Volume 32 Number 2, 77–160 Volume 32 Number 2 February...

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The Future of Miniature Mass Spectrometers

Chromatographic Modeling for LC Method Development in a

Quality-by-Design Framework

Pesticide Analysis in Fruits and Vegetables

Using QuEChERS, GC–MS, and LC–MS

The Role of Selectivity in Extractions

Volume 32 Number 2 February 2014www.chromatographyonline.com

Triazole Bonded Stationary Phase

Alternative Selectivity for HILIC Analysis

www.nacalaiusa.com

COSMOSIL HILIC

Column size: 4.6mmI.D. 250mm

Mobile phase: Acetonitrile/50mmol/l CH3COONH4 = 80/20

Flow rate: 1.0ml/min Temperature: 30

Detection: ELSD

Sample: 1. meso-Erythritol (1.0mg/ml)

2. Tris (1.0mg/ml)

3. Glyceric Acid (2.0mg/ml) Injection Vol: 3.0μl 1. meso-Erythritol

Neutral2. TrisBasic

3. Glyceric Acid Acidic

COSMOSIL HILIC TSKgel Amide-80(Basic/Neutral)=1. 48,

(Acidic/Neutral)=4.36(Basic/Neutral)=3.07,

(Acidic/Neutral)=2.04

Atlantis HILICZIC-HILIC(Basic/Neutral)=4.45,

(Acidic/Neutral)=1.71(Basic/Neutral)=5.03,

(Acidic/Neutral)=1.47

COSMOSIL HILIC HPLC ColumnThe positively charged triazole bonded stationary phase

provides alternative selectivity to other HILIC columns.

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CONTENTS








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LCGC North America (ISSN 1527-5949 print) (ISSN 1939-1889 digital) is published monthly with 1 additional issue in August as Buyers Guide by Advanstar Communica-tions Inc., 131 West First Street, Duluth, MN 55802-2065, and is distributed free of charge to users and specifiers of chromatographic equipment in the United States and Canada. Single copies (prepaid only, including postage and handling): $15.50 in the United States, $17.50 in all other countries; back issues: $23 in the United States, $27 in all other countries. LCGC is available on a paid subscription basis to nonqualified readers in the United States and its possessions at the rate of: 1 year (13 issues), $74.95; 2 years (26 issues), $134.50; in Canada and Mexico: 1 year (13 issues), $95; 2 years (26 issues), $150; in all other countries: 1 year (13 issues), $140; 2 years (26 issues), $250. Periodicals postage paid at Duluth, MN 55806 and at additional mailing offices. POSTMASTER: Please send address changes to LCGC, P.O. Box 6168, Duluth, MN 55806-6168. PUBLICATIONS MAIL AGREEMENT NO. 40612608, Return Undeliverable Canadian Addresses to: IMEX Global Solutions, P. O. Box 25542, London, ON N6C 6B2, CANADA Canadian GST number: R-124213133RT001. Printed in the USA.

COLUMNS

92 SAMPLE PREP PERSPECTIVESThe Role of Selectivity in Extractions: A Case Study

Douglas E. RaynieThe selective removal of a fat substitute in food products is discussed to demonstrate options for obtaining selectivity during extraction.

98 LC TROUBLESHOOTINGLC Method Scaling, Part I: Isocratic Separations

John W. DolanWhat kind of adjustments need to be made when scaling an isocratic method?

104 MS – THE PRACTICAL ARTThe Future of Miniature Mass Spectrometers and a Path

Forward: A Few Thoughts from an Academic Researcher

Zheng OuyangA discussion of the future role of miniature MS systems, the need for simplification in operation, the role of ambient ioniza-tion, and challenges in development and commercialization.

158 THE ESSENTIALSTroubleshooting Real GC Problems

Key considerations for setting up or troubleshooting a GC method.

PEER-REVIEWED ARTICLES

116 Significant Improvements in Pesticide Residue

Analysis in Food Using the QuEChERS Method

Walter J. Krol, Brian D. Eitzer, Terri Arsenault, Mary Jane Incorvia Mattina, and Jason C. WhiteThe analysis of pesticide residues in food samples from the state of Connecticut’s regulatory monitoring pro-gram are compared to USDA and US FDA results.

126 Rapid UHPLC Method Development for Omeprazole

Analysis in a Quality-by-Design Framework and

Transfer to HPLC Using Chromatographic Modeling

Alexander H. Schmidt and Mijo StanicQuality-by-design principles are applied to build in a more scien-tific and risk-based multifactorial strategy in the development of a UHPLC method for analyzing a drug and its related impurities.

ON THE WEBWEB SEMINARS

Editors’ Series: Analytical Tools for the Characterization of Bio-pharmaceuticals: Part 1, Chromatographic Methods Davy Guillarme, University of Geneva and University of Lausanne

Editors’ Series: Analytical Tools for the Characterization of Bio-pharmaceuticals: Part II, Mass Spectrometry Detection Sarah Cianférani, University of Strasbourg

Multi-Antibiotic Residue Detection in Food: An Improved Method for Screening and Confirmation Testing, in Accordance with EU Commission Decision 2002/657/ECNelli Jochim, Eurofins WEJ Contaminants

chromatographyonline.com/WebSeminar.

Like LCGC on Facebook: www.facebook.com/lcgcmagazine

Follow LCGC on Twitter:https://twitter.com/LC_GC

Join the LCGC Group on LinkedInhttp://linkd.in/LCGCgroup

DEPARTMENTSPeaks of Interest . . . . . 90 Product Showcase . . .149 Ad Index . . . . . . . . . . . 156

v�� 32 n�� 2 february 2014

Cover photography by Joe Zugcic, Joe Zugcic Photography

Cover materials courtesy of Perkin- Elmer and Restek Corporation

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Within their existing workflows. Across every sample. Without training. A lab where uncertainty about compounds

is replaced by fast, efficient confirmation and the confidence that comes with crossing the LC/MS divide like never

before. Now imagine all this happening at the push of a button. This goes far beyond the power of mass detection.

This is the ACQUITY QDa™ Detector from Waters. SEPARATING BEYOND QUESTION.™ Visit waters.com/separate

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©2014 Waters Corporation. Waters and The Science of What’s Possible are registered trademarks of Waters Corporation.

ACQUITY QDa and Separating Beyond Question are trademarks of Waters Corporation.

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Publishing & Sales

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Executive Vice-President, Chief Administrative Officer &

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Kevin D. Altria GlaxoSmithKline, Ware, United Kingdom

Jared L. Anderson The University of Toledo, Toledo, Ohio

Daniel W. Armstrong University of Texas, Arlington, Texas

Michael P. Balogh Waters Corp., Milford, Massachusetts

Brian A. Bidlingmeyer Agilent Technologies, Wilmington, Delaware

Dennis D. Blevins Agilent Technologies, Wilmington, Delaware

Peter Carr Department of Chemistry, University

of Minnesota, Minneapolis, Minnesota

Jean-Pierre Chervet Antec Leyden, Zoeterwoude, The Netherlands

John W. Dolan LC Resources, Walnut Creek, California

Michael W. Dong Genentech, San Francisco, California

Roy Eksteen Sigma-Aldrich/Supelco, Bellefonte, Pennsylvania

Anthony F. Fell School of Pharmacy, University of

Bradford, Bradford, United Kingdom

Francesco Gasparrini Dipartimento di Studi di Chimica e Tecnologia delle

Sostanze Biologicamente Attive, Università “La Sapienza,” Rome, Italy

Joseph L. Glajch Momenta Pharmaceuticals, Cambridge, Massachusetts

Davy Guillarme University of Geneva, University

of Lausanne, Geneva, Switzerland

Richard Hartwick PharmAssist Analytical Laboratory,

Inc., South New Berlin, New York

Milton T.W. Hearn Center for Bioprocess Technology,

Monash University, Clayton, Victoria, Australia

Emily Hilder University of Tasmania, Hobart, Tasmania, Australia

John V. Hinshaw BPL Global, Ltd., Hillsboro, Oregon

Kiyokatsu Jinno School of Materials Science, Toyohashi

University of Technology, Toyohashi, Japan

Ira S. Krull Northeastern University, Boston, Massachusetts

Ronald E. Majors LCGC columnist and analytical

consultant, West Chester, Pennsylvania

R.D. McDowall McDowall Consulting, Bromley, United Kingdom

Michael D. McGinley Phenomenex, Inc., Torrance, California

Victoria A. McGuffin Department of Chemistry, Michigan

State University, East Lansing, Michigan

Mary Ellen McNally E.I. du Pont de Nemours

& Co., Wilmington, Delaware

Imre Molnár Molnar Research Institute, Berlin, Germany

Glenn I. Ouchi Brego Research, San Jose, California

Colin Poole Department of Chemistry, Wayne

State University, Detroit, Michigan

Fred E. Regnier Department of Chemistry, Purdue

University, West Lafayette, Indiana

Pat Sandra Research Institute for Chromatography, Kortrijk, Belgium

Peter Schoenmakers Department of Chemical Engineering,

University of Amsterdam, Amsterdam, The Netherlands

Kevin Schug University of Texas, Arlington, Texas

Dwight Stoll Gustavus Adolphus College, St. Peter, Minnesota

Michael E. Swartz Ariad Pharmaceuticals, Cambridge, Massachusetts

Thomas Wheat Waters Corporation, Milford, Massachusetts

CONSULTING EDITORS: Jason Anspach, Phenomenex, Inc.; Stuart Cram,

Thermo Fisher Scientific; David Henderson, Trinity College; Tom Jupille, LC

Resources; Sam Margolis, The National Institute of Standards and Technology;

Joy R. Miksic, Bioanalytical Solutions LLC; Frank Yang, Micro-Tech Scientific.

