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ES301918_LCA0913_CVTP1_FP.pgs 08.20.2013 18:51 ADV blackyellowmagentacyan

LC TROUBLESHOOTING

Baseline drift problems

GC CONNECTIONS

A long-distance GC run

PERSPECTIVES IN

MODERN HPLC

Fundamental concepts of HPLC

September 2013

Volume 16 Number 3

www.chromatographyonline.com

in Water Samples Screening Pollutants

From passive samplers and extracts using LC–MS and GC–MS

ES301447_LCA0913_CV1.pgs 08.19.2013 20:21 ADV blackyellowmagentacyan

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3

Editorial P olicy:

All articles submitted to LC•GC Asia Pacific

are subject to a peer-review process in association

with the magazine’s Editorial Advisory Board.

Cover:

Original materials: Image Source

Columns15 LC TROUBLESHOOTING

Gradient Elution: Baseline Drift Problems

John W. Dolan

Can anything be done to correct for baseline drift in gradient

separations?

18 GC CONNECTIONS

A Long Distance Run

John V. Hinshaw

In this instalment, John Hinshaw compares the GC separation process

to a long-distance run through a long corridor with some unique

properties. The runners are separated in various ways.

24 PERSPECTIVES IN MODERN HPLC

The Essence of Modern HPLC: Advantages, Limitations,

Fundamentals, and Opportunities

Michael W. Dong

The reasons that make HPLC so ubiquitous; the fundamentals on

how we conduct HPLC separations; and a few opportunities with

far-reaching impacts in life sciences for separation scientists are

discussed in this article.

32 THE ESSENTIALS

Secrets to Successfully Translating and Transferring HPLC

Methods

There are many parameters that need to be considered when

transferring HPLC methods between instruments. Knowledge of all

the potential pitfalls assists with designing robust methods.

Departments30 Products

33 Application Notes

COVER STORY6 Screening of Pollutants in Water

Samples and Extracts from

Passive Samples and Extracts

from Passive Samplers Using

LC–MS and GC–MS

A Gravell, G.A. Mills and W. Civil

This article describes the GC–MS

and LC–MS screening methods

developed for the analysis of both

low-volume water samples and

extracts obtained from various designs

of passive samplers.

September | 2013

Volume 16 Number 3


ES301327_LCA0913_003.pgs 08.19.2013 18:21 ADV blackyellowmagentacyan

4 LC•GC Asia Pacific September 2013

The Publishers of LC•GC Asia Pacific would like to thank the members of the Editorial Advisory Board

for their continuing support and expert advice. The high standards and editorial quality associated with

LC•GC Asia Pacific are maintained largely through the tireless efforts of these individuals.

LCGC Asia Pacific provides troubleshooting information and application solutions on all aspects

of separation science so that laboratory-based analytical chemists can enhance their practical

knowledge to gain competitive advantage. Our scientific quality and commercial objectivity provide

readers with the tools necessary to deal with real-world analysis issues, thereby increasing their

efficiency, productivity and value to their employer.

Editorial Advisory Board

Kevin AltriaGlaxoSmithKline, Harlow, Essex, UK

Daniel W. ArmstrongUniversity of Texas, Arlington, Texas, USA

Michael P. BaloghWaters Corp., Milford, Massachusetts, USA

Coral BarbasFaculty of Pharmacy, University of San

Pablo – CEU, Madrid, Spain

Brian A. BidlingmeyerAgilent Technologies, Wilmington,

Delaware, USA

Günther K. BonnInstitute of Analytical Chemistry and

Radiochemistry, University of Innsbruck,

Austria

Peter CarrDepartment of Chemistry, University

of Minnesota, Minneapolis, Minnesota, USA

Jean-Pierre ChervetAntec Leyden, Zoeterwoude, The

Netherlands

Jan H. ChristensenDepartment of Plant and Environmental

Sciences, University of Copenhagen,

Copenhagen, Denmark

Danilo CorradiniIstituto di Cromatografia del CNR, Rome,

Italy

Hernan J. CortesH.J. Cortes Consulting,

Midland, Michigan, USA

Gert DesmetTransport Modelling and Analytical

Separation Science, Vrije Universiteit,

Brussels, Belgium

John W. DolanLC Resources, Walnut Creek, California,

USA

Roy EksteenSigma-Aldrich/Supelco, Bellefonte,

Pennsylvania, USA

Anthony F. FellPharmaceutical Chemistry,

University of Bradford, Bradford, UK

Attila FelingerProfessor of Chemistry, Department of

Analytical and Environmental Chemistry,

University of Pécs, Pécs, Hungary

Francesco GasparriniDipartimento di Studi di Chimica e

Tecnologia delle Sostanze Biologica-

mente Attive, Università “La Sapienza”,

Rome, Italy

Joseph L. GlajchMomenta Pharmaceuticals, Cambridge,

Massachusetts, USA

Jun HaginakaSchool of Pharmacy and Pharmaceutical

Sciences, Mukogawa Women’s

University, Nishinomiya, Japan

Javier Hernández-BorgesDepartment of Analytical Chemistry,

Nutrition and Food Science University of

Laguna, Canary Islands, Spain

John V. HinshawServeron Corp., Hillsboro, Oregon, USA

Tuulia HyötyläinenVVT Technical Research of Finland,

Finland

Hans-Gerd JanssenVan’t Hoff Institute for the Molecular

Sciences, Amsterdam, The Netherlands

Kiyokatsu JinnoSchool of Materials Sciences, Toyohasi

University of Technology, Japan

Huba KalászSemmelweis University of Medicine,

Budapest, Hungary

Hian Kee LeeNational University of Singapore,

Singapore

Wolfgang LindnerInstitute of Analytical Chemistry,

University of Vienna, Austria

Henk LingemanFaculteit der Scheikunde, Free University,

Amsterdam, The Netherlands

Tom LynchBP Technology Centre, Pangbourne, UK

Ronald E. MajorsAgilent Technologies,

Wilmington, Delaware, USA

Phillip MarriotMonash University, School of Chemistry,

Victoria, Australia

David McCalleyDepartment of Applied Sciences,

University of West of England, Bristol, UK

Robert D. McDowallMcDowall Consulting, Bromley, Kent, UK

Mary Ellen McNallyDuPont Crop Protection,Newark,

Delaware, USA

Imre MolnárMolnar Research Institute, Berlin, Germany

Luigi MondelloDipartimento Farmaco-chimico, Facoltà

di Farmacia, Università di Messina,

Messina, Italy

Peter MyersDepartment of Chemistry,

University of Liverpool, Liverpool, UK

Janusz PawliszynDepartment of Chemistry, University of

Waterloo, Ontario, Canada

Colin PooleWayne State University, Detroit,

Michigan, USA

Fred E. RegnierDepartment of Biochemistry, Purdue

University, West Lafayette, Indiana, USA

Harald RitchieThermo Fisher Scientific, Cheshire, UK

Pat SandraResearch Institute for Chromatography,

Kortrijk, Belgium

Peter SchoenmakersDepartment of Chemical Engineering,

Universiteit van Amsterdam, Amsterdam,

The Netherlands

Robert ShellieAustralian Centre for Research on

Separation Science (ACROSS), University

of Tasmania, Hobart, Australia

Yvan Vander HeydenVrije Universiteit Brussel,

Brussels, Belgium

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KEY POINTS• There are increased demands on regulators for monitoring

water quality.

• Conventional ‘targeted’ analysis of aquatic pollutants may

not always give reliable information on overall water quality.

• New high resolution analytical techniques and associated

software routines allow for rapid “multi-target”screening of

complex environmental samples.

• Analysis of low volume spot water samples often give an

inaccurate indication of the presence of pollutants in a

water body and can this can give rise to misleading risk

assessments.

Screening of Pollutants in Water Samples and Extracts from Passive Samplers Using LC–MS and GC–MS A. Gravell1, G.A. Mills2 and W. Civil3, 1Environment Agency, National Laboratory Service, Llanelli, Wales, UK, 2School

of Pharmacy and Biomedical Sciences, University of Portsmouth, UK, 3Environment Agency, National Laboratory Service,

Starcross, UK.

Water pollution is of major worldwide environmental concern and therefore a priority for all environmental authorities and regulators. Although water pollution reduction measures taken over the past decades have significantly reduced the presence of many known contaminants in water, the number of new and emerging contaminants that can reach the environment is steadily increasing. The use of various passive sampling devices in conjunction with gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–mass spectrometry (LC–MS) techniques to screen pollutants has proved invaluable in identifying these new and emerging contaminants in various water bodies. Used together, these new analytical approaches offer a robust solution to address specific future monitoring needs, particularly those prompted by legislative change.

pollutants is typically accomplished by low resolution

gas chromatographic–mass spectrometric (GC–MS)

analytical methods using simple mass spectral library

searching routines. GC–MS is a powerful technique for the

separation and determination of volatile and semi-volatile

compounds, but even with the use of high-resolution

capillary columns, it is unable to resolve the multitude of

compounds that can be present in complex environmental

samples.

Screening of samples via low unit mass resolution GC–MS

is also susceptible to interference from other compounds

of a similar molecular mass. This implies that by using

conventional single quadrupole GC–MS techniques many

compounds can remain unidentified, which could have a

significant aquatic toxicity (6). It is therefore desirable to

introduce other instrumental techniques to improve the

quality of environmental assessments and also to benefit

from resource reduction as further analytical developments

become available (7).

Environmental legislation such as the European Union’s

Water Framework Directive (WFD), the US Environmental

Protection Agency’s (EPA) Clean Water Act and Australia’s

Water Act 2007 have the objective of providing for the

planning and delivery of better quality surface water, ground

water and coastal waters (1–4). Various types of monitoring

activities are described including: investigative, operational

and surveillance. In particular, they set out to deal with diffuse

pollution that remains a serious environmental concern.

Most of the strategies currently used for the identification

of pollutants in a body of water focus on the measurement

of the concentrations of specific substances. The measured

concentrations are compared to the proscribed environmental

quality standards for each of these pollutants (5). Each subset

of pollutants, such as polyaromatic hydrocarbons (PAHs) or

specific classes of pesticides, may require a separate water

sample to be taken in the field and the combined cost of these

analyses can prove labour intensive and expensive

In such surveillance monitoring campaigns, many compounds

that could have a significant toxicological impact on the fauna and

flora within the given aquatic environment will remain unidentified.

This could result in the environmental objectives for a particular

body of water not being met, and the causes of the failure need to

be better understood. Further investigative monitoring may then

be undertaken to gather further information on the likely reason for

the failure. Additionally, the typical current monitoring practice of

taking low volume (1–10 L) bottle, grab or spot samples of water

followed by their laboratory analysis may not always provide a

useful indication of the environmental status of a water course and

alternative approaches, such as bio-monitoring, sensors, passive

sampling, and strategies may be warranted

Existing Approaches to Investigative MonitoringFor many years investigative monitoring by analysis of

spot samples for unknown (non-target) organic chemical

LC•GC Asia Pacific September 20136


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New Analytical Technologies for Environmental AnalysesSeveral advanced instruments for environmental

analyses have been developed recently; including high

resolution GC–MS and liquid chromatography–mass

spectrometry (LC–MS) systems. These techniques use

higher resolution spectrometers such as time-of-flight

(TOF), quadrupole-time-of-flight (Q-TOF) and some trap

technologies; all have been used effectively to identify

complex mixtures of pollutants in water samples (8–12). In

addition, nuclear magnetic resonance (NMR) spectroscopy

(to confirm structures) in conjunction with high resolution

LC–MS has also been proposed (13, 14). Two-dimensional

GC (GC×GC) has also seen significant advances, especially

with the use of fast TOF-MS detectors (15). GC×GC allows

for enhanced separation of complex mixtures through greater

chromatographic peak capacity and allows for the detection

of trace contaminants that would not have otherwise been

identified through conventional single dimension GC–MS

techniques (16).

Multi-target ScreeningMany hazardous chemicals have impacts on the aquatic

environment at extremely low concentrations and these

can be challenging for analytical detection. Complex

sample matrices can often make the identification of target

compounds very difficult due to significant “chemical noise”,

resulting in sub-standard mass spectral library match factors.

In order to address these issues, and, in particular the

‘chemical challenges’ of the WFD, the Environment Agency

commissioned the National Laboratory Service (NLS) to

develop new low-cost and effective methods to screen

pollutants in water samples.

GC–MS Screen: The NLS developed the GC–MS

target-based multi-residue method (TBMR) which allows

for the identification of virtually all GC-amenable pesticides

as well as hundreds of other organic pollutants in a single

sample. At the heart of the GC–MS screening capability is

the de-convolution reporting software (DRS) application for

target compound analysis (17). This application combines

results from the GC–MS Chemstation (Agilent Technologies,

Santa Clara, California, USA), the automated mass spectral

de-convolution and identification software (AMDIS —

http://chemdata.nist.gov/mass-spc/amdis/ ) and the mass

spectral search program from the National Institute of

Standards and Technology (NIST) in a single report. A

website that provides an explanation of how AMDIS works

can be found at http://chemdata.nist.gov/mass-spc/amdis/

explanation.html.

Extraction and Analytical Method: Aqueous samples

(including surface water, groundwater and effluent) are

collected into 1 L glass bottles and stored in the dark at

5 °C ± 3°C without further additives. As a result of a wide

Figure 1: Total ion chromatogram of an extracted 1 L

groundwater sample analysed using a GC–MS instrument

running in full scan mode (35–566 Da). The extract (1.5 µL) is

introduced via cold splitless injection and separated on a HP5-

MS UI capillary column (30 m × 25 µm × 0.25 µm film) at the

conditions described in the text.

Table 1: Example of a report from a GC–MS DRS analysis of a contaminated groundwater sample. Compounds identified include the

commonly found molluscicide metaldehyde, the herbicide bentazone and the plant growth regulator chlorpropham.

