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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
www.chromatographyonline.com
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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ES301442_LCA0913_004.pgs 08.19.2013 19:48 ADV blackyellowmagentacyan
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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
ES301330_LCA0913_006.pgs 08.19.2013 18:21 ADV blackyellowmagentacyan
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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
LC•GC Asia Pacific September 20138
Gravell et al.
ES301328_LCA0913_008.pgs 08.19.2013 18:21 ADV blackyellowmagentacyan
Material relationships
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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
LC•GC Asia Pacific September 201310
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
©2013 Sigma-Aldrich Co. LLC. All rights reserved. SIGMA-ALDRICH and SUPELCO are trademarks of Sigma-Aldrich
Co. LLC, registered in the US and other countries. Ecoporous and Titan are trademarks of Sigma-Aldrich Co. LLC.
N 1.9 μm UHPLC ColumnsS l ® d T ™ C UHPLC l b dSSupelco® introduces Titan™ C18 UHPLC columns, basedS
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These particles are the result of a newly developed, patent-T
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ES301332_LCA0913_011.pgs 08.19.2013 18:21 ADV blackyellowmagentacyan
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
8-2 Fluorotelomer alcohol
10-2 Fluorotelomer alcohol
6-2 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
LC•GC Asia Pacific September 201312
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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)
13www.chromatographyonline.com
Gravell et al.
ES301346_LCA0913_013.pgs 08.19.2013 18:22 ADV blackyellowmagentacyan
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).
LC•GC Asia Pacific September 201314
Gravell et al.
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15www.chromatographyonline.com
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?
ES301394_LCA0913_015.pgs 08.19.2013 18:23 ADV blackyellowmagentacyan
LC•GC Asia Pacific September 201316
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.
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17www.chromatographyonline.com
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).
(3) J.W. Dolan, LCGC Europe 26(5), 260–264
(2013).
(4) J.W. Dolan, LCGC Europe 26(6), 330–337
(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
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.
ES301395_LCA0913_017.pgs 08.19.2013 18:23 ADV blackyellowmagentacyan
LC•GC Asia Pacific September 201318
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”.
ES301348_LCA0913_018.pgs 08.19.2013 18:22 ADV blackyellowmagentacyan
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LC•GC Asia Pacific September 201320
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.
ES301351_LCA0913_020.pgs 08.19.2013 18:22 ADV blackyellowmagentacyan
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ES300459_LCA0913_021_FP.pgs 08.14.2013 22:19 ADV blackyellowmagentacyan
LC•GC Asia Pacific September 201322
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
23www.chromatographyonline.com
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,
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
LC•GC Asia Pacific September 201324
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.
ES301407_LCA0913_024.pgs 08.19.2013 18:24 ADV blackyellowmagentacyan
PerSPectiveS in Modern HPLC
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)
Universal HPIC Analysis
less time, better results
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ICS-5000+ HPIC system enables fast separations and high resolution with the new 4
µm particle IC columns. This Reagent-Free™ HPIC system simplifi es your analysis and
increases reproducibility by removing the inconsistencies of manually prepared eluents.
© 2
013
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reserv
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ES301404_LCA0913_025.pgs 08.19.2013 18:24 ADV blackyellowmagentacyan
LC•GC Asia Pacific September 201326
PerSPectiveS in Modern HPLC
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.
ES301405_LCA0913_026.pgs 08.19.2013 18:24 ADV blackyellowmagentacyan
27www.chromatographyonline.com
PerSPectiveS in Modern HPLC
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
LC•GC Asia Pacific September 201328
PerSPectiveS in Modern HPLC
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
www.chromatographyonline.com
PerSPectiveS in Modern HPLC
(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.
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.
ES301403_LCA0913_029.pgs 08.19.2013 18:24 ADV blackyellowmagentacyan
LC•GC Asia Pacific September 201330
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.
ES301352_LCA0913_030.pgs 08.19.2013 18:22 ADV blackyellowmagentacyan
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LC TROUBLESHOOTING
Problem solving
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poisoners
THE ESSENTIALS
Multidimensional GC
September 2012
Volume 15 Number 3
www.chromatographyonline.com
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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LC•GC Asia Pacific September 201332
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
ES301373_LCA0913_032.pgs 08.19.2013 18:23 ADV blackyellowmagentacyan
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.
ES301372_LCA0913_033.pgs 08.19.2013 18:22 ADV blackyellowmagentacyan
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.
ES301446_LCA0913_034.pgs 08.19.2013 20:21 ADV blackyellowmagentacyan
BE
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Our revolutionary Dried Blood Spot (DBS) autosampler is almost
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(Booth No.12008, 12007, 12031, 12032) or scan the QR-code for
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ES300455_LCA0913_CV3_FP.pgs 08.14.2013 22:19 ADV blackyellowmagentacyan
www.gerstel.com
Polymer Quality
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Extraction, SPE, addition of standards
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For the highest Product Quality you can rely on
GERSTEL Solutions for GC/MS and LC/MS.
ES301549_LCA0913_CV4_FP.pgs 08.20.2013 00:40 ADV blackyellowmagentacyan