For analyzing biological, natural, and synthetic...

LC TROUBLESHOOTING

LLOQ: A case study

GC CONNECTIONS

Practical GC

COLUMN WATCH

The top 10 column myths

November 2013

Volume 16 Number 4

www.chromatographyonline.com

For analyzing biological, natural, and synthetic polymers

Field Flow

Fractionation

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LC•GC Asia Pacific November 2013

Editorial Policy:

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 courtesy: Hong Li

Columns

17 LC TROUBLESHOOTING

What’s the Problem with the LLOQ? — A Case Study

John W. Dolan

Two different methods of calculating the LLOQ disagree. Which, if

either, is correct?

22 GC CONNECTIONS

Practical Gas Chromatography

John V. Hinshaw

Questions about how practical proposed gas chromatography (GC)

method changes are often come up during optimization for speed

and resolution, or while converting to a different carrier gas. Related

objective measurements such as the optimum practical carrier

gas velocity were defined more than 40 years ago. This instalment

reviews such metrics in the light of their relevance to today’s GC

challenges.

26 COLUMN WATCH

The Top 10 HPLC and UHPLC Column Myths

Ronald E. Majors

In any field, there are “misconceptions” or “myths” that arise and

are perpetuated and passed on to the next generation. These

myths are often driven by a lack of understanding of the real issues

by practitioners. In the first of a two-part feature from Ron Majors,

the top 10 high performance liquid chromatography (HPLC) column

myths are presented and attempts are made to demystify them by

offering some evidence that they are untrue. This part will feature

myths 10 to six.

Departments

33 Application Notes

COVER STORY

8 Field-Flow Fractionation for

Biological, Natural, and

Synthetic Polymers: Recent

Advances and Trends

Carmen Bria, Frédéric Violleau, and

S. Kim Ratanathanawongs Williams

A review of the latest trends in

field-flow fractionation (FFF) for

various types of polymer analysis.

November | 2013

Volume 16 Number 4

4

ES339284_LCA1113_004.pgs 10.17.2013 17:29 ADV blackyellowmagentacyan

Are Wyatt’s MALS instruments the product ofintelligent design or evolution? Yes.

© 2013 Wyatt Technology, Optilab, DAWN and the Wyatt Technology logo are registered trademarks of Wyatt Technology Corporation.

There’s no debate about it. We invented the first commercial Multi-Angle Light Scattering (MALS) detectors for GPC/SEC, then built on them to develop a complete family of related instruments, all of which provide unparalleled performance for our customers.

Our MALS products have multiplied and are the most widely-used on earth. Thousands of chemical, biotechnol-ogy, pharmaceutical, academic and government laboratories around the world rely on them to characterize proteins, polymers and macromolecules of all kinds. And many of our customers have published, resulting in nearly 10,000 peer-reviewed articles based on their work using Wyatt’s MALS instruments. These articles, application notes and other remarkable feedback in the scientific community have helped our 18+ PhD scientists and other innovators to refine our instruments more rapidly to meet our customers’ needs. Which oftentimes results in an unprecedented new product.

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DAWN® HELEOS. The most advanced multi-angle light scattering (MALS) detector for macromolecular charac-terization.

Optilab® T-rEX. The refrac-tometer with the greatest combination of sensitivityand range—and absolute refractive index, too.

ViscoStar®. The viscom-eter with unparalleled signal-to-noise ratios, stable baselines and a 21st-century interface.

Eclipse. The ultimate sys-tem for the separation of proteins and nanoparticles in solution.

DynaPro® Plate Reader. The only automated dynamic light scattering for 96 or 384 or 1536 well plates—now with an on-board camera!


6 LC•GC Asia Pacific November 2013

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

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

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

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

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

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

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

efficiency, productivity and value to their employer.

Editorial Advisory Board

Kevin AltriaGlaxoSmithKline, Harlow, Essex, UK

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

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

Coral BarbasFaculty of Pharmacy, University of San

Pablo – CEU, Madrid, Spain

Brian A. BidlingmeyerAgilent Technologies, Wilmington,

Delaware, USA

Günther K. BonnInstitute of Analytical Chemistry and

Radiochemistry, University of Innsbruck,

Austria

Peter CarrDepartment of Chemistry, University

of Minnesota, Minneapolis, Minnesota, USA

Jean-Pierre ChervetAntec Leyden, Zoeterwoude, The

Netherlands

Jan H. ChristensenDepartment of Plant and Environmental

Sciences, University of Copenhagen,

Copenhagen, Denmark

Danilo CorradiniIstituto di Cromatografia del CNR, Rome,

Italy

Hernan J. CortesH.J. Cortes Consulting,

Midland, Michigan, USA

Gert DesmetTransport Modelling and Analytical

Separation Science, Vrije Universiteit,

Brussels, Belgium

John W. DolanLC Resources, Walnut Creek, California,

USA

Roy EksteenSigma-Aldrich/Supelco, Bellefonte,

Pennsylvania, USA

Anthony F. FellPharmaceutical Chemistry,

University of Bradford, Bradford, UK

Attila FelingerProfessor of Chemistry, Department of

Analytical and Environmental Chemistry,

University of Pécs, Pécs, Hungary

Francesco GasparriniDipartimento di Studi di Chimica e

Tecnologia delle Sostanze Biologica-

mente Attive, Università “La Sapienza”,

Rome, Italy

Joseph L. GlajchMomenta Pharmaceuticals, Cambridge,

Massachusetts, USA

Jun HaginakaSchool of Pharmacy and Pharmaceutical

Sciences, Mukogawa Women’s

University, Nishinomiya, Japan

Javier Hernández-BorgesDepartment of Analytical Chemistry,

Nutrition and Food Science University of

Laguna, Canary Islands, Spain

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

Tuulia HyötyläinenVVT Technical Research of Finland,

Finland

Hans-Gerd JanssenVan’t Hoff Institute for the Molecular

Sciences, Amsterdam, The Netherlands

Kiyokatsu JinnoSchool of Materials Sciences, Toyohasi

University of Technology, Japan

Huba KalászSemmelweis University of Medicine,

Budapest, Hungary

Hian Kee LeeNational University of Singapore,

Singapore

Wolfgang LindnerInstitute of Analytical Chemistry,

University of Vienna, Austria

Henk LingemanFaculteit der Scheikunde, Free University,

Amsterdam, The Netherlands

Tom LynchBP Technology Centre, Pangbourne, UK

Ronald E. MajorsAgilent Technologies,

Wilmington, Delaware, USA

Phillip MarriotMonash University, School of Chemistry,

Victoria, Australia

David McCalleyDepartment of Applied Sciences,

University of West of England, Bristol, UK

Robert D. McDowallMcDowall Consulting, Bromley, Kent, UK

Mary Ellen McNallyDuPont Crop Protection,Newark,

Delaware, USA

Imre MolnárMolnar Research Institute, Berlin, Germany

Luigi MondelloDipartimento Farmaco-chimico, Facoltà

di Farmacia, Università di Messina,

Messina, Italy

Peter MyersDepartment of Chemistry,

University of Liverpool, Liverpool, UK

Janusz PawliszynDepartment of Chemistry, University of

Waterloo, Ontario, Canada

Colin PooleWayne State University, Detroit,

Michigan, USA

Fred E. RegnierDepartment of Biochemistry, Purdue

University, West Lafayette, Indiana, USA

Harald RitchieThermo Fisher Scientific, Cheshire, UK

Pat SandraResearch Institute for Chromatography,

Kortrijk, Belgium

Peter SchoenmakersDepartment of Chemical Engineering,

Universiteit van Amsterdam, Amsterdam,

The Netherlands

Robert ShellieAustralian Centre for Research on

Separation Science (ACROSS), University

of Tasmania, Hobart, Australia

Yvan Vander HeydenVrije Universiteit Brussel,

Brussels, Belgium

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Macromolecules are ubiquitous in many areas of science and

technology. Depending on the macromolecule, it is important

to analyze properties like size, molar mass (MM), chemical

composition, degree of branching, and their respective

distributions to understand their behaviour. However, because

of the complex nature of polymers, current separation

techniques are not always capable of comprehensive analyses.

Size-exclusion chromatography (SEC) is widely regarded as the

workhorse for polymer characterization, but is limited by high

molar mass (HMM) macromolecules, weakly bound complexes

and aggregate species, and highly branched polymers.

Field-flow fractionation (FFF) is a versatile family of techniques

that complements SEC with additional separation capabilities

based on analyte size, mass, composition, or architecture

depending on the field used (Figure 1).

The open channel FFF design results in a soft separation

mechanism that is well suited for analysis of high and ultrahigh

MM polymers and samples containing microgel. Some key

advantages of FFF over SEC arises from its ability to separate

analytes over a broad size range (0.001 to 100 µm) using a

single channel, and the absence of column packing, which

greatly reduces shear degradation. SEC of protein aggregates

often requires the addition of cosolvents or preconditioning

of columns to reduce adsorption (1). However, addition of

cosolvents may induce aggregation, dissociate aggregates,

or cause sample specific adsorption (Figure 2) (2).

Preconditioning columns is often practised but not reported

in the literature, and even when preconditioning is used

poor recoveries and sample specific adsorption have been

observed (3). In FFF the ability to use formulation buffer allows

separations and measurements under solution conditions that

are more representative of actual use. For polymer analysis,

the shear degradation and co-elution of small and large

analytes observed in SEC for highly branched polymers

are attributed to effects caused by the column packing

material (4).

In practice, FFF offers users additional benefits. Prior to

SEC, filtering is often implemented as a sample preparation

step to remove large components and help prolong the life

of the column. Sample filtering has been shown to remove

soluble and insoluble microgels leading to erroneous MM

and polydispersity results (Figure 3) (5). Filtering is not

required in FFF and soluble polymers and microgels can

be simultaneously characterized. Many syntheses require

the addition of excess reagents, which may interfere with

subsequent product analyses. Such reagents or interfering low

MM sample components either elute in the void peak or can be

removed on-line through a semi-permeable membrane used in

some FFF techniques.

Separations in FFF are dependent on the strength of

an externally applied field which can be easily adjusted.

Therefore, resolution and separation speed are readily

controlled without the need to change channels. In addition,

the open channel design greatly reduces the chance of

contamination and inexpensive membranes can be replaced

when contaminated. Finally, FFF is easily coupled on-line with

detectors frequently used for SEC analysis, including multi-

angle light scattering (MALS), differential refractive index (dRI),

and mass spectrometry (MS) detectors. For those interested

in FFF, building a simple homemade system requires a FFF

channel and standard high performance liquid chromatography

(HPLC) components common to many laboratories. The recent

advances in FFF over the last three years are highlighted in this

review.

Field-Flow Fractionation for Biological, Natural, and Synthetic Polymers: Recent Advances and Trends

Carmen Bria1, Frédéric Violleau2, and S. Kim

Ratanathanawongs Williams1, 1Laboratory for Advanced

Separations Technologies, Department of Chemistry and

Geochemistry, Colorado School of Mines, Golden, Colorado,

USA, 2Université de Toulouse, INPT, Ecole d’Ingénieurs

de Purpan, Département de Sciences Agronomiques et

Agroalimentaires, Toulouse Cedex, France.

Field-fl ow fractionation (FFF) is a family of techniques that is increasingly used for separating and characterizing macromolecules. This review discusses recent advances in the characterization of biological, natural, and synthetic polymers. Applications of FFF are contrasted with size-exclusion chromatography to illustrate practical considerations when characterizing macromolecules. The use of different FFF à elds allows separations based on size, mass, composition, and architecture. The open channel design and subsequent low shear rate is well suited for analyzing weakly bound complexes, highly branched polymers, high molar mass analytes, and aggregates. Other beneà ts of FFF that are highlighted in this paper include simplià ed sample preparation, fl exibility in carrier fl uid choice, and on-line removal of low-molecular-weight contaminants.


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LC•GC Asia Paciàc November 201310

Williams et al.

Principles of FFFSeparation takes place in a thin, open, ribbon-like channel

where carrier fluid transports components down the separation

axis of the channel. Frictional drag at the channel walls creates

a parabolic flow profile across the channel thickness, w, with

the fastest flows in the middle of the channel and the slowest

flows near the walls (Figure 1). An external field (flow, thermal, or

sedimentation) is applied perpendicular to the separation axis

of the channel to drive components towards the accumulation

wall. This field-induced transport is counteracted by diffusion

of components away from the high concentration region near

the accumulation wall. Equilibrium is reached when the two

transport processes are balanced and there is no net flux of

sample in either direction. The equilibrium position is different for

each sample component depending on the magnitude of their

interaction with the applied field and their diffusion coefficient.

Components of smaller sizes diffuse further into the channel

than larger components based on the inverse relationship

between diffusion coefficient (D) and hydrodynamic diameter

(dh) given by the Stokes-Einstein equation for spherical analytes.

The smaller components experience the faster flows further

from the accumulation wall and therefore elute before larger

components in normal mode FFF. This normal mode elution

order is the reverse of that observed for SEC.

The main FFF techniques relevant to this review are

asymmetrical flow field-flow fractionation (AF4), hollow fibre flow

field-flow fractionation (HF5), and thermal field-flow fractionation

(ThFFF). AF4 utilizes a single permeable wall that allows a

crossflow to act as the perpendicular field (Figure 1[a]). The

permeable wall is composed of a porous frit covered with a

semipermeable membrane, the latter acting as the sample

accumulation wall. The retention time (tr) for AF4 is given in

equation 1:

w 2πηt 0v

c

tr=

2V0kTAF4d

h

.