Amount (µg/L) approx AMDIS NIST

Retention Time

Cas# Compound Name Chem Station Match Retention Time

Difference in Seconds

Match Hit Num

5.5524 98828 Isopropylbenzene 0.06 93 0.2 87 1

7.7070 108623 Metaldehyde 0.96 98 -1.6 92 1

16.1887 13194484 Ethoprophos 0.2 62 -0.7 90 1

16.3978 101213 Chlorpropham 2.25 97 -0.4 92 2

17.9536 23950585 Propyzamide 0.12 86 -0.6 81 1

18.4394 2303175 Tri-allate 0.16 85 -0.8 75 1

18.6333 58082 Caffeine 0.47 93 0.6 85 1

18.7245 23103962 Pirimicarb 0.17 91 -3.2 87 1

19.6931 886500 Terbutryne 0.16 74 -1.6 73 1

19.7281 2032657 Methiocarb 0.1 70 -1.3 74 1

19.796 330552 Linuron 0.4 61 0.1 78 1

20.2459 16118493 Carbetamide 0.07 69 -1.9 75 1

20.3776 25057890 Bentazone 0.36 88 -1.8 92 1


Gravell et al.


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range of compounds contained within the target database

and their variety of chemical characteristics, a liquid-liquid

extraction method was chosen as the initial isolation method.

An internal standard (D10-phenanthrene) is added to a water

sample (1 L) which is extracted using dichloromethane

(50 mL). The extraction solvent is removed and the remaining

aqueous layer acidified (pH ~ 1–2) using sulphuric acid.

The extraction procedure is then repeated on the acidified

sample. The combined extracts are then carefully evaporated

to avoid significant losses of volatile compounds to 1 mL

using a nitrogen ‘blow down’ concentrator. The resultant

extract is dried using anhydrous sodium sulphate and

transferred to an auto-sampler vial for analysis. Typically, a

batch of twenty-four samples can be prepared in a single

day. Extracts are analysed using a single quadrupole

GC–MS (Agilent 7890-5975 instrument) running in full

scan mode (mass range: 35–566 Da) with de-convolution

reporting software (DRS) that incorporates mass spectral

de-convolution with conventional library searching and

quantification. Sample extracts (1.5 µL) are introduced

via cold splitless injection and separated on a HP5-MS UI

capillary GC column (30 m × 25 µm × 0.25 µm film). (Agilent

Technologies, Santa Clara, California, USA). The initial oven

temperature of 40 °C (2 min) is increased to 300 °C at 10 °C/

min and held for 8 mins. Over 990 target compounds can be

analysed by this method without further clean-up. Figure 1

shows a typical chromatogram for a groundwater sample

extracted and analysed under the above conditions.

Data Analysis: Firstly, the GC–MS Chemstation software

performs a quantitative analysis for target compounds using

the target ion and up to three qualifying ions under retention

time locking (RTL) conditions. RTL has the ability to very

closely match chromatographic retention times in any Agilent

GC system even if there are subtle differences in columns

differing from the nominal length because of “end trimming”

or when there are different column outlet pressures. The

DRS software then sends the data file to AMDIS which

de-convolutes the component spectra and searches the

NLS generated target database using the de-convoluted full

spectra (provided peak apices are > half a scan apart). The

NLS target database comprises the Agilent RTL Hazardous

Industrial Chemicals Database (a mass spectral database

of 567 pesticides, solvents and endocrine disrupting

compounds), plus a further 423 compounds added by the

NLS based on environmental risk assessments. These mass

spectra are generated under RTL conditions and entries

include the retention times from the locked method. The

de-convoluted spectra for all hits identified by AMDIS are

sent to the NIST mass spectral library for confirmation. The

routine searches against all of the 160,000 compounds

contained in the NIST library. This is also a full spectrum

search, but now against a different library than that used

by AMDIS. This approach provides three independent

complementary steps of confirmation, thereby significantly

increasing the accuracy of target identification, even in the

most challenging of matrices.

The amounts shown in the quantitation and DRS reports

are an estimate of concentration based on target ion

response compared to response of the internal standard.

This new method has been essential for identifying emerging

environmental pollutants. Table 1 shows a report obtained

from the GC–MS DRS analysis of an extracted groundwater

sample containing several pesticides. The de-convoluted

spectrum obtained from AMDIS is searched against the NLS

RTL target database and a match factor assigned. This is

followed by the retention time comparison to that in the RTL

database (R.T. Diff. Sec.). As a final compound verification,

the match factor obtained from the independent NIST library

is shown together with its rank (Hit Num.) in the top 100 hits.

High match factors obtained from the two spectral libraries

provides high confidence in the data.

LC–TOF-MS The NLS have also developed a LC–TOF-MS screening

method for the more polar pollutants that are not amenable to

direct GC–MS analysis.

Extraction: HLB SPE cartridges (200 mg) (Waters

Corporation, Milford, Massachusetts, USA) with an automated

extraction system are used. Cartridges are conditioned

with methanol (8 mL) followed by de-ionized water (8 mL)

and the water sample (500 mL, flow-rate 5 mL/min) is then

loaded onto the cartridge. After loading, the cartridge is

washed with de-ionized water and the sorbent dried fully

with high purity nitrogen. The column is then eluted (10 mL)

with dichloromethane:iso-propylalcohol:trifluoroacetic acid

(80:20:0.1 v/v/v). The eluate is evaporated to dryness in a

vacuum centrifugal evaporator and the residue re-solvated

(75 μL) in acetonitrile:methanol (1:1 v/v) and de-ionized water

added (425 μL). The sample is vortexed, mixed, filtered,

transferred to a screw cap silanized vial and stored (2–4 °C)

until analysis. Typically, a batch of twenty samples can be

prepared in a single day. The extracts are analysed by

LC–MS (Agilent 1200 LC system coupled to a Bruker

Micro-TOF MS) (Bruker Daltonics GmbH, Bremen, Germany).

LC Conditions: The SPE extract (100 μL) is injected onto

a Atlantis T3 LC column (Waters Corporation, Milford,

Massachusetts, USA) (150 mm × 2.1 mm i.d., 3 μm particle

size) held at 45 °C. The mobile phase is 2 mM ammonium

formate and 0.01% formic acid in de-ionized water (solvent

A) and 2 mM ammonium formate and 0.01% formic acid

in methanol (solvent B). The gradient is: 5% B (0 min) with

a linear increase to 100% B over 25 min, held for 5 min.

Equilibration is 10 min at 5% B.

TOF Conditions: The interfaced TOF-MS is equipped with an

electrospray ionization source. The above LC gradient is run

twice for each sample: once with the TOF in positive ionization

mode and once in negative mode. The nebulizer pressure, dry

gas flow, dry gas temperature and capillary voltage are held

constant at 50 psig, 10 L/min, 180 °C and 4500 V (negative

ionization mode), and 50 psig, 10 L/min, 180 °C and 3000 V

(position ionization mode), respectively. The resolution at

m/z = 316.962 is approximately 12 000. Scan data is acquired

across the mass range: 125–1,000 Da at a rate of 1 Hz with

the optimized parameters shown in Table 2.

Table 2: Scan data.

AML concentration (mg/L) Negative Positive

Hexapole RF (V): -175 125

Cap exit (V): -70 100

Skimmer 1 (V): -30 50

Lens transfer (μs): 52 52

Pre-pulse storage (μs): 10 10


Gravell et al.

ES301329_LCA0913_010.pgs 08.19.2013 18:21 ADV blackmagentacyan

Data Analysis: Using the Bruker target analysis software, the

TOF data files for each sample are searched for compounds

listed in a database. Analysis was performed for the

chemicals listed in Table 3.

Approach: First a calibration is performed to ensure good

mass accuracy. Next, extracted ion chromatograms (EICs)

are created with a width of ± 0.005 mDa for each compound

in the database. The software then looks for peaks in each

chromatogram at the retention time specified in the database

± 0.5 min. If a peak is detected, an average mass spectrum

(background subtracted) is taken across the peak and the

mass and isotope pattern present are scored against the

theoretical values. Figure 2 shows an EIC and extracted mass

spectrum for the anti-convulsant drug carbamazepine found

in a groundwater sample. Analytes were identified using

target analysis software (Bruker) which scores compounds in

the database against the following criteria:

1. Comparison to known chromatographic retention times,

2. Accurate mass database of environmental contaminants,

3. Isotope patterns for compounds identified in 1 and 2

compared against the theoretical values.

Results for a groundwater sample spiked with a mixture of

pesticides, non-ionic surfactants and pharmaceuticals are

shown in Figure 3.

Passive SamplingThe reliability of taking spot samples of water is questionable,

as there is a chance that potentially harmful pollutants can

be missed if a sample is taken at the wrong time over a

pollution event. The ‘spot check’ approach is only able to

collect pollutants present in the column of water the moment

the sample is taken. Passive sampling is a technique where

pollutants are sequestered by an in-situ device over extended

periods of time; typically 1–6 weeks (18). Such devices have

been used historically to measure time-weighted averaged

concentrations of pollutants in air. This approach increases the

likelihood of capturing different pollution events, whether they

are point or diffuse. The technique measures the ‘toxicologically

relevant fraction’ of contaminant mixtures and can also indirectly

lower analytical detection limits for the various pollutants.

Typically, the extracts obtained from passive samplers are

chemically complex, especially from those deployed, for

example, downstream of a wastewater treatment plant. The

analytical challenge is how to cope with the huge number of

potential contaminants that may be present in any given sample.

Passive SamplersA wide range of passive sampling devices is now available

commercially or as laboratory prototypes. A book (19) and

a number of reviews (7, 20) are available that describe their

construction and use in monitoring the aquatic environment.

Two of the main types of sampler used by the NLS in various

trials in the United Kingdom are described below:

Semi-permeable Membrane Device (SPMD): SPMDs

consist of layflat, low-density polyethylene (LDPE) tubing

(approximately 98 cm × 3 cm) containing triolein lipid (1 mL)

as the absorption matrix (21). SPMDs are used to monitor

non-polar organic compounds, defined as those with a log

11www.chromatographyonline.com

Gravell et al.

11708

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Kow > 3, with maximum cross-sectional diameters of 1 nm

and a molecular mass of less than 600 Da. Hydrophobic

compounds such as PAHs, polychlorinated biphenyls (PCBs)

and organochlorine pesticides in the dissolved state (that is,

those that are readily bio-available) will partition into the SPMD

and become concentrated over time (22). Figure 4 shows a

SPMD sampler before deployment alongside its protective

stainless steel housing. The processing, enrichment and

fractionation of SPMDs have been described in a number of

publications (23–25) and involve the following steps:

1. Removal of exterior surficial periphyton and debris,

2. Solvent dialysis or extraction using n-hexane,

3. Size-exclusion chromatography and collection of the

fraction that contains the chemical classes of interest, for

example, PAHs, PCBs and organochlorine pesticides,

4. Class-specific fractionation using adsorption

chromatography.

The fraction collected is carefully evaporated to avoid

losses of volatile compounds and is reduced in volume

to approximately 0.5 mL using a nitrogen blow-down

concentrator. The SPMD extracts are analysed by GC–MS

using conditions identical to those for the spot water samples.

Polar Organic Chemical Integrative Sampler (POCIS):

The POCIS is designed to monitor more polar organic

compounds which are not accumulated by SPMDs,

that is, compounds with a log Kow < 3 with maximum

cross-sectional diameters of ~ 0.1 µm (26). The POCIS

consist of specific SPE resin sandwiched between two

polyethersulphone membranes clamped together by steel

rings (9 cm outside diameter, 5 cm inside diameter). POCIS

are manufactured in-house and the SPE material (200 mg)

used is the Oasis HLB phase (Waters). Figure 5 shows a

POCIS prior to deployment alongside its protective stainless

steel housing.

Table 3: Range of substances analysed in the LCÐMS

screening method.

Table 3: (continued).

AML concentration (mg/L)

Albendazole Mebendazole Fenbendazole

Doramectin Moxidectin Tiamulin

Emamectin 1a Thiabendazole Monensin

Eprinomectin 1a Triclabendazole Tylosin

Ivermectin 1a Tilmicosin

Herbicides

Carbetamide Metoxuron Propazine

Chloroxuron Metsulfuron methyl Simazine

Chlortuloron Monolinuron Terbutryn

Diflubenzuron Monuron Trietazine

Diuron Neburon Metazachlor

Fenuron Atrazine Propyzamide

Isoproturon Atrazinedesethyl Ethofumesate

Linuron Cyanazine Asulam

Methabenzthiazuron Desmetryn Atrazinedesisopropyl

Metobromuron Prometryn Napropamide

Pharmaceuticals

Fluoxetine Tamoxifen Spectinomycin

Paroxetine Thioridazine Oxytetracycline

Sertraline Phenacetin Minocycline

Clotrimazole (fragment)

Warfarin Sulfamethoxazide

Carbamazepine Florfenicol Streptomycin

Nor-fluoxetine Lincomycin Sulphamethazine

4-Acetamidophenol (Paracetamol)

Sulfadiazine Atenolol

Ciprofloxacin Trimethoprim Metoprolol

Citalopram Apramycin Propranolol

Dextropropoxyphrene Amoxicillin Sotalol

Diclofenac Chlortetracycline Celiprolol

Fluvoxamine Demeclocycline Oxprenolol

Ibuprofen Anhydrotetracycline Labetalol

Mefenamic acid Doxycycline

Erythromycin Tetracycline

Insecticides

Sulcofuron Aldicarb sulfone Iodofenphos

Flucofuron Aldicarb sulfoxide Malathion

Azinphos Methyl Carbaryl Mevinphos

Carbophenothion Carbofuran Parathion ethyl

Chlorfenvinphos Ethiofencarb Parathion methyl

Chlorpyrifos-ethyl Methiocarb Pirimiphos-ethyl

Chlorpyrifos-methyl Oxamyl Pirimiphos-methyl

Coumaphos Propoxur Propetamphos

Diazinon Pirimicarb Phorate

Dichlorvos Bendiocarb Triazophos

Dimethoate Methomyl Ethion

Demeton-s-methyl Fenthion Fenchlorphos

Aldicarb Fonofos Fenitrothion

Fungicides

Iprodione Prochloraz Carbendazim

Metalaxyl Fenpropimorph

Perfluorinated alcohols

4-2 Fluorotelomer alcohol




Anti-foulant

Irgarol 1051

Acid herbicides

2,3,6–TBA Bromoxynil MCPA

2,4-D Clopyralid MCPB

2,4–DB Dicamba MCPP (Mecoprop)

2,4,5–T Dichlorprop Phenoxyacetic acid

4-Chlorophenoxyacetic acid

Fenoprop Phenoxybutyric acid

Benazolin Fluroxypyr Phenoxypropionic acid

Bentazone Ioxynil Picloram

Organophosphate flame retardants

tert-Butylphenyl diphenyl phosphate

Iso-decyl diphenyl phosphate

Triphenylphosphate

2-Ethylhexldiphenyl phosphate

Tricresyl phosphate Trixylenyl phosphate


Gravell et al.

