Resolving Volume-Restricted...

COLUMN WATCH

Total peak shape analysis

SAMPLE PREPARATION

PERSPECTIVES

Extraction technologies in food

safety studies

QUESTIONS OF QUALITY

Data integrity metrics for

chromatography

CE

LEBRATING OUR 30th YEA

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EB

RATING OUR 30th YEAR

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December 2017 Volume 30 Number 12

www.chromatographyonline.com

The benefits of CE–MS for analyzing limited amounts of polar

and charged biological samples

Resolving Volume-Restricted Metabolomics

Q

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c

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That’s the problem with relying on the assumptions of column calibration to characterize macromolecules.

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LC•GC Europe December 2017654

Editorial Policy:

All articles submitted to LC•GC Europe

are subject to a peer-review process in association

with the magazine’s Editorial Advisory Board.

Cover:

Original materials courtesy: Nik Merkulov/

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Columns662 SAMPLE PREPARATION PERSPECTIVES

The Use of Extraction Technologies in Food Safety

Studies

Changling Qiu and Doug Raynie

Traditional extraction methods for food samples, such as liquid–

liquid extraction and Soxhlet extraction, are often time-consuming

and require large amounts of organic solvents. Therefore, one of

the objectives of analytical food safety studies currently has been

the development of new extraction techniques that can improve

the accuracy and precision of analytical results and simplify the

analytical procedure.

670 COLUMN WATCH

Peak Shapes and Their Measurements: The Need and the

Concept Behind Total Peak Shape Analysis

M. Farooq Wahab, Darshan C. Patel, and Daniel W. Armstrong

A new “total peak shape analysis” approach is suggested that

facilitates detection and quantifi cation of concurrent fronting and

tailing in peaks. Several remediation approaches are proposed to

chromatographers, which can help them analyze and improve the

peak shapes.

679 QUESTIONS OF QUALITY

Data Integrity Metrics for Chromatography

Mark E. Newton and R.D. McDowall

This article discusses metrics for monitoring data integrity within

a chromatography laboratory, from the regulatory requirements to

practical implementation.

Departments686 Products

688 Events

689 The Applications Book

COVER STORY

658 Resolving Volume-Restricted Metabolomics Using

Sheathless Capillary Electrophoresis–Mass Spectrometry

Rawi Ramautar

Advances that signifi cantly

improved the performance of

capillary electrophoresis–mass

spectrometry for in-depth

metabolic profi ling of limited

sample amounts are considered.

Attention is also devoted to

various technical aspects that

still need to be addressed to

make capillary electrophoresis–

mass spectrometry a viable

approach for volume-restricted

metabolomics.

December | 2017

Volume 30 Number 12

.

A major challenge in bioanalysis and metabolomics is the

profiling of (endogenous) metabolites in volume-restricted

and often scarce biological samples. Though the analytical

techniques currently used in metabolomics are powerful, the

need for relatively large amounts of sample for pre-analytics

and injection prevent their use in many biomedical and clinical

applications. For example, metabolic profiling of small-volume

biological samples, such as cerebrospinal fluid (CSF) from

mouse models, spheroids or microtissues, liquid biopsies,

and samples from microfluidic organ-on-a-chip systems,

is seriously hindered by the current analytical technologies

because of the limited sample material. The standard

analytical techniques also do not allow a maximum amount of

biochemical information from valuable and scarcely available

biological samples to be obtained because the material is

completely consumed for a single metabolomics measurement.

Despite important developments in analytical separations

technology over recent years, the analysis of small-volume

biological samples remains a challenging task. Therefore, there

is a critical need for the introduction of analytical methods to

allow highly sensitive metabolic profiling of volume-restricted or

mass-limited biological samples.

Capillary electrophoresis–mass spectrometry (CE–MS) can

be considered an attractive microscale analytical method for

metabolomics because in CE nanolitre injection volumes are

used from microlitre sample amounts or less in the injection

vial (1–3). Therefore, CE–MS is highly suited for the profiling

of metabolites in ultrasmall biological samples, as has been

recently demonstrated for the analysis of CSF from mice and

extracts from small tissues or a single cell (4–7). Moreover,

CE (denoting here capillary zone electrophoresis [CZE])

separates compounds based on differences in their intrinsic

electrophoretic mobility, which is dependent on the charge and

size of the analyte and, therefore, well-suited for the analysis of

polar and charged metabolites. As the separation mechanism

of CE is fundamentally different from chromatographic-based

separation techniques, a complementary view on the

composition of (endogenous) metabolites present in a

given biological sample is provided. In comparison with

chromatographic-based methods the separation efficiency

of CE is very high because there is no mass transfer betwe

phases, and under perfect experimental conditions the only

source of band broadening in CE is from longitudinal diffus

Over the past few years, various new CE–MS approaches

been developed that show a strong potential for improving

sensitivity and metabolic coverage in metabolomics (8). T

article will focus on advances that significantly improved t

analytical performance—especially with regards to impro

the metabolic coverage—of CE–MS for volume-restricted

metabolomics studies.

Sheathless CE–MS Using a Porous Tip Spraye

CE is essentially a low-flow microscale separation techn

that reaches its optimal separation performance at very

flow rates—typically in the range of 20 nL/min to 100 nL

min—depending on the pH of the separation buffer wh

a bare fused-silica capillary. A high separation resolutio

obtained in CE by merely separating the compounds b

Resolving Volume-Restricted

Metabolomics Using Sheathless

Capillary Electrophoresis–

Mass Spectrometry

Rawi Ramautar, Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research,

Leiden University, The Netherlands

The analytical toolbox used in present-day metabolomics encounters difficulties for the analysis of

limited amounts of biological samples. Therefore, a significant number of crucial biomedical and clinical

questions cannot be addressed by the current metabolomics approach. Capillary electrophoresis–

mass spectrometry (CE–MS) has shown considerable potential for the profiling of polar and charged

metabolites in volume-restricted or mass-limited biological samples. This article considers advances

that significantly improved the performance of CE–MS for in-depth metabolic profiling of limited sample

amounts. Attention is also devoted to various technical aspects that still need to be addressed to make

CE–MS a viable approach for volume-restricted metabolomics.

KEY POINTS

t� There is a strong need for microscale analytica

technology and workflows for volume-restricte

metabolomics.

t� CE–MS using a sheathless porous tip interfac

attractive tool for highly sensitive analysis of p

charged metabolites.

t� New pre-analytics are needed in combination

CE–MS to effectively profile metabolites in ult

samples.

-$r($�&VSPQF

658

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A major challenge in bioanalysis and metabolomics is the

profiling of (endogenous) metabolites in volume-restricted

and often scarce biological samples. Though the analytical

techniques currently used in metabolomics are powerful, the

need for relatively large amounts of sample for pre-analytics

and injection prevent their use in many biomedical and clinical

applications. For example, metabolic profiling of small-volume

biological samples, such as cerebrospinal fluid (CSF) from

mouse models, spheroids or microtissues, liquid biopsies,

and samples from microfluidic organ-on-a-chip systems,

is seriously hindered by the current analytical technologies

because of the limited sample material. The standard

analytical techniques also do not allow a maximum amount of

biochemical information from valuable and scarcely available

biological samples to be obtained because the material is

completely consumed for a single metabolomics measurement.

Despite important developments in analytical separations

technology over recent years, the analysis of small-volume

biological samples remains a challenging task. Therefore, there

is a critical need for the introduction of analytical methods to

allow highly sensitive metabolic profiling of volume-restricted or

mass-limited biological samples.

Capillary electrophoresis–mass spectrometry (CE–MS) can

be considered an attractive microscale analytical method for

metabolomics because in CE nanolitre injection volumes are

used from microlitre sample amounts or less in the injection

vial (1–3). Therefore, CE–MS is highly suited for the profiling

of metabolites in ultrasmall biological samples, as has been

recently demonstrated for the analysis of CSF from mice and

extracts from small tissues or a single cell (4–7). Moreover,

CE (denoting here capillary zone electrophoresis [CZE])

separates compounds based on differences in their intrinsic

electrophoretic mobility, which is dependent on the charge and

size of the analyte and, therefore, well-suited for the analysis of

polar and charged metabolites. As the separation mechanism

of CE is fundamentally different from chromatographic-based

separation techniques, a complementary view on the

composition of (endogenous) metabolites present in a

given biological sample is provided. In comparison with

chromatographic-based methods the separation efficiency

of CE is very high because there is no mass transfer between

phases, and under perfect experimental conditions the only

source of band broadening in CE is from longitudinal diffusion.

Over the past few years, various new CE–MS approaches have

been developed that show a strong potential for improving the

sensitivity and metabolic coverage in metabolomics (8). This

article will focus on advances that significantly improved the

analytical performance—especially with regards to improving

the metabolic coverage—of CE–MS for volume-restricted

metabolomics studies.

Sheathless CE–MS Using a Porous Tip SprayerCE is essentially a low-flow microscale separation technique

that reaches its optimal separation performance at very low

flow rates—typically in the range of 20 nL/min to 100 nL/

min—depending on the pH of the separation buffer when using

a bare fused-silica capillary. A high separation resolution is

obtained in CE by merely separating the compounds based

Resolving Volume-Restricted Metabolomics Using SheathlessCapillary Electrophoresis–Mass SpectrometryRawi Ramautar, Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research,

Leiden University, The Netherlands

The analytical toolbox used in present-day metabolomics encounters difficulties for the analysis of limited amounts of biological samples. Therefore, a signifi cant number of crucial biomedical and clinical questions cannot be addressed by the current metabolomics approach. Capillary electrophoresis– mass spectrometry (CE–MS) has shown considerable potential for the profi ling of polar and charged metabolites in volume-restricted or mass-limited biological samples. This article considers advances that signifi cantly improved the performance of CE–MS for in-depth metabolic profi ling of limited sample amounts. Attention is also devoted to various technical aspects that still need to be addressed to make CE–MS a viable approach for volume-restricted metabolomics.

Ph

oto

Cre

dit: N

ik M

erk

ulo

v/S

hu

tte

rsto

ck.c

omKEY POINTS

• There is a strong need for microscale analytical

technology and workflows for volume-restricted

metabolomics.

• CE–MS using a sheathless porous tip interface is an

attractive tool for highly sensitive analysis of polar and

charged metabolites.

• New pre-analytics are needed in combination with

CE–MS to effectively profile metabolites in ultrasmall

samples.


on their electrophoretic mobilities, that is, under (near-)zero

electroosmotic flow conditions. The inherently low flow rates of

CE are also useful with regards to the electrospray ionization

(ESI) mechanism. In ESI, smaller droplets are generated

under low flow separation conditions, which results in a more

efficient desolvation and an improved transfer of ions to the MS

system (9–11). Moreover, at very low flow rates (≤20 nL/min) ion

suppression is significantly reduced, resulting in an improved

concentration sensitivity (9), which is important for ultratrace

detection of metabolites in limited sample amounts.

In a conventional CE system, both ends of the separation

capillary are immersed in buffer vials to which electrodes

are added to provide a high voltage gradient. To couple CE

to MS, the outlet vial must be replaced by an interface to

close the electrical circuit and to provide contact with the ESI

stream. Therefore, a CE–MS interface needs to apply voltage

to the capillary outlet while maintaining independent CE and

ESI electrical circuits. A sheath-liquid interface and various

other interfacing techniques have been developed to allow

the coupling of CE to MS. Until now, most CE–MS-based

metabolomics studies have been performed with a sheath-liquid

interface (12). CE–MS approaches using a sheath-liquid interface

for metabolomics were first developed by Soga and co-workers

(13,14). The sheath-liquid interface, originally designed by

Smith and co-workers (15), has been used for a broad range

of bioanalytical applications with acceptable analytical figures

of merit. However, the sheath-liquid is generally provided at a

flow rate between 2 μL/min to 10 μL/min, thereby significantly

diluting the CE effluent and resulting in compromised detection

sensitivities for metabolomics applications. Moreover, this flow

rate is not compatible with nano-ESI-MS. However, an important

benefit of the sheath-liquid interface is that the composition can

be modified to improve the ionization efficiency without affecting

the selectivity and efficiency of the electrophoretic separation

(16,17). The influence of these agents on metabolomics studies

by CE–MS still needs to be explored. Overall, as both CE

and ESI-MS perform optimally at low flow-rate conditions, the

coupling of CE to MS should preferably be performed via an

interface that effectively makes use of the intrinsically low flow

separation property of CE and the improved ESI efficiency under

these conditions.

At present, the development of new or improved interfacing

designs for CE–MS and the evaluation of their potential for

bioanalysis and metabolomics remains an active area of

research (18–25). So far, the most encouraging results for

volume-restricted metabolomics studies have been obtained by

CE–MS using a sheathless porous tip interface. In this design,

which was developed by Moini (19), a porous capillary tip

inside a cylindrical metal tube maintains electrical contact via

transport of ions and electrons through the capillary wall while

spraying the CE effluent into the nano-ESI-MS system. The

sheathless porous tip design is especially useful for interfacing

narrow (<30 μm internal diameter [i.d.]) capillaries and for low

flow-rate (<20–30 nL/min) nano-ESI-MS analyses (26).

In-Depth Metabolic Profi ling of Volume-Restricted SamplesThe capabilities of CE–MS using a sheathless porous tip

interface for global profiling of cationic metabolites in human

urine have been assessed (27). Low nanomolar limits of

detection (LODs) were obtained for a wide range of metabolite

classes in human urine by using only an injection volume of

9 nL. As a follow-up to this work, the potential of sheathless

CE–MS for metabolic profiling of very minute amounts of

biological samples was assessed. To this end, mouse CSF

was selected as a typical example of a biological material only

available in minute quantities, that is, only a few microlitres

can be obtained under proper conditions (4). Figure 1 shows

a metabolic profile obtained by sheathless CE–MS for mouse

CSF after only a 1:1 dilution with water, thereby fully retaining

sample integrity. By using an injection volume of 45 nL from a

vial containing only 2 μL of a 1:1 diluted CSF, more than 300

compounds could be observed. As only 45 nL of the sample

was consumed, the proposed method allows multiple analyses

on a single highly valuable and precious mouse CSF sample,

enabling repeatability studies and, even more interesting,

analysis of the same sample at different separation conditions

to further increase the metabolic coverage. Performing multiple

analyses on a single, scarce biological sample is not possible

with conventional analytical techniques.

Apart from volume-restricted biological samples, such as

mouse CSF, there is currently a strong interest in analytical

tools capable of providing highly sensitive metabolic profiles

for microscale cell culture samples. The performance of

sheathless CE–MS has therefore been assessed for the

profiling of cationic metabolites in an extract from approximately

10,000 HepG2 cells. Figure 2 shows that a reasonable number

of metabolites could be detected by sheathless CE–MS using

an injection volume of 60 nL, which in this case equals the

aliquot of only 5 HepG2 cells. An improvement of the injection

part is needed to further enhance the detection sensitivity of

the sheathless CE–MS method, however, the results obtained

so far clearly indicate the strong potential of the method for

metabolic profiling of limited sample amounts.

Recently, the utility of the sheathless CE–MS method

was also examined for the profiling of anionic metabolites in

biological samples (28), using the same separation conditions

used for the profiling of cationic metabolites—only the MS

detection and CE separation voltage polarity were switched.

A wide range of anionic metabolite classes could be profiled

under these conditions, including sugar phosphates,

nucleotides, and organic acids. An injection volume of 20 nL

resulted in nanomolar detection limits, which corresponded to

659www.chromatographyonline.com

Ramautar

50

1

2

3

4

5

x106

10 15 20 25

Migration time (min)

Inte

nsi

ty (

cou

nts

)Figure 1: Metabolic profile (m/z 65–1000) of mouse cerebrospinal

fluid obtained with sheathless CE–MS using a porous tip

sprayer. Conditions: separation buffer: 10% acetic acid (pH 2.2);

electrophoretic separation at +30 kV; sample injection: circa

45 nL. Adapted with permission from reference 4.

a significant enhancement when compared to the micromolar

detection limits typically obtained with classical sheath-liquid

CE–MS methods. Structural isomers of phosphorylated sugars

as well as isobaric metabolites could be selectively analyzed

by the proposed sheathless CE–MS method without using

any derivatization. The approach was used for the profiling of

anionic metabolites in extracts from the glioblastoma cell line.

Around 100 compounds were found for an injection volume of

20 nL, corresponding to the content of approximately 400 cells.

Future DirectionsThe examples highlighted here clearly indicate the ability

of sheathless CE–MS using a porous tip sprayer for

in-depth metabolic profiling of minute amounts of sample

material. Although (sub-)nanomolar detection limits can

be obtained for a wide range of polar compounds by only

using nanolitre injection volumes, as required for trace-level

analysis of metabolites in limited sample amounts, the next

important step is to examine the utility of sheathless CE–MS

for large-scale volume-restricted metabolomics studies.

Hirayama and co-workers have recently shown that a single

sheathless porous tip capillary can be used for more than

180 successive runs of a 10-fold diluted human urine sample

(29). Still, the long-term performance of sheathless CE–MS for

volume-restricted metabolomics needs to be assessed in more

extended studies analyzing large numbers of diverse biological

samples.

When using a highly sensitive microscale analytical

method for in-depth metabolic profiling, attention should also

be paid to the design of reliable sampling techniques for

volume-restricted or mass-limited samples. Nemes

and co-workers have recently developed a microprobe

single-cell CE–MS method, which integrates capillary

microsampling, microscale metabolite extraction, and

CE–MS analysis, for in situ mass spectrometric profiling of

metabolites in single cells (7). It is anticipated that microprobe

single-cell CE–MS will allow metabolomics studies in larger

populations of single cells and other model organisms. Proper

sample pretreatment strategies are needed for the selective

extraction of polar and charged metabolites from minute

amounts of sample. In this context, it would be interesting to

evaluate strategies like in-line or on-line solid-phase extraction

(SPE)-CE–MS or to explore the possibilities of electrodriven

sample pretreatment techniques, such as electromembrane

extraction or three-phase electroextraction (30,31). Overall,

further developments in this area may result in a more viable

CE–MS approach for probing the polar metabolome in

volume-restricted samples.

AcknowledgementsRawi Ramautar would like to acknowledge the financial support

of the Veni grant scheme of the Netherlands Organization for

Scientific Research (NWO Veni 722.013.008).

