two prominent fragment ions at m/z 173.082 and 85.03 match the loss of trimethylamine from 232 and the loss of butyric acid group and a trimethylamino group from 232, respectively. These are consistent with the identification of 232.1554 as butyryl carnitine. To provide yet another piece of supporting evidence, the relative isotope abundance was generated for the two formula above and these were compared to what was observed in high (fatty/obese) and low (lean) intensity samples. These f indings are provided in Table II.


Table II: Mass accuracy in the assignment of identities to doxylamine metabolites

Assignment Expected m/z Observed m/z Relative Error (ppm)

Didesmethyldoxylamine 243.149190 243.14925 0.2

Desmethyldoxylamine 257.164840 257.16497 0.5

Doxylamine 271.180490 271.18049 0.0

Doxylamine + O 287.175404 287.17543 –0.1

Figure 5: Comparison of relative isotope abundance measurements for the analyte at m/z

232.1554. The percent of the theoretical abundance is provided versus the expected values

for each option and is shown as Option 1/Option 2. The options are the two highest hits in the

formula match. Isotope abundance was calculated using the ChromaTOF-HRT tool. Signals were

considered from each of five samples, which covered a range of response intensities, and all

three phenotypes.

C11

Perc

en

t th

eo

reti

cal

H21

C12

H17

N5

NO4

180

160

140

120

100

80

60

40

20

12C

12C

0

1* 13

C 1* 13

C 2* 13

C 2* 13

C

lsotope examined

Figure 6: Standard curve of butyryl carnitine from single injections: (a) 0.16–500 nM, (b) zoomed

region of 0.16–20 nM.

3500000 65000

55000

45000

35000

25000

15000

5000

-5000 0 10 20 30

Analyte concentration (nM)

Are

a r

esp

on

se

Are

a r

esp

on

se

Analyte concentration (nM)

3000000

2500000

2000000

1500000

1000000

500000

0

0 200 400 600

For the monoisotopic peak and first isotope the agreement with the theo-retical distribution is better than ±5% (relative) in all cases versus Option 1 (butyryl carnitine). For the second isotope (2 13C), the agreement is still better than ±17% (relative) for four of the five samples, with the fifth being of very low signal intensity. In all cases, the relative isotope abundance defines which of the two formula op-tions best fits the data. This is most clearly seen in the first isotope data shown in Figure 5. This combination of accurate mass precursor analysis, accurate mass fragment ion analysis, and high integrity relative isotope evaluation is a clear demonstration of the impact that high-performance MS can have in metabolomic analysis and the characterization of analytes.

Of additional importance in metab-olomic analysis is the dynamic range of the instrument. To effectively inter-rogate both abundant and trace level analytes simultaneously, the linear-ity of response across several orders of magnitude is necessary. To explore this and the limit of detection of the accurate mass system, a standard curve of butyryl carnitine was created that covered the range of 0.16 through >500 nM. The plots of response versus concentration are shown in Figure 6 (Figure 6a: 0.16–500 nM; Figure 6b: 0.16–20 nM) and demonstrate that the dynamic range achievable here is clearly in excess of the 500 nM point shown (2500 nM not shown). This is equivalent to a linear dynamic range greater than 3000-fold (15,000-fold at 2500 nM). The experiment has its lowest detectable level at 0.16 nM or approximately 200 fg of butyrylcarni-tine on-column. This translates to the ability to examine either dramatic dif-ferences in concentrations of a single analyte or a range of analytes at sub-stantially different concentrations in the same analysis.

Analysis and Characterization of

Drug Metabolites in Urine: One of the most complex, but also most read-ily available, biological f luids is urine. It is difficult to standardize and is af-fected by diet, physiological state, dis-ease, and other hard-to-control factors


800000

700000

600000

500000

400000

300000

200000

100000

0

Times (s) 25 50 75 100 125

243.149190+0.000486 (Parent Ions)

257.164840+0.000514 (Parent Ions)

271.180490+0.000542 (Parent Ions)

434.217329+0.000868 (Parent Ions)

258.185241+0.000516 (Parent Ions)

272.200891+0.001361 (Parent Ions)

BPI

150 175 200 225

(a)

Figure 7: (a) Base peak signal and extracted ion chromatograms of active ingredients and

potential metabolites in urine. The signal for PEGs is clear between 50–100 s. (b) Precursor

(low energy) and product (high energy) spectra from m/z 271 showing key fragment ions. The

extraction window is at ±2 ppm for each analyte.

(24). With all of this said, urine is still able to indicate the health of indi-viduals and to provide insights into what the body has been exposed to, as it does give up its metabolites easily through renal clearance mechanisms. Here, urine from a healthy donor was analyzed by UHPLC with high-per-formance detection. A minimal level of cleanup was performed with the objective of providing a global survey for metabolites of doxylamine and dextromethorphan. Figure 7 shows a representative set of chromatograms for actives and metabolites detected in urine. The extraction window of ±2 ppm shows the necessary stability of the mass accuracy across the entire

peak. This facilitates accurate integra-tion of signal with high resolution and accuracy relative to other peaks. Al-though there are known compounds which should be present from the dos-ing, urine is a complex matrix, and as such, the identity of the analytes was confirmed by accurate mass analysis (Table II), which showed less than 0.5 ppm mass errors for the proposed dox-ylamine-related peaks. Similar to what was achieved with the butyryl carnitine peak, relative isotope abundance for the doxylamine provided highly accu-rate agreement with the theoretical iso-tope abundance. This is shown in the top of Table III. In addition, the appli-cation of “in-source CID” to generate

fragment ions provided both accurate mass fragment identification and ac-curate relative isotope abundance of the fragment ions. These findings are shown in the bottom of Table III. All but the lowest of signals provide bet-ter than 5% relative difference versus the theoretical isotope abundance. Furthermore, the measured resolving power was in excess of 40,000 at m/z 271, and even at m/z 90, the resolving power was a robust 35,000.

ConclusionsThe above applications of high-perfor-mance MS to the analysis of physiologi-cal analytes and metabolites in complex biological matrices provide a represen-tative overview of the impact that this type of experimentation can have on analyte identification and quantitation. High mass accuracy, mass resolving power, and relative isotope abundance prove to be invaluable tools for analyte identification in the experiments dis-cussed and facilitated the identification of analytes as potential biomarkers for obesity and as metabolic byproducts of pharmaceutical agents. Having high mass accuracy, resolving power, and isotope measures available in both precursor and fragment ions make the confidence an analyst might have in the identification of analytes extremely high. As this can be achieved on fem-togram amounts of material using high acquisition rates with analysis times of a few minutes and provide high per-formance under all acquisition condi-tions, high-performance MS is both fast and of high information content. These findings clearly define the ability of high-performance MS to provide a positive impact in the advancement of metabolomic and metabanomic analyses.

AcknowledgmentsThe authors wish to thank Celine Aguer and Mary-Ellen Harper from the University of Ottawa (Ottawa, Canada), Oliver Fiehn from UC Davis (Davis, California), and Sean Adams from the Western Human Nutrition Research Center (WHNRC) at UC Davis for butyryl carnitine standards through work funded by the Na-



tional Institute of Health (DK 078328, Bethesda, Maryland).

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Table III: Mass accuracy and relative isotope abundance for doxylamine using “in source CID.” Parent ion data are provided at the top of the table and product ion data at the bottom.

Parent Ion Channel

Assignment Observed m/zMass Resolving Power

m/z Error (ppm)

Expected Relative Abundance

Observed Relative Abundance

Relative Isotopic Accuracy

[C17H23N2O]+ 271.18049 49409 0.0

M+1 272.18398 49044 0.1942 0.1854 –4.5%

M+2 273.18671 41602 0.0180 0.0173 –3.8%

Product Ion Channel

Assignment Observed m/zMass Resolving Power

m/z Error (ppm)

Expected Relative Abundance

Observed Relative Abundance

Relative Isotopic Accuracy

[C4H12NO]+ 90.09145 35232 1.2

[C13H12NO]+ 182.09672 45503 1.6

M+1 183.10023 43879 0.1456 0.1487 2.1%

M+2 184.10332 42877 0.0091 0.0097 5.8%

[C17H23N2O]+ 271.18105 48606 2.1

M+1 272.18446 49421 0.1942 0.1853 –4.6%

M+2 273.18732 49147 0.0180 0.0232 29.2%

Introduction

The U. S. Environmental Protection Agency (EPA) has been

investigating and regulating PFOA, PFOS and other fl uorinated

telomers, because a number of recent studies have indicated serious

health effects in various animal models. Perfl uorinated compounds

(PFCs) have been manufactured for more than 50 years, and have

been widely used as surfactants and surface protectors in carpets

and fabric, and as performance chemicals in products such as fi re-

fi ghting foams, fl oor polishes and shampoos.

Sampling methods utilizing LC/MS/MS have been recommended in

EPA Method 537 to analyze the concentration of PFCs in ground water

as a source of the hazardous contact. To improve the detection limit of

the PFCs with this equipment, scientists have tried to replace fl ow lines

containing fl uoropolymers (such as PTFE, FEP) with PEEK™ or stainless

steel to eliminate interference effects. Membrane degassers, which

are widely used in HPLC, are generally manufactured from TFE/PDD

copolymer or from PTFE. These fl uorinated polymer membranes slowly

release PFCs into the solvent fl ow which can cause changes in MS

background, and can signifi cantly affect the accuracy of the analysis.

As a major supplier of degassers, IDEX Health & Science introduces

Systec™ Interference-Free Degassing for Mass Spectrometry applications

such as the measurement of trace PFOA. These premium degassers have

the same degassing capability as standard degassers, but are free of

PFOA and offer a signifi cant reduction of other PFCs. When coupled with

a fl uorinated-polymer-free fl ow path, this degassing technology enables

much more accurate analysis of PFCs.

Why Do We Need Degassing in HPLC?

In low-pressure-mixing HPLC, two or more solvents are mixed together

prior to introduction into the HPLC pump. The seminal engineering

study in 1 76 by Tokunaga, which analyzed the concentration of air

in alcohols and in water and their mixtures, clearly demonstrated the

need to remove air from solvents prior to mixing in order to eliminate

bubbles. Simply put: alcohol contains nearly seven times as much air

as water, but mixtures of water and alcohol cannot contain the total

amount of air each solvent brings to the mixture. Tokunaga found

that at atmospheric pressure, water-methanol mixes can contain

only 38% of the amount of air each brings to the mix —between 30%

and 70% methanol. Studies of other solvent-solvent interactions also

demonstrated a similar reduction of solubility along a mixing curve.

In Figure 1, the excess air in the mixture forms bubbles in the low-pressure, gradient-forming portion of the HPLC pumping system. Various other solvents (where one solvent dissolves more air than another with which it will be mixed) will produce similar outgassing and bubble formation.

FIGURE 1. Solubility of Air in Water–Methanol Mixtures During HPLC Gradient Formationbased on Tokunaga, J Chem & Eng, Vol 20, No 1, 1975

0

0.02

0.04

0.06

0.08

0.1

0.12

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0.18

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Millilite

r o

f A

ir P

er M

illilite

r o

f M

ixtu

re

Percent Methanol in a Water-Methanol Mixture

Mixture saturation limit

Total air from water a

nd ethanol

Excess air i

n the m

ixture

Figure 2 shows the difference in HPLC chromatography performed with and without a degasser. The retention times for the peaks are very consistent when using a degasser, yet vary signifi cantly when not using a degasser. Also bubble formation can cause unstable back pressure, and detector baseline shift.

FIGURE 2. Effect of Degassing on Chromatography

1. 65:35 MeOH/WATER, Column: Agilent Zorbax SB-C18 length: 4.6 X 100 mm, 3.5 µm, Flow Rate: 2.00 mL/min, Wavelength: 254 nm, Injection Volume: 0.5 µL, Temperature: Not Controlled. 2. Vacuum Degassing Conditions: 50 mm Hg, Part Number: 000-1385. 3. Vacuum Degassing Conditions: 760 mm Hg, other conditions the same as in 2.