ES301331_LCA0913_012.pgs 08.19.2013 18:21 ADV blackmagentacyan

Extraction of POCIS: The SPE material contained within the

POCIS is transferred into an empty 6 mL glass SPE column

with a PTFE frit. A further PTFE frit is then placed on top of

the material to keep it in place. The SPE column is then eluted

(10 mL) with dichloromethane:isopropanol:trifluoroacetic acid

(80:20:0.1 v/v/v). This elution mix has been found to give good

recoveries for all compounds within the LC–MS database used

by the NLS. The evaporation of the eluates and their analyses

by LC–TOF-MS is the same as for spot water samples, except

that the injection volume is reduced to 10 µL.

Results from the Deployment of Passive SamplersThe NLS has used both designs of passive sampler for

a number of field trials in the United Kingdom, and these

devices have been used alongside spot water sampling for

comparison. The most recent trial involved eighteen protected

drinking water areas in England and Wales. Article 7 of the

WFD requires member states to put such additional monitoring

and associated measures in place to prevent the deterioration

of raw water quality so that the need for treatment is reduced

(1). Table 4 lists the individual WFD priority substances plus

other compounds identified in the SPMD and POCIS from a

site on the River Mersey, England. Many of the WFD priority

substances identified in the extracts from the SPMDs deployed

river have not been identified previously using spot sampling

and the GC–MS TBMR screen.

The SPMD can effectively sequester large volumes

(10–100 L depending on the analyte of concern) of water

over their deployment time. This both permits time integrated

sampling that compensates for fluctuating discharges and

also gives much lower analytical detection limits. If uptake

rates (expressed as the volume of water cleared per unit time

for each compound) for the different pollutants are known,

time-weighted average concentrations can also be calculated

allowing for more realistic mass loadings of a pollutant on a

water course to be estimated.

A number of pollutants were also identified by the POCIS

and these include the newly classified emerging contaminants

such as the fluorinated acids and sulphonates; PFOA, PFHS

and PFOS. Various classes of pharmaceuticals including beta

blockers (used to treat hypertension), antibiotics, anti-fungals

0.8

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ty (

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Inte

nsi

ty (

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0.6

0.4

0.2

0.0

0

6

32.237.1022

Carbamazepine +

1895696_1-A,4_01_5593.d: ElC 237.1022±0.01 +, -Constant Bkgrnd, Smoothed (0.00,1,GA), Carbamazepine +, C15H12N201 (17.2)

Carbamazepine +, C15H12N201, 236.0944, err[mDa]:-027,mSigma: 10.1),-Paek Bkgrnd

17.1 min32

5

4

3

2

1

0100 200 300 400 500 600 700 800 900 1000

m/z

5 10 15 20 25 30 35Time (min)

Figure 2: Extracted ion chromatogram and mass spectrum for

the anti-convulsant drug carbamazepine found in a groundwater

sample. The protonated molecular ion for carbamazepine

appears in the mass spectrum at m/z = 237.1022.

Figure 3: An example of the data output obtained from

the analysis of a spiked groundwater. Compounds identifed

with the symbol +++ meet all three identifcation criteria;

chromatographic retention times and accurate masses

match those contained within the data base of environmental

contaminants. The isotope patterns also match theoretical

values.

Table 4 : Different pollutants identified in SPMD and POCIS

passive sampling devices deployed in the River Mersey,

one of eighteen drinking water protected areas chosen in

England and Wales for field trial in 2011. Pollutants identified

comprised several WFD priority substances including: the

trichlorobenzenes, fluoranthene, benzo(b)fluoranthene, benzo(k)

fluoranthene, benzo(a)pyrene, isoproturon and diuron.

SPMD POCIS

1,3,5-Trichlorobenzene Carbendazim

1,2,4-Trichlorobenzene Thiabendazole

1,2,3-Trichlorobenzene Metamitron

Acenaphthene Aldicarb sulfoxide

Fluorene Isoproturon

Phenanthrene Diuron

Homosalate Carbamazepine

o-Terphenyl Prometryn

Fluoranthene Terbutryn

Triclosan Sulfamethoxazole

Pyrene Oxprenolol

Octyl-methoxycinnamate Atenolol

2-Ethylhexyl diphenyl phosphate

Napropamide

Benz[a]anthracene Sotalol

Chrysene Trimethoprim

Dicyclohexyl phthalate Flufenacet

Benzo[k]fluoranthene Phenoxyacetic acid

Benzo[b]fluoranthene Ibuprofen

Benzo[a]pyrene Mecoprop (MCPP)

Perfluorohexane sulphonate (PFHS)

Perfluorooctanoic acid (PFOA)

Perfluorooctanesulfonic acid (PFOS)


Gravell et al.


and analgesics were also found. These substances

were not identified by spot sampling during the field trial.

As a result of a lack of uptake rate data for most of the

compounds identified by the POCIS, time-weighted average

concentrations could not be determined. Further work in this

area is now ongoing. This does not detract, however, from

the main objective of the trial which was to identify if passive

sampling could produce additional data that would benefit

the Agency’s investigative monitoring programmes. These

trials have shown that passive sampling techniques should

have an important role to play in the WFD chemical monitoring

requirements. It is possible that the use of these devices

will allow for certain water catchments to be discounted

while others are targeted and monitored more intensively for

regulatory and investigative purposes.

ConclusionsThe new screening methods fill the gaps created by historically

reliant risk assessments, and validate the data through a

refining of the original risk assessment. Passive sampling

together with the GC–MS and LC–MS target-based screening

methods will also identify many emerging contaminants that

provide a powerful combined monitoring tool in assessing

the pressures on a given water course, helping to ensure

that legislation is met. Together these new techniques offer a

robust solution to address specific future monitoring needs,

particularly those prompted by legislative change.

References(1) European Commission, The European Water Framework Directive

(WFD;2000/60/EC) (http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?

uri=OJ:L:2000:327:0001:0072:EN:PDF)

(2) US EPA Clean Water Act http://epw.senate.gov/water.pdf

(3) http://www.environment.gov.au/water/australia/water-act/#water-act

(4) European Commission, The European Marine Strategy Framework

Directive (MSFD; 2008/56/EC) (http://eur-lex.europa.eu/LexUriServ/

LexUriServ.do?uri=OJ:L:2008:164:0019:0040:EN:PDF)

(5) European Commission, Directive on Environmental Quality Standards

(Directive 2008/105/EC) (http://eur-lex.europa.eu/LexUriServ/

LexUriServ.do?uri=CELEX:32008L0105:EN:PDF)

(6) S. Pedersen-Bjergaard, S.I. Semb, J. Vedde, E.M. Brevik and T.

Greibrokk, Chemosphere, 32(6), 1103–1115 (1996).

(7) I.J. Allan, B. Vrana, R. Greenwood, G.A. Mills, B. Roig and C. Gonzalez,

Talanta, 69(2), 302–322 (2006).

(8) M. Krauss, H. Singer and J. Hollender, Analytical and Bioanalytical

Chemistry, 397(3), 943–951 (2010).

(9) T. Portolés, E. Pitarch, F.J. López, J.V. Sancho and F. Hernández,

Journal of Mass Spectrometry, 42(9), 1175–1185 (2007).

(10) I. Bobeldijk, J.P.C. Vissers, G. Kearney, H. Major and J.A. Van Leerdam,

Journal of Chromatography A, 929(1–2), 63–74 (2001).

(11) R. Dıaz, M. Ibanez, J.V. Sancho and F. Hernandez, Analytical Methods,

4(1), 196–209 (2012).

(12) A. Muller, W. Schulz, W.K.L. Ruck and W.H. Weber, Chemosphere,

85(8), 1211–1219 (2011).

(13) M. Godejohann, L. Heintz, C. Daolio, J.-D. Berset and D. Muff,

Environmental Science & Technology, 43(18), 7055–7061 (2009).

(14) M. Godejohann, J.-D. Berset and D. Muff, Journal of Chromatography A,

1218(51), 9202–9209 (2011).

(15) J. Beens and U.A.Th. Brinkman, Analyst, 130(2), 123–127 (2005).

(16) E. Skoczyñska, P. Korytár and J. de Boer, Environmental Science &

Technology, 42(17), 6611–6618 (2008).

(17) P. Wylie, M. Szelewski, C.-K. Meng and C. Sandy, Comprehensive

Pesticide Screening by GC/MSD using Deconvolution Reporting

Software, Agilent Technologies, publication 5989-1157EN.

(18) O. Gangfeng and J. Pawliszyn, Journal of Chromatography A, 1168

(1–2), 226–235 (2007).

(19) R. Greenwood, G.A. Mills and B. Vrana (eds.), Passive sampling

techniques in environmental monitoring, Comprehensive Analytical

Chemistry series, D. Barcelo (series Editor), Elsevier, Amsterdam (May

2007).

(20) B. Vrana, G.A. Mills, R. Greenwood, I.J. Allan, E. Dominiak, K.

Svensson, J. Knutsson and G.M. Morrison. Trends in Analytical

Chemistry, 24(10), 845–868 (2005).

(21) J.N. Huckins, M.W. Tubergen and G.K. Manuweera, Chemosphere,

20(5), 533–552 (1990).

(22) B. Vrana, A. Paschke, P. Popp and G. Schüürmann, Environmental

Science and Pollution Research, 8(1), 27–34 (2001).

(23) J.N. Huckins, G.K. Manuweera, J.D. Petty, D. Mackay and J.A. Lebo,

Environmental Science & Technology, 27(12), 2489–2496 (1993).

(24) V. Yusà, A. Pastor and M. de la Guardia, Analytica Chimica Acta,

540(2), 355–366 (2005).

(25) K.-D. Wenzel, B. Vrana, A. Hubert and G.G. Schüürmann, Analytical

Chemistry, 76(18), 5503–5509 (2004).

(26) D.A. Alvarez, J.D. Petty, J.N. Huckins, T.L. Jones-Lepp, D.T. Getting

and J.P. Goddard, Environmental Toxicology and Chemistry, 23(7),

1640–1648 (2004).

Anthony Gravell is a technical specialist at the Environment

Agency’s National Laboratory Service laboratory based

in Llanelli, Wales. He specializes in passive sampling in

conjunction with HPLC–MS techniques for the analysis of

pesticides, pharmaceuticals and endocrine disruptors in

various environmental compartments. He is responsible for the

development of methods to meet future Environment Agency

needs such as the European Union’s Water Framework

Directive and the Marine Strategy Framework Directive.

Graham Mills is a professor of environmental chemistry (since

2008) at the School of Pharmacy and Biomedical Science,

University of Portsmouth, Portsmouth, UK. His research

interests include the monitoring of pollutants in water and he

is the co-inventor of a novel and low-cost passive sampling

device - the Chemcatcher. He has been involved in a number

of national and international research projects related to the

EU’s Water Framework Directive.

Wayne Civil is a technical specialist with the National

Laboratory Service of the Environment Agency of England

and Wales. His team, based at NLS Starcross, is responsible

for the development and implementation of analytical

solutions to meet the ever changing environmental monitoring

requirements, driven largely by the introduction of the

European Union Water Framework Directive.

Figure 4: The SPMD (bottom left) is used to sequester

non-polar compounds such as the PAHs, PCBs and

organochlorine pesticides (with log Kow

> 3), whereas the

POCIS (bottom right) adsorbs polar compounds such as

pharmaceuticals, some herbicides and fuorinated acids (with

log Kow

< 3) from the water column. Both devices are deployed

within a protective stainless housing (middle).


Gravell et al.



LC TROUBLESHOOTING

This is the latest “LC Troubleshooting”

instalment in a series focusing

on gradient elution (1–4) in liquid

chromatography (LC). In an earlier

column (4) we considered problems

related to the system dwell volume.

This month, we’ll continue looking at

gradient problems with a focus on

baseline drift. If you’re just moving

from isocratic separations to gradients,

one of the first observations you make

when you examine a chromatogram is

that the gradient baseline is often not

flat. With both isocratic and gradient

separations, the baseline can drift when

the column temperature is not stable,

but if you use a column oven and the

laboratory temperature is relatively

stable, this is usually not a problem.

Drifting baselines under gradient

conditions are common. Usually the

drift is minor, and you learn to live with

it. In other cases, it may be possible to

compensate for the drift by adjusting

the mobile phase. In still other cases,

there isn’t much you can do. Let’s look

at each of these cases next.

Mobile-Phase AbsorbanceWhen ultraviolet (UV) absorbance is

used for detection, it is common to find

that the A and B mobile phases differ

in their UV absorbance at the detection

wavelength. This difference means that

the baseline will drift during a gradient

run, as is seen in the upper trace in

Figure 1. In this case, a gradient is run

from 100% water (A) to 100% methanol

(B) at 215 nm. Because methanol has

significantly stronger UV absorbance

at 215 nm than water, the baseline

rises — approximately 1 absorbance

unit (AU) in this case. If the display

setting is set to a range of <1 AU,

the baseline will drift off scale during

the run. This is inconvenient, but with

many detectors today, the detector

range is >1 AU, so peak data will still

be collected, even though they do not

appear on the computer monitor until

the scale is changed. However, in the

days of strip-chart recorders, before

computerized data collection was

used, an off-scale baseline or peak

meant that no data were collected

under those conditions. In any event,

we would like to be able to see the

entire chromatogram without having

to change from one display scale to

the next. For this reason, drift, such as

that observed for methanol in Figure 1,

is unacceptable for most of us. From

a practical standpoint, methanol

has sufficient absorbance at low

wavelengths that full-range

water–methanol gradients are seldom

used below approximately 220 nm.

Contrast the plot for methanol at

215 nm with that for acetonitrile at

200 nm in Figure 1. The

water–acetonitrile gradient baseline

looks flat at the same display scale

because acetonitrile has very low UV

absorbance relative to water under

these conditions. This is one reason

why acetonitrile is often the preferred

organic solvent when low-wavelength

(<220 nm) UV detection is used.