Editorial Advisory Board

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PEAKS of Interest

National Institute of Standards

and Technology Makes Polycyclic

Aromatic Hydrocarbon Structure

Index Publicly Available

The National Institute of Standards

and Technology (NIST), an agency of

the U.S. Department of Commerce,

has made a polycyclic aromatic hydro-

carbon (PAH) structure index data-

base publicly available on-line

(http://pah.nist.gov/). A by-product of

hydrocarbon fuel combustion, PAHs

can have significant adverse health

and environmental impacts. The web-

site contains data on more than 650

PAH compounds, with more to be

added in the future.

According to NIST, the Chemical

Informatics Research Group of NIST’s

Material Measurement Laboratory

created the site to provide standard

reference data to industry, academia,

and the US public, and builds on

NIST Special Publication 922: Polycy-

clic Aromatic Hydrocarbon Structure

Index (SP922) by Lane C. Sander and

Stephen A. Wise of NIST. Publication

SP922 indexed a large number of PAH

structures and provided parameters

for estimating retention indices for

liquid chromatography using a simple

model. The new database expands on

this by providing data from further

experimental data including a collec-

tion of thermochemical data on gas-

phase PAH compounds, and UV–vis-

ible spectra.

Duke Molecular Physiology

Institute Receives Agilent Grant

Agilent Technologies (Santa Clara,

California) has awarded a grant to

the Duke Molecular Physiology Insti-

tute, Duke University (Durham, North

Carolina) to support research into the

metabolic and physiological aspects

of major chronic diseases such as

cardiovascular disease. The institute

researchers perform a range of ana-

lytical chemistry techniques, including

liquid chromatography and gas chro-

matography coupled to mass spec-

trometry, to characterize molecular

pathways in disease.

The group is headed by Christopher

Newgard, a professor at Duke Uni-

versity School of Medicine’s Depart-

ment of Pharmacology and Cancer

Biology and director of the Sarah W.

Stedman Nutrition and Metabolism

Center and the Institute for

Molecular Physiology.

“The Duke Molecular Physiology

Institute seeks to combine strong

genomics, epigenomics, transcrip-

tomics, and metabolomics platforms

with computational biology, clinical

translation, and basic science exper-

tise to gain new insights into the

mechanisms of cardiometabolic dis-

ease,“ Newgard said, adding “We

thank Agilent for supporting our

research and look forward to collabo-

rating to advance the understanding

of cardiovascular and undiagnosed

metabolic diseases.” ◾

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SAMPLE PREP PERSPECTIVES

Many of the extraction

techniques developed over

the past generation tout

selectivity among their

advantages. In reality,

solvent selection and the

use of stationary (sorbent)

phases are the main

mechanisms for providing

selectivity. Therefore,

selectivity is often limited

to isolation of classes of

compounds rather than

individual structures. In

this column installment,

the selective removal of

a fat substitute in food

products is discussed

to demonstrate options

for obtaining selectivity

during extraction.

Douglas E. Raynieis the guest author

of this installment.

Ronald E. Majorsis the editor of Sample

Prep Perspectives.

Over the past generation or so,

myriad extraction techniques were

developed that have generally

improved yields, lessened the amount of

organic solvent used, and minimized time.

Additionally, many of these techniques

claim advantages concerning selectivity.

Selectivity is the ability to determine

the analytes of interest in preference

to other sample components (potential

interferents). A recent installment of

this column (1) advocated that selec-

tivity can stem from any point in the

analytical process, but as a general rule,

selectivity arises from separations, selec-

tive detection schemes, and selective

chemical reactions. These approaches

can balance each other. For example, if

an analytical separation is not completely

sufficient, the use of a selective detection

method like mass spectrometry (MS) or

fluorescence spectroscopy can offer the

balance of the required selectivity pro-

vided that the unseparated components

do not suppress the detector signal.

Majors described “just enough” sample

preparation (2) in which method selectivity

is matched to the qualitative or quantitative

analytical requirements. For example, the

QuEChERS (quick, easy, cheap, effective,

rugged, and safe) method for extract-

ing pesticides from fruits and vegetables

combines salting out partitioning with

dispersive solid-phase extraction (SPE)

to remove matrix components, allowing

effective chromatography and MS detec-

tion. As Majors points out and illustrates

in Figure 1 from his original column,

increasing complexity in an analytical pro-

cedure typically leads to greater selectivity.

Turning our attention back to modern

extraction methods, the fundamental

driving force of the technique leads to

the element of selectivity. A number of

sorbent-based methods, such as SPE,

solid-phase microextraction, and stir-bar

sorbent extraction, use chromatographic

stationary phases to isolate solutes of

interest from gaseous or liquid samples.

Analytes are retained by their attraction

to a stationary phase of similar polarity

and are selectively eluted via choice of an

appropriate solvent. The techniques aimed

at solid samples, including supercritical

fluid extraction (SFE), pressurized fluid

extraction, microwave extraction, and

ultrasound extraction, rely on the applica-

tion of energy (often heat) to drive the

analyte into an appropriate solvent. In all

of these techniques, both sorbent- and

solvent-based, the key to selectivity is

the match between analyte polarity and

polarity of the extracting phase. In other

words, “like dissolves like.” Thus, extrac-

tions are usually considered crude separa-

tion techniques, providing compound class

selectivity and less utility for the selective

isolation of specific, individual compounds.

Of course, volatility is the major contribu-

tor to selectivity for gas-phase techniques.

If the primary selectivity mechanism

in extractions is solute polarity (that is,

matching solute polarity with the solvent

or sorbent following the “like dissolves

like” principle), is selectivity possible dur-

ing chemical extraction? Is selectivity

beyond compound class selectivity pos-

sible? Do extractions need to be selective

or is selectivity solely a function of subse-

quent chromatography and detection?

To look at an example of extraction

selectivity within the “like dissolves

like” polarity context, let’s consider

the example of fat analysis in food

products and, more specifically, the

example of sucrose ester fat substitutes.

Fatty Acid Methyl Ester Analysis

The United States Nutrition Labeling and

Education Act (NLEA) of 1990 requires

The Role of Selectivity in

Extractions: A Case Study


the labeling of selected nutrients on pre-

packaged food products. One issue with

this requirement deals with the concept

of “total fat.” What is a “fat”? Are lipo-

proteins considered lipid or protein? The

next concern is their analysis. If “fats” are

based on the fatty acid moiety, how can

they be measured? Fatty acids are not

volatile enough for gas chromatography

(GC) analysis. They do not contain any

chromophores necessary for ultraviolet

detection in liquid chromatography

(LC). (Remember, at the time, LC–MS

was not as widely accepted as it is cur-

rently.) The polarity of the acidic group

can irreversibly adsorb to active sites on

chromatographic stationary phases via

hydrogen bonding, depending on the

type of chromatography. Consequently,

the total fat listed on nutritional labels

is based on the acid hydrolysis and for-

mation of methyl esters of an organic

extract, using a nonpolar solvent, of the

food product. The “total fat” listed on the

nutritional label is that of a triglyceride

based on the resulting fatty acid methyl

ester (FAME) composition (3,4). An

overview of the formation of methyl esters

from triglycerides is presented in Figure 2.

In the FAME method, samples are dis-

solved in a nonpolar solvent and a catalyst

like BF3 dissolved in methanol is added.

Sometimes methanolic acid or base is used.

After mild heating, back-extraction with

water removes the polar components. The

FAME sample is dried and characterized

by GC with flame ionization detection

(FID). The esterification is facilitated

with an alkylation derivatizing agent to

condense the carboxyl group of the fatty

acid with the methanol hydroxyl. The

catalyst aids the reaction by protonating

the acid group to promote the formation

of the ester and water. The stability of the

methyl ester, or FAME, allows GC separa-

tion by boiling point or unsaturation.

The Procter and Gamble Company

began marketing sucrose esters, called

olestra or the tradename Olean, as fat sub-

stitutes in the mid-1990s. The sucrose ester

structure is shown in Figure 3. In this fig-

ure, the R group is either hydrogen or any

fatty acid. By varying the number of fatty

acids connected to the sucrose molecule by

ester linkages or by changing the carbon

chain length of the fatty acids, the proper-

ties of the olestra molecule can be altered.

Under appropriate conditions, the olestra

molecule can have boiling points, viscosity,

mouth feel, and other properties similar

to common vegetable oils. Because they

are not naturally occurring lipids (though

they are made from naturally occurring

compounds), the sucrose esters are not sub-

ject to enzymatic digestive action. Hence,

they can be substituted for vegetable oils

in selected applications, such as the frying

of potato chips and similar salted snacks.