Interference-Free Degassing for Mass SpectrometryAuthors: Quan Liu, Ph.D., Michael Keller, Carl Sims

IDEX Health & Science LLC

Limitation of Existing Degasser on PFC Detection

There are a number of limitations to instrumental analyses of PFCs. One of

the most severe limitations is high levels of background. The background

comes from the presence of PFCs in a number of the instrumental

components. This contamination can come from everything from the

septa used in autosampler vials, from pump seals, or even tubing.

To conduct the analysis of PFCs with suffi ciently high sensitivity

and reproducibility, it has been recommended to substitute stainless

steel or PEEK tubing for Tefl on™ in all parts of the instrument. It has also

been recommended that the instrument should be operated without the

degasser. Unfortunately, this will cause the unstable chromatographic

result demonstrated in Figure 2. Other researchers have tried to fl ush the

entire HPLC system with solvent for upwards of several days to decrease

or eliminate the contaminants from the instrument.

Test Result of Systec Interference-Free Degassers

In cooperation with Thermo-Fisher Scientifi c, IDEX Health & Science

provided samples of the new Systec Interference Free degassers for

their evaluation. The Interference-Free degasser (referenced as the “pre-

cleaned PFC-free” degasser in Thermo Scientifi c notes), incorporating

PEEK tubing from IDEX Health & Science rather than Tefl on tubing,

enabled Thermo Scientifi c to develop a new UHPLC/MS method for

sensitive, precise, and reproducible trace-level analysis of PFCs.

Other PFCs, including PFBS, PFHxS, PFHpA, PFUnA, and PFDoA are

also analyzed. Excellent linearity in detector response was observed over

the range of 0.04-2.5 ppb. Figure 4 shows the separation and detection

of 10 ppt PFBS and 10 ppt PFDS at different SRM transitions, and the

corresponding blanks as comparisons.

The sensitivity of the method is dependent on the levels of interferences

that are present in the blank and in the solvent used. Limits of detection

(LODs) and limits of quantitation (LOQs), defi ned as S/N ratio of 3 and 10,

respectively, are shown in Table 1.

The result shows that by using the Systec Interference-Free degasser,

very low background (very low interference) could be achieved, thus a

highly sensitive, accurate and reproducible HPLC/MS/MS method for PFC

analysis could be developed.

More details about the tests can be found in the Thermo Scientifi c

Application Note 51 36 – “Sensitive and Accurate Quantitation of

Perfl uorinated Compounds in Human Breast Milk using Selected Reaction

Monitoring Assays by LC/MS/MS”.

Conclusion

The Systec Interference-Free Degasser, with the PFC-free pump and

fl ow path, enables our customer to reliably achieve the maximum

sensitivity and repeatable accuracy of their instrument for PFC

detection and quantitation.TABLE 1. LODs and LOQs of the PFC Standard*

Compounds SRM LOD (ppt) LOQ (ppt)

PFBS 298.9>80.2 2 5

298.9>99.2 5 12

PFHxS 398.9>80.2 21 83

398.9>99.2 12 66

PFHpA 362.9>169.0 174 756 blank con amina ion

362.9>319.0 120 457 blank con amina ion

PFDS 598.9>80.2 2 7

598.9>99.2 3 9

PFUnA 562.9>269.0 35 156

562.9>519.0 52 235

PFDoA 612.9>169.0 59 196

612.9>569.0 64 195

Figure 3 shows an HPLC/MS/MS test on PFOA by using different degassers. The TSQ Vantage� triple-stage quadrupole MS was used as detector. The fi rst two chromatograms show the analysis using a standard degasser. The PFOA peak is still signifi cantly noticeable after the fi rst wash out. This is a common phenomenon in current degasser usage.

The last two chromatograms show the analysis using the new Interference-Free degasser. The PFOA peak visible in Run #1 might come from other components, such as a pump seal, etc. after the instrument idled over weekend. But the PFOA peak disappeared after the fi rst wash out. This clearly shows that the PFOA contamination from the degasser has been eliminated.

FIGURE 3. PFOA test using TSQ MS as detector*

FIGURE 4. Separation and Detection of 10 ppt of PFBA and PFDS*

Acknowledgements: The authors thank Mark Joiner and Joe Rotter of IDEX Health & Science, and Guifeng Jiang and Robert Szilasie of Thermo-Fisher Scientifi c for their assistance with this work.

Disclaimers: PEEK� polymer is a trademark of Victrex plcTefl on™ is a registered trademark of E.I. du Pont de Nemours and Company. Only DuPont makes Tefl on. Vantage� is a trademark of Thermo Fisher Scientifi c, Inc.

* Figures and table used with the permission of Thermo Scientifi c

® 2011, IDEX Health & Science LLC All Rights Reserved.

www.idex-hs.com I North/South America +1 866 339 4653

Europe +49 1801 808 800 I Asia +86 10 6566 9090

Rela

tive A

bu

nd

an

ce

NL: 7.76E2

NL:1.13E2

NL:1. 4E2

NL: 1.11E1

Eastern Plastics I Innovadyne I Ismatec I Isolation Technologies I Rheodyne I Sapphire Engineering I Systec I Upchurch Scientifi c


Why Use Signal-To-Noise As a Measure of MS Performance When It Is Often Meaningless?

Greg Wells, Harry Prest, and Charles William Russ IV

In the past, the signal-to-noise ratio of a chromatographic peak determined from a single mea-surement served as a convenient figure of merit used to compare the performance of two dif-ferent mass spectrometry (MS) systems. The evolution in the design of MS instrumentation has resulted in very low noise systems that have made the comparison of performance based upon signal-to-noise increasingly difficult, and in some modes of operation, impossible. This is espe-cially true when using ultralow-noise modes, such as high-resolution MS or tandem MS, where there often are no ions in the background and the noise is essentially zero. This occurs when analyzing clean standards used to establish the instrument specifications. Statistical methodol-ogy that is commonly used to establish method detection limits for trace analysis in complex matrices is a means of characterizing instrument performance that is rigorously valid for both high and low background noise conditions. Instrument manufacturers should begin to provide customers an alternative performance metric, in the form of instrument detection limits based on the relative standard deviation of replicate injections, to allow analysts a practical means of evaluating an MS system.

For decades, signal-to-noise ratio (S/N) has been a pri-mary standard for comparing the performance of chro-matography systems including gas chromatography–

mass spectrometry (GC–MS) and liquid chromatography (LC)–MS. Specific methods of calculating S/N have been codified in the U. S., European, and Japanese Pharmacopeia (1–3) to produce a uniform means of estimating instrument detection limits (IDL) and method detection limits (MDL). The use of S/N as a measure of IDL and MDL has been use-ful, and is still in use, for optical-based detectors for LC, and flame-based detectors for GC. As MS instrument design has evolved, the ability to accurately compare performance based on S/N has become increasingly difficult. This is especially true for trace analysis by MS using ultralow-noise modes, such as high-resolution mass spectrometry (HRMS) or tan-dem MS (MS-MS). S/N is still a useful parameter, particu-

larly for full scan (EI) MS, but the comparison of high-per-formance MS analyzers should be based upon a metric that is applicable to all types of MS instruments and all operating modes. Statistical means have long been used to establish IDL and MDL (4–7) and are suitable to all operating modes for mass spectrometers.

Evolution of Instrumentation

Many sources of noise have been reduced by changes to the MS design such as low noise electronics, faster electronics allowing longer ion sampling (signal averaging), modified ion paths to reduce metastable helium (neutral noise), and signal processing (digital filtering). HRMS and MS-MS are also effective means to reduce chemical background noise, particularly in a complex sample matrix. For the signal, a longer list of improvements to the source, analyzer, and detec-


Signal

Time

Noise

Signal

Start End

Figure 1: Analyte signal as a function of time for a chromatographic peak, demonstrating the

time dependence in the amount of analyte present.

Time (min)

(a) (b) (c)

Ab

un

dan

ce

54

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8000

7000

6000

5000

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3000

2000

1000

04.00 6.806.606.406.206.005.805.605.405.205.004.804.604.404.20

Ion 272.00

m/z 272.00

6 120

Figure 2: EI full-scan extracted ion chromatograms of m/z = 272 from 1 pg OFN.

tor components have resulted in more ions produced per amount of sample. In combination, the improvements in S/N and the increase in sensitivity have resulted in significant and real lowering of the IDL and MDL.

Lack of Guidelines for S/N MeasurementsThe measurement of the “signal” is gen-erally accepted to be the height of the maximum of the chromatographic sig-nal above the baseline (Figure 1). How-ever, some of the changes in GC–MS S/N specifications have been artificial. The industry standard for GC–electron ionization (EI) MS has changed from methyl stearate, which fragments ex-tensively to many lower intensity ions, to compounds that generate fewer, more intense ions, such as hexachloroben-zene (HCB) and octaf luoronaphtha-lene (OFN). The change to OFN held a secondary benefit for noise: the m/z 272 molecular ion is less susceptible to baseline noise from column bleed ions (an isotope of the monoisotopic peak of polysiloxane at m/z 281 increased base-line noise for HCB at m/z 282).

Improvements in instrument design and changes to test compounds were accompanied by a number of different approaches to measuring the noise. In the days of strip chart recorders and rul-ers, a common standard for noise was to measure the peak-to-peak (minimum to maximum) value of baseline noise, away from the peak tails, for 60 s before the peak (Figure 1) or 30 s before and after the peak. As integrator and data systems replaced rulers, the baseline segments for estimation of noise were autoselected and noise was calculated as the standard deviation (STD) or root-mean-square (RMS) of the baseline over the selected time window.

Automation of the calculation holds many conveniences for a busy labora-tory, but the noise measurement criteria have become less controlled. In some cases, vendors have estimated noise based on a very narrow window (as short as 5 s) and location that can be many peak widths away from the peak used to calculate signal. These variable “hand-picked” noise windows make it easy for a vendor to claim a higher S/N

by judiciously selecting the region of baseline where the noise is lowest. Gen-erally, the location in the baseline where the noise is calculated is now automati-cally selected to be where the noise is a minimum. Figure 2 shows three dif-ferent values of RMS noise calculated at different regions of the baseline. The value of the noise at positions a, b, and c is 54, 6, and 120, and the factor of 20 variation in S/N is exclusively because of where in the baseline the noise is

measured. However, these S/N values will not correlate to the practical IDL for automated analyses that the labo-ratory performs. Therefore, the use of signal-to-noise as an estimate of the de-tection limit will clearly fail to produce usable values when there is low and highly variable ion noise and the choice of where to measure it is subjective.

The situation becomes even more indeterminate when the background noise is zero, as shown in the MS-MS


Ab

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nce

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6000

4000

3000

2000

1000

0

272 m/z 222 m/z

Time (min)

4.00 6.806.606.406.206.005.805.605.405.205.004.804.604.404.20

Figure 3: EI MS-MS extracted ion chromatogram of m/z 222.00 from 1 pg OFN exhibiting no

chemical ion noise.

chromatogram in Figure 3. In this case, the noise is zero and S/N then becomes infinite. The only “noise” observed in Figure 3 is because of the electronic noise, which is several orders of mag-nitude lower than noise because of the presence of ions in the background. This situation can be made more se-vere by increasing the threshold for ion detection. Under these circumstances, it is possible to increase the ion detector gain, and hence the signal level, without increasing the background noise. The signal of the analyte increases, but the noise does not. This is obviously mis-leading since the signal increased, but there was no increase in the number of ions detected, and therefore, no change to the real detection limit. This allows S/N to be adjusted to any arbitrary value without changing the real detec-tion limit.