Compensating for DriftIn the water–acetonitrile gradient

of Figure 1, water and acetonitrile

have approximately the same UV

absorbance at 200 nm, so the baseline

does not drift. It may be possible to

create analogous conditions with other

solvents by adjusting the absorbance

of a solvent mixture used as the A and

B solvents of the mobile phase. An

example of this is shown in Figure 2,

where 10 mM potassium phosphate

(pH 2.8) is used instead of water as

the A-solvent and methanol is used as

B. Under these conditions, phosphate

has nearly the same UV absorbance

as methanol, so the baseline has

very little drift. Note that the y-axis of

Figure 2 is 0.1 AU full scale compared

to 1 AU full scale in Figure 1, so the

reduction in drift is impressive. From a

practical standpoint we’ve solved the

gradient drift by adding phosphate

buffer to the A solvent. Because

phosphate is such a common buffer

for reversed-phase LC, its use means

that methanol can be used as the B

solvent at much lower wavelengths

than when water is used as A.

Most organic solvents have lower

UV absorbance as the detection

wavelength is increased, so simply

increasing the wavelength may also

help to flatten out the baseline. For

example, the lower plot of Figure 2

is under the same conditions as the

upper one, but at 254 nm the baseline

is flat. So even if we don’t add a UV

absorbing compound to the A solvent,

simply increasing the detection

wavelength may be a sufficient change

to mitigate baseline drift. Of course,

a reduction in sample response may

also occur with an increase in detection

wavelength, so making this change

may not be a viable option.

Although using a buffer instead of

water as the A solvent may correct

for baseline drift, it doesn’t always

produce the desired results. An

example of this is seen in Figure 3,

where the A solvent is 25 mM

ammonium acetate (pH 4) and B is

Gradient Elution: Baseline Drift Pr oblemsJohn W. Dolan, Walnut Creek, LC Resources, California, USA.

Can anything be done to correct for baseline drift in gradient separations?



LC TROUBLESHOOTING

80% methanol in water. A negative

baseline drift of >1 AU is seen at

215 nm for this gradient, and the

baseline curves sharply downward as

the gradient progresses. A negatively

drifting baseline can cause additional

problems besides the inability to

fit the entire chromatogram on a

reasonable vertical scale. Many

data systems stop collecting data

when the baseline drifts more than

approximately 10% below the initial

baseline. In the example at 215 nm,

the only way to collect this baseline

for display was to turn off the autozero

function on the data system and

manually set the baseline at 1.0 AU

before the gradient was started. In

this manner, the baseline signal was

always >0 AU, so it could be collected

by the data system. This certainly

is not a technique that is amenable

to unattended sample analysis. The

reduced UV absorbance at higher

wavelengths that was mentioned

earlier holds here, as well, where

the same gradient at 254 nm is flat.

Another option that might help to

flatten out the baseline, would be

to add ammonium acetate to both

the A and B solvents to try to cancel

the negative drift as the gradient

progresses. It should also be noted

that although the present conditions

at 215 nm are unacceptable for UV

detection, if mass spectrometry (MS)

was used for detection instead of

UV absorbance, the baseline drift

would not be a problem because UV

absorbance does not affect the MS

signal; ammonium acetate–methanol

gradients are commonly used with

LC–MS.

In still other cases, the baseline

drift during a gradient may not be

amenable to correction by adding

something to the mobile phase.

An example of this is seen in

Figure 4, where 50 mM ammonium

bicarbonate is used as the A solvent

and methanol as the B solvent. At

215 nm, the baseline drifts downward

as it approaches the middle of the

gradient, then starts back up again. In

this case, the change in absorbance

is worse for a mixture of A and B

than with either solvent alone, so it is

unlikely that the absorbance of either

mobile phase could be manipulated

to compensate for the midgradient

dip. As with the other examples of

baseline drift with methanol as the B

solvent, an increase in the detection

wavelength to 254 nm minimizes the

problem.

Trifluoroacetic Acid: A Special CaseTrifluoroacetic acid is an additive

commonly used in LC separations of

biomolecules, such as proteins and

peptides. Trifluoroacetic acid acts

1.0

0.0 Time

Ab

sorb

an

ce (

AU

)

Methanol (215 nm)

Acetonitrile (200 nm)

Figure 1: Baselines obtained from linear gradients of water–methanol at 215 nm and

water–acetonitrile at 200 nm.

0 2 4 6 8 10

0.0

0.1

Time (min)

Ab

sorb

an

ce (

AU

)

215 nm

254 nm

0 10 20 30 40

-0.5

-1.0

0.0

Time (min)

Ab

sorb

an

ce (

AU

)

215 nm

254 nm

Figure 2: Baselines for phosphate–methanol gradients of 5–100% B in 15 min at 215 nm

and 254 nm. A: 10 mM potassium phosphate (pH 2.8); B: methanol. Adapted from reference 5.

Figure 3: Baselines for ammonium acetate–methanol gradients of 5–100% B in 40 min at

215 nm and 254 nm. Mobile phase A: 25 mM ammonium acetate (pH 4); B: 80% methanol

in water. Adapted from reference 5.



LC TROUBLESHOOTING

detection (ELSD) or charged-aerosol

detection (CAD).

Figure 5 shows gradient baselines

at selected wavelengths where A

is water with 0.1% trifluoroacetic

acid added and B is acetonitrile

with 0.1% trifluoroacetic acid

added. It is seen that the curvature

of the baseline depends on the

wavelength chosen. At 215 nm, the

baseline is nearly flat, making this

an especially attractive wavelength

for the detection of proteins and

peptides at trace concentrations. At

other wavelengths, a little additional

trifluoroacetic acid (for example,

0.11% instead of 0.1%) can be added

to acidify the mobile phase (0.1%

trifluoroacetic acid gives pH ≈ 1.9)

as well as acting as an ion-pairing

reagent, both of which are beneficial

to many biomolecule separations.

In addition, trifluoroacetic acid

has low UV absorbance at

wavelengths < 220 nm, making it

especially attractive as an additive

for acetonitrile-containing mobile

phases. Trifluoroacetic acid is

volatile, so it is easily evaporated

with the aqueous acetonitrile

mobile phase for compatibility with

LC–MS detection, as well as other

evaporative detection methods, such

as the evaporative light scattering

to the A or B solvent to help reduce

the baseline drift.

ConclusionsWe have seen that a major component

of baseline drift in gradient LC methods

and UV detection is often the result

of differences in detector response

to the A and B components of the

mobile phase. At higher wavelengths,

such as >250 nm, the UV absorbance

of mobile-phase components is

usually minimal, so baseline drift

under these conditions is seldom a

concern. At wavelengths <220 nm,

however, baseline drift caused by

differential solvent absorbance can

be sufficient to prevent practical use

of certain solvents, such as methanol

or tetrahydrofuran. Sometimes it is

possible to compensate for differences

in UV absorbance by adding a UV

absorbing component to one solvent

or the other. A good example of this

was shown in Figure 2 for the addition

of phosphate buffer at 215 nm. In other

cases, the drift characteristics are such

that it is not possible to compensate

for drift by modifying the mobile phase.

However, by judiciously choosing

the mobile-phase components and

detection wavelength, it is usually

possible to find gradient LC conditions

where baseline drift does not

compromise the analysis.

References(1) J.W. Dolan, LCGC Asia Pacific 16(2),

14–18 (2013).

(2) J.W. Dolan, LCGC Europe 26(4), 210–215

(2013).


(2013).


(2013).

(5) N.S. Wilson, R. Morrison, and J.W. Dolan,

LCGC North Am. 19(6), 590–596 (2001).

(6) C.T. Mant and R.S. Hodges,

High‑Performance Liquid Chromatography

of Proteins and Peptides: Separation,

Analysis, and Conformation (CRC Press,

Boca Raton, Florida, USA, 1991), p. 90.

John W. Dolan is vice president

of LC Resources, Walnut Creek,

California, USA. He is also a member

of LC•GC Asia Pacific’s editorial

advisory board. Direct correspondence

about this column should go to “LC

Troubleshooting”, LC•GC Asia Pacific,

4A Bridgegate Pavillion, Chester

Business Park, Wrexham Road,

Chester, CH4 9QH, UK, or email the

editor-in-chief, Alasdair Matheson, at

[email protected]

0 2 4 6 8 10

-0.2

0.0

-0.1

0.1

Time (min)

Ab

sorb

an

ce (

AU

)

215 nm

254 nm

0.0

-0.1

0.1

Ab

sorb

an

ce (

AU

)

0 20 40 60 80 100

Acetonitrile (%)

220 nm

215 nm

210 nm

205 nm 200 nm

Figure 4: Baselines for ammonium bicarbonate–methanol gradients of 5–60% B in

10 min at 215 nm and 254 nm. A: 50 mM ammonium bicarbonate (pH 9); B: methanol.

Absorbance scale is relative, not absolute. Adapted from reference 5.

Figure 5: Baselines for trifluoroacetic acid–acetonitrile gradients of 0–100% B in 100 min.

A: 0.1% trifluoroacetic acid in water; B: 0.1% trifluoroacetic acid in acetonitrile. Absorbance

scale is relative, not absolute. Adapted from reference 6.



GC CONNECTIONS

Summer is in full swing this month.

Many chromatographers, myself

included, look forward to participating

in some of the plethora of 10-km,

half-marathon, or full-marathon

long-distance running events

offered worldwide. Besides the

obvious benefits to health and

fitness, long-distance running offers

opportunities for free-running thoughts

that can turn the imagination to

musing upon seemingly disparate and

otherwise unnoticed phenomena.

Long-distance runners often

experience the sensation of travelling

along a narrow passage — the

“tunnel-vision” effect — with reduced

awareness of their surroundings

beyond a few metres. This is a helpful

adaptation of the senses that avoids

undesirable events such as tripping

over a curb or colliding with the next

runner ahead. For a chromatographer,

a long-distance run can be perceived

as if moving through a flattened

separation column. The race starts

out with runners bunched tightly

together and finishes with runners

distributed according to their abilities.

At first glance, this result resembles a

chromatography separation, but how

true is the likeness?

Long-distance runs and

chromatography separations do

share some attributes such as a long,

narrow course and a chromatography

column; runners and solute

molecules; segregation of runners

into groups and the formation of

discrete peaks; timing-chip sensors

and a chromatography detector; run

completion times and retention times;

an organized run start and an injection

system. Coincidentally, if the width of

a long-distance race course is about

3 m then the length-to-width ratio of

a 42.2 km marathon course is similar

to that of a 10 m × 0.75 mm gas

chromatography (GC) column.

Some chromatography authors

have remarked on the parallels

between long-distance runs and a

chromatography experiment (1,2).

The concept of a chromatography

theoretical plate can be applied to

the statistical distribution of runners

finishing a race. For example, the

finishing times from a half marathon

in 2012 had the distribution shown in

Figure 1. The resemblance to a tailing

peak is unmistakable, but really this

is just the statistical nature of two

disparate processes as we will see

shortly. The finish-time distribution

has the equivalent of about 40 total

plates on the basis of the time of the

distribution maximum and its width at

half-height. Blumberg (3) calculated

that the 2001 New York City Marathon

spread the runners into a distribution

with about 70 equivalent plates.

Between these two races, the average

plate height for a half to full marathon

comes to about 560 m. Scaled

down to the proportionately sized

10 m × 0.75 mm GC column, that is a

plate height of about 130 mm — more

than two orders of magnitude larger

(worse) than might be expected for this

size GC column. I’d send that one back

to the manufacturer right away.

Clearly, there are significant

similarities and differences between

chromatography and long-distance

running. This article examines the

basic elements of a chromatography

experiment — flow, diffusion, and

retention — and imagines how a

long-distance race might be arranged

to better represent chromatography.

Along the way I will try to describe

rules for a modified long-distance

chromatography “fun run” as a

challenge to anyone who would like to

try it out for a short distance, perhaps

as a 5-km (3.1-mile) course.

Flow

First of all, is a normal long-distance

race a form of chromatography?

Definitely not. The basic definition of

chromatography is “a physical method

of separation in which the components

to be separated are distributed

between two phases, one of which

is stationary (stationary phase) while

the other (the mobile phase) moves

in a definite direction” (4). Here are

two principal differences between a

chromatography experiment and a

long-distance race: The former has

both mobile and stationary phases, and

the latter has neither. Long-distance

runners generally do move in a defined

direction at least.

This discussion will be limited to

column chromatography, in which

solutes move through a column under

the influence of mobile-phase flow.

Thin-layer chromatography (TLC) could

be simulated by travel along the length

of a football field, but the pace would

be quite slow. Sedimentary field-flow

fractionation (SFFF) might take the

form of a carnival ride, but it would be

prohibitively dangerous to the riders

acting as the particles being separated.

When not retained by a stationary

phase, all solutes move along a

chromatography column at the same

average speed as the mobile phase.

Long-distance runners, in the absence

of a mobile phase, move along at

individual speeds or paces that

depend on their abilities, how they

A Long Distance RunJohn V. Hinshaw, BPL Global Ltd, Hillsboro, Oregon, USA.

In this month’s instalment, we examine long-distance running as a metaphor for gas chromatography (GC) separations. For those readers who cannot stop thinking about work while on vacation, here is a light treatment of the separation process and a proposal for a chromatography “fun run”.


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GC CONNECTIONS

feel that day, what they last ate, the

weather, how much sleep they got the

night before, and any other number

of human factors. The statistical

distribution of their finishing times

derives from the range and distribution

of their speeds and not from a process

resembling chromatography. However,

a chromatographic process can be

simulated by runners with a little

creative imagination.

Although it does not seem practical

to create a physical equivalent to

mobile-phase flow in a long-distance

race — imagine thousands of

“mobile-phase” runners pushing

the true competitors along — major

long-distance races do include a

similar feature: Pace runners. A number

of experienced runners who can travel

at a near-constant pace are designated

as pacers. They can usually be seen

with balloons for visibility and a sign

that indicates their pace, such as 10:00

min/mile, 9:30 min/mile, 9:00 min/

mile, 8:30 min/mile, and faster. It is

often difficult for the average runner to

maintain a constant pace throughout.

Pacers provide a reference for the

runners to follow, should they choose to

do so. In a chromatography simulation,

runners could use the pacers as

mobile-phase flow references.

A chromatography column

encompasses some complex physical

phenomena that are without a clear

equivalent in a race. For one thing,

mobile-phase velocities in the column

are slower near the column wall and

faster near the centre, an effect caused

by shear forces at the wall under

laminar flow conditions. At normal GC

linear velocities a parabolic laminar

flow profile forms, as shown in Figure 2.

Solute molecules that reside between

the average velocity zone (b) and the

column wall move along more slowly,

while those that are close to the column

centre area approach the maximum

velocity (a).