[1]

where η is the carrier fluid viscosity, t0 is the void time, V.

c is the

crossflow rate, V0 is the void volume, k is Boltzmann’s constant,

and T is the temperature.

In HF5 a semipermeable hollow fibre membrane is used and

an outward radial flow acts as the perpendicular force (Figure

1[b]). The benefits of HF5 over AF4 are lower sample volumes

and a potentially disposable channel. Thermal FFF (ThFFF)

employs a hot and cold wall to create a temperature difference

(∆T) that subsequently induces thermal diffusion of components

towards the cold wall (in most cases) (Figure 1[c]). The retention

time is given in equation 2

DTΔπηt 0

tr= =

2kTThFFFd

h

DT Tt0

6D

Δ [2]

where DT is the thermal diffusion coefficient.

Biopolymers Biopolymers are a diverse class of macromolecules that

includes polypeptides, polynucleotides, and polysaccharides.

The versatility of AF4 separation and the characterization of

biopolymers is well established. Several review papers and a

book focusing on the analysis of biological polymers using FFF

have recently been published (6–8).

(a)

(b)

(c)

Separation axis

D

DT

D

W

Cross fow

Outward radial fow

Semi-permeablemembrane

Hot wall

Cold wall(Accumulation wall)

Porous frit

Semi-permeablemembrane

(Accumulation wall)

∆T

Figure 1: Types of FFF separation. (a) In AF4 a crossflow

passes through a semi-permeable membrane and porous frit. (b)

In HF5 a cylindrical semipermeable membrane is used and a

radial outward flow creates the perpendicular field. (c) In ThFFF

a temperature gradient (∆T) is formed between a hot wall and a

cold wall, and sample migrates towards the cold wall because of

thermal diffusion (DT).

(a) (b)

107 106

105

104

106

10 12 14 16 18 20

0.010

0.008

0.006

0.004

0.002

0.000

105

104

0 2 4 6 8 10 12 14 16 18 20 220.00

De

tect

or

vo

lta

ge

(V

)

De

tect

or

vo

lta

ge

(V

)

Mo

lecu

lar

we

igh

t (D

alt

on

s)

Mo

lecu

lar

we

igh

t (D

alt

on

s)

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18 0.14

0.12

0.10

0.08

0.06

0.04

0.02

0.00

Elution time (min)

0 2 4 6 8 10 12 14 16 18 20 22

Elution time (min)

Figure 2: An IgG1 recombinant fully humanized monoclonal

antibody was analyzed by FFF in two different carrier fluids: (a)

0.1% acetic acid containing 50 mM magnesium chloride and (b)

10 mM phosphate buffer pH 7.1. High molar mass aggregates

(peak at ~18.5 min) present in (a) are absent in (b) as a result

of weak aggregate interactions stabilized by the magnesium

chloride. Adapted and reproduced with permission from B.

Demeule, M.J. Lawrence, A.F. Drake, R. Gurny, and T. Arvinte,

(2007), Biochim. Biophys. Acta-Proteins. Proteomics 1774,

146-153. © Elsevier.


11www.chromatographyonline.com

Williams et al.

Characterizing protein—protein and

protein—macromolecule complexes

is important for understanding the

efficacy and functions of proteins.

AF4’s gentle separation mechanism is

well suited to analyze complexes with

weak interactions (Figure 4) (8). Current

techniques for characterizing protein

dissociation constants (Kd), such as

surface plasmon resonance (SPR),

analytical ultracentrifugation (AUC),

and SEC, are limited in their ability to

analyze more than two components

or to detect weak binding affinities

(Kd > µM). Protein—protein binding

between a neonatal Fc receptor (FcRn),

immunoglobulin (IgG), and human serum

albumin (HSA) was recently studied by

AF4 (9). FcRn is involved in removing

IgG proteins from lysosomal degradation

pathways and IgG transportation in the

body. AF4 separation of the IgG-FcRn

complex allowed for the determination

of a relatively low binding affinity (Kd

of 3.74 μM). In addition, FcRn, HSA,

IgG, and their associated complexes

were separated using AF4. By using an

internal standard curve, the formation

of multi-protein complexes were

determined, including a previously

unreported protein complex (HSA/FcRn/

IgG/FcRn, 303 kDa). The separation of

intact, weakly bound protein complexes

shows great promise for AF4 studies

of protein pharmacokinetics and

aggregation kinetics. Analysis of protein

aggregates, especially those in the

submicron size range, is of particular

interest in the development of therapeutic

proteins. Development of an AF4 method

for separating IgG monomer and

submicron IgG aggregates was recently

shown by Hawe et al. (3). Better size

resolution and recoveries of submicron

IgG aggregates were achieved by AF4

compared to SEC.

Lipoproteins are assemblies of

proteins and lipids that function as

carriers for lipids and cholesterols in

blood. AF4 has been used to analyze

low-density lipoproteins (LDL) and

high-density lipoproteins (HDL) (10).

LDLs have been associated with

an increased risk of coronary artery

disease (CAD). In addition to the

conventional AF4 channel, a hollow

fibre guard channel placed before

the AF4 channel was evaluated using

serum from healthy patients and

CAD patients. The guard channel

removed contaminants and improved

reproducibility in retention, and

fluorescence detection reduced

adsorption of serum proteins to the

membrane and reduced the amount

of serum required for each injection

(0.13 μL).

On-line coupling with a variety of

detection methods has expanded the

breadth of AF4’s characterization ability

in recent years. The use of MALS, dRI,

and quasi-elastic light scattering (QELS)

detectors has become more common

for characterizing macromolecules.

Characterization of dh, MM, radius of

gyration (rg), and chemical composition

was shown for a PEGylated protein

conjugate and its aggregates using

on-line AF4–UV-MALS–QELS–dRI,

SEC–UV-MALS–QELS–dRI, and

matrix-assisted laser-desorption/

ionization time-of-flight mass

spectrometry (MALDI–TOF -MS) as

complementary techniques (11).

PEGylated protein, unreacted protein

traces, and aggregated species were

detected by both AF4 and SEC, with

AF4 providing superior size resolution,

while MALDI–TOF-MS was unable to

detect aggregates. Detection by UV–

MALS-QELS–RI enabled chemical

composition characterization of

PEGylated proteins (1/1 PEG to protein

ratio) and allowed identification of

aggregates present using different

storage buffers.

Interest in characterizing protein

complexes by 2D off-line coupling of

AF4 with other separation techniques

and a variety of detection methods has

grown in recent years. Lectin-treated

N-linked glycopeptides in serum from

lung cancer and healthy patients were

separated by AF4 and subsequently

analyzed by nanoflow liquid

chromatography-electrospray ionization–

tandem mass spectrometry (nLC–

ESI-MS–MS) (12). Binding of various lectin

types to glycoproteins enabled a size-

based separation by AF4. Removal of

non-lectin bound glycopeptides and size

sorting of lectin-glycopeptide complexes

during AF4 allowed semi-quantitative

analysis and improved identification

of biomarkers by nLC–ESI–MS–MS.

Similarly, characterizing cholesterols and

triglycerides in lipoprotein complexes is

also important for understanding their

function in the body. Off-line coupling of

AF4 and gas chromatography (GC)–

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Williams et al.

improved ionization of the HDL and LDL lipids, and in a CAD

plasma sample 28 phospholipids, 18 triacylglycerides, and

six cholesteryl esters were identified.

Natural Polymers Starches are macromolecules essential to human beings and

are used in a variety of industrial and food applications. The

properties of starches (for example, digestion and thickening

abilities) are dependent on starch structure, which in turn is

dependent on the degree of branching. However, wide size

distributions and variation in branching makes characterizing

starches difficult (20). AF4 coupled with MALS and dRI

detectors has been successfully used to determine starch dh,

MM, and rg (21,22). An in-depth review of FFF characterizing

food macromolecules has been recently published (23).

Wahlund et al. demonstrated the power of AF4–MALS–RI to

rapidly separate amylose and amylopectin in maize, wheat, rice,

potato, and tapioca starches (24). Qualitative results for amylose

and amylopectin ratios demonstrate the feasibility for relatively

fast characterization of starches by AF4 and provide a starting

point for more extensive starch analyses. Studies by Juna et

al. have examined various starches (waxy maize, tapioca,

corn, sago) to better understand AF4 conditions and starch

processing parameters (25–30). Changes in size distributions

were observed with changes in AF4 conditions. For example,

at high cross flow rates, the dh, MM, and rg distributions of

tapioca, sago, and corn starch shifted to lower values because

of increased retention (or potential degradation of HMM

components). The effect of AF4 conditions is therefore important

and must be considered for accurate analyses of starches.

Coupling a separation technique with MALS and dRI

detectors can provide information on structural and branching

characteristics of starches. SEC is the most common separation

technique used to characterize starches, but low exclusion limits

and shear scission may bias results. AF4 has the potential to

reduce artifacts observed in SEC such as changes in MM and

size distributions as a result of shear degradation or aggregation

and large branched polymers that co-elute with smaller

components (4,20). A more in-depth comparison of AF4 and

SEC as separation techniques for starches is available (31). To

characterize size distributions and gain structural information

for a commercial starch and a waxy yam starch, Perez et al.

compared AF4–MALS–dRI and SEC–MALS–dRI (32). AF4 and

MS allowed cholesterols and triglycerides to be profiled from

human serum samples, and results showed agreement with

the current enzymatic determination methods (13).

The use of FFF has shown promise as a pre-MS separation

technique in proteomics analyses. To improve MS detection of

poorly soluble proteins, the effect of protein–SDS complexation

on protein solubility was examined by HF5 and nLC–MS (14).

SDS-denatured serum samples, unfractionated or fractionated,

showed improved solubility of the SDS–protein complexes,

and allowed for a greater number of proteins to be identified by

nLC–MS. Furthermore, the HF5 process was shown to remove

low MM (< 30 kDa) components, which subsequently led to

lowered background noise in the MS spectrum.

Protein phosphorylation is a post-translational modification

which plays an important role in protein regulation and can

be used as a biomarker for diseases like cancer. The 2D

on-line coupling of an isoelectric focusing (IEF) step and

AF4 step prior to nLC–ESI–MS–MS enabled separation of

phosphorylated proteins from a proteome sample based

on isoelectric point (pI) and dh (15). IEF-AF4 separation

was evaluated for unphosphorylated and phosphorylated

α-casein. Peptides with higher degrees of phosphorylation

eluted in the lower pH channels and at longer AF4 retention

times as expected. Relative abundances of phosphorylated

protein biomarkers were determined by IEF–AF4 and nLC–

ESI–MS-MS for a prostatic cancer line and a normal cell line.

In another study, improvements in direct on-line coupling of

AF4 with ESI–MS were shown in a small chip-type channel

for top-down proteomics that operates in the micro-flow rate

regime (Figure 5[a]) (16). The chip-type channel effectively

separated carbonic anhydrase (29 kDa) and transferrin (78

kDa) while using much lower, and more ESI–MS compatible,

channel flow rates (<12 µL/min) than previous on-line studies

(Figure 5[b]) (17,18). Resolution of monomer and aggregate

species as well as desalting during AF4 led to higher

signal-to-noise for ESI–MS detection (Figure 5[c] and 5[d]).

Lipodomic analysis of HDL and LDL from human serum

was also shown by chip-type AF4 (19). On-line desalting

0.8

Unfltered

Filtered0.6

0.4

0.2

0.0

0

Vl, mL

2

Vo

4 6 8 10

10

100

rrm

s, n

m

dR

I, V

1

Figure 3: ThFFF–MALS–dRI analysis of unfiltered (solid line,

black symbols) and 0.5-µm filtered (dashed line, grey symbols)

microgel-containing poly(vinyl acetate). The lines and symbols

represent the dRI fractograms and rg, respectively. Significant

polymer loss in the filtered sample is evident in the lower MM

distribution. Adapted and reproduced with permission from

D. Lee and S.K.R. Williams, (2010), J. Chromatogr. A. 1217,

1667-1673. © Elsevier.

SECbindingaffnity

(Kd) 10-10M

lgG-antigen lgG-Fcγ RI7

lgG-FcRn-HSA

Proteasebinding

Lower affnityaggregates8,9

Higgher affnityaggregates9

lgG-Fcγ RII/lll7

lgG-FcRnFc-FcRn FcRn-HSA10-9M 10-8M 10-7M 10-6M 10-5M 10-4M 10-3M

FFFSPR

AUC10

Figure 4: A comparison of FFF to other currently used techniques

(analytical ultracentrifugation [AUC]; surface plasmon resonance

[SPR]) for protein–protein characterization. The open channel

FFF design and flow-based separation extends the current ability

to detect weak protein–protein interactions into the µM binding

affinity range. Adapted and reproduced with permission from

J. Pollastrini, T.M. Dillon, P. Bondarenko, and R.Y.T. Chou,

(2011), Anal. Biochem. 414, 88–98. © Elsevier.



Williams et al.

the physiological effects of soluble fibre. Several recent studies

have used AF4–MALS–dRI to analyze β-glucans (36–38). In

one of the studies, β-glucan aggregates under gastric digestion

conditions were disrupted while, after undergoing small

intestinal digestion, aggregates were reformed (Figure 6) (36).