Two decades ago, instrument speci-fications often described the detailed analytical conditions that affected the signal or noise value, such as chromato-graphic peak width, data rate, and time constant. Today, those parameters are missing or hard to find, even though a novice chromatographer realizes a nar-row peak will have a larger peak height than a broad peak. In many cases, the GC conditions are selected to make the chromatographic peak extremely nar-

row and high, with the result that the peak is under-sampled (only one or two data points across a chromatographic peak). This may increase the calculated S/N, but the under-sampling degrades the precision and would not be accept-able for most quantitative methods. Once again, a false perception of per-formance has been provided. Accurate, meaningful comparison of instrument performance for practical, analytical use based on published specifications is increasingly difficult, if not impos-sible. Alternative means are required to determine the instrument performance and detection limit, which is generally applicable to all modes of MS operation.

What Is the Alternative for S/N?S/N is still useful and represents a good first estimate to guide other statisti-cally based estimates of performance. Every analytical laboratory should un-derstand and routinely use statistics to evaluate and validate their results. The analytical literature has numerous ar-ticles that utilize a more statistical ap-proach for estimating an IDL or MDL. The U.S. EPA has dictated a statistical approach to MDL, and can be found in the recommended EPA “Guidelines Establishing Test Procedures for the Analysis of Pollutants” (6). The Euro-pean Union also has supported this po-

sition. A commonly used standard in Europe is found in “The Official Jour-nal of the European Communities;” Commission Decision of 12 August 2002; Implementing Council Directive 96/23/EC concerning the performance of analytical methods and the interpre-tation of results (8).

Both of these methods are generally similar and require injecting multiple replicate standards to assess the uncer-tainty in the measuring system. A small number of identical samples having a concentration near the expected limit of detection (within 5–10) are mea-sured, along with a comparable num-ber of blanks. Because of the specificity of MS detection, the contribution from the blank is negligible and is often excluded after the significance of the contribution has been confirmed. The mean value and standard deviation (STD) of the set of measured analyte signals, X

n (that is, integrated areas

of the baseline subtracted chromato-graphic peaks), is then determined. Even for a sensitivity test sample, the “sampling” process is actually a com-plex series of steps that involves draw-ing an aliquot of analyte solution into a syringe, injecting into a GC system, and detecting by MS. Each step in the sampling process can introduce varia-tions in the final measured value of the chromatographic peak area, resulting in a sample-to-sample variation or “sampling noise.” These variances will generally limit the practical IDL and MDL that can be achieved.

The variance in the measured peak areas includes the analyte signal varia-tions, the background noise, and the variance from injection to injection. Distinguishing the statistical signifi-cance of the mean value of the set of measured analyte signals from the combined system and sampling noise can then be established with a known confidence level. The IDL is then the smallest signal or amount of analyte that is statistically greater than zero within a specified probability of being correct. The IDL (or MDL) is related (9,10) to the standard deviation STD of the measured area responses of the replicate injections and a statistical confidence factor t

α by

X


Time (min)

500

400

300

200

100

0

8.0 8.1 8.2 8.3 8.4

m/z 272.00

Peak area = 810 counts

STD 41.3 counts

Peak area RSD = 5.1%

Ab

un

da

nce

Figure 4: EI full-scan extracted ion chromatograms of m/z = 272 from 200 fg OFN, eight replicate

injections, 3.3 Hz data rate.

Time (min)

500

400

300

200

100

0

8.0 8.1 8.2 8.3 8.4

m/z 272.00


S/N - 568

Ab

un

da

nce

Figure 5: EI full-scan extracted ion chromatograms of m/z = 272 from 200 fg OFN, first entry in

Table I, 3.3 Hz data rate.

Table I: Comparison of S/N for eight injections

Injection Number Signal RMS Noise S/N IDL = 3×RMS (fg)

1 795 1.4 568 1.1

2 821 2.5 328 1.8

3 835 10.9 77 7.8

4 854 2.8 305 2.0

5 818 2.9 282 2.1

6 776 26.6 29 20.6

7 853 2.6 328 1.8

8 735 2.7 272 2.2

IDL = (tα)( STD ), where the STD and

IDL are expressed in area counts.

Alternately, many data systems re-port relative standard deviation (RSD = STD/Mean Value). In this case, the IDL can be determined in units of the amount of the standard (ng, pg, or fg) injected by

IDL = (tα)( RSD )(amount stan-

dard)/100%

When the number of measure-ments n is small (n < 30), the one-sided Student t-distribution (11) is used to determine the confidence factor t

α.

The value of tα comes from a table of

the Student t-distribution using n–1 (number of measurements minus one) as the degrees of freedom, and 1–α is the probability that the measure-ment is greater than zero. The larger the number of measurements n, the smaller is the value of t

α, and less the

uncertainty in the estimate of the IDL or MDL. Unlike the statistical method of determining IDL and MDL, the use of signal-to-noise from a single sample measurement does not capture the “sampling noise” that causes multiple measurements of the same analyte to be somewhat different.

As an example, for the eight replicate injections in Figure 4 (n = 7 degrees of freedom) and a 99% (1–α = 0.99) confi-dence level, the value of the test statistic from the t-table is t

α = 2.998. For the

eight 200-fg samples, the mean value of the area is 810 counts, the standard de-viation is 41.31 counts, and the value of the IDL is: IDL = (2.998)(41.31) = 123.85 counts. Since the calibration standard was 200 fg and had a measured mean value of 810 counts, the IDL in femto-grams is: (123.85 counts)(200 fg)/(810 counts) = 30.6 fg. Alternately in terms of RSD, the IDL = (2.998)(5.1%)(200 fg)/100 = 30.6 fg. Thus, an amount of analyte greater or equal to 30.6 fg is de-tectable and distinguishable from the background with a 99% probability. In contrast, S/N measured on one single chromatogram (Figure 5) would give an IDL of 1.1 fg, assuming that IDL = 3 × rms noise (first entry in Table I). The high S/N is the result of a commonly


Figure 6: EI full-scan extracted ion chromatograms of m/z = 272 from 1 pg OFN, eight replicate

injections, 3.3 Hz data rate.

Time (min)

2.5

2.0

1.5

1.0

0.5

0.0

8.5 8.6 8.7 8.8

m/z 272.00


STD 254.0 counts

Peak area RSD = 2.1%

Ab

un

da

nce

1/√N

√5 = 2.24

used algorithm that calculates the noise using the lowest noise sections of the baseline adjacent to the peak where the noise is unusually low. The individual S/N and IDL values measured for each injection in Figure 4 are tabulated in Table I. The IDL values range from 1.1 fg to 20.5 fg, and even the largest value is significantly smaller than the IDL de-termined by statistical means, which is more realistic.

Estimating Relative SensitivityA secondary benefit to specifying de-tection limits in statistical terms is to use the RSD as an indirect measure of the relative number of ions on a chro-matographic peak. It is known (12) that if a constant flux of ions impinge on a detector and the mean number of ions detected in a particular time interval averaged over many replicate measure-ments is N, then the RSD in the number ions detected is

Hence, decreasing the number of ions in a chromatographic peak will in-crease the area RSD. This effect can be seen by comparing the RSD in Figure 6 and Figure 4 where the ana-lyte amounts are 1 pg and 200 fg, re-spectively. The increased RSD at the lower sample amount is, in large part,

because of ion statistics. Five times lower number of ions means

times increase in RSD. It follows that the 2.1% RSD at 1 pg will become 4.7% at 200 fg just from ion statistics. It is not unexpected that, at lower sample amounts, there also will be some ad-ditional variance from the chromatog-raphy. Thus, RSD measurements of the same amount of analyte on two differ-ent instruments can be used to indi-cate the relative difference in sensitiv-ity near the detection limit, assuming that other contributions to the total variance of the signals are small. The more sensitive instrument will have the smaller RSD if all other factors are the same (that is, peak width and data rate). This avoids the uncertainty of inferring sensitivity from the mea-surement of peak areas where there is no baseline noise.

Impact of Changing from S/N to RSD For MS vendors, there is complexity and cost in providing customers this valuable information. If the GC is configured with an autosampler and a splitless inlet, it should be fairly simple to add 7–10 replicate injections. This might add a little time to the instal-

lation, but the use of an autosampler should keep this at a minimum. But how do you test a system that does not have an autosampler or the appropri-ate inlet? Manual injections will add operator-dependent imprecision to measuring the peak area. Reproduc-ible injection using a manually oper-ated injector device like the Merlin MicroShot Injector (Merlin Instru-ment Co., Half Moon Bay, California), are necessary to reduce the sampling noise. If the method uses headspace or gas analysis, it could be costly to test as a liquid injection and then re-configure for the final analysis. These costs must be managed appropriately, but factors like cost and complexity for some configurations should not pre-vent a transition to a better, statisti-cally based standard such as %RSD for the majority of the systems that do not have these inlet limitations.

From the customer’s perspective, however, there is a significant benefit to having a system level test of performance that offers a realistic estimate of the IDL and the system precision near the limits of detection where it is the most critical. The purchase of a mass spectrometer is a significant capital expenditure, and often purchasing decisions are based on a customer’s specific application need. Instrument manufacturers do not have the resources to demonstrate their prod-uct’s performance for every application. It would be valuable to have a simple, but realistic, means of evaluating a par-ticular MS instrument’s performance in the form of the IDL and the system precision. These will form lower bounds to the MDL and method precision for specific applications.

SummaryIn the past, the S/N of a chromato-graphic peak determined from a single measurement has served as a convenient figure of merit used to compare the performance of two dif-ferent MS systems. However, as we have seen, this parameter can no lon-ger be universally applied and often fails to provide meaningful estimates of the IDL. A more practical means of comparing instrument performance is to use the multi-injection statistical



methodology that is commonly used to establish MDLs for trace analysis in complex matrices. Using the mean value and RSD of replicate injections provides a way to estimate the statistical significance of differences between low level analyte responses and the combined uncertainties of the analyte and background measurement, and the uncertain-ties in the analyte sampling process. This is especially true for modern mass spectrometers for which the background noise is often nearly zero. The RSD method of characteriz-ing instrument performance is rigorously and statistically valid for both high and low background noise conditions. Instrument manufacturers should start to provide custom-ers with an alternative performance metric in the form of RSD-based instrument detection limits to allow them a practical means of evaluating an MS system for their intended application.

References

(1) European Pharmacopoeia 1, 7th Edition.

(2) United States Pharmacopeia, XX Revision (USP Rockville, Mary-

land, 1988).

(3) Japanese Pharmacopoeia, 14th Edition.

(4) ASTM: Section E 682–93, Annual Book of ASTM Standards,

14(1).

(5) P.W. Lee, Ed. Handbook of Residue Analytical Methods for Ag-

rochemicals 1, Chapter 4 (2003).

(6) U.S. EPA – Title 40: Protection of Environment; Part 136 –

Guidelines Establishing Test Procedures for the Analysis of

Pollutants; Appendix B to Part 136 – Definition and Procedure

for the Determination of the Method Detection Limit – Revision

1.11.

(7) “Uncertainty Estimation and Figures of Merit for Multivariate

Calibration,” IUPAC Technical Report, Pure Appl. Chem. 78(3),

633–661 (2006).

(8) Official Journal of the European Communities; Commission

Decision of 12 August 2002; Implementing Council Directive

96/23/EC concerning the performance of analytical methods

and the interpretation of results.

(9) “Guidelines for Data Acquisition and Data Quality Evaluation in

Environmental Chemistry,” ACS Committee on Environmental

Improvement Anal. Chem. 52, 2242–2249 (1980).

(10) “Signal, Noise, and Detection Limits in Mass Spectrometry,”

Agilent Technologies Technical Note, publication 5990–7651EN.

(11) D.R. Anderson, D.J. Sweeney, and T.A. Williams, Statistics (West

Publishing, New York, 1996).

(12) P.R. Bevington and D.K. Robinson, Data Reduction and Error

Analysis for the Physical Sciences (WCB McGraw-Hill, Boston,

2nd Edition, 1992).