Here is the first chromatography-

related characteristic that can be

applied to a modified long-distance

race being run more like it was a

separation: Conduct it in a way that

better reflects mobile-phase flow by

instructing all of the pacers to run at the

same constant speed while asking the

runners to establish the semblance of a

laminar flow profile.

Rule 1, Flow: Groups of runners start

at gated times and follow a designated

pacer; all pacers run at the same

constant speed. Individual runners near

the centre of the course should exceed

their pacer’s speed by up to 50%;

runners halfway between the centre

and the wall should match the pace;

runners close to the sides of the course

should reduce their speed to 50% less

than the pace or slower. In other words,

walk at the course boundaries and run

faster in the centre.

With the average pace remaining

constant in a laminar profile along the

length of the course, this arrangement

is missing one detail that applies to

GC. Gaseous column flows are more

complex because the mobile phase is

a compressible fluid, as opposed to

the noncompressible nature of a liquid

chromatography (LC) mobile phase.

The GC mobile phase enters at the

column inlet pressure and exits at the

outlet pressure, either atmospheric or

vacuum. The mobile phase expands

down the length of the column and

the local linear velocity increases

accordingly as a function of the

distance along the column in the

direction of flow. This gas expansion

effect is larger at the higher pressure

drops encountered with narrow-bore

columns. For a 0.75-mm i.d. wide-bore

column running at 10 mL/min,

which represents an average linear

velocity close to 40 cm/s, the inlet

gauge pressure is only about 4.4 kPa

(0.64 psig). The velocity at the column

entrance will be only several percent

lower than at the exit, so it is easy

to ignore this effect in the case of a

long-distance run simulation. This

is fortunate, because otherwise the

runners would have to increase their

pace by a factor of four or more over

the course of the run.

DiffusionSo far our chromatography–race

simulation has runners moving along

the race course in a roughly parabolic

profile. Runners in the centre are

moving faster than the average pace

250

200

150

100

50

000:00 00:30 01:00 01:30 02:30 03:30 04:3002:00

Time (h)

03:00 04:00 05:00

Nu

mb

er

of

run

ners

(a)

(b)

y

z

x

Figure 1: Distribution of finishers in a half marathon from 2012. Total number of

runners = 2416; maximum Tmax occurs at 2.0 h; width at half-height wh ≈ 0.75 h;

apparent plate number = 5.54 • (Tmax / wh)2 = 40.

Figure 2: Laminar flow and diffusion: (a) Maximum velocity is at the centre of tube;

(b) the average velocity defines a cylinder, shown in orange, approximately 65% of

the distance to the wall. Velocity at the wall is zero. At an average helium carrier-gas

velocity of 40 cm/s in a 10 m × 0.75 mm column at 100 °C, the maximum velocity

will be approximately 65 cm/s. x, y, and z are solute diffusion vectors. The y-vector

would be zero in a flat footrace.


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GC CONNECTIONS

while runners at the edge are moving

much slower. Slower, less-capable

runners would naturally prefer positions

closer to the outer boundary, and faster

runners would favour the centre. If

undisturbed, this situation would create

a very wide distribution of finishing

times. Fast runners would run fast

throughout the race and slow runners

would lag far behind the average pace.

It is essentially the same as a real race

and would result in a distribution similar

to Figure 1.

Solutes have some degrees of

freedom when travelling down a

chromatography column in the

mobile phase; they are free to diffuse

in random directions relative to the

containing mobile-phase flow. The rate

of gas-in-gas diffusion is significant:

n-Hexane, for example, diffuses in

helium at about 0.5 cm2/s at 130 °C and

101 kPa, therefore, a helium atom could

quickly span the width of a 0.75-mm i.d.

column if it moved in a straight line.

Diffusion of solute molecules through

the mobile and stationary phases plays

a pivotal role in any chromatography

separation. The diffusion path of

a solute can be envisioned as a

series of random movements along

three vectors: In the longitudinal

flow or z-direction, and in the x- and

y-directions at right angles to the flow,

shown in Figure 2. Constant diffusion

through the mobile phase causes a

degree of solute band broadening

as solutes move both forward and

backward relative to the local velocity,

as well as back and forth across the

velocity gradient between the column

centre and the inner wall.

Extracourse Contributions to Runners’ Bandwidth

Chromatographers routinely encounter effects that increase band broadening of peaks

beyond the expected theoretical levels. For example, a poor column connection at

the inlet or detector or an improperly packed inlet liner can broaden peaks. Nonlinear

interactions such as adsorption or decomposition of solutes in the column or on

other components can cause peaks to tail. Some running effects that might produce

additional broadening in the simulation could include the following. Many have

analogues in chromatography separations.

• Extra stops to tie a shoelace or for water.

• Slowing down for a muscle cramp, overheating, or because out of breath.

• Chatting with other runners or otherwise loosing track of time while paused.

• Failing to pause for the same interval each time.

• Failing to follow the average pace while running in the race course.

• Colliding with or trying to run through other runners on the course.

• Running on only the inside or outside of a circular course (see racetrack, below).

Additional Options

Here are some additional simulation options to try:

• To better simulate solute behaviour in a liquid stationary phase, runners could walk

around randomly while paused, as if they were slowly diffusing into and along the

course boundary. The longer they pause, the farther they should stray from their

original pause point.

• The simulation can be run multiple times with one or several small groups. By

combining the resulting finishing times a larger number of runners is simulated.

By including more runners in the results the statistical nature of the simulation will

become more apparent. For example, 1000 runner-results is a good number to aim

for. With sufficient numbers, a good measurement of equivalent plate counts can

be made for each group. Compare this to the plate numbers noted at the beginning

of this article.

• The simulation can be run on a circular track; 5 km is 12.5 times around a 400-m

track. This would represent a form of recycle chromatography. The faster groups

would lap the slower ones multiple times, and it would not be necessary to wait for

one group to finish before the next started. Unlike normal GC columns, however, the

racetrack effect will be significant, so runners should try to alternate the side of the

track when they pause and as they move across the flow profile.

In the real long-distance race,

runners are not constrained to fixed

lanes; they are free to move from the

inside to the outside of the course and

back again. This movement resembles

diffusion, and rule 2 induces them to

do the same in the chromatography

race, as a simulation of solute diffusion

through the carrier gas.

Rule 2, Diffusion: Runners should

move at random back and forth across

the width of the course while continuing

to follow rule 1. While in the running

lane, each runner should match the

official pace on average.

According to rule 1 they also

must speed up at the middle of the

course and slow down at the sides.

The biggest challenge: To mimic

solute diffusion the runners should

continuously move across the zones,

not just once in a while. They must

constantly change directions and

speeds during the whole race, while

always moving in a net forward

direction in a type of fartlek run

scheme. (Note: A fartlek run is a

training technique in which varying the

pace during a run to move between

anaerobic and aerobic exercise is

intended to develop better speed and

endurance.) They will end up covering

a longer distance than if they ran in a

single linear direction, but that is part

of a chromatography long-distance run

challenge.

With all runners’ average speeds now

close to their pacer, the runners will all

finish at nearly the same net time. Their

distribution at the end should approach

a Gaussian shape if their cross-course

zone shifting is truly random and the

associated forward speed changes are

consistent. These runners represent

unretained solutes in a simplified

chromatography experiment on a

column with no stationary phase or

packing. Those competitors used to

running at a slower pace might be

nearly exhausted, while those with

faster abilities would be left less taxed

and perhaps would wonder what the

challenge was.

RetentionThus far in the chromatography

simulation, runners move along the

course and, if they follow the rules, find

themselves spread in a single peak-like

distribution at the finish line. There is

nothing in place to differentiate the

runners: They all move along at the

ES301350_LCA0913_022.pgs 08.19.2013 18:22 ADV blackmagenta


GC CONNECTIONS

same average pace. The simulation

needs a retention mechanism to

separate the runners into multiple

groups that represent peaks.

Diffusion delivers solute molecules

to the column wall for retention

and subsequent release back

into the mobile phase. Retained

chromatography solutes spend time on

or in the stationary phase, in proportion

to their affinity for it relative to the

mobile phase. A number of retention

mechanisms are used in GC, the most

common being gas–liquid partitioning

in which solute molecules dissolve

into the stationary phase in proportion

to their solubility. The more soluble

molecules spend less time overall in

the mobile phase than less-soluble

ones and so they are eluted later

in the run. The ratio of a solute’s

concentration in the stationary phase to

its concentration in the mobile phase is

called the partition coefficient, K.

One potential retention parameter for

the runners’ simulation is each runner’s

average pace in a normal long-distance

race. Faster runners achieve a lower

average pace, which makes pace

a good substitute for a partition

coefficient. Long-distance runners

travel at average paces from around

6:00 min/mile (3:43 min/km) up to about

14:00 min/mile (8:41 min/km). Walkers

travel at a slower pace; 15:00 min/mile

(9:19 min/km) is considered a brisk

walk.

To make the simulation easier to

achieve, 8:00 min/mile (4:58 min/km)

on average could be set as the pacer

speed that represents unretained

runners. All runners who can keep

an 8:00 min/mile average pace while

following the rules are assigned a race

number that starts with 08. Slower

runners and walkers receive higher

race numbers as outlined in Table 1.

Chromatographers usually express

retention in terms of the retention factor,

k, which is a function of retention time

normalized to the unretained peak time:

k =(t

R - t

M)

tM

[1]

A peak with a retention factor of 1.0

will have taken twice as long to pass

through the column as a completely

unretained peak. Table 1 includes the

average expected finishing times for

each group, as well as their apparent

retention factors.

Rule 3, Retention: Runners with race

numbers of 1000 or higher receive

two small bags. One contains 50 small

tokens and the other is empty; both

are to be pinned to the runner’s bibs.

(Optional: Place a flag every 100 m

along the 5-km course).

When a course boundary is

encountered, runners with tokens will

step out of the boundary and out of

the way of other runners, then stand

in place for the count of the first two

numbers of their race bib minus 8 s.

While pausing, runners will transfer

one token from the full bag to the other.

All tokens are to be transferred before

the finish line. Throwing tokens on the

ground is grounds for disqualification.

The 50 flags at 100-m intervals serve

as a reminder to pause and transfer

a token, although it is acceptable to

use all of the tokens, one at a time, at

random points along the course.

As the race progresses, four

groups of runners should develop:

The unretained 8 min/mile group,

the 10 and 14 min/mile runners, and

the 14–22 min/mile run–walkers.

For increased accuracy it would be

advisable to launch additional

8 min/mile pacers at regular intervals so

the retained runners can better judge

the correct average linear velocity.

The regime outlined in rule 3 will

cause some stress for less-capable

runners: Each has to be able to travel

at an average 8 min/mile pace for

about 100 m while in the race course,

punctuated by the specified rest

periods at the sides. Those assigned to

the slowest group won’t actually do any

walking. Their average pace will work

out to the equivalent of a walk, but they

will have to run at an 8 min/mile pace

on average to each stopping point and

then wait for 22 s before continuing.

That should be enough time to catch

their breath if necessary. All runners will

need to move randomly out and across

the width of the course while speeding

up or slowing down according to the

laminar flow and diffusion schemes

outlined in rules 1 and 2.

The Finish LineSo, there you have it: A long-distance

running version of a GC separation.

Average column flow is represented

by pace runners, solute diffusion is

simulated by the random movement

of runners back and forth across the

race course, and retention is achieved

by runners’ pausing at the course-side

boundaries for variable periods.

Following the scheme in Table 1 that

divides the runners into four categories

each with its own retention factor, four

distinguishable groups of runners

should arrive at the finish line of a 5-km

run at average times that represent

their retention factors. The degree of

resolution of the runner groups is a little

difficult to predict since it relies on so

many human factors (see the sidebar

“Extracourse Contributions to Runners’

Bandwidth”), but the plate numbers

may be larger than is observed in a

standard long-distance race, especially

for the last group to finish.

References(1) H. Iwase, Curr. Sep. 20(4), 147–149

(2004).

(2) Anonymous, “Continuous Analytical

Measurement—Chromatography,” update

blog entry in http://iamechatronics.

com/notes/general-engineering/420-

continuous-analytical-measurement-

chromatography.

(3) L.M. Blumberg, Temperature-Programmed

Gas Chromatography (Wiley-VCH,

Weinheim, Germany, 2010), p. 224.

(4) L.S. Ettre, Pure and Appl. Chem., 65(4),

819–872 (1993).

John V. Hinshaw is a senior research

scientist at BPL Global Ltd., Oregon,

USA, and is a member of LC•GC

Asia Pacific’s editorial advisory board.

Direct correspondence about this

column should be addressed to “GC

Connections”, LC•GC Asia Pacific, 4A

Bridgegate Pavillion, Chester Business

Park, Chester, CH4 9QH, UK, or email

the editor-in-chief, Alasdair Matheson,

at [email protected]

Table 1: Runner pace and retention for a 5-km chromatography simulation.

Calculated for a total of 50 stops along the race course.

Runner’s Normal

Pace (min/mile)

First Two Digits of

Race Bib; Goal Pace

(min/mile)

Delay

Time (s)

Average Finish

Time (min)

Retention

Factor, k

8 or better 08 0 24.8 0

8–10 10 7 31.5 0.3

10–14 14 21 43.2 0.7

14–22 22 49 68.2 1.7

ES301347_LCA0913_023.pgs 08.19.2013 18:22 ADV blackmagenta


PerSPectiveS in Modern HPLC

this is the second instalment of a

new column in LCGC Asia Pacific

titled “Perspectives in Modern HPLc,”

which will be published every quarter

and will feature fresh perspectives,

innovative approaches, best

practices, megatrends, and emerging

opportunities in this ever‑evolving

field of separation science. the first

instalment in the April 2013 issue was

devoted to new high performance

liquid chromatography (HPLc)

products introduced at Pittcon 2013 (1).

HPLc is a dominant analytical

technique with “mature” technologies

that have been widely practiced

for five decades. innovations

such as ultrahigh‑pressure liquid

chromatography (UHPLc), liquid

chromatography–mass spectrometry

(Lc–MS), two‑dimensional liquid

chromatography (2d‑Lc), chiral

separations, core–shell columns, and

novel stationary phases have helped

drive HPLc to higher performance

in diverse applications, yielding

faster speed, higher resolution,

greater sensitivity, and increased

precision. the practice of HPLc is

no longer limited to specialists or

“chromatographers,” but is now widely

performed by students, chemists,

biologists, production workers, and

other novices in academia, research,

and quality control laboratories. More

than $4 billion of HPLc equipment,

columns, and accessories were sold

worldwide in 2012 (2).

there is no shortage of information

on HPLc (3–9). Hundreds of books,

thousands of articles, and millions of

web citations are available. My goals

are to reexamine the big picture of

HPLc and its applications from a

user’s perspective. i will strive to find

approaches to make it more productive

or relevant. i am excited to be a new

columnist for LCGC Asia Pacific and

promise to dig deeper and comment

on ideas to make HPLc more exciting

and less arduous for all practitioners.