If we review the acid hydrolysis and

esterification reactions for the FAME

analysis, olestra in food products would

be hydrolyzed along with triglycerides

and other fats. The resulting FAMEs

would be indistinguishable regarding

their source, olestra or triglyceride. Thus,

a selective analysis to determine NLEA

“total fats” in the presence of olestra is

needed. Here we will present three pos-

sibilities to garner the necessary selectivity

during the sample preparation process.

Supercritical Fluid Extraction

Perhaps the easiest method, conceptu-

ally, to address the isolation of FAME

from total fats from those originating

from olestra would be at the level of the

extraction, meaning we would selectively

extract olestra from the total fats. (That

is, we’re assuming that we must perform

FAME analysis of total fats to comply

with the requirements of the NLEA.)

This brings us back to the issue of solvent

polarity or “like dissolves like.” Because

olestra is designed to have properties sub-

stantially similar to vegetable oils, which

are composed primarily of triglycerides,

the solvents used in the dissolution and

extraction of either olestra or triglycerides

would likely be very similar. This brings

us to the solvent extraction method where

we have the most variation in solubility

conditions with a single solvent: SFE.

SFE almost always uses carbon dioxide,

perhaps mixed with small amounts of

Methodology

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Figure 1: Just-enough sample preparation represents a continuum of methodologies.

Figure 2: Triglycerides are hydrolyzed and esterified with methanol to form FAME. The R groups of the triglyceride are typically fatty acids with a carbon chain length of 14–24 and up to two double bonds.


organic cosolvents, near or above its criti-

cal point of 31.1 °C and 72.9 atm. Lipids

and lipophilic materials are highly soluble

in supercritical carbon dioxide, and the

use of this solvent for the extraction and

fractionation of lipids is well-reviewed

(5–7). By making subtle changes in the

operating temperature or pressure, some-

what dramatic changes in solvating ability

can occur. These changes may allow the

fractionation of members of a single com-

pound class on the basis of either polarity

or molecular weight. Because the molecu-

lar weights of olestra molecules are at least

double that of triglycerides, it is conceiv-

able that SFE could be used to either

selectively extract olestra and triglycerides

from each other, or to selectively precipi-

tate one from the other. This has not been

reported in the peer-reviewed literature, so

will remain theoretical for now. No other

solvent extraction methods will be able to

achieve this level of selectivity as easily.

Solid-Phase Extraction

The next step up in complexity toward

gaining the requisite selectivity during the

sample preparation of olestra-containing

food products would be to use a sorbent-

based method to separate olestra from

total fats postextraction. SPE is the most

basic of these techniques and perhaps the

most directly applicable to our hypotheti-

cal scenario. SPE can, in many ways, be

regarded as an elementary form of LC. A

stationary phase is placed onto a support

material and put into a cartridge, disk, or

other vehicle. Liquid samples are placed

onto the SPE sorbent where total retention

is achieved. Then analytes and interfer-

ents are isolated from each other by the

judicious elution with selective solvents.

Tallmadge and Lin (8) used reversed-

phase LC to determine the percent

olestra in lipid samples. They found an

octadecylsilane column (Zorbax, Agilent

Technologies) appropriate to separate

olestra from other lipophilic sample com-

ponents in samples of soybean-oil olestra

and heated or unheated cottonseed-oil

olestra in soybean oil. The percentage

of olestra in these samples varied from

5% to 90% and relative recoveries of

99.2% to 106.0% were reported. Thus,

it seems possible that with minimal

additional method development a pro-

tocol could be developed that involves

an extraction of total fats and olestra

from the sample food product, SPE

separation of olestra from the total fats,

and FAME analysis of the total fats.

Lipase Hydrolysis

Simultaneously, perhaps the most obvious

and the most direct means of address-

ing the proposed situation is to explore

the fundamental chemistry behind the

problem. Again, olestra is created by esteri-

fication of sucrose with fatty acids, but

because it lacks the glycerol backbone, it is

not subject to enzymatic digestion as are

triglycerides. Can this resistance to diges-

tion be exploited in the conversion of total

fats to FAMEs to the exclusion of olestra?

This is the approach taken in a method

validated under the Association of Official

Analytical Chemists (AOAC) Peer-Verified

Methods Program (9). A modified version

of AOAC Method 983.2.3 was used, in

which a chloroform–methanol extraction

of olestra-containing snacks was performed.

This extract contained both the total fat

and olestra. The hydrolysis portion of the

FAME analysis used a lipase to hydrolyze

the total fats, leaving the unaltered olestra.

The fatty acids resulting from the lipase

hydrolysis were precipitated as calcium

salts and the olestra was extracted with

hexane. The fatty acid salts were redis-

solved and esterified before GC analysis.

Recoveries of 101% (6% relative standard

deviation [RSD]) for total fat and 104%

(6% RSD) for saturated fat were reported.

Repeatability and reproducibility were also

studied and the method was standardized

for fatty acid carbon chains of 6–24. This

Figure 3: Structure of sucrose ester (oles-tra) fat substitutes created from sucrose esterified with six to eight fatty acids.

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Summary

Analytical selectivity can occur during

any step of an analytical method, but

typically it occurs during the separation or

detection steps rather than during sample

preparation. Selectivity during chemical

(analytical) extractions is almost exclusively

limited to solute polarity. Consequently,

selective extractions beyond compound

class separations will be difficult. Although

selective sample preparation is not the

typical case, increasing complexity of the

procedures can lead to selective analy-

sis. This column installment presented a

scenario in which selectivity during the

characterization procedure was not pos-

sible, but selectivity during solvent extrac-

tion (SFE) and sorbent-based extraction

(SPE) or via selective reactions was shown.

References

(1) R. Majors and D. Turner, LCGC North Am.

30(2), 100–110 (2012).

(2) R. Majors, LCGC North Am. 30(12), 1024–

1031 (2012).

(3) AOCS Method Ce 1-62, “Fatty Acid Composi-

tion by Gas Chromatography,” American Oil

Chemists Society Official Methods (2005).

(4) AOAC Method 996.06, “Fat (Total, Saturated,

and Unsaturated) in Foods,” 18th edition

Association of Official Analytical Chemists

Methods.

(5) J. Martinez and A.C. deAguiar, Curr. Anal.

Chem. 10, 67–77 (2014).

(6) F. Sahena, I.S.M. Zaidul, S. Jinap, A.A. Karim,

K.A. Abbas, N.A.N. Norulaini, and A.K.M.

Omar, J. Food Eng. 95, 240–253 (2009).

(7) F. Temelli, J. Supercrit. Fluids 47, 583 (2009).

(8) D.H. Tallmadge and P.Y. Lin, J. AOAC Intl. 76,

1396–1400 (1993).

(9) D. Schul, D. Tallmadge, D. Burress, D. Ewald,

B. Berger, and D. Henry, J. AOAC Intl. 81,

848–849 (1998).

Douglas Raynieis an Associate Research Professor at South Dakota State University. His research interests include green chemis-try, alternative solvents, sample preparation, high resolution chromatogra-phy, and bioprocessing in supercritical fluids. He earned his PhD in 1990 at Brigham Young University under the direction of Milton L. Lee.

Ronald E. Majors“Sample Prep Perspec-tives” Editor Ronald E. Majors is an analyti-cal consultant and is a member of LCGC’s editorial advisory board. Direct correspondence about this column to

“Sample Prep Perspectives,” LCGC, Woodbridge Corporate Plaza, 485 Route 1 South, Building F, Suite 210, Iselin, NJ 08830, e-mail [email protected].

For more information on this topic,

please visit

www.chromatographyonline.com/majors

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LC TROUBLESHOOTING

What kind of adjustments

need to be made when

scaling an isocratic method?

LC Method Scaling, Part I:

Isocratic Separations

John W. DolanLC Troubleshooting Editor

Today we are often confronted with

many different types of liquid chro-

matography (LC) methods. These

may use conventional 250 or 150 mm ×

4.6 mm, 150 mm × 2.1 mm, 50 mm ×

2.1 mm, and many other column con-

figurations packed with particles generally

ranging from <2 μm to 5 μm, and some-

times even 10 μm in diameter. One of the

challenges this variety presents is transfer-

ring a method from one column configu-

ration to another and still obtaining the

same resulting separation. For example,

you may use an ultrahigh-pressure LC

(UHPLC) system to develop methods

quickly in your research and development

(R&D) laboratory, but want to transfer it

to a conventional LC system for routine

use. Or your conventional method with

ultraviolet detection (LC–UV) may need

to be transferred to an LC system with

mass spectrometry detection (LC–MS).

Alternatively, you may want to adjust a

pharmacopeial method to use a different

column configuration. In many of these

cases, the method must be moved from

one column size to another, yet maintain

the same separation. The conversion is

not difficult, but you do have to be care-

ful to make the appropriate adjustments.

Isocratic separations, in which the mobile-

phase concentration is constant, are sim-

pler to convert than gradient methods,

where special care has to be taken to avoid

inadvertent chromatographic changes.

This month’s “LC Troubleshooting” dis-

cussion focuses on isocratic separations,

and next month we’ll look at gradients.