Greg Wells, Harry Prest, and Charles William

Russ IV are with Agilent Technologies, Inc., Santa Clara,

California. ◾

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Food Metabolomics: Fact or Fiction?

Leon Coulier, Albert Tas, and Uwe Thissen

Comprehensive analysis of both volatile and nonvolatile metabolites in food, combined with information on sensory properties and multivariate statistics, can be a valuable tool in under-standing and improving the taste of food. However, performing food metabolomics studies is challenging and requires the analytical measurements to be of a very high quality. Although in some cases more-targeted approaches are adequate, it is expected that new developments in analytical chemistry will increase the value of food metabolomics in the future.

Metabolomics plays an important role in systems biology. It enables better understanding of com-plex interactions in biological systems. There are

many definitions and interpretations of metabolomics, but generally, it aims to analyze as many — if possible, all — small molecules (<1500 Da) in a biological system. The idea is that the biochemical level of the metabolome is closest to that of the daily function of a cell. It is an emerging tool in many disciplines such as plant physiol-ogy, drug discovery, human disease and nutrition, and others. Developments in analytical chemistry such as high-resolution mass spectrometry (HRMS), ultrahigh-pressure liquid chromatography (UHPLC), and software programs for fast processing of large analytical data sets have been responsible for the popularity and evolution of metabolomics.

Metabolomics has also found its place in food science, as recently reviewed by Wishart (1) and Cevallos-Cevallos and colleagues (2). Within the area of food science, three main applications of metabolomics can be distinguished:• Foodconsumptionmonitoringandphysiologicalmoni-

toring of diet and nutrition focuses mainly on analyzing nutrients and their metabolites in biof luids or health effects of food on an organism. Although this is a very exciting application, concomitant analysis is performed on biof luids or tissue of humans or animals and not on the food itself.• Foodauthenticity.Thistypeofanalysis isalsoper-

formed on food itself and the main purpose is to classify samples according to, for example, origin and age. The advantage of this approach is that one is only interested in accurate classification and that can, in many cases, be

achieved by a single method like nuclear magnetic reso-nance (NMR) spectroscopy covering a limited number ofmetabolites.Furthermore,understandingthereason

why different samples are separated and thus identify-ing discriminating metabolites is not of primary inter-est.• Foodsafetyorqualityimprovementwheretheanalyst

wants to correlate a specific property to metabolite pat-terns using biostatistics. Taking taste as an example, the main goal is often to understand the taste of food in terms of chemical composition and physical properties so it can be optimized by processing and growth condi-tions, or to find markers that can be measured routinely and easier than taste itself. This puts strong demands ontheexperimentaldesign.First,thespecificproperty,

such as sensory scores, should be quantifiable and the variance herein should be well represented by the differ-entsamples.Furthermore,thecoverageoftheanalytical

methods used should be high to detect all relevant me-tabolites that lead to understanding and optimizing the specific property. Depending on the research question, in some cases the relevant metabolites can be analyzed by a target analytical platform, whereas in other cases a more holistic view (that is, multiple platforms) is re-quired.

Sample PreparationThe typical workf low of a metabolomics experiment is showninFigure1.Everyaspectofthisworkflowhastobe

optimized to make metabolomics studies a success. With respect to the analytical chemistry involved in metabolo-mics, sample work-up, analysis of the samples, and data-


preprocessing are items that have to be dealt with.

The sample preparation procedures strongly depend on the type of ma-trix and compounds of interest. In the case of volatiles, food samples are preferably analyzed directly by head-space, solid-phase microextraction (SPME),orstir-barsorptiveextrac-tion(SBSE)incombinationwithgas

chromatography–mass spectrometry (GC–MS). Choices should be made with respect to adding agents to stop enzymatic reactions, like salt-solu-tions. This can have both positive and negative effects on the response, depending on the specific compound.For lessvolatile andnonvolatile

compounds, the sample extraction is determined by the polarity range of interest. In the case of metabolo-mics, where all metabolites can be of importance, extraction using chloro-form–methanol–water mixtures are most often used to extract both polar and nonpolar compounds. Samples should be quickly frozen before ex-traction to stop any further reactions. Extraction should a lways be per-formed at low temperature to prevent loss of volatile compounds or thermal degradation.

Usually, a single analytical method will not be sufficient to analyze all relevant metabolites and thus a com-bination of different methods should be applied to increase the coverage. In the case of taste, it is essential to cover both volatile and nonvolatile compounds. Hence, the combination of methods for very volatile compo-nents,likeSPME–GC–MS,andless-

or nonvolatile components, like GC–

MS and LC–MS, is essential but can hardly be found in literature.

In this article, an example of food metabolomics in food quality will be described using tomato sensory prop-erties as a demonstration case. Some highlights of this study will be shown; an extensive paper on this study can be found elsewhere (3). The challenges of this approach will be described, es-pecially from an analytical chemical point of view, and recommendations will be made for future developments in food metabolomics with respect to analytical chemical requirements.

ExperimentalSample generation and work-up: A total of 19 different tomato samples from different origins were used. Fromeachsample,22sensoryattri-butes were quantified in duplicate by a sensory panel consisting of nine as-sessors.ForSPME–GC–MS,tomatoeswere

cut into equal parts and stored for 20 min to allow enzymatic reactions to take place. Next, the samples were pulped on ice and liquid nitrogen was added to stop enzymatic reactions. Before analysis, an equal weight of saturated calcium chloride solution was added.

The sample extract ion method applied for GC–MS and LC–MS was adapted from a procedure developed for microbial metabolomics samples where it is essential to quench sam-plesasquicklyaspossible (4).For

oximation silylation-GC–MS (OS-GC–MS) and LC–MS, tomatoes were freeze-dried and grinded followed by extraction using a 20:11:9 chlo-

roform–methanol–water mixture. After extraction for 30 min at −40 °C, the extracts were centrifuged. The aqueous phase was pipetted off and freeze-dried. Before analysis, the freeze-dried samples were redis-solved in 3:1 (v/v) water–methanol for LC–MS or derivatized using a so-lution of ethoxyamine hydrochloride in pyridine fol lowed by si lylation withMSTFA(5).

Instrumentation and operational

conditions: GC–MS was performed on an Agilent 6890 gas chromato-graph with an Agilent 5973 mass selective detector (Agilent Technol-ogies, Palo Alto, California). Detec-tion was performed using MS detec-tioninelectronionizationmode.For

SPME–GC–MS,volatilesfrommixed

tomato–CaCl2 samples were absorbed on a 50/30 μm DVB/Car/PDMS fiber (Supelco, Bellefonte, Pennsylvania) for 15 min at 50 °C. Desorption was performed at 250 °C for 10 min fol-lowed by splitless injection on a 30 m × 0.25 mm, 1-µm df HP5-MS column (Agilent) with a 0–320 °C tempera-ture gradient at a rate of 10 °C/min. ForOS-GC–MS,derivatizedsamples

were analyzed by 1-μL PTV injection on a 30 m × 0.25 mm, 0.25-µm d f

HP5-MS column (Agilent) with a 70–325 °C temperature gradient at a rate of 15 °C/min. LC–MS was per-formed on an LTQ linear ion-trap system consisting of a Surveyor AS autosampler, Surveyor MS pump, and LTQ LT–10000 mass detector with an OptonESIprobe(ThermoFisherSci-entific, San Jose, California). Separa-tion of tomato extracts (10 μL) was performed on a 100 mm × 2.1 mm

Figure 1: Schematic diagram of the different steps in a food metabolomics experiment.

Experi-

mental

design

Analysis

of the

samples

Data

Preproces-

sing

Biological

questionSample

generation

Data

analysis

Biological

Inter-

pretation

ValidationSample

work-upValidation

Sensory evaluation

Biologicalquestion

Experimentaldesign

Samplegeneration

Samplework-up

Analysisof thesamples

DataPreprocessing

Dataanalysis

BiologicalInterpretation


Figure 2: Typical chromatograms of tomato samples obtained by (a) SPME–GC–MS, (b) GC–MS,

and (c) LC–MS–(ESI+).

1.4e+07

1.5e+07

1.6e+07

34

8 000 000

9 000 000

1e+07

1.1e+07

1.2e+07

1.3e+07 3) Hexenal

4) 2,3-Dimethylpentene

5) Cyclohexanol

6) Heptenal

2 000 000

3 000 000

4 000 000

5 000 000

6 000 000

7 000 000

12

56

7

8

7) 6-Methyl-5-hepten-2-one

8) Octenal

5.00 10.00 15.00 20.00 25.00 30.00

1 000 000

Time (min)

1) 1-Pentene-3-one

2) Pentanal

8 000 000

9 000 000

1e+07

14

57

8 1) Phosphate

2) Threonine

3) Malic acid

4 000 000

5 000 000

6 000 000

7 000 000

1

369

10

4) 4-Aminobutyric acid/aspartic acid

5) Glutamic acid

6) Glutamine

7) Citric acid

10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00 55.00

1 000 000

2 000 000

3 000 000

2

8) Monosaccharides

9) Myo-inositol

10) Disaccharide

Time (min)

85

90

95

1001 4 .3 16 3 5 .8

1) Phenylalanine

50

55

60

65

70

75

80 3) 3-Caffeoylquinic acid

4) 5-Caffeoylquinic acid

5) Coumaroylquinic acid

6) Rutin

10

15

20

25

30

35

40

451 6 .3 82 2 7 .2

1 0 .5 62 1 7 .1

1 3 .2 06 2 3 .8

2 .1 02 8 4 .1

1 1 .6 23 3 9 .1

8 .3 12 2 7 .2

9 .3 83 9 4 .2

1 2 .3 96 1 4 .8

3 .8 11 6 6 .1

2 6 1 8 8 7

1

3

45 6

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Time (min)

0

5

2 .6 11 7 7 .1

8 .8 74 4 6 .2

6 .4 53 8 0 .1

1 5 .7 22 3 8 .2

3 .2 92 2 4 .1

4 .8 73 2 4 .2

1 .5 82 2 2 .1

2

Re

lative A

bu

nd

an

ce (

%)

(a)

(b)

(c)

2) Tryptophan

i.d., 3.5-μm dp Xbridge C18 column (Waters, Milford, Massachusetts) using a linear mobile phase gradi-ent from 0.1% formic acid in water (100%) to 0.1% formic acid in aceto-nitrile (30%) in 30 min with a f low rate of 300 μL/min. Compounds were detected by electrospray ionization in

both positive and negative mode (m/z 150–2000).

Results and Discussion

Analysis of the samples: SPME–GC–

MS was chosen as the method for vol-atilesforthetomatosamples(Figure

2a) and resulted in the analysis of 101

metabolites, including aldehydes, al-cohols,andthiazoles.VariousSPME

fibers were tested but the fiber used here showed, on average, the best results for a broad range of volatile compounds.Ingeneral,SPMEshows

better sensitivity and coverage than headspace GC–MS but requires more method optimization with respect to adsorption conditions and the type ofSPMEfiber.Othertechniques,like

SBSE,seemtohavesuperiorperfor-manceoverSPME.Couplingofthese

techniques to GC×GC–time of f light (TOF)-MSwillincreasethesensitiv-ity and coverage signif icantly but nontargeted metabolite quantifica-tion of GC×GC–TOF-MSdataisstilla challenge and very time-consuming (6).Extremevolatilemetabolitesare

of course dif f icult to ana lyze by techniqueslikeSPME–GC–MS,and

more-specific techniques have to be used when it is expected that these types of compounds might be of in-terest.