A listing of my tentative topics for

2013 and beyond can be found in the

addendum.

in this instalment, the essence

of modern HPLc is discussed —

first, by examining the reasons that

make HPLc so ubiquitous; second,

by reviewing the fundamental

chromatographic principles on how

they can guide the way we conduct

HPLc separations; and finally, by

commenting on some less‑obvious

opportunities with far‑reaching impacts

or immediate job prospects for

separation scientists.

Why Is HPLC So Ubiquitous? Advantages and Perceived Limitations of HPLCWhy is HPLc so ubiquitous

as practiced by thousands of

practitioners around the world?

table 1 lists the advantages and

perceived limitations of HPLc

(3). the reader, without a doubt,

has seen similar lists elsewhere.

Here, i would like to discuss a few

highlighted advantages with an

example of a stability study followed

by a description of its perceived

limitations.

Advantages of HPLC: the dominance

of HPLc as a premier analytical

technique is no accident. the most

prominent advantage is its applicability

to diverse analytes types, from small

organic molecules and ions to large

biomolecules and polymers. the

successful coupling of HPLc to MS

gave it an invincible edge as “the

perfect analytical tool” — combining

excellent separation capability with

the unsurpassed sensitivity and

specificity of MS. HPLc–MS is rapidly

becoming the standard platform

technology for bioanalytical testing

(drugs in biological fluids); trace

analysis for residues in food, forensic,

and environmental samples; and life

science research (3–6). Finally, the

excellent precision and robustness of

HPLc with Uv detection makes it an

indispensable tool for quality control

(Qc). this last point is illustrated by a

case study on stability evaluation of

a pharmaceutical product shown in

Figure 1 and table 2.

Figure 1 shows chromatograms

of a retention marker solution and

a three‑month stability sample of a

drug tablet formulation. the retention

marker solution contains the active

pharmaceutical ingredient (APi)

The Essence of Modern HPLC: Advantages, Limitations, Fundamentals, and opportunitiesMichael W. Dong, Genentech, South San Francisco, california, USA.

This article reexamines the fundamental concepts of high performance liquid chromatography (HPLC) to bring fresh insights to how we perform HPLC today. It reviews the prominent advantages that render HPLC indispensable and comments on its many perceived limitations. Several opportunities with far-reaching impacts in life science for the separation scientist are described.



Table 1: Advantages and limitations of HPLc.

Advantages Perceived Limitations

Amenable to diverse analyte or sample types

Lack of an ideal universal detector

Precise and highly reproducible quantitative analysis

Less separation efficiency than capillary gas chromatography

LC–MS relatively difficult for novices

Flexible, customizable, automated operation Still arduous for regulatory testing

High separation power with sensitive detection

with its ability to track changes of drug

impurities over time. data are highly

reproducible with a high degree of

confidence and can be repeated by

different labs. this high level of data

reliability and reproducibility is taken

for granted in HPLc applications for

quality control to such an extent that

it becomes mundane — a feat less

achievable by capillary electrophoresis

(ce), MS, or supercritical fluid

chromatography (SFc).

Perceived Limitations of HPLC:

Limitations of HPLc are rarely

discussed and are therefore more

interesting. “Perceived limitations” is the

terminology used here since most have

been mitigated by recent advances and

are no longer real practical issues.

Lack of a Universal Detector: the lack

of a universal detector for HPLc is

often mentioned, although the

Uv–vis detector comes close to

one for chromophoric compounds.

refractive index detection fits the

bill, but suffers from low sensitivity

and incompatibility with gradient

elution. evaporative light scattering

detection (eLSd) was a contender, but

• All components readily identified by

MS for assays with volatile mobile

phases

table 2 is a stability report summarizing

data from the three‑month time point

of this accelerated stability study

of the oral tablet formulation under

various storage and packaging

conditions, indicating increased levels

of degradants at 40 °c/75%rH and

50 °c/75%rH, particularly for hydrolytic

degradant M399. the remarkable

aspect lies in the exceptional quality of

the stability data generated by HPLc,

spiked with its expected impurities

and degradants. this type of testing is

conducted routinely by pharmaceutical

laboratories to establish shelf lives

and storage conditions for APi and

drug products (5,6). the HPLc

conditions use a multisegment gradient

with ammonium formate buffer and

acetonitrile. Peak designations shown

in the chromatograms are APi (Srr,

absolute configuration for the drug

molecule with three chiral centres);

SrS and rrr (process impurities‑

diastereomers); M235, M416, and

M399 (degradants designated by

their MS parent ions); ketone (an

oxidative degradant); and BHA (butyl

hydroxyanisole, an antioxidant additive).

the bottom chromatogram shows the

extract of a tablet formulation kept in

a stability chamber at 50 °c/75%rH

for three months, indicating increased

levels of degradants for M416, SrS,

rrr, ketone, and M399 (data captured

in the stability table in table 2). the

chromatograms and the operating

conditions are fairly unremarkable by

today’s standard though they serve

to illustrate some of the less obvious

strengths of HPLc in Qc applications,

such as:

• the ability to quantitate all

components (APi and all related

substances, including isomers)

• very precise retention times and peak

areas using Uv detection (<0.1%

relative standard deviation [rSd] is

routinely achievable for UHPLc versus

0.2–0.3% rSd for HPLc)

• Highly reproducible assays

(robustness) by different laboratories

(with instruments from different

vendors and columns from different

batches)

• High‑sensitivity assays for trace

impurities ~0.01% in this assay (limit

of quantitation of 0.05% or 0.10% is

required by regulations)

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Still Arduous, Particularly for Regulated

Testing: HPLc is versatile, quantitative,

sensitive, and extremely precise. it

can also be time‑consuming and

arduous, particularly for regulated

analysis under good manufacturing

practices (GMP). For instance, these

are the steps in a typical operation:

Weighing reference standards;

preparing samples and mobile

phases; setting up the column and

all modules; performing system

suitability testing; injecting standards

to calibrate the system followed by

samples analysis; performing peak

integration; reporting; reviewing; and

sign‑offs. Fortunately, most steps are

automated by precision instruments

for routine testing and are therefore

highly reproducible. compare it with

spectroscopic analysis such as the

identification of raw materials using

a hand‑held raman spectrometer

— point the laser to the sample,

press a button and a pass or fail

result with GMP documentation is

available in seconds. one piece of

advice: don’t use HPLc unless you

have to quantitate analytes with high

accuracy and precision.

HPLC Fundamentals and Insights on Performing HPLC SeparationsLet’s briefly review the fundamental

principles to look for some fresh

insights on how we perform HPLc

analysis. First, the goal of most HPLc

analysis is to quantitate analytes

in a sample (mixture) by physically

separating its components. it is useful

to categorize samples as simple or

complex since the analytical approach

is quite different. in chromatography,

three factors control the separation or

resolution (Rs) of several components

in the sample:

• Retention or having k values

(retention factor) greater than one

• Selectivity (α) or differential

migration of analytes in the

column

• Separation power or column

efficiency (N) — the ability of

the column to separate many

components in the chromatogram.

these factors are defined and

explained in every HPLc textbook

(3–6). For isocratic analysis in which

the mobile phase composition remain

constant, the relationship of Rs to k, α,

further increase Pc for comprehensive

analysis of very complex samples in

proteomics and metabonomics (9,16).

Relatively More Difficult for Novices:

the bewildering number of HPLc

modules, columns, mobile phases,

and operating parameters renders

HPLc difficult for the novice.

Surprisingly, with a single‑point

control of the HPLc system by the

data system, it becomes relatively

easy to teach a new person to run an

existing HPLc method. For example,

just place the sample vial into the

autosampler tray and the assay can

be started with few mouse clicks with

formal‑looking reports automatically

printed afterwards. nevertheless,

substantial experience and scientific

judgment are needed to develop

a new method, interpret a strange

result, or troubleshoot a problem. the

good thing is that chromatographic

principles are well documented

and can easily be explained by

more experienced colleagues in

your laboratory. HPLc is complex,

predictable, and not particularly

complicated to a typical scientist with

a strong chemistry background.

was surpassed by charged aerosol

detection (cAd). cAd uses a nebulizer

with corona discharge detection and

has better sensitivity (low ng) and

ease‑of‑use than eLSd (3,10).

Mass spectrometry is becoming a

universal detection method for ionic or

ionizable compounds with incredible

speed, sensitivity, and selectivity. the

developments of triple‑quadrupole

MS–MS, high‑resolution MS (for

example, time‑of‑flight [toF] and

orbital trap), and hybrid MS (Q‑toF

or ion trap–orbital trap) (11) in

combination with UHPLc and 2d‑Lc

have transformed our abilities to

develop and perform bioanalytical

assays, multiresidue analysis of

complex samples, and life science

research (9).

Less Separation Efficiency

Than Capillary Gas Chromatography:

conventional HPLc has a practical

peak capacity (Pc) of ~200 using

columns with ~20,000 plates under

gradient conditions — not particularly

effective for very complex samples (3).

the advent of UHPLc has extended

Pc to 400–1000 range in a time span

of ~60 min (9,12–16). 2d‑Lc can

175

(a)

150

125

100

Ab

sorb

an

ce

(m

AU

)A

bso

rba

nc

e (

mA

U)

75

50

25

-252 4 6

Time (min)

Time (min)

8 10 12

2 4

M416

M235

M416SRS RRR

M399

API (SRR)

BHAKetone

SRS RRR

API (SRR)M399

BHA

Ketone

5.3

09

1.1

96

5.3

02

5.5

09

6.9

41

6.6

42

6.7

29

8.1

53

9.7

96

11

.18

0

13

.37

4

5.8

18

6.0

52 6

.53

00

.00

86

.93

8

8.1

77

8.8

68

9.7

99

13

.15

21

3.3

80

6 8 10 12

8

(b)

6

4

2

-2

0

0

Figure 1: UHPLc chromatograms of (a) a retention marker solution and

(b) a three‑month stability sample (extract of a tablet kept in a stability chamber at

50 °c/75%rH). this is an example of a stability‑indicating assay used extensively in

the pharmaceutical industry to establish shelf life. column: 100 mm × 3.0 mm,

2‑μm dp Ace excel 2 c18; mobile‑phase A: 20 mM ammonium formate (pH 3.7);

mobile‑phase B: 0.05% formic acid in acetonitrile; flow: 0.8 mL/min; temperature:

40 °c; pressure: 450 bar; gradient: 5–15% B in 2 min, 15–40% B in 10 min, 40–90%

B in 1 min; detection: Uv absorbance at 280 nm; sample: tablet extract in 20%

acetonitrile in 0.1 n Hcl; injection volume: 3 μL.




Table 2: results of a three‑month accelerated stability study in a new drug product formulation.

Peak IDM235

(area%)

M416

(area%)

SRS

(area%)

API

(area%)

RRR

(area%)

Ketone

(area%)

M399

(area%)

tR (min) 1.20 5.30 6.53 6.73 6.94 8.15 9.80

temp/rH% rrt 0.18 0.79 0.97 1.00 1.03 1.21 1.46

5 °c coated‑yellow tablet, 100 mg 0.01 99.9

25 °c/60

coated‑yellow tablet, 100 mg 0.01 0.01 99.9 0.01 0.01

coated‑yellow tablet, 100 mg, 1 dessicant

0.01 0.01 99.8 0.03 0.01

coated‑yellow tablet, 100 mg, open dish

0.01 0.01 99.8 0.03 0.01

30 °c/65 coated‑yellow tablet, 100 mg 0.01 0.01 99.9 0.02 0.02

40 °c/75

coated‑yellow tablet, 100 mg 0.02 0.02 99.8 0.04 0.06

coated‑yellow tablet, 100 mg, 1 dessicant

0.03 0.02 99.7 0.05 0.06

coated‑yellow tablet, 100 mg, open dish

0.02 0.03 99.7 0.01 0.03 0.10

50 °c/75 coated‑yellow tablet, 100 mg 0.01 0.02 0.09 99.3 0.06 0.04 0.35

rrt = relative retention time

broad for quantitation and the gain

in resolution becomes negligible.

in reversed‑phase Lc, ‑log k is

proportional to the solvent strength

of the mobile phase or % organic

solvent (%B). these relationships are

well‑behaved and very predictable

(3,4).

Gradient analysis with increasing

mobile phase strength is typically

preferred for complex samples or

mixtures with diverse polarities or

for assays in which all components

must be reported (such as impurity

testing of pharmaceuticals). Gradient

analysis yields higher peak capacity

(Pc, defined as the number of peaks

that can be resolved in chromatogram

with an Rs value of 1.0; for example,

Pc is ~200 for gradient vs. ~50–100

for isocratic) (2) and sharper peaks

because peak widths are similar for all

peaks irrespective of retention times.

Pc is useful to measure performance

under gradient conditions since one

can only measure N isocratically.

Gradient methods are more difficult

to develop because retention and

selectivity (and Pc) are affected by

many factors, including initial and

ending solvent strengths (%B) and

gradient time (tG), in addition to

the typical mobile phase factors.

Secondary factors are flow rate

(F ) and column temperature (3–5).

Gradient analysis is less susceptible

to extracolumn band broadening

Second, a selectivity value of 1.0 of

the critical pair means coelution (that

means interference of an analyte with

another component), which precludes

accurate quantitation because the

method is not specific to that analyte.

in HPLc, it is often easier to change

the mobile phase (organic solvent

type, buffer type or strength, and pH)

because they can be continuously

varied. the next step is to change

column type or column temperature. in

HPLc, adjusting or fine‑tuning α is the

main, and most time‑consuming part of

the method development process.

Finally, N or plate count is a measure

of the separation power of the column

and is proportional to column length

and inversely proportional to particle

size (dp) (3–5). N can be reasonably

low (for example, 5000 plates) for

simple mixtures using a short column.