Resolution Is the Key

Equivalent separations require that the

resolution stays the same when conditions

are changed. In the method development

classes we teach, we use what is often

referred to as the “fundamental resolu-

tion equation” as a guide for the method

development process. We can use this

same equation to guide us in method

conversion:

Rs = ¼N 0.5(α – 1)(k/[1+k]) [1]

where Rs is resolution, N is the column

plate number, α is the separation factor,

and k is the retention factor. The first

caveat is that the chemistry of the system

cannot change when a change in the col-

umn or other conditions changes. This

means that the mobile phase must remain

the same, as well as the column tempera-

ture and column chemistry. With today’s

columns, it usually is valid to assume that

the same brand name description of a col-

umn (for example, ACE C18 [Advanced

Chromatography Technologies Ltd.) will

have the same column chemistry, no mat-

ter what the particle size is (2, 3, 5 μm,

and so forth). You’ll recall that the reten-

tion factor is defined as:

k = (tR – t0)/t0 [2]

where tR is the retention time of a solute

and t0 is the column dead time (retention

time of an unretained peak). If we keep

the chemistry of the system constant, the

retention time relative to the dead time

should stay constant, so k will remain

unchanged. Any change in retention

because of a change in column length,

diameter, or flow rate will have a propor-

tional change for tR and t0, so k will stay

constant in this case as well. For example,

doubling the flow rate will halve tR and t0,

and k will be unchanged.

The separation factor α is simply the

ratio of k values for two adjacent peaks, k1

and k2:

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α = k2/k1 [3]

So if we keep k constant, as discussed

above, α will be unchanged. If k and α

are kept constant, to keep Rs constant

(equation 1), all that remains is to make

sure that the column plate number stays

constant.

Keeping the Plate

Number Constant

Fortunately, the relationship between the

column plate number, N, the column

length, L, and particle diameter, dp, are

well defined and the effect of a change

in L or dp can be determined by a simple

calculation. And for you purists, yes, flow

rate does affect N, but for real samples

under real operating conditions, this effect

usually can be ignored for a change in

flow rate by a factor of two with particles

of dp ≤ 5 μm. N is directly proportional to

column length and inversely proportional

to particle size, so if L/dp is kept constant,

the plate number should remain constant.

Because the number of available col-

umn lengths and particle sizes is some-

what limited, the U.S. Pharmacopeial

Convention (USP) (1) suggests that

the plate number should be considered

equivalent if L/dp is a constant -25% to

+50%. For example, we can consider the

following columns to have equivalent plate

numbers (all lengths are in millimeters

and particle diameters in micrometers):

L/dp: 150/5 ≈ 100/3 ≈ 50/1.8

Ratio: (30) (33) (28)

You can see by the ratios shown below

each column configuration that each of

these columns has a ratio in a range of

±10%, so they can be considered equiva-

lent in terms of plate number. It should

be noted that the USP (1) also suggests

allowing other combinations of L and

dp to be used as long as N stays within

-25% to +50%, as would be the case for

core–shell particles, which provide larger

plate numbers than their particle diameter

might suggest.

Maintaining the Linear Velocity

It is customary to keep the linear velocity

(in units of millimeters per second) of the

mobile phase constant when the column

size is changed. The linear velocity is

independent of the column length, but

proportional to the cross-sectional area of

the column. So,

F2 = F1(πr22)/(πr1

2) [4]

or

F2 = F1(dc2/dc1)2 [5]

Where F is the flow rate, r is the col-

umn internal radius, and dc is the

column internal diameter; subscripts

1 and 2 are for the original and new

column, respectively. For a change from

a 4.6-mm i.d. column to a 2.1-mm

column, equation 5 generates a ratio of

(4.6/2.1)2 = 4.8 ≈ 5. Because this change

in diameter is the most common one we

encounter, I like to remember the fac-

tor of five so I can do the quick mental

math. Thus, a 4.6-mm i.d. column

operated at 1 mL/min would mean that

an equivalent linear velocity would be

obtained with a 2.1-mm i.d. column at

0.2 mL/min.

What About Pressure?

Pressure in LC separations is a result of

the separation conditions used, and in

itself is important only relative to the

pressure capability of the instrument.

The exception to this generalization is

that sometimes selectivity (peak spacing)

will change when large changes in pres-

sure occur, such as when switching from

conventional LC pressures to UHPLC

pressures. Although we generally run

conventional LC systems at half to three

quarters of their pressure capability,

all can generate pressures of 400 bar

(6000 psi). UHPLC comprises instru-

ments with a pressure capability of >400

bar, and in some cases up to 1300 bar

(19,000 psi), but most workers operate

their UHPLC systems at 600–1000 bar

(8700–14,500 psi).

Pressure is directly related to column

length and inversely related to the col-

umn cross-sectional area. It is directly

related to the flow rate and inversely

related to the square of the particle size.

Pressure also is influenced by the mobile-

phase viscosity and the column tempera-

ture, but for the present discussion, we’re

assuming that these remain constant so

that selectivity is not changed. Combin-

ing these factors, we have the following

relationship for pressure, P:

P2 = P1(L2/L1) (dc1/dc2)2 (dp1/dp2)

2 (F2/F1)

[6]

where P1 and P2 are the initial and new

pressures, respectively. If we want to

maintain linear velocity, F2 will be deter-

mined by the column diameter change

as in equation 5. If we are not concerned

about linear velocity (usually the case

for ≤3 μm dp particles and often for ≤5

μm ones), F2 may be adjusted to obtain

a desired pressure P2. Examples are dis-

cussed below.

And the New

Retention Time Is . . .

Many of the changes discussed above

will result in a change in the retention

time of each peak. Because we’re only

considering isocratic separations and we

have been careful to avoid making any

chemical changes to the system, we can

calculate what the new retention time

will be. We can use an equation similar

to equation 6 (but be careful which terms

are in the numerator and denominator):

tR2 = tR1(L2/L1) (dc2/dc1)2 (F1/F2) [7]

where tR1 and tR2 are the original and new

retention times, respectively. That is, the

same factors affect retention time as those

that affect pressure, with the exception

that particle diameter does not come into

play for retention. An example is included

in the discussion below.

Example

Let’s look at two examples. In the first

example, let’s consider a hypothetical,

compendial method that uses a 250 mm

× 4.6 mm, 5-μm dp column operated

at 1.0 mL/min, as is typical for many

of the older pharmacopeial methods.

Let’s assume that the retention time of

the analyte of interest is 17 min. The

current method conditions generate a

system pressure of 110 bar (1600 psi),

but for the new method we’re willing

to tolerate 300 bar (4300 psi). To speed

things up, let’s move the method to a

3-μm dp column and to save solvent,

we’ll use a 2.1-mm i.d. column. How do

we go about this?

First, to maintain resolution, we want

to keep N constant, which means L/dp

should be constant within -25% to +50%.

So we can calculate the desired column

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length and then choose an available one that is close to satisfying

this relationship:

L2 = L1dp2/dp1 [8]

or (250 mm × 3 μm/5 μm) = 150 mm. Because 150-mm col-

umns are readily available, the new column’s dimensions will be

150 mm × 2.1 mm and it will be packed with 3-μm dp particles.

Next, we might optionally calculate the flow rate required

for a constant linear velocity with the new column. Using

equation 5, 1.0 mL/min × (2.1 mm/4.6 mm)2 = 0.21 mL/

min, so a new flow rate of 0.2 mL/min would keep the linear

velocity constant.

Now we can calculate what the new pressure would be using

equation 6: 110 bar × (150 mm/250 mm) × (4.6 mm/2.1 mm)2

× (5 μm/3 μm)2 × (0.2 mL/min/1.0 mL/min) = 175 bar (2550

psi). Because this is an isocratic method, we don’t have to be too

concerned about linear velocity, so we can increase the flow rate

to 0.3 mL/min to speed up the method, which would generate an

expected pressure of ~265 bar (3850 psi), well within our desired

maximum pressure.

Finally, we can calculate the new retention time using equation

7: 17 min × (150 mm/250 mm) × (2.1 mm/4.6 mm)2 × (1.0

mL/min/0.3 mL/min) = 7.1 min. All the other peaks in the chro-

matogram will change by the same factor, so for a shortcut, use

the ratio 7.1 min/17 min = 0.41 as the multiplier to determine the

retention times of the other peaks.

As a second example, let’s convert an existing conventional

method to UHPLC conditions. Our original method uses a 150

mm × 4.6 mm column packed with 5-μm dp particles operated

at 1.5 mL/min. The retention time of the active ingredient is 12

min and the pressure is 150 bar (2175 psi). We want to use a 1.7-

μm column with a maximum pressure of 1000 bar (14,500 psi).

First, find the desired column length with the help of equation

8: 150 mm × 1.7 μm/5 μm = 51 mm, so we’ll use a 50 mm ×

2.1 mm column packed with 1.7-μm dp particles. For the flow

rate conversion, let’s just use our factor of five for the 4.6 mm

to 2.1 mm i.d. change. This gives us (1.5/5) = 0.3 mL/min as a

starting flow rate with constant linear velocity.

The pressure calculation, using equation 6, gives a new pres-

sure of 415 bar. Because we can tolerate up to 1000 bar, we can

increase the flow rate to 0.7 mL/min and have a predicted pressure

of 970 bar. The predicted retention time using equation 7 will be

reduced from 12 min down to 1.8 min. The retention times for

the remaining peaks will change by a factor of 1.8/12 = 0.15.