A common method used in me-tabolomics in many application areas is GC–MS, in which metabolites are first oximated and silylated (5). With this method, various classes of small polar compounds can be analyzed by GC–MS, including organic acids, amino acids, sugars, sugar monophos-phates, alcohols, aldehydes, amines, and acyl monophosphates. These classes of compounds are present in almost every biof luid or food sample, which explains the popularity of this method. As a result, this method can be easily applied to a new (food) ma-trix without extensive optimization. Moreover, large databases exist of mass spectra of reference compounds, facilitating identification. Derivatized water–methanol extracts of tomato analyzed by OS-GC–MS resulted in the detection of 112 metabolites, in-cluding organic acids, amino acids, sugars, amines, sugar phosphates, andnucleosides(Figure2b).However,

a significant number of metabolites could not be identified using exist-ing databases. The coverage and sen-sitivity of the OS-GC–MS method can be increased signif icant ly by


using GC×GC–TOF-MS,ashasbeendemonstrated for other matrices (7). However, as mentioned before, non-targeted metabolite quantification of GC×GC–TOF-MSdatainlargescalestudies is mainly hampered by the limitation of existing data preprocess-ing tools.

Nonvolatile metabolites are usually analyzed by LC–MS. Reversed-phase LC–MS methods using a C18 column with a mobile phase gradient from water to acetonitrile or methanol are most commonly used for aqueous extracts of food and plant material. Tomato extracts were analyzed by reversed-phase LC–MS in both the positive ionization (PI) and negative ionization (NI)mode (Figure 2c).

Data preprocessing finally resulted in 1394 features that were used for further data analysis. Compounds identif ied were mainly secondary metabolites like (poly)phenolic com-pounds and derivatives thereof.

Small differences exist between methods used in literature and usu-ally include additives used for the mobile phases and different types and suppliers of C18 columns. Un-targeted screening is performed using ion-traporTOFmassspectrometers,

preferably in the PI and NI mode to increase the coverage. These methods are especially capable of analyzing larger, nonvolatile compounds such as phenolics and peptides. These classes of compounds do exist in many different foods and plant ma-terials, but the exact structures are very type-specific. Because of this and the fact that many LC–MS meth-ods exist with somewhat different ex-perimental conditions, no databases are available that can be used for identification. Databases have been set up, but primarily starting from a specific matrix, such as tomato (8). However, these are often of little use for other matrices. As a result, many peaks can often be detected by LC–MS, but many remain unidentified. This is a key problem in the success of LC–MS in metabolomics stud-ies. Consequently, standardization of LC–MS methods, in combination with proper databases and successful

identification strategies, are essential for the future of the use of LC–MS in (food) metabolomics.

Recent developments in LC, such as columns with sub-2-μm particles in UHPLC, have led to increased throughput, better separation, and higher sensitivity in metabolomics applications (9). The issue with re-spect to the large number of unknown peaks and their identification will then become even more urgent. Other developments, such as robust hydro-philic interaction chromatography (HILIC) and mixed-mode columns or ion-pair methods, may lead to the use of more than one LC–MS method in metabolomics studies, resulting in increased coverage (10). Ideally, these methods will be used for high-throughput detection of small polar metabolites without derivatization, in addition to the larger nonpolar metabolites detected with reversed-phase LC–MS. Some examples do exist in literature, especially in the field of microbial metabolomics, and have shown that it is possible to detect a wide range of underivatized small polar compounds in complex matrices with LC-based methods (11–13). One limitation of these LC–MS methods is their separation efficiency compared with GC–MS, especially when dealing with isomers like monosaccharides and sugar-phosphates. However, the speed, sensitivity, and sample prepa-ration of the LC–MS methods can be superior to GC–MS (for example, UHPLC coupled to high-resolution MS or MS–MS in HILIC or ion-pair mode). Quantification of many metabolites in a complex matrix is

challenging for both types of meth-ods. Although one always has to deal with ion-suppression in LC–MS, the GC–MS method with oximation and silylation shows strongly deviating analytical performance for differ-ent classes of compounds, mainly because of the characteristics of the derivatizationstep(5).Furthermore,

with the GC–MS method, the results obtained for some metabolites in cal-ibration solutions are very different from that in a complex matrix (5). As a result, the use of labeled standards for LC–MS methods might be suffi-cient for quantification but not for the GC–MS method, although each me-tabolite will require its own labeled standard. Therefore, these profiling methods are unlikely to be used for absolute quantification in the near future.Futuredevelopmentsinboth

GC–MS and LC–MS will determine whether LC–MS methods might re-place the existing GC–MS methods used in metabolomics.

Data PreprocessingAfter collection of the data, the next step in the metabolomics workf low is data preprocessing (that is, con-verting the raw data to reliable peak areas and peak lists). Various pro-grams exist for the different analyti-caltechniques.ForGC–MSmethods,

either peak picking or deconvolution isapplied.BecauseofthenatureofEI,

peak-picking routines usually lead to enormous peak lists, represented by a mass and retention time, in which a metabolite is represented by many entries, which is not ideal for statisti-cal analysis. Therefore, deconvolution

Figure 3: Standardized analysis scheme used for metabolomics studies.

STD samples QC

QC samples QC samples QC samples QC STDQC….

n batches

STD samples QC

QC samples QC samples QC samples QC STDQC….

Samples: Randomized study samples QC: Pooled study sample, biological calibration


Figure 4: Improvement of the analytical data quality by (a) internal standard (IS) normalization

and (b) batch correction.

770 780 790 800 810 820 8303

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routines are much more practical and ideally lead to metabolite lists — one entry per metabolite represented by the whole mass spectrum and area basedonallmassesof theEImass

spectrum.TheSPME–GC–MSand

OS-GC–MS data of the tomato sam-ples were deconvoluted using TNO-DECO(14)andresultedin101and

112metabolites, respectively. For

LC–MS data, deconvolution is under development but peak picking is usu-ally applied using different freeware software packages (for example, Met-Align, XCMS, and MzMine). Because of the soft ionization in ion-trap and TOFequipment,themassspectrum

of a metabolite often contains one main ion, such as [M−H]− or [M+H]+, or common adducts, such as [M+Na]+, depending on the nature of the me-tabolite and the mobile phases used. Thus, the metabolite can be repre-sented by one mass without losing too much information. Software tools exist that combine masses belonging to one metabolite, taking into ac-count chemical rules and knowledge, like natural isotopes and common adduct.FortheLC–MSdataofthe

tomato samples, in-house developed alignment and peak-picking software was used, resulting in 1181 peaks ex-cluding isotopes and adducts for as

much as possible. These are typical numbers of entries obtained with LC–MS in combination with peak-picking algorithms, but so far, it is not known what percentage of the number of en-tries are real metabolites as no one has annotated these peak lists.

Many different analytical methods have been developed and applied in metabolomics-related research areas. Interestingly, attention has only very recently been paid to the data qual-ity of these methods using quality control (QC) samples, such as pooled studysamples(Figure3).Thesesam-ples ref lect the average metabolite concentrations and the performance of the analytical platform can be as-sessed using the relative standard deviation (RSD) of the metabolites in these samples. This approach has only been applied in a qualitative way — by visual inspection — and no at-tempt was made to really improve the data quality in a quantitative way. However, an explicit way of reducing the analytical error is better (15). Its workf low first consists of data nor-malization using internal standards (IS), which reduces differences in sample extraction, derivatization, and injection volume. With the use of the RSDs of the analyte response in the QC samples to quantify the amount of analytical variation, the best IS is the one that gives a minimum RSD. Adjustments of the analytical instru-ment between batches of samples can be the cause of analytical errors. This behavior results in different response factors between, and even within, batches. The data quality can be improved further using two types of QC samples. The first type, calibra-tion QC samples, is used to perform a one-point calibration, and the second type, validation QC samples, is used to assess how well the calibration procedure improved the data qual-ity.Figure4showstheresultofthe

batch calibration procedure on both the study samples and QC samples. It can be seen that both the intra- and inter-batch trends are corrected for by the procedures. Table I shows the re-sult of the calibration procedures on the data quality of the OS-GC–MS

Table I: Relative standard deviation (RSD) of 118 metabolites in QC tomato samples in raw data and after calibration procedures

RSD (%) Raw Data After Calibration

< 10 96 110

10–20 12 5

20–30 4 1

> 30 6 2


tomato data. It can be seen that the raw data are already of good quality and that the calibration procedures lead to further improvement of the data quality.

It is essential to integrate the ana-lytical data coming from the different analytical platforms before statistical analysis to find better correlations with the taste attributes. This so-cal led data fusion procedure (16) has been applied to the three differ-ent data sets obtained for the tomato samples.

Data AnalysisMultivariate data, such as metabo-lomics data, can be analyzed best by multivariate statistics. In contrast to univariate statistics, which looks at one metabolite at a time, multivari-ate statistics analyzes all metabolites simultaneously. This approach has shown to be more powerful and sen-sitive because it exploits existing rela-tions between metabolites. Principal component analysis (PCA) is a com-mon multivariate statistical method that is often used to summarize and to visualize the relations that exist within variables, such as metabolites or sensory scores, within samples, and between samples and variables. Methods like regression analysis that build models on both metabolomics and sensory data are much more suitable for food metabolomics stud-ies because they explicitly model the relation between two data sets (that is, multivariate metabolomics data and a selected sensory attribute). The quality of the regression model deter-mines if the model is good enough for furtherinterpretation.Forthat,the

correlation coefficient (R2) between the predicted and the true outcome is used. Double cross validation is used to determine the R2 in an unbiased way.

Both the sensory data and (fused) analytical data were analyzed by typi-cal multivariate techniques such as PCA and partial least squares (PLS). Figure5ashowsaso-calledPCAbi-plot displaying both the tomato sam-ples and the sensory attributes. In this plot, it can be directly seen which

tomato samples score high or low on specifictasteattributes.Forexample,

samples 1 and 3 score high on sour, while samples 8 and 12 score high on sweet. It is also clear that tomatoes 16 and 17 are clearly different from the other tomatoes. A similar PCA plot can be made of the fused analytical data(Figure5b).Again,itcanbeseen

that tomato samples 16 and 17 are differentfromtheothers.Fromthese

plots, it can be concluded that these samples have a significantly differ-ent taste and a significantly different metabolite pattern. However, this is not true for all samples. Samples 4, 14, and19clustertogetherinFigure5a,

butinFigure5b,sample4isseparated

0.4(a)

(b)

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Figure 5: (a) PCA plot of the sensory data showing both the tomatoes (numbers) and the

attributes to enable interpretation about the relations. In total, 63% of the variation of the

sensory data is represented by PC1 (39%) and PC2 (24%); (b) PCA plot of the fused metabolomic

data. In total, 39% of the variation of the sensory data is represented by PC1 (25%) and PC2

(14%).


significantly from samples 19 and 14. In this case, the taste of these three samples is similar but the metabolite patterns show significant differences. One other striking example is samples 8and10.InFigure5b,thesesamples

are almost on the same spot, indicat-ing very similar metabolite patterns. However, in Figure 5a, samples 8

and 10 are clearly separated, sample 8 scoring high on sweet and sample 10highonbitter.Fromthis, itcan

be concluded that, despite the large differences in taste, the metabolite patterns are very similar, indicating that taste differences can be caused by very subtle differences in metabolites. Summarizing, the relative positions of the tomato samples in the sensory PCA plot and metabolomics PCA plot are very different. Hence, it can be concluded that PCA analysis does not suffice in correlating tomato sensory data and instrumental metabolomics data. Nevertheless, from the overview of Cevallos-Cevallos (2), it is seen that methods like PCA are still used.

PLS was carried out for dif fer-entsensoryattributes.Forsourness,

R2 = 0.92 as can be seen in Table II,

which indicates that at least 92% of the sensory attribute variation is in-dependently described by the metabo-lomicsdata.Forothertasteattributes,

similar high R2 values were found after variable selection (see Table II).