Confl ict of InterestThe author has no other relevant affiliations or financial

involvement with any organization or entity with a financial

interest in or financial conflict with the subject matter or

materials discussed in the manuscript apart from those

disclosed.


Ramautar

Time (min)

Inte

nsi

ty (

cou

nts

)

9e4

8e4

7e4

6e4

5e4

4e4

3e4

2e4

1e4

0e013 14 15 16 17

m/z 284,1157

m/z 76,0462

m/z 118,0943

m/z 130,0945

m/z 192,1707

m/z 90,0624

m/z 133,0693

m/z 120,0888

m/z 120,0735

m/z 106,0575

m/z 134,0532

m/z 150,0674

m/z 116,0786

m/z 156,0862

m/z 132,1103

m/z 147,1219

m/z 130,0582

m/z 147,0856

m/z 182,0921

m/z 182,0588

m/z 166,0963

m/z 241,0558

m/z 137,0543

m/z 175,1296

m/z 132,0852

m/z 537,9050

m/z 148,0695

18 19 20 21 22 23 24 25 26 27

Figure 2: Multiple extraction ion electropherograms for a selected number of metabolites obtained with sheathless CE–MS using a porous

tip sprayer in an extract of HepG2 cells. Conditions: separation buffer: 10% acetic acid (pH 2.2); electrophoretic separation at +30 kV; sample

injection: circa 60 nL.

References(1) W. Zhang, T. Hankemeier, and R. Ramautar, Curr. Opin. Biotechnol.

43, 1–7 (2017).(2) N.L. Kuehnbaum and P. Britz-McKibbin, Chem. Rev. 113, 2437–2468

(2013).(3) A. Slampova and P. Kuban, J. Chromatogr. A 1497, 164–171 (2017).(4) R. Ramautar, R. Shyti, et al., Anal. Bioanal. Chem. 404, 2895–2900

(2012).(5) J.X. Liu, J.T. Aerts, S.S. Rubakhin, X.X. Zhang, and J.V. Sweedler,

Analyst 139, 5835–5842 (2014).(6) R.M. Onjiko, S.A. Moody, and P. Nemes, Proc. Natl. Acad. Sci. USA

112, 6545–6550 (2015).(7) R.M. Onjiko, E.P. Portero, S.A. Moody, and P. Nemes, Anal Chem. in

press (2017).(8) P.W. Lindenburg, R. Haselberg, G. Rozing, and R. Ramautar,

Chromatographia 78, 367–377 (2015).(9) A. Schmidt, M. Karas, and T. Dulcks, J. Am. Soc. Mass Spectrom. 14,

492–500 (2003).(10) M. Wilm and M. Mann, Anal. Chem. 68, 1–8 (1996).(11) G.A. Valaskovic, N.L. Kelleher, and F.W. McLafferty, Science 273,

1199–1202 (1996).(12) A. Hirayama, M. Wakayama, and T. Soga, TrAC Trends in Analytical

Chemistry 61, 215–222 (2014).(13) T. Soga, Y. Ohashi, Y. Ueno, H. Naraoka, M. Tomita, and T. Nishioka,

J. Proteome Res. 2, 488–494 (2003).(14) T. Soga, Y. Ueno, H. Naraoka, Y. Ohashi, M. Tomita, and T. Nishioka,

Anal. Chem. 74, 2233–2239 (2002).(15) R.D. Smith, C.J. Barinaga, and H.R. Udseth, Anal. Chem. 60,

1948–1952 (1988).(16) T.J. Causon, L. Maringer, W. Buchberger, and C.W. Klampfl, J.

Chromatogr. A 1343, 182–187 (2014).(17) G. Bonvin, S. Rudaz, and J. Schappler, Anal. Chim. Acta 813, 97–105

(2014).(18) E.J. Maxwell, X. Zhong, H. Zhang, N. van Zeijl, and D.D. Chen,

Electrophoresis 31, 1130–1137 (2010).(19) M. Moini, Anal. Chem. 79, 4241–4246 (2007).(20) R. Wojcik, O.O. Dada, M. Sadilek, and N.J. Dovichi, Rapid Commun.

Mass Spectrom. 24, 2554–2560 (2010).(21) X. Guo, T.L. Fillmore, Y. Gao, and K. Tang, Anal. Chem. 88,

4418–4425 (2016).

(22) S.B. Choi, M. Zamarbide, M.C. Manzini, and P. Nemes, J. Am. Soc. Mass Spectrom. 28, 597–607 (2017).

(23) T.T. Nguyen, N.J. Petersen, and K.D. Rand, Anal. Chim. Acta 936, 157–167 (2016).

(24) V. Gonzalez-Ruiz, S. Codesido, J. Far, S. Rudaz, and J. Schappler, Electrophoresis 37, 936–946 (2016).

(25) J. Krenkova, K. Kleparnik, J. Grym, J. Luksch, and F. Foret, Electrophoresis 37, 414–417 (2016).

(26) J.M. Busnel, B. Schoenmaker, et al., Anal. Chem. 82, 9476–9483 (2010).

(27) R. Ramautar, J.M. Busnel, A.M. Deelder, and O.A. Mayboroda, Anal. Chem. 84, 885–892 (2012).

(28) C. Gulersonmez, S. Lock, T. Hankemeier, and R. Ramautar, Electrophoresis 37, 1007–1014 (2016).

(29) A. Hirayama, M. Tomita, and T. Soga, Analyst 137, 5026–5033 (2012).(30) A. Gjelstad and K.F. Seip, Bioanalysis 7, 2133–2134 (2015).(31) R.J. Raterink, P.W. Lindenburg, R.J. Vreeken, and T. Hankemeier,

Anal. Chem. 85, 7762–7768 (2013).

Rawi Ramautar strongly believes in the power of analytical

technology to contribute to a better understanding of

biochemical mechanisms underlying diseases. He therefore

studied both pharmacochemistry and analytical sciences

at the Vrije Universiteit of Amsterdam (The Netherlands) to

have the necessary background to undertake a Ph.D. in this

direction. In 2010, he completed his Ph.D. on the development

of capillary electrophoresis–mass spectrometry methods

for metabolomics at Utrecht University (The Netherlands).

Intrigued by metabolomics for disease prediction and

diagnosis, Rawi switched to the Leiden University Medical

Center (The Netherlands) to broaden his horizon on this topic.

Currently, he is a principal investigator (tenured) at the Leiden

Academic Center for Drug Research of the Leiden University

where his group is developing microscale analytical workflows

for volume-restricted biomedical problems.

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SAMPLE PREPARATIONPERSPECTIVES

Food safety has become a top

concern in our society. In general

the public is increasingly concerned

about the safety of the food products

they consume every day as more and

more food contamination incidents

and widespread recalls arise. Such

incidents have alerted the authorities

and the public that more efforts and

deeper investigations are needed.

As a result, reliable and efficient

methods for food safety analyses are

required. Even with modern detection

techniques, because of the low

concentrations of contaminants and

complicated food matrices, efficient

sample preparation is necessary.

Traditional extraction methods

for food samples, such as liquid–

liquid extraction (LLE) and Soxhlet

extraction, are often time-consuming

and require large amounts of

organic solvents. Therefore, one

of the objectives of analytical food

safety studies has currently been

the development of new extraction

techniques that can improve the

accuracy and precision of analytical

results and simplify the analytical

procedure.

Because of increased concerns

for food safety, attention is given to

developing methods for determination

of contaminants and other harmful

substances from food samples. The

analysis of food samples is usually a

complicated procedure involving many

steps. It requires extensive sample

extraction before further analysis.

Sample extraction is a crucial step

in food sample analysis because it

can affect the concentration of the

analyte and the cleanliness of the

sample. Traditional sample extraction

techniques used in food safety

studies are based on the suitable

choice of solvents and the use of

heat and agitation to improve the

solubility of the desired compounds

and the mass transfer (1), like in

Soxhlet, liquid–liquid, and shake-flask

extraction. Some of the traditional

extraction techniques can require

a great deal of time—Pedersen

and Olsson (2) performed Soxhlet

extraction of acrylamide from potato

chips, and it took seven days to get

a complete extraction. Frenich and

coworkers (3) reported a method

for the determination of residues of

organochlorine and organophosphorus

pesticides using Soxhlet extraction.

This extraction method involved

laborious steps with the use of large

amounts of solvent. Analysis of

polychlorinated biphenyls (PCBs),

polychlorinated dibenzo-p-dioxins

(PCDDs), and polychlorinated

dibenzofurans (PCDFs) in butter

based on three different liquid–liquid

extraction methods was studied

by Ramos and coworkers (4). The

reported methods also involved

time-consuming and large solvent

consumption steps. These traditional

extraction techniques are quite

laborious, time-consuming, and involve

large quantities of organic solvents,

which are flammable, expensive, and

generate hazardous waste.

In recent years, several new

extraction techniques emerged

as alternatives to the conventional

sample preparation methods,

including microwave-assisted

extraction (MAE), ultrasound-assisted

extraction (UAE), accelerated

solvent extraction (ASE) (also

known as pressurized solvent

extraction [PSE]), supercritical fluid

extraction (SFE), and solid-phase

microextraction (SPME). These new

extraction techniques have numerous

advantages over traditional extraction

methods, including shortened

extraction times, reduced solvent

consumption, reduced cost, and

automation. In this column, we will

provide an overview of these newer

extraction techniques that have been

applied to food safety studies.

Liquid–Liquid ExtractionLiquid–liquid extraction is the most

widely used method for the extraction

of analytes from aqueous food

samples. In LLE, the sample

is distributed or partitioned between

two immiscible solvents in which the

analyte has different solubilities. The

solution containing the analyte must

be immiscible with the solvent used

to extract the analyte. The

main advantages of this method

are the wide availability of solvents

and the use of low-cost apparatus.

However, low recoveries, limited

selectivity, and time-consuming

procedures limit LLE. A variety of

microscale variants of LLE have been

The Use of Extraction Technologies in Food Safety StudiesChangling Qiu1 and Douglas E. Raynie2, 1Department of Chemistry and Biochemistry, University of Texas at Arlington, Arlington,

Texas, USA, 2Sample Preparation Perspectives Editor

Traditional extraction methods for food samples, such as liquid–liquid extraction and Soxhlet extraction, are often time-consuming and require large amounts of organic solvents. Therefore, one of the objectives of analytical food safety studies currently has been the development of new extraction techniques that can improve the accuracy and precision of analytical results and simplify the analytical procedure.


SAMPLE PREPARATION PERSPECTIVES

reported and food applications of

these have been recently reviewed

(5).

Solid-Phase ExtractionIn SPE, the sample passes over the

stationary phase (solid phase). The

analytes separate according to the

degree to which each component

is partitioned or adsorbed by the

stationary phase. The analytes may

favourably adsorb to the solid phase

or they may remain in the liquid

phase. If the analytes are adsorbed,

a stronger eluting solvent selectively

Liquid–liquid extraction is the most widely used method for the extraction of analytes from aqueous food samples. The sample is distributed or partitioned between two immiscible solvents in which the analyte has different solubilities.

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Figure 1: Schematics of: (a) the fibre SPME extraction procedure, (b) thermal desorption in a GC injection port, and (c) solvent desorption using an SPME interface.



desorbs the analytes. If the analytes

remain in the liquid phase, they

can be collected and prepared

properly for further analysis, provided

the interferents are bound to the

solid-phase sorbent.

Effective separation by SPE can

be achieved by choosing suitably

selective solid-phase sorbent

and eluting solvents. With proper

selection of the sorbent and solvents,

SPE is capable of being used for

gases, solids, and liquids. However,

the primary area of application of

SPE is in the selective extraction and

enrichment of liquids samples. SPE

is used widely in the environmental,

pharmaceutical, biological, clinical,

forensic science, and food and

beverage areas.

SPE is also used to concentrate

and clean a sample before using

a chromatographic or other

analytical method. SPE has very

extensive applications in food

safety studies because of its low

cost, good selectivity, small solvent

consumption, and high recovery.

However, long sample preparation

times and multistep procedures are

among its disadvantages.

Solid-Phase MicroextractionSPME, developed by Pawliszyn

and coworkers (6) in 1990, involves

the use of a fibre coated with

suitable stationary phase extracting

material for the removal of analytes

of interest from the sample. The

sample molecules adsorb onto the

fibre and subsequently desorb into

the gas chromatograph’s injection

port for analysis. It is a simple, fast,

inexpensive, and efficient extraction

method that has been applied to both

headspace and aqueous sample

analysis with great sensitivity and

selectivity (7).

SPME is most effective when

coupled to gas chromatography

(GC). It has also been used with high

performance liquid chromatography

(HPLC) separations (8). Figure 1

shows the SPME device (9). It

consists of a fibre bonded to a

stainless steel plunger and installed

in a holder. The fibre, which is coated

with a suitable stationary phase, is

immersed in the sample or exposed

to the headspace above the sample.

Analytes in aqueous samples are

extracted by direct immersion, where

To fume

hood

IR

temperature

sensors

Air flow

Solvent

detector

Magnetic

stirring

Microwave

field

Direct

temperature

sensor

Multimode

microwave

cavity

Figure 3: Schematic diagram of instrumentation for microwave-assisted extraction (24).

Figure 2: Steps in the original and official versions of QuEChERS sample preparation for pesticide residues in food commodities.



analytes partition between the aqueous sample and

the fibre coating. When equilibrium is reached, the fibre

is removed and exposed to the injection port of a gas

chromatograph for analysis. Headspace analysis can be

used for the extraction of volatile or semivolatile analytes

from solid, liquid, or gaseous samples. In the headspace

extraction mode, the analytes first partition between

the sample and the headspace, then the analytes are

adsorbed by the fibre, which is then inserted directly into

the injection port of a GC system (10).

Since SPME is an equilibrium extraction technique,

several factors influence the extraction efficiency, such

as fibre-coating thickness and characteristics, sample

size, vial size, and adsorption and desorption conditions

(temperature and time) (10). To achieve successful

quantitative analysis, it is vital that each of these variables

is constant between analyses.

SPME has become increasingly popular in the analysis

of volatile and semivolatile compounds because of its

advantages over conventional extraction methods. It is a

simple, effective, and low-cost technique. The extraction

combines sampling, isolation, and concentration in one

step. SPME is also considered environmentally friendly

because of the elimination of organic solvents. The

technique has been widely applied to food samples (7,11–

13). One of these recent reviews (11) provided a summary

of fibre coatings, while another (13) discussed techniques

that are considered variants of SPME.

Shake-Flask ExtractionThe most common approach for extraction from solids

is conventional liquid-solid extraction, in the form of

shake-flask extraction. Shake-flask extractions can be

easily performed by putting a sample into a flask, adding

solvent, and agitating. After extraction, the extract is

separated from the solid residue by filtration. Shake-flask

extraction requires minimal glassware, small amounts of

organic solvent, and is comparatively fast (10–50 min).

It is one of the oldest and most widely used extraction

methods. However, because of its poor recovery and low

efficiency, the application is limited.

Soxhlet ExtractionSoxhlet extraction is a traditional extraction technique

for many food samples. It was originally designed for the

extraction of a lipid from a solid material by Franz von

Soxhlet in 1879 (14). The technique uses a specialized

piece of glass apparatus where the solid sample is placed

and is semicontinuously extracted with a sub-boiling

solvent. Though Soxhlet extraction is simple, standard,

and robust, there are disadvantages (15). Soxhlet

extraction usually requires long extraction times (8–24 h)

and large amounts of solvent. The operation lacks

automation, but several samples can be extracted in

parallel.

QuEChERSQuEChERS (quick, easy, cheap, effective, rugged, and

safe) extraction has become a very attractive sample

extraction method for various food samples. This method

was developed by Lehotay and Anastassiades in 2003

for the analysis of pesticides in vegetables and fruits (16).

Now, QuEChERS has been widely used in pharmaceutical,

clinical, and environmental analysis including steroids,

hormones, acetaminophen, acrylamide, perfluorinated

compounds, polycyclic aromatic hydrocarbons, alkaloids,

mycotoxins, and other applications. Overall, this procedure

has two main steps: extraction with a solvent and

partitioning salts, and clean up with dispersive solid-phase

extraction (dSPE). Figure 2 displays the sequence of

events in QuEChERS in the original and the AOAC and

European official methods (17). The QuEChERS method

has many advantages over traditionally used techniques.

QuEChERS provides accurate analytical results with high

recoveries, saves time and labour, reduces hazardous

solvent consumption and waste disposal, and uses less

laboratory glassware with a minimal number of steps.

Rajczak and Tuzimski (18) recently reviewed QuEChERS,

including food applications.

Ultrasound-Assisted ExtractionUltrasound-assisted extraction is also employed in food

safety studies for the extraction of contaminants or

bioactive components from food materials. The principle

of UAE is based on the propagation of ultrasound pressure

Sample extraction is a crucial step in food sample analysis because it can affect the concentration of the analyte and the cleanliness of the sample.



waves and resulting in a cavitation

phenomena. Ultrasound waves are

elastic waves that have a frequency

above the threshold of human

hearing, approximately 20 kHz. The

extraction mechanism involves two

steps, diffusion through the cell walls

and releasing the cell content after

the walls are disrupted (19). In one

configuration, the sample is immersed

in an ultrasonic bath with a solvent

and subjected to ultrasonic radiation.

Higher energy extractions use a

horn or probe device. Ultrasound

waves create bubbles in the solvent

and produce high local negative

pressure that can cause the collapse

of cavitation bubbles. The collapse

of cavitation bubbles near cell walls

produces cell disruption, and as a

result, solvent penetrates into the cells

and causes the release of extractable

compounds. The ultrasound waves

can also facilitate the diffusion process

and increase mass transfer.

UAE can reduce extraction time

and solvent consumption, thus

resulting in higher extraction

rates and good extraction efficiency.

Compared to other extraction

techniques, UAE is simple, fast,

productive, inexpensive, and

capable of operating with many

samples at one time. UAE usually

provides good results for food

samples (20–22). The benefits

for using UAE for food samples

include enhancement of extraction

yield or rate and extraction of

heat-sensitive bioactive and food

components under lower processing

temperatures. Food components

such as antioxidants, phenols,

aromas, carotenoids, anthocyanins,

and oils can be isolated from fruits

and vegetables, herbs and spices,

and seeds using UAE (22).