In both of these examples, our next step would be to run the

desired new conditions and observe what happens. Because we

expect the chemistry to be the same and the same plate number

was chosen, the resolution should remain the same. The retention

times should also be close to the calculated values. The observed

pressure will likely deviate from the calculated one somewhat —

for example, because of the additional pressure generated by very

small tubing diameters used for UHPLC.

A Simpler Way

The calculations above are not difficult, but they can be tedious

to perform on a routine basis. You can do as I’ve done and put the

equations into an Excel spreadsheet, making the calculations sim-

ple. Some of the data system software packages now have method

conversion calculators built in. Alternatively, there are several of

these calculators that are free on the internet. Just use a search term

such as “HPLC method transfer calculator,” and you will find sev-

eral choices. I used one of these to double-check my calculations.

Stay tuned for next month’s “LC Troubleshooting,” where we’ll

extend the current discussion to include scaling gradient methods.

Reference

(1) General Chapter 621 “Chromatography, Pharmacopeial Forum

PF38(2)” in United States Pharmacopeia 35–National Formulary 30

(United States Pharmacopeial Convention, Rockville, Maryland,

2012), www.usppf.com.


please visit www.chromatographyonline.com/dolan

John W. Dolan“LC Troubleshooting” Editor John Dolan has been writing “LC Troubleshooting” for LCGC for more than 30 years. One of the industry’s most respected professionals, John is currently the Vice President of and a principal instructor for LC Resources in Walnut Creek, California. He is also a member of LCGC’s editorial advisory board. Direct correspondence about this column via e-mail to [email protected]

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MS – THE PRACTICAL ART

Mass spectrometry (MS)

serves as a versatile and

effective tool in chemical

analysis. It provides

highly specific molecular

information at excellent

sensitivity. The emerging

miniature MS systems

could potentially be

used outside analytical

laboratories by personnel

not trained in analytical

chemistry, and thereby

the range of applications

for MS could also be

significantly broadened.

This column installment

defines the future role

of miniature MS systems

in specialized analysis,

justifies the need for

simplification in operation,

proposes a development

approach involving ambient

ionization, and delineates

challenges in development

and commercialization.

Zheng Ouyang is the guest author of this

month’s installment.

Kate Yu is the editor of

MS—The Practical Art.

When we mention miniature

mass spectrometers, it often

brings to mind handheld

research prototypes such as the Mini 10

or Mini 11 systems developed at Purdue

University (West Lafayette, Indiana) or

commercial products that specialize in

homeland security applications (1). A

marked advance in the same category

is the recent development at Purdue of

a backpack mass spectrometer that has

a sampling probe that can scan ground

surfaces for in-field detection of explo-

sives (2). In this column installment,

however, we contemplate a different

type of miniature mass spectrometry

(MS) analysis systems, such as the Mini

12 system (3). Also developed at Pur-

due, the Mini 12 system weighs 25 kg,

is as compact as a desktop computer,

and could prove useful in the field of

biomedicine as well in the pharma-

ceutical, chemical, and agrochemical

industries. A primary motivation for

developing such a system is to enable

physicians, nurses, and biologists to

analyze samples at their desks, obviat-

ing the need to send the samples to an

analytical laboratory.

Miniature Mass Spectrometry

Analysis System Defined

In the past, the term miniature mass

spectrometer has been used for a vari-

ety of devices that fall within a broad

range of system completeness or self-

sustainability. The miniature mass

analyzers or vacuum manifold assem-

blies by themselves have all been called

miniature mass spectrometers previously.

Finally, complete instrument packages

were developed to perform vacuum

pumping, ionization, mass analysis,

instrument control, and data acquisi-

tion. As demonstrated by the Mini 11,

a mass spectrometer, even with multi-

stage MS-MS capability, can be made

to weigh only 4 kg (4). Such a minia-

turized instrument by itself, however,

would not be practically useful because

it could not perform complete chemi-

cal analyses starting from raw samples

(1). As for a mobile chemical analysis

laboratory, additional equipment for

sample preparation and chromato-

graphic separation is always needed and

could require more space than the mass

spectrometer. Thus, sample prepara-

tion before MS analysis must also be

done using miniaturized equipment and

highly autonomous procedures.

Assuming such miniaturization is

feasible at a system level, a biologist

doing a preclinical study could almost

effortlessly perform routine work such

as finding the concentration of a drug

metabolite in blood. To do that, he or

she would draw about 0.5 μL of blood

from a study animal (for example, a

mouse), drop the blood onto a paper

substrate inside a disposable sample

cartridge, push the cartridge into the

analysis system, and then wait 60 s

for a report of the concentration. With

such a small amount of sample required,

this type of analysis would be mini-

mally invasive. More importantly, the

biologist would not need to program the

instrument, telling it what to look for or

what to do. Instead, a bar code on the

cartridge would be scanned, and a pre-

saved scan function would be automati-

cally loaded and executed. The biologist

also would not need to be concerned

The Future of Miniature Mass

Spectrometers and a Path

Forward: A Few Thoughts

from an Academic Researcher


about the accuracy of measuring a 0.5-

μL sample, because he or she would use

a precut capillary, taking the blood by

capillary action. The biologist could also

expect a high degree of precision for the

quantitation result because the internal

standard (IS) would be precoated on

the inside wall of the capillary (5), and

the concentration calculated according

to the analyte/IS ratio measured and a

presaved calibration curve.

A similar system could be used by a

nurse performing a blood test to iden-

tify a smoker (6), by a physician who

must perform therapeutic drug monitor-

ing to prescribe the correct dosage of

cancer or immunosuppressive drugs (7),

or by a police officer or anxious parent

who wants to determine the presence or

absence of illicit drugs in urine (8). To

do these things, complete systems much

smaller than the current systems used

in analytical laboratories must be devel-

oped. Nevertheless, though we could

never overemphasize the importance

of a system’s operational simplicity, we

might indeed overemphasize the impor-

tance of its small size. Yet we must avoid

doing so. If we can accommodate print-

ers or copiers of various sizes, it might

be ok for us to accept MS systems of 50

kg, as long as they can be operated like

a printer or copier and fit unobtrusively

in our offices.

Development Strategy

The focus of miniaturization used to

apply mainly to instrumentation. How-

ever, the development of applications

for small instruments such as the Mini

11 revealed that beyond the mass spec-

trometer itself, much remained to be

addressed before a complete solution for

chemical analysis could be offered out-

side the laboratory. Mass spectrometers

are always at the end of the food chain

and they just don’t take raw stuff very

well! The Mini 12, as a proof-of-concept

prototype, was developed for exploring a

solution at the system level.

The incremental approach to reduc-

ing equipment size (for instance, by

adopting microextraction or microflu-

idic technologies for traditional sample

preparation and chromatographic sepa-

ration) might eventually deliver some

good integrated solutions. However,

direct MS analysis using ambient ion-

ization has certainly already shown its

potential (9–11). The term ambient ion-

ization, coined by Professor R. Graham

Cooks at Purdue, originally referred to a

class of sampling ionization technologies

for direct ionization of chemicals from

samples in their raw or unprocessed

“ambient” state (12). I often wonder

whether by now Graham has regretted

using this term, for so many research-

ers misconstrue ambient ionization as

meaning ambient pressure ionization and

therefore atmospheric pressure ionization,

which refers to electrospray ionization

(ESI) or atmospheric pressure chemical

ionization (APCI). Both ESI and APCI

are used under atmospheric pressure,

but traditionally only with compounds

extensively purified following sample

preparation.

The potential of ambient ioniza-

tion was originally demonstrated with

desorption electrospray ionization

(DESI) and direct analysis in real

time (DART) (13), not to mention

another 30-plus ambient ionization

methods developed thereafter (9–11).

Remarkable limits of detection have

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been achieved using direct sampling

analysis without any chromatographic

separations. Examples include the

detection of chemical warfare agents

at low-parts-per-billion (ppb) levels

using DART (14), 0.62 pg/mL nicotine

in gas-phase samples using extractive

electrospray ionization (EESI) (15), 100

fmol of peptides using electrospray-

assisted laser desorption ionization

(ELDI) (16), and 0.2–40 ng amounts

of drug molecules in plasma using

DESI (17).

A promising strategy for the future

development of miniature MS analysis

systems, as the Mini 12 system has dem-

onstrated, would be to combine ambient

ionization methods with miniature mass

spectrometers (18). In the Mini 12 sys-

tem, paper spray ionization (7) (Figure

1) is used with a miniature ion-trap mass

spectrometer. A blood sample is depos-

ited on the triangle paper substrate inside

a sample cartridge, forming a dried blood

spot. After it is pushed into the system,

about 10–30 μL of organic solvent is

automatically added to the cartridge, and

a voltage of about 4 kV is applied. The

solvent elutes the organic compounds,

such as drugs and their metabolites,

and spray ionization occurs at the tip of

the paper substrate. Two MS-MS scans

are automatically performed on the

analyte and its internal standard, which

can be mixed in the sample by using

an IS-coated capillary (5) or IS-printed

paper substrate (19). As an example of

the quantitation performance possible

using the Mini 12, consider the analy-

sis of amitriptyline in blood (3), which

returned an limit of detection (LOD) of

7.5 ng/mL and a relative standard devia-

tion (RSD) better than 10% (Figure 2).

