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TRANSCRIPT
March 2015
Volume 18 Number 1
www.chromatographyonline.com
LC TROUBLESHOOTING
Identifying the causes of method
failure
GC CONNECTIONS
GC ovens
COLUMN WATCH
The possibilities of SFC
Principles, advantages, and potential problems
Translating Methods from HPLC to UHPLC
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T O M A K E
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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: traffic_analyzer/Getty Images
Columns17 LC TROUBLESHOOTING
Listen to the Data
John W. Dolan
A stepwise process is described to help isolate and identify the
cause of a method failure.
22 GC CONNECTIONS
Gas Chromatography Ovens
John V. Hinshaw
The transit of peaks through a gas chromatography (GC) column
depends strongly on the thermal profile they encounter on the way.
Gas chromatographers primarily rely on the classic hot-air-bath
type of oven, but several alternatives are also in use. This
instalment examines ovens for GC in several forms plus how oven
thermals affect peak retention behaviour.
27 COLUMN WATCH
Modern Supercritical Fluid Chromatography — Possibilities
and Pitfalls
Torgny Fornstedt and Ronald E. Majors
There has been a revival of supercritical fluid chromatography
(SFC) in recent years, especially in the chiral preparative field,
but also more recently in the analytical area. However, SFC is
considerably more complex than liquid chromatography (LC),
mainly because of the compressibility of the mobile phase. One
can say that SFC is a “rubber variant” of LC where everything
considered constant in LC varies in SFC. In this review, we
go through advances in theory, instrumentation, and novel
applications.
Departments33 Events
34 Application Notes
COVER STORY 6 Translations Between Differing Liquid Chromatography
Formats: Advantages, Principles, and Possible
Pitfalls
Patrik Petersson, Melvin R. Euerby,
and Matthew A. James
The numerous advantages of
translating gradient chromatographic
methods between the differing
formats of LC have been explored
and discussed. While translations
in principle obey well defined
chromatographic theories, the authors
investigate a number of potential pitfalls
that may result in poor translations as
exhibited by selectivity differences,
changes in efficiency, and hence failure to
meet resolution system suitability criteria.
March | 2015
Volume 18 Number 1
www.chromatographyonline.com
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4 LC•GC Asia Pacific March 2015
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
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 RitchieTrajan Scientific and Medical. Milton
Keynes, 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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resolution, while reducing the gradient analysis time by 50%
(same velocity typically used for large molecules) to 70% (higher
velocity typically used for small molecules) and substantially
increasing productivity. This approach is vitally important for
the analysis of increasingly larger numbers of samples (that
is, to better describe a process or formulation performance),
increased utilization of instruments, the analysis of labile
samples, and rapid at-line analysis (that is, process analytical
technology).
A compromise between the approaches of increased
resolution and speed of analysis has been the use of 100-mm
columns with sub-2-µm particles, which results in a 60%
reduction in gradient time and an approximate 10% increase in
resolution for small molecules.
Reduced Solvent Consumption: Converting a standard
HPLC method that uses a 150 mm × 4.6 mm, 3–3.5 µm
column to a 100 mm × 2.1 mm, 1.7-µm or 100 mm × 1.0 mm,
1.7-µm column in theory offers a possible reduction in solvent
consumption of approximately 86% to 97%. In practice, it is less
often because of the necessity to prime the LC lines. During a
global implementation of UHPLC within AstraZeneca (during the
period of 2007–2010) involving 41 UHPLC systems, a reduction
in solvent consumption of 63% was realized compared to the
theoretical reduction of 77% (9).
KEY POINTS• Translations between differing formats of liquid
chromatography (LC) obey well-defined theories.
• Typically translations work very well.
• There are potential pitfalls that result in changes in
selectivity or poor efficiency, but these can in most
cases be addressed.
As a result of the introduction of commercially available
sub-2-µm porous particles (1), sub-2-µm, 3-µm, and 5-µm
superficially porous (2,3) particles, and ultrahigh-pressure
liquid chromatography (UHPLC) instrumentation (4,5) from
2004 onwards, there has been an increasing interest in the
ability to perform accurate translations between different
liquid chromatography (LC) formats. An example would be
translating between 150 mm × 4.6 mm, 5-µm dp formats on
standard high performance liquid chromatography (HPLC)
systems and 50 mm × 2.1 mm, sub-2-µm formats on UHPLC
systems while maintaining the same resolution. The findings
of a recent survey of major chromatographic users predicted
that the use of standard HPLC systems is expected to steadily
decline from 2011 to 2015 with a concomitantly higher usage
and purchase of UHPLC systems predicted over the same
time frame (6).
There are a plethora of reasons for this shift in LC format
usage and purchase, all of which are based on sound
chromatographic theory (5,7,8). From the extensive experience
of the authors within the pharmaceutical industry, the major
drivers for this shift appear to be increased productivity
(that is, reduced analysis time) coupled with minimal loss of
information quality or an increased quality of data with no loss of
productivity.
Advantages and DriversIncreased Resolution: Reduction of the packing material
particle size by a factor of two (that is, substitution of 3–3.5 µm
particles by 1.7-µm particles), while keeping other operation
factors constant, should result in an increase of resolution of
approximately 30–40%.
Speed of Analysis: A reduction in column length (L) and
particle size (dp) while keeping the L/dp ratio constant (for
example, substitution of a 150-mm column with 3–3.5 µm
particles for a 75-mm column with 1.7-µm particles) should
maintain the same chromatographic efficiency and hence
Translations Between Differing Liquid Chromatography Formats: Advantages, Principles, and Possible Pitfalls Patrik Petersson1, Melvin R. Euerby2,3, and Matthew A. James3, 1Novo Nordisk A/S, Måløv, Denmark, 2Strathclyde Institute
of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK, 3Hichrom Ltd, Berkshire, UK.
The numerous advantages of translating gradient chromatographic methods between the differing formats of liquid chromatography (LC) have been explored and discussed. Although translations in principle obey well-defi ned chromatographic theories, the authors investigate a number of potential pitfalls that may result in poor translations as exhibited by selectivity differences, changes in efficiency, and hence failure to meet resolution system suitability criteria. The consequences of these pitfalls are examined and the regulatory implications of method translation are explored.
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6 LC•GC Asia Pacià c March 2015
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Ease of Method Transfer: Within many industries it is often standard practice to transfer the chromatographic testing from the research and development (R&D) laboratory to a contract research organization (CRO), operation, or quality control (QC) sites. This method transfer exercise can be made even more problematic because it is now quite common for many R&D departments to develop only UHPLC methods. However, not all receiving laboratories have sufficient UHPLC capacity or experience and, thus, method translations become necessary. The reverse of this is becoming true in that QC laboratories, which have moved predominantly to UHPLC, may have to use UHPLC methods for the analysis of legacy products or methods that use HPLC columns.Increased Instrument Utilization: The drive for increased productivity and efficiency has necessitated an increased flexibility and utilization of available instrumentation. Many companies have a rolling programme to replace their worn out HPLC systems with a reduced number of UHPLC systems capable of running LC methods based on both 5-µm and sub-2-µm particles. In addition, valve arrangements are used that allow queuing of both HPLC and UHPLC methods on the
same LC system, hence a reduced number of LC systems allow continuous operation.
Principles of Method TranslationBecause many translation guides successfully describe translations of isocratic LC methodologies (10), this article will focus entirely on the translation of gradient LC methodologies, which can be more difficult to perform. In theory, the translation required to maintain the chromatographic selectivity and performance of either an isocratic or gradient separation is very simple (7,11). The additional consideration that has to be accounted for in gradient separations is that a constant ratio must be maintained between the volume of each segment in the gradient over the column dead volume (8,12–15). After the introduction of sub-2-µm particles and UHPLC in the mid-2000s, several articles were published that focused exclusively on translations between 4.6- and 2.1-mm i.d. columns (7,8,10,16–18). To assist the practicing chromatographer in all types of translations, several commercial and academic computer applications have been developed (19); however, although these computer applications undoubtedly aid chromatographers, they all possess certain drawbacks to successful method translation.
In contrast to previous publications, this article only briefly describes the underlying theory and equations relating to chromatographic method translation principles (readers are encouraged to see the sidebar “Theory for Translations” for more information). Instead, this article will focus on how to
8 LC•GC Asia Paciàc March 2015
Petersson et al.
(a)
(b)
1 tR 0.53 min
2 3 4 5
6 7
8
9 tR 1.95 min
3 4 7
8
9 tR 2.80 min
1 tR 0.53 min
(c)
3 4 7
8
9 tR 2.77 min
1 tR 1.37 min
Figure 1: An illustration of how a larger VD/VM for the translated method can be compensated using a delayed sample injection (Δ negative). The data also illustrates how VD/VM differences can affect the selectivity. The chromatograms have been scaled by alignment of the first and last eluted peaks. (a) binary model 1290 Agilent system, VD/VM = 1.8, injection in mainpass; (b) quaternary model 1290 Agilent system, no correction, VD/VM = 9.0, injection in mainpass; (c) quaternary model 1290 Agilent system, 0.93-min injection delay, VD/VM = 8.2, injection in bypass. Other conditions for (a)–(c): column: 50 mm × 2.1 mm, 1.8-µm dp; flow rate: 0.75 mL/min; mobile-phase A = 0.1% formic acid in water; mobile-phase B = 0.1% formic acid in acetonitrile; gradient: 5–27% B in 2 min; temperature: 40 °C. Peaks: 1 = paracetamol, 2 = 4-hydroxybenzoic acid, 3 = caffeine, 4 = 3-hydroxybenzoic acid, 5 = salicylamide, 6 = acetanilide, 7 = aspirin, 8 = salicylic acid, 9 = phenacetin.
0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00
4+5
1+2
3 6 7
8 9
10
11
5
1+2
3 6 7
8 9 10
11
4
(a)
(b)
Time (min)
Figure 2: An illustration of how a translation from one column to another may affect both retention and selectivity if the translation is incorrectly performed (that is, assuming equal porosity between materials). Conditions: (a) Column: 150 mm × 2.1 mm, 3.6-µm dp, 3.2 µm dcore, VM = 0.27 mL, 200-Å superficially porous particle Aeris widepore XB-C18; flow rate: 0.3 mL/min; gradient: 27–65% B in 30 min; mobile-phase A: 0.1% trifluoroacetic acid in water; mobile-phase B: 0.08% trifluoroacetic acid in acetonitrile; temperature: 40 °C. Conditions (b): Gradient scaled assuming the same porosity as for 300 Å porous particles, that is, VM = 0.38 mL and a gradient time of 41.8 min. Other conditions same as (a). Sample: proteins with pI ranging from 3.6 to 9.3 (Sigma-Aldrich I3018 IEF mix 3.6–9.3).
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perform successful translations of gradient chromatographic methods between HPLC and UHPLC systems, the accuracy that can be expected, and the potential pitfalls that chromatographers must be aware of and how to successfully avoid them. If the necessary precautions are taken, it is the experience of the authors that gradient translations work extremely well. For example, 11 LC methods were translated to or from UHPLC within AstraZeneca; the maximum deviations in relative retention times were only between 0.02 and 0.05, which was deemed acceptable (9,18).
ExperimentalExperimental work was performed on Agilent 1100, 1260, and 1290 LC systems of which the dwell volume and system volumes had been previously well characterized. Any experimental data including the injector programmes used can be supplied by the authors on request. For the delayed injection work on the Agilent LC systems, the injector must be in the bypass mode (that is, no flow through the injector). After the calculated delay time, the flow was returned to the mainpass position (that is, flow through the injector), then after a predefined time (time = [5 × [injection volume + 5]]/flow rate) to flush the sample onto the column, the flow was returned to the bypass position. At the top of the gradient the flow was returned to the mainpass position to wash the injector
with the mobile phase to avoid carryover and subsequently flush the injector with the starting mobile phase composition prior to the next injection. Zorbax Eclipse XDB C18 columns (Agilent Technologies) of 1.8-, 3.5-, and 5-µm particle sizes were selected for the small molecule work because they exhibit a good scalability with respect to efficiency and particle size (R2 = 0.9 for N versus 1/dp) (20).
Translations were made using a Microsoft Excel spreadsheet and the equations described in the sidebar “Theory for Translations.” A corresponding software application for these types of translations is now freely accessible at the ACD/Labs website (21). In addition, a more complete set of translation tools is included in version 2014 of the ACD/Chrom Workbook software (22).