Microwave-Assisted ExtractionMicrowave-assisted extraction is an

extraction technique that combines

microwave and traditional solvent

extraction. Its use in food safety

analysis has become one of the more

common and low-cost extraction

methods today. Typically, a microwave

system includes a microwave

power generator, a waveguide for

transmission, a resonant cavity, and

a power supply (23). The microwave

power generator is a magnetron. At

the common microwave frequency

of 2.45 GHz, electromagnetic

energy is conducted from the

magnetron to the cavity using a

waveguide. The sample and solvent

placed inside the resonant cavity is

subjected to microwave energy. This

arrangement is outlined in Figure 3

(24). After typically 5–30 min, the

extraction is complete, and the extract

can be filtered and prepared for

analysis.

Compared to other extraction

techniques, an important advantage of

MAE is the extraction rate acceleration

resulting from the high temperatures

employed. Therefore, short extraction

times are obtained. Other advantages

include reduced solvent consumption

and improved extraction yield and

product quality. On the other hand,

disadvantages include an additional

filtration step needed to remove the

solid residue after the extraction, the

poor efficiency of microwaves when

the solvents are nonpolar and volatile,

and the use of high temperatures

that might degrade heat-sensitive

compounds. The stated requirement

that analytes or samples must be

polar to absorb microwave energy

is usually ignored with food analysis

because of the typically high water

content.

MAE has been applied to a

diverse range of sample types (soils,

sediments, sewage sludge, plants,

food). MAE is employed extensively

in the extraction of pesticides,

pigments, bioactive compounds

from vegetables, plants, and

natural products as an alternative to

traditional techniques of extraction

(25,26).

Compared to other extraction techniques, an important advantage of MAE is the extraction rate acceleration resulting from the high temperatures employed.

Solvent

Mixing

valve

Relief

valve

Solvent

Oven

Pump

Extraction cell

Static valve

Collection bottle

Solvent

Figure 4: Schematic diagram of accelerated solvent extraction instrumentation.



Supercritical Fluid ExtractionSupercritical fluid extraction uses a fluid phase having

unique properties between a gas and a liquid to effect

the solubilization of solutes. Compared to traditional

liquid solvents, supercritical fluids have lower viscosities

and high diffusivities, thus allowing more-efficient mass

transfer of solutes from sample matrices. SFE can be

operated in two modes, off-line and on-line (27). In the

on-line mode, the SFE instrument is coupled directly to

the analytical instrument, such as with SFE–GC. Off-line

SFE focuses on the sample preparation only, for analytical

purposes or on a larger scale to either remove unwanted

components from a product or collect desired components

(28).

An SFE system contains reservoirs for the supercritical

fluid and cosolvent, a thermostated extraction cell, a

restrictor to maintain flow and pressure, and a collection

vial. Typically, the supercritical fluid is pumped to a

heating zone, where it is heated to supercritical conditions.

It then passes into the extraction cell, where it rapidly

diffuses into the sample and dissolves the components

to be extracted. The dissolved components flow from the

extraction cell into a collection vial. The supercritical fluid

can then be condensed and recycled, or discharged to

atmosphere.

The most commonly used supercritical fluid is carbon

dioxide, which has a critical point of 31.3 °C and 72.8 bar.

This low critical temperature and pressure allows

extraction to occur near room temperature and at a

mild pressure. Carbon dioxide is inexpensive, nontoxic,

nonflammable, inert, and a good solvent for nonpolar

molecules. In general, supercritical carbon dioxide

extraction has a very wide range of applications, such as

in food, cosmetics, pharmaceutical, environmental, and

other related industries. Pesticides, organic pollutants,

fats and lipids, flavours, and natural bioactive components

are all classes of compounds that can be separated and

extracted from food samples (29). One attractive feature

for the use of carbon dioxide in food analysis is that it is

“generally regarded as safe” by the U.S. Food and Drug

Administration.

Accelerated Solvent Extraction Accelerated solvent extraction is a fast and automatic sample

extraction technique that uses elevated temperatures and

pressures with liquid solvents to obtain fast and efficient

extractions. It allows a high extraction efficiency with a small

volume of solvent (10–40 mL) and a short extraction time

(5–20 min).

ASE is mostly applicable to solid or semisolid samples

that can be held in the extraction cell during extraction.

A schematic of the ASE apparatus is presented in

Figure 4 (30). With ASE, a solvent or a mixture of

solvents is pumped into an extraction cell containing

the sample, which is then brought to elevated pressure

and temperature for extraction. Following extraction, the

sample is purged with compressed gas and prepared for

analysis. Application of ASE in food safety studies has

been reported for the extraction of various compounds

and contaminants like residual pesticides, fats and

lipids, food additives, and microbial contaminants in food

samples (31).

Optimization of various parameters in ASE, including

solvent, temperature, pressure, extraction cycles,

and time, is necessary to achieve good efficiency,

quantification, and reproducibility. For an efficient

extraction, the solvent must be able to solubilize the

desired analyte while keeping the sample matrix intact.

Most organic solvents and buffered aqueous solutions can

be used in ASE, so the need for extraction and the cost

of the solvent should be considered when developing a

method. ASE uses high temperatures to accelerate the

extraction processes. As the temperature is increased,

solvent diffusivity increases and viscosity decreases,

increasing the extraction rate. The temperatures used

in most ASE applications are in the 50–150 °C range.

Changing pressure has little impact on ASE extraction,

as the main effect of pressure is to maintain the solvent

in its liquid state. Most accelerated solvent extractions

use 1500 psi as the standard operating pressure. Static

extraction cycles are used to introduce fresh solvent

during the extraction process, which maintains favourable

extraction equilibrium. Time is also optimized to obtain a

complete and efficient extraction.

Thermal DesorptionThermal desorption is a well-known sample introduction

technique for GC determination of volatile or semivolatile

organic compounds. For gaseous samples, volatile

organic compounds are collected onto a sorbent, then

thermally desorbed for GC analysis, while volatile and

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semivolatile analytes in solid samples

can be determined directly by

thermal desorption.

Thermal desorption has numerous

benefits for analysis of trace-level

volatile and semivolatile organic

compounds. Thermal desorption

performs sample collection and

concentration at the same time. The

use of sorbents enables accurate

and efficient analyses of volatile

organic compounds in large sample

volumes even at low concentration.

Thermal desorption uses heat instead

of solvent to desorb analytes from

the sorbent and transfers the entire

sample to a GC system for analysis.

This technique enables a complete,

fast and solvent-free desorption of

the analytes. Thermal desorption is

a flexible, efficient, and convenient

sample introduction method. It

has myriad applications, including

fragrances and flavours.

Static Headspace ExtractionHeadspace extraction is usually

defined as a vapour-phase extraction,

involving the partitioning of analytes

between a nonvolatile liquid or solid

phase and the vapour phase above

the liquid or solid. In this process, the

sample is placed in a sealed glass vial

with a septum-lined cap. The vial is

then heated to a specific temperature

so that the volatile compounds diffuse

into the headspace above the sample.

After the equilibrium is reached,

the analytes in the headspace are

collected with a gas-tight syringe

and injected into a GC system for

analysis. The extraction of volatile

and semivolatile organic compounds

in solid, liquid, and gas samples

can be achieved by headspace

analysis. This extraction technique is

simple and fast, and it can provide

acceptable sensitivity. Common

applications include analyses of

flavour compounds in beverages and

food products.

Purge-and-Trap ConcentrationThe purge-and-trap method is a

dynamic headspace technique that

involves the purging of inert gas

through a liquid or solid sample,

followed by trapping of the volatile

analytes on a sorbent and desorption

into a GC system for separation and

identification. This method uses

the inert gas to strip the volatile

analytes from the sample matrix

and concentrate them on a sorbent.

Purge-and-trap concentration reduces

matrix effects and increases sensitivity.

This sampling method has been used

extensively in food analysis (32,33).

ConclusionsThe various extraction methods

described here provide an overview of

methods that are widely used in food

safety analysis. Conventional methods

such as Soxhlet extraction,

liquid–liquid extraction, and

Thermal desorption is a flexible, efficient, and convenient sample introduction method. It has myriad applications, including fragrances and flavours.

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shake-flask extraction are laborious,

require the use of large amount

of solvents and tedious extraction

steps, and have limited applications.

Modern extraction methods such as

those presented here have numerous

advantages when compared to the

traditional methods, such as shortened

extraction time, reduced solvent and

energy consumption, and improved

extraction efficiency. They are usually

considered to be green techniques

and have been used extensively for

determination of various contaminants

and harmful substances in food

samples.

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Sci. Technol. 17, 300–312 (2006).

(2) J.R. Pedersen and J.O. Olsson, Analyst

128, 332–334 (2003).

(3) A. Garrido Frenich, J.L. Martínez

Vidal, A.D. Cruz Sicilia, M.J. González

Rodríguez, and P. Plaza Bolaños, Anal.

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(4) L. Ramos, E. Eljarrat, L.M. Hernández,

J. Rivera, and M.J. González,

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AOAC Intl. 99, 1383–1394 (2016).

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Chem. 62, 2145–2148 (1990).

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Schmitt-Kopplin, Crit. Rev. Anal. Chem.

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Europe 18, 145–154 (2005).

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Zhou, Environ. Geochem. Health 25,

69–75 (2003).

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Y.-X. Fan, X.-C. Wang, and Y. Liu, TrAC

Trend. Anal. Chem. 80, 12–29 (2016).

(12) J. Li, Y.-B Wang, K.-Y. Li, Y.-Q. Cao,

S. Wu, and L. Wu, TrAC Trend. Anal.

Chem. 72, 141–152 (2015).

(13) T.A. Souza-Silva, E. Gionfriddo, and J.

Pawliszyn, TrAC Trend. Anal. Chem. 71,

236–248 (2015).

(14) F. Soxhlet, Polytechnisches J. 232,

461–465 (1879).

(15) M.L. De Castro and L. Garcıa-Ayuso,

Anal. Chim. Acta 369, 1–10 (1998).

(16) M. Anastassiades, S.J. Lehotay, D.

Štajnbaher, and F.J. Schenck, J. AOAC

Intl. 86, 412–431 (2003).

(17) S.J. Lehotay, R.E. Majors, and M.

Anastassiades, LCGC North Am. 28,

504–516 (2010).

(18) T. Rejczak and T. Tuzimski, Open

Chem. 13, 980–1010 (2015).

(19) D. Veli̷koviǅ, D. Milenoviǅ, M. Ristiǅ,

and V. Veljkoviǅ, Ultrasonics Sonochem.

13, 150–156 (2006).

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Stasiak, Acta Sci. Pol., Technol. Aliment

6, 89–99 (2004).

(21) K. Vilkhu, R. Mawson, L. Simons, and D.

Bates, Innov. Food Sci. Emerg. Techn.

9, 161–169 (2008).

(22) F. Chemat, N. Rombaut, A.-G. Sincaire,

A. Meullemiestre, A.-S. Fabiano-

Tixier,and M. Abert-Vian, Ultrasonics

Sonochem. 34, 540–560 (2017).

(23) E. Thostenson and T.-W. Chou,

Composites Part A: Appl. Sci. Manufac.

30, 1055–1071 (1999).

(24) B. McManus, M. Horn, B. Lockerman,

S. Smith, and G. LeBlanc, LCGC North

Am. 31(11), 22–27 (2013).

(25) H. Wang, J. Ding, and N. Ren, TrAC

Trend. Anal. Chem. 75, 197–208 (2016).

(26) L. Angilillo, M.A. Del Nobile, and A.

Conte, Curr. Opin. Food Sci. 5, 93–98

(2015).

(27) J.W. King, J. Chromatogr. Sci. 27,

355–364 (1989).

(28) C. Turner, C.S. Eskilsson, and E.

Björklund, J. Chromatogr. A 947, 1–22

(2002).

(29) M. Herrero, J.A. Mendiola, A. Cifuentes,

and E. Ibáñez, J. Chromatogr. A 1217,

2495–2511 (2010).

(30) A. Kettle, LCGC North Am. 31(11),

28–33 (2013).

(31) P. Vazquez-Craig and Y. Rico, TrAC

Trend. Anal. Chem. 71, 55–64 (2015).

(32) J.G. Wilkes, E.D. Conte, Y. Kim, M.

Holcomb, J.B. Sutherland, and D.W.

Miller, J. Chromatogr. A 880, 3–33

(2000).

(33) L. Zoccolillo, L. Amendola, C. Cafaro,

and S. Insogna, J. Chromatogr. A 1077,

181–187 (2005).

Changling Qiu is a postdoctoral

associate in the Department of

Chemistry and Biochemistry at the

University of Texas at Arlington,

where she is investigating

vacuum-UV detection for capillary

GC. She earned degrees in food

engineering at universities in China

and completed her Ph.D. in chemistry

at South Dakota State University,

studying extraction development for

food safety applications.

Douglas E. Raynie is Department

Head and Associate Professor in

the Department of Chemistry and

Biochemistry at South Dakota State

University. His research interests

include green chemistry, alternative

solvents, sample preparation, high

resolution chromatography, and

bioprocessing in supercritical fluids.

He earned his Ph.D. in 1990 at

Brigham Young University under the

direction of Milton L. Lee.

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COLUMN WATCH

Why do separation scientists care

about peak shapes so much? The

analysis of peak shape is critical during

the development and optimization

of synthetic approaches of new

stationary phases as well as the quality

of packing for all stationary phases

(1). To the end-user, Gaussian peaks

are more amenable to integration

than non-Gaussian shapes, provide

higher detection sensitivity, and allow

a higher number of peaks within a

given run time—that is, increased

peak capacity. Peak distortion

may also indicate a closely eluted

component. From an instrumentation

designer’s perspective, obtaining a

narrow dispersion of analyte peaks

is an indication of a well-behaving

instrument. Unfortunately, peak shapes

encountered in practice are rarely

ideal in the majority of the separation

modes. The shape of the peak can be

affected by factors such as the column

packing, secondary interactions of the

analyte with the stationary phase, the

connection tubing from the injector

to the detector inlet, the detector

sampling rate, and the nature of the

digital filter (mathematical elimination

of noise) in the chromatographic

software (2). These factors result in a

different efficiency, retention time, and

selectivity or lower overall resolution

than expected for the particle size of a

given support. Table 1 summarizes the

origins of undesired peak shapes with

their underlying causes.

How to Make an Initial Assessment of an Experimental Peak Shape The simplest way to assess the

quality of the chromatographic

signal is to visually inspect the peak

for mirror image symmetry and a

measurement of its width W, which

can be measured at a given height.

These measurements allow the

calculation of two chromatographic

figures of merit: the theoretical plates

(N) and the peak asymmetry. Like an

engine’s horsepower, N indicates the

“horsepower” of a chromatography

column. A larger value of N indicates

that the column can produce narrower

peak widths and can separate more

peaks in a given time window. The

features of a typical peak obtained

in chromatography are labelled

in Figure 1 (3). Figure 1(a) shows

how N can be calculated. Similarly,

Figure 1(b) shows how peak shapes

are measured at various heights using

two popular quantities: the United

States Pharmacopeia (USP) tailing

factor and the asymmetry factor. If the

users assume that peaks are visually

Gaussian (the judgement depends on

the user), the equation to calculate N is

given by equation 1:

N = a 2t

R

W [1]

where tR is the retention time, and W

is the width of a peak at given height.

Because it is possible to measure W at

Peak Shapes and Their Measurements: The Need and the Concept Behind Total Peak Shape AnalysisM. Farooq Wahab1, Darshan C. Patel2, and Daniel W. Armstrong1, 1University of Texas at Arlington, Texas, USA, 2AbbVie

Inc., North Chicago, Illinois, USA

Gaussian peak shapes in chromatography are indicative of a well-behaved system. Such peak shapes are highly desirable from the perspective of column packing technology. From an analyst’s point of view, Gaussian peaks provide improved sensitivity (lower detection limits) and allow ease of quantitation. In practice, one can obtain peaks that tail, front, or concurrently front and tail for reasons such as column packing issues, chemical and kinetic effects, and suboptimal high performance liquid chromatography (HPLC) system plumbing and detector settings. Here, we discuss a number of approaches for peak shape measurement that are available in modern chromatography software, along with their advantages and drawbacks. A new “total peak shape analysis” approach is suggested that facilitates detection and quantifi cation of concurrent fronting and tailing in peaks. Several remediation approaches are proposed that can help chromatographers analyze and improve peak shapes.


COLUMN WATCH

various heights, the factor a is adjusted

accordingly. Unfortunately, even for

extremely high efficiency columns,

perfect Gaussian peaks are rarely

observed. Because we are interested

in measuring the actual peak shapes,

the method of moments is the most

accurate measure of peak properties.

It involves slightly tedious calculations,

but several computer data systems

(CDS) readily allow users to calculate

moments as shown in Figure 2.

N =2

m1

m2 [2]

In equation 2, m1 is the centroid or

centre of gravity of the peak, and m2

is the second moment or variance of

distribution of the analyte in time. The

definitions are provided in Figure 2. The

definition of N described in equation 2

does not assume any peak shape,

because the centroid and variance

can be determined for any peak shape

encountered in chromatography (that

is, fronting, tailing, split, shouldering,

horned, and so forth). The main

drawback of moments is that they are

very sensitive to peak start (t1) and

peak end (t2), and noise in the signal

S(t) (Figure 2). Secondly, moments

also depend on the data sampling rate.

Typically, the signal-to-noise ratio (S/N)

should be 200 or above for obtaining

reliable moment values (4). All the first

three moments can be calculated in

Microsoft Excel by estimation of the

equations shown in Figure 2 (4). Slightly

incorrect peak integration could lead to

poor precision; therefore, the moment

analysis for total shape analysis is too

extensive and sensitive for routine work,

despite their easy availability in modern

data acquisition and analysis software.

Let us compare the width-based

measurements of the two peaks

obtained on a 15 × 0.46 cm,

2.7-μm dp C18 core–shell column.

The manufacturer’s quality control

test reports an exceptionally high

plate number (39,000 per column).

When tested, indeed the Gaussian

efficiencies are 38,000 and 39,700

for uracil and phenol, respectively

(equation 1). However, N values

obtained from moment analysis are

24,000 and 26,000, respectively, as

calculated by Agilent’s ChemStation

software (equation 2). There is a

surprising difference of more than

10,000 plates, proving the point that

even the most efficient columns today

Table 1: Origin of peak distortions in chromatography (thermodynamic, kinetic, peak processing, and fluid dynamical reasons)

Origin of Peak Distortion Peak Shape Phenomenological Cause Correctable by User?