Challenges and Solutions

The challenges associated with bring-

ing miniaturized MS analysis systems

to end users are quite considerable. The

instrumentation and application must

be researched and developed much

further, and a good strategy will even-

tually be needed to develop the initial

market for the products. Before we even

begin to address those challenges, we

must overcome a psychological barrier.

MS has established itself as the “gold

standard” for chemical analysis, and it

is proudly announced as the most sensi-

tive and specific technique for “general

purpose” analysis. The development and

continuous refinement of conventional,

general-purpose MS analysis systems

has, in turn, led to better resolution,

mass accuracy, and wider dynamic

ranges for both mass-to-charge ratio

(m/z) and concentration. MS systems

meeting with these criteria are therefore

highly effective for analyzing a plethora

of chemical and biological compounds

over wide ranges of concentration and

molecular weight. For example, Figure 3

shows a subset of chemical and biologi-

cal compounds, ranging in mass from

several hundred daltons to 16 kDa, in

human blood that includes therapeutic

drugs, amino acids, lipids, and proteins.

In concentration, these compounds vary

by more than nine orders of magnitude.

Following delicate sample prepara-

tion and chromatographic separation,

they can all be quantitatively analyzed

using modern, commercial mass spec-

trometers. Indeed, the success of mass

spectrometers has produced the high

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standard by which we judge their

performance and guide instrument

development. However, any attempt to

transfer the capability of conventional

mass spectrometers to miniature MS

systems would sink the ship before the

journey starts. To gain the convenience

of using small systems, we must forgo

something; we must compromise, and

we must do so in a significant way.

Can we make small MS systems with

each specialized only for one compound

from one sample? In such a case, we

need to worry about only a narrow

concentration range, a narrow m/z

range (though perhaps not for hemoglo-

bin — at least not yet), a single SRM

(MS-MS) scan, and a single calibration

curve. The chance for packaging these

functions into a unit operated with

minimal human intervention would be

significantly larger. One must question

whether manufacturers would profit by

systems of restricted application range,

but which would, nonetheless, reflect

significant development and production

costs. At the moment, we might have to

blindly believe that the high-volume sale

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kV

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LOQ: 1ng/mL

Measu

red

co

nce

ntr

ati

on

(μg

/mL)

RSD < 8%

T

4

3

2

1

00 1 2

Theoretical concentration (μg/mL)3 4

Figure 1: Paper spray ionization and direct quantitative analysis of imatinib in blood. Adapted from reference 7.

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of units and their associated consum-

ables, simply because of the convenience

of use, would take care of the profit.

This would make it critical to identify

killer applications for launching the

product.

Here I am depicting a future of MS

with two distinct paths: one for the cur-

rent commercial MS systems further

advanced for discovery and research

work and the other for the specialized

and turnkey miniature MS systems

developed for routine analysis.

Some technical challenges have

already been identified. They include

efficient extraction of target compounds

from the complex sample; efficient

transfer of the ions into the miniature

mass spectrometer; and adequate per-

formance for compound identification,

in light of “compromised” instrument

capability. Rapid development in the

field of ambient ionization offers us

hope that miniature MS systems using

consumable sample cartridges would

perform with adequate sensitivity.

A critical development in operation

procedure would be the accurate trans-

fer of small amounts of samples and the

incorporation of internal standards for

quantitation. The practical challenge

lies in the associated procedures, which

must be simple enough for users who are

untrained in analytical techniques (5).

The interface for coupling an ambient

ionization source with a miniature mass

spectrometer is another challenge. For

instruments fitted with small pumps,

ion transfer from air to mass analyzer

has proved difficult. Currently, the only

method developed is the discontinuous

atmospheric pressure interface (20) used

in the Mini 12 (3) and its predecessors,

the Mini 10 (20) and Mini 11 (4). This

is one area in the instrument development

that particularly needs some major effort.

People are also generally nervous about

the mass accuracy and mass resolution

3

2

A/IS

1

00 100 200

Concentration (ng/mL)

300 400 500

Therapeutic range

RSD < 10%

LOQ: 7.5 ng/mL

Figure 2: Plot showing the performance of the Mini 12 MS system for the quantitation of therapeutic drugs in a blood sample. Adapted from reference 3.


of the miniature mass spectrometer,

which no doubt would be somewhat

compromised to achieve smaller size,

lower weight, and lower cost of the

system. Before we put enormous effort

into tackling this problem, we should

ask this question: How much could we

tolerate the mass shifts and overlap of

the isobaric peaks if MS-MS transitions

are used for compound identification?

Formulating a general answer would

be difficult right now, and we probably

should not look for a general solution

in the future for miniature MS systems

either. For a special package with a sam-

pling ionization method optimized for

target analytes, the specificity and reli-

ability based on MS-MS can easily be

tested. On-cartridge, real-time reactions

can also be incorporated, improving the

specificity based on the chemical struc-

tures of the target compounds (19).

The Path Forward in Research

and Commercialization

The miniaturization of mass spectrom-

eters used to be solely for instrumental-

ists. Now, however, the future devel-

opment of complete analysis systems

requires a major contribution by ana-

lytical chemists who possess extensive

knowledge and experience in sample

treatment and chromatography. We will

need to persuade many of them to shift

their interest from liquid chromatogra-

phy (LC) columns to sample cartridges

with integrated functions for real-time

extraction and sampling ionization.

Developing miniature MS analy-

sis systems requires a comprehensive

engineering capability for research and

development, certainly a stretch for

academic, analytical chemistry research

groups, which have historically made

major efforts to develop instrumenta-

tion for chemical analysis. In the past,

the Jonathan Amy Facility for Chemical

Instrumentation (JAFCI) at Purdue has

served as an effective model for enabling

the engineering capability to analyti-

cal chemists. In fact, the JAFCI model

has been adopted by other chemistry

departments nationwide. Current eco-

nomic constraints, however, make estab-

lishing new facilities or even maintain-

ing current ones difficult. Searching for

alternatives, some analytical chemistry

divisions have exploited their intrinsic

connections with academic engineering

departments such as chemical engineer-

ing and biomedical engineering. Some

new initiatives nationwide among these

departments suggest that their faculty’s

holding positions in both chemistry

and engineering departments might

be a sustainable way of creating and

maintaining multidisciplinary research

environments for developing chemical

instrumentation. Such a setup would

also provide an opportunity for engi-

neering students and researchers to play

a more active role, versus a supportive

one in the JAFCI model, in the research

and development of chemical instru-

mentation; as long as we tell them we

are now stepping into the era of “MS

sensors” and that Rapid Communications

in Mass Spectrometry (RCM), Inter-

national Journal of Molecular Sciences

(IJMS), Journal of Mass Spectrometry

(JMS), or Journal of The American Soci-

ety for Mass Spectrometry (JASMS) are

actually all engineering journals.

The future of the miniature MS

analysis systems could be very bright;

the path for their commercialization,


Dedication to R. Graham Cooks:

An “Acorn” in Mass Spectrometry

This article is based on my thoughts

and those of my former mentor

and current colleague at Purdue

University, Professor R. Graham

Cooks (though without his knowl-

edge). Each year, the university’s

vice president for research hosts

a dinner for “Boilermakers” who

have brought in substantial grants,

and awards each of them a “brass

acorn,” the university’s symbolic

“Seed for Success.” With more

than 100 PhD students graduated,

Graham is truly one of the most

productive acorns in the field of

MS. He has promoted analytical

instrumentation his entire, lengthy

career and has been one of the most active advocates for mass spectrometer minia-

turization. In recognition of his contribution to the advances in chemical instru-

mentation, Graham was awarded the 2013 Dreyfus Prize in the Chemical Sciences.

In his plenary talk at the 50th American Society for Mass Spectrometry (ASMS)

Conference in 2002, Graham cited a poem by Stephen Spender as a dedication to the

pioneers in mass spectrometry. Here I dedicate the same poem to him, R. Graham

Cooks, a warrior, a wanderer, and a gentleman in the field of mass spectrometry.

“I Think Continually of Those Who Were Truly Great”

by Stephen Spender

I think continually of those who were truly great.

Who, from the womb, remembered the soul’s history

Through corridors of light where the hours are suns

Endless and singing. Whose lovely ambition

Was that their lips, still touched with fire,

Should tell of the Spirit clothed from head to foot in song.

And who hoarded from the Spring branches

The desires falling across their bodies like blossoms.

What is precious is never to forget

The essential delight of the blood drawn from ageless springs

Breaking through rocks in worlds before our earth.

Never to deny its pleasure in the morning simple light

Nor its grave evening demand for love.

Never to allow gradually the traffic to smother

With noise and fog the flowering of the spirit.

Near the snow, near the sun, in the highest fields

See how these names are feted by the waving grass

And by the streamers of white cloud

And whispers of wind in the listening sky.

The names of those who in their lives fought for life

Who wore at their hearts the fire’s centre.

Born of the sun they travelled a short while towards the sun,

And left the vivid air signed with their honour.