Potential Pitfalls in Method TranslationsObserved Selectivity Anomalies as a Result of Differences
in Dwell Volume: The system dwell volume (VD) is defined as the volume from the point where the two solvents A and B first meet to the inlet of the column. For accurate translation of gradient methods it is of critical importance that the ratio between the dwell and dead volume (VM) is kept constant; that is, the difference in ratio between the system dwell volume and the column dead volume needs to be negligible (Δ = [VD/VM]
original – [VD/VM]new must approach 0). If this is not the case, then
Theory for TranslationsIn theory a volumetric translation of a chromatographic method is simply a matter of keeping the ratio between the volume of each segment in the gradient and the column dead volume constant
tG1iF1
=V
M1
tG2iF2
VM2
[1]
where subscript 1 corresponds to the method being translated, subscript 2 the translated method, tGi the time for segment i in the gradient, F is the flow rate, and VM the column dead volume (12–15). The dead volume can be measured by injecting a dead time marker such as uracil or thiourea for reversed-phase LC. This step is strongly recommended to get an accurate translation. The dead volume can also be estimated by the following approximation:
VM≈π Lε
pp
d
2
2
[2]
where d is the internal diameter of the column, L the length of the column, and εpp is the porosity of the column (typically ~0.6 for 100 Å porous particles).
In previous publications and software related to translations, it is assumed that the porosity of the columns is the same
and VM in equation 1 can be replaced with d2L. However, when translating between a porous and a superficially porous particle it is unacceptable since the dead volume is reduced by the solid core of the latter. Differences in porosity will result in inaccurately scaled gradient times. For example, when translating from a porous particle to a superficially porous wide pore material the error in gradient time will be 36% (for the Aeris phase, dp = 3.6 µm and dcore = 3.2 µm).
Based on the knowledge of the diameter of the particle (dp) and the solid core (dcore) it is possible to estimate the porosity of the column packed with superficially porous particles.
εspp
= (xinter
+x intra
)εpp [3]
xinter
=
π
61– [4]
xintra
=– d 3
core)π(d 3
p
6d 3p
[5]
where xintra and xintra are the fraction of the dead volume corresponding to inter- and intraparticle volume of mobile phase, respectively.
Since the porosity of columns packed with porous particles varies significantly, up to ±28% as described in the text, it is necessary to use experimentally
determined dead volumes to obtain accurate translations.
The flow rate is often scaled against column internal diameter to obtain the same linear velocity for columns with the same porosity.
F2=
F1d 2
2
d 21
[6]
UHPLC is usually operated at higher linear velocities than HPLC because of the more favourable van Deemter curves. Thus, going from HPLC to UHPLC requires a flow higher than the scaled flow and vice versa. To translate to a flow rate scaled for another particle size, the reduced interstitial linear velocity (43) should be kept constant. This can be achieved by a scaling based on the following equation:
F2=
F1d
p1d 2
2
dp2
d 21
[7]
Translating flow rates, however, is difficult for superficially porous particles since both the A term and the C term in the van Deemter equation are smaller than for porous particles with the same diameter. In addition, it should not be assumed that the original method is running at an optimal flow rate. To make a translation to a truly optimal flow rate it is therefore recommended to translate
10 LC•GC Asia Paciàc March 2015
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differences in chromatographic selectivity and relative retention
time shifts may be observed. One such example is shown in
Figure 1, where a series of analgesic related drugs have been
chromatographed on the same 50 mm × 2.1 mm, 1.8-µm dp
column using a binary high-pressure mixing system with low
dwell volume (that is, 202 µL) and a quaternary low-pressure
mixing system with a higher dwell volume (that is, 990 µL). Note
that in this example a translation from a system with VD/VM = 1.8
(Figure 1[a]) to a system with VD/VM = 9 (Figure 1[b]) resulted in
coelution (peaks 7 and 8) as well as a reversal of elution order
(peaks 3 and 4).
To compensate for a larger VD/VM on the translated method
(that is, Δ = [VD/VM]original – [VD/VM]new = negative), the gradient
must be started before injecting the sample — so-called
injection delay or pre-injection volume — which can be easily
implemented on certain LC systems (such as the Agilent 1290
or the Waters Acquity H-class systems). Equations 9 and 10 in
the sidebar describe this process in detail. It should be noted
that it is only necessary to determine the VD (23) once for a
certain LC configuration. Figure 1 illustrates how the injection
delay principle can be used to compensate for VD differences
and thereby maintain the original selectivity and relative retention
(Figure 1[a] versus 1[c]).
To compensate for a smaller VD/VM on the translated method
(that is, Δ = [VD/VM]original – [VD/VM]new = positive) the translated
gradient must possess an isocratic hold before commencing
the gradient.
A small VD/VM results in a gradient with sharp changes
(a Z-shaped gradient), whereas larger VD/VM will result in
smoother, more S-shaped changes. This difference in gradient
shape may also result in selectivity changes, mainly for fast
separations on short columns (for example, 2-min gradients
on 50 mm × 2.1 mm columns). To address this problem with
possible selectivity changes, Agilent Technologies developed
an approach (24) in which the company’s model 1290 UHPLC
systems can mimic the gradient shape of systems with a larger
system dwell volume.
Observed Selectivity Anomalies as a Result of Incorrect
Dead Volume Estimation: The column dead volume is
a fundamental parameter that has a significant impact on
translations and their subsequent accuracy (equations 1, 9–13
in the sidebar). To the best of our knowledge, all available
translation applications erroneously assume an equal porosity
for all stationary phases irrespective of their nature (equation
2 in the sidebar). In cases where this assumption is not valid,
significant errors in translated gradient times are possible.
For example, large differences in porosity and hence dead
volume can be expected and observed between porous and
superficially porous particles (that is, 10–29% difference was
observed for seven different types of superficially porous
to a series of flow rates using equation 1
and thereby determine the optimal flow
experimentally.
An approximate estimation of the
maximal pressure for the translated
method can be determined based on a
pressure reading of the maximal pressure
for the original method. Maximal pressure
is obtained at approximately 20% (v/v)
acetonitrile or 50% (v/v) methanol in
water. It should be noted, however, that
this estimate may be unreliable since
both packing procedures and particle
size distributions differ between columns
and vendors (20). Furthermore, the
system’s contribution to the pressure
also differs.
Δp2=
Δp1d 2
1d 2
p1 F
2L
2
d 22 d 2
p2F
1L
1
[8]
For an accurate scaling it is of critical
importance that the ratio between dwell
and dead volume is kept constant; that
is, the difference in ratio between the
system’s dwell volume and the column
dead volume needs to be negligible.
VD1
– =ΔV
M1
VD2
VM2
[9]
where VD is the system dwell volume
and Δ the difference in dwell and dead
volume ratio between the systems.
The dwell volume for an HPLC system
is typically 1–2 mL and for a UHPLC
system it is 0.1–0.5 mL depending
on configuration and model. For an
accurate translation it is recommended
to measure (23) the dwell volume (only
necessary once for a certain instrument
model and configuration).
If Δ is positive, it is necessary to
introduce an initial isocratic step of z min
to the translated method corresponding
to
z =∣Δ∣
VM2
F2
[10]
If Δ is negative, it is necessary to start
the gradient z min before the sample is
injected, a function available in certain
chromatographic data systems. A delayed
injection after the start of the gradient
can be easily implemented on certain
LC systems (such as the Waters Acquity
H-class system or the Agilent 1290
system). Another alternative that partially
solves the problem is to use a larger
column internal diameter (equation 9).
If the translated method is not
corrected for, it may display different
relative retention times and potentially
also different selectivity. The injection
volume is scaled against the dead
volume to obtain the same relative
amount on column.
Vinj2=
Vinj1V
M2
VM1
[11]
Because of the higher efficiency
of sub-2-µm and superficially porous
columns, asymmetry will be higher for
overloaded peaks due to the higher
concentration at peak apex. This can
be compensated for by a scaling
against the number of plates for a
nonoverloaded peak chromatographed
under isocratic conditions.
V ′inj2=
Vinj1
N1V
M2
N2V
M1
[12]
For porous particles the efficiency
is proportional to the ratio of column
length or particle size. Thus, for porous
particles:
V ′inj2≈
Vinj1
L1d
p2V
M2
L2d
p1V
M1
[13]
To minimize poor efficiency for early
peaks it is important to match the
column dead volume with the system’s
contribution to peak volume:
N =V
M (1+k)
σ2col+σ
2ext
√ [14]
where k is the retention factor and σ
is the contribution to the peak volume
from band broadening in column
(σcol) and system (σext) expressed as
standard deviation.
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columns [25] and 13–37% predicted difference according to equations 2–5 in the sidebar). Consequently, translated gradient times may have an error of the same magnitude; for nonporous particles the error will be even greater (that is, 52%).
Equations 2–5 in the sidebar describe how VM can be estimated for columns based on superficially porous particles using their reported particle and core diameters. However, these and other VM estimates are all associated with an error that can be quite substantial. The use of quoted particle sizes may be misleading because there are many differing ways that particle size can be determined and reported (20). The pore size of the particles also affects the porosity (100-Å columns have a porosity of ~0.60, whereas 300-Å columns have a porosity of ~0.75). VM estimations may also be inaccurate because of the fact that columns packed with different particle sizes are often packed with different pressures, which can result in lower porosities for columns designed for UHPLC. Consequently, for columns packed with porous particles, the difference between theoretical and measured dead volumes can be quite substantial. A comparison of 14 brands of modern columns from various vendors packed with porous particles exhibited a dead volume prediction error of ±28% (25). This value corresponds to an error in gradient time of up to 28%. Figure 2 exemplifies how a 29% error in VM can affect retention time as well as chromatographic selectivity.
Figure 2(a) demonstrates the experimental chromatogram derived by using a 200-Å superficially porous material with a dp and dcore of 3.6 µm and 3.2 µm, respectively, with a measured VM for the column of 0.27 mL. In comparison, when the methodology is translated using the same column (to eliminate any stationary-phase selectivity differences), but assuming that it is 300 Å and fully porous in nature, the VM is calculated as 0.38 mL and the new gradient yields a significant change in selectivity (see Figure 2[b]).
Given the above discussion, the authors strongly recommend that VM values are experimentally determined to obtain accurate translations. VM can be easily determined by injecting a reversed-phase-LC dead time marker such as uracil or thiourea.Observed Selectivity and Peak Width Anomalies as a
Result of Differences in Column Thermostat Design
Between LC Instrumentation: Differences in column thermostat design used by differing LC instrument vendors will result in different temperatures being achieved within the column despite identical “set-point and feedback” temperatures being recorded in the column compartment. The extent of this temperature deviation is dependent on instrument design, flow rate, and temperature set point. According to our experience, deviations of 5 °C or more are commonplace (25). This degree of temperature difference is sufficient to cause significant selectivity differences when transferring a method from one type of LC instrument to another. Consequently, when translating from HPLC to UHPLC or transferring a method from one type of HPLC system to another, it is very likely that observed selectivity differences are related to differences in column temperature.
Column thermostat design affects not only selectivity, but also efficiency. It has been found that a precolumn heat exchanger in combination with a still air column compartment can reduce radial temperature gradients in the column and thereby results in narrower peaks compared to a water bath or a thermostat based on a forced air flow principle (26,27).
To address this problem, we recommend that the system suitability test (SST) section of the method describes how the
resolution is affected by temperature and how corrections can be employed; this description can be achieved, for example, by illustrating chromatograms corresponding to 30 °C, 35 °C, and 40 °C for a method developed on an instrument at 35 °C or by the construction of van ’t Hoff plots (that is, log k versus 1/temperature). By following this procedure, users can see in what direction and approximately how much the temperature
12 LC•GC Asia Paciàc March 2015
Petersson et al.
1
2 3
3
(a)
(b)
(c)
(d)
3+4
3+4
4
4
5
6
78 9
10
11 tR 37.0 min
11 tR 12.9 min
11 tR 12.4 min
11 tR 13.7 min
Figure 3: A translation while maintaining constant linear velocity from an HPLC method based on (a) a 150 mm × 4.6 mm, 5-µm column to the corresponding UHPLC method based on a 50 mm × 2.1 mm × 1.8 µm column (b) without and (c) with dwell volume compensation. The chromatograms have been scaled by alignment of the first and last eluted peaks. (a) binary Agilent 1100 system, VD/VM
= 0.8 (injection in mainpass), 103 bar; (b) binary Agilent 1290 system, no correction, VD/VM = 1.9 (injection in mainpass), 231 bar; (c) binary Agilent 1290 system, 0.12 min injection delay, VD/VM = 1.0 (injection in bypass), 228 bar; (d) as in (b) but with a postcolumn restrictor, 714 bar. Conditions (a): Flow rate: 1 mL/min; gradient: 5–100% B in 40 min; mobile-phase A: 20 mM monobasic potassium phosphate (pH 2.7) in water; mobile-phase B: 20 mM monobasic potassium phosphate (pH 2.7) in 65:35 (v/v) acetonitrile–water; temperature: 40 °C. Conditions for (b), (c), and (d) are the same as (a) except for a gradient time of 13.3 min, a flow of 0.208 mL/min. Peaks: 1 = terbutaline, 2 = N-acetylprocainamide, 3 = phenol, 4 = eserine, 5 = quinoxaline, 6 = quinine, 7 = ARD12495, 8 = diphenhydramine, 9 = carvediol, 10 = amitriptyline, 11 = reserpine.