Fromthesemodels,themostimpor-tant metabolites can be determined that contribute most to the model (i.e., explain the most variation in the sensory attribute). Table III shows an example of such important metabo-lites for the PLS model of sourness, including the analytical platform they were derived from and whether the metabolites showed a positive or negative correlation with the sensory score. The results in Table III dem-onstrate the added value of applying and integrating different analytical platforms while all methods used in this study are represented. It should be mentioned that part of the impor-tant metabolites found for the dif-ferent taste attributes could not be identified and these were mostly me-tabolites detected by LC–MS. It was discussed earlier that identification of metabolites, especially those detected by LC–MS, is one of the critical issues in metabolomics that should be solved in the near future to make metabolo-mics a more successful technology. It should be noted that the metabolites themselves do not necessarily lead to the sensory experience with which they correlate. They can also be pre-cursor molecules, cause synergetic or masking effects with other metabo-lites, or have a regulatory role. In this study, most of the important metabo-lites could be identified, and of those

29 compounds, 20 were reported ear-lier to occur in tomatoes.

The next step in the whole process is the biological interpretation of the results (that is, understanding the observed relation between the iden-tif ied metabolites and the specific taste attribute). This of course needs profound knowledge of the chemistry of taste in the specific matrix.

Validation is another key step in the metabolomics workf low. In order to demonstrate the validity of the results obtained, a separate second study with new samples and sensory data should be conducted and treated in the same way as described here. With respect to the analytical chem-istry involved in metabolomics, this requires good reproducibility of the analyticalmethodsused.Forbioana-lytical methods, it is standard proce-dure to validate analytical methods accordingtoFDAguidelinesinclud-ing reproducibility. This part has been largely overlooked in metabo-lomics and only recently has more attention been paid to the stability of analytical methods over long time periods for, as an example, large-scale metabolomics studies (17).

After positive validation, the results obtained can be used to optimize or improve the taste of food products by approaches such as adapting growth or food processing conditions.

Conclusions

The different aspects of food metabo-lomics were described using tomato taste as an example. It is shown that the different parts of the metabo-lomics workf low should be of high quality in order to be successful. With respect to analytical chemistry, it is essential to have as much metabolite coverage as possible. Therefore, dif-ferent complementary analytical tech-niques should be used and the data should be integrated. Also, aspects like data quality, data preprocessing, and metabolite identification are es-sential within a food metabolomics study. This makes food metabolo-mics studies often laborious, tedious, and costly. However, in cases where targeted approaches using existing

Table III: Selected metabolites on basis of PLS regression on metabolomics data and sensory data on sourness

Metabolite Analytical Method Correlation

Terpineol SPME–GC–MS Negative

Cysteine OS-GC–MS Positive

Coumaroyl-quinic acid LC–MS Negative

Tryptophan LC–MS Positive

Dimethyl-3-cyclohexene-1-aldehyde SPME–GC–MS Negative

Table II: Double cross validated correlation coefficients (R2) for PLS models obtained for several taste attributes

R2

Flavor intensity 0.72

Taste intensity 0.89

Sweetness 0.87

Sourness 0.92

Aftertaste duration 0.82



knowledge are not sufficient, the food metabolomics approach can be a valu-able tool in food science. New tech-nological developments in analytical chemistry and statistics will increase the value of food metabolomics even further.

AcknowledgmentsWe would like to thank Karin Over-kamp, Bianca van der Werf, Jan Jetten, Luco Ravensberg, Miriam Kort, Richard Bas, Bas Muilwijk, Maarten Hekman, Marc Tienstra, Marloes ter Beek, Wilbert Oostrom, and Jack Vogels for their contribution to the different aspects of the metab-olomics workf low. Toon van de Ven (Monsanto Holland B.V., Antwerp, Belgium) is acknowledged for his con-tribution to the tomato sensory study.

References (1) D.S. Wishart, Trends Food Sci. Tech-

nol. 19, 482–493 (2008).

(2) J.M. Cevallos-Cevallos et al., Trends Food

Sci. Technol. 20, 557–566 (2009).

(3) U. Thissen et al., Food Qual. Prefer.,

(in press) (2010).

(4) B. Pieterse et al., J. Microbiol. Meth-

ods 64, 207–216 (2006).

(5) M.M. Koek et al., Anal. Chem. 78,

1272–1281 (2006).

(6) M.M. Koek et al., Metabolomics, in press.

(7) M.M. Koek et al., J. Chromatogr. A

1186, 420–429 (2008).

(8) S. Moco et al., Plant Physiol. 141,

1205–1218 (2006).

(9) D. Guillarme et al., Trends Anal.

Chem. 29, 15–27 (2010).

(10) K. Spagou et al., J. Sep. Sci. 33, 716–

727 (2010).

(11) H. Yoshida et al., J. Agric. Food Chem.

55, 551–560 (2007).

(12) W. Lu et al., Anal. Chem. 82, 3212–

3221 (2010).

(13) L. Coulier et al., Anal. Chem. 78,

6573–6582 (2006).

(14) R.H. Jellema et al., Chemom. Intel.

Lab. Syst. 104(1), 132–139 (2010).

(15) F. van der Kloet et al., J. Prot. Res. 8,

5132–5141 (2009).

(16) A.K. Smilde et al., Anal. Chem. 77,

6729–6736 (2005).

(17) S. Bijlsma et al., Anal. Chem. 78,

567–574 (2006).

Leon Coulier is a research scientist

and project manager analytical research

at TNO. His current research interests

include development and application of

MS-based metabolomics technology for

food, microbial, and medical applications.

Albert Tas is a consultant in analyti-

cal research and statistics at TNO. He has

been active in the field of mass spectrom-

etry, NMR, and multivariate statistics for

more than 30 years.

Uwe Thissen is a research scientist

and project manager life sciences at TNO

Triskelion BV. His main interests regard the

development and application of analytical

technologies for innovations in food. ◾

patent pendingwww.optimizetech.com/exp

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Determining High-Molecular-Weight Phthalates in Sediments Using GC–APCI-TOF-MS

The analysis of phthalates in environmental samples is an important application because some phthalates are considered to be potential endocrine-disrupting

chemicals (EDCs) (1). Most classical environmental meth-ods focus on the analysis of the dialkyl esters of phthalic acid ranging from dimethylphthalate (DMP) to dioc-tylphthalate (DOP) (2), among which di-isobutylphthal-ate (DiBP), dibutylphthalate (DBP), and bis(2-ethylhexyl)phthalate (DEHP) are the most important single isomer phthalates found in environmental samples (3,4). These solutes are single isomer compounds and relatively easy to analyze by gas chromatography–mass spectrometry (GC–MS) because only one peak for each compound is obtained by chromatographic analysis (5).

Using electron ionization (EI), the phthalic acid diesters strongly fragment, the most abundant ion being a phthalic anhydride ion at m/z = 149, common for all saturated alkyl phthalates (except DMP). This ion can be used for trace anal-ysis in selected ion recording (SIR) mode, provided that the target solutes are well separated by GC (5).

More recently, attention has been paid to the analysis of di-isononylphthalate (DiNP) and di-isodecylphthalate (DiDP). These compounds are synthesized from phthalic acid and a mixture of C9 or C10 alcohols, respectively. Consequently, the GC analysis of these solutes results in a cluster of peaks corresponding to different branched isomers.

In the case of DiNP and DiDP determination, the C9 isomers present in commercial DiNP samples par-tially overlap with the C10 isomers present in DiDP. No column or temperature program conditions could be found to completely separate C9 from C10 isomers, mak-ing quantification of DiNP and DiDP using the most abundant ion at m/z =149 impossible. To differentiate DiNP and DiDP, another ion should be measured. This can be done using ions 293 and 307, respectively, cor-responding to the M-alkyl ion (5). However, these ions are relatively low in abundance, and consequently, the same sensitivity as for the single isomer phthalates can-not be reached.

The environmental analysis of phthalates is established and widespread with most classical methods based on gas chromatography–mass spectrometry (GC–MS). Recently, attention has turned to the analysis of di-isononylphthalate (DiNP) and di-isodecylphthalate (DiDP), but their analysis by GC results in an unresolved cluster of peaks that all predominantly frag-ment to the phthalic anhydride ion at m/z = 149. Consequently, quantification of DiNP and DiDP using the most abundant ion is impossible and the same sensitivity as for the single isomer phthalates cannot be reached. GC, combined with atmospheric-pressure chemi-cal ionization (APCI), was used to analyze the high-molecular-weight phthalates. DiNP and DiDP were successfully ionized in proton transfer mode yielding spectra that contained the molecular ion as the base peak. The method was also applied to measure DiNP and DiDP in a sediment extract at relevant levels for environment samples.

Frank David, Pat Sandra, and Peter Hancock


Figure 1: GC–EI-MS total ion chromatogram (TIC) obtained for a DiNP/DiDP mixture (10 ppm).

Ab

un

dan

ce

20 00 21 00 22 00 23 00 24 00 25 00 26 00 27 00 28 00 29 00

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Figure 2: EI-MS spectrum of di-isodecyl phthalate (C28H46O4, MW = 446).

40,000

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Possible solutions are offered by the use of soft ionization techniques, such as chemical ionization (using ammonia), or by liquid chromatog-raphy (LC)–MS. Recently, the com-bination of GC with atmospheric-pressure chemical ionization (APCI) became commercially available (6) and this technique was tested for the analysis of the high-molecular-weight phthalates. The method was also applied to measure DiNP and DiDP in a sediment extract from the Netherlands.

ExperimentalR e f e r e n c e s a m p l e s o f D i N P (C26H42O4) and DiDP (C28H46O4) were obtained from BASF (Ludwig-shafen, Germany). Individual stock solutions at 1000 ppm were prepared in cyclohexane. Calibration solu-tions containing DiNP and DiDP at the 1–10 ppm level were prepared by dilution in cyclohexane.

A sediment sample, obtained during an environmental study (4), was dried by lyophilization and a 10-g sample was extracted with 50 mL of cyclohex-ane in an ultrasonic bath. The extract was concentrated to 10 mL in a Zymark Turbovap system (Caliper Life Sciences, Teralfene, Belgium).

Analysis was performed on an Agi-lent 7890 GC (Palo Alto, California) coupled to a Waters Xevo QTOF MS system (Waters, Manchester, United Kingdom). Sample introduction was performed using a CTC GC-Pal au-tosampler (Basel, Switzerland) by means of a split–split less injector. The MS system was operated in GC–MS mode using an atmospheric-pres-sure GC (APGC) source (Waters). Water was continually bled into the source during acquisition to promote proton transfer conditions.

The analysis used a 1-µL splitless injection at 300 °C with a 1.05-min purge delay. A 30 m × 0.25 mm, 0.25-μm df DB-5MS column was used (J & W Scientific, Folsom, California). The carrier gas was helium at a con-stant f low of 2 mL/min. The oven temperature program was as follows: 50 °C for 1 min, then 20 °C/min to 320 °C, hold for 2.5 min.

The instrument was tuned and calibrated using perf luorotripentyl-amine (FC 70) before acquisition, so that the resolution of the instrument was greater than 10,000 full width half maximum (FWHM). Exact-mass spectra were obtained (5 Hz) using a single-point lock mass (column bleed, m/z = 281.0517) infused into the source continuously during the temperature program.

The MS system had a corona current of 4.0 μA, a sampling cone voltage of 10 V, and a source temperature of 140 °C.

The cone gas was nitrogen at 30 L/h, the source auxiliary gas was nitrogen at 300 L/h, and the make-up gas was nitrogen at 300 mL/min. The GC interface tem-perature was 310 °C.

Results and DiscussionGC–EI-MS (Single-quadrupole sys-tem): A typical total ion chromatogram (TIC) obtained for the 10 ppm stan-dard mixture of DiNP and DiDP using electron impact ionization (performed on a single quadrupole instrument) is shown in Figure 1. A complex cluster of


Figure 4: Proton-transfer APCI spectrum for DiDP (m/z = 447.3494 = [C28H46O4 + H]+).