Professor R. Graham Cooks of Purdue University and two of his apprentices, Scott McLuckey (left) of the Department of Chemistry, and the author, Zheng Ouyang (right), of the Weldon School of Biomedical Engineering. Photo taken at the 2013 “Acorn Awards” dinner at Purdue University.

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however, could still be very difficult.

It is not a natural move for any of the

current major instrument companies to

initiate the production of small systems.

No doubt they all retain top-quality

instrumentation scientists who can

produce products of novel capabilities;

however, it can be mentally torching to

ask the builders of Mercedes-Benz auto-

mobiles to shift their interests to mak-

ing scooters. Besides, market research

plays such an important role nowadays

in deciding which products to develop,

and no valid data would be available for

anything truly groundbreaking. Unless

a “dictator” with a vision like Steve Jobs

appeared in one of the large instrument

corporations, development of miniature

MS products would more likely be pur-

sued by some desperate startups that

really want to go beyond the homeland

security market. Even then, however,

patent issues can be formidable for such

small companies because technical areas

are well-covered by the major players in

the industry.

China, however, is uniquely positioned

to assume a major role in commercial-

izing miniature MS analysis systems.

Traditionally, instrumentation companies

have not applied for patent protection for

their technologies in China. Therefore,

it is easier to produce a product package

that includes the best suitable technolo-

gies in China than to do so in North

America, Europe, Japan, or Australia.

Would the size of the market in China

justify such a development? Indeed it

would. China has become the world’s

number-two market for MS products.

Given the high cost of materials in the

production-based economy, improper use

of cheap materials and illicit additives is

a problem in China that calls for product

quality control. This is a major appli-

cation area well suited for specialized

MS systems. Since 2004, the Chinese

government has invested in MS product

development, and the funding amount

was dramatically increased recently, with

each individual project funded at $10M

or higher. Although the direct product

outcomes of these investments remain

to be seen, the development activities

have certainly trained many researchers

and developers in the requirements of

10-1 10-4

Concentration (g/mL)

Hemoglo

bin

Sphingom

yelin

Cyclophosp

hamid

e

Lidocain

e

Amin

o acids

Amitr

ipty

line

Tamoxife

n

Topotecan

10-5 10-6 10-7 10-8 10-9

Figure 3: Exemplary chemical and biological compounds in human blood.


the MS instrument industry. Because

it lacks a major player in that industry,

China has been striving to establish one,

and the concept of small instruments for

specialized applications has long been

considered as a good foothold for break-

ing into the business of MS manufacture.

Also, the culture of “wide mass range,”

“high dynamic ranges,” and “ultimately

high resolution and precision” is not so

deeply rooted in China as it is in other

places. Thus it would come as no surprise

to soon see some miniature MS products

manufactured there and packaged into

different systems suitable for various

needs in different global regions.

References

(1) Z. Ouyang and R.G. Cooks, Annu. Rev.

Anal. Chem. 2, 187–214 (2009).

(2) P.I. Hendricks, J.K. Dalgleish, J.T. Shel-

ley, M.A. Kirleis, M.T. McNicholas, L. Li,

T.-C. Chen, C.-H. Chen, J.S. Duncan, F.

Boudreau, R.J. Noll, J.P. Denton, Z. Ouy-

ang, and R.G. Cooks, submitted to Analyti-

cal Chemistry, 2013, under review.

(3) L. Li, T.-C. Chen, Y. Ren, P.I. Hen-

dricks, R.G. Cooks, and Z. Ouyang,

submitted to Analytical Chemistry, 2013,

under review.

(4) L. Gao, A. Sugiarto, J.D. Harper, R.G.

Cooks, and Z. Ouyang, Anal. Chem. 80,

7198–7205 (2008).

(5) J. Liu, R.G. Cooks, and Z. Ouyang, Anal.

Chem. 85, 5632–5636 (2013).

(6) H. Wang, Y. Ren, M.N. McLuckey, N.E.

Manicke, J. Park, L. Zheng, R. Shi, R.G.

Cooks, and Z. Ouyang, Anal. Chem. 85,

11540–11544 (2013).

(7) H. Wang, J. Liu, R.G. Cooks, and Z. Ouy-

ang, Angew. Chem., Int. Ed. 49, 877–880

(2010).

(8) Y. Su, H. Wang, J. Liu, P. Wei, R.G. Cooks,

and Z. Ouyang, Analyst 138, 4443–4447

(2013).

(9) R.G. Cooks, Z. Ouyang, Z. Takats, and J.M.

Wiseman, Science 311, 1566–1570 (2006).

(10) Z. Ouyang and X. Zhang, Analyst 135,

659–660 (2010).

(11) M.E. Monge, G.A. Harris, P. Dwivedi, and

F.M. Fernández, Chem. Rev. 113, 2269–

2308 (2013).

(12) Z. Takáts, J.M. Wiseman, B. Gologan, and

R.G. Cooks, Science 306, 471–473 (2004).

(13) R.B. Cody, J.A. Laramee, and H.D. Durst,

Anal. Chem. 77, 2297–2302 (2005).

(14) J.M. Nilles, T.R. Connell, and H.D. Durst,

Anal. Chem. 81, 6744–6749 (2009).

(15) C. Berchtold, L. Meier, and R. Zenobi, Int.

J. Mass Spectrom. 299, 145–150 (2011).

(16) I.X. Peng, R.R.O. Loo, E. Margalith, M.W.

Little, and J.A. Loo, Analyst 135, 767–772

(2010).

(17) J.H. Kennedy and J.M. Wiseman, Rapid

Commun. Mass Spectrom. 24, 309–314

(2010).

(18) L. Gao, R.G. Cooks, and Z. Ouyang, Anal.

Chem. 80, 4026–4032 (2008).

(19) J. Liu, H. Wang, N.E. Manicke, J.-M. Lin,

R.G. Cooks, and Z. Ouyang, Anal. Chem.

82, 2463–2471 (2010).

Zheng Ouyangis an Associate Professor in the Wel-don School of Bio-medical Engineering at Purdue University (West Lafayette, Indiana). He has a research interest in developing instrumentation and applica-tions for mass spectrometry, with results published in more than 110 peer-reviewed publications. He has received a number of awards including the Wallace H. Coul-ter Foundation Early Career Translational Research Award in Biomedical Engineering, a China National Natural Science Foundation Award for Distinguished Overseas Young Scholars, a USA National Science Foundation Early Career Award, the American Society for Mass Spectrometry Research Award, and the International Mass Spectrometry Society Curt Brunnée Award for outstanding contri-butions to the development of instrumenta-tion for mass spectrometry.

Kate Yu“MS — The Practical Art” Editor Kate Yu joined Waters in Mil-ford, Massachusetts, in 1998. She has a wealth of experience in applying LC–MS technologies to vari-ous application fields such as metabolite identification, metabolomics, quantitative bioanalysis, natural products, and environ-mental applications. Direct correspondence about this column to [email protected]

For more on mass spectrometry, see the

full “MS–The Practical Art” column at

www.chromatographyonline.com/MSPA.

Also, see our ongoing supplement series,

Current Trends in Mass Spectrometry, under

“Publications,” then “Supplements.”

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Walter J. Krol, Brian D. Eitzer, Terri Arsenault, Mary Jane Incorvia Mattina, and Jason C. WhiteThe Connecticut Agricultural Experiment Station, Department of Analytical Chemis-try, New Haven, Connecticut. Direct corre-spondence to: [email protected]

Significant Improvements in Pesticide

Residue Analysis in Food Using the

QuEChERS Method

The quick, easy, cheap, effective, rugged, and safe (QuEChERS) sample

preparation procedure combined with both gas chromatography–mass

spectrometry (GC–MS) and liquid chromatography–mass spectrometry (LC–

MS) was adopted in our laboratory for the analysis of pesticide residues in

food samples as part of the state of Connecticut’s regulatory monitoring

program. In 2006, data from a QuEChERS-based sample preparation

procedure were compared to data from our previous analytical method. In

this article, these results are further compared to those of the U.S. Food and

Drug Administration’s pesticide residue monitoring program and the U.S.

Department of Agriculture’s pesticide data program.

Since its inception in 1963, the pesti-

cide residue program in the Depart-

ment of Analytical Chemistry at

The Connecticut Agricultural Experiment

Station (CAES) has made major advance-

ments in the analyses of pesticide resi-

dues present in food — primarily but not

exclusively produce. In 1992, the method

of Pylypiw (1) was used in our laboratory

to replace our older methods (2) for the

extraction of organochlorine and organo-

phosphorous pesticides from food samples.

At the time, residues were analyzed by

gas chromatography (GC) with element-

selective detection. Beginning in 1993,

mass spectrometry (MS) was introduced

for the confirmation of violative residues.

By 1999, all samples were subjected to

MS analysis for the presence of pesticide

residues (3). In 2006, following the acqui-

sition of a ion trap liquid chromatogra-

phy–mass spectrometry (LC–MS) system,

a direct comparison was made between

the Pylypiw method and the then newly

published quick, easy, cheap, effective,

rugged, and safe (QuEChERS) method

(4). In 2011, an orbital trap LC–MS sys-

tem (Thermo Scientific Exactive Orbitrap)

was added to our program and is currently

used for the exact-mass confirmation of

violative pesticide residues.