ES572912_LCA0315_012.pgs 02.20.2015 22:24 ADV blackyellowmagentacyan
needs to be changed to meet the SST acceptance criteria.
This approach is supported by the United States, European,
and Japanese pharmacopeias in that they allow a ±5 °C
adjustment of temperature to obtain the right selectivity
(23,40,44).
Observed Selectivity Anomalies as a Result of Differences
in Heat of Friction: When the mobile phase is depressurized
in the column, frictional heat is generated, resulting in an axial
temperature gradient. The heat generated is proportional to both
the pressure drop over the column, but also the flow has an
impact on heat generation and dissipation (28,29). On a 2.1-mm
i.d. column operated at a pressure close to 1000 bar, the
difference between the column outlet and inlet temperature can
be on the order of 5–10 °C for a 150-mm column and 10–18 °C
for a 50-mm column (28,29).
Selectivity differences caused by heat of friction can
be compensated for by increasing the temperature when
translating from UHPLC to HPLC and thereby compensate
for the reduced heat of friction. Alternatively, the temperature
is decreased to compensate for an increased heat of friction
when translating from HPLC to UHPLC. One approach that is
recommended is to change the temperature ±2 °C, 4 °C, 6 °C,
and 8 °C and thereby investigate if the observed change in
selectivity is related to heat of friction and can be compensated
for (18).
Observed Selectivity Anomalies as a Result of Differences
in Pressure: A change in pressure affects the molar volume of
solvated analytes as well as their degree of ionization (30–34).
This effect typically results in an increased retention as pressure
increases; however, there are exceptions to this rule (34).
Differing degrees of pressure-induced retention changes can
result in either enhanced or reduced chromatographic resolution.
It has been reported that proteins are more sensitive to
pressure-induced retention effects than lower-molecular-weight
analytes because their secondary and tertiary structures are
additionally affected by pressure and flow (35).
The majority of previous studies on pressure-induced
retention effects have been conducted with a postcolumn
restrictor to increase the pressure while maintaining the same
flow rate and do not reflect typical chromatographic conditions.
Furthermore, it has been suggested that heat of friction should
counteract pressure-related retention and selectivity changes
to some extent (28,35). Nevertheless, pressure-related
selectivity differences are also seen during typical
chromatographic conditions and for quite modest differences
in pressure (for example, 142 bar) (36). Figure 3 illustrates
a typical example of a translation from an HPLC method
based on a 150 mm × 4.6 mm, 5-µm column (Figure 3[a])
to a UHPLC method based on a 50 mm × 2.1 mm, 1.8-µm
column (Figure 3[c]) where a pressure increase of only 125 bar
resulted in a significant change in selectivity (peaks 3 and 4).
Unfortunately, it is difficult to compensate for these types of
pressure effects. While it does not provide a solution, adding
a postcolumn restrictor can enable confirmation that pressure
is the cause of the problem (Figure 3[d]). To compensate for
pressure-related selectivity changes, it is probably necessary
to reoptimize the method by adjustments of parameters such
as gradient shape and temperature.
13www.chromatographyonline.com
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ES572914_LCA0315_013.pgs 02.20.2015 22:24 ADV blackyellowmagentacyan
Poor Efficiency Because of Differences in Extracolumn
Band Broadening Between LC Instrumentation: Efficiency
is dependent on retention as well as the ratio between column
dead volume and the volume of the peak (equation 14 in the
sidebar). Since the dead volume of an HPLC column is typically
much larger than that of an UHPLC column, it is necessary to
reduce the UHPLC instrument’s contribution to peak volume
significantly (7). This extracolumn band broadening (ECBB) —
usually measured as 4σ — is typically in the order of 30–50 µL
and 10 µL for HPLC and UHPLC, respectively. Even the low
volume associated with modern UHPLC instrumentation has
been found to be insufficiently low to compensate completely
for the extracolumn band broadening contribution associated
with peaks of low retention (7,37). This effect is illustrated in
Figure 4, where an isocratic translation from HPLC to UHPLC
exhibited the expected efficiency for late eluted peaks; however,
the efficiency observed for early eluted peaks was significantly
lower. Figure 5 illustrates the efficiency versus the retention factor
for the same separation for other combinations of column and LC
system ECBB volumes. In this example as well as in reference
7, the expected plate numbers are not reached until a retention
factor of approximately 4 is reached when translating to a
50 mm × 2.1 mm column operated on an UHPLC system.
Modifying the ECBB of an LC system is usually quite difficult
(38). A more realistic alternative is to increase the column internal
diameter. Today, 3-mm i.d. UHPLC columns are commercially
available, and they can significantly reduce this problem. The
drawback to this approach is that a flow rate twice as high as
for a 2.1-mm i.d. column must be used, resulting in a reduced
saving in solvent consumption and a potential for increased heat
of friction, which could cause band broadening and selectivity
differences (29).
In general, the minimized ECBB associated with UHPLC
systems is advantageous; however, there is a possibility that
it may cause significant band broadening when performing
HPLC methods with the sample dissolved in a high proportion of
organic solvent compared to the initial mobile phase. This may
appear contradictory, but it can be explained by the narrower
capillaries on the UHPLC system reducing the mixing between
the sample and the mobile phase and thereby reducing peak
focusing of the sample on top of the column. The solution to
this problem is to reduce the sample volume or the amount of
organic solvent in the sample solution. Alternatively, the capillary
between the injection valve and the column can be replaced with
a large internal diameter capillary to increase the mixing.
Differences in Linearity, Response, or Repeatability Related
to Injector Design: Different LC systems possess differing
injection flow principles and materials in their autosampler
construction. For example, in a loop injector the sample is
typically exposed to larger surface areas and other materials
than in a flow through needle injector design. This larger
surface area may result in a more pronounced adsorption and,
consequently, also a more pronounced nonlinear response at
low concentrations for loop injectors.
Other potential problems can be related to differences in the
internal diameter of the injector needle and capillaries. UHPLC
systems have significantly narrower injector needle and capillary
diameters, which can result in poor injection repeatability
because of bubble formation if the draw speed is set too high in
relation to the viscosity of the sample. Another related problem
is that differences in viscosity between samples and standards
because of matrix differences may result in differing amounts
being injected, thereby the sample concentration can be under
or over-estimated.
Consequently, it is important to validate linearity, response,
and repeatability of the LC method when transferring a method
from one type of LC system to another.
Differences in Peak Asymmetry Related to Differences in
Efficiency: The transfer of HPLC methodology to UHPLC when
overloaded peaks are involved results in more-pronounced
14 LC•GC Asia Paciàc March 2015
Petersson et al.
Nu
mb
er
of
theo
reti
cal p
late
s (N
)
Retention factor (k)
20000
18000
16000
14000
12000
10000
8000
6000
4000
2000
00 2 4 6
100 × 2.1 mm, 1.8-μm, ECBB 9 μL
100 × 2.1 mm, 1.8-μm, ECBB 11 μL
100 × 2.1 mm, 1.8-μm, ECBB 23 μL
150 × 4.6 mm, 3.5-μm, ECBB 9 μL
150 × 4.6 mm, 3.5-μm, ECBB 11 μL
150 × 4.6 mm, 3.5-μm, ECBB 23 μL
250 × 4.6 mm, 5-μm, ECBB 23 μL
Figure 5: Number of theoretical plates versus retention factor
for the separations shown in Figure 4 and for five additional
combinations of columns and LC systems with different ECBB
contributions.
(a)
1
k = 0.1
N = 8727
3
k = 1.3
N = 13,294
4
k = 3.4
N = 14,971
5
k = 5.6
N = 17,707
tR = 17.3 min
k = 0.1
N = 3602
k = 1.4
N = 8571
k = 3.9
N = 17,108 k = 6.4
N = 18,699
tR = 7.7 min
(b)
2
Figure 4: An isocratic translation from (a) a 250 mm × 4.6 mm,
5-µm column and a binary Agilent 1100 HPLC system to (b) a
100 mm × 2.1 mm, 1.8-µm column and a quaternary Agilent
1290 UHPLC system, showing how ECBB becomes critical for
peaks with low retention when using columns with small dead
volumes. The chromatograms have been scaled by alignment
of the first and last eluted peaks. Flow rate (a): 1.0 mL/min;
flow rate (b): 0.21 mL/min; mobile phase: 2:400:600 (v/v/v)
phosphoric acid 85% w/v–acetonitrile–water; temperature:
22 °C. Peaks: 1 = 4-hydroxybenzoic acid, 2 = acetylsalicyclic
acid, 3 = salicylic acid, 4 = acetylsalicylsalicylic acid,
5 = salsalate.
ES572910_LCA0315_014.pgs 02.20.2015 22:24 ADV blackyellowmagentacyan
peak asymmetries being observed even
if injection volumes have been correctly
scaled against column volume. The
explanation for this phenomenon is that
the higher efficiency associated with
the UHPLC method results in higher
concentrations of the analyte at the apex
of the peak and thus a higher degree of
overloading is observed (39). Despite
this overloading, the resolution of peaks
adjacent to the overloaded peak is
typically higher in the UHPLC method
than in the HPLC method. It is possible
to compensate for this overload effect
by simply scaling the injection volume
against column dead volumes as well as
the isocratic efficiencies (equations 12
and 13 in the sidebar).
Other Issues: In the early days
of UHPLC, selectivity differences
were commonly observed between
columns of nominally the same
material but differing particle size.
These differences were impossible to
explain by differences in the heat of
friction or pressure differences (20).
It was assumed that the base silica
of the smaller particle size material
was subtly different compared to its
larger particle size counterparts. Today
this difference is less of a problem.
However, if a selectivity difference is
observed that cannot be compensated
for by either increasing or decreasing
the temperature by a few degrees to
mimic heat of friction or by adding
a postcolumn restrictor to mimic a
pressure-induced retention change, it is
more than likely that it is related to subtle
differences in the heterogeneity of the
base silica used.
Regulatory AspectsThe degree of revalidation that is required
for a translated method depends on the
intended purpose for that method and
where in the R&D process it is to be
used.
For pharmacopeia methods, LC
translations can, in principle, be made
without any formal validation exercise
being performed provided that the
operating ranges described in the
appropriate monographs (23,40,44) are
not exceeded. Unfortunately, the current
operating ranges limit the ability to
translate from HPLC to UHPLC. However,
the United States Pharmacopeia (40)
recently opened up for translation of
isocratic methods from 5-µm porous
particles to sub-2-µm porous particles.
Hopefully other pharmacopoeias will
also follow this example and, in addition,
allow translations of gradient methods
as long as the specified selectivity
and efficiency is maintained (that is,
by maintaining or increasing the ratio
between column length and particle
diameter [41]).
For original pharmaceutical products
it is necessary to validate all versions of
the method that are used for analysis
of samples to be used in toxicological
or clinical studies. Do both the HPLC
and the UHPLC methods require
a full International Conference on
Harmonisation (ICH) validation (42)?
From a scientific point of view, it should
be sufficient to submit a full ICH
validation for either the HPLC or the
UHPLC version of the method. For the
other version, it should be sufficient to
submit a minimalistic validation report
claiming that the translation has been
made according to first principles
(that is, fundamental chromatographic
theory). In principle, it should be enough
to validate selectivity and linearity for
15www.chromatographyonline.com
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“Chromatography” First Supplement to USP 37-NF 32 (United States Pharmacopeial Convention, Rockville, MD, USA).
(41) U.D. Neue, D. McCabe, V. Ramesh, H. Pappa, and J. DeMuth, Pharmacopeial Forum 35, 1622–1626 (2009).
(42) International Conference on Harmonisation, ICH Q2(R1), Validation
of Analytical Procedures: Text and Methodology (ICH, Geneva, Switzerland, 2005).
(43) U.D. Neue, HPLC columns: Theory, technology and practice (Wiley-ICH, New York, USA, 1997), pp. 14.
(44) Japanese Pharmacopoeia 16th Edition, section 2.01 “Liquid Chromatography” (JP XVI 2.01, Pharmaceuticals and Medical Devices Agency, Shin-Kasumigaseki Building, 3-3-2 Kasumigaseki, Chiyoda-ku, Tokyo 100-0013 Japan, 2011).