100

%

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500 550 600 650

461.3615

448.3539

447.3494

Figure 3: GC–APCI-TOF-MS analysis of a 10-ppm standard mixture containing DiNP and DiDP. (a) TIC; (b) XIC at 419.316 (DiNP); (c) XIC at 447.347 (DiDP).

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DiNP and DiDP isomers is observed. The spectrum taken at 25.6 min, corre-sponding to the most abundant DiDP isomer, is given in Figure 2. The mo-lecular ion at m/z = 446 (C28H46O4) cannot be detected and the main ions are at m/z =149 (phthalic anhydride, C8H5O3

+) and 307 (M-C10H19).Differentiation of the two high-mo-

lecular-weight phthalates can only be completed by monitoring the ions at 293 (DiNP) and 307 (DiDP). However, because of the low abundance of these ions compared to the base peak, lower sensitivity is obtained, and, moreover, confirmation by a second ion is not possible.GC–APCI-TOF: The analysis of the

10 ppm standard mixture was also per-formed using the conditions described in the experimental section. The TIC and the extracted ion chromatograms (XIC) at m/z = 419.316 (DiNP) and


Figure 5: Detection of high-molecular-weight phthalatesin sediment sample by GC–APCI-TOF-MS (a) XIC

at 419.316 (DiNP); (b) XIC at 433.332 (C9/C10); (c) XIC at 447.347 (DiDP); (d) XIC at 461.363 (C10 /C11).

%

%

Time (min)

(a)

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13.28 13.58 13.81

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13.64 13.87

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11.00 11.50 12.00 12.50 13.00 13.50 14.00 14.50 15.00 15.50 16.00

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%

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0

For more information on this topic, please visit our homepage at www.spectroscopyonline.com

447.347 (DiDP) are shown in Figure 3. The mass spectrum at 13.7 min (DiDP fraction) is shown in Figure 4. DiNP and DiDP give strong pseu-domolecular ions, [M + H]+ with no fragmentation compared with EI ion-ization. The main ion in the spectrum of DiDP is at m/z = 447.3494, a devia-tion of 4.5 ppm from the theoretical value for [C28H46O4 + H]+. The XICs for DiNP and DiDP, included in Figure 3, show that partial chromatographic separation is possible between these two compounds. However, the XICs are needed to allow quantification. The exact-mass detection enhances selectivity and allows confirmation of the presence of the individual solutes. Interestingly, the spectrum for DiDP shown in Figure 4 also contains m/z = 461.3615, −3.5 ppm from the cal-culated mass of C29H49O4. This can probably be explained by the pres-ence of mixed undecyl–decyl phthal-ates in the DiDP standard. If traces of C11 alcohols were present in the C10 alcohols used in DiDP synthesis, this would result in these impurities. Next, a sediment extract was also analyzed by GC–APCI-time of flight (TOF)-MS. The extracted ion chromatograms at 419.316 (DiNP) and 447.347 (DiDP) are given in Figure 5. Both solutes can be detected easily. It is, however, interesting to observe that the iso-mer distribution of DiNP and DiDP detected in the sediment sample are different from the standard mixture. Moreover, by using XICs at 433.332 and 461.363, additional phthalate solutes are detected. The masses cor-respond to C27H44O4 and C29H48O4, respectively, and are probably mixed C9/C10 and C10/C11 phthalates. At the end of each phthalate elution window, a single abundant peak is detected (for instance, at 13.74 min for DiNP and at 14.41 min for DiDP). These probably correspond to the linear di-C9 and di-C10 phthalates, respectively. The cal-culated concentrations of DiNP and DiDP in this sediment sample are 0.2 and 2 ppm, respectively.

Conclusions

DiNP and DiDP were successfully ionized in proton transfer APCI,

yielding spectra that contained the molecu lar ion as t he base pea k . T h e s e h i g h - m o l e c u l a r -w e i g h t phthalates could be detected below 1 ppm in sediment samples using A PCI by ex t rac t i ng exac t-mass chromatograms. At least two addi-tional phthalate moieties were also identified in the extracted sediment sample using exact-mass chromato-gra ms. The ca lcu lated formu las C 27H4 4O 4 a nd C 29H4 8O 4 suggest mixed C9/C10 and C10/C11 phthalates, respectively. Good exact-mass calcu-lation was obtained for all solutes, with mass errors less than 5 ppm. GC–APCI-TOF-MS therefore offers interesting perspectives for detailed characterization of high-molecu-lar-weight phthalates and for their sensitive and selective detection in environmental samples. The APGC source used allows all of the acqui-sition modes of tandem quadrupole and QTOF spectrometers to be used with a GC inlet combined with a soft ionization mechanism.

References

(1) S. Jobling et al., Environ Health Per-

spect. 103(6), 582–587 (1995).

(2) DIN EN ISO 18856.

(3) H. Fromme et al., Water Research

36(6), 1429–1438 (2002).

(4) W.J.G.M. Peijnenburg and J. Struijs, Ec-

otoxicology and Environmental Safety

63(2), 204–215 (2006).

(5) F. David et al., in Phthalate Esters, C.A.

Staples, Ed. (Springer, Berlin, 2003),

pp. 9–56.

(6) K. Worrall and P. Silcock, The Column

6(10), 3–6 (2010).

Frank David is R&D Manager at RIC,

Kortrijk, Belgium and visiting professor at

Ghent University, Belgium.

Pat Sandra is director of the Research

Institute for Chromatography (RIC),

Kortrijk, Belgium, professor in Separation

Science at Ghent University, Belgium, and

director of the Pfizer Analytical Research

Centre-UGent, Belgium.

Peter Hancock is Technical Manager in

the Chemical Analysis Business Operations

team working for Waters Corporation,

Manchester, United Kingdom, where he and

his team are actively involved in developing

analytical methods on new MS technologies

in application areas such as food and envi-

ronmental testing and chemical materials. ◾


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Preview of the 59th Annual ASMS Conference

Megan Evans, Spectroscopy Managing Editor

We present a brief overview of this year’s ASMS conference, which takes place June 5–9, 2011, in Denver, Colorado.

A popular theme song from the 1980s comes to mind when thinking about the 59th Annual ASMS Conference on Mass Spectrometry and Allied Topics — “Sometimes

you want to go where everybody knows your name, and they’re always glad you came.” While the conference floor is a long way from the Boston-based Cheers bar, the sentiment of welcome and familiarity among friends and colleagues is always present at this show.

This year’s conference will be held at the Colorado Convention Center in Denver, Colorado, June 5–9, 2011. All oral sessions, poster sessions, and exhibit booths will be located in the conven-tion center. Although the conference does not officially kick off until Sunday, there will be several two-day short courses start-ing on Saturday, June 4 but attendees must preregister for those. On-site registration for the conference only will be from 2:00 to 5:00 pm on Saturday, 10:00 am to 8:00 pm on Sunday, and 7:30 am to 5:00 pm on Monday through Thursday.

Sunday will mark the opening of the conference with short courses going on during the day, tutorial lectures from 5:00 to 6:30 pm, the plenary lecture from 6:45 to 7:45 pm, and the wel-

come reception from 7:45 to 9:30 pm. The tutorial lectures will be given by Mark W. Duncan of the University of Colorado and James Jorgenson of the University of North Carolina. Duncan’s lecture topic will focus on good mass spectrometry (MS) and its place in good science. Jorgenson, who is winner of the LCGC Lifetime Achievement award this year, will discuss liquid chro-matography (LC) and MS. This year’s opening plenary lecture, entitled, “Our Stellar Origins Revealed by Stardust Grains,” will be given by Ernst Zinner of Washington University. The lec-ture will address the MS analysis of tiny stardust grains from primitive meteorites and what that analysis reveals about stellar production of the elements.

Program ScheduleStaying consistent with previous year’s schedule, Monday through Thursday will consist of oral sessions, poster sessions, workshops, plenary lectures, and corporate hospitality suites. Oral sessions will take place daily from 8:30 to 10:30 am and 2:30 to 4:30 pm. Poster sessions, exhibits, and lunch breaks will be from 10:30 am to 2:30 pm.


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There are so many interesting sessions to attend at this year’s conference that it can often be difficult to choose which ones to attend. Some of LCGC and Spec-troscopy’s Editorial Advisory Board mem-bers offered recommendations to help our readers narrow it down.Sunday, June 5:

• TutorialLecturebyJamesJorgenson,

University of North Carolina, “LC and MS: A Match Made in Heaven”

Monday morning, June 6:

• IntegratedQualitativeandQuantitative

LC–MS for Elucidation of PK/PD• ProteinTherapeuticsinDrugDiscov-

eryandDevelopment:LC–MSQuanti-fication

• ImagingMS:InstrumentationandIon-ization Sources

Monday afternoon, June 6:

• ChallengesinQuantitativeBioanalysis

in a Regulated Environment for Bio-markers

Tuesday morning, June 7:

• DriedBloodSpotforDrugandMe-tabolite Analysis

• ClinicalChemistry:AdvancesinSepa-ration Technologies

Tuesday afternoon, June 7:

• SmallMoleculeDrugDiscovery:Ad-vances in Micro- and Nano-flow Sepa-rations

Wednesday morning, June 8:

• PKAssays:NovelApproachestoIn-crease LC–MS Throughput

Thursday Afternoon, June 9:

• BiomarkersofDrug/MetaboliteToxic-ity: LC–MS Methods

Workshops and Interest Group MeetingsWorkshops and interest group meetings will be held Monday through Wednesday from 5:45 to 7:00 pm. Topics include MS fundamentals, polymer mass spectrom-etry, mass spectrometry in drug target identification, MS-MS based protein iden-tification and characterization strategies, LC–MS libraries, career development in MS, and a discussion to generate content to stimulate undergraduate student inter-est in MS.

AwardsTwo awards will be presented at this year’s conference: the award for a Distinguished Contribution in Mass Spectrometry and

the Biemann Medal. The award for a Distinguished Contribution in Mass Spectrometry will presented on Monday, June 6, during the plenary lecture sched-uled from 4:45 to 5:30 pm. The Biemann Medal will be presented on Tuesday, June 7, during the plenary lecture scheduled from 4:45 to 5:30 pm. The award recipi-ents were not announced as of press time.

Conference FinaleThe conference will conclude on Thurs-day, June 9, with a plenary lecture from Arthur Shapiro of American University. Shapiro’s presentation, titled, “Why Are We Surprised by Only Some of the Things thatWeSee?VisualIllusions,theBrain,

and Baseball,” will present recently de-veloped visual illusions and discuss the implications of our perceptive (in)abili-ties. After the lecture there will be a clos-ing toast, officially ending the 59th ASMS Conference.

Plan Your Visit to Denver LogisticsDiscounted pricing for ASMS attendees is available for travel from the airport to downtown hotels. Rates from the ASMS website are $32 round trip on the Su-perShuttle and $70 one-way on the Ex-ecuCarfromtheDenverInternational

Airport; reservations are required. The ASMS room block features all major downtown hotels with very attractive conference rates, including: Crowne Plaza ($150), The Curtis ($169), Denver Mar-riot ($169), Embassy Suites ($179), Grand Hyatt($174),HiltonGardenInn($169),

Hyatt Regency ($189 single, $199 double), and the Sheraton ($157).

Beer, Biking, and BaseballDenver features several enjoyable attrac-tions for attendees looking to do some sightseeing in their free time. The Den-ver area is often referred to as the “Napa Valley of Beer” and there are plenty of breweries and microbreweries for visitors to explore. For outdoor enthusiasts, Den-ver offers more than 850 miles of paved off-road trails that connect to hundreds of miles of dirt trails for hiking and bik-ing. There is also Coors Field, home of the Colorado Rockies who will be facing the Los Angeles Dodgers toward the end of this conference.

Registration and Further InformationAdvance conference registration fees are $150 for ASMS members, $300 for non-members, $75 for ASMS students, and $120 for student nonmembers. One-day registration is available onsite for $175 and includes a printed program, but no conference proceedings or access to we-bcasting.