The disruption and re-formation of aggregates is likely to impact

the behaviour and function of β-glucan. To demonstrate the

effect of processing and storage on aggregates, Ulmius et al.

subjected barley β-glucan samples to several conditions (such

as storage time, heating, freeze time, freeze-thaw, and change

in solution conditions) and performed AF4–MALS–dRI analysis

(37). Disruption, structural change, or elimination of β-glucan

aggregates was observed under most conditions. Properties of

individual and aggregated β-glucans from oat and barley were

also compared using AF4–MALS–dRI (38). Individual molecules

could be distinguished from supramolecular species based

on conformational differences across the size distribution. In

addition, dissolution of both β-glucans under harsh alkaline

conditions showed that barley β-glucan aggregates were not

dissolved as previously proposed.

AF4 has been applied to hyaluronan (HA) and sodium

hyaluronate (NaHA) polysaccharides, which have

important biological functions and industrial applications

(39). Characterization of HA MM and conformation by

AF4-MALS-dRI yielded results that were consistent with other

methods, including SEC–MALS–dRI. Both AF4 and SEC were

able to measure low MM (<1 × 106) samples. Molar mass

distributions, an important parameter for HA characterization,

were also similar between AF4 and SEC measurements.

NaHA is used commercially in pharmaceutical and cosmetic

products (40). Molar mass distributions and structural properties

of NaHA and commercially blended NaHA mixtures were

characterized and compared by frit inlet (FI) AF4–MALS–dRI.

Frit inlet is a particularly gentle FFF method without the initial

focusing step. Significant aggregation was not observed

SEC results both yielded smaller sizes for the commercial starch

than the waxy yam starch, while a more quantitative recovery

for AF4 (100%) was seen compared to SEC (62%). Structural

characterization of the starches was also accomplished by

SEC and AF4. Plotting the rg and MM of the same fraction, and

using the exponent, νg, from the equation rgi = KgMiνg where

Kg is a constant, the polymer shape can be described (νg of

0.3, 0.5–0.6, and 1 describe the polymer shape for a sphere,

a linear random coil, and a rod, respectively). Values for AF4

and SEC were all close to 0.4, which fell between a sphere and

a random coil. Rolland-Sabaté et al. examined the differences

between hydrodynamic chromatography (HDC)–SEC and

AF4 for characterizing starches (33). Better separation of

amylose and amylopectin was achieved with AF4 and allowed

determination of dh and MM distributions and better structural

characterization (especially for large amylopectin fractions). In

addition, the branching parameter distributions showed that

WTPS and WTRS amylopectins could be discerned by AF4, but

not HDC–SEC.

Characterizing aggregates is important for understanding the

solution behaviour and physical properties of polysaccharides.

Arabinoxylan and its aggregates were characterized by AF4–

MALS–dRI and SEC–MALS–dRI (34). Although aggregate

concentrations were low, co-elution of individual polymers and

aggregates in SEC led to larger molar masses and rg’s reported

compared to AF4. The MM, size, and conformation of dextrans

with varying amounts of α(1-3) glycosidic linkages has also

been investigated (35). Using νg values, dextrans containing

the most α(1-3) linkages were found to be the smallest and

densest while dextrans with the least amount displayed a

quasi-linear conformation. Understanding the physical and

structural properties of glucan allows for further development of

biomaterials.

β-glucan’s solution behaviour and ability to form aggregates

may be associated with beneficial health effects. For example,

understanding β-glucan digestion can aid in comprehending

1010 0.8

Mo

lar

mass

(g

/mo

l) 0.6

0.4

0.2

0.0

109

108

107

106

105

104

0 1 2 3Elution time (min)

Flu

ore

scen

ce-s

ign

al (V

)

4

Water

Gastric digestion

Small intestinal digestion

5 6to

Figure 6: AF4 fractograms of barley β-glucan (lines represent

fluorescence and symbols represent molar mass). Samples

dispersed in water (grey-dashed line, circles), after in vitro gastric

digestion (grey full line, squares), and undergoing additional

small intestinal digestion (black line, triangles) were analyzed

by AF4–MALS. Gastric digestion samples show a reduction in

aggregate species, while the re-formation of higher density is

shown after small intestinal digestion. Adapted and

reproduced with permission from M. Ulmius, S. Adapa, G.

Onning, and L. Nilsson,(2012), Food Chem. 130, 536–540.

Split

#1

#2

#3

transferrin

dimer

0 3 6 9Time (min)

15 1812

CA

ACN+acid

emitter

Rela

tive in

ten

sity

Rela

tive in

ten

sity

Rela

tive in

ten

sity

Crossfowout

MS

(a)

(c)

(b)

(d)

Pump

Ion count:

Ion count:1.4E6

1.4E6

+461696.6

+451734.4

a. AF4-ESI-MS of #2

Mr=78,008t

r=9.6~9.9 min

+451626.0

500 1000 1500 2000

b. Direct ESI-MS of transferrin

m/z2500 3000

500 1000 1500 2000m/z

2500 3000

+421857.8

+392001.4

+352229.8

Figure 5: (a) A schematic of the chip-type miniaturized AF4

channel interfaced with electrospray ionization mass spectrometry

(AF4–ESI–MS); (b) Base peak fractogram (BPF) of AF4–ESI–MS

for the separation of CA and transferrin (V. out/V

. c = 0.012/0.49 mL/

min; (c) Full scan ESI–MS for peak #2 (transferrin) after AF4 shown

in (b); (d) Full scan ESI–MS spectrum of transferrin (0.01 μg/

μL) without AF4. Considerably better S/N is observed in the

fractionated transferrin as a result of monomer/dimer resolution

and contaminant removal during AF4. Adapted and reproduced

with permission from K.H. Kim and M.H. Moon, (2011), Anal.

Chem. 83, 8652–8658. © American Chemical Society.



Williams et al.

degrees of branching and HMMs were analyzed. As a result

of the co-elution of large and small macromolecules in SEC, a

correct calculation of the MM distribution and the MM average

or branching ratio was not possible (Figure 7). In contrast, AF4

allows the precise determination of the MM distribution, the

MM averages, and the degree of branching because the MM

versus elution volume curve and the conformation plot were

not affected by the co-elution issues encountered in the SEC

analysis.

In addition, because of the absence of significant shear

degradation in the channel, characterization of linear and

branched HMM polyethylene by AF4 has been developed

under high temperature (HT) conditions (145 ºC) in organic

solvent (1,2,4-trichlorobenzene [TCB]) (46). Compared to

HT-SEC, HT–AF4 allows for a more complete separation of

highly branched polyethylene with limited co-elution of large

and small macromolecules. The HT–AF4 technique coupled

with MALS detection was used for quantification and size

determination of the co-eluting molecules. Furthermore, HT–AF4

induced lower shear and thermo-oxidative degradation of HMM

PE and PP than HT–SEC (47). As a consequence, the HMM

averages obtained from HT–AF4 are significantly higher than

those obtained from HT–SEC. It was shown that most of the

observed limitations of SEC could be overcome by using AF4.

AF4 has also been applied to dendritic polymer

characterization. Different poly(amidoamine) (PAMAM)

dendrimers have been characterized by AF4–MALS (48). The

separation between different generations (4 to 9) of PAMAM

particles has been shown under different pH conditions and

AF4 highlighted the presence of some impurities. Coupled with

other on-line characterization techniques (for example, MALS

or a differential viscometer), AF4 allows for a more detailed

physical characterization of each separated size fraction.

Aggregation and complexation of dendritic glycopolymers

used as drug delivery systems has been demonstrated using

AF4–MALS (49–51). In addition, removal of small sample

components through the ultrafiltration membrane during

AF4 can be used to quantitatively determine the amount of

complexed small guest dye molecules in core–shell polymers.

This feature of AF4 can potentially be used for the separation

and quantification of drugs encapsulated in polymers and

makes the AF4 technique very promising for the analysis of drug

delivery systems.

Other types of drug delivery systems such as micelles have

been characterized by AF4. Poly(ethyleneoxide-b-ε-caprolacto

ne) (PEO-b-PCL) self-assemblies in water were characterized by

AF4 with on-line MALS–dRI–UV–vis–QELS detection (52). This

study underlined the impact of the mass of the PEO and PCL

fragments on the micelle size. Hydrodynamic radii measured

by QELS were in good agreement with values calculated by

AF4 retention times. AF4 illustrated that in some instances the

number of self-assemblies present was very low compared

to the number of unassembled diblock copolymers. Finally,

quantification of photosensitizers used in photodynamic therapy

encapsulated by these micelles has been performed. This

approach was used to characterize several diblock copolymer

micelles (PEG-PVP, PEG-PLA, PEG-PLGA, and PEG-PCL)

and determine their in vitro half-lives in human serum (53). The

impact of human serum on the micelle size and stability was

shown by AF4. Indeed, micelle disassembly was observed for

PEG-PVP micelles, while PEG-PLA, PEG-PLGA, and PEG-PCL

micelles were far more stable.

while samples subjected to gamma ray sterilization showed a

significant breakdown of NaHA. Exudate gums are complex

polysaccharides with industrial applications. They are used

as emulsifiers and stabilizers and contain a small amount of

proteinaceous material. Molar mass, rg, dh, conformation,

apparent densities, and distribution of proteinaceous material

were determined for gum arabic (GA) and mesquite gum (MG)

by AF4–MALS–dRI (41). The separation of polysaccharide and

proteinaceous populations and the characterization of important

molecular data over the entire size range were demonstrated by

AF4. Using AF4, it was possible to conclude that GA-stabilized

emulsions were more stable against coalescence than

MG-stabilized emulsions.

The characterization of gelatine by AF4 has also been

demonstrated (42). In denatured native gelatine an increase

in MM during renaturation was attributed to α-, β-, and

γ-chain interactions. However, an increase in MM for thermally

pre-treated gelatine was not seen, indicating an inhibition of α-,

β-, and γ-chains in gelatine and therefore limiting renaturation.

The effect of available lysine (lysine with a hydrogen-bonding

amino group) on the formation of HMM compounds in gelatine

was also characterized by AF4 (43). A decrease in available

lysine with thermal treatment led to higher MMs.

Tannins play an important role in the colour, taste, and overall

quality of wine. Oxidized tannins formed macromolecules

and were characterized by AF4–MALS, showing soluble

and insoluble populations (44). Both AF4 and small-angle

X-ray scattering (SAXS) showed that the MM of insoluble

macromolecules was much higher than the soluble

macromolecules.

Synthetic Polymers In recent years, advances in the characterization of synthetic

polymers have included the introduction of an elevated

temperature AF4 instrument and new applications in AF4

and ThFFF. Low MM polyethylene samples and a number of

narrowly distributed polystyrene standards were analyzed

by AF4–MALS–dRI in organic solvent and compared to

SEC–MALS–dRI (45). At ambient temperature, low-density

polyethylene, polypropylene, and polybutadiene containing high

1011

1010

109

108

107

106

105

104

1011

1010

109

108

107

106

105

104

10 20 30

Retention time (min) Retention time (min)

MW

CSTR LDPE 1M

W CSTR LDPE 2

MW

CSTR LDPE 1

HT-AF4 HT-SEC(a) (b)

MW

CSTR LDPE 2

Mo

lar

ma

ss (

g/m

ol)

Mo

lar

ma

ss (

g/m

ol)

40 50 10 20 30 40 50

Figure 7: Separation of a low density polyethylene sample

(CSTR-LDPE 1) by (a) HT–AF4 and (b) HT–SEC, with MALS and

dRI detection. The abnormal curvature of the molar mass from

HT–SEC indicates co-elution as a result of the high branching

of the polymers. AF4 shows complete separation over the entire

size range into the ultrahigh molar mass range not detected by

SEC because of shear degradation. Adapted and reproducd

with permission from T. Otte, H. Pasch, T. Macko, R. Brull,

F.J. Stadler, J. Kaschta, F. Becker, and M. Buback, (2011), J.

Chromatogr. A. 1218, 4257–4267. © Elsevier.



Williams et al.

is proportional to DT/D. If D can be measured independently,

that is, by QELS, DT can be calculated. When on-line D

measurements are made, DT can be calculated as a function

of tr and subsequently correlated with polymer composition.

Using this premise, the DT was found to be independent of

MM for copolymers with similar compositions and dependent

on composition of copolymers with similar MM in a non-

selective solvent. The ThFFF–MALS–dRI–QELS combination

allowed rapid determination of copolymer MM and chemical

composition distributions. ThFFF has recently been coupled

to NMR off-line (61) and on-line (62) in the analysis of triblock

copolymers and PS, poly(methyl methacrylate) (PMMA),

polyisoprene (PI), and PS-b-PMMA block copolymers,

respectively. NMR provided an independent measurement

of copolymer composition and confirmed compositional

separation by ThFFF.

To date, ThFFF method development has been predominantly

through trial-and-error based on other published work. A

recent paper demonstrated that a theoretical approach

based on temperature-dependent osmotic pressure gradient

and polymer–solvent interaction parameters can be used

to successfully estimate DT and retention times for different

polymer–solvent pairs (57). Experiments confirmed the

calculation of poly(n-butyl acrylate) (PBA), poly(methyl acrylate)

(PMA), and PS retention times in different solvents. This provides

a potential route to predicting good solvents for polymer

retention.

Thermal diffusion is an intriguing phenomenon with hidden

potential for other important analyses. A recent development

has shown that the correlation between theoretical and

experimental DT values can provide information about the

number of chain ends for branched polymers (63). The

uniqueness of this study lies in the fact that the chain ends can

be determined without the need for a linear polymer analogue.

The ThFFF–MALS–dRI–QELS combination allows simultaneous

determination of MM, composition, and number of chain ends.