Patrik Petersson is a principal scientist with Novo Nordisk A/S
in Måløv, Denmark. Please direct correspondence to ppso@
novonordisk.com
Melvin R. Euerby is a professor at the Strathclyde Institute
of Pharmacy and Biomedical Sciences in the University of
Strathclyde in Glasgow, UK and the head of research and
development and training at Hichrom Ltd. in Berkshire, UK.
Matthew A. James is a research analyst with Hichrom Ltd.
the translated version of the method. It is advisable, at least
during this transition period, to perform a full ICH validation
and be prepared to submit the “missing” parts of the validation
if requested by the authorities. It may also be prudent to
include both the HPLC and UHPLC versions of the method
in the experimental design used for validation of intermediate
precision.
Postsubmission changes to methods are possible according
to both the European Medicines Agency (EMA) and the Food
and Drug Administration (FDA) regulations. However, it is
uncertain what is required in the rest of the world. In addition, the
cost associated with a postapproval change is often considered
prohibitive.
ConclusionsThis article demonstrates how the practical advantages of
translating existing HPLC methodologies to those using newer
column formats and UHPLC instrumentation can be realized.
Advantages include increased resolution, enhanced speed of
analysis, reduced solvent consumption, more efficient utilization
of LC equipment, and ease of method transfer between HPLC
and UHPLC. The authors describe a number of potential pitfalls
that must be taken into consideration and avoided if accurate
and reliable translations are to be achieved. These pitfalls
include differences in LC instrumentation dwell volumes, which
can lead to coelution of peaks and even reversal of elution
order. The difference between the original and new VD/VM ratios
must be accounted for: In the case of the new methodology
having a larger VD/VM, a delayed injection must be used, or
if it has a smaller VD/VM, an isocratic hold must be inserted
before commencing the gradient. Errors in translated gradient
times of up ~30% can be encountered unless experimentally
determined VM values are used. This may affect both retention
and selectivity. Hence, the authors strongly recommend that
chromatographers practically determine their VM values.
The effect of instrument differences between HPLC and
UHPLC models have been highlighted as possible sources of
translation error; these include differences in column thermostat
design, the effect of extracolumn band broadening, and the
influence of differing injector design. In addition, the effect of
only moderately elevated pressures on selectivity has been
demonstrated, coupled with the effect of increased heat of
friction associated with smaller particles and the influence of
increased efficiency on peak asymmetry has been described.
A free translation tool is available to assist the successful
translation between HPLC and UHPLC methods (21). The tool
is based on the principles described in this article and permits
scaling of gradient times, flow rates, and injection volume as
well as accounting for differences in VD/VM ratios between LC
systems. A more comprehensive translation tool will also be
available (22).
References(1) J.W. Thompson, J.S. Mellors, J.W. Eschelbach, and J.W. Jorgenson,
LCGC North Am. 24(s4), 16–21 (2006). (2) J.J. Kirkland, F.A. Truszkowski, C.H. Dilks Jr., and G.S. Engel, J.
Chromatogr A 890, 3–13 (2000).(3) J.J. DeStefano, T.J. Langlois, and J.J. Kirkland, J. Chromatogr. Sci. 46,
254–260 (2008).(4) M. Swartz, LabPlus International 18, 6–9 (2004).(5) J.R. Mazzeo, U.D. Neue, M. Kele, and R.S. Plumb, Anal. Chem. 77,
460A–467A (2005).(6) Strategic Directions International (SDi) Tactical Sales and Marketing
Report (HPLC: End Users Influencing Development for Advanced HPLC Systems, Columns and Chemistry). (2013).
16 LC•GC Asia Paciàc March 2015
Petersson et al.
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17www.chromatographyonline.com
LC TROUBLESHOOTING
One of the most frequent times that
we discover a problem with a liquid
chromatography (LC) method is when
we examine a data set following the
analysis of a batch of samples. This
month’s “LC Troubleshooting” looks
at some data submitted by a reader
who suspected that something wasn’t
right with their results. These data
give us a good example of how we
can examine data for abnormalities
and formulate some experiments to
try to identify the problem source so
we can correct the problem. I have
somewhat obfuscated the details so
that the reader and company remain
anonymous. The sample comprises a
pharmaceutical formulation that was
being assayed for potency following
a particular stress test. A single
batch of product was divided into 12
samples, which were then treated
in the same manner. For analysis,
two subsamples were weighed from
each sample, diluted, and injected,
for a total of 24 sample injections.
The potency was determined by
comparing the area response of
each injection to the response of
a reference standard. The method
stipulates that if the two subsamples
disagree by more than 1.0% in assay
value, the source of disagreement
must be investigated. The reader
reported that normally these
“duplicate” samples agree within
0.5%.
The data I received are listed in
the first two columns of Table 1.
Each sample is numbered, and
the associated letter identifies the
subsample (for example, 1a and
1b are subsamples of sample 1). I
have noted the absolute difference
between the two subsamples in the
third column. The abnormality that
triggered the reader’s inquiry was the
1.41% difference between samples
4a and 4b. This difference exceeded
the limit allowed by the method and
required that the chemist perform an
investigation to identify the source
of the problem so that it could be
corrected.
Initial Examination
When I try to solve a problem like
this, I like to examine the data in
several ways. Often I find that a
table of data, such as that of Table 1,
makes my eyes glaze over. I do much
better with a graphic representation.
To get an idea of how atypical the
1.41% difference is, I constructed
the frequency plot shown in Figure 1.
Here I simplified the data set by
“binning” the absolute differences into
groups with 0.25% increments, so you
can see, for example, that there were
five samples in which the difference
between injections was 0–0.25%.
All the data points except the 1.41%
value were <0.75% difference.
The big gap between the 11 good
sample pairs and the one bad one
makes the problem pair seem like an
obvious outlier. But is there any more
quantitative measure of this? One
simple technique to test for outliers
is the Dixon’s Q -test. A test value is
calculated as:
|suspect – nearest|/(largest – smallest)
[1]
For this example, |1.41 – 0.73|/(1.41 –
0.07) = 0.51. The critical value of Q
for n ≥ 10 is 0.464 (1), meaning that
any test value larger than the critical
value is an outlier. Now we have
some statistical support in stating
that the difference in assay values
for sample 4 is an outlier. As I look
at the results of the Q -test, however,
it looks to me like the 1.41 value isn’t
very much of an outlier. I checked this
by looking at different suspect values
using the data set of Table 1, and it
is easy to show that the critical value
is exceeded only when the suspect
value is >1.3%. This says to me that
if the current data set is typical for
this method, the requirement for
differences of <1.0% may be a bit
too tight. That is, a value >1.0% will
trigger an investigation, but unless
it is >1.3%, it is not likely that it can
be proven an outlier with the Dixon’s
Q -test. Such limits should be set as
part of the method validation, where
large data sets are available and the
normal variation of the method can be
determined more easily than with the
limited data available here.
Digging a Bit Deeper
Sometimes it is useful to examine
the data for any trends that might
be obvious. An easy way to make a
first pass at this is to simply plot the
assay values over time. In Figure 2, I
have plotted the assay values in order
for the 24 injections. There doesn’t
seem to be any trend to larger or
smaller values over the course of the
analysis. The variability for the first
12 injections seems to be larger than
for the last 12, but these were run
on two separate days, so it may be
a day-to-day difference as much as
anything. The overall variability in the
data is shown at the bottom of Table 1
with the percent relative standard
deviation (%RSD) of only 0.5%.
Considering that many autosamplers
have %RSD in the 0.3–0.5% range
using reference standards under
carefully controlled conditions, it
looks to me like this method (including
the autosampler) is operating with
acceptable precision.
Listen to the DataJohn W. Dolan, LC Resources, Lafayette, California, USA.
A stepwise process helps isolate and identify the cause of a method failure.
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LC•GC Asia Paciàc March 201518
LC TROUBLESHOOTING
Although the %RSD is good when
comparing samples, I wondered
how good the precision was for
the same sample with multiple
injections. I asked the reader if
such data were available, and I was
supplied with the data in Table 2.
In Table 2, I have shown the results
for the original data from Table 1
(4a and 4b), the reinjection data of
the same vials (4a-ri and 4b-ri), and
the transfer data of the contents to
a new vial before reinjection (4a-nv
and 4b-nv). I am considering all 4a
samples to be equivalent and 4b
samples to be equivalent. You can
see from the data at the bottom of
the table that the variability (≤0.5%)
is approximately the same as it is
for the between-sample variability
for the data of Table 1 (0.5%). This
reinforces the conclusion I drew
in the previous paragraph that the
injection process is working properly.
What Is at Fault?
At this point, we’ve observed that
sample 4 exceeded the maximum
allowable difference between
subsamples and confirmed that the
difference between subsample 4a
and 4b is indeed an outlier using the
Dixon’s Q-test. We have also shown
that the results for both samples
4a and 4b have the same level of
precision as the remaining samples,
so it appears that the problem is not
related to the injector. Let’s see if we
can further narrow the source of the
problem to the primary sample 4 or
one of the subsamples 4a or 4b. We
have enough data now that we can
compare the assay values and see if
they are consistent.
First, let’s compare sample 4a
and 4b. With the three “equivalent”
injections for each sample from
Table 2, we can see if there is a
statistical difference between the
mean assay value of 4a and 4b.
We do this with the Student’s t-test
that is available as part of the
data analysis add-in for Microsoft
Excel. We select the two-tailed test
because we want to know if there is a
difference in the mean values. From
the six data points in Table 2, we can
calculate a test value of t = 3.19; for
a probability α = 0.05, the critical
value is t = 2.78, so the Student’s
t-test shows that there is indeed a
significant difference between the
mean assay values of sample 4a
and 4b. The Excel report (not shown)
refines this a bit and indicates that
there is only a 3.3% chance that
there is not a significant difference in
means.
Now we know that samples 4a and
4b are not equivalent. Can we extend
the process further and decide if
one or both of them are likely to have
an error in assay value? We can do
the same Student’s t-test for sample
4a and sample 4b compared to
the remaining samples. One might
argue that this is stretching the test
a bit, because the larger data set
compares variation between samples,
whereas 4a and 4b test within-sample
variation, but let’s ignore that for the
moment and see what we get. First,
we’ll take the data from Table 1 and
remove the injections for 4a and 4b,
leaving 22 data points. Then we’ll take
the three data points for 4a and run
the t-test comparison, then repeat it
for 4b.
When we compare the larger data
set to sample 4a, we get a test value
0
1
2
3
4
5
6
0 0.25 0.5 0.75 1 1.25 1.5
Subsample difference
Fre
qu
en
cy
Figure 1: Frequency distribution of subsample differences from Table 1,
binned into 0.25-unit increments.
Table 1: Percent assay values
for individual injections of a
pharmaceutical product.
Sample* % Assay Difference†
1a 86.180.49
1b 85.69
2a 86.450.45
2b 86.90
3a 86.100.55
3b 86.65
4a 86.111.41
4b 87.52
5a 85.810.14
5b 85.67
6a 86.640.73
6b 85.91
7a 86.180.14
7b 86.32
8a 86.680.24
8b 86.44
9a 86.580.07
9b 86.51
10a 86.830.59
10b 86.24
11a 86.580.45
11b 86.13
12a 86.160.19
12b 85.97
Average 86.34
SD 0.42
%RSD 0.5%
*Samples 1–12 are divided into
subsamples a and b, which should be
equivalent. †Difference in assay value
between subsamples a and b of each
sample.
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LC•GC Asia Paciàc March 201520
LC TROUBLESHOOTING
What’s Next?
Now that we’ve identified sample 4b
as being an outlier, what else can we
do to track down the source of the
problem? First, we should eliminate
the simple and obvious possibilities.
The ones that come to my mind are
transcription errors and integration
errors. If the analytical balance has
a printer attached, check the printer
tape to make sure that the weight for
sample 4b was transcribed correctly
into the calculation of the assay value.
For example, the reported weight (not
shown) was 100.29 mg; if the decimal
values were reversed in transcription
from a true value of 100.92, the
correct weight (100.92 mg) would
change the 87.52 assay value to
86.92, and the difference between
4a and 4b would drop from 1.41% to
0.86%, and would pass the maximum
difference test (1.0%) and bring
sample 4b within 1.5 SD of the mean.
Another possible error is in integration
of the peak; double-check that the
baseline was drawn properly.