Advance registration for the confer-ence and short courses will close April 30. Those who have not registered by April 30 may register onsite at the con-vention center beginning Saturday, June 4, 2:00–5:00 pm, Sunday, June 5, 10:00 am–8:00 pm, and Monday through Thursday 7:30 am–5:00 pm. An addi-tional $50 will be charged for onsite reg-istration. There is no onsite registration for short courses.

For further information about the con-ference please contact: ASMS, Tel. (505) 989-4517, fax (505) 989-1073, email: [email protected], website: www.asms.org. ◾


PRODUCT RESOURCEShead_productstext_products text_products text_products text_products text_prod-ucts text_products text_products

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MS systemBruker Daltonics’ maXis 4G UHR-QTOF MS system is designed to offer full sensi-tivity resolution greater than 60,000 and mass accuracy greater than 600 ppb. The system reportedly maintains maximum sensitivity at full mass resolution, and can acquire data at speeds of up to 30 Hz during UHPLC separations. According to the company, the system can discover, identify, and quantify low-level compounds and metabolites in a single LC–MS-MS run. Bruker Daltonics, Billerica, MA; www.bdal.com

Column selection resourceThe Reversed Phase Column Screener from Phenomenex is a website-based resource designed to help chromatographers select the best HPLC or UHPLC column for their applications from the compa-ny’s product line. With the column-screening resource, users reportedly can search by compound characteristics, pharmacopeia classification, column, or application, and access application notes. According to the company, the resource gives primary and secondary recommendations for fully porous particle columns, complementary selec-tivities, and core-shell particle options, and recommends mobile phase start-ing conditions. The resource is available online at www.ColumnMatch.com. Phenomenex, Torrance, CA; www.phenomenex.com

Ion-trap GC–MS systemsAgilent Technologies’ model 220 and 240 Ion Trap GC–MS systems are designed with the multiple ionization and scan modes of an ion-trap mass spectrometer and the capil-lary flow technology of a gas chromatograph. According to the company, the model 240 has MS-MS and MSn capabili-ties and can perform EI, CI, and MS-MS in the same run. The model 220 reportedly is designed for educational laboratories and labora-tories needing rugged operation for high-throughput routine testing. Agilent Technologies, Santa Clara, CA; www.agilent.com

Capillary inlet tubesPhotonis USA’s capillary inlet tubes, manufactured with resistive glass, are designed to control the speed and direction of ions, significantly increasing their transfer efficiency. Resistive glass utilizes lead silicate, which acts as a semiconductor, reportedly creat-ing uniform electric fields within the capillary inlet tubes. When voltage is applied, positive or negative ions are preferentially attracted to more effec-tively draw them into the mass spectrometer. According to the company, the resistive glass prevents collisions between the ions and the tube walls, reducing ion loss and increasing transfer efficiency. The capillary inlet tubes are available as single-channel tubes or as six-channel multicapillary arrays. Photonis USA, Sturbridge, MA; www.photonis.com

Portable GC–MS systemTorion’s Guardion GC-TMS system is designed to be fast and portable for field GC applications. According to the company, the system combines a high-speed, EPC-controlled capillary chromatograph with an amplitude-scanning toroidal ion trap mass spectrometer. The system reportedly is sup-ported by the company’s SPME syringes, calibration standards, and data processing software. Torion, American Fork, UT; www.torion.com

Multipurpose samplerGerstel’s MPS multipur-pose sampler is designed to perform sample preparation and introduc-tion techniques such as SPE, dynamic headspace, stir-bar sorptive extrac-tion, thermal desorption, and SPME. According to the company, with the sampler, matrix residues can be eliminated, standards or reagents can be added, dilution series can be created, and analytes can be concentrated. The system reportedly can operate independently as a workstation or can be integrated with GC–MS or LC–MS. Gerstel, Linthicum, MD; www.gerstelus.com

Mass spectrometerThe BenchTOF-dx mass spec-trometer from Almsco Interna-tional is designed to identify, quantify, and differentiate the nature of known and unknown compounds at trace levels. According to the company, the instrument can screen for these compounds at lower levels with a higher degree of definition and clarity. The instrument reportedly provides full-scan sensitivity at typical GC–quad SIM levels and can integrate with existing gas chromatographs and data handling and control software. Almsco International, Llantrisant, United Kingdom; www.almsco.com

TOF mass spectrometerThe Waters Xevo G2 time-of-flight (TOF) mass spectrometer is designed to identify and quantify unknown compounds in complex samples. According to the com-pany, the bench-top mass spectrometer incorporates features from research-grade mass spectrometers. The mass spec-trometer reportedly uses the company’s UPLC-MSE method of data acquisition, which provides both mass precursor and fragment ion data from a single data set. Waters Corporation, Milford, MA; www.waters.com


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Mass spectrometerShimadzu’s LCMS-8030 triple-quadrupole mass spectrometer is designed to complement UHPLC systems, offering power and speed in the detection of target analytes. According to the company, the system features multiple reaction monitoring (MRM) transitions that enable data acquisition of as many as 500 channels/s, 15-ms polarity switching, and mass spectrum measure-ment speeds of 15,000 u/s. The instrument reportedly accelerates ions out of the collision cell by forming a pseudo-potential surface, producing high-efficiency collision-induced dissociation (CID) and high-speed ion transport. Shimadzu Scientific Instruments, Inc.,Columbia, MD; www.ssi.shimadzu.com

MicroplatesMicroLiter’s microplates for chromatography are available in 2-mL square, deep well, round, and conical bottom; 1.3-mL round well round bottom; and 0.75-mL round medium well round bottom designs. According to the company, the microplates feature clear poly-propylene for visual reference of samples and have pierce-able covers in either EVA or silicone with sprayed on PTFE barriers. Microliter Analytical Supplies, Inc., Suwanee, GA; www.microliter.com

AutosamplerThe PAL-xt autosampler from CTC Analytics includes the company’s XCHANGE option for automatic syringe exchange. The system is designed for adding internal standards without cross contamination, injection of small and large volumes in one sample sequence, and preventative exchange of syringes when using harmful media. The autosampler’s coupling system reportedly comprises a mating mechanical piece that captures the syringe and a magnet that automati-cally aligns. CTC Analytics AG, Zwingen, Switzerland; www.palsystem.com

Concentration systemFluid Management Systems’ PowerVap Automated Direct to Vial Concentration system is designed to automate the manual steps in the sample evaporation and con-centration process. According to the company, the compact, stand-alone system can be used for con-centrating samples such as the by-products of pesticides, herbicides, persistent organic pollutants, polychlorinated biphenyls, polycyclic aromatic hydrocarbons, pharmaceuticals, and personal care products. The system reportedly uses no water, can concentrate up to six samples simultaneously, and provides method documentation. Fluid Management Systems, Watertown, MA; www.fmsenvironmental.com

Mass spectrometerThe Waters SYNAPT G2 mass spec-trometer is designed for use in biophar-maceutical, metabolite identification, metabonomics, proteomics, biomarker studies, and food and environmental applications. According to the company, the quadrupole time-of-flight system features greater than 40,000 FWHM resolution, high sensitivity, a data acqui-sition rate of 20 spectra/s, exact mass (1 ppm RMS) information, and a dynamic range of up to five orders of magnitude. Waters Corporation, Milford, MA; www.waters.com

Trap columnsOptimize Technologies’ hand-tight OPTI-TRAP EXP trap col-umns are sample purification and preconcentration products designed for use at pressures as high as 20,000 psi (1400 bar). According to the company, the columns can connect directly to any injection valve (with 10-32 threads) or in-line with the company’s OPTI-LOK EXP fittings. Optimize Technologies, Inc., Oregon City, OR; www.optimizetech.com

Chromatography vialsVerex chromatography vials, caps, and inserts from Phenomenex are designed to meet specifications for performance and recoveries and to exceed industry standards. The vials’ borosilicate glass reportedly has the lowest ion content possible. According to the company, the vials can be sup-plied as kits and with mix-and-match cap choices, including crimp, snap, or screw, with septa available in preslit or nonslit formats. The vial kits reportedly are provided in dispenser packages made from recycled materials.Phenomenex, Inc., Torrance, CA; www.phenomenex.com

MS systemAB Sciex’s TripleTOF 5600 mass spectrometry system is designed for high-performance qualitative and quantitative analysis. According to the company, the system features Smart-Speed 100-Hz acquisition; Accelerator TOF Analyzer for high-resolution data at high speed; and EasyMass Accuracy to achieve stable ~1 ppm mass accu-racy without continuous user calibra-tion. AB Sciex, Foster City, CA; www.absciex.com

50 Current Trends In Mass Spectrometry May 2011 www.spec t roscopyonl ine .com

Ad IndexADVERTISER PG#

1st Detect .............................................................................................................................................................................................................. 9

Agilent Technologies ......................................................................................................................................................................................... 21

Biotage ................................................................................................................................................................................................................ 17

Bruker Daltonics ............................................................................................................................................................................................. CV2

Gerstel GmbH & Co. KG .................................................................................................................................................................................. CV4

Idex Health & Science ..................................................................................................................................................................................26–27

MicroLiter ...........................................................................................................................................................................................................33

Optimize Technologies ...................................................................................................................................................................................... 41

Phenomenex ............................................................................................................................................................................................ Covertip

Photonis .............................................................................................................................................................................................................. 13

Shimadzu Scientific Instruments ..................................................................................................................................................................... 11

Thermo Fisher Scientific ..................................................................................................................................................................................... 5

Torion Technologies............................................................................................................................................................................................. 3

Valco Instruments ........................................................................................................................................................................................... CV3

Waters Corporation ............................................................................................................................................................................................. 7

LCGC and Spectroscopy magazines are seeking contributed manuscripts for the 2011 supplement series, Cur-rent Trends in Mass Spectrometry. These issues will be published in July and October 2011.

These supplements will examine developments in mass spectrometry and MS-hyphenated methods and their application to analytical prob-lems in many fields, including but not limited to pharmaceutical and biopharmaceutical discovery, devel-opment, and manufacturing, biology and medicine, energy, the environ-ment, forensics, defense, and food safety.

Manuscripts should be approxi-mately 3500 words long, plus figures and tables as needed, and follow a standard experimental article format, including an abstract of approxi-mately 150–200 words. Figures and tables, along with their captions, should appear at the end of the man-uscript, and figures also must be sent

as separate files, preferably in JPG, TIF, PNG, or XLS format. References should be called out using numbers in parentheses and listed at the end of the manuscript in numerical order.

Submissions from equipment manufacturers will be considered but must be devoid of promotional content.

Submission Deadlines

October 2011 issue:

Abstracts (150-200 words): July 8, 2011Completed manuscripts: August 8, 2011

Where to Submit

Send all proposals and completed articles to Editor Laura Bush, at [email protected] (tel. +1.732.346.3020).

About LCGCFor nearly 30 years, LCGC has been the gold standard relied upon by

chromatographers across all tech-nique areas for objective, unbiased, nuts-and-bolts technical information they can use in their labs every day. LCGC’s columns and peer-reviewed articles continue to bring readers technical advice from the most re-spected, veteran industry experts in the areas of UHPLC, HPLC, GC, LC–MS, GC–MS, CE, SFC, and more.

About SpectroscopyFor more than 25 years, Spectroscopy has been providing information to enhance productivity, efficiency, and the overall value of spectroscopic in-struments and methods as a practical analytical technology across a variety of fields. Scientists, technicians, and laboratory managers gain proficiency and competitive advantage for the real-world issues they face through unbiased, peer-reviewed technical ar-ticles, trusted troubleshooting advice, best-practice application solutions, and educational web seminars. ◾

Call for PapersCurrent Trends in Mass Spectrometry2011 Supplements to LCGC and Spectroscopy

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