ConclusionsFFF is a versatile family of techniques for characterizing

biological, natural, and synthetic macromolecules. As

a complementary technique to SEC, more detailed

macromolecule characterizations are possible using both

FFF and SEC. The open channel design and soft separation

mechanism of FFF make it a powerful technique for analyzing

weak macromolecule interactions, polymer aggregates, and

HMM and highly branched polymers. The benefits to users are

also evident in simplified sample preparations, ultrafiltration of

contaminants during separation, and flexibility in carrier fluid

choice among others. A conference dedicated to this subject

— The 16th International Symposium on Field- and Flow-Based

Separations (FFF2013) — was held in Pau, France in July, and

FFF2014 will be held in Salt Lake City, Utah, USA in October

next year. Further interesting developments are anticipated,

along with a flurry of associated publications.

AcknowledgementsCB and SKRW thank the National Science Foundation

CHE-1013029 for financial support.

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1674–1692 (2010).

ThFFF has been mainly used to fractionate and characterize

lipophilic polymers in organic solvents. The applied force is a

temperature gradient that causes thermal diffusion of analytes.

The magnitude of the thermal diffusion coefficient DT has been

empirically observed to depend on the polymer—solvent

interface and other factors (54–56). Thermal diffusion in liquids

is a complex phenomenon that is not yet fully understood (57–

60). However, its usefulness in ThFFF polymer separations has

been demonstrated and new interesting capabilities are being

developed. For example, the observation that different polymer

chemistries in the same solvent or the same polymer chemistry

in different solvents can have different DT and hence tr (see

Equation 2) allows for chemical composition (in addition to size)

analyses of polymers.

ThFFF coupled with MALS–dRI–QELS was used

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100(a)

(b)

Th

FFF P

S w

eig

ht

perc

en

tTh

FFF P

S w

eig

ht

perc

en

t

100

80

80

y=.99x +.17

R2=.98

y=1.0x +1.21

R2=.98

60

60

Nominal PS weight percent in PS-PBA

40

40

20

100

80

60

40

20

20

1008060

Nominal PS weight percent in PS-PMA

4020

Figure 8: Weight percent composition of (a) PS-PBA and (b)

PS-PMA copolymers were determined through averaged on-line

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Chromatogr. A. 1218, 6774 –6779. © Elsevier.



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518–523 (2012).

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42. K. Rbii, O. Surel, N. Brambati, A.M. Buchert, and F. Violleau, Food

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43. K. Rbii, F. Violleau, N. Brambati, A.M. Buchert, and O. Surel, Food

Hydrocolloids 25(6), 1409–1412 (2011).

44. A. Vernhet, S. Dubascoux, B. Cabane, H. Fulcrand, E. Dubreucq,

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6774–6779 (2011).

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7016–7022 (2011).

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9935–9942 (2000).

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8(2), 133–153 (2003).

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61. C.A. Ponyik, D.T. Wu, and S.K.R. Williams, Anal. Bioanal. Chem. In

Press (2013).

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Macromolecules. 46(7), 2544–2552 (2013).

63. J.R. Runyon, D.T. Wu, and S.K.R. Williams, Characterizing branched

Polymers Using Thermal Field-Flow Fractionation — In Preparation.

Carmen Bria is a PhD student in the Chemistry Department at the

Colorado School of Mines (Colorado, USA). His research focuses

on the use of FFF and light scattering to characterize proteins

and probe protein aggregation processes. He is also working on

developing improved membrane surfaces for flow FFF.

Frédéric Violleau graduated from ENSCT (INPT – University

of Toulouse, France) with a “Diplôme d’Ingénieur en chimie”

(equivalent to a MSc in chemistry) and from the National

Polytechnic Institute of Toulouse (University of Toulouse) with

a PhD in organic chemistry. He joined Ecole d’Ingénieurs

de PURPAN (EI Purpan – INPT – University of Toulouse) in

2003 and he is currently vice head of the Agricultural and

Food Sciences Department. He has experience in using

AsFlFFF technology for various applications involving proteins,

polysaccharides, polymers, and particles.

S. Kim R. Williams is a professor of chemistry and the Director

of the Laboratory for Advanced Separations Technologies at

the Colorado School of Mines. She began her journey with FFF

as a postdoctoral fellow with the late J. Calvin Giddings at the

University of Utah (Utah, USA) and has acquired more than

25 years of experience in this field. Research in the Williams

group focuses on developing new capabilities for nanoparticle

and macromolecular analyses using FFF and related methods.

Dissemination of these new technologies are done through

collaborations with scientists at universities, companies, and

national laboratories. She recently edited a book entitled Field-

Flow Fractionation in Biopolymer Analysis.8

ES339972_LCA1113_016.pgs 10.18.2013 20:05 ADV blackmagentacyan


LC TROUBLESHOOTING

Recently, a reader emailed me with a

problem he was having determining the

lower limit of quantification (LLOQ) for his

method, which had a target LLOQ of 0.01

µg/mL for his analyte. He compared the

LLOQ calculated using the International

Committee on Harmonization guidelines

(ICH) (1) with replicate injections of a

reference standard and found that the

two differed by more than an order of

magnitude. He came to me to help him

figure out what was wrong. The method

was proprietary, and the reader needed

to stay anonymous, so I’ve disguised

things a bit, but this case study helps us

to better understand how to evaluate a

calibration curve.

The ICH (1) presents a formula to

calculate what they call the quantitation

limit (QL), but what most users call the

limit of quantification (LOQ) or LLOQ:

QL = 10σ/S [1]

where σ is the standard deviation of the

response (the standard error [SE]) and S

is the slope of the calibration curve. This

is calculated easily from the regression

statistics generated in Microsoft Excel or

your data system software. Let’s see how

this works.

Table 1 includes the initial data

from the calibration curve. The user

injected eight concentrations of his

analyte, ranging from 0.01 to 1.0 µg/

mL, generating the peak areas shown

in the “Response” column of Table 1. I

used Excel’s regression tool to generate

the regression statistics, part of which

I’ve included in Table 2. These include

the coefficient of variation (r2), the

standard error of the curve (SE‑curve),

the y‑intercept (intercept‑coefficient),

the standard error of the y‑values

(intercept‑SE‑y), and the slope of the

curve (X variable). Calculated values for

these variables are shown in the second

two columns of Table 2, headed “With

1.0 µg/mL”.

The user used equation 1 with the

standard error of the curve (SE‑curve)

and slope, and found that the LLOQ was

predicted to be ~0.15 µg/mL (summarized

as the first entry of Table 3). (Here I’ll

pause to remind you that I’ve rounded

and truncated numbers in the tables

for ease of viewing; if you try to repeat

my calculations, your results may differ

slightly.) Yet, when he injected n = 10

replicates of a 0.01 µg/mL solution, he

found the percent relative standard

deviation (%RSD) was 1.1% (last entry,

Table 3), which he felt indicated the LLOQ

was considerably lower than the 0.15‑µg/

mL prediction using the ICH technique. At

this point he contacted me.

Examine the Calibration CurveThe calibration curve shown in

Figure 1(a) was supplied to me with the

data set. You can see that the value

of r2 = 0.9986 is excellent. The linear

regression line is shown in blue; at first

glance, this looks good too. However, a

closer examination of the regression line

shows that it is above the data points

at low concentrations and below the

data points at the high concentrations,

passing through the data points at middle

concentrations. This kind of behaviour

tends to send up a caution flag for me

because the higher concentrations tend

to dominate the calculation. It is time to

examine the data a little more carefully.

Although it is part of the reporting

requirements for most methods, we

should be a little careful about putting

too much confidence in values of r2.

The reason for this is that the coefficient

of variation is meant to be used with

homoscedasic data; that is, data in which

the standard deviation is approximately

the same throughout the data range.

Chromatographic data, however, are not

homoscedastic, but heteroscedastic. The

relative standard deviation (%RSD) tends

to be constant throughout the range. In

plain English, chromatographic data don’t

have, for example, standard deviations of

±1 ng/mL throughout the concentration

range, but they might instead have

±0.1% RSD throughout the range.

The coefficient of variation, r2, doesn’t

describe heteroscedastic data very well,

so if we use r2 as our sole determinant

of the goodness of a calibration curve,

we may be misled. This all means that

r2 = 0.9986 for these data does not

guarantee that all is well.

Back to Table 1. I’ve used the

regression equation to calculate the

expected response at each concentration

and compared this to the actual response

to determine the percent error. These

values are listed in the third column of

Table 1 (%‑error; with 1.0 µg/mL). You can

see that the deviations from the expected

values increase at lower concentrations,

as expected, but they are also larger at

high concentrations than in the middle

of the curve. One technique to find out

if there is a problem with the highest

concentration is to drop it from the data

set and repeat the calculations. I did

this by dropping the 1.0‑µg/mL point;

the data are shown in column four of

Table 1 (%‑error, without 1.0 µg/mL).

Notice how this reduces the deviations

from the expected values. Also, the error

increases at the lower concentrations,

as expected, but is very small at higher

concentrations (with the exception of 1.0

µg/mL). The regression results for the data

without the 1.0‑µg/mL point are shown in

the last two columns of Table 2 (headed

“without 1.0 µg/mL”). You can see that

the SE‑curve, SE‑y, and y‑intercept are

all reduced by approximately an order

of magnitude, yet r2 changes very little

(0.9986 versus 1.000). In Figure 1(b),

I’ve plotted the revised regression line,

What’s the Problem with the LLOQ? — A Case StudyJohn W. Dolan, LC Resources, Walnut Creek, California, USA.

Two methods of calculating the lower limit of quantifi cation (LLOQ) disagree. Which, if either, is correct?



LC TROUBLESHOOTING

which visibly fits the data better than the

original if the 1.0‑µg/mL point is ignored.

Another way to evaluate these differences

is to compare the absolute values of the

%‑error, as shown in the last two columns

of Table 1. The sum of these absolute

values is shown at the bottom. Notice that

eliminating the 1.0‑µg/mL point from the

regression calculation reduced the total by

more than 2.5‑fold from 54% to 20%. This

is definitely a better fit of the data.

An additional way to visualize the data

is shown in Figure 2, where I’ve taken just

the lower (Figure 2[a]) and higher (Figure

2[b]) portions of the concentration curve

and expanded the scale. Now the original

regression (blue line) is obviously an

inferior fit to the revised one (red line) at

both ends of the scale.

At this point, it might be interesting to

determine what the problem is with the

1.0‑µg/mL point, but I don’t have any

additional information to help me with this

task. It would be nice to make several

replicate injections to be sure the 1.0‑µg/

mL data point isn’t an outlier. If the problem

persists over replicate injections, a new

preparation of the standard should be

checked to eliminate the possibility of

formulation errors. Another possibility is

that the peak is large enough to cause a

slightly nonlinear behaviour of the detector,

which often happens as the detector signal

nears its upper limit. In any event, I think

it is prudent to exclude this point from the

regression without further indications that it

should be included as a valid point.

Another question that often comes up

is whether the calibration curve should be

forced through x = 0, y = 0 or not. This

is a simple test that was discussed in an

earlier “LC Troubleshooting” column (2).

If the value of the y‑intercept calculated

from the regression process is less than

the standard error of the y‑intercept, it

means that the y‑value is within 1 standard

deviation (SD) of the 0,0 point. Most

statistical tests will tell you that there is no

statistical difference between a point <1

SD from the mean and the mean, so the

curve can be forced through zero. How do

you check this? The data are in the Excel

regression summarized in Table 2 on the

line labelled “intercept.” The “coefficient”

column lists the calculated value of the

y‑intercept, so if this is less than the

standard error (SE‑y), you can force the

curve through zero. You can see that in

both cases (with or without 1.0 µg/mL

included), the y‑intercept is greater than

the standard error, so the curve should not

be forced through zero.

Double-Check the CalculationsNow that we’ve decided to exclude 1.0 µg/

mL from the regression calculations, let’s

see why the ICH method predicted such

a large LLOQ. When I tried to reproduce

the user’s results, I found the problem. He

was using the standard error of the curve

(SE‑curve, line 2 of Table 2) instead of

the standard error of the y‑intercept. The

SE‑curve value represents the variability

around the regression curve throughout

the whole range of the curve. But for

determination of the LLOQ, we want to use

the standard error in that region instead,

so SE‑y is more appropriate. Otherwise we

often find that the variability of the larger

concentrations overpowers the variability of

the lower ones and gives an unrealistically

high value of the LLOQ. When I used the

SE‑y value with equation 1, the LLOQ was

reduced by approximately two‑fold with

Figure 1: Plot of data of Table 1 with overlay of regression lines. Regression (a)

including and (b) excluding the 1.0‑µg/mL point.

0 0.2 0.4 0.6 0.8 1.0

Concentration (μg/mL)

0

4

8

12

16

(a)

Resp

on

se (

x10

-6)

r2 = 0.9986

y = 14.9E6x+133832

0

4

8

12

16

0 0.2 0.4 0.6 0.8 1.0


r2 = 1.0000

y = 15.4E6x+39211

Resp

on

se (

x10-6

)

(b)

Table 1: Input data and error calculations.

Concentration (µg/mL)

Response%-Error Absolute %-Error

With 1.0 µg/mL

Without 1.0 µg/mL

With 1.0 µg/mL

Without 1.0 µg/mL

0.01 207,028 37% ‑7% 37% 7%

0.05 853,543 3% ‑5% 3% 5%

0.10 1,548,352 5% 2% 5% 2%

0.20 3,096,704 1% 1% 1% 1%

0.40 6,193,568 ‑1% 0% 1% 0%

0.60 9,290,112 ‑2% 0% 2% 0%

0.80 12,386,816 ‑2% 0% 2% 0%

1.00 14,686,085 3% 5% 3% 5%

Sum 54% 20%


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LC TROUBLESHOOTING

be near or prepared at the quantitation

limit.” In the last line of Table 3, you can

see that the n = 10 replicate injections

at 0.01 µg/mL gave imprecision of 1.1%

RSD, an excellent value at the LLOQ for

most methods. This strongly suggests

that the method will perform adequately

at the desired LLOQ of 0.01 µg/mL of the

target analyte.