At this point, you may feel that
the investigation is complete. We’ve
identified sample 4b as an outlier, and
its companion 4a gives a reasonable
value. Depending on your laboratory’s
standard operating procedures
(SOPs), you may be able to drop 4b
from the data set and use the data
from 4a for reporting purposes. Write
up your investigation report and you
are done. Of course, you should keep
your eyes open for similar failures in
the future to determine if there is a
of t = 0.76, whereas the critical value
is t = 2.07. This tells us that there is
no statistical difference between the
mean assay value for sample 4a and
that of the remaining samples. With
sample 4b, the test value of t = 3.20,
which exceeds the critical value,
means we can conclude that there is
a significant difference between the
mean assay value for sample 4b and
the remaining samples. We may get
a better concept of this if we view
the data in Figure 3. In Figure 3, I
have binned all the assay values
from Table 1 into 0.25% increments.
As expected, the general form is
that of a Gaussian distribution,
which would be the case for a large
number of points containing random
error. The mean (bottom of Table 1)
is 86.34, which falls in the 86.5
bin. The value for 4a (86.11) falls in
the 86.25 bin, which confirms what
we found above: sample 4a is not
significantly different than the mean
of the remaining 22 values. The
value for 4b (87.52), however, falls in
the 87.5 bin at the extreme right of
Figure 3. With a standard deviation
(SD) of 0.42 for the data of Table 1
this means that 4b is 2.8 SD from the
mean; for a Gaussian distribution,
99.4% of the values will fall within
±2.8 SD of the mean. Contrast this to
the smallest value of Table 1 (85.67),
which is 1.6 SD below the mean;
89% of values should fall within ±1.6
SD of the mean, so it is much less
likely that 85.67 is an outlier than is
87.52.
high enough frequency of failure to
merit further investigation.
If you want to investigate further,
you need to consider all the possible
sources of variation and determine
if they are potentially important
and if they can be reduced in
magnitude. If the sources are
independent of each other, the
overall coefficient of variation
(CV = %RSD/100) can be determined
as the square root of the sum of
squares of each variable:
CV = (CV12 + CV2
2 + . . . + CVn2)0.5
[2]
where the subscripts represent
each independent operation. The
operations that come to mind in this
instance are sampling, weighing,
dilution, mixing, filtration, injection,
and integration; there are probably
others I’ve overlooked. Each of these
will contribute uncertainty to the overall
measurement. Sampling errors reflect
how representative the subsamples
are. For example, if the sample were
granular sugar or a cup of coffee, the
primary sample is very homogeneous,
so taking a random sample should be
fairly representative of the whole. On
the other hand, if the sample were a
bag of M&M candies, the distribution
of the different colours in a small
subsample would likely have much
more variation. Thus, the homogeneity
of the sample and the ability to take a
representative sample would influence
the sampling step. The variation in
weighing could be tested by weighing
a fixed standard weight multiple times.
The reader did not specify how dilution
Table 2: Data for multiple injections of
samples 4a and 4b.
Sample* % Assay Sample* % Assay
4a 86.11 4b 87.52
4a-ri 86.33 4b-ri 86.67
4a-nv 85.98 4b-nv 86.87
Average 86.14 Average 87.02
SD 0.18 SD 0.44
%RSD 0.2% %RSD 0.5%
*a and b are original values from Table
1; ri is a reinjection of the original sample
from the same vial; nv is a reinjection
of the original sample after it was
transferred to a new vial.85.0
85.5
86.0
86.5
87.0
87.5
88.0
Injection order
Ass
ay v
alu
e
1 5 10 15 20
Figure 2: Plot of assay values from Table 1 in injection order.
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21www.chromatographyonline.com
LC TROUBLESHOOTING
was done; however, more uncertainty
would be expected if a graduated
cylinder were used to measure the
liquid as compared to preparing the
sample in a volumetric flask. Is the
mixing sufficient for the concentrated
sample and the diluent? Should
mixing time be extended, agitation
or sonication increased, or other
variations in the mixing process be
changed to improve the homogeneity
of the diluted sample? Does filtering
the sample affect the final result? This
possible effect could be checked by
comparing centrifugation to filtering
and seeing if the results were any
different. Check the precision of the
injector by making replicate injections
of a well-behaved analyte. If errors are
constant, such as an error of ±0.1 mg
on the analytical balance, a fixed
volumetric error with a volumetric flask,
or ±0.2 µL for sample injection, they
can usually be reduced by increasing
the sample mass, dilution volume,
or injection volume, respectively, to
reduce the percent contribution of the
fixed error to the total.
Before embarking on a detailed
investigation of the method CV, you
should step back and consider if it
is likely that you will really improve
the results of the analysis. A basic
principle of statistics tells us that for
independent errors, as in equation
2, the largest error will have the most
influence on the overall error, that the
overall error will never be smaller than
the error of the largest contribution,
and that the overall error will usually
fall between the value of the largest
error and twice that value. We
determined in Table 1 that the overall
method %RSD was 0.5% based on
single injections of multiple samples.
An autosampler that is operating well
should have errors in the range of
0.3–0.5%, so it is unlikely that the
overall error can be reduced much
below the observed value of 0.5%.
In other words, after a brief mental
evaluation of the problem, I don’t think
I’d waste my time trying experiments
to reduce the overall error. Instead,
I’d stay alert to see if I could correlate
future failures to some pattern in the
analysis.
Conclusions
Let’s review what we’ve been able to
observe about the present problem:
• An error was found when the
difference in assay values between
equivalent subsamples exceeded
the 1.0% threshold.
• By evaluating the difference
between subsamples 4a and 4b
both visually (Figure 1) and with the
Dixon’s Q-test, we showed that the
difference was indeed an outlier
from the remaining samples.
•We also concluded from the Q-test
that the 1.0% threshold may have
been a bit too tight because, based
on the current data set, differences
of 1.0–1.3% would fail the test
criteria, but would not be proven
outliers by the Q-test.
• Based on multiple injections of
samples 4a and 4b, we used the
Student’s t-test to show that there
was statistical difference between
the mean assay values of the two
samples, so they are not equivalent.
•We also used the t-test to find that
the assay value for sample 4a
fit within the normal range of the
remaining samples, whereas sample
4b did not. This test correlated the
cause of the problem with sample
4b, not 4a.
•We confirmed the association of the
problem with sample 4b by plotting
a frequency distribution of the assay
values in Figure 3. Sample 4b was
clearly at the extreme edge of the
plot, whereas the value for 4a was
near the middle.
• Before concluding the investigation,
it was suggested that we check
for obvious errors in numeric
transcription and peak integration.
•We mentally evaluated possible
sources of uncertainty with the
method and concluded that it was
unlikely that a thorough investigation
of these sources would yield
information that would reduce overall
uncertainty of the method.
Although the present discussion
centred on a specific data set, it
illustrates how we can use simple
graphic and statistical tools to
investigate the failure. We were able
to assign the error to a single sample
(4b) and demonstrate that its paired
subsample (4a) behaved in the same
manner as the remaining samples, so
it may be possible to use its results to
obtain reliable assay data.
References(1) J.C. Miller and J.N. Miller, Statistics for
Analytical Chemistry (Ellis Horwood
Limited, 1984).
“LC Troubleshooting” Editor John Dolan
has been writing “LC Troubleshooting”
for LCGC for more than 30 years.
One of the industry’s most respected
professionals, John is currently the Vice
President of and a principal instructor for
LC Resources in Lafayette, California,
USA. He is also a member of LCGC
Asia Pacific’s editorial advisory board.
To contact John directly please e-mail:
contact the editor-in-chief, Alasdair
Matheson, please e-mail: amatheson@
advanstar.com
0
1
2
3
4
5
6
7
8
85.5 85.75 86 86.25 86.5 86.75 87 87.25 87.5
Assay value
Fre
qu
en
cy
Figure 3: Frequency distribution of assay values for all samples from Table 1,
binned into 0.25% increments.
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LC•GC Asia Pacià c March 201522
GC CONNECTIONS
Gas chromatography (GC) ovens
were not developed for cooking
purposes, but rather to provide a
suitable thermal environment for the
elution of chromatographic peaks
from GC columns. That being said, I
have seen a number of inappropriate
applications for GC ovens including
reheating of cold pizza for lunch
and the warming of casseroles for
company potluck dinners. Some of
us have even been known to trick out
the oven door switch and use an idle
GC system as a room heater on cold
days.
GC ovens have gone through an
evolutionary development just like
other GC components have. Today,
the majority of GC ovens use rapidly
circulating air inside an insulated
enclosure to transfer heat to and from
the column, much like the convection
ovens found in many high-end
kitchen ranges. Some readers may
be familiar with other means that
chromatographers use to control the
thermal environment surrounding
a column; some of these are quite
ingenious. First, though, let’s take
a look at what kind of thermal
conditions a column needs to deliver
excellent separation performance.
GC Oven Requirements
In gas chromatography, the
relationship between peak
retention and column temperature
is fundamental. Changes in
temperature cause significant
exponential retention shifts, so GC
instruments control solute retention
times by carefully managing the
column temperature along with the
column pressure drop. In general,
retention times decrease by a factor
of two for every 15–20 °C increase
in column temperature (Tc). Peak
retention depends differently on
column temperature with isothermal
versus temperature-programmed
elution, but in both cases tight
control of the temperature is
paramount. This same temperature
dependency also causes minor
temperature shifts to randomize peak
retention times by a small amount
from one run to the next. Slightly
higher overall oven temperatures
cause peaks to be eluted a little
earlier, and lower temperatures
shift retentions to longer times.
In addition to changes in the
average oven temperature from
one run to the next, other important
temperature variabilities include
spatial fluctuations along the column
because of temperature gradients in
the GC oven and dynamic short-term
oven temperature fluctuations
because of electronic and software
temperature-control processes.
If large enough, such spatial and
short-term changes will affect peak
shapes and resolution.
Run-to-Run Average Temperature DeviationsIn general, run-to-run retention
time deviations should stay within
a window of about ±10% of the
peak width at base. That way there
is little ambiguity concerning peak
identification by absolute retention
time, including for partially separated
peaks. The narrowest practical peak
widths will put the most demands on
the oven temperature repeatability.
Relatively short, 10-m long, 0.20-mm
i.d., thin-film capillary columns
elute significantly retained peaks
— those with retention factors of
two or greater — with base widths
of about 1.5 s minimum. This figure
assumes that the column operates
at an optimum average linear
helium carrier gas velocity of about
30 cm/s with the best theoretical
efficiency. Chromatographers can
obtain narrower peaks by operating
columns at higher than optimum
carrier-gas velocities, or by choosing
narrower or shorter columns — in
other words, by going to “fast” GC.
But in general this is about as fast as
peaks go for the vast majority of GC
systems in operation today.
The oven is not the only factor
that contributes to run-to-run
retention time variations. The
pneumatic system also influences
retention times significantly, and the
data handling system introduces
other uncertainties including its
determination of the exact run start
time and the peak apex or centroid.
I assembled a statistical analysis
of these factors and the GC oven
using typical values for modern
GC instrumentation and columns.
Gas Chromatography OvensJohn V. Hinshaw, Severon Corporation, Oregon, USA.
The transit of peaks through a gas chromatography (GC) column depends strongly on the thermal proà le they encounter on the way. Gas chromatographers primarily rely on the classic hot-air-bath type of oven, but several alternatives are also in use. This instalment examines ovens for GC in several forms plus how oven thermals affect peak retention behaviour.
GC ovens have gone through an evolutionary development just like other GC components have. Today, the majority of ovens use rapidly circulating air inside an insulated enclosure to transfer heat to and from the column.
ES572941_LCA0315_022.pgs 02.20.2015 22:25 ADV blackyellowmagentacyan
23www.chromatographyonline.com
GC CONNECTIONS
Interested readers can refer to
a website for this supplemental
information (1). In this analysis,
the room temperature and GC line
voltage supply are assumed to
be stable and to not influence the
actual oven temperature and column
pressure drop. This is not always the
case, of course, and such problems
fall into the category of instrument
“environmental” difficulties that
should be resolved before attempting
to obtain the best retention time
reproducibilities.
Isothermal Elution: According
to this analysis, the average oven
temperature needs to repeat from
run to run within less than ±0.05 °C
to hold retention times inside a
window of ±10% of peak base
widths, for commonly encountered
solutes. Lee, Yang, and Bartle
cite a similar requirement in their
1984 book (2). These figures are
for typical test mixture solutes
separated at moderate isothermal
temperatures on short narrow-bore
thin-film capillary columns, which
represent the more difficult cases.
Columns with thicker stationary
films, columns that are longer,
wide-bore columns, and, of course,
packed columns generate broader
peaks and may tolerate a less
stringent temperature environment
while still delivering retention time
repeatability that is adequate for
peak identification purposes. On
the other hand, fast GC separations
performed on microbore columns
with inner diameters under 0.2 mm,
on short columns under 10 m, or
with elevated carrier-gas velocities
over 100 cm/s, may require better
temperature control to contain
retention times within a ±10% of
base-width window.