Longer columns with higher N are

preferred for more complex mixtures

or for closely eluting analytes (for

example, isomers). the practical

maximum for N in conventional HPLc

is ~20,000 plates for routine testing;

equivalent to the N of a 150‑mm‑long

column packed with 3‑μm particles.

in isocratic analysis, the analyte

peak or band is continuously

broadened with higher retention times

by the inherent chromatographic

process (3–5). values of k exceeding

20 are typically not feasible

because the peaks would be too

and N are described by the resolution

equation shown below (3,4).

Rs

=

k

k + 1

α – 1

α

N4

√ [1]

retention Selectivity and efficiency

conventional wisdom leads us to the

following “rules of thumb” for isocratic

analysis.

First, k or retention factor is the ratio

of retention times of the analyte to that

of an unretained component. Keep k

from 1 to 20 by adjusting mobile phase

strength (% organic in reversed‑phase

Lc). For potency or performance

testing of the main component (for

example, assays of drug substances

or products, dissolution, or content

uniformity testing), adjust the k

to be ~1 and use a short column

(length = 50 mm) for fast analysis

(<2 min). For multicomponent analysis

of a simple mixture, increase k by

lowering the mobile phase strength

until all components are retained and

separated from each other. if there

are four components and four distinct

peaks are observed, then this can

be a preliminary method condition. if

there is a pair of coeluting or partially

resolved peaks with Rs < 1.0–1.5, then

the selectivity (α) of the two peaks

or “critical pair” (α, which is the ratio

of the two k values) should be adjusted.

ES301406_LCA0913_027.pgs 08.19.2013 18:24 ADV blackyellowmagenta



addressed the essence of modern HPLc

by reviewing its advantages, limitations,

and fundamental principles. HPLc is the

dominant analytical technique because

of its versatility, reproducibility, and wide

applicability in research and quality

control. HPLc is a complex technique

because of its myriad combinations of

modules, columns or mobile phases,

and operating parameters. A deeper

understanding of the principles is

becoming more important for the

effective use of UHPLc, the new

standard platform of HPLc. Passionate

separation scientists with expertise in

Lc–MS plus an in‑depth understanding

of the great problems in biology are in

an excellent position to develop new

approaches to make real impacts in life

science for a better tomorrow.

Acknowledgmentsthe author is grateful to drs. Sam

Yang, Mohammad Al‑Sayah, and c.J.

venkatramani, and Midco tsang and

Bob Garcia, Jr., of Genentech; drs. davy

Guillarme of University of Geneva, and

ron Majors of Agilent technologies; and

Professors Kevin Schug of University

of texas at Arlington and Milton Lee of

Brigham Young University for providing

useful inputs and comments. the

opinions expressed in this column are

solely those of the author and bear

no reflections on those of LCGC Asia

Pacific or other organizations.

AddendumAgenda for 2013 and beyond for

“Perspectives in Modern HPLc”:

2013:

• new HPLc product introductions at

Pittcon 2013

• essence of modern HPLc

• A three‑pronged template approach

for rapid HPLc method development

• Myths in UHPLc

Some Potential Future Topics:

• Seven common faux pas in modern

HPLc

• High‑resolution UHPLc

• Analytical platform technologies

• Key equations in pharmaceutical

analysis

• HPLc in drug discovery,

development, and quality control

• trends in modern food and

environmental testing by HPLc

• Lc–MS in clinical diagnostics

• Best practice of HPLc

for characterization of

biopharmaceuticals

Opportunities for Separation Scientiststoday, i believe that biology and life

sciences are the research areas that

offer opportunities for separation

scientists to make the greatest impact.

Biology is the “Wild West” of the 21st

century with a lot of fertile ground for

scientific discovery. Unfortunately,

most biologists are not experts in the

versatile tools for discovery, HPLc or

Lc–MS (8), and separation scientists

(mostly analytical chemists) don’t

usually have the intimate knowledge

of the great biological problems (such

as cell signalling and curing cancer

or Alzheimer’s disease). it would be

ideal if scientists could straddle both

analytical chemistry and biology to

tackle pressing problems, such as the

identification of disease biomarkers

used for clinical diagnostics in

personalized medicine (17–19). Many

instrumentation and pharmaceutical

companies are already investing

heavily in this area (20), though new

approaches are needed such as

automated procedures to isolate the

key analytes in these complex matrices

(19). Here, 2d‑UHPLc coupled

with hybrid high‑resolution MS can

be a powerful generic tool — but

only for those scientists with a good

understanding of the problem and the

analytical technologies.

My next comments are some

immediate job opportunities for

separation scientists in our recovering

economies. in 2012, six of the 15

top selling drugs were monoclonal

antibodies (mAb) (21). With hundreds

of on‑going mAb research projects as

therapeutics, many job opportunities

are available in the characterization

and quality control of mAbs (22,23).

However, analysts experienced in

assessing the critical quality attributes

of biological drugs are rare and

graduate students are not trained in

this area because it is not the funding

source of their professors. So, there

appears to be a disconnect between

graduate training and job opportunities

that goes beyond summer internships

in the pharmaceutical industry. Perhaps

a closer collaboration or partnership

between academia and industry is the

right solution.

Summary and Conclusionsin this instalment, my first real column for

“Perspectives in Modern HPLc,” i have

because sample band dispersion

before the column and large injection

volumes are inconsequential (for

injection of samples in lower‑strength

diluents) — an important fact for

UHPLc using columns with smaller

internal diameters (9,13).

the advent of UHPLc (systems

with low dispersion and pressure

limits of 15,000 to 19,000 psi)

together with the use of smaller

internal diameter columns packed

with sub‑2‑μm particles, accentuated

the need for better understanding

of chromatographic fundamentals

such as Rs, k, N, α, particle size (dp),

column internal diameter (dc), column

void volume (Vm), peak volume, peak

width, instrument bandwidth or system

dispersion, flow‑cell volume, and dwell

volumes (3,9,13). Because UHPLc is

becoming the modern standard HPLc

platform, better understanding of these

concepts will be helpful for efficient

operation and method development

and transfer (9,16).

in summary, the biggest strength

of HPLc is its versatility for reliable

quantitation of analytes in complex

mixtures through physical separations

of the analyte peaks from coeluted

components. to effect separations,

one must have retention (k), selectivity

(α), and adequate plate counts (N).

retention is related to the partition

coefficient of the analyte molecule

between mobile and stationary

phases. this partitioning process is

repeated millions of times down the

column to allow separation of analytes

with minute differential migration (α).

Selectivity (α) can be “tweaked” by

changing column or mobile phase

parameters. the unlimited number

of combinations of columns, mobile

phases, and controlling factors makes

HPLc complex but gives endless

possibilities for the quantitation of

all or specific components in many

sample types. HPLc works reliably

in practice because of the gentle,

predictable nature of the liquid phase

chromatographic processes and the

availability of precise instrumentation

with efficient and reproducible columns.

very complex samples with thousands

of analytes can be separated by

“brute force” with UHPLc and 2d‑Lc

coupled with Uv, MS, or MS–MS (16).

the complexity (versatility) of HPLc is

its greatest strength and also its key

weakness (laborious).

ES301408_LCA0913_028.pgs 08.19.2013 18:24 ADV blackyellowmagenta



(10) M. Swartz, M. emmanuel, A. Awad, and

d. Hartley, “Advances in HPLc Systems

technology” supplement to LCGC North

Am. 27(4), 40–48 (2009).

(11) W.A. Korfmacher, ed. Mass Spectrometry

for Drug Discovery and Drug Development,

(Wiley, Hoboken, new Jersey, USA, 2013).

(12) J.e. Macnair, K.c. Lewis, and J.W.

Jorgenson, Anal. Chem. 69, 983–989 (1997).

(13) M.W. dong, LCGC North Am. 25(7),

656–666 (2007).

(14) n. Wu and A.M. clausen, J. Sep. Sci. 30,

1167–1182 (2007).

(15) d. Guillarme and M.W. dong, Amer.

Pharm. Rev., (2013) submitted.

(16) M.W. dong, d. Guillarme, S. Fekete, r.

rangelova, J. richards, d. Prudhomme,

and n.P. chetwyn, J. Chromatogr. A.

submitted.

(17) L. Sannes, “commercializing Biomarkers in

therapeutic and diagnostic Applications –

overview,” insight Pharma report, May 2011.

(18) A. tessitore, A. Gaggiano, G. cicciarelli,

d. verzella, d. capece, M. Fischietti,

F. Zazzeroni, and e. Alesse, Int. J.

Proteomics 2013, 1–15 (2013).

(19) F.e. regnier, LCGC North Am. 30(8),

622–623 (2012).

(20) M. Hollmer, “2012’s top 10 diagnostics

companies,” Fierce Medical

devices, november 27, 2012, www.

fiercemedicaldevices.com/special‑reports/

top‑10‑diagnostics‑companies.

(21) J.d. carroll, “the 15 best‑selling drugs of

2012,” Fierce Pharma, october 9, 2012,

http://www.fiercepharma.com/special‑

reports/15‑best‑selling‑drugs‑2012.

Your ideas and inputs are solicited on

areas deserving further investigation

or discussions. Please send your

comments and suggestions to: dong.

[email protected]

References(1) M.W. dong, LCGC Asia Pacific 16(2) 27–32

(2013).

(2) Strategic directions inc. Market Analysis

and Perspectives Report for Analytical and

Life Science Instruments Industry (Los

Angeles, california, USA, 2012).

(3) M.W. dong, Modern HPLC for Practicing

Scientists (Wiley, Hoboken, new Jersey,

USA, 2006).

(4) L.r. Snyder, J.J. Kirkland, and J. W.

dolan, Introduction to Modern Liquid

Chromatography, 3rd ed. (John Wiley &

Sons, Hoboken, new Jersey, USA, 2009).

(5) S. Ahuja and M.W. dong, eds. Handbook

of Pharmaceutical Analysis by HPLC,

(elsevier/Academic Press, 2005).

(6) Y.v. Kazakevich and r. LoBrutto, eds.

HPLC for Pharmaceutical Scientists, (Wiley,

Hoboken, new Jersey, USA, 2007).

(7) c.F. Poole, Essence of Chromatography

(elsevier Science, Amsterdam, the

netherlands, 2002).

(8) r.L. Wixom and c.L. Gehrke, eds.

Chromatography: A Science of Discovery,

(Wiley, Hoboken, new Jersey, USA, 2010).

(9) d. Guillarme, J‑L veuthey, and r.M. Smith,

eds. UHPLC in Life Sciences, (royal

Society of chemistry, cambridge, United

Kingdom, 2012).

(22) t. Zhang, J. Zhang, d. Hewitt, B.tran,

X. Gao, Z.J. Qiu, M. tejada, H.

Gazzano‑Santoro, and Y‑H. Kao, Anal.

Chem. 84, 7112–7123 (2012).

(23) S. Fekete, M.W. dong, t. Zhang, and

d. Guillarme, J. Pharm. Biomed. Anal.

submitted.

Michael W. Dong is a senior scientist

in Small Molecule drug discovery at

Genentech in South San Francisco,

california, USA. He is responsible

for new technologies, automation

and supporting late‑stage research

projects in small molecule analytical

chemistry and Qc of small molecule

pharmaceutical sciences. He holds a

Phd in analytical chemistry from the

city University of new York, USA, and

a certificate in Biotechnology from U.c.

Santa cruz, USA. He has conducted

numerous courses on HPLc/UHPLc,

pharmaceutical analysis, HPLc method

development, drug development

process and drug quality fundamentals.

He is the author of Modern HPLC for

Practicing Scientists and a co‑editor of

Handbook of Pharmaceutical Analysis

by HPLC. He is a member of the editorial

advisory board of LCGC North America.

http://bit.ly/1cqH6Wt

LCGC Asia Pacific is pleased to present

an EXCITING new E-Book, Innovations in

Environmental AnalysisNo matter what type of environmental testing you do, you need the

latest methods. This new special issue brings together vital

information on methods for environmental analysis.



PRODUCTS

Microwave prep

The sample preparation

portfolio of the Gerstel

MultiPurpose Sampler

MPS has been expanded

to include advanced

microwave technology

for accelerated solvent

extraction and rapid

chemical reactions. An

application example

is automated saponification of fats in food in combination with

esterification and FAME analysis by GC. The MPS can be

coupled directly to a GC–MS or LC–MS system or used in

stand-alone mode.

www.gerstel.com

Gerstel, Mülheim an der Ruhr, Germany.

SEC–MALS detector

Wyatt has introduced

the DAWN HELEOS– II,

a highly sensitive MALS

detector for absolute

molecular weight and

size determination

of polymers and

biopolymers in solution.

It can be connected to

any chromatographic

system without the use of reference standards or column

calibration.

www.wyatt.com

Wyatt Technology, California, USA.

Positive pressure manifold

UCT has announced the

introduction of a positive

pressure manifold. The company

report that using positive

pressure to push samples

through SPE columns is more

efficient than using a vacuum.

It allows for a more even flow

across the samples as well

as more flow control of liquids

passing through the columns.

It is designed to handle up

to 48 samples and is entirely

pneumatically operated.

www.unitedchem.com

United Chemical Technologies, Pennsylvania, USA.

Exhaust filter

VICI has announced

the introduction of

an exhaust filter that

prevents the pollution

of laboratory air with

VOCs and other toxic or

hazardous compounds.

It includes a detector

which indicates the filter

saturation. The filter works with all common safety caps

(adapters available) for lab bottles and canisters and has a

long lifetime because of its high capacity filter material.

www.vici-jour.com

VICI AG International, Schenkon, Switzerland.

Convergence

Chromatography

UltraPerformance Convergence

Chromatography from Waters is

a broad-based, complementary

analytical platform that is taking

its place alongside LC and GC

for modern laboratory analysis,

according to the company. The

system routinely provides reliable orthogonal data. The

company report that the impact on workflow starts with

the time saved in sample preparation, analysis, and data

interpretation.

www.waters.com

Waters, Massachusetts, USA.

GPC/SEC detector

Malvern has

introduced the

Viscotek SEC-MALS

20, a multi-angle light

scattering detector for

measuring absolute

molecular weight of

proteins, synthetic and

natural polymers, and

molecular size (Rg). According to the company, it brings

enhanced performance and increased choice in GPC/SEC

analysis, extending the range to include low-, right-, and

multi-angle light scattering detectors. The 20 detector array

and vertical flow cell ensure exceptional accuracy.

www.malvern.com/mals

Malvern Instruments, Malvern, Worcestershire, UK.