SummaryThis data set has served as a good

example of how easy it is to misinterpret

the results of a calibration curve. We saw

that the value of r2 can be misleading

about how good the calibration curve is.

the original dataset (0.15 versus 0.08 µg/

mL), as shown in the first two lines of Table

3. When the SE‑y of the revised calibration

curve (without 1.0 µg/mL) is used, the

predicted LLOQ drops to 0.011 µg/mL. As

mentioned above, the revised calibration

curve generates values of SE‑curve and

SE‑y that are approximately an order of

magnitude smaller than the original data

set (Table 2).

The predicted LLOQ that we just

calculated using the ICH method is not

sufficient, however. The ICH document

(1) clearly states, “the limit should be

subsequently validated by the analysis of

a suitable number of samples known to

It was shown that it is useful to examine

both a visual and tabular expression of

the data. The original plot (Figure 1[a])

suggested that the highest concentration

might be biasing the regression, and

when this point was eliminated, the new

trend line (Figure 1[b]) fits all the other

points better. Expanding the scale on

the plots (Figure 2) also helped to get

a better picture of what is happening.

Comparing the sum of absolute values

of the deviations of experimental data

points from those calculated from the

regression is a simple way to see if a new

data treatment reduces the overall error.

In the present case, error was reduced by

more than 2.5‑fold simply by dropping the

highest concentration point (Table 1).

When using estimating techniques,

such as the ICH method used here, it is

imperative to use the correct coefficients

or the wrong conclusions may be

drawn. Fortunately, the user noticed that

something was wrong and searched for

further help. If, instead, he believed the

calculations, he might have discarded a

good method or spent unnecessary time

trying to improve an already acceptable

method. Finally, regression curves,

percent‑error tables, and data plotting

techniques are merely tools to help us

understand the data better. When it

comes to determining the LLOQ, there

is nothing that can compare with the

measured performance from multiple

injections at the target LLOQ.

References(1) Validation of Analytical Procedures: Text and

Methodology Q2(R1), International Conference

on Harmonization, Nov. 2005, http://www.ich.

org/LOB/media/MEDIA417.pdf.

(2) J.W. Dolan, LCGC North Am. 27(3), 224–230

(2009).

John W. Dolan is the vice president of

LC Resources, Walnut Creek, California,

USA. He is also a member of the LC•GC

Asia Pacific editorial advisory board. Direct

correspondence about this column should

go to “LC Troubleshooting”, LC•GC Asia

Pacific, 4A Bridgegate Pavilion, Chester

Business Park, Wrexham Road, Chester,

CH4 9QH, UK, or e‑mail the editor‑in‑chief,

Alasdair Matheson, at amatheson@

advanstar.com

Table 2: Summary of Excel regression statistics.

Concentration

(µg/mL)With 1.0 µg/mL Without 1.0 µg/mL

r2 0.9986 1.0000

SE‑curve 222,989 28,842

Coefficient SE‑y Coefficient SE‑y

Intercept 133,832 119,416 39,211 16,245

X variable 14,934,035 15,417,430

Figure 2: Expanded sections of Figure 1(a) (blue) and 1(b) (red): (a) 0.01–0.2 µg/

mL region, (b) 0.6–1.0 µg/mL region.

0 0.05 0.10 0.15 0.20 0

10

20

30


Resp

on

se (

x10

-5)

(a)

0.6 0.8 1.0 8

10

12

14

16


Resp

on

se (

x10

-6)

(b)

Table 3: Summary of LLOQ calculations.

Technique LLOQ (10σ/S’)

SE‑curve 0.149 µg/mL

SE‑y with 1.0 µg/m 0.080 µg/mL

SE‑y without 1.0 µg/mL 0.011 µg/mL

0.01 µg/mL, n = 10 1.1% RSD


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GC CONNECTIONS

One of the classical trade‑offs in gas

chromatography (GC) separations

lies between speed of analysis and

peak resolution. Chromatographers

can increase the speed of analysis

in a number of ways, including the

use of shorter and narrower columns,

higher temperatures and temperature

programme ramp rates, and faster

flow rates, but higher speeds do

not guarantee equal or better peak

resolution. The relationships between

flow or velocity and resolution have

recently received attention in the

context of a drive towards faster

separations, and the on‑going

substitution of hydrogen carrier gas for

helium in many laboratories also fuels

the discussion. This instalment of “GC

Connections” discusses the effects of

increased carrier‑gas flow or velocity

in an example separation that includes

two pairs of solutes.

Optimum Practical Velocity One of the more neglected separation

metrics is the optimum practical

carrier‑gas velocity (OPGV). This

idea is not new: The pioneers of

gas chromatography formulated the

OPGV as one way to measure the

trade‑offs between speed of analysis

and resolution. As the carrier‑gas flow

increases above an optimum value,

peaks become broader and their

resolution starts to decline but they

are eluted sooner in proportion to

the higher flow. Scott and Hazeldean

(1) proposed that an optimum

compromise between the two could

be found by increasing the flow until

the corresponding increase in a plot of

plate height versus average carrier‑gas

velocity becomes essentially linear. An

optimum velocity would be reached

at the point where additional losses of

resolution because of further increases

in velocity could not be compensated

for by a corresponding increase in

column length.

Without experimental measurements

from multiple columns, the OPGV has

been considered as the velocity at

which a tangent line from the origin

meets a plot of measured values of the

plate height, Hmeas, versus the average

linear carrier‑gas velocity, u–. This is the

velocity at which the quantity H/u– hits

a minimum (2). A plot of experimental

Hmeas versus u– departs from the

linear at higher velocities because of

secondary gas‑compression effects

at higher pressures and extracolumn

broadening from the detector if the

peaks become narrow enough. The

basic Golay equation, however,

neglects such effects. A plot of the

theoretical plate height, Htheor, versus u–

will never become completely linear:

H = (B/u–) + Cu– [1]

where H is the height of one

theoretical plate, u– is the average

carrier‑gas linear velocity, B describes

the broadening of a peak because

of gas diffusion along the direction

of carrier‑gas flow, and C describes

broadening because of the effects

of solute molecules entering and

leaving the stationary phase. As the

linear velocity (flow) increases, a

decreasingly small fraction of the total

theoretical plate height is a result of

the B term, and the C term dominates.

These effects are shown in Figure 1

for the basic Golay equation using a

25 m × 0.53 mm column. In this, and

the subsequent column treatments,

the influence of the stationary phase

on solute broadening is minimal; the

stationary‑phase film thickness used

was 0.4 µm, which influenced this plot

by less than 2%. Plot (a) is the total

theoretical plate height; plot (b) is the

B term contribution; and plot (c) is

the C term contribution. Plot (c) also

represents a tangent that meets the

total plate height (a). That this junction

occurs at infinite linear velocity shows

the fundamental difficulty with using

the basic Golay equation this way for

OPGV calculations.

The basic Golay equation yields an

infinite linear velocity if a tangent‑line

construction is used to find the OPGV

because the theoretical relationship

neglects the effects on plate height

of operating at higher inlet pressures

and of producing potentially very

narrow peaks. The theoretical plot

does not curve away from a linear

relationship at elevated velocities,

but experimental data do. Although

it is a convenient way to explain

idealized column band‑broadening

behaviour without making arduous

measurements of Hmeas versus u– data

for multiple columns, widespread

use of the basic Golay equation has

resulted in the neglect of OPGV as a

means of expressing a practical upper

limit for average linear velocity in

specific separations.

A simple approach to determining

a finite value for OPGV from the

basic Golay equation is to choose an

arbitrary point that sets the OPGV at

the velocity where gas–gas diffusion

contributes a fixed percentage of

the overall band broadening. Figure

1 illustrates an example at the

point labelled (d), where gas–gas

diffusion contributes 10% of the total

band‑broadening and u– = 117 cm/s.

The optimum velocity, u–opt — the point

at which H is at a minimum — is shown

as well at point (e), where u– = 39.2

Practical Gas ChromatographyJohn V. Hinshaw, BPL Global Ltd, Hillsboro, Oregon, USA.

Questions about how practical proposed gas chromatography (GC) method changes are often come up during optimization for speed and resolution, or while converting to a different carrier gas. Related objective measurements such as the optimum practical carrier gas velocity were deà ned more than 40 years ago. This instalment reviews such metrics in the light of their relevance to today’s GC challenges.



GC CONNECTIONS

cm/s. But this idealized and arbitrary

OPGV point is not connected to

physical properties that accommodate

the effects of an increasing inlet

pressure gradient on peak broadening;

the velocity of 117 cm/s seems too

high for a reasonable upper limit.

Extended band‑broadening theories

that do include such effects can

produce a better theoretical picture of

the effects of increasing the velocity.

Practical TheoryA number of authors have proposed

more‑complete theories, including

Golay himself, although his extended

equations address porous‑layer

open‑tubular (PLOT) and support‑

coated open‑tubular (SCOT) columns

and do not consider how gas–gas

diffusion is affected by carrier‑gas

compression inside the column. A

relationship proposed by Giddings

(3) works well as a theoretical model

for determining OPGV values that are

closer to experimental data. Such

calculations are only as good as the

model and the accuracy of the applied

physical parameters, of course, but

they can provide useful insight for the

selection of practical operating gas

velocities or flows.

The B and C terms of the Golay

equation (equation 1) are proportional

to the rate at which solutes diffuse

through the carrier gas. Thus,

band‑broadening increases as the

gas–gas diffusion rates increase.

The Golay equation considers these

diffusion rates at the column outlet

pressure (atmospheric pressure) alone

1.0

0.8

(e)

(d)

(a)

(c)

(b)

0.6

0.4

0.2

0.0

0 20 40 60 80 100 120 140 160 180

H (

mm

)

u (cm/s)

Figure 1: Plot of the basic Golay equation for n‑hexane: (a) total plate height, (b) B

term contribution to the plate height, (c) C term contribution to the plate height, (d)

OPGV where the B term contribution accounts for 10% of the total plate height, and

(e) optimum carrier‑gas velocity. Theoretical column parameters: 25 m × 0.53 mm,

0.4‑µm nonpolar stationary‑phase film thickness, 130 °C, helium carrier gas.

11708

©2013 Sigma-Aldrich Co. LLC. All rights reserved. SIGMA-ALDRICH and SUPELCO are trademarks of Sigma-Aldrich

Co. LLC, registered in the US and other countries. Ecoporous and Titan are trademarks of Sigma-Aldrich Co. LLC.

N 1.9 μm UHPLC ColumnsS l ® d T ™ C UHPLC l b dSSupelco® introduces Titan™ C18 UHPLC columns, basedS

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These particles are the result of a newly developed, patent-T

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and real time availability, visit

sigma-aldrich.com/titan



GC CONNECTIONS

accommodates the net effect

of increasing diffusion rates on

band‑broadening as solutes progress

along the column. The correction is

larger with increasing inlet pressure

and, thus, follows the influence of

higher carrier‑gas velocities. (The

details of this correction factor and the

Giddings equation are available for

interested readers as supplementary

material on‑line at http://wiki.hrgc.com.)

In addition to the influence of the

carrier‑gas pressure, diffusion rates

are different for different solutes. They

grow smaller with increasing molecular

weight. The diffusion rate of n‑hexane,

for example, in helium (or in any other

and does not consider the effect of

the higher pressures and gradient

inside the column itself. Physical

measurements as well as theoretical

treatments of gas diffusion show that

diffusion is inversely proportional to

pressure: Diffusion slows down as

pressure increases. The effect of

intracolumn carrier‑gas pressure on

diffusion rates is not very large at low

inlet pressures, such as in a 0.53‑mm

i.d. column. As inlet pressures rise the

effect becomes more significant — for

example, with a 0.25‑mm i.d. column

— especially at higher linear velocities.

Giddings’ equation applies a

pressure correction factor that

carrier gas) is about 40% faster than

that of n‑dodecane. Temperature plays

a role too; gases diffuse more rapidly

at higher temperatures. The current

discussion is limited to isothermal

conditions so variable temperature

isn’t a concern, but its influence is

significant when temperatures or

programming rates are changed as

part of optimization.

Figure 2 shows a series of Giddings

theoretical plate height versus u–

curves for n‑C6, n‑C8, n‑C10, and

n‑C12 on a 25 m × 0.25 mm column

at 130 °C. Theoretical diffusion

coefficients for each solute were used

as listed by Ettre (4). The column

pressure drops were calculated

from theory as well, using the same

relationships found in instrumental

electronic pneumatics.

The influence of the individual solute

diffusion rates on plate height is quite

clear. Each solute takes on its own

optimum average linear velocity, as

marked on each plot in Figure 2. That

different solutes have different u–opt

values is not new information, although

the span of the optima in this case

— from 34 cm/s for n‑C12 to 44 cm/s

for n‑C6 — appears wider than might

be expected. Without considering

the OPGV for the moment, the optima

plainly show that biasing the carrier‑

gas velocity on the high side appears

to be a good idea: Small losses in

efficiency are taken while achieving a

faster separation. But what about the

high end of linear velocity for speed

optimization purposes?