Thermodynamic and Kinetic Origins

Stationary-phase

characteristics: carbon

load, particle or pore

size distribution

Variable

Thermodynamics–kinetics;

higher bonding coverage

may slow kinetics; wide

particle size distribution

results in poor bed structure

No: Stationary phase

synthesis issues

Sample concentration

effects (analyte and

other components)

Tailing, fronting, split

tailing and split fronting,

retention time shift

When analyte concentration

exceeds the adsorption

capacity of the stationary

phase/mobile phase

Yes: Lower analyte

concentration

Solvent mismatch between

diluent and the mobile phase

Possibly distorted or split

peaks, viscous fi ngering

The elution strength of the

diluent is signifi cantly stronger

than the mobile phase or

possible immiscibility issues

Yes: Match the diluent

composition with the

mobile phase

Frictional effects of

the mobile phase

Peak distortion, shoulders

in worst case

Radial frictional heating of the

column at high fl ow rates

Yes: Use narrow inner

diameter columns or decrease

the fl ow rate; use still-air–

based temperature control

Fluid Mechanics

Slurry packing process

of columns

All shapes possible because

of axially and radially

heterogeneous bed

Suspension rheology

during column packing

under pressure

No: Can only be changed

by the column packer

Injector–connection tubing,

frits, detector design

Tailing, peak broadening, and

shoulders (in case there are

dead volumes or clogged frits)

Fluid dynamics

Partly yes: Use the shortest

possible and narrower

inner diameter tubing,

low-volume fl ow cell,

zero-dead-volume fi ttings

Mathematical Processing of the Chromatographic Data

Digital fi lters in the

instrument’s software

Symmetric widening or tailing,

never fronting; can produce

dips on chromatogram;

can deceptively make

peaks look more symmetric

(with a Gaussian fi lter)

Mathematical operations

on the raw chromatogram

to decrease noise level

No: The fi lters are

mathematics embedded

in the software; can be

circumvented by collecting

analog output and users

can apply smoothing fi lter of

their choice to improve S/N


COLUMN WATCH

up with an improved measure to assess

peak shapes. There are several ways

to measure peak asymmetry, many of

which are included in chromatography

data acquisition software for reporting.

The definitions of various peak shape

measurements are shown in Table 2.

Figure 3 shows that USP tailing factor

of peak 1 is 1.22, the asymmetry factor

do not produce pure Gaussian peaks. If

the peaks were perfectly Gaussian, the

plate numbers from equations 1 and 2

would match. Chromatographers are

used to looking at large numbers for

efficiency; most column manufacturers

use the simplified calculation of

efficiency since the method of moments

often results in very low values that do

not reflect favourably on a column’s

performance. The efficiency only

carries the information about the width

of a peak, and nothing about the nature

of its entire shape.

With chiral separations, even

when the peaks may have very

high efficiency, the peaks are often

asymmetric. In the unusual case of

β-blockers or compounds such as

hydantoins on chiral columns, the peaks

have a slight ascending fronting as well

as a tailing. Both of these shapes can

originate because of column packing

or kinetic band broadening effects, or

both. We have aptly named such peaks

Eiffel Tower peaks because the top is

mostly very narrow, yet the ascent and

the descent are rather bent like the

Eiffel Tower (Figure 3) (5). The Gaussian

efficiencies of peaks 1 and 2 in Figure 3

are 10,000 and 6400, respectively;

however, keep in mind that these peaks

are not even visually Gaussian. The

plates are vastly overestimated and

the efficiency by moments tells us the

actual efficiencies are 3300 and 1800

for peaks 1 and 2, respectively. These

points again show that we need to come

100

(a) (b)

tR

tR

Wi

Wi

W

WN

a

a

a

b

b

a

W0.5

W0.5

W0.05

W0.05

W3σ

W3σ

W4σ

W4σ

W5σ

W5σ

Wtan

Wtan

f0.05

2f0.05

Tangent

4

5.54

9

16

25

16

Inflection

Method

2

FWHM

3σ

4σ

5σ

=

==

Asymmetry factor

USP tailing factor

% P

eak h

eig

ht

% P

eak h

eig

ht60.7

10

5

50

32.4

13.4

4.4

Figure 1: (a) Illustration of the features of a Gaussian peak profile. The theoretical

plates can be calculated for various heights with an appropriate factor a.

(b) Definitions of the commonly used measures of peak shape.

Pure

•

•

•

•

•


COLUMN WATCH

is 1.33, the symmetry is 1.72, and the

moment-based measure called skew

is 1.64. All of these numbers tell us that

the peaks are tailing in a net fashion.

These methods assign a unique number

to a given peak, an approach that may

not present the full picture. Indeed, they

only indicate the contributions to the

asymmetry that are in excess. A careful

examination of the chromatographic

peaks in Figure 3 reveals that tailing is

coupled with fronting, which is rarely

detected and never quantified. The

USP tailing (T) is the most common

measurement and is required by the

U.S. Food and Drug Administration

(FDA). The FDA recommends a tailing

factor of ≤2. A peak shape T of ~2 is

visually very asymmetric and deformed.

The Concept of Total Peak Shape AnalysisHow can we detect peak deformations

throughout the entire peak rather than

rely on a single value? Such an analysis

for the whole of the peak is beneficial

in troubleshooting the peak shape

problems highlighted in Table 1. We

developed two simple tests to study

complete peak shapes graphically (5).

Both methods are intuitive and can be

used with Microsoft Excel. The first one

is the derivative test and the second

one is the Gaussian test.

The Derivative Test: Taking the

derivative with respect to time of a

given peak is the most straightforward

approach to assess total symmetry

and peak shapes. If S is the

chromatographic signal, then the

derivative is

=S

2 –S

1dSdt t

2–t

1 [3]

Equation 3 merely states that we find

the difference between two consecutive

signal values (S2 and S1) and divide it

by the sampling interval. Like moment

analysis, this peak shape test does

not preassume any peak model.

Table 2: Peak shape measurements and their availability in chromatography data acquisition software

Names Defi nition Data Acquisition Software Details

USP tailing factor

T = W0.05/2f0.05

where W0.05 = peak width

at 5% peak height, and f0.05

= distance from the leading

edge of the peak to the peak

maxima at 5% peak height

Universally present in all

major software. Symmetry

factor (JP) or (EP) is

identical with the tailing

factor (USP). Chromeleon

also calls it “skewness”.

1, perfect symmetry;

<1, net fronting

>1, net tailing

Measured at 5% height

Asymmetry

T = b0.1/a0.1

where a0.1 = distance

from leading edge of the

peak to the peak maxima

at 10% peak height, and

b0.1 = distance from peak

maxima to the trailing edge

at 10% peak height

OpenLAB, Empower,

Atlas, Chromeleon,

ChromNAV, ChemStation

1, perfect symmetry;

<1, net fronting

>1, net tailing

Measured at 10% height

Symmetry

m1+m

2

m3+m

4

mi is the ith moment, where i =

1, represents mean; i = 2

represents the variance; i = 3,

vertical symmetry; and i = 4

is a measure of the

compression or stretching of

the peak along a vertical axis

ChemStationProprietary formula

based on moments (6)

Skew (7)m

3

m23/2

ChemStation, Empower,

many software programs

use their own equations.

0, perfect symmetry

<0, net fronting

>0, net tailing

with respect to the centroid

(mean retention time)

Derivative test =S

2 –S

1dSdt t

2–t

1

Chromeleon

Test of peak symmetry,

no shape assumed

Same absolute value of the

maximum and minimum

indicates the peak is

perfectly symmetric

Gaussian test

Graphical representation,

divides the peak in half and

assumes the top 15–20%

of the peak is a perfect

Gaussian; measures residuals

New approach suggested for

graphical peak shape analysis

Minimum fronting and

tailing residuals


COLUMN WATCH

Thus any peak shape can be analyzed. The sampling

interval can be determined as follows: If the data are

sampled at 160 Hz (160 data points per second), the

sampling interval is 1/160 s or 1/(60×160) min. The only

requirements of the derivative test is to ensure a high

sampling rate (80 Hz and above), to have low response

time settings (< 0.1 s), and a high signal-to-noise ratio.

When time and the derivative are plotted on the same

axis as the original chromatogram, the derivative

intersects the x-axis at the same position as the

maximum of the original peak (Figure 4). Figure 4(a)

shows how the derivative of a pure Gaussian peak

(ideal chromatography) would look. The maximum and

minimum values are identical, which would be true for

any symmetric peak. If a peak has a very slight tail as in

Figure 4(b), then the left maximum has a larger absolute

value than the right minimum. This tailing is coming from

a very short column (0.5 cm × 0.46 cm). As indicated in

Table 1, the tailing is originating from the column packing

as well as from extracolumn effects. If there is concurrent

fronting or tailing, then the absolute values will depend

on which half of the peak is dominating, as we will show

later. The gist of the derivative test is that if the absolute

values of the maxima and minima of the first derivative

of a peak do not match, the slope of the leading edge of

the peak differs from the slope of the trailing edge and

reveals the presence of asymmetry. For the derivative

test, the excess magnitude of the positive end indicates

tailing and the excess magnitude of the negative end

suggests a fronting element to the peak. The derivative

test is a very sensitive test to detect the peak asymmetry,

and it does not rely on the user to choose peak start

and end time, which makes it independent of integration

errors.

The Gaussian Test: The Gaussian test is a graphical

and quantitative approach for analyzing the total peak

shape and its departure from an ideal Gaussian shape. It

concurrently determines the extent of a peak’s deviation

from a perfect Gaussian form on the leading and the

trailing edge and allows detection and quantitation of

fronting and tailing peaks. As discussed earlier, Table 2

compares what measurement methods are available in

chromatographic software for analyzing experimental

peaks. Ideal chromatography peaks typically follow

the Gaussian peak profile G(t) and amplitude A can be

modelled using equation 4:

=(t

–t

R)

–G (t) A exp

2

[4]

The standard deviation or σ of a peak can be obtained at

any peak height using equation 5:

wH

2√2ln(1H

(

[5]

If we know the experimental standard deviation and

retention time, we can construct a complete Gaussian

peak using equation 4. This method makes the critical

assumption that even in the most distorted peaks, the

upper regions of a peak (80% peak height or above)

follow the Gaussian peak shape; the peak shape


COLUMN WATCH

analysis is approached from the top

in this method. The idea behind the

Gaussian test is as follows:

1. Normalize the experimental peak

height to unity. This process simplifies

the visualization and calculations.

2. The top section of most

chromatographic peaks is almost

an ideal Gaussian shape—for

example, the >80% height (Figure 1).

To confirm this hypothesis, the

standard deviation should be

extracted at other heights (for

example, 85%, 90%). The σ

values should closely match.

3. Extract the standard deviation of

the experimental peak at a given

height (>80% peak height) from

equation 5, and determine the peak

maximum (that is, the retention time).

4. Plot a pure Gaussian peak, using

equation 4 with the retention

time and standard deviation

extracted from the experimental

peak. Graphically, superimpose

the ideal peak on a real peak.

5. Find the differences at each point

of the pure Gaussian peak and

the experimental peak. These

values are called residuals. Plot

the residuals on the same graph

against retention time. The residuals

show both if the peak fronts or if

there is a shoulder, or if the peaks

tail and how much they tail.

6. Express the percentage of fronting

residual and tailing residual as the

fraction of the sum of all residuals.

Herein we assume that S/N is high,

and the baseline is not drifting. We

wish to emphasize here that this test is

not simply a curve-fitting procedure,

where the only goal is to minimize

the residuals by the method of least

squares. Although we fit the curve on

the experimental peak, mathematical

constraints are placed in this process

(see steps 2 and 3). To make the

technique easily accessible, a useful

Excel template with prefilled formulas is

available (5) that ultimately automates

the derivative and the Gaussian

test. In the Excel file, the user simply

pastes the retention time as well as a

chromatographic signal (absorbance,

fluorescence, refractive index, and so

forth) and allows Excel to perform the

rest of the calculations.

Figure 5 provides examples where

we show the utility of this Gaussian

test. During initial column packing

experiments, a core–shell material

was producing peak shapes that

were seemingly Gaussian, but only

deceptively symmetric. The column

efficiency was acceptable (for a 5-cm,

2.7-μm column, ~8000–9000 plates).

The USP tailing factor was 0.93

(Figure 5[a]), which can indicate a

relatively symmetric peak shape

with a net fronting; however, a visual

examination showed that there is

coupled fronting and tailing, which

had to be eliminated experimentally

by optimizing the packing conditions.

Modern columns are packed as a

slurry of particulates suspended

in an appropriate solvent. The

concentration of the slurry and the

nature of the solvent affects the

peak shape of the packed column.

440

390

340

290

240

190

140

90

40

-10

1 1.5 2

Retention time (min)

Ab

so

rb

an

ce

5-Methyl-5-phenylhydantoin

USP tailing factor

Asymmetry factor

Symmetry

Skew

1.22

1.33

0.72

1.64

1.44

1.71

0.59

2.32

o

o

NH

NH

2.5

1

1

2

2

Figure 3: Comparison of single valued measures of peak shapes of the enantiomers

of 5-methyl-5-phenyl hydantoin on a chiral column. The USP tailing factor, symmetry,

and skew were obtained using Agilent Chemstation software. The asymmetry factor

was obtained using Microsoft Excel. Manually calculated values may slightly differ,

however the trend is same. Eiffel Tower–shaped peaks were obtained in this case. A

visual examination shows that the peaks have fronting and tailing attributes yet the

single value descriptors only indicate tailing.

The Concept of Moments

Meaning

Peak area

Mean

S(t) dt

(t – m1)2 S(t)

dt

dtVariance

∫

0

190Plates 1. Uracil

38,000

24,000Nmoments

t1

t2

NG

(W0.5

) 39,700

26,000

1

2. Phenol

2

140

=

tS (t)

m0

m0

t1

t2

∫t1

t2

=

=

90A

bso

rb

an

ce

40

-10

0.2 0.4 0.6 0.8 1


m0

m1

m2

Calculated by

Figure 2: A comparison of different indicators of peak width. The calculation of

column efficiency using two different approaches for calculating theoretical plates;

NG assumes that the experimental peaks are perfect Gaussian peaks and efficiency is

measured using peak width at half height (W0.5). Moment analysis considers the exact

peak shape. The discrepancy in plates Nmoments shows that even high-efficiency

columns may not produce ideal Gaussian peaks. See moment equations on the left.

S refers to the chromatographic signal. The peak start and the end times are indicated

by t1 and t2.


COLUMN WATCH

In Figure 5(a), the Gaussian test

(steps 1 to 5) was performed on this

initial packing condition with a low

slurry concentration; the derivative

test confirms the presence of peak

distortion (maximum = 84.839 and

minimum = −82.822). Only the full

peak shape analysis identifies the

problematic regions of the entire peak.

The presence of residuals shows

significant areas of fronting plus tailing

elements despite an indication from

the USP tailing factor that the peak

only fronts. The contribution to peak

distortion is 58% from fronting and

42% from tailing. In another set of

conditions, the slurry concentration

was increased for optimization. As

shown in Figure 5(b), the fronting

element has become negligible and

the left side of the peak has nearly

a perfect Gaussian character. The

value of the derivative still detects

asymmetry (maximum = 86.598,

minimum = –80.282). The peak

shape distortion from the Gaussian

tests shows 10% contribution from

fronting and 90% from tailing. Although

a visual inspection may lead users

to assume that the original peak

shown in Figure 5(a) is desirable over

the optimized condition shown in

Figure 5(b) based on the USP tailing

factor, the Gaussian test disproves this

assumption by showing the problematic

1

0.8

0.6

Sig

nal S (

mA

U)

Sig

nal S (

mA

U)

0.4

0.2

170 3000

2500

2000

-2000

1500

-1500

1000

-1000

500

-500

+1.44

+2876(b)(a)

-1377-1.44

0 Fir

st d

eriv

ativ

e

Fir

st d

eriv

ativ

e

150

130

110

90

70

50

30

10

-10

0

-0.2

15.0 17.5 20.0 22.5 25.0

Time (s) Time (s)

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

0.0 0.4 0.8 1.2 1.6 2.0

Figure 4: Illustration of a derivative test for testing peak symmetry (a) for a perfect

Gaussian peak and (b) for a peak obtained on a 0.5-cm column at 5 mL/min on a

custom-built chromatographic system. Note how the absolute values of the maximum

and minimum match for a symmetric peak as in (a). Any symmetric peak shape will

show the same values of maximum and minimum.

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COLUMN WATCH

regions of the peak with concurrent

fronting and tailing.

Indeed, where resolution from a

closely eluted impurity or a low-level

analyte eluted before the main peak

is of concern, the peak shape shown

in Figure 5(a) may conceal the smaller

impurity despite its seemingly more

symmetric peak shape. In such a case,

despite a larger USP tailing factor, the

peak shape shown in Figure 5(b) would

be desired because it may facilitate

improved resolution of the impurity; the

main peak has practically no fronting.

Note that since uracil is an early eluted

analyte on a narrow-bore column, the

persistent presence of tailing has its

origins in extracolumn connections plus

a nonoptimized slurry solvent.

ConclusionsAn ideal Gaussian peak shows that the

system (column and the instrument)

are well-behaved. When a peak is

asymmetric, single-valued descriptors

of peak shapes such as USP tailing,

asymmetry factor, symmetry, and

skew can be inadequate because

they do not give a complete picture of

the overall peak shape. Researchers

engaged in instrument design and

stationary-phase development need to

analyze peak shapes during synthesis

and column packing. The derivative

test is based on the concept that if a

peak is symmetric, their inflection points

will be mirror images. The Gaussian

test superimposes a Gaussian

model on a normalized peak with

its set of constraints and shows the

problematic regions of the peak. The

standard deviation is extracted from

the upper section (>80% height) of

the peak rather than the conventional

half-height approach. This total

peak shape analysis approach can

uncover the concurrent fronting and

tailing in peaks. The origin, underlying

phenomenological cause, and possible

remedies are highlighted.

References(1) M.F. Wahab, D.C. Patel, R.M.

Wimalasinghe, and D.W. Armstrong, Anal.

Chem. 89, 8177–8191 (2017).

(2) M.F. Wahab, P.K. Dasgupta, A.F. Kadjo,

and D.W. Armstrong, Anal. Chim. Acta

907, 31–44 (2016).