Temperature Programming: With
isothermal elution, peak base
widths increase approximately in
proportion to the retention time,
but with single-ramp programmed
temperature elution the base widths
stay approximately constant across
a GC run, for peaks that are eluted
during the temperature ramp. Most
peaks are essentially immobilized
upon injection at the initial column
temperature; as the oven temperature
increases, solutes begin to move
along the column. Each spends
about the same time moving through
the column (3), which results in the
peaks having approximately the
same widths as they are eluted. For
temperature programming, oven
temperature ramps need to repeat
the time and temperature curve with
the same precision as required for
isothermal elution. The oven heating
system must have sufficient power
to maintain a linear programme
ramp from the start temperature to
the final temperature. The maximum
rate at which the oven temperature
can increase linearly is related
to the thermal mass of the oven
cavity, including the column, the
power that the heater can dissipate
into the oven, the efficiency of the
oven boundary insulation, and the
differential temperature between the
inside of the oven and the external
environment.
One principal difference between
isothermal and programmed elution
is the slight time lag between the
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ES572942_LCA0315_023.pgs 02.20.2015 22:25 ADV blackyellowmagentacyan
LC•GC Asia Paciàc March 201524
GC CONNECTIONS
the average column temperature
along the length of the column. Minor
temperature fluctuations of less than
1 °C along the column do not affect
retention times seriously, as long
as these gradients are stable and
repeatable. Unstable gradients that
change from run to run, however,
will impose additional uncertainty on
retention times because they affect
the average temperature that solutes
experience.
Some temperature gradients
are normal and necessary. The
column inlet and outlet connect to
the injector and detector, which are
normally at elevated temperatures
relative to the oven. Where GC
column ends are positioned inside
the inlet and detector, a negative
temperature gradient exists from the
inlet into the column and a positive
gradient out into the detector. The
net effect on retention is negligible,
since solutes will be weakly retained,
if at all, in the short sections of
heated column ends, which act more
like connecting tubing. Gradients
also exist close to the oven walls
because of heat losses or gains
from cooler oven walls or warmer
heated inlets and detectors. In a
conventional air-bath oven, the
column is normally positioned in
the centre of the oven to immerse it
in the most thermally uniform area.
These stable temperature gradients
do not seriously affect performance if
they are stable both during a run and
in the long term from run to run.
Gradients that fluctuate from run to
run will affect retention time stability,
but gradients that fluctuate rapidly
during a run will affect peak shapes.
A poorly sealed air-bath oven can
generate this type of gradient. A
controlled amount of air normally
circulates through an air-bath oven,
to prevent the buildup of explosive
hydrogen gas. This is done in such
a way that significant temperature
gradients are not generated. But if
the oven door or vent isn’t sealed
properly, significant amounts of
cold air can enter and blow directly
on the column and create a rapidly
fluctuating localized temperature
gradient. For fused-silica columns
in particular, with their thin walls
and rapid thermal time constants,
fluctuating gradients will alternately
trap and release portions of solute
actual column temperature and
the setpoint temperature of the
temperature ramp. As the oven
temperature controller increases
the setpoint temperature according
to the demands of the programme
ramp, the actual oven temperature
lags behind by a small amount and
the temperature inside the column
lags a little more. The temperature
lag is larger at higher programme
rates. This temperature differential
is a necessary by-product of both
the nature of temperature-control
algorithms and the physics of heat
transfer from the heater to the
column. For our purposes, it is the
temperature in the column itself that
counts.
The electronics and software
must repeat not only the same time
and temperature profile across
the run, but also the thermal
conditions in the oven must be
consistent. This is one reason why
temperature-programmed ovens
require a temperature equilibration
period after cooling down before
a new run can commence. Shortly
after the oven cools down, the oven
temperature probe may indicate the
initial oven temperature, but there
is still a significant amount of heat
remaining in the oven; the walls and
the column can be significantly hotter
than the indicated temperature. This
is the reverse of the temperature lag
encountered with oven heating, and
good practice dictates that sufficient
time be allowed to dissipate the
residual oven heat and bring the
column as close as possible to
the initial oven temperature. An
equilibration time of 2–4 min is
typical for most air-bath GC ovens
with temperature programming
capability. With isothermal elution, no
equilibration time is required after the
oven reaches its temperature for the
first time, because the temperature is
not changed during the run.
Oven Gradients: A high degree
of run-to-run average temperature
repeatability ensures the best
retention time precision. GC ovens
also must maintain a uniform and
stable temperature profile across the
column during elution. The process
of solute partitioning and migration
along the column averages out
temperature gradients so that peaks
are eluted as if they experienced
bands as they pass by, which will
distort the peak shapes and may
even split peaks up into apparent
multiples.
GC operators play an important
role in ensuring the minimum
possible gradients, by installing
columns correctly. As much as
possible, columns should be centred
within the column oven, with no
length of the column closer than
about 1 in. (2.5 cm) from the oven
walls, except of course for the inlet
and detector connections. Column
mounting brackets provided by the
manufacturer are the best way to
provide secure and centred column
mounting.
Oven Heating Schemes
Over the years, GC researchers
have envisioned and built a wide
variety of column heating schemes.
Early workers were well aware of
the requirements imposed by the
sensitivity of GC retention times
to temperature changes and by
the problem of transient thermal
gradients. They had it easier than
present-day chromatographers,
however, because they had to
accommodate packed columns and
metal or thick-walled glass capillary
tubing, but not fused-silica tubing.
Thin-walled fused-silica tubing
responds more rapidly to oven
temperature changes and is more
susceptible to the effects of rapidly
changing oven gradients. The newest
generation of GC ovens includes
features designed to maximize
performance with all columns.
Oven heating schemes fall into
two broad categories: those that
rely on a fluid to transfer heat to and
from the column, and those that
use solid-state materials for heating
purposes. Today, of course, air is
the heat-transfer fluid of choice for
most of us, but that was not always
the case. Chromatographers have
experimented with various vapours
and liquids, as well. Solid-state ovens
have been applied to temperature
programming metal capillary
columns, and small, completely
solid-state ovens are found primarily
in portable instruments because of
their low power requirements and
good long-term stability. Columns
installed inside resistively heated
metal tubing or directly heated
ES572945_LCA0315_024.pgs 02.20.2015 22:25 ADV blackyellowmagentacyan
25www.chromatographyonline.com
GC CONNECTIONS
column tubing, suspended inside a
conventional air-bath oven, deliver
very fast programming rates for
high-speed GC.
Fluid Ovens: The earliest report
I could find of a fluid oven for GC
involves the suspension of the
column inside a vapour bath over
a boiling pure substance (4). This
technique of temperature regulation
had been used in various related
disciplines even before partition GC
came into its prime in the 1950s, and
it was a natural step to use it for the
then-new technique. The operator
could change the temperature of the
vapour bath up or down by regulating
the pressure inside a sealed oven
container. For example, p-xylene
boils at 138 °C at 1 atmosphere and
temperatures down to about 30 °C
lower were attainable at reduced
pressures (5). Changing the oven
temperature outside this limited
range was not very convenient
because the pure substance had
to be exchanged for another with a
higher or lower boiling point. This
arrangement produced a fairly stable
thermal environment for the column,
as long as the atmospheric pressure
didn’t change too much. There were
also problems with decomposition
of the boiling substance at higher
temperatures, as well as the
flammability of the vapours, and
chromatographers abandoned this
heating system fairly early.
Oil baths were also used as oven
thermostats (6). After the operating
temperature was reached, if the
heating vessel was well insulated
and efficiently stirred, a very uniform
thermal environment was possible.
Temperature control circuitry
provided fairly good temperature
stability. Such baths could be
temperature programmed, but at
a limited rate because of the large
heat capacity of the oil. A main
drawback of oil bath ovens was
the inconvenience of working with
high-molecular-weight oils that would
coat the column and connections.
The slightest leak would permit the
oil to enter and destroy the column
or, at the very least, drastically
modify its retention characteristics.
Changing the column was difficult
and messy. A water bath was also
used, but with a limited temperature
range.
Air-Bath Ovens: GC researchers
understood at the onset that an
air-bath oven was potentially superior
to liquid or vapour bath designs.
The temperature in an air bath
could be varied rapidly over a wide
range without changing the working
fluid, and it would be much easier
to change columns. However, early
air-bath oven designs from the 1950s
suffered from large temperature
gradients and a significant lag
time between the oven setpoint
and actual column temperatures.
Improved fans and better insulation
helped to remedy the situation, and
by the mid-1960s the air-bath oven
was almost universally accepted.
The subsequent introduction of
microprocessor temperature control
in the 1970s and the adoption
of a number of other important
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ES572938_LCA0315_025.pgs 02.20.2015 22:25 ADV blackyellowmagentacyan
LC•GC Asia Paciàc March 201526
GC CONNECTIONS
separation, but this is certainly an
impressive capability. This type of
hybrid arrangement is available as
a special oven door with integrated
electronics and controller.
ConclusionGas chromatographers have invented
and adapted various methodologies
for controlling column temperature.
Far removed from simple cake
baking requirements, the physics
and chemistry of GC separation and
elution determine the requirements
for temperature control. The need for
application flexibility or specificity
determines the kind of heating
system that is appropriate. A broadly
applicable system such as the
conventional air-bath oven gives the
greatest flexibility at the expense
of portability, power consumption,
and space. Dedicated systems
in which the column and heater
are permanently mated yield low
power consumption, portability, and
compactness. Interest in high-speed
and high-resolution GC will continue
to spur developments in column
heating technologies.
References(1) See http://wiki.hrgc.com
(2) M.L. Lee, F.J. Yang, and K.D Bartle,
Open Tubular Gas Chromatography.
Theory and Practice (Interscience, New
York, USA, 1984), p. 106.
(3) J.C. Giddings, J. Chem Educ. 39, 569
(1962).
(4) A.T. James and A.J.P. Martin, Biochem.
J. 50, 679–690 (1952).
(5) A.I.M. Keulemans, Gas
Chromatography (Reinhold Publishing
Corp., New York, USA, 1957), pp.
59–61.
(6) D.H. Desty and B.H.F Whyman, Anal.
Chem. 29, 320–329 (1957).
(7) L.S. Ettre, J. Chrom. Sci. 15, 90–110
(1977).
(8) L.S Ettre, Amer. Lab 31(14), 30–33
(1999).
(9) S.R. Lipsky and M.L. Duffy, J. High
Resolut. Chromatogr. 9(376–382),
725–730 (1986).
John V. Hinshaw is a senior scientist
at Serveron Corporation in Beaverton,
Oregon, USA, and is a member of the
LCGC Asia Pacific editorial advisory
board. Direct correspondence about
this column should be addressed
to “GC Connections”, LCGC Asia
Pacific, Honeycomb West, Chester
Business Park, Chester, CH4 9QH,
UK, or e-mail the editor-in-chief,
Alasdair Matheson, at amatheson@
advanstar.com
asbestos tape. A variable voltage
gave rudimentary temperature control
(7). Another approach connected a
source of electrical current across
the metal packed column itself, and
at least one commercial GC used this
method (8). This approach has also
been applied to metallic open-tubular
columns and metal-coated
fused-silica columns (9).
A different solid-state column
heating design packages the
column and a separate heater into a
monolithic block. One or more metal
capillary columns are wound with a
heating element around a core and
the entire assembly is then encased
in thermoplastic resin. The column
tubing terminates outside the oven
assembly in adjacent valves, inlet
systems, and detectors, and the
entire assemblage is placed inside
an insulated enclosure. This type of
construction is attractive for portable
isothermal GC systems because
once heated, it doesn’t require a lot
of power to maintain temperature
and it can be very small in size.
Changing the column requires a
complete exchange of the inner oven
assembly, however.
Micro GC: GC systems that use
nonconventional micromachined
inlet, column, and detector modules
have experienced significant
development in the past decade.
Such devices may use columns
etched into a planar substrate
with integral or attached heating
elements. Small both in size and
power consumption, these GC
systems are finding a niche in field
portable applications.
Hybrid Ovens: Some of the most
recent high-speed GC arrangements
for benchtop instruments use a
hybrid combination of an air-bath
that surrounds solid-state heated
columns. By using short columns
with smaller internal diameters in
combination with fast heating, such
a device can deliver the resolution
of longer, larger diameter columns
in less time. In one arrangement,
the column runs coaxially inside a
coiled, externally insulated metallic
tubular heater that is suspended
on a cage in the air-bath GC oven.
Such devices can achieve ramp
rates up to 1200 °C/min, although
at such rates the oven temperature
will likely get ahead of or escape the
features — including improvements
made specifically for fused-silica
columns — resulted in modern GC
oven designs that are ubiquitous
today. GC air-bath ovens are
available in both cylindrical and
cubic forms.