With the Giddings equation a tangent

line from the origin intersects each

curve at a well‑defined point, and

these points correspond to the original

definition of the OPGV. The tangent

line for n‑C6 is drawn in Figure 2; it

intersects the plot at u– = 68 cm/s.

Figure 3 illustrates another way of

expressing the OPGV by plotting the

number of plates generated per second

as a function of the average carrier‑gas

velocity. The maxima of these plots

correspond to each solute’s OPGV.

Similar to the span of u–opt, the OPGV

values range from 54 cm/s for n‑C12 up

to 68 cm/s for n‑C6.

Taken together, Figures 2 and 3

would define a range for minimum and

maximum optimized average velocities

across the scope of the idealized

normal hydrocarbons that were

employed, from the highest optimum

0 10 20 30 40 50 60 70 80 90 100

400

300

200

100

0

(d)

(c)

(b)

(a)

N/s

u (cm/s)

Figure 3: Plots of theoretical plates per second from the Giddings equation:

(a) n‑hexane, (b) n‑octane, (c) n‑decane, and (d) n‑dodecane. Vertical tick marks

on each plot show the maximum, at the OPGV. Theoretical column parameters,

same as in Figure 2.

1.0

0.8

0.6

0.4

0.2

0.0

0 10 20 30 40 50 60

OPGV

70 80 90 100

H (

mm

)

(d)

(c)

(b)

(a)

u (cm/s)

Figure 2: Plots of the Giddings equation for a series of hydrocarbons: (a) n‑hexane,

(b) n‑octane, (c) n‑decane, and (d) n‑dodecane. Dashed line: tangent that intersects

plot (a) at its OPGV. Theoretical column parameters, same as Figure 1 except for

column internal diameter of 0.25 mm.



GC CONNECTIONS

velocity of 44 cm/s to the lowest OPGV

of 54 cm/s. Compared to operating

at the lowest optimum of 34 cm/s, a

velocity of 54 cm/s would decrease

the analysis time by roughly 38% while

sacrificing about 20% of the theoretical

plates generated for the later‑eluted

n‑C12. Running the separation at 44

cm/s would restore much of the plate

number while sacrificing some of the

gain in analysis time.

These trade‑offs in column

efficiency for speed are also not

new information, but here the OPGV

values provide a more meaningful

upper end for a range of velocities

than do arbitrary velocities based on

simple percentages or multipliers,

while preserving most of the column’s

separating power. And as always,

the best procedure is to determine

actual experimental performance for

the analytes of interest. Theoretical

predilections provide useful guidelines

and place boundaries on the range of

practical conditions, but they are no

substitute for real data.

Practical Resolution Chromatography is all about peak

resolution, not just the efficiency of

individual solutes. The discussion so

far has considered solutes standing

alone, ones that are well separated

under almost any conditions at that.

Applying the Giddings equation to

pairs of closely eluted solutes provides

some more information and guidance

for selecting optimized velocities by

putting the modelled separation into

a context of resolution. Two pairs

of peaks were chosen so that the

resolution, Rs, of each pair will range

around 2.0. This exceeds the minimum

“baseline” resolution of 1.5 that is

often considered good enough, but a

resolution of 2.0 does provide some

working room for eventual performance

losses because of column degradation

and also makes for a more robust

method. Many separations do exhibit

more than one critical pair of peaks,

those that are resolved at close to the

minimum, so it is useful to consider

what happens to solute pairs at the

beginning and end of a separation as

the velocity is optimized.

Figure 4 shows the resolution that

would be obtained in theory between

two pairs of solutes, n‑hexane with

a closely following hypothetical

analogue, and n‑dodecane with

another closely eluted analogue.

Each solute pair is separated from its

neighbour with a separation factor, α,

of 1.03. Each pair of solutes shows

an optimum resolution at close to

the optimum linear velocity, which is

expected because they both share

nearly identical properties. The later‑

eluted pair (Figure 4[b]) does gain

some resolution at optimum over the

earlier pair (Figure 4[a]), which is

also expected because the later pair

simply has more time in the column for

resolution to develop. A bias towards

higher velocities up to the OPGV is

again clear, even more so than is

apparent for the individual solutes’

efficiencies as shown in Figure 2. It is

also interesting to see that both pairs’

resolution declines linearly with nearly

the same slope when the carrier‑gas

velocities are pushed higher above

their OPGV levels. If a minimum

resolution of 1.5 were the goal for

these peak pairs, then a velocity of

around 82 cm/s would be acceptable

and would realize a gain in speed of

analysis of approximately 2.5‑fold.

ConclusionBoth efficiency and resolution are

critical for obtaining acceptable

separations. With extra resolution

available, a column can be pushed to

higher linear velocities or flows while

still obtaining a minimum performance

level. Short of experimental data,

theoretical treatment of a separation

can yield useful information about

how performance would be affected

by increasing the speed of analysis,

but only to the degree that the

theory reflects the chromatographic

process. Application of an extended

theory such as the Giddings equation

provides additional insight beyond the

basic Golay equation, and this helps to

frame practical limits on optimization

for speed of analysis. But in cases

where peak resolution is minimal,

there is no substitute for careful

experimental evaluation of a faster

separation scheme.

References(1) R.P.W. Scott and G.S.F. Hazeldean, in

Gas Chromatography 1960, R.P.W. Scott,

Ed. (Butterworths, London, UK, 1960), pp.

144–161.

(2) W. Jennings, Analytical Gas

Chromatography (Academic Press,

Orlando, Florida, USA, 1987), pp. 77–79.

(3) J.C. Giddings, S.L. Seager, L.R. Stucki,

and G.H. Stewart, Anal. Chem. 32,

867–870 (1960).

(4) L.S. Ettre and J.V. Hinshaw, Basic

Relationships of Gas Chromatography

(Advanstar, Cleveland, Ohio, USA, 1993),

p. 47.

John V. Hinshaw is a senior scientist

at BPL Global Ltd., Oregon, USA,

and is a member of the LC•GC Asia

Pacific editorial advisory board.

Direct correspondence about this

column should be addressed to “GC

Connections”, LC•GC Asia Pacific, 4A

Bridgegate Pavillion, Chester Business

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

the editor‑in‑chief, Alasdair Matheson,

at [email protected]

0 10 20 30 40 50 60 70 80 90 100

3.0

2.5

2.0

1.5

1.0

0.5

0.0

Rs (a)

(b)

u (cm/s)

Figure 4: Plots of resolution for two pairs of solutes, using the Giddings equation:

(a) n‑hexane and a closely eluted analogue and (b) n‑dodecane and a closely

eluted analogue. Separation factor for both pairs α = 1.03. Theoretical column

parameters, same as in Figure 2.



COLUMN WATCH

In any field there are often

“misconceptions” or “myths” that are

perpetuated and passed on to the next

generation. These myths are often driven

by a lack of understanding by practitioners

of the real issues, and can change as time

moves on. Originally, seven years ago, in

a “Column Watch” instalment (1), the 10

most popular myths of the time around

high performance liquid chromatography

(HPLC) column technology were

demystified by discussing the issues at

hand. Because HPLC is approaching

its 50th year, many column myths have

already been passed down to two

generations of liquid chromatographers.

Recently, ultrahigh-pressure liquid

chromatography (UHPLC) has come into

its own and a new set of myths are arising.

The purpose of this instalment of “Column

Watch” is to revisit and update readers on

the most popular column myths of today

and try to dispel some of these myths

before they get perpetuated. This column

is an adaptation of an oral presentation at

the HPLC2013 conference in Amsterdam,

the Netherlands (2). In keeping with the

“countdown theme,” I will start with number

10 and work my way up to the top myth.

Myth 10: Air Will Kill an HPLC ColumnFalse: HPLC and UHPLC columns are

shipped with plugs of either stainless steel

or polymeric construction installed at both

end. Users are told that a column should

always be capped tightly after the column

is disconnected from the instrument. The

thought is that large amounts of air can

get inside the column, perhaps damaging

the packing material, causing bubbles in

the detector flow cell when installed into

the HPLC system in the future, and maybe

disrupting the packed-bed morphology.

One should first realize that the tiny hole

in the endfitting is less than 0.02 in. in

diameter and therefore has an extremely

small cross-sectional area. If left open, the

small amount of air that diffuses into the

column could hardly cause irreparable

damage. Depending on the volatility of

the solvent used to store the column,

there could be some evaporation near the

end of the column. But large quantities

of air would have a hard time diffusing

through the microparticles in the packed

bed seeing that we need thousands of

pounds per square inch of pressure to

push liquid mobile phases through these

micrometre-sized particles. The small

amount of air that could conceivably enter

into the ends of the column would be

immediately dissolved once the system

was pressurized or, at least be flushed out

in the initial pressurization in a short time

and should not cause any problems with

the chromatography later on. However,

if you feel more secure by capping the

endfittings, by all means do so.

Myth 9: All C18 (L1) Columns Are the SameFalse: All of our HPLC column surveys

have shown that C18 is, by far, the most

popular bonded phase in existence (3).

Because pharmaceutical manufacturers

were the earliest adopters of HPLC, the

United States Pharmacopeial Convention

(USP), not wanting to favour any particular

manufacturer of HPLC columns,

developed a classification system that

gave a generic description for each

type of bonded phase column that was

submitted under a new drug application.

For HPLC columns, an “L” designation

was given, and because C18 is used for a

majority of submittals, its designation was

“L1.” As additional phases came along,

they were given their own “L” number such

as C8 (L7), CN (L10), phenyl (L11), and so

on. The implication with this system was

that each C18 column that was submitted

also designated as L1, was the same as

the last L1 column. Unfortunately, this

system proved to be unreliable because

columns from different manufacturers,

produced from different base silicas and

bonded with different silane reagents

using different synthetic routes, were not

chromatographically the same and one

could therefore not be substituted for

another. With more than 800 different L1

columns introduced into the marketplace,

it has proven to be a confusing system.

Several approaches, including the use of

the hydrophobic subtraction model (4,5)

that gives a more detailed classification

of reversed-phase columns, have

been proposed but to this day the “L”

classification is still in widespread use.

Thus, some chromatographers who

do not really understand the issues still

believe that “all C18 columns are the

same.” Simple examples that this is not

the case are shown in Figures 1 and

2. In Figure 1, four different C18 silica

bonded phase columns are shown for

the same separation under the same

operating conditions; each phase

provides a different chromatogram.

The Top 10 HPLC and UHPLC Column Myths: Part 1Ronald E. Majors, Agilent Technologies, Wilmington, Delaware, USA.

Webster’s New Collegiate Dictionary deà nes a myth as “an ill-founded belief held uncritically, especially by an interested group.” Could that group be misinformed chromatographers? In the à rst of a two-part feature from Ron Majors, the top 10 high performance liquid chromatography (HPLC) column myths are presented and attempts are made to demystify them by offering some evidence that they are untrue. This part will feature myths 10 to six. Since ultrahigh-pressure liquid chromatography (UHPLC) has come about, new myths are popping up and these shall also be dealt with here.



COLUMN WATCH

To demonstrate that the L system also

doesn’t hold for other bonded phases

as well, Figure 2 provides an example

of three different C8 (L7) columns, one

of which (Figure 2[b]) was very similar

to the original chromatogram and could

probably be substituted in an HPLC

method while the third column (Figure

2[c]) is quite different and might even

be considered as orthogonal to the first

two columns. The Fs designation shown

alongside each chromatogram is a

numerical classification of how “close”

of a fit columns are to one another

(4,5). Close Fs numbers are potentially

replacement columns while large values

of Fs imply that the column would not be

a “drop-in” replacement in a particular

HPLC method and, in fact, might be

a useful column when first performing

method development because it offers a

different selectivity to the other columns.

So, the bottom line is: All C18 (L1) and

other reversed phase columns are not the

same.

Myth 8: Never Use 100% Water with a Reversed-Phase LC ColumnFalse: This myth was brought about by

users who experienced a phenomenon

popularly known as “phase collapse”

when using reversed-phase columns with

a low percentage of organic solvent or

100% water as a mobile phase. Phase

collapse really is a misnomer as the

phenomenon was better explained as

phase dewetting. Phase dewetting is

highly undesirable since retention times

decrease and are not reproducible,

peaks may become distorted and

reequilibration times may be quite long.

Earlier, we published two detailed papers

on this subject (6,7). The phase dewetting

conditions most often occur when users

are trying to increase the retention of very

polar compounds in reversed-phase LC

by decreasing the percentage of organic

solvent in the mobile phase to low values

to increase the retention of these polar

compounds, which have a tendency to

be eluted very early in the chromatogram.

Nowadays, this problem is frequently

addressed by using hydrophilic interaction

liquid chromatography (HILIC).

With the help of Figure 3, I will try to

explain phase dewetting. Figure 3 shows

two situations: Situation A, where the

aqueous mobile phase has a significant

portion of water-soluble organic solvent,

such as methanol or acetonitrile — a

densely chemically bonded C18 (or other

hydrophobic bonded phase) prefers to be

solvated with organic solvent (for example,

like-like relationship); and situation B,

when the mobile phase has a very low

percentage of organic content (<10%)

or even 100% water. A very simplistic

visualization of phase collapse can be

observed in the upper portion of Figure 3.

Situation A shows the C18 bonded group

being solvated with methanol and in this

state the hydrophobic moieties are able

to interact with the hydrophobic portions

of solute molecules and provide retention.