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LC TROUBLESHOOTING

Problem solving

GC–MS

Quantitative analysis of catalyst

poisoners

THE ESSENTIALS

Multidimensional GC

September 2012

Volume 15 Number 3


Detecting herbicides in tap water

Biomolecule AnalysisUsing reversed-phase liquid

LC TROUBLESHOOTING

The role of the injection solvent

GC CONNECTIONS

Developments in SPME

COLUMN WATCH

Achiral stationary phases

November 2012

Volume 15 Number 4


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THE ESSENTIALS

We very often have a need to transfer or

translate an existing high performance

liquid chromatography (HPLC) method

to a different instrument, or to gain

speed by altering our column geometry,

stationary phase particle size, or particle

morphology.

To do this successfully we need to

understand the underlying principles and

important instrument aspects that govern

this translation or transfer. No matter if

you are transferring to a smaller column

geometry with the same particle size or to

a column using smaller particles, certain

simple relationships hold that can help

us to geometrically scale to the optimum

eluent flow, pressure, sample volume, or

the expected efficiency we might obtain.

Furthermore, there are simple formulas

to help us transfer the gradient profile to

maintain selectivity and retention order.

There may also be times when we

need to transfer an existing method from

one instrument to another, without any

change other than the actual instrument

used, the instrument manufacturer, or the

laboratory in which it is situated. Many

of the considerations highlighted below

will also be pertinent to this situation. It

should be noted that in all of the situations

below, the nature of the stationary phase

chemistry does not change between the

original and translated method.

Flow rate between columns with

different diameter and particle size can

be scaled using the following simple

relationship.

F2=F

1×

dc2

2

dc1

(

( dP1

dP2

× (

(

[1]

where F is flow rate in millilitres per minute,

dc is column diameter in millimetres, dp

is particle diameter in micrometres, 1 is

the original value, and 2 is the translated

(new) value.

Injection volume can be translated

using equation 2:

Vinj2=V

inj1×

dc2

2

dc1

(

( L2

L1

× (

(

[2]

where Vinj is the injection volume in

microlitres and L is the column length

in millimetres. All other terms are the

same as in equation 1.

The expected pressure from the new

column can be approximated using

equation 3:

P2=P

1×

dc1

2

dc2

((

×

dp1

2

dp2

((×

L2

2

L1

((

×

F2

F1

((

[3]

where P is the pressure in bar or pounds

per square inch and all other terms are

the same as in equations 1 and 2.

The expected change in efficiency can

be calculated using equation 4:

N2=N

1×

dp1

dp2

(

( L2

L1

×(

(

[4]

where N is the plate count and all other

terms are the same as in the other

equations.

Please bear in mind that the

measurement of plate count in gradient

HPLC is virtually meaningless —

although for comparative purposes

it will give an idea of the expected

improvement in the peak width

and therefore the chromatographic

performance.

One of the most important parameters

in scaling methods is the gradient time (tg)

for each gradient segment. By keeping

the gradient composition the same at

each point, altering the gradient time will

alter the slope of the gradient, which is

the important thing in terms of preserving

retention order and selectivity. There are

a host of more complex relationships that

are featured in the CHROMacademy

webcast and tutorial, but, as pragmatic

chromatographers, we prefer simple rules

of thumb that can be performed quickly

and easily within the laboratory and will

give us results that are fit for purpose.

The time for each gradient segment

can be calculated using equations 5

and 6:

tg2=t

g1×

VM2

VM1

(( F

1

F2

×((

[5]

where tg is the gradient time in minutes,

VM is the column interstitial volume

(volume of mobile phase inside the

column) in microlitres, and other terms

are the same as previously mentioned.

VM≈ 0.5 × L × d

c

2 [6]

For example, solving equation 6 for a

50 mm × 2.1 mm column would result

in a mobile phase volume of 110 μL.

Of course, when translating gradient

methods between instruments, one

would need to consider the gradient

dwell volume (time) for each system

to compensate properly for these

differences. This discussion is outside the

scope of this short article but full details

can be found in the accompanying

CHROMacademy Essential Guide.

So, just to test out your calculator, we

have translated a method and shown the

original and final values in Table 1 — see

if you can match these values and prove

to yourself that you can easily translate

method variables in HPLC!

Secrets to Successfully Translating and Transferring HPLC MethodsAn excerpt from LC•GC’s e-learning tutorial on HPLC methods at CHROMacademy.com

Get the full tutorial at www.CHROMacademy.com/Essentials

(free until 20 October).

More Online:

Table 1: Translated HPLC method variables using the various equations shown in the text.

Parameter Original Method (1) Translated Method (2) (Figures Used in Reality)

F (mL/min) 1.0 0.58 (0.6)

dc (mm) 4.6 2.1

L (mm) 150 100

dp (μm) 5 1.8

Vinj (μL) 15 2.1 (2)

Gradient (tg) 40–60% B in 15 min 40–60% B in 3.5 min

Plate count (N) 8000 14,814

Pressure (bar) 60 859


LC•GC Asia Pacific September 2013 33

ADVERTISEMENT FEATURE

This application note presents a simple and cost-effective method for the

fast determination of pesticides in bananas. The method employs the AOAC

QuEChERS approach, which yields higher recovery for several sensitive

pesticides, such as pymetrozine and Velpar. A 15 g sample of homogenized

banana is hydrated with 5 mL of reagent water to give a sample with >80%

water. The hydrated sample is extracted using 15 mL acetonitrile with 1%

acetic acid, this is followed by the addition of magnesium sulphate and

sodium acetate. After shaking and centrifugation, 1 mL supernatant is

cleaned in a 2-mL dSPE tube containing 150 mg MgSO4, 50 mg primary

secondary amine (PSA), and 50 mg C18. MgSO4 absorbs residual water

in the extracts; PSA removes organic acids and carbohydrates; while C18

retains fatty acids and other non-polar interferences. The result is a clean

extract for LC–MS–MS analysis.

QuEChERS Extraction

1. Weigh 15 ± 0.15 g of peeled and homogenized banana sample

into a 50-mL centrifuge tube (RFV0050CT).

2. Add 5 mL of reagent water to increase the water content in

banana from 74% to >80%.

3. Add an internal standard to all samples, and appropriate amounts

of pesticide spiking solution to fortif ed samples.

4. Add 15 mL of acetonitrile with 1% acetic acid.

5. Cap and shake for 1 min at 1000 strokes/min using a Spex 2010

Geno/Grinder.

6. Add salts (6 g MgSO4 and 1.5 g NaOAc) in Mylar pouch

(ECMSSA50CT-MP) to each tube, and vortex for 10 s to break up

salt agglomerates.

7. Shake for 1 min at 1000 strokes/min using Spex Geno/Grinder.

8. Centrifuge the samples at 3830 rcf for 5 min.

dSPE Cleanup

1. Transfer 1 mL supernatant into 2-mL dSPE tube (CUMPSC18CT).

2. Shake for 2 min at 1000 strokes/min using Spex Geno/Grinder.

3. Centrifuge at 15300 rcf for 5 min.

4. Transfer 0.3 mL of the cleaned extract into a 2-mL auto-sampler vial.

5. Add 0.3 mL of reagent water, and vortex for 30 s.

6. The samples are ready for LC–MS–MS analysis.

LC–MS–MS Method

System: Thermo UltiMate 3000 LC with Vantage MS/MS, ESI+

Determination of Pesticides in Banana by AOAC QuEChERS and LC–MS–MS DetectionXiaoyan Wang, UCT

UCT, LLC 2731 Bartram Road, Bristol, Pennsylvania19007, USA

Tel: (215) 781 9255

Email: [email protected]

Website: www.unitedchem.com

Extraction and Clean-up Products

RFV0050CT 50 mL polypropylene centrifuge tube

ECMSSA50CT-MP 6 g MgSO4 and 1.5 g NaOAc in Mylar pouch

CUMPSC18CT 150 mg MgSO

4, 50 mg PSA, and 50 mg C18

in 2 mL centrifuge tube

Table 1: Accuracy and Precision Data (n = 5)

AnalyteSpiked at 10 ng/g Spiked at 50 ng/g

Recovery (%) RSD (%) Recovery (%) RSD (%)

Methamidophos 97.3 5.9 100.2 4.6

Pymetrozine 96.5 4.7 99.3 3.8

Carbendazim 103.5 3.3 107.3 5.3

Dicrotophos 101.8 4.1 104.8 4.8

Acetachlor 121.0 2.8 126.2 4.5

Thiabendazole 133.8 5.8 111.0 4.9

DIMP 89.2 6.0 92.1 7.7

Tebuthiuron 105.2 7.9 112.2 5.1

Simazine 96.3 4.6 101.2 4.8

Carbaryl 93.3 10.8 96.4 7.1

Atrazine 97.6 12.8 101.5 7.1

DEET 86.9 12.8 93.6 7.3

Pyrimethanil 100.6 8.0 97.0 5.7

Malathion 103.9 2.6 100.2 4.8

Bifenazate 84.4 13.7 85.4 3.2

Tebuconazole 90.0 1.2 88.2 1.5

Cyprodinil 97.3 3.1 96.0 1.8

Diazinon 104.1 1.7 99.8 2.9

Zoxamide 104.3 2.7 98.9 4.4

Pyrazophos 105.4 3.3 106.1 5.2

Profenofos 95.8 8.8 96.4 8.7

Chlorpyrifos 86.8 14.3 90.7 12.3

Abamectin 81.7 7.8 80.6 16.3

Bifenthrin 90.9 2.6 88.4 7.8

Overall mean 98.7 6.3 98.9 5.9

Injection: 10 μL at 10 ºC

LC column: Thermo Accucore aQ, 100 × 2.1 mm, 2.6 μm, at 40 ºC

Mobile phase: (A) 0.3% formic acid and 0.1% ammonia formate in

water; (B) 0.1% formic acid in methanol

Gradient programme and SRM transitions are available upon request.

Conclusion

A simple, fast, and cost-effective method has been developed for the

determination of pesticides in banana samples. Pesticide residues

in bananas were extracted using the AOAC version of the QuEChERS

approach, followed by dSPE cleanup using MgSO4, PSA, and C18.

Excellent accuracy and precision were obtained, even for pymetrozine

(recovery >95%), a sensitive pesticide with limited

recovery when the original or EN versions of QuEChERS

approach is employed. The analytical run time was 20 min

and the overall mean recovery for the 24 pesticides tested

were 98.7% and 98.9% for the fortif ed banana samples

at 10 ng/g and 50 ng/g, respectively.


34 LC•GC Asia Pacific September 2013

ADVERTISEMENT FEATURE

A novel method for Drug Antibody Ratio (DAR) determinations

based on size-exclusion chromatography-multi-angle light

scattering (SEC-MALS) in conjunction with ultraviolet (UV)

absorption and differential refractive index detection.

There has been a signif cant resurgence in the development of

anti-body-drug conjugates (ADC) as target-directed therapeutic agents

for cancer treatment. Among the factors critical to effective ADC design

is the Drug Antibody Ratio (DAR). The DAR describes the degree of

drug addition which directly impacts both potency and potential toxicity

of the therapeutic, and can have signif cant effects on properties such

as stability and aggregation. Determination of DAR is, therefore, of

critical importance in the development of novel ADC therapeutics.

DAR is typically assessed by mass spectrometry (MALDI-TOF or

ESI-MS) or UV spectroscopy. Calculations based on UV absorption

are often complicated by similarities in extinction coeff cients of

the antibody and small molecule. Mass spectrometry, though a

powerful tool for Mw determination, depends on uniform ionization

and recovery between compounds — which is not always the case

for ADCs.

Here we present a method for DAR determination based on

SEC-MALS in conjunction with UV absorption and differential

refractive index detection. Figure 1 shows UV traces for two

model ADCs; molecular weights of the entire ADC complexes are

determined directly from light scattering data.

Component analysis is automated within the ASTRA 6 software

package by using the differential refractive index increments (dn/dc)

and extinction coeff cients, which are empirically determined for each

specie or mined from the literature, to calculate the molar mass of the

entire complex as well as for each component of the complex.

In this example an antibody has been alkylated with a compound

having a nominal molecular weight of 1250 Da (Figure 2). Molar masses

of the antibody fractions are similar, which indicates that the overall

differences between the two formulations ref ect distinct average DARs

which are consistent with values obtained by orthogonal techniques. Note

that the molar mass traces for the conjugated moiety represent the total

amount of attached pendant groups; the horizontal trends indicate that

modif cation is uniform throughout the population eluting in that peak.

Antibody Drug Conjugate (ADC) Analysis Wyatt Technology Corporation

Wyatt Technology Corporation6300 Hollister Avenue, Santa Barbara, California 93117, USA

Tel: +1 (805) 681 9009 fax: +1 (805) 681 0123

Website: www.wyatt.com

Antibody-Drug Conjugate Analysis

(■) Mw of complex

(+) Mw of antibody

(x) Mw of conjugated drug

1.0x105

1.0x104

9.0 9.5 10.0 10.5 11.0 11.5 12.0time (min)

Complex Antibody Drug

DAR

ADC1

ADC2

167.8 (±1.2%)

163.7 (±1.2%)

155.2 (±1.8%)

155.6 (±1.2%)

12.6

8.1 6.5

10.1

Mw (kDa)

Mo

lar

Ma

ss (

g/m

ol)

ADC1

ADC2

2.0x105

Molar mass vs. time

167.8 kDa

ADC1

ADC2

163.7 kDa1.8x105

1.6x105

1.4x105

1.2x105

Mo

lar

Ma

ss (

g/m

ol)

1.0x105

8.0x104

9.0 9.5 10.0 10.5Time (mn)

11.0 11.5 12.0

Figure 2: Molar masses for the antibody and total appended drug are calculated in the ASTRA software package based on prior knowledge of each component’s extinction coeff cent and dn/dc, allowing determination of DAR based on a nominal Mw of 1250 Da for an individual drug.

Figure 1: Molar masses for two distinct ADC formulations are determined using SEC-MALS analysis.


BE

TT

ER

SAMPLE

CA

RE

Spark Holland B.V.P.O. box 3887800 AJ EmmenThe Netherlands

P. +31 591 631 700F. +31 591 630 035E. [email protected]

Head Offi ce:P. de Keyserstraat 87825 VE EmmenThe Netherlands

Our revolutionary Dried Blood Spot (DBS) autosampler is almost

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