(3) B.A. Bidlingmeyer and F.V. Warren, Anal.

Chem. 56, 1583A–1596A (1984).

(4) P.G. Stevenson, H. Gao, F. Gritti, and

G. Guiochon, J. Sep. Sci. 36, 279–287

(2013).

(5) M.F. Wahab, D.C. Patel, and D.W. Armstrong,

J. Chromatogr. A 1509, 163–170 (2017).

(6) Agilent Technologies, “Evaluating

System Suitability CE, GC, LC and

A/D ChemStation” Revisions: A.03.0x-

A.08.0x.

(7) E. Grushka, J. Phys. Chem. 76,

2586–2593 (1972).

M. Farooq Wahab is currently a

research engineering scientist at the

University of Texas at Arlington (Texas,

USA). He received a postdoctoral

fellowship with Professor Armstrong

after completing his Ph.D. at the

University of Alberta. His research

interests include fundamentals of

separation science, ultrahigh-efficiency

phases, hydrophilic interaction

chromatography, supercritical fluid

chromatography, data processing, and

subsecond separations in LC. For Excel

template and correspondence contact:

[email protected]

Darshan C. Patel received his

Ph.D. from the University of Texas at

Arlington, where he developed new

technologies for ultrahigh-throughput

LC under the direction of Dr. Armstrong.

In his current role, as a senior scientist

at AbbVie Inc., in Process Research

and Development, he focuses on the

development of process analytical

technologies for in-process sampling

and automation.

Daniel W. Armstrong is the Welch

Distinguished Professor of Chemistry

at the University of Texas at Arlington.

Professor Armstrong has received

more than 30 national and international

research and teaching awards.

His current interest involves chiral

recognition, macrocycle chemistry,

synthesis and use of ionic liquids,

separation science, and mass

spectrometry. He has more than 600

publications, one book, and 30 patents.

For correspondence and a copy of the

Excel template email [email protected]

David S. Bell is a manager in

Separations Technology and Workflow

Development at MilliporeSigma (formerly

Sigma-Aldrich/Supelco). With a B.S.

degree from SUNY Plattsburgh (New

York, USA) and a Ph.D. in analytical

chemistry from The Pennsylvania

State University (Pennsylvania, USA),

Dave spent the first decade of his

career within the pharmaceutical

industry performing analytical method

development using various forms of

chromatography and electrophoresis.

During the past 20 years, working

directly in the chromatography industry,

Dave has focused his efforts on the

design, development, and application

of stationary phases for use in HPLC

and hyphenated techniques. In his

current role at MilliporeSigma, Dr. Bell’s

main focus has been to research,

publish, and present on the topic of

molecular interactions that contribute

to retention and selectivity in an array

of chromatographic processes. Direct

correspondence to: LCGCedit@ubm.

com


0.60

1.0

0.8

0.6

0.4

0.2

0.0

-0.2

1.0

0.8

0.6

0.4

0.2

0.0

-0.2

1.00

0.95

0.90

0.85

0.80

0.75

0.70

0.65

0.60

0.55

0.50

0.45

0.40

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0.00 Re

sid

ua

ls

Re

sid

ua

ls

1.00

0.95

0.90

0.85

0.80

0.75

0.70

0.65

0.60

0.55

0.50

0.45

0.40

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0.00

0.65 0.70

Normalized

peak

First derivative

Max = 84.839

Min = -82.822

First derivative

Max = 86.598

Min = -80.282

Gaussian

test

Ga

ussia

n t

est

(a) (b)

Ga

ussia

n t

est

Gaussian

test

Normalized

peak

0.75 0.80 0.60 0.65 0.70 0.75 0.80


Figure 5: The Gaussian test applied on examples of (a) asymmetric peak (which

deceptively appears symmetric) obtained from the use of a nonoptimal (2.3% w/v)

slurry concentration and (b) improvement of peak shape by using an optimal (16%

w/v) slurry concentration during column packing. The experimental peaks were

obtained using 5 cm × 0.3 cm columns packed with native 2.7-μm superficially porous

particles at different slurry concentrations. The black curve shows the raw data, and

the superimposed blue dashed lines show the ideal peak shape. The residuals in red

show the regions where the experimental peak does not match the ideal peak.



Key performance indicators

(KPIs) or metrics are used by

many laboratories to measure the

operational and quality performance

of processes and the laboratory

itself: If you can’t measure it, you

can’t control or manage it. Metrics

are also a requirement of the ISO

9001 quality standard and six sigma

initiatives for continuous improvement

of processes. In this instalment of

“Questions of Quality”, we look at

the regulatory requirements for data

integrity metrics so that laboratory

managers can understand how their

laboratories are performing with

respect to data integrity and identify

where they may have potential

problems. For those that don’t

understand laboratory metrics, let us

start with a primer.

Understanding Laboratory MetricsConsider a laboratory analysis, a

supervisor or manager needs to

know how many samples are being

analyzed and how long it takes to

analyze them. They need to know

if there are any bottlenecks and

if they have enough resources

assigned for the job. Metrics can

provide this information to measure a

process or activity. If used correctly,

metrics can help staff understand

the performance measures that a

laboratory is judged against. Some

common laboratory metrics are

shown in Table 1.

Metrics Must be Generated AutomaticallyA key requirement for collection of

metrics is that the process must be

automatic. Why is this? The simple

reason is that if humans are used

to collect and collate the data

manually it becomes an error-prone,

tedious, and labour-intensive

process, and could also be subject to

falsification.

Automatic metric generation is the

only way to create metrics that are

timely, accurate, and repeatable. This

is where suppliers of chromatography

data systems and other laboratory

informatics applications could help

by implementing applications that

generate both general and data

integrity metrics automatically.

FDA Quality Metrics GuidanceThe Food and Drug Administration

(FDA) are also focusing on quality

metrics as a way of identifying

facilities that have lower risks. The

Agency issued two draft guidance

for industry documents on quality

metrics in July 2015 and November

2016. To monitor the performance

of QC laboratories, the Agency has

selected out-of-specification (OOS)

results. The metric chosen was

invalidated out-of-specification rate

(IOOSR), which is defined as the

number of OOS test results for lot

release and long-term stability testing

invalidated (1).

From a laboratory perspective,

knowing the OOS rate is an important

criterion for both quality and data

integrity. One of the key questions

to ask when auditing or inspecting a

laboratory is the OOS results for the

past six months. However, answering

“we don’t have any” can result in a

regulatory surprise:

Since beginning manufacturing

operations in 2003, your

firm has initiated a total

of 0 out-of-specification

investigations for finished

product

FDA 483 Observation,

November 2014.

As analytical procedures are

subject to variation we expect

to see not only OOS but also

out-of-expectation (OOE) and

out-of-trend (OOT) results. There

is a specific EU GMP regulation

that requires trending of laboratory

results:

6.9 Some kinds of data

(e.g. tests results, yields,

environmental controls) should

be recorded in a manner

permitting trend evaluation.

Any out of trend or out of

specification data should be

addressed and subject to

investigation (2).

6.16 The results obtained

should be recorded. Results of

parameters identified as quality

attribute or as critical should be

Data Integrity Metrics for ChromatographyMark E. Newton1 and R.D. McDowall2, 1Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana, USA, 2R.D. McDowall Ltd, Bromley, Kent, UK

The authors discuss metrics for monitoring data integrity within a chromatography laboratory, from the regulatory requirements to practical implementation.

Key performance indicators (KPIs) or metrics are used by many laboratories to measure the operational and quality performance of processes and the laboratory itself: If you can’t measure it, you can’t control or manage it.



trended and checked to make

sure that they are consistent

with each other…… (2).

What would be an OOS rate in

a regulated laboratory? If you

don’t know there might be some

issues during the next inspection.

Why Metrics for Data Integrity?We now need to ask why data

integrity metrics are important.

Put simply, it is now a regulatory

expectation as we can see from the

PIC/S guidance PI-041 in Table 2

(3). There is an expectation that

data integrity metrics of processes

and systems are collected for

management review. Of course, there

is the implicit expectation to act if the

metrics indicate an activity or trend

that has the potential to compromise

data integrity.

Metrics Lead Behaviour?The second row of Table 2 states that

you should be careful when selecting

a KPI or metric so as not to result in

a culture in which data integrity is

lower in priority. Let us consider this

statement further. Take the example

of turnaround time (TAT) in Table 1.

Imagine that the laboratory target

for TAT is set for 10 days and over

the past few months the target has

been missed and the laboratory

manager is not pleased. The sample

you are starting to analyze has been

in the laboratory for nine days and

your analysis will take two days to

perform. You will miss the TAT target

unless…

This is where quality culture,

management, and data integrity

collide and it is where the PIC/S

guidance caution about metrics

comes into play—can some metrics,

intended to monitor activities,

become the means of inducing

behaviours that compromise data

integrity?

One other factor to consider

here: As you monitor some actions,

they will improve because they get

attention, but this can be at the

expense of other activities that

are not being monitored. Be

aware of what you do not monitor

as well, for example, measuring

metrics on quality control production

TAT can cause stability tests to be

neglected.

Overview of Data Integrity Metrics in An OrganizationThis is an evolving subject but

the aim in this column is an

overview of data integrity metrics

that could be generated as part of

a data integrity programme of work

as well as for routine work. Let us

look at where in an organization

metrics for data integrity could

be generated (Figure 1). In this

column, we will not consider

production manufacturing metrics.

Although focused in a quality control

laboratory, the same principles

apply to an analytical development

laboratory in R&D. The scope of

data integrity metrics can cover

Automatic metric generation is the only way to create metrics that are timely, accurate, and repeatable.

Data IntegrityMetrics

Data IntegrityMetrics

Data Governance orData Integrity

Policy

• DI Policy Status• Number of Staff Trained• Understanding Measured• Audit Findings & Management Review

• Computer Systems and Manual Processes Assessed• Short-Term Remediation Completed Versus Plan• Long-Term Solutions

• Process and Analytical Dev• Manufacturing• Quality Control• Outsourcing CMO and CRO

• DI Audits• Audit Findings and CAPA• For Cause Data Integrity Audits• Data Integrity Investigations

Assessment andRemediation

R&D andProduction

Batches

Quality AssuranceOversight

Figure 1: Scope of data integrity metrics within an organization.



the four main areas within an

organization:

• Data governance such as

data integrity policy(ies) and

• Development and production

activities including outsourcing

of laboratory analysis;

• Quality assurance oversight.

the associated training;

• Assessment and remediation of

laboratory manual processes

and computerized systems;

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Overall Process Data Integrity MetricsTurnaround Time (TAT)

% Right First Time

% Out of Specification (OOS) & Invalidated OOS (IOOSR) Results

Sampling Preparation Acquisition Reviewing ReportingIntegration and

Calculation

Sample Preparation

Records

Manual Data

Input to CDS

Multiple Runs

Identical Data

Duplicate Files

Second Person Review

Focus on Work Correctly Performed

Focus on Potential Poor Data Management Practices

% Manual

Integration or

Intervention

Time to Review

Analyst Throughput

Post Release Activity

Figure 2: Some data integrity metrics for laboratory processes.



DI Policies and Assessment and Remediation of Processes and SystemsThe first two areas from Figure 1 to

consider for data integrity metrics are

data integrity policies and procedures

and assessment and remediation of

processes and systems.

Data integrity policies and

procedures should include the

following:

• DI policy outlining company

expectations, behaviours, and

culture;

• Good documentation practices SOP

covering paper, hybrid and electronic

processes and systems;

• Second person review SOP including

audit trail and e-records review;

• Updating current procedures

to include validation processes

that ensure the integrity of

records and detection of

improper actions;

• Metrics for policies and procedures

in Table 3 focus on the training

and its effectiveness. This is

a subject where a read and

understand approach is not tenable

and demonstrable evidence of

understanding is required.

Assessment of processes and

systems should cover the following

activities:

• Assessment of new systems for

integrity gaps prior to purchase;

• Identification of existing

computerized systems and

paper processes for assessment

including access databases and

spreadsheets and their listing in an

inventory;

• Processes and systems are

prioritized by risk and record impact;

• Assessment of systems and paper

process;

• Data flow mapping to highlight

generated records and their

vulnerabilities;

• For each process and system there

Table 1: Some common laboratory key performance indicators (KPIs) or metrics

Metric Measurement

Turnaround time

(TAT)

• Time of sample receipt to delivery of final report or certificate of

analysis

• If required, further subdivision is possible, for example:

Time between receipt and start of analysis

Time for the actual analysis

Time for review of results

Time for report writing

Chromatograph

use

• Percentage utilization per instrument (weekends included or excluded)

• Machine breakdowns: time off-line

• Samples analyzed between maintenance periods

Injections • Number of injections per chromatograph per run

• Peaks analyzed with automated versus manual integration

Table 2: Requirements for data integrity metrics from PIC/S PI-041 guidance (3)

PIC/S PI-041 Section

Requirements

6.4 Modernizing

the pharmaceutical

quality system

6.4.2 The company’s Quality Management System should be able

to prevent, detect, and correct weaknesses in the system or their

processes that may lead to data integrity lapses.

The company should know their data life cycle and integrate the

appropriate controls and procedures such that the data generated

will be valid, complete, and reliable.

Specifically, such control & procedural changes may be in the

following areas:

Quality metrics and reporting to senior management

Regular

management

review of quality

metrics

6.5.1 There should be regular management reviews of quality metrics,

including those related to data integrity, such that significant issues are

identified, escalated, and addressed in a timely manner.

Caution should be taken when key performance indicators are selected

so as not to inadvertently result in a culture in which data integrity is lower

in priority.

Table 3: Data integrity metrics for policies and assessment and remediation of paper

processes and computerized systems

DI Subject Area Suggested Metrics

Data governance

integrity policy

and associated

procedures

• DI policy status: drafting, issued, in-revision versus schedule

• Staff training in DI policies and procedures

Staff training: number completed versus plan

Number of staff passing first time

• Metrics on HR issues related to DI

Assessment of

processes and

systems

• Number of systems and processes, risk priority

• Percentage of high-, medium-, and low-risk systems and process

assessments completed versus plan

Remediation of

processes and

systems

• Percentage of short-term quick fixes completed versus plan: high-,

medium-, and low-risk processes and systems

• Longer term remediation projects completed versus plan: high-,

medium-, and low-risk processes and systems

• Manual processes: percentage of uncontrolled blank forms replaced

by a computerized process or control over master templates and

controlled blank forms

As you monitor some actions, they will improve because they get attention, but this can be at the expense of other activities that are not being monitored.



should be short- and long-term

remediation plans;

• The metrics shown in Table 3 are

focused on the completion of

assessments for high-, medium-,

and low-risk systems.

Remediation plans executed for

processes and systems:

• Implementation order of both

short- and long-term remediation

should be based on inventory

(total) risk;

• Short-term quick fixes to remediate

critical risks to records, for

example, eliminate shared user

identities or restrict access to

data directories to meet ALCOA+

criteria;

• Longer term remediation (for

example, update the system to

have a database or effective audit

trail, or replace hybrid systems with

electronic systems);

• Replacement of manual processes

using uncontrolled blank forms with

either a computerized process, or

controlled master templates and

blank forms with reconciliation.

Table 4: Some data integrity metrics for laboratory processes


Reprocessed

sample analysis • Sample results that were released, withdrawn, and released again.

(This is usually done for errors discovered after release)

Injections • Run times longer than average

• Run times shorter than average

Exported runs

• A missing metric from some chromatography data systems is a list

of all runs exported to LIMS or ELN systems. This missing piece

can permit users to run a sample multiple times and release their

“favourite”. A report of runs with 1–2 injections per vial can help

detect some of this behaviour, but in general, accounting for all

chromatography injections is an issue.

Runs stopped or

aborted

• This metric could indicate behaviour where the analyst does not like

what they see as they look at the run in progress, so they stop data

collection.

Table 5: Data integrity metrics for quality assurance oversight


QA oversight

metrics

• DI audits progress against plan for scheduled and unscheduled

audits

• Number of DI investigations

• CAPAs raised from audits and investigations

Number and classification of findings

Number of CAPAs closed versus planned completion

CAPA effectiveness on follow-up

Might consider some time metrics here: for example, average, min, max

time to release test results, sorted by method

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Metrics for progress of short-term

and long-term remediation versus

plans are shown in Table 3.

Laboratory Data Integrity MetricsWe can see some of the data integrity

metrics that could be generated in

Figure 2.

Preliminary Considerations:

• Most data integrity reports can

provide only points of concern. It

is rare where 1 record = 1 data

integrity breach. You must carefully

select what areas will be monitored

by metrics because too many

can bury you in data. Start small,

evaluate the data, and adjust as

necessary.

• Not all metrics are equal; therefore,

their review should not be equal.

• It is nearly impossible to generate

and analyze metrics for manual

or hybrid activities. For example,

you could detect missing files

in a system by comparing the

instrument use log (paper) against

a report of sample IDs from all

runs stored on a computer system.

However, this approach is too

tedious to be done on a regular

basis and is best left for either a

data integrity audit or investigation.

In contrast, sample IDs in a

laboratory information management

system (LIMS) or electronic

laboratory notebook (ELN) could

be compared to the data files on

laboratory data systems using an

automated script. Owing to the

time involved in their preparation,

manual reports should be limited

to specific scenarios over a

specific time period (for example,

detecting fraud from a single

suspected individual as part of an

investigation).

• There are some activities that are

just difficult to catch with a report.

For example, once a user changes

the naming convention on repeat

injections, you will probably not

detect it on a routine report. And

trying to catch everything that

could be done is like testing every

possible branch of a program. It

could be done if you had infinite

resources.

• Some things can’t be reported.

For example, chromatographers

can delete data on a balance or

other simple analytical system.

The data existed outside the system

and so the records are gone. This

is where data capture to a central

informatics system can improve

the situation compared with the

following citation: “He said that

he recalibrated the balance and

prepared new documentation, and

subsequently discarded the original

record. Furthermore, we learned

that additional original calibration

records of other balances had

similarly been discarded” (4).

• Metrics could also include

assessment of the performance of

individuals, for example, measuring

the time to complete a method

for several different people and

looking for someone working too

quickly. This is where regulatory

compliance can clash with the

works council—who are concerned

with protecting the rights of

workers— in some countries.