On the other hand, for the right hand side

of the upper portion of Figure 3, situation

B shows a C18 phase in a 100% water

mobile phase. The C18 functionality

prefers to be in a self-associated state

(like prefers like) and folds upon itself in

a collapsed state. The bottom portion of

Figure 3 shows the situation as it actually

happens. Most of the interactions with

an LC stationary phase occur inside the

pores (rather than on the outer surface),

so when an organic solvent is present at

higher concentrations (greater than 10%),

the pores are filled with the water–organic

mixture that allows the C18 bonded

groups to be solvated, and everything

behaves normally. However, when the

solvent within a pore becomes unfriendly

(for example, very low %B or 100% A),

there is a tendency to force the water out

of the pore, which results in a dewetting

phenomenon. This dewetting doesn’t

occur instantaneously, but can happen

over a number of column volumes as

the organic solvent is leached out of

the solvated bonded phase. As this

is happening, the retention of organic

solutes may decrease with time and

retention times also decrease accordingly.

Selectivity can change during this time

and peak shapes can become distorted.

Phase dewetting most often occurs with

very hydrophobic, very dense chemically

bonded phases. Phases that are highly

1 2

3

4

1 2 3 4 5

1 2

3

4

1 2 3 4 5

1 2,3

4

1 2 3 4 5

1 2 3

4

1 2 3 4 5

(d)

(c)

(b)

(a)

Time (min)

Figure 1: Chromatograms obtained using C18 bonded phases with the same base

material but different chemistries: (a) Zorbax Eclipse Plus C18 (different surface

treatment for same base silica, double endcapping, same bonding chemistry as

Eclipse XDB-C18); (b) Zorbax StableBond SB-C18 (same base silica, sterically

protected C18 phase, no endcapping); (c) Zorbax Eclipse XDB-C18 (same base

silica, monomeric bonding chemistry, double endcapping); (d) Zorbax Extend-C18

(same base silica, bidentate bonding chemistry, double endcapping). Column

dimensions: 50 mm × 4.6 mm, 1.8-µm dp; mobile phase: 69:31 acetonitrile-water;

flow rate: 1.5 mL/min; temperature: 30 °C; detection: single-quadrupole

electrospray ionization MS, positive mode scan. Peaks: 1 = anandamide,

2 = palmitoylethanolamide, 3 = 2-arachinoylglycerol, 4 = oleoylethanolamide.



COLUMN WATCH

after the stationary phase solvated with an acetonitrile-buffered water mixture. Then, the mobile phase was 90% ammonium dihydrogen phosphate buffer and 10% acetonitrile, which allowed solvation of the hydrophobic stationary phase. Next, the column was rinsed for a period of time with an aqueous buffer (no organic). The mobile phase was returned to the original conditions and the sample was reinjected. Note the greatly reduced elution times and the change in selectivity that occurred with the sample chromatogram (Figure 4[b]). Clearly, this separation is different from what would be expected. The water rinse caused a change in the retention characteristics of the stationary phase most likely by a phase dewetting phenomenon. Next, the column was treated with a 50:50 mixture of aqueous buffer and acetonitrile, followed by the mobile phase. Indeed, the chromatogram returned to the original one shown in Figure 4(a).

Table 1 provides a list of phases that do not show the phase dewetting phenomenon. These phases all have a polar functional group of some kind close to the surface, near or on the chemically bonded phase. Polar embedded phases are among the most popular of these special phases. In this case, a polar functional group is located on the alkyl phase itself usually only a few carbon atoms removed from the silica surface. Different commercial phases utilize different embedded functional groups, the most popular being amide, urea, and carbamate. With these polar groups, the water in the mobile phase can interact and solvate the phase so that collapse or dewetting doesn’t occur. Some columns have incorporated polar functional groups in other ways such as endcapping with a polar functionality (for example, diol). These phases are usually given an AQ designation. Very short chain phases (such as C2) do not show phase dewetting because they may allow residual silanol groups to hydrogen bond with the aqueous component of the mobile phase. Surprisingly, very long alkyl chain phases do not show phase dewetting, most likely because the steric requirement allow surface silanols to remain on the bonded silica; hence, water can interact with these silanols and allow surface solvation. Phases with wide pore diameters (for example, 300 Å) do not show phase dewetting because the pores are wide enough not to force water out of them, although I am not aware of

endcapped with non-polar silane reagents may encourage the situation. The % organic in which dewetting may occur varies with a number of parameters including type of bonded phase, bonded phase coverage (density), pore size, and the presence and availability of residual surface silanol groups among others. Phase dewetting does not permanently damage the column and it can be recovered as described below. However, over the years many chromatographers have been totally baffled by the presence of phase dewetting and much time has been lost trying to solve the problem of shifting retention times. Hence, they believe that one shouldn’t run reversed-phase columns in highly aqueous media.

There are two approaches to overcome the phase dewetting phenomenon: Subject the column to a high back pressure according to the Laplace-Young equation (see reference

6 for an example of this approach); or resolvate the stationary phase with a higher % organic in an organic–water mixture or mobile phase.

The first approach is inconvenient and requires a lot of experiments to get the right back pressure. The second approach is the easiest to perform because the column is already installed in the instrument and the experimental conditions can be adjusted to ensure that sufficient organic solvent is present to resolvate the phase. Of course, a third approach is to use a phase that is solvated under all mobile phase conditions (see below).

To illustrate what can happen in a phase dewetting situation, Figure 4 provides an example of the separation of procainamides on a very hydrophobic-C8 phase. The sequence of the experiments is outlined in the figure caption and will not be repeated here. Figure 4(a) depicts the normal isocratic separation that occurs

1

1

(a)

(b)

(c)

0 2

2

2

3

3

3

4

4

4

5

5

5

Time (min)

6

6

6

0 2

2

+

+

1

4

4

6

0 2 4 6

6

7

7

7

8

8

8

9

9

9

Fs= 0.0

Fs= 3.1

Fs= 37

Figure 2: Separation of the same mixture on three reversed-phase columns under the same conditions: (a) Ace C8 (Advanced Chemical Technologies); (b) Precision C8 (Mac-Mod); (c) Inertsil C8 (GL Sciences). Column dimensions: 15 cm × 4.6 mm; flow rate: 2.0 mL/min; temperature: 35 °C; mobile phase: 50:50 30 mM potassium phosphate buffer (pH 2.8)–acetonitrile. Peaks: 1 = N,N-diethylacetamide, 2 = nortriptyline, 3 = 5,5-diphenylhydantoin, 4 = benzonitrile, 5 = anisole, 6 = toluene, 7 = cis-chalcone, 8 = trans-chalcone, 9 = mefenamic acid. (Courtesy of Lloyd Snyder and John Dolan, LC Resources).



COLUMN WATCH

any studies on the wettability of these

wide-pore phases.

Figure 5 shows the use of an AQ-type

phase in a situation with a low percentage

of organic mobile-phase content. The

sequence of experiments with this

column was very similar to that shown

in Figure 4. Because this phase was

developed to work in mobile phases with

a low percentage of organic content and

100% water, it did not undergo phase

dewetting when subjected to the same

conditions of the highly hydrophobic

phase of Figure 4. Such phases are to be

recommended for the separation of small

polar compounds that are lowly retained

on many reversed-phase chromatography

columns.

Myth 7: It Takes a Minimum of 10 Column Volumes to Reequilibrate an LC ColumnFalse: Equilibration time is very important

in gradient chromatography because

it is a limiting factor in the throughput

of the technique. At the conclusion of

gradient, the column must be returned to

its original state before another injection

can be made. The longer it takes for

this reinstatement to occur, the longer

the overall gradient run. In addition, if

one takes longer to reequilibrate the

column than is actually required, solvent

is wasted. In modern two-dimensional

liquid chromatography (2D LC×LC), the

throughput is dictated by the speed of the

secondary column because the flow on

the primary column is not stopped during

the second chromatographic step. If 10

column volumes are required instead

of just a few, then the comprehensive

chromatography, already a fairly slow

process, is made even slower. Finally, if

reequilibration time is too short and the

column has not been stabilized, then

repeatability of retention time, important

when this parameter is used to help

identify components, may be limited.

There have been a number of studies

of the reequilibration times, but for the

purposes of brevity, I would like to cite

two of the more comprehensive ones

(8,9). Schellinger, Stoll, and Carr (8)

studied high-speed gradient elution in the

reversed-phase LC of neutral compounds

and bases in buffered eluents with regard

to retention repeatability and column

reequilibration and conducted a follow-up

study of full equilibrium conditions (9).

There are many variables affecting the

reequilibration in reversed-phase LC. In

isocratic LC, there is no equilibration time

at the conclusion of a chromatographic

run, but there may be a considerable

waiting time when changing solvent

composition. In gradient LC, the eluent

composition, the bonding density of

the reversed-phase LC packing, the

instrumental design (particularly, the

gradient delay volume also known as the

dwell volume), the flow rate, the use or

lack of use of bonded phase additives

to wet the bonded phase, and the types

of solutes (ionic, ionizable, neutral) and

flushing times all play a part.

I would like to summarize the major

outcomes of these reequilibration

studies. First, the instrument gradient

delay volume, although important in

the real world, must be subtracted from

the total volume of the eluent passed

through the column because the delay

volume itself has little to do with the

reequilibration time that actually occurs

within the chromatography column.

Earlier liquid chromatographs often had

several millilitres of delay volume. The

delay volume is the total volume from

H2O

H2O

H2O

H2O

H2O

H2O

H2O

H2O

H2O

H2O

H2O

CH3OH

CH3OH

CH3OH

CH3OH

CH3OH

CH3OH

CH3OH

CH3OH

CH3OH

SiO2

SiO2

H2O

H2O

H2O

H2O

H2O

H2O

H2O

H2O

H2O

H2O

H2O

H2O

H2O

H2O

H2O

H2O

Situation A Situation B

Water forced out of pore Pores are wetted with methanol

In A, analytes are properly retained

In B, analytes partially retained or unretained

Time (min) 0 5 10

1

2

3

Time (min) 0 5 10

1

2

4

5 4

5

3

(a) (b)

Figure 3: Phase collapse (or more correctly, phase dewetting).

Figure 4: Inconsistent retention in a highly aqueous mobile phase as demonstrated

by the separation of procainamides on a hydrophobic C18 column. Sequence

of events: Condition with a 50:50 mixture of phosphate buffer and acetonitrile for

15 min; run mobile phase for 5 min; inject the sample and obtain the chromatogram

shown in (a); switch to 100% aqueous for 30 min; switch back to mobile phase for

5 min; inject the sample and obtain the chromatogram shown in (b); repeat the

first three steps; chromatogram returns to (a). Column: 150 mm × 4.6 mm Eclipse

XDB-C8; mobile phase: 90% 50 mM KH2PO4 (pH 3.5), 10% acetonitrile; flow rate:

1.0 mL/min; temperature: room temperature. Peaks: 1 = uracil, 2 = procainamide,

3 = N-acetylprocainamide, 4 = N-propionylprocainamide, 5 = caffeine.



COLUMN WATCH

the point of solvent mixing to the head

of the column. There are many volumes

to flush out before the actual gradient

reaches the column. For high-pressure

mixing, the volume of the mixing tee,

mixer (if present), pulse damper, pressure

transducer, all the connecting tubings,

injector including the loop or by-pass

volume, and guard column can be

substantial. For low-pressure gradient

systems, the proportioning valve, inlet

and outlet check valves, the pump piston

chamber, and various tubing adds even

more to the gradient delay volume. More

recently, newer UHPLC instruments have

addressed these problems by greatly

decreasing the instrumental contributions

to gradient delay volume as well as

extracolumn volumes.

The reequilibration study came up with

several conclusions. There are two types

of equilibrium: Repeatable equilibrium

and full equilibrium. Repeatable

equilibrium means that full equilibrium

may not have been achieved, but on

a practical basis, if the retention time

repeatability on subsequent runs is less

than 0.002 min then for non-ionizable

solutes in unbuffered eluents and for

basic compounds using the popular

trifluoroactic acid and formic acid

additives, repeatable equilibrium can be

achieved within two column volumes.

For a non-endcapped phase, 1% (v/v)

n-butanol added to the mobile phase was

required to achieve rapid full equilibrium in

two column volumes. For an endcapped

phase, the n-butanol was not required.

Myth 6: Superàcially Porous (Solid-Core) Particles Have a Signiàcantly Lower Sample Loading Capacity Compared to Totally Porous ParticlesFalse: The sample capacity of an HPLC

packing material is proportional to the

available surface area, which, of course,

is related to the amount of bonded phase

chemically attached to the available

silanols through monomeric bonding.

If one goes through the mathematical

calculations of the volume of a 2.7-µm

spherical totally porous particle (TPP)

and compares the volume of a 2.7-µm

superficially porous packing (SPP) with

a 0.5-µm porous shell, the total volume

available on the SPP particle is about 25%

less than the TPP. This assumes that the

porous portion of both particles has the

same characteristics, which may not be

the case. Nevertheless, the loss in surface

volume of the SPP is nowhere near that

of the pellicular packings of yesteryear

in which the shell thickness was 1–2 µm

and the particle size was 45–50 µm. If the

surface area of the SPP is actually larger

than that of the TPP, then the difference in

sample capacity between the two could

be less (this may be the case).