Common features among air-bath
ovens include high-speed fans for
turbulent mixing that minimizes
internal gradients. The columns and
temperature sensor are shielded
from direct thermal radiation emitted
by the oven heating element.
Incrementally positioned oven
vents supply regulated amounts of
cooling air for improved control at
temperatures close to ambient and
open fully for maximum cool-down
rates.
These ovens can routinely deliver
retention time repeatability well
within the criteria cited earlier in this
article. Today, GC ovens are capable
of linear temperature programming
rates up to at least 75 °C/min with a
standard heating element. However,
the maximum linear programme rate
declines to around 20–30 °C/min at
temperatures above about 175 °C
because of increasing heat losses
through the temperature gradient,
through the oven insulation, and out
to the surrounding environment. More
powerful oven heaters available with
some instruments can yield higher
programming rates up to 120 °C/min
that are suitable for higher speed
GC separations. When the columns
occupy only a fraction of the full
oven volume, even faster heating and
cooling are obtainable by reducing
the oven air volume with an internal
insulating blanket.
The fastest GC total run-to-run
times require not only fast heating,
but also rapid cooling back to the
initial oven temperature. The use
of an extra fan that forces cooling
air through the oven vents helps to
speed cool-down time. Cryogenic
cooling is another option that
minimizes cool down, but is not
always convenient or available.
Solid State Ovens: GC
experimenters also developed
a number of different solid-state
column heating schemes over the
years. An early design used heating
wire that was simply wrapped
around a straight or U-shaped
packed column and insulated with
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COLUMN WATCH
Recently, both instrumental manufacturers and the pharmaceutical industry have increased their interest in supercritical fluid chromatography (SFC) because of the lower environmental impact and the considerably shorter separation times compared to traditional high performance liquid chromatography (HPLC). The use of low-viscosity carbon dioxide as the main solvent enables operation at higher flow rates as compared to liquid chromatography (LC) (1), and the low toxicity of this mobile phase and its ease of recycling have created another important incentive. Even though the SFC revival has focused on preparative chiral separations (2), it has also been successfully used in many achiral applications. However, LC still dominates analytical applications because of its versatility and, so far, its superior robustness compared to SFC. If the reproducibility of modern SFC could be considerably improved, we believe its revival would be strongly boosted.
The inherent problem with SFC that makes it so much more complex than LC is the compressibility of the mobile phase, which makes SFC a “rubber variant of LC”. Everything considered as constant in LC is not constant in SFC (3,4). This ultimately results in radial and axial density and temperature gradients
in the column that affect the thermodynamics of adsorption and cause a volumetric flow rate gradient through the column (5–7). The practical consequences are serious. We cannot be guaranteed that the set operational conditions reflect the true conditions over the columns like we can in LC. This results in poor system-to-system reproducibility and transfer between analytical methods, which in turn hampers the wider use of SFC in the pharmaceutical industry. These phenomena also result in poor predictions in scaling up from analytical SFC instruments to preparative SFC instruments (8). The consequence is that optimization in process- and preparative-scale SFC must be done in the large scale, which costs money and energy and may even be impossible in some cases. Therefore, a proper understanding of how operational parameters such as pressure, temperature, and solvent composition affect the separation and its reproducibility is of paramount importance.
In this brief investigation, with both new and review material, we start out with some illustrative examples of poor method transfer among different analytical systems. Thereafter, we report on recent investigations on which experimental parameters are the most important to improve reproducibility and method of transfer in SFC. Some
guidelines to users and some advice for improvements to manufacturers is also provided.
Results and DiscussionExamples of Poor Reproducibility
Between Systems: First, the reproducibility between different SFC systems was studied by injecting 20 µL (full-loop) of 350 g/L antipyrine solution on four different SFC systems using identical instrumental settings and the same column (see Figure 1). Liquid carbon dioxide was supplied to systems 1, 3, and 4 (AGA Gas AB, Sweden) and system 2 was fed gaseous carbon dioxide. The same 150 mm × 4.6 mm Kromasil silica column, packed with 5-μm nominal particle size, 100-Å pore size media (AkzoNobel Eka) was used for all systems. The back pressure (P) was 150 bar, the column temperature was 35 °C, and the eluent composition was 85:15 (v/v) carbon dioxide–methanol. All experiments were carried out at volumetric flow of 2 mL/min.
Modern Supercritical Fluid Chromatography — Possibilities and Pitfalls Torgny Fornstedt1 and Ronald E. Majors2, 1Karlstad University, Karlstad, Sweden, 2Column Watch Editor
There has been a revival of supercritical fl uid chromatography (SFC) in recent years, especially in the chiral preparative fi eld, but also more recently in the analytical area. However, SFC is considerably more complex than liquid chromatography (LC), mainly because of the compressibility of the mobile phase. One can say that SFC is a “rubber variant” of LC where everything considered constant in LC varies in SFC. In this review, we go through advances in theory, instrumentation, and novel applications.
The inherent problem
with SFC that makes it so
much more complex than
LC is the compressibility
of the mobile phase,
which makes SFC a
“rubber variant of LC”.
ES572892_LCA0315_027.pgs 02.20.2015 22:23 ADV blackyellowmagentacyan
LC•GC Asia Paciàc March 201528
COLUMN WATCH
The resulting overloaded elution
profiles, corrected for system void
volumes, is clearly system dependent
as can be seen in Figure 1. This
figure also shows that the elution
volume is larger on system 4 while
systems 1 and 2 have lower, and
similar, retention volumes. However,
system 3 (same instrument model as
system 1) had a retention volume that
was more than 0.5 mL larger than
that of system 1 when using the same
column and the same set conditions.
This observation indicates that either
the set conditions are not manifested
in the systems or that the operational
column conditions were different,
although identical set values were
used. We further investigated if the
volumetric flow rate was the reason
for the observed difference between
the elution profiles shown in Figure 1,
but the column dead volume was
determined and was estimated to
be approximately 1.9 mL on each
system. This observation means that
the volumetric flow rate was more
or less identical on all systems;
otherwise the void volumes would
have been different.
Other important parameters
governing retention are the density
of the mobile phase, the fraction
of organic modifier (CM), and the
column temperature (T ). The density
is a nonlinear function of pressure
(P) and temperature; to calculate the
density of the carbon dioxide and
methanol stream inside the column,
the pressure at the column inlet and
outlet as well as the actual column
temperature must be known. An
immediate conclusion was that none
of the systems measure pressure
directly before or after the column,
only at a point upstream from the
column. Furthermore, this pressure
reading cannot simply be correlated
between different systems because
the pressure drop upstream to the
back-pressure regulator will be
divided between capillaries and
column. Pressure drop from the
capillaries will depend on their length
and diameter, which cannot be
assumed to be identical between the
different systems. So the reason for
the problem is probably due to the
fact that in SFC — in contrast to LC
— the set operational values are not
necessarily identical with the actual
operational values.
12System 1
System 2
System 3
System 4
6
Co
nce
ntr
ati
on
(g
/L)
0
5 6 7
Volume (mL)
8
Figure 1: Experimental chromatograms recorded on the various SFC systems. The
set volumetric flow was 2 mL/min and the injection volume was 20 µL of a 350-g/L
sample.
160
150
Back
pre
ssu
re (
bar)
140
Methanol content (%)
14
7.2
0
7.0
06.8
0
6.6
0
6.4
0
6.2
0
15 16
Figure 2: Retention volume of analytical peaks as a function of methanol content
and pressure determined using 14, 14.5, 15, 15.5, and 16 (v/v) % methanol content at
140, 145, 150, 155, and 160 bar on system 1. All analytical injections were 5 µL of a
0.35-g/L sample.
ES572891_LCA0315_028.pgs 02.20.2015 22:23 ADV blackyellowmagentacyan
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ES572496_LCA0315_029_FP.pgs 02.20.2015 01:56 ADV blackyellowmagentacyan
LC•GC Asia Paciàc March 201530
COLUMN WATCH
Deeper Investigation of the
Causes of the Poor Reproducibility
Between Systems:
Initial Experiments: We used a
design-of-experiment (DoE)-based
approach (9) to find out the most
important parameters affecting
the reproducibility of retention for
different neutral model compounds
in this system and similar ones. In
DoE, the effects of variations of
operational parameters on a certain
response, such as retention factors
or selectivity in chromatography,
are statistically investigated under
controlled experiments. For this
particular study, we had to find out
the real temperature, pressures,
and the cosolvent fractions inside
the column. Thus, we used external
devices to measure the real mass
flows over the column and external
sensors for measuring temperature
and pressure at different positions
along the column (5).
Before the DoE-based experiments
and modelling, we investigated
experimentally how retention volumes
are affected by changes in system
back pressure and modifier content
(10). Analytical injections of 5 µL
of 0.350-g/L antipyrine solutions
were performed at back pressures
of 140–160 bar and modifier
fractions of 14–16%, on system 1
(see Figure 2). As seen in Figure 2,
the retention volume was strongly
affected by changes in the modifier
concentration and to a smaller extent
to the “set” back pressure. When
performing the system reproducibility
experiments described in the
previous section, we noticed that
system 4 had a much lower pressure
drop compared to systems 1 and 2.
5
-5
Co
eff
cien
ts ×
100
-10
-15
P
CM
P.C
M
CM
.T
P.T
C2
M
P 2
T 2T P
CM
P.C
M
CM
.T
P.T
C2
M
P 2
T 2T
0
-5
-10
-15TSO k1
TSO k2
BINOL k1
BINOL k2
0
(a) (b)
7
CM (
v/v
%)
CM (
v/v
%)
24
19
14140 160
P (bar)
k1 BINOL k
2 BINOL
P (bar)
180 200 140 160 180 200
5
3
130 170 210 130 170 2102
6
9
6
3
8(a) (b)
(c) (d)
k2
TSOk1
TSO
k TSO
k B
INO
L
4
Figure 3: Centred and normalized coefficients from the model fit for the first and
second retention factor, respectively, for (a) TSO and (b) BINOL. The error bars
represent the 95% confidence interval of the coefficients. The height of a negative
or positive bar in the plot shows the relative degree of impact of corresponding
parameter. Adapted with permission from reference 9.
Figure 4: Contour plots showing the retention factor (k) as a function of pressure
and amount of methanol in the eluent: (a) k1 for TSO, (b) k2 for TSO, (c) k1 BINOL,
and (d) k2 BINOL. Adapted with permission from reference 9.
SFC is a complicated
chromatographic
method and the same
instrumental set
conditions do not
necessarily give the
same actual working
conditions over the
column on different
systems for a variety of
reasons.
ES572890_LCA0315_030.pgs 02.20.2015 22:23 ADV blackyellowmagentacyan
31www.chromatographyonline.com
COLUMN WATCH
This sensitivity and back pressure
difference between systems probably
explains the observed differences in
retention volumes mentioned earlier.
Design of Experimental-Based
Investigation: We used DoE to
investigate the impact on retention by
cosolvent, pressure, and temperature
in the column (9). These three
factors and their interactions were
studied for two different solutes.
The coefficients of the second
order polynomial were estimated
by least squares regression. The
regression model for each response
was evaluated with analysis of
variance (ANOVA). The model was
refined by first removing statistical
outliers and then removing any
statistically insignificant terms from
the polynomial at a 95% confidence
level.
To study the dependency
and variation of common
chromatographic parameters such
as retention factors, selectivity, and
productivity with parameters such as
amount of cosolvent, temperature,
and pressure, the true parameter
values in the chromatographic
column must be used rather than the
values set on the instrument. The
true values were determined by using
external measurements of pressure,
temperature, and mass flow. To
verify the set volumetric flow rate,
the corresponding density of the
mobile phase fluid was calculated
using the Kunz and Wagner equation
of state as implemented by the
National Institute of Standards and
Technologies in a database software
used in science research (11).
Interestingly, the deviation between
the set and estimated volumetric flow
rate, temperature, and pressure was
found to be relatively small for most
experimental conditions used.