The balance between ensuring

data integrity of the organization

producing and marketing

pharmaceutical products needs

to be balanced with the rights of

individuals. However, the regulatory

expectation is that processes must

ensure the integrity of records

generated.

Some metrics for laboratory

processes are shown in Table 4

and cover the main areas of

chromatographic analysis.

Quality Assurance Data Integrity (DI) MetricsThe main areas for quality assurance

oversight in data integrity are DI

audits and investigations and the

resulting corrective and preventive

actions (CAPAs) that are raised

following them. Typically, there will

be:

• DI audits following a schedule

of both announced and

unannounced visits for all

processes, systems, and areas

of an organization. The reason

for unannounced audits is to see

a better picture of work carried

out although there is a risk of

disrupting work;

• Data integrity investigations raised

either by an audit or inspection

finding or a staff concern;

• CAPAs arising from audits or

investigations, close out versus

planned completion, and how

effective each action plan was.

Management Review of DI MetricsData integrity metrics need to be

reviewed by management because

they are responsible for the whole of

the quality management system. The

review should be formal with minutes

kept of the meeting and action items

raised and their progress monitored.

This is especially true for high risk or

impact systems—along with rapid

implementation of short-term fixes

to ensure any major data integrity

gaps are remediated. Demonstrable

progress is important and management

activity in this area is best evidenced

by actions and not words. Management

review and follow-up emphasizes the

importance of data integrity to the

organization and ensures that process

and resource bottlenecks are exposed

and removed.

It’s Déjà Vu All Over Again! For those with short memories, data

integrity is the third major IT systems

improvement program that has faced

the pharmaceutical industry over the

past 20 years, the other two being

Year 2000 (Y2K) and electronic

records and signatures (Part 11)

assessment and remediation. Is

the pharmaceutical industry going

to make the same mistakes again?

Let us explore this question. The

Y2K program was simply replacing

applications and operating systems

that could handle dates past 31st

December 1999. Typically, it was a

case of updating, rather than process

improvement, to complete the work

before the deadline; this was a

technical project with a fixed due

date.

In contrast, a 21 CFR 11

assessment and remediation program

was an opportunity to upgrade and

provide substantial business benefit

by changing the business process to

Data integrity metrics need to be reviewed by management because they are responsible for the whole of the quality management system.



use electronic signatures and eliminate paper. However,

not many laboratories took advantage of this approach

and simply replaced noncompliant with technically

compliant software.

Is the industry going to repeat the Part 11 behaviour?

Reading the various guidance documents (3, 5–9),

you see the storm clouds on the horizon; tight and

bureaucratic control of blank forms and discouraging use

of hybrid systems. The message is clear—get rid of paper

or control it rigorously. The cost of electronic management

is steady or declining, but the cost of using paper records

is rising. Consider not just the physical storage cost but

also the time to access reports and trend data—paper’s

management cost is considerable and highly labour

intensive. Conversion to electronic records is the only

option for the pharmaceutical industry.

An alternative view for data integrity remediation

is seeing it as a second chance to get Part 11 right

by looking at the intent rather than the letter of the

regulation. Seen in this way, industry can both align

with regulators and position themselves for

productivity—and data integrity—improvements in their

processes.

However, many organizations complain that that this

will cost money. Yes, but what is the impact on the

organization’s cash flow if every batch can be released

a few days earlier? Do the sums and then put in your

upgraded systems project proposals.

SummaryMetrics can be used to monitor for some potential

integrity issues but not all. Care should be taken

to ensure that metrics do not drive behaviours that

compromise data integrity. To be sustainable,

accurate, and timely, collection of metrics must be

automated.

It is essential that metrics enable management

monitoring of data integrity because this is a key part

of success for an overall data governance and data

integrity programme in an organization. However, not

all metrics are equal, they need to be chosen carefully.

Use long-term data integrity remediation as an

opportunity to also improve productivity in laboratory

processes.

References(1) US Food and Drug Administration, Guidance for Industry

Submission of Quality Metrics Data, Revision 1 (FDA,

Rockville, Maryland, USA, 2016).

(2) EudraLex, Volume 4 Good Manufacturing Practice

(GMP) Guidelines, Chapter 6 Quality Control (European

Commission, Brussels, Belgium, 2014).

(3) PIC/S, PI-041 Draft Good Practices for Data Management

and Integrity in Regulated GMP / GDP Environments.

Pharmaceutical Inspection Convention/Pharmaceutical

Inspection Co-Operation Scheme, Geneva, Switzerland (2016).

(4) FDA Warning Letter Sun Pharmaceuticals, (Food and Drug

Administration, Rockville, Maryland, USA, 2014).

(5) MHRA, GMP Data Integrity Definitions and Guidance for Industry

2nd Edition. Medicines and Healthcare Products Regulatory

Agency, London, UK (2015).

(6) US Food and Drug Administration, Draft Guidance for Industry

Data Integrity and Compliance with cGMP (Silver Spring,

Maryland, USA, 2016).

(7) WHO, Technical Report Series No.996 Annex 5 Guidance on

Good Data and Records Management Practices. World Health

Organization, Geneva, Switzerland (2016).

(8) MHRA, GxP Data Integrity Definitions and Guidance for

Industry, Draft version for consultation July 2016. Medicines

and Healthcare Products and Regulatory Agency,

London, UK, 2016.

(9) EMA, Questions and Answers: Good Manufacturing Practice:

Data Integrity. 2016; Available from: http://www.ema.europa.eu/

ema/index.jsp?curl=pages/regulation/general/gmp_q_a.jsp&mid

=WC0b01ac058006e06c#section9.

Mark E. Newton is a laboratory informatics QA

representative at Eli Lilly and Company, Indianapolis,

Indiana, USA. He is also a co-lead of the ISPE/GAMP Data

Integrity Interest Group.

“Questions of Quality” editor Bob McDowall is Director

at R.D. McDowall Ltd., Bromley, Kent, UK. He is also a

member of LCGC Europe’s editorial advisory board. Direct

correspondence about this column should be addressed

to the editor-in-chief, Alasdair Matheson, at alasdair.

[email protected]

It is essential that metrics enable management monitoring of data integrity because this is a key part of success for an overall data governance and data integrity programme in an organization.


PRODUCTS

Procainamide glycan labelling

Procainamide labelling permits glycan

identifi cation by either MS or

(U)HPLC, and because of its improved

ionization efficiency compared to 2AB

labelling it can permit identifi cation of

minor glycans (<1% relative peak area)

by ESI-MS. Ludger’s procainamide

labelling kit, LT-KPROC-VP24, is

suitable for N-glycans, O-glycans,

GSL-glycans, heparin, or any

sugar with a reducing terminus.

Procainamide-labelled purifi ed glycan

standards are also available.

https://www.ludger.com/procainamide

Ludger Ltd, Oxford, UK.

FID gas station

The VICI fl ame ionization detector (FID)

gas station combines the reliability

of the VICI DBS hydrogen and zero

air generators into one compact and

convenient package. Available in high

and ultrahigh purity for all GC detector

and carrier gas applications. The

generator is available in two styles,

fl at for placement under a GC, or the

Tower. Available in H2 fl ow ranges up to

1 L/min and 11 bar.

www.VICIDBS.com

VICI AG International, Schenkon, Switzerland.

Thermal desorber

Designed for material emissions-,

fl avour-, and air analysis, the TD

3.5+ processes 3.5” tubes or Gerstel

plus tubes for enhanced recovery

and sensitivity. The liner-in-liner

design replaces the transfer line

reducing analyte loss and memory

effects. Up to 240 samples can

be processed automatically. In

combination with DHS 3.5+, dynamic

headspace up to 1 L volume is

performed.

www.gerstel.com

Gerstel GmbH & Co. KG,

Mülheim an de Ruhr, Germany.

(U)HPLC method development

DryLab 4 is a (U)HPLC method

development and optimization

software that predicts

chromatograms under a much

wider range of experimental

conditions than would be

possible in the laboratory,

according to the company.

DryLab can reportedly

quickly and easily determine how a specifi c separation

behaves as the user simultaneously varies multiple

method parameters, such as pH, temperature, buffer

concentration, and many more.

www.molnar-institute.com

Molnár-Institute, Berlin, Germany.

HILIC columns

Hilicon has launched its novel

iHILIC-Fusion, iHILIC-Fusion(+), and

iHILIC-Fusion(P) UHPLC/HPLC HILIC

columns, based on spherical silica

or polymer particles. According to

the company, their surface-bonding

technologies provide customized

selectivity, high separation efficiency,

and ultra-low column bleeding. The columns are

versatile and can be used for the analysis of polar

compounds in “-omics” research, pharmaceutical

discovery, food and beverage, clinical diagnostics, and

environmental monitoring.

www.hilicon.com

Hilicon AB, Umeå, Sweden.

Microchambers

Markes’ Micro-Chambers offer

a compact, stand-alone unit for

the rapid, method-compliant

screening of chemicals emitted

from a wide variety of products,

materials, and foodstuffs. They are used in numerous

industries from automotive, building products, and

consumer goods, to food and drink. For more information

please watch this video:

http://chem.markes.com/UCTE

Markes International Ltd., Llantrisant, UK.


PRODUCTS

MALS detector

The μDAWN is,

according to the

company, the world’s

fi rst multi-angle light

scattering (MALS)

detector that can be

coupled to any UHPLC

system to determine

absolute molecular weights and sizes of polymers, peptides,

and proteins or other biopolymers directly, without resorting

to column calibration or reference standards. The WyattQELS

Dynamic Light Scattering (DLS) module, which measures

hydrodynamic radii “on-the-fl y”, reportedly expands the

versatility of the μDAWN.

www.wyatt.com

Wyatt Technology, Santa Barbara, California, USA.

HPLC system

The VWR Hitachi Chromaster delivers highly

reliable results, according to the company.

The high precision delivered by the pump,

the low carryover and high precision of the

autosampler, the stability of the column oven,

and the sensitivity of the detectors combined

contribute to this reliability. Furthermore, the

modular nature of the Chromaster system

allows a multitude of confi gurations to suit all

needs.

https://vwr.com/chromaster

VWR International GmbH, Darmstadt,

Germany.

Portable detectors

Biotech in co-operation with

Runge are proud to announce

a unique series of portable

pocket-sized detectors for liquid

chromatography. The product

line consists of a photometer, a

fl uorimeter, and a conductivity

meter that can be combined

depending on the situation.

Light sources, fi lter, and measuring cell can be adapted

to changing tasks. With these devices you can take the

detector to the measurement site and not vice versa.

https://www.biotech.se/products/detectors/mikron/

Biotech AB, Onsala, Sweden.

LC solvents

Romil-UpS ultra LC solvents

have been developed to

meet the demands of today’s

hyphenated techniques.

Each batch is produced using

exacting purifi cation techniques,

use-tested, and supplied in

bottles specially pre-treated to maintain ionic impurities

at trace levels, to ensure the best possible performance,

according to the company.

www.romil.com

Romil Ltd, Cambridge, United Kingdom.

Full details upon request

Please send applications by email to: [email protected]

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Supplies & Services


EVENT NEWS

24–26 January 201815th International Symposium

on Hyphenated Techniques In

Chromatography and Separation

Technology (HTC-15)

Cardiff City Hall, Cardiff, UK

E-mail: [email protected]

Website: www.ilmexhibitions.com/

htc

18–21 February 201834th International Symposium

on Microscale Separations and

Bioanalysis

Rio de Janeiro, Brazil


Website: www.msb2018.org

26 February–1 March 2018Pittcon Conference and Expo

(Pittcon 2018)

Orange County Convention Center,

Orlando, Florida, USA


Website: https://pittcon.org/

pittcon-2018/

12–14 March 2018The 10th Conference of the World

Mycotoxin Forum

Amsterdamn, The Netherlands

E-mail: WMF@bastiaanse-communi-

cation.com

Website: www.worldmycotoxinforum.

org

10–13 April 201826th International Trade Fair for

Laboratory Technology, Analysis,

Biotechnology, and Analytica

Conference (Analytica 2018)

Munich, Germany


Website: www.analytica.de/index-2.

html

17–18 April 2018International Scientific

Conference Ion Chromatography

and Related Techniques

Zabrze, Poland

E-mail: rajmund.michalski@ipis.

zabrze.pl

Website: http://ipis.pan.pl/en/

Please send any upcoming event

information to lewis.botcherby@

ubm.com

42nd International Symposium on Capillary Chromatography and the 15th GC×GC Symposium

The 42nd International Symposium on

Capillary Chromatography (ISCC) and 15th

GC×GC Symposium will be held at the Palazzo

dei Congressi, Riva del Garda, Italy, from

13–18 May 2018.

The International Symposium on Capillary

Chromatography (ISCC) has established its

reputation as a leading forum for microcolumn separation techniques. From

the first meeting in Hindelang in 1975, the most important developments in

capillary gas chromatography (GC), microcolumn liquid chromatography (LC),

and electromigration techniques have been presented in this symposium

series. The format and the atmosphere of the 42nd meeting will be similar to

the previous meetings, however, this year there will be a particular emphasis

on mass spectrometry (MS).

The six-day event will feature recent findings from leading academic and

industrial experts in the form of lectures and posters revealing the most

recent advances in pressure- and electrodriven microcolumn separation

techniques, and comprehensive two-dimensional gas chromatography

(2D GC). As previously mentioned a particular emphasis will be directed

to comprehensive separation technologies that are combined with various

forms of MS from unit-mass to high resolution, and from single- to hybrid

analyzers. The conference will offer sessions on capillary GC, microcolumn

LC, electromigration methods, and microfabricated analytical systems,

which are expected to cover lab-on-a-chip, column technology, coupled

and multidimensional techniques, comprehensive techniques, hyphenated

techniques, sampling and sample preparation, trace analysis, and automation.

Application sessions include environmental applications, energy, petrochemical,

and industrial applications, biomedical and pharmaceutical applications, and

the analysis of natural products, food, flavours, and fragrances.

Workshop seminars from instrument manufacturers and an extensive

exhibition of instrumentation, accessories, and supplies will run in parallel to

the scientific programme.

At the meeting, the 2018 Marcel Golay Award will be presented in

recognition of outstanding contributions in the field of separation science.

The Leslie Ettre Award will be presented to a young scientist for research

on capillary GC applied to environmental or food analyses. The Giorgio

Nota Award will be presented to a scientist in recognition of a lifetime of

achievement in capillary LC. The John Phillips Award will be awarded to

individuals who have made outstanding contributions to the field of GC×GC

analysis. The GC×GC Lifetime Achievement Award honours an experienced

GC×GC scientist who has made significant contributions to the field.

Scholarships for young researchers will also be promoted by the Interdivisional

Group of Separation Science of the Italian Chemical Society (Italy) and its

partners.

To encourage scientific exchange and friendship building, the scientific

programme will be enhanced with the well-known “Riva Social Programme”,

which consists of a welcome reception, cocktail party, classical concert,

wine and cheese evening, and farewell cocktails. Considering the interest in

comprehensive techniques, the 15th GC×GC Symposium will be organized

during the same period to allow scientists to attend both meetings. The 15th

GC×GC Symposium will start on 13 May 2018 with a course presented

by experts in the field covering the fundamental aspects of comprehensive

techniques and a plenary session on 14 May 2018. For both meetings,

abstracts for consideration as lecture or poster presentations can be submitted

on-line at: http://www.chromaleont.it/iscc

For more information please visit www.chromaleont.it/iscc or e-mail: iscc@

chromaleont.it

EEVVEENNTT NNEEWWSSEVENT NEWS

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THEAPPLICATIONS

BOOK

December 2017


CONTENTS

THE APPLICATIONS BOOK

Environmental

691 Analysis of Organophosphates in Lithium Ion Battery Electrolytes by HILIC–ESI-MS

Jonas Henschel1, Martin Winter1,2, Sascha Nowak1, and Wen

Jiang3, 1MEET Battery Research Center, University of Münster, 2Forschungszentrum Jülich GmbH, IEK-12, Helmholtz Institute Münster

(HI MS), 3HILICON AB

693 Overcome the Memory Effect in Dioxins Extraction with ETHOS X Powered by FastEX-24

Diego Carnaroglio and Matteo Volpi, Milestone Srl

Food and Beverage

694 Determination of Chloramphenicol in Honey with Automated Solid-Phase Extraction and HPLC–MS/MS Detection

Hans Rainer Wollseifen, Johannes Brand, and Detlef Lambrecht,

MACHEREY-NAGEL GmbH & Co. KG

696 Determination of Ephedra Alkaloids and Synephrine in Dietary Supplements via Strong Cation-Exchange SPE and LC–MS/MS DetectionBrian Kinsella, UCT, LLC

Medical/Biological

697 Monitoring Emissions From Respiratory Medical Devices in Accordance With ISO 18562-3

David Barden and Gareth Roberts, Markes International

699 Definitive EtG/EtS LC–MS/MS Analysis: A Rugged 4-Min Method for High-Throughput Laboratories

Justin Steimling and Frances Carroll, Restek Corporation

Cover Photography: Shutterstock.com

690 THE APPLICATIONS BOOK – DECEMBER 2017

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CONTENTSCONTENTS

THE APPLICATIONS BOOK – DECEMBER 2017 691

ENVIRONMENTAL

New separation techniques for the analysis of polar and ionic

analytes have aroused great interest in the fi eld of metabolomics and

environmental investigation in the past two decades. Hydrophilic

interaction liquid chromatography (HILIC) is a promising tool to

address this challenge. HILIC separation is based on the polarity

of analytes, which generally show stronger retention with increasing

polarity according to the HILIC separation mechanism. Furthermore,

the high content of organic solvent in the mobile phase leads to

good ionization properties in the electrospray ionization (ESI), and

consequently enhances the detection sensitivity by hyphenated

mass spectrometry (MS) detector.

Analysis of organophosphates as the degradation products

of pesticides, flame retardants, plasticizers, or batteries is very

important for safety evaluation because their chemical structures

are similar to chemical warfare agents with high toxic potential

(1). Lithium ion batteries possess electrolytes in cells that usually

consist of different formulations of linear and cyclic organic

carbonates and hexafluorophosphate (LiPF6) conducting salts.

One of the major issues is the degradation of LiPF6 as a result

of thermal and chemical instability of the P-F bond. Subsequent

reactions lead to battery performance loss. The presence of

traces of moisture and elevated temperatures in a battery cell

promote the decomposition of PF6 to the reactive intermediate

PF5 after a reaction cascade culminating in fluorinated and

alkylated phosphates (3). Therefore, it is vital to monitor all the

organophosphates in batteries, which helps to better understand

battery performance loss and to avoid its potential threat to the

environment during the disposal process.