Rather than relying on mathematical

estimates, experiments were actually

performed on comparing the TPP

and SPP with similar chromatographic

conditions for basic compounds. For the

basic compound dextromethorphan,

successively larger concentrations were

injected onto the four columns. Three of

the columns were SPP columns Poroshell

120 EC-C18 (100 mm × 3.0 mm, 2.7-µm

dp, Agilent Technologies); Ascentis

Express C18 (100 mm × 3.0 mm, 2.7-µm

dp, Sigma Aldrich/Supelco); and Kinetex

C18 (100 mm × 4.6 mm, 2.6-µm dp,

Phenomenex). The TPP column was the

Zorbax Eclipse Plus C18 (100 × 3.0 mm,

1.8-µm dp, Agilent Technologies). At the

time, only a 100 mm × 4.6 mm Kinetex

column was available, so adjustments

were made in the experimental

conditions to accommodate the different

column dimensions.

One definition of overload is when

the sample size injected causes a 10%

dropoff in efficiency (or alternatively a 10%

increase in peak width). One can view

the results of the experiment in Figure 6,

which shows a plot of the peak width

versus sample concentration injected

at a constant volume. A 10% increase

in peak width for the SPP columns

occurred at roughly the same sample

loading as for the totally porous column

at a concentration value of 0.05 mg/

mL indicating a comparable sample

capacity for both types of columns.

Interestingly, a recent similar study

by Fallas and colleagues (10) came

to the same conclusion. Table 2 is an

abbreviated extract of data from their

work, which shows that, for both basic

and acidic compounds on the Poroshell

120 EC-C18 and the totally porous Zorbax

Eclipse Plus C18 columns of the same

dimensions and same conditions used,

has nearly the same sample capacity.

Interestingly, other SPP columns and

porous particle columns were evaluated

in the same study. All of them weren’t very

Table 1: Phases to address the dewetting problem in reversed-phase chromatography.

Polar-embedded alkyl phases (such as amide, urea, carbamate, ether, other polar

function)

Hydrophilic, polar-endcapped, and polar-enhanced stationary phases (such as AQ,

hydroxyl, amide)

Non-endcapped, short chain alkyl phases

Long chain alkyl phases (such as C30)

Wide-pore diameter phases

0 10 15 5 0 10 15 5

(a) (b)

Time (min) Time (min)

Figure 5: Consistent retention of procainamides. Sequence of events: condition with

a 50:50 mixture of phosphate buffer and acetonitrile for 15 min; run mobile phase for

5 min; inject the sample and obtain the chromatogram shown in (a); switch to 100%

aqueous for 30 min; switch back to mobile phase for 2 min; inject the sample and

obtain the chromatogram shown in (b). Column: 150 mm × 4.6 mm, 5-µm dp Zorbax

SB-Aq; mobile phase: 90% 50 mM KH2PO4 (pH 3.5), 10% acetonitrile; flow rate:

1.0 mL/min; temperature: room temperature. Peaks: 1 = uracil, 2 = procainamide,

3 = N-acetylprocainamide, 4 = N-propionylprocainamide, 5 = caffeine.


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different, indicating the sample capacity

of a number of porous and SPPs when

tested under the same conditions were

essentially the same. However, with some

of the newer SPPs with a thinner shell

thickness, there may be different sample

capacities than TPPs of the same size.

References(1) R.E. Majors, LCGC North Am. 24(11),

1172–1182 (2006).

(2) R.E. Majors, “Top Ten LC Column Myths,

Lecture PL2” presented at HPLC 2013,

Amsterdam, Amsterdam, The Netherlands,

2013.

(3) R.E. Majors, LCGC North Am. 25(1), 31–39

(2012).

(4) L.R. Snyder and J.W. Dolan, LCGC North Am.

22(12), 1146–1152 (2004).

(5) L.R. Snyder and J.W. Dolan, LCGC North Am.

23(2), 118–127 (2005).

(6) M. Przybyciel and R.E. Majors, LCGC North

Am. 20(6) 516–523 (2002).

(7) R.E. Majors and M. Przybyciel, LCGC North

Am. 20(7), 584–593 (2002).

(8) A.P. Schellinger, D.R. Stoll, and P.W. Carr, J.

Chromatogr. A 1192(1), 41–53 (2008).

(9) A.P. Schellinger, D.R. Stoll, and P.W. Carr, J.

Chromatogr. A 1192(1), 54–61 (2008).

(10) M.M. Fallas, S.M.C. Buckenmaier, and D.V.

McCalley, J. Chromatogr. A 1235, 49–59

(2012).

“Column Watch” Editor Ronald E. Majors

is a senior scientist at the Columns and

Supplies Division, Agilent Technologies,

Wilmington, Delaware, USA, and is a

member of the LC•GC Asia Pacific editorial

advisory board. Direct correspondence

about this column should be addressed

to “Column Watch”, LC•GC Asia Pacific,

4A Bridgegate Pavilion, Chester Business

Park, Wrexham Road, Chester, CH4 9QH,

UK, or e-mail the editor-in-chief, Alasdair

Matheson, at [email protected]

Table 2: Sample capacity for two C18 columns.

20 mM ammonium formate (pH 3)

Column C0.5 (mg/L) Nortriptyline C0.5 (mg/L) 2-NSA

Zorbax Eclipse Plus C18 (TPP, 1.8 µm) 97 163

Poroshell 120 EC-C18 (SPP, 2.7 µm) 100 128

100 mM ammonium formate (pH 3)

Column C0.5 (mg/L) Nortriptyline C0.5 (mg/L) 2-NSA

Zorbax Eclipse Plus C18 (TPP, 1.8

µm)300 472

Poroshell 120 EC-C18 (SPP, 2.7 µm) 360 451

Extracted and adapted from reference 10

0.8

0.7

0.6

0.5

0.4

Peak w

idth

(s)

0.3

0.2

0.1

0.001 0.01

HBr OCH3

CH3

N

pKa = 8.3

Concentration of dextromethorphan (mg/mL)

0.1 1

0

Figure 6: Sample loading of a basic compound (dextromethorphan) onto superficially

porous and sub-2-µm totally porous columns. A 10% increase in peak width for

the superficially porous particle columns occurs roughly at the same loading as

the 1.8-µm totally porous particle column. Mobile phase: 80% 25 mM Na2HPO4

buffer (pH 3.0), 20% acetonitrile; detection: UV absorbance at 205 nm; temperature:

30 °C. Columns: blue diamonds: 100 mm × 3.0 mm, 2.7-µm dp Agilent Poroshell 120

EC-C18; orange squares: 100 mm × 3.0 mm, 2.7-µm dp Supelco Ascentis Express

C18; green triangles: 100 mm × 4.6 mm, 2.6-µm dp Phenomenex Kinetex C18; yellow

X’s: 100 mm × 3.0 mm, 1.8-µm dp Agilent Zorbax Eclipse Plus C18.


LC•GC Asia Pacifi c November 2013 33

ADVERTISEMENT FEATURE

Gamma-Hydroxybutyrate (GHB) is a naturally occurring

substance found in the human central nervous system. It is also

easily synthesized. GHB has been used medically as a general

anesthetic, as well as to treat conditions such as insomnia, clinical

depression, narcolepsy, and alcoholism. Illegally, it has been used

as an intoxicant or as an agent in Drug Facilitated Sexual Assaults

(DFSA) or to improve athletic performance. GHB has a very short

window of detection in urine and blood making it diff cult to detect

in support of date rape cases. Using hair samples has been shown

to be a viable alternative for detecting the presence of GHB above

endogenous levels (1). This application note describes the extraction

and subsequent analysis of GHB from decontaminated hair samples.

After incubation in methanol, the extract is dried and then dissolved in

deionized water (D.I.) prior to sample clean-up using anion exchange

SPE (CUQAX156). The recoveries are greater than 90%, and matrix

effects are less than 5%.

Sample Preparation

To a clean glass tube, add 100 mg of decontaminated hair

sample.

Add 1 mL of CH3OH and internal standard, vortex mix.

Incubate the sample at 40 °C for approximately 12 h.

Centrifuge sample at 3000 rpm for 10 min.

Transfer organic phase to a clean glass tube.

Evaporate to dryness <40 °C.

Dissolve residue in 3 mL of D.I. H2O (pH 7).

Vortex mix.

Sample Extraction

Condition the CLEAN-UP extraction column with 3 mL of CH3OH

followed by 3 mL D.I. H2O. Aspirate at <3 in. Hg to prevent the

sorbent bed from drying.

Load the sample onto the column at 1–2 mL/min.

Wash the column with 3 mL D.I. H2O followed by 3 mL of CH

3OH.

Dry the column for 10 min at >10 in. of Hg.

Analyte Elution

Elute the GHB with 2 mL × 3 mL aliquots of CH3OH w/ 6% acetic

acid. The eluate collection rate should be 1–2 mL/min.

Dry the Eluate

Evaporate the extract to dryness under nitrogen <40 °C. Then

reconstitute in 100 μL of mobile phase.

Determination of Gamma-Hydroxybutyrate (GHB) in Hair Samples Using Solid-Phase Extraction and LC–MS–MSJeffery Hackett, UCT

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

Tel: (800) 385 3153

E-mail: [email protected]

Website: www.unitedchem.com

Sample Preparation Products

CUQAX156CLEAN-UP® SPE Column - Quaternary Amine w/

Chloride Counter Ion, 500 mg/6 mL

LC–MS–MS Method

System: Agilent 1200 LC

Injection: 10 μL

LC column: Thermo Fisher Gold C18, 50 mm × 2.0 mm, 1.9 μm

Column temp: 40 ºC

Mobile phase: Acetonitrile w/ 0.1% formic acid: D.I. H20 w/ 0.1% formic

acid; (50:50)

Flow rate: 0.2 mL/min

LC–MS–MS Conditions

Detector: API 4000 MS/MS

Conclusion

This method offers analysts working in the area of DFSA a viable,

eff cient method for determining the presence of GHB in hair

samples. The isolation and quantif cation of this drug (performed

by SPE and LC–MS–MS) is a robust alternative to GC–MS where

chemical derivatization is required for the analysis of this compound.

Reference

(1) P. Kintz, V. Cirimele, C. Jamey, and B. Ludes, J. Forensic

Science 48(1), 195–200 (2003).

Ion Source ESI

Ion Mode Negative

Ion Spray Voltage - 4500V

Curtain Gas 10

Gas 1 40

Gas 2 40

CAD Gas Medium

Source Temp 650 °C

Mode Positive

XIC of -MRM(4 pairs): 103.020/57.000 Da ID: gnb from Sample 7 (H3) of GHB-SSQAXhair1123010.wiff (Turbo Spray)

XIC of -MRM(4 pairs): 109.130/90.000 Da ID: gnb-d6 from Sample 7 (H3) of GHB-SSQAXhair1123010.wiff (Turbo Spray)

Max. 1.7e4 cps

Max. 7490 ps

1.6e4

Inte

nsi

ty (

cps)

Inte

nsi

ty (

cps)

1.4e4

1.2e4

1.0e4

8000.0

6000.0

74907000

6000

5000

4000

3000

2000

1000

0

2000.0

1.18

1.23

4000.0

0.00.5 1.0 1.5 2.0 2.5

Time (min)3.0 3.5 4.0 4.5

0.5 1.0 1.5 2.0 2.5Time (min)

3.0 3.5 4.0 4.5

Figure 1: GHB chromatogram.

Table 1: Mass spec table.

Compound RT (min) Precursor Product 1 Product 2

GHB 1.23 103.0 57.0 84.0

GHB-D6 1.18 109.1 90.0 60.9


34 LC•GC Asia Pacifi c November 2013

ADVERTISEMENT FEATURE

Polydimethylsiloxane (PDMS) is the world’s most common silicone.

Its applications range from contact lenses and medical devices

to elastomers, caulking, lubricating oils, and heat resistant tiles.

For all of its applications, the weight-average molar mass (and

its distribution) is directly associated with the performance of the

product. A DAWN multi-angle light scattering (MALS) detector

coupled with a size-exclusion chromatograph (SEC) provides the

perfect tool for making molecular weight determinations without

reference to standards or column calibration.

For this application note, a polydimethylsiloxane sample was

analysed by SEC in toluene, using Wyatt Technology’s DAWN

and an Optilab refractometer as the respective MALS and

concentration detectors.

Figure 1 shows the chromatograms of polydimethylsiloxane with

signals from the light scattering at 90° (top) and the RI (bottom)

detectors. The RI signal is negative because the refractive index

increment (dn/dc) of polydimethylsiloxane in toluene is negative.

A positive signal can be obtained if the polarity of the signal output

is reversed. Because the light scattering signal is proportional to

dn/dc squared, its signal is positive.

By combining the DAWN and Optilab data, the absolute

molar masses of this siloxane were calculated without making

any assumptions about the polymer’s conformation or elution

time.

A polystyrene standard with a molar mass of 200 kD was

analysed under the same conditions, as it is frequently used to

calibrate columns for conventional chromatography. Both results

are plotted in Figure 2. Even though polydimethylsiloxane is a linear

polymer, just as this polystyrene standard is, the molar masses at

the same elution time are not identical for the two polymers.

If polystyrenes had been used as calibration standards, the molar

mass for polydimethylsiloxane would have been erroneous. The

results once again demonstrate the power of MALS in determining

absolute molar masses of polymers without any reference to

calibration routines or polymer standards — even when those

polymers appear to share the same conformation as the standards.

SEC-MALS of Silicones 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

90˚ light scattering signal

Optilab RI signal(negative dn/dc)

Figure 1: Chromatograms obtained by SEC of a PDMS sample with signals from the DAWN (top, red) and the Optilab RI (bottom, blue).

90˚ LS for

PDMS

260K

PDMS

200K

PS

PSSTD

Figure 2: Plots of the molar mass versus elution time superimposed over the signals from the DAWN, for the PDMS sample and the polystyrene “standard”, showing the large errors associated with conventional column calibration.


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