Parameters of Importance
for Retention Factors: Before a
successful method development can
be performed in SFC, it is essential
to have knowledge about and to be
able to predict how the retention
factor is affected by a certain
change of the operating parameters
(that is, the variables). The centred
and normalized coefficients for
the retention factor models are
presented in Figure 3. Here the test
solutes studied were trans-stilbene
oxide (TSO) and 1,1′-bi-2-naphthol
(BINOL). In particular, Figure 3
shows the impact of different
operation parameters on the retention
factors for the first and second
enantiomer of TSO and BIONOL. The
height of positive or negative bar in
the plot shows the relative degree
of impact of the corresponding
operational parameter. The
operational parameters are
denoted in the x-axis: pressure
(P), methanol fraction (CM), and
column temperature (T ); significant
values of the quadratic and more
complex term on the x axis indicate
nonlinear dependencies. In Figure 3,
it is clear that the volumetric CM
was the most important parameter,
which is represented by the largest
coefficient value for methanol. The
methanol fraction also had a large
quadratic term (C2M), meaning that
the methanol dependence is not
linear. Pressure was the second most
important factor for the retention
factor, and the retention factors all
have similar pressure dependencies
with a slight nonlinearity (see
Figure 3). In Figure 3, it can also be
seen that the interaction terms are all
significant, that is, the coefficients
of the interaction terms are shown
to be significant. These interaction
terms would be missed if one factor
at a time were varied. Interestingly,
the effect of temperature in the
investigated region was low for both
components enantiomers, however,
slightly larger for BINOL (see
Figure 3).
Figure 4 contains contour
plots illustrating the combined
dependency of the retention factors
on methanol fraction and pressure
for TSO and BINOL, respectively,
at the fixed column temperature
30 °C. For both enantiomers of both
components, we can see that the
larger the methanol fraction and the
larger the pressure the lower (more
blue colour) the retention factors
(see Figure 4). The highest values
of the retention factors are obtained
for the second enantiomer (for both
TSO and BINOL) at combined low
methanol fractions and low pressures
(deeply red colour). It is evident
that the retention factors for both
solutes have similar trends when
studied in the methanol–pressure
plane, despite the different density
variations seen between the
experimental conditions for TSO and
BINOL.
(a) (b)TSO
2
0
Co
eff
cien
ts X
10
-224 29 34
0.4
3
5
71.2
0.8
P CM C2
MT
T (°C)
CM (
v/v
%)
pr (m
gm
in)
Figure 5: Centred and normalized coefficients with 95% confidence interval from the
model fit to the productivity for the optimum touching-band chiral separation of TSO
are plotted. In (b) the productivity is plotted as a function of the amount of modifier in
the eluent and the temperature. Adapted with permission from reference 9.
ES572894_LCA0315_031.pgs 02.20.2015 22:23 ADV blackyellowmagentacyan
LC•GC Asia Paciàc March 201532
COLUMN WATCH
Parameters of Importance for
Productivity: In preparative chromatography, it is important to purify the desired components at the desired purity and for maximum productivity (that is, the amount of purified product per unit time). In a separation of the enantiomers of a racemate, the first step is to find suitable separation systems. This is achieved by screening combinations of chiral stationary phases (CSPs) and cosolvents. Typically, selectivity is maximized while k ´ is minimized. Loading studies utilizing maximum sample concentration on suitable candidate systems can then be performed. In this study, the dependency and variation of productivity on cosolvent, pressure, and temperature was studied. Either of the enantiomers of TSO was selected as target. The optimal chromatogram was chosen to maximize injection volume while maintaining touching-band separation (that is, having a yield of 100%). The productivity was calculated by assuming that stacked injections could be performed for the separation of either enantiomer of TSO at 30 °C, 160 bar, and 5% methanol. The trends in productivity were very clear and analysis of parameter importance revealed that the most important factor to increase productivity was to increase the fraction of cosolvent followed by an increase of temperature (see Figure 5[a]). Therefore, by simultaneously increasing methanol content and temperature, the productivity in this case could be efficiently maximized as can be seen in Figure 5(b). By decreasing pressure, the productivity could be further increased but only marginally (data not shown). It should be noted that a complete optimization would also entail further work on the volumetric flow or concentration of the sample, but that was beyond the scope of this study. Interestingly, the selectivity for the separation of TSO increases with decreasing methanol content. Hence, in this case, maximizing selectivity will yield lower productivity.
ConclusionsThis study shows that SFC is a complicated chromatographic
method and that the same instrumental set conditions do not necessarily give the same actual working conditions over the column on different systems for a variety of reasons. Recorded elution profiles for the model compound antipyrine injected onto the same silica column on three different systems at the same set conditions were clearly different. This means that it can be hard to transfer an established separation method from one system to another, even if one is using the same column and identical instrument settings. This method transfer difficulty has implications both for analytical methods and preparative applications. For example, different separation methods are usually screened on an analytical instrument and then the selected method is transferred to a larger preparative system. A design of experimental approach was used to study the impact of a change of the most common operational parameters using neutral model compounds on mostly chiral systems. The model systems presented here — except for antipyrine separated with a silica column — consisted of racemic mixtures of TSO and BINOL and were separated using a chiral cellulose-derivative silica-based column. All input parameters were carefully measured using external sensors. The parameters investigated were column temperature, pressure, and cosolvent fraction. The methanol fraction was the most important factor for governing the retention factors, and the pressure was the second most important factor. On the other hand, the temperature did not have a significant effect on the retention factor. Increasing either the temperature or the pressure resulted in reductions of the retention factors. Although not reported here, this result was also obtained with another chiral system studied (racemic 1-phenyl-1-propanol as an analytical model compound) (10).
For preparative purification of either enantiomer of TSO, the most important parameters were found to be methanol content and temperature. The selectivity for the separation of TSO increases
with decreasing methanol content. Therefore, in this case, maximization of the selectivity will yield lower productivity. As in most forms of chromatography, compromises must also be made in SFC.
References(1) G. Guiochon and A. Tarafder, J.
Chromatogr. A. 1218, 1037–1114 (2011).
(2) A. Rajendran, J. Chromatogr. A. 1250, 227–249 (2012).
(3) A. Tarafder, K. Kaczmarski, D.P. Poe, and G. Guiochon, J. Chromatogr. A. 1258, 136–151 (2012).
(4) K. Kaczmarski, D.P. Poe, and G. Guiochon, J. Chromatogr. A 1218, 6531–6539 (2011).
(5) M. Enmark, P. Forssén, J. Samuelsson, and T. Fornstedt, J. Chromatogr. A 1312, 124–133 (2013).
(6) F. Kamarei, F. Gritti, and G. Guiochon, J. Chromatogr. A 1306, 89–96 (2013).
(7) M. Enmark, J. Samuelsson, E. Forss, P. Forssén, and T. Fornstedt, J.
Chromatogr. A 1354, 129–138 (2014). (8) A. Tarafder, C. Hudalla, P. Iraneta, and
K.J. Fountain, J. Chromatogr. A 1362, 278–293 (2014).
(9) D. Åsberg, M. Enmark, J. Samuelsson, and T. Fornstedt, J. Chromatogr. A 1374, 254–260 (2014).
(10) M. Enmark, D. Åsberg, J. Samuelsson, and T. Fornstedt, Chrom. Today 7, 14–17 (2014).
(11) E.W. Lemmon, M.L. Huber, and M.O. McLinden, NIST Standard
Reference Database 23: Reference
Fluid Thermodynamic and Transport
Properties-REFPROP, Version 9.1, (National Institute of Standards and Technology, Standard Reference Data Program, Gaithersburg, Maryland, USA, 2013).
Torgny Fornstedt is a professor in analytical chemistry and heads the internationally competitive Fundamental Separation Science Group (www.separationscience.se) at Karlstad University. The group develops and applies numerical tools to characterize, validate, and optimize analytical and preparative separation methods and has recently turned towardssupercritical fluid chromatography.Ronald E. Majors is the editor of “Column Watch,” an analytical consultant, and a member of LCGC
Asia Pacific ’s editorial advisory board. Direct correspondence about this column should be addressed to “Column Watch”, LCGC Asia
Pacific, Honeycomb West, Chester Business Park, Wrexham Road, Chester, CH4 9QH, UK, or e-mail the editor-in-chief, Alasdair Matheson, at [email protected]
ES572893_LCA0315_032.pgs 02.20.2015 22:23 ADV blackyellowmagentacyan
33
31st International Symposium on MicroScale Bioseparations
The 31st International
Symposium
on MicroScale
Bioseparations (MSB
2015) will be held from
26–29 April 2015 at the
Crowne Plaza Hotel,
Harbour City, Pudong,
Shanghai, P.R. China.
The symposium series
was first established
to support the field
of high-performance
capillary electrophoresis,
but now covers all major microscale bioseparation techniques including
capillary electrophoresis, nano-liquid chromatography, microfluidics, mass
spectrometry, hyphenated detection methods, pharmaceutical analysis,
biotechnology, clinical diagnostics, food and health, nanoparticles, and
advanced detection and instrumentation.
Around 70% of the planned programme will be built from submitted
abstracts, where a blind peer review with an enhanced submission requirement
has been developed to ensure a very high calibre of science being presented.
A layer of confidentiality has been introduced to protect conversations and
presentations to allow the discussion of unpublished work. The format of
MSB 2015 has evolved to create a new biotechnology track with a focus on
the practical applications of microscale separations. The presentation format
has been adapted to create an extended discussion period with each paper,
moderated by a session chair. The top three posters presented at the meeting
will be selected for oral presentation in a special session as part of the finale
of the symposium. Overall, it is the intent of this symposium to foster the
exchange of ideas at the frontiers of separation science.
Four short courses are scheduled before the conference on 26 April,
including “Immunoaffinity Capillary Electrophoresis for use in Biotechnology
Applications” presented by Prof. Norberto Guzman from New Jersey,
USA; “Multidimensional HPLC” presented by Dr. Gerard Rozing from
Karlsruhe, Germany; and “Microfluidic Devices in the Life Science: Basics
and Applications” presented by Prof. Jörg P. Butter from the University of
Copenhagen, Denmark. More details are available from the website.
Current printer and laser technology has enabled a method termed print,
cut, and laminate (PCL) fabrication that is capable of creating printable
microfluidic devices in a simple, cost-effective, and rapid manner, using
relatively commonplace office equipment. Professor James Landers will
present a lecture discussing the theory and methodology associated with each
of the steps, as well the material challenges associated with substrates and
chemistries used in this method. This will be followed by a hands-on part of
the workshop, where those involved will carry out PCL fabrication using the
necessary materials and some standard microfluidic designs.
The historical city of Shanghai has been chosen for this meeting, which will
provide excellent networking opportunities to perform scientific discussions.
On the 28 April, the organizers have planned the MSB Gala Banquet that
promises food, live music, and the opportunity to network. Registration for
the event will be limited to 395 participants to establish a “smaller meeting”
ambience with a focus on the vigorous scientific exchange between delegates.
E-mail: [email protected]
Website: msb2015.org
Co-Chairs: Yukui Zhang, Pengyuan Yang, Norman Dovichi, and Amy Y. Guo
15–17 April 2015Vietnam Analytica 2015
Saigon Exhibition & Convention Centre,
Ho Chi Minh City, Vietnam
Contact: Eva Sauerborn
E-mail: [email protected]
Website: www.analyticavietnam.com/
en/Home
17–21 May 2015ISCC and GC×GC 2015
Hilton Fort Worth Hotel, Fort Worth,
Texas, USA
Contact: Danielle Schug
E-mail: [email protected]
Website: www.isccgcxgc2015.com
30 June–3 July 201521st International Symposium on
Separation Sciences (ISSS 2015)
Grand Hotel Union, Ljubljiana,
Slovenia
Tel: +38614760265
E-mail: [email protected]
Website: www.isss2015.si
22–24 July 20159th International Conference on
Packed Column SFC
Loews Hotel, Philadelphia, USA
Tel: +1 412 805 6296
E-mail: [email protected]
Website: www.greenchemistrygroup.
org/Registration.html
21–25 September 201543rd International Symposium
on High-Performance Liquid
Phase Separations and Related
Techniques
Beijing International Convention Center
Bejing, China
Contact: HPLC Secretariat
E-mail: [email protected]
Website: hplc2015-beijing.org/
3–6 November 2015Recent Advances in Food Analysis
(RAFA)
Clarion Congress Hotel, Prague, Czech
Republic
Tel: +386 1 4760265
E-mail: [email protected]
Website: www.rafa2015.eu
Please send any event news to
Bethany Degg
33www.chromatographyonline.com
EVENT NEWSP
ho
to C
red
its:
Bla
cksta
tio
n/G
ett
y I
mag
es
ES572913_LCA0315_033.pgs 02.20.2015 22:24 ADV blackyellowmagentacyan
34 LC•GC Asia Pacifi c March 2015
ADVERTISEMENT FEATURE
There has been a signif cant resurgence in the development of
antibody-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 that 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
size-exclusion chromatography combined with multi-angle light
scattering (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 species 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 that 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 with SEC–MALS Wyatt Technology Corporation
Wyatt Technology Corporation6300 Hollister A venue, 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
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.
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 (min)
11.0 11.5 12.0
Figure 1: Molar masses for two distinct ADC formulations are determined using SEC–MALS analysis.
ES572911_LCA0315_034.pgs 02.20.2015 22:24 ADV blackyellowmagentacyan
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