Organophosphates cover a wide spectra of ionic and high polar

species as well as nonpolar analytes. Multiple separation techniques

such as gas chromatography (GC), ion chromatography (IC), or

reversed-phase liquid chromatography (LC) are often required for

the comprehensive determination of the organophosphates and

their formed degradation products (1,2). However, we aimed to

develop an LC method using either HILIC or RP column that leads

to a simultaneous separation of all organophosphorus species

by one chromatography run for our study. In this application,

we demonstrate the separation potential of polar and ionic

organophosphate species with an iHILIC®-Fusion(+) column

packed with charge modulated hydroxyethyl amide silica. A

mixed interaction, for example, hydrophilic partitioning and ionic

interactions, may be involved in the HILIC separation mechanism

depending on the chemical properties of the analytes.

Experimental

LC–MS system 1: Thermo Ultimate 3000 LC system and AB SCIEX

3200 QTrap® equipped with an ESI source, operated in ESI(+) and

ESI(-) mode for analysis of standards.

Analysis of Organophosphates in Lithium Ion

Battery Electrolytes by HILIC–ESI-MS Jonas Henschel1, Martin Winter1,2, Sascha Nowak1, and Wen Jiang3, 1MEET Battery Research Center, University of Münster, 2Forschungszentrum Jülich GmbH, IEK-12, Helmholtz Institute Münster (HI MS), 3HILICON AB

TEP

DBP DEP DMP

DFP

TMP DMFP

O

O

O

O P

O

O

O

O P

O

O

O

O P

O

F

O

O P

O

F

F

O P

–

O

O

O

O P–

O

O

O

O P–

–

100

50

0

0 4 8

Time (min)

DEP DMP

DBP

DFP

TMP

TEP

DMFP

Rel. in

ten

sit

y (

%)

12 16

Figure 1: Chemical structures of organophosphate standards used in the study.

Figure 2: Extracted ion chromatograms of organophosphate standards in HILIC separation with iHILIC®-Fusion(+).


ENVIRONMENTAL

LC–MS system 2: Shimadzu Nexera X2 LC system and Shimadzu

LC-MS-IT-TOF™ equipped with an ESI source, operated in ESI(+)

mode for analysis of thermally treated battery electrolyte.

Column: 150 × 2.1 mm, 3.5-μm iHILIC®-Fusion(+) (P/N

100.152.0310, HILICON AB)

Gradient elution 1: A) acetonitrile; B) deionized water-1 M

ammonium formate, pH 2.9 (99.5:0.5); 0–3.5 min (90:10) A–B;

3.5 to 6.5 min, gradient elution from (90:10) A–B to (50:50) A–B;

6.5–14.5 min, (50:50) A–B.

Gradient elution 2: A) acetonitrile; B) deionized water-1 M

ammonium acetate, pH 6.1 (99:1); 0–3.5 min (95:5) A–B; 3.5 to

8 min, gradient elution from (95:5) A–B to (75:25) A–B; 8–16 min,

(75:25) A–B.

Flow rate: 0.3 mL/min

Column temperature: 40 °C

Injection volume: 10 μL

Organophosphates: Trimethyl phosphate (TMP), triethyl phosphate

(TEP), dimethyl phosphate (DMP), diethyl phosphate (DEP),

dibutyl phosphate (DBP), difl uorophosphate (DFP), dimethyl

fl uorophosphates (DMFP). Their detailed chemical structures are

illustrated in Figure 1. A 50 μM measure of TMP, TEP, DMP, and

DEP, 100 μM of DFP and DBP, and 350 μM of DMFP were prepared

in 90:10 acetonitrile–water solution.

Battery electrolyte: 1 M LiPF6 in ethylene carbonate–ethyl methyl

carbonate–vinylene carbonate (30:70:+2 w%, LP 572, BASF) with

2v% water addition was stored at 80 °C for 14 days.

Results and Conclusion

As shown in Figure 2, seven standards of organophosphates

can be simultaneously separated and determined using an

iHILIC®-Fusion(+) column hyphenated to ESI-MS detection. The

nonpolar organophosphates TEP, TMP, and DMFP were eluted fi rst.

This is expected due to weaker interaction with the HILIC stationary

phase through the hydrophilic partioning. The other four ionic species,

DFP, DBP, DEP, and DMP, show stronger retention and good peak

shapes by hydrophilic partitioning and ionic interaction.

The applicability of the iHILIC®-Fusion(+) column for the study

of battery electrolyte degradation products was investigated and is

illustrated in Figure 3 and Table 1. The hyphenation of HILIC with an

IT-TOFTM-MS system allowed us to separate and elucidate structural

information of several formed organophosphates in thermally treated

battery electrolyte.

This work emphasizes the potential application of using

HILIC–ESI-MS methods for the analysis of organophosphates in the

battery industry and for environmental investigation.

References

(1) V. Kraft et al., RSC Advances 6, 8–17 (2016).

(2) I. Van der Veen and J. de Boer, Chemosphere 88, 1119–1153 (2012).

(3) C. Campion et al., J. Electrochem. Soc. 152(12), A2327–A2334 (2005).

HILICON ABTvistevägen 48, SE-90736 Umeå, Sweden

Tel.: +46 (90) 193469


Website: www.hilicon.com

Rel. in

ten

sit

y (

%)

Time (min)

100

50

0

0 4 8 12 16

TEP

155.046

171.041

183.077

199.072

213.089

Figure 3: Extracted ion chromatograms of possible degradant organophosphate products in lithium ion battery electrolyte.

Table 1: Retention times (tR) and suggested structures of

degradation products from LiPF6 electrolyte

tR (min) m/z (M+H)+ Suggested Structure

1.3 213.089

1.3 183.077

1.3 199.072

8.8 199.072

9.1 155.046

9.4 171.041

9.7 171.041

13.2 171.041


ENVIRONMENTAL

pressure reactor. This

approach ensures

the lowest analytical

b l a n k s a n d

completely avoids

carryover between

runs.

Instrumentation

Milestone’s new

ETHOS X microwave

extraction system

can extract organic

c o n t a m i n a n t s

from soils, in full

compliance with EPA

3546 (100–115 °C

and 50–150 psi). Disposable glass vials and contactless temperature

control in all positions makes it a unique and innovative solution for the

extraction of contaminants from soils, providing unmatched ease of use

and low running costs. The ETHOS X is capable of processing up to

30 g of sample per vessel (up to 24 samples simultaneously), thereby

improving the limit of quantitation (LOQ) for analysis. The handling is very

easy because the sample is weighed directly into the disposable glass

vial, 1:1 hexane–acetone or CH2Cl

2–acetone is added, and the vessel is

loaded into the FastEX-24 rotor. After 10–20 min of microwave heating,

the sample is ready to be fi ltered and analyzed by gas chromatography

(GC). ETHOS X provides full data traceability of the entire process.

Results

Table 1 shows recoveries of all the molecules in the range of

80–120% with great reproducibility (RSD%). It also shows the

blank values of the following run with new disposable glass vials (all

the blanks were below 0.065 μg/kg).

Conclusion

The ETHOS X enables simultaneous dioxins extraction of up to 24

samples (from weighing to fi ltration steps) in less than 1 h offering

superior productivity than any other technology in the market. In

addition, the use of contactless temperature control and disposable

glass vials fully solves constant issues in dioxins determination by

avoiding tedious cleaning, memory effect, and

carryover. The ETHOS X with all its unique features

fully addresses the needs of environmental

laboratories in terms of productivity, ease of use,

running costs, extraction quality, and is fully

compliant with EPA 3546 method.

Microwave extraction becomes faster and easier with the new

Milestone ETHOS X with its FastEX-24. This application note

shows the use of Milestone technology for extraction of dioxins

from environmental matrices, with emphasis on one of the most

common challenge of this application: the carryover effect. The

unique design of the FastEX-24 rotor with disposable glass vials

allows easy and effi cient dioxin extraction, and other organic

pollutants, to be performed, avoiding any memory effect. The

FastEX-24 simplifi es the routine pollutants extraction process

and provides superior productivity at lower costs.

Dioxin is known as one of the most toxic chemicals, a serious

public health threat. According to the US Environmental Protection

Agency (EPA) reports, dioxin and dioxin-like chemicals have been

associated with adverse health effects in the population.

The analysis of this class of pollutants is fundamental for

environmental and human health protection, but because of the

persistent nature of those compounds, the sample preparation

and analysis is challenging for many extraction techniques. Many

solutions are available for the extraction of dioxins such as Soxhlet,

automated Soxhlet, sonication, pressurized liquid extraction (PLE),

and closed microwave vessels. A common and very critical issue

in all sample preparation techniques is dioxin extraction and the

subsequent memory effect or carryover in the extraction cells.

ETHOS X equipped with the unique FastEX-24 rotor is specifically

designed to overcome the memory effect and cleaning by using

disposable glass vials as reaction vessels, which are placed into a

Overcome the Memory Effect

in Dioxins Extraction with

ETHOS X Powered by FastEX-24

Diego Carnaroglio and Matteo Volpi, Milestone Srl

Milestone SrlVia Fatebenefratelli, 1/5 24010 Sorisole (BG), Italy

Tel.: +39 035 573857


Website: www.milestonesrl.com

Figure 1: ETHOS X microwave extraction system.

MILESTONE

Table 1: Recovery (n = 4) of PCDD and PCDF from sandy

soil standard reference material BCR®-529 (2 g) and blank

value after extraction with replaced disposable glass vial

AnalyteCertifi ed Value

(μg/kg)

Ethos X

Recovery (%)

RSD

(%)

Blank Value

(μg/kg)

2,3,7,8 -TCDD 4500±0.6 94 3.4 0.0185

1,2,3,7,8-PeCDD 440±0.05 117 2.8 0.02122

1,2,3,4,7,8-HxCDD 1220±0.21 106 3.1 0.00312

1,2,3,6,7,8-HxCDD 5400±0.9 85 2.1 0.02198

1,2,3,7,8,9-HxCDD 3000±0.4 84 1.9 0.06412

2,3,7,8-TCDF 78±0.013 96 2.7 0.02524

1,2,3,7,8-PeCDF 145±0.028 80 3.5 0.01939

2,3,4,7,8-PeCDF 360±0.07 91 2.6 0.01889

1,2,3,4,7,8-HxCDF 3400±0.5 100 1.9 0.03112

1,2,3,6,7,8-HxCDF 1090±0.15 99 3.8 0.00633

1,2,3,7,8,9-HxCDF 22±0.010 82 3.6 0.04043

2,3,4,6,7,8-HxCDF 370±0.05 120 2.2 0.05186


FOOD AND BEVERAGE

A sensitive determination method with automated

solid-phase extraction (SPE) is presented and the

recovery rates are compared with manual extraction. The

automated extraction was developed with the SPE robot

FREESTYLETM SPE module (LCTech GmbH). According to the

European Regulation (EC) No 470/2009, CHROMABOND®

HLB was applied for SPE (1). A high performance liquid

chromatography tandem mass spectrometry (HPLC–MS/MS)

method with the biphenyl phase NUCLEODUR® π2 was used

for the subsequent analysis.

Chloramphenicol is a broad-spectrum antibiotic against

Gram-positive and Gram-negative bacteria. It has many side

effects. Thus, it is only used nowadays as a reserve antibiotic (2).

For warranty of the food safety, many countries declare limits for

pharmaceuticals in animal products. The necessary determination

methods demand a high-performance solid-phase extraction

(SPE) to reliably detect and quantify residues in trace level with

the subsequent analysis.

Automated Solid-Phase Extraction (3)

Sample pretreatment: Weigh out 5 g of honey. Add 4 mL water

and shake rigorously for 30 s. Spike with 1 mL standard solution

(chloramphenicol , chloramphenicol-d5 , c = 5 ng/mL in methanol)

and shake rigorously for 30 s. Add 15 mL ethyl acetate and shake

rigorously for 30 s. Centrifuge at 3000 rpm for 10 min. Take 12 mL

of supernantant and evaporate extracts to dryness at 40 °C under

a stream of nitrogen. Redissolve residue in 10 mL water.

Extraction:

SPE robot: FREESTYLETM SPE module, LCTech

SPE column: CHROMABOND® HLB, 3 mL, 200 mg,

MACHEREY-NAGEL REF 730924

Column conditioning: 3 mL methanol (dispensing speed 1 mL/min),

then 5 mL dest. water (dispensing speed 1 mL/min)

Sample application: 9 mL pretreated sample (dispensing speed

3 mL/min over sample loop)

Washing: 10 mL dest. water (dispensing speed 3 mL/min)

Drying: 100 mL air (dispensing speed 100 mL/min)

Elution: 5 mL 80:20 (v/v) ethyl acetate–methanol

Determination of Chloramphenicol in Honey with Automated

Solid-Phase Extraction and HPLC–MS/MS DetectionHans Rainer Wollseifen, Johannes Brand, and Detlef Lambrecht, MACHEREY-NAGEL GmbH & Co. KG

1.00e5

9.00e4

8.00e4

7.00e4

6.00e4

5.00e4

4.00e4

3.00e4

2.00e4

1.00e4

0.0

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0

Time (min)

Max 27e5 cps. (min)

Inten

sit

y (

cp

s)

8.0 9.0 10.0 11.0 12.0 13.0 14.0

Figure 1: Chromatogram of spiked honey sample (1 μg/kg).


FOOD AND BEVERAGE

Drying: 100 mL air (dispensing speed 100 mL/min)

Evaporation: under nitrogen, 40 °C

Reconstitution: in 1 mL 95:5 (v/v) dest. water–acetonitrile

Manual Solid-Phase Extraction

After identical sample pretreatment as used for automated

solid-phase extraction, the sample was manually extracted by a

vacuum manifold under comparable SPE conditions.

Subsequent Analysis: HPLC–MS/MS (4)

HPLC column: EC 150/2 NUCLEODUR® π2, 5 μm, MACHEREY-NAGEL

REF 760624.20

Eluent A: dest. water

Eluent B: acetonitrile

Gradient: 5–95% B in 7.5 min, 95% B for 1 min, 95–5% B in 1 min,

5% B for 5 min


Temperature: 35 °C


MS/MS detection: API 5500 (AB Sciex), ion source: ESI, negative

ionization mode, scan type: MRM (chloramphenicol: [M-H]- 320.9,

152.0 [Q1], 256.0 [Q2]; chloramphenicol-d5: [M-H]- 325.9, 156.6

[Q1], 261.8 [Q2]), curtain gas: 35 psig, ion spray voltage: -4500 V,

temperature: 450 °C, nebulizer gas: 45 psig, turbo gas: 45 psig,

CAD medium.

Results

Solid-phase extraction of chloramphenicol from the matrix honey

with CHROMABOND® HLB results in an outstanding recovery.

The recovery rates presented in Table 1 show that higher

recovery rates up to 90% with very good reproducibility (< 5%)

are achieved by automation with the SPE robot FREESTYLETM

SPE module. A correction with an internal standard is especially

advantageous for the manual sample preparation. Figure 1

shows the chromatogram of a sample extract from spiked honey.

A definite identification and quantification is possible as a result

of a clear separation of matrix interferences from the analyte

peak.

The liquid chromatographic method could be accelerated by using

a shorter HPLC column because of good retention of chloramphenicol

by the additional π-π-interactions of NUCLEODUR® π2.

Conclusion

A determination method for chloramphenicol in honey has been

developed, which receives very good results—especially by the

automation of solid-phase extraction.

References

(1) Regulation (EC) No 470/2009 of the European Parliament and of the

Council.

(2) T. Tomaszewski, Br Med J. 384–2, 388–392 (1951).

(3) Application No. 306350, MACHEREY-NAGEL, available from www.mn-net.

com/apps

(4) Application No. 128140, MACHEREY-NAGEL, available from www.mn-net.

com/apps

MACHEREY-NAGEL GmbH & Co. KGNeumann-Neander-Str. 6 – 8, 52355 Düren, Germany

Tel.: +49 24 21 969 0 Fax: +49 24 21 969 199

E-mail: [email protected] Website: www.mn-net.com

Table 1: Comparison of recovery rates from automated to manual solid-phase extraction

Recovery Rates (%) ChloramphenicolChloramphenicol

(Corrected by Int. Standard)Chloramphenicol-d

5

Automated SPE 86.8 ± 4.4 (n = 4) 90.8 ± 4.6 (n = 4) 95.6 ± 4.5 (n = 4)

Manual SPE 74.6 ± 2.7 (n = 3) 92.9 ± 3.4 (n = 3) 80.3 ± 4.7 (n = 3)


FOOD AND BEVERAGE

Ephedra alkaloids are phenethylamines that occur naturally

in plants, including the herb Ma Huang used in traditional

Chinese medicine. Ephedra alkaloids are potent CNS stimulants

and also have a sympathomimetic effect on the peripheral

nervous system. The aim of this study was to develop a

multi-analyte procedure for the extraction, cleanup, and

quantifi cation of the ephedra alkaloids in functional foods

and natural products. High capacity strong cation-exchange

SPE cartridges were used for the isolation of ephedrine,

pseudoephedrine, norephedrine, norpsuedoephedrine,

methylephedrine, and synephrine from dietary supplements.

Procedure

1. Sample Pretreatment

a) Weigh 1 ± 0.1g of dietary supplement into a 15 mL polypropylene

centrifuge tube. For this study, a SPEX® SamplePrep®GenoGrinder®

was used to pulverize the tablets.

b) Add 10 mL of 1% formic acid to each sample.

c) Shake or vortex sample for 15 min to fully extract the ephedra

alkaloids. Ensure samples are fully dissolved.

c) Centrifuge the sample for 10 min at ≥3000 × g and 4 °C.

2. SPE Procedure

SPE Conditioning

a) Add 2 × 4 mL of methanol to CUBCX1HL56 SPE cartridge.

b) Add 4 mL of ultrapure water.

c) Add 4 mL of 1% formic acid.

Note: Do not let the cartridge go dry otherwise repeat steps a)

through c).

Sample Extraction

a) Load supernatant from step 1.

b) Allow sample to percolate through the cartridge or apply a

vacuum if necessary (adjust vacuum for fl ow of 1–3 mL per minute).

Wash Cartridge

a) Add 2 × 4 mL of 0.1% formic acid.

b) Add 2 × 4 mL methanol.

c) Dry under vacuum for ≈30 s to remove excess solvent.

Elution

a) Elute the ephedra alkaloids using 8 mL of methanol containing

2% ammonium hydroxide.

b) Evaporate off the methanol solvent at 40 °C under a gentle

stream of nitrogen until it reaches a volume of ≈1 mL.

c) Add 1 mL of aqueous mobile phase (10 mM ammonium acetate)

d) Evaporate off any remaining methanol*

e) Vortex the samples for 1 min and fi lter through a 0.2 μM syringe

fi lter directly into a HPLC vial.

*Ephedra alkaloids are similar to amphetamines, which are known to be

volatile compounds. Therefore, extra care was taken during the evaporation

step to avoid any potential loss in recovery that may occur during this step.

Results

Conclusion

A method was successfully developed for the extraction, cleanup, and

quantifi cation of the ephedra alkaloids and synephrine in functional

foods and natural products. Strong cation-exchange SPE was used to

isolate the phenethylamines from the complex sample

matrix, which consisted of nine herbal extracts containing

a high concentration of calcium, caffeine, and additional

excipients. A high capacity SPE sorbent was used because

it offers better retention than standard SCX sorbent.

Determination of Ephedra Alkaloids and

Synephrine in Dietary Supplements via Strong

Cation-Exchange SPE and LC–MS/MS DetectionBrian Kinsella, UCT, LLC

UCT, LLC2731 Bartram Road, Bristol, Pennsylvania 19007, USA

Tel: (800) 385 3153

E-mail: [email protected] Website: www.unitedchem.com

NL: 3.35E4TICF: + C ESI SRMms2 150.142(91.019-91.021,107.009-107.011)MS Standard_10ppb

0

100

80

60

40

20

0

100

80

60

40

20

0

100

80

60

40

20

0

100

80

60

40

20

0

100

80

60

40

20

0

2 4 6 8 10

Time (min)

Re

lati

ve

Ab

un

da

nce

17.66

14.74

13.72

11.43

9.86

13.818.30

11.449.91

8.95

6.72

4.20

5.59

3.31

2.12RT: 0.00- 19.99 SM 5G

12 14 16 18





Figure 1: Chromatogram of a 10 ng/mL standard.

Table 1: Extraction and analytical materials

CUBCX1HL56 High-load benzenesulfonic acid – 500 mg / 6 mL

SLPFPP100ID21-3UMSelectra® PFPP HPLC column

100 × 2.1 mm, 3-μm

SLPFPPGDC20-3UMSelectra® PFPP guard column

10 × 2.1 mm, 3-μm


MEDICAL/BIOLOGICAL

This study describes the monitoring of potentially harmful

volatile organic compounds (VOCs) emitted from respiratory

medical devices, by pumped sampling and thermal

desorption–gas chromatography–mass spectrometry

(TD–GC–MS) analysis in accordance with ISO 18562 part 3.

Emissions from two sets of face-mask supply tubing and

three nasal cannulas were compared, and all were found to

emit VOCs at levels that may give cause for concern.

Respiratory medical devices (RMDs) are widely used in medical

practice, but the potential for polymeric components in the gas

pathways to release harmful volatile organic compounds (VOCs) is

giving rise to concern that prolonged use could be detrimental to

patient health. This is the driving force behind ISO 18562 (1), which

was released in March 2017. The predecessor to ISO 18562 (ISO

10993) did not cover VOC emissions directly, and was partly based

on animal implant tests of uncertain scientifi c value. In contrast, the

new method is based on tests performed directly on the device, and

does not involve animal experimentation.

The ultimate outcome of testing in accordance with ISO 18562 is

to assess the patient’s exposure to chemicals from the RMD during

their treatment (expressed in units of μg/day), which is then related

to thresholds of toxicological concern (TTCs) specified in ISO 18562.

Devices requiring testing under ISO 18562 include medical devices

and accessories containing gas pathways, which includes ventilators,

anaesthesia workstations, oxygen-conserving equipment, nebulizers,

gas monitors, masks, mouthpieces, breathing tubes, and breathing

accessories.

Part 3 of ISO 18562 deals with testing for emissions of VOCs, and

references two analytical options: ISO 16000-6 for sorbent-tube

sampling and ASTM D5466 for canister sampling. Both are

well-established and popular for air monitoring and related applications,

but generally speaking, tube-based methods such as ISO 16000-6 are

preferred for investigating emissions from products and materials, for

reasons of wider analyte range and also greater ease of use. Analysis

in accordance with the tube-based standard ISO 16000-6 is therefore

the focus of this study.

Experimental

Three sets of face-mask supply tubing (A, B, C), and two nasal

cannulas (D, E) were assessed. In brief, VOC emissions were

collected on a 3½″ × ¼″ sorbent tube after being swept from the

RMD by a fl ow of clean air, followed by analysis of the tubes using

a TD100-xr™ thermal desorption tube autosampler connected to

a GC–MS system. Compounds were identifi ed by screening the

chromatograms against a 540-component library of compounds

typically encountered in polymeric consumer goods and

construction materials. Full analytical conditions are available in

Markes International’s Application Note 132 (http://chem.markes.

com/RMD).

Results and Discussion

VOC Profi les: As an example of a typical profi le, Figure 1 shows the

analysis of Sample A (face-mask supply tubing). The large number

of compounds released is immediately apparent, as is the presence

of a broad hydrocarbon/oil-like response, which was confi rmed as

a real feature by its absence from a sampling run without the RMD

in position.

Across the five samples A–E, screening of the chromatographic

profiles indicated the presence of between 45 and 60 components

from the 540-component library. The most abundant components

are shown in Figure 2.

Monitoring Emissions From Respiratory Medical

Devices in Accordance With ISO 18562-3David Barden and Gareth Roberts, Markes International

Ab

un

dan

ce (

x 1

09 c

ou

nts)


3

2

1

0

2 4 6 8 10 12 14 16 18 20 30

10396

92

90

89

86

84

7950

2

2

50

79

84

86

89

90

92

96

103

Acetone

Cyclohexanone

2-Ethylhexan-1-ol

n-Undecane

1,2,4,5-Tetramethylbenzene

n-Dodecane

n-Tridecane

n-Tetradecane

Diethyl phthalate

Bis(2-ethylhexyl) phthalate

Figure 1: VOC profi le of a sample of face-mask supply tubing. Selected peaks are labelled.


MEDICAL/BIOLOGICAL

Two compounds dominate the profiles of all five samples:

Cyclohexanone is used as a binding agent for polymer fittings,

while 2-ethylhexan-1-ol is a precursor in the synthesis of phthalate

plasticizers. The large number of other compounds present includes

solvents, such as dichloromethane (now declining in use as a binding

agent, but present in all five samples), methyl ethyl ketone (sometimes

used in conjunction with cyclohexanone, and found in Samples B and

D), and toluene (present in all five samples).

Phthalates were also present in all five samples, with diethyl

phthalate and bis(2-ethylhexyl) phthalate being confidently identified.

The latter was the least volatile of the components, eluting as the last

peak. A specification for phthalate-free medical tubing is available,

but these results show that at least some manufacturers are not yet

producing phthalate-free products.

TVOC Values and Exposures: ISO 18562-3 is primarily concerned

with the “dose to the patient of a substance or sum of substances”

within three exposure categories, which are: “Limited” (≤24 h),

“Prolonged”(>24 h and <30 days), and “Permanent” (≥30 days).

The sampling time of 1 h and sampling rate of 0.1 L/min used in

the current study means that the results are of greatest relevance to

breathing volumes of newborns and to devices used in the “Limited”

category.

To assess how Samples A–E perform within this category, total VOC

(TVOC) values were determined by integrating the entire chromatogram

(Table 1). Extrapolating these 1-h TVOC values to 24 h on the basis of

a separately determined decay curve allowed derivation of cumulative

exposure values for the first 24 h of use. Values are in the range

1370–7000 μg, which exceeds three times the TTC for adults of

360 μg/day, and is over 500 times the TTC for newborns of ~2.5 μg/day.

Conclusions

This study shows that the sorbent sampling tubes and

TD–GC–MS analytical equipment used is versatile and fully

compliant with the requirements of ISO 18562-3 (and of the

cited method ISO 16000-6). In particular, the thermal desorption

instrument used to pre-concentrate the vapours is highly sensitive

and easily automated, and in combination with GC–MS is able to

reliably identify compounds released from the gas pathways of

respiratory medical devices.

The results obtained indicate that some respiratory medical devices

emit a broad range of VOCs, at levels that indicate potential issues

with manufacturing quality. The use of low flow rates, and the fact

that the products were sampled immediately after being removed

from the packaging, means that these results represent a “worst-case

scenario” for VOC emissions, but nevertheless one that may be typical

of products used for the care of certain patients.

References

(1) ISO 18562: Biocompatibility evaluation of breathing gas pathways in

healthcare applications, International Organization for Standardization,

2017. Part 3 (“Tests for emissions of volatile organic compounds (VOCs)”)

is available at www.iso.org/standard/62894.html.

For the complete study, including a full table of results, download

Markes International’s Application Note 132 (http://chem.markes.

com/RMD).

Markes InternationalGwaun Elai Medi-Science Campus, Llantrisant, Wales, UK

Tel.: +44 (0)1443 230935

E-mail: [email protected] Website: www.markes.com

80

60

40

20

0

A B C D E

Sample

Ma

ss (

μg

to

lue

ne

eq

uiv

ale

nts)

Tolu

ene

Cyclo

hexanone

Cum

ene

o-C

ym

ene

2-E

thylh

exan-1

-ol

Aceto

phenone

n-U

ndecane

n-T

ridecane

n-D

odecane

Bis(2

-eth

ylh

exyl)

phth

ala

te

Figure 2: Comparison of the masses of the 10 compounds present at levels above 2000 ng in any of the samples.

Table 1: Estimated TVOC exposures in μg (toluene

equivalents) over the first 1 and 24 h of use of Samples

A–E, based on integration of the entire chromatogram and a

50% 24-h decay rate

A B C D E

1 h 371 172 429 75 122

24 h 6777 3131 7830 1369 2234


MEDICAL/BIOLOGICAL

Methods for monitoring alcohol consumption biomarkers EtG

and EtS are generally limited by poor retention and coelution

with matrix interferences, as well as by long analysis times and

short column lifetimes. The dilute-and-shoot EtG/EtS LC–MS/MS

analysis developed here using the novel Raptor EtG/EtS column

easily resolves EtG and EtS from matrix interferences, providing

consistent, accurate results for high-throughput labs testing

human urine samples for alcohol consumption.

Ethyl glucuronide (EtG) and ethyl sulfate (EtS) are important

biomarkers of alcohol use. The detection of these metabolites offers

many advantages for abstinence monitoring, including a three-day

detection window, good stability in properly stored specimens, and

analytical specifi city. However, typical methods for EtG/EtS analysis

have several shortfalls: poor resolution of EtG and EtS from matrix

components, long run times that limit sample throughput, and short

column lifetimes.

In this study, a simple dilute-and-shoot method was developed,

validated, and applied to patient samples for EtG/EtS LC–MS/MS

analysis in human urine. The method was developed on a Raptor

EtG/EtS column because it provides the retention characteristics that

are needed to consistently elute the target analytes away from matrix

interferences. In addition, it is rugged and long-lasting, which is

benefi cial to high-throughput laboratories.

Experimental Conditions

Standard and Sample Preparation: Human urine (alcohol free) was

fortifi ed with EtG and EtS to prepare calibration standards and QC

samples. The concentration of calibration standards ranged from

50–5,000 ng/mL for both analytes. Four QC levels were prepared at

50, 150, 750, and 4000 ng/mL.

Aliquots (50 μL) of urine were diluted with 950 μL of the working

internal standard (100 ng/mL EtG-d5 and 25 ng/mL EtS-d5 in

0.1% formic acid in water). Samples were vortexed at 3500 rpm for

10 s to mix followed by centrifugation at 3000 rpm for 5 min at 10 °C.

Additional double blanks were extracted for column equilibration.

Defi nitive EtG/EtS

LC–MS/MS Analysis: A

Rugged 4-Min Method for

High-Throughput LaboratoriesJustin Steimling and Frances Carroll, Restek Corporation

Time (min)

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0

MatrixInterference

MatrixInterference

MatrixInterference

MatrixInterference

EtG

EtG

EtS

Injection 1

Injection 1,000

EtS

Figure 1: Rugged Raptor EtG/EtS columns provide reliable results for EtG/EtS LC–MS/MS analysis allowing more samples to be analyzed between column changes.

Table 1: Analyte transitions

Analyte Precursor Ion Product Ion Product Ion

EtG-d5 225.9 84.9 –

EtG 220.8 84.9 74.8

EtS-d5 129.7 97.7 –

EtS 124.7 96.8 79.7

LC–MS/MS Analysis: In order to ensure good response, peak

shape, and retention time consistency, the analytical column

was conditioned prior to use with 30 matrix injections that were

run through the full gradient program. Instrument parameters for

EtG/EtS LC–MS/MS analysis are shown below and the analyte

transitions are given in Table 1.

Analytical column: Raptor EtG/EtS, 2.7 μm, 100 mm × 2.1 mm

(cat.# 9325A12)

Guard column: UltraShield UHPLC precolumn fi lter, 0.2 μm frit

(cat.# 25809)

Mobile phase A: 0.1% formic acid in water

Mobile phase B: 0.1% formic acid in acetonitrile

Gradient Time (min) %B

0.00 5

2.50 35

2.51 5

4.00 5



Column temp.: 35 °C

Ion mode: Negative ESI

Results

Chromatographic Performance: As shown in Figure 1, a fast,

4-min EtG/EtS LC–MS/MS analysis was obtained from the direct

(dilute-and-shoot) injection of supernatant. Both EtG and EtS are

clearly resolved from matrix interferences, making accurate peak

identifi cation an easy task. Even after 1000 sample injections on a

Raptor EtG/EtS column, all chromatographic peaks maintained the


MEDICAL/BIOLOGICAL

Restek Corporation110 Benner Circle, Bellefonte, Pennsylvania 16823, USA

Tel.: (800) 356 1688 Fax: (814) 353 1309

Website: www.restek.com

Time (min)

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0

EtG

(overlay of 5,000 ng/mL standard)

Matrix double

blank injection with

post-column EtG

infusion.

Figure 2: EtG elutes outside the matrix suppression zones (double blank injection with post-column EtG infusion).

initial peak shape, retention time, and response.

Linearity: Using linear 1/x weighted regression for EtG and EtS, both

compounds showed good linearity with r2 values of 0.999 or greater

over a calibration range of 50–5000 ng/mL. Accurate calibration

down to 50 ng/mL allows low-level positive results to be reported with

confi dence.

Accuracy and Precision: Precision and accuracy analyses were

performed on three different days. The method accuracy was

demonstrated to be within 6.3% of the nominal concentration

for all QC levels for both EtG and EtS. The %RSD was

1.28–9.19% and 4.01–6.82% for intra- and inter-run,

respectively, at the QC LLOQ. The %RSD was 0.675–7.78% and

1.00–4.99% for intra- and inter-run, respectively, at the QC low,

mid, and high levels, indicating good method precision for EtG/EtS

LC–MS/MS analysis (Table 2).

Matrix Effect: Samples prepared in matrix show approximately 80%

(EtG) and 100% (EtS) of the signal obtained when samples are

prepared in solvent across all QC levels, demonstrating that matrix

effect is minimal. The sensitivity and consistency of the EtG response

at the LLOQ is only possible because retention on the Raptor EtG/EtS

column was optimized so that EtG elutes outside the zones of matrix

suppression (Figure 2). Because the optimal EtG elution time was

established here during method development, clinical labs adopting

the method can be confi dent that matrix suppression has already

been minimized. This, in combination with the established calibration

range and LLOQ, demonstrates that the method provides excellent

sensitivity for EtG, which is generally harder to detect at low levels

than EtS.

Sample Analysis: The robustness of the method was further tested by

monitoring fi ve patient samples with positive results for both EtG and EtS

in the lower end of the linear range across multiple instrument platforms

and nine lots of Raptor EtG/EtS columns. The precision of the results

Table 2: Accuracy and precision of QC samples

QC LLOQ (50 ng/mL) QC Low (150 ng/mL)QC Mid

(750 ng/mL)

QC High

(4000 ng/mL)

Analyte

Average

Conc.

(ng/mL)

Average

Accuracy

(%)

%RSD

Average

Conc.

(ng/mL)

Average

Accuracy (%)%RSD

Average

Conc.

(ng/mL)

Average

Accuracy

(%)

%RSD

Average

Conc.

(ng/mL)

Average

Accuracy (%)%RSD

EtG 51.2 102 6.82 143 95.2 4.99 749 99.8 3.68 3949 98.7 2.04

EtS 46.9 93.7 4.01 143 95.6 1.42 762 102 1.00 3958 98.9 1.59

Table 3: Inter-run precision of patient samples across

multiple instrument platforms and nine column lots

Patient

Sample

Average EtG

Concentration

(ng/mL)

%RSD

Average EtS

Concentration

(ng/mL)

%RSD

A 216 6.13 78.0 3.56

B 1,167 4.81 300 3.24

C 98.2 9.76 82.4 4.01

D 319 8.33 233 3.63

E 247 11.2 163 8.70

(n = 9) was found to range from 3.24–11.2% for both analytes over

multiple days and sample preparations, indicating excellent robustness

and ease of method transfer (Table 3). High-throughput labs in particular

will benefi t from implementing this rugged EtG/EtS LC–MS/MS analysis

method as highly consistent performance is seen across a wide range

of scenarios.

Conclusions

It was demonstrated that the Raptor EtG/EtS column is excellent for the

rapid and accurate analysis of EtG and EtS in human urine. Isobaric

matrix interferences are easily resolved, preventing issues with peak

identifi cation and quantitation. In addition, superior sensitivity for EtG

is achieved because it elutes outside the zones of matrix suppression

that are typically observed with dilute-and-shoot assays. With a fast

and simple sample preparation procedure and only four minutes

of chromatographic analysis time, the EtG/EtS LC–MS/MS method

established here provides accurate, high-throughput monitoring of

alcohol consumption. For the full article, visit www.restek.com and

enter “CFAN2736” in the search box.

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