In 2006, we compared the Pylypiw

method (1), which offers petroleum ether

extracts that are amenable to GC analysis,

with an adaptation of the recently intro-

duced QuEChERS method (4), which

offers acetonitrile or toluene extracts that

are amenable to both GC–MS and LC–

MS. Approximately 181 samples obtained

for analyses in the Connecticut program

were tested using a paired sample blind

study protocol (vide infra). The extracts

from the Pylypiw method were analyzed

by GC–MS and GC with micro electron-

capture detection (ECD), and the QuECh-

ERS extracts were analyzed by GC–MS

and LC–MS as outlined in Figure 1.

The Connecticut program is similar to the

larger United States (US) Food and Drug

Administration (FDA) program in that it

tests a wide variety of samples available to

the consumer in the market place. The sam-

ples tested in these two surveys can be com-

prised of nearly any type of food offered for

sale to the consumer. On an average annual

basis from 1990 to 2010 the Connecticut

program tested 37 different commodity

types of fresh food and 14 different com-

modity types of processed food. These two

programs contrast to the US Department of

Agriculture (USDA) pesticide data program

(PDP) which, on average, targets 12 fresh

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MAKING A DIFFERENCE

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CT no residue found

CT violative samples

Pe

rce

nt

of

sam

ple

s w

ith

no

re

sid

ue

s

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

Pe

rce

nt

of

sam

ple

s w

ith

to

lera

nce

vio

lati

on

Left ordinate axis

FDA no residue found

Right ordinate axis

FDA violative samples

PDP no residue found

PDP violative samples

12.080.0

70.0

60.0

50.0

40.0

30.0

20.0

10.0

0.0

10.0

8.0

6.0

4.0

2.0

0.0

Year

Figure 2: Comparison of the Connecticut, FDA, and PDP monitoring programs for pesticide residues in food.

and four processed samples per year. Owing

to the fact that the results obtained from the

Connecticut and FDA programs are derived

from nontargeted sources (5), as opposed to

those in the PDP (6), the results obtained

through the Connecticut program are

thought to be more representative of those

in the larger FDA program.

From 1990 through 2005, the results

obtained from the Connecticut program

closely matched those obtained in the larger

FDA pesticide residue monitoring program

(Figure 2). During this timeframe, the FDA

program analyzed 167,215 samples (5); the

Connecticut program separately analyzed

4871 food samples (3). The Connecticut

program analyzed only about 3% (2.91%)

of the total samples in the FDA program. It

is noteworthy that there is not a significant

difference in the proportions of pesticide

residue–free samples, 63.3% reported by

Connecticut and 64.2% reported by FDA

(5) (Figure 2), over the 16-year timeframe

(1990–2005) when the data are compared

using a z-test (P = 0.230; z = 1.200). The

average violation rate over the same period

was similar, 1.5% for the Connecticut pro-

gram and 2.8% (5) for the FDA program,

but statistically different (P = <0.001; z =

5.114). These results imply that the sam-

pling design in Connecticut closely paral-

lels the larger program of the FDA, that

the analytical methodology used in the two

surveys was comparable over the timeframe

1990–2005, and that the smaller Con-

necticut subsample is representative of the

larger with respect to samples containing

pesticide residues.

From the inception of the USDA PDP

study in 1992 and through 2005, the results

obtained from the Connecticut program

contrasted sharply to those obtained in the

PDP study by as much as 38% (Figure 2).

During this timeframe the PDP targeted

112,395 samples (6), and the Connecticut

program tested 4150 samples. When com-

pared, the percentages of pesticide resi-

due–free samples over the inclusive 14-year

timeframe (1992–2005), 62.7% reported

by Connecticut and 38.8% reported by the

PDP, was not similar nor was it statistically

significant (P = <0.001; z =34.117). The

average violation rate reported, 3.5% by

the PDP (6) and 1.7% by Connecticut, was

likewise statistically dissimilar (P = <0.001;

z = 6.208). These results suggested that the

sampling designs of the two programs were

dissimilar and that the analytical method-

ology used in the two studies was likely

dissimilar.

Experimental

Sample Collection

All of the fresh and processed fruit and

vegetable samples examined in this work

were collected by inspectors from the Con-

necticut Department of Consumer Pro-

tection (DCP). The samples consisted of

fruits and vegetables grown in Connecti-

cut, other states, or foreign countries and

were collected at different Connecticut

farms, producers, retailers, and wholesale

outlets located within the state. The sam-

ples collected were brought to our labora-

tory in New Haven by the DCP inspectors

for pesticide-residue testing. In all cases,

these samples were collected without prior

knowledge of any pesticide application.

Sample Homogenization

In most cases, each sample was prepared

in its natural state as received, unwashed

and unpeeled, but in all cases samples were

processed according to the Pesticide Ana-

lytical Manual (7). Whole food samples

were homogenized before extraction using

Homogenized sample

QuEChERS extracts

181 Samples41 Processed

140 Fresh

Pylypiw extract

181 VegPrep extracts (petroleum ether)

Blind362 total extracts

181 QuEChERS extracts (acetonitrile–toluene)

GC–MS

GC–ECD

GC–MS

LC–MS

Figure 1: Flowchart of 2006 sample extract and analysis.


a food chopper or a commercial blender

equipped with an explosion-proof motor.

Liquid and powdery samples were mixed

thoroughly before subsampling for extrac-

tion. In all cases, a portion of each sample

(approximately 500 g) was retained and

frozen in plastic bags until analysis and

reporting of the results were completed.

Sample Extraction

Pylypiw Method

The method described by Pylypiw (1)

was used with minor modifications. A

50-g subsample of homogenized material

was weighed into a blender jar (1 L) and

blended with 50 mL of isopropyl alcohol

and 100 mL of petroleum ether for approx-

imately 5 min. After the mixture settled, it

was filtered through a plug of glass wool

into a 500-mL separatory funnel to remove

insoluble particulates. Interfering coex-

tracted compounds and the isopropyl alco-

hol were removed from the petroleum ether

extract by sequential washing with water

(3 × 200 mL). Saturated sodium sulfate

solution (50 mL) was added to the first

and the final wash to enhance partitioning

and phase separation. After the final wash,

the organic extract was collected in 40-mL

glass vials containing anhydrous sodium

sulfate (approximately 10 g) as a drying

agent. After 2 h, a portion of the sample

extract was transferred to a chromatogra-

phy vial and stored at room temperature

until analysis. It should be noted that this

extraction method results in a twofold dilu-

tion factor of the original sample.

QuEChERS Method

The QuEChERS method described by

Anastassiades and colleagues (4) was mod-

ified for this work. For the 990 samples

tested in this work, a 15-g subsample of

homogenized material was weighed into

a 50-mL disposable polypropylene centri-

fuge tube. The tube was then amended

with [U-ring]-13C6-Alachlor internal

standard (600 ng; prepared from mate-

rial purchased from Cambridge Isotope

Laboratories), anhydrous magnesium sul-

fate (6 g), anhydrous sodium acetate (1.5

g), and acetonitrile (15 mL) and the mix-

ture was shaken on a Burrell Model 75

Wrist Action shaker (Burrell Scientific)

for approximately 1 h. The tube was cen-

trifuged using a Centra GP6 centrifuge

(Thermo IEC) at 3000 rpm (2087g) for 10

min at approximately 25 °C to separate the

acetonitrile from the aqueous phase and

solids. Acetonitrile (10 mL) was decanted

into a 15-mL polypropylene Falcon cen-

trifuge tube (Corning) containing anhy-

drous magnesium sulfate (1.5 g); primary

and secondary amine (PSA) bonded silica

(0.5 g) and toluene (2.0 mL). The mixture

was shaken by hand for approximately 5

min and centrifuged at 3000 rpm (2087g)

for 10 min at 25 °C. Then 6 mL of the

extract was added to a concentrator tube

and blown down to just under 1 mL (but

not to dryness) under a stream of nitrogen

at 50 °C. The concentrated material was

reconstituted to a final volume of 1.0 mL

with toluene. It should be noted that this

extraction method results in a fivefold con-

centration of the original sample.

Whereas QuEChERS normally requires

the use of acidified acetonitrile when sodium

acetate is used for salting out (4,8,9), it was

intentionally not used in this modification.

The exclusion of acid provides better PSA

cleanup yet may lead to lower recoveries of

a few base sensitive pesticides (10). To mini-

mize potential lower recoveries, extracts are

stabilized by reconstitution in toluene fol-

lowing their concentration. Excluding the

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acid during the extraction eliminates the need for its neutralization

before concentrating the extract. Failure to neutralize the added acid

would expose the extracts to a concentrated acid environment fol-

lowing the concentration step used in the present protocol and lead

to rapid GC column degradation.

Instrumental Analysis

Pylypiw Extracts

From 1990 to 1999, 3547 extracts were analyzed by GC as

described by Pylypiw (1). Beginning in 1994, violative samples

were confirmed by MS using a model 5890 GC system equipped

with an HP-7673 autoinjector and an HP 5972A MSD system

(all from Hewlett-Packard Co.). Injections (2–4 μL) were made

onto a 30 m × 0.53 mm, 0.5-μm df SPB-5 fused-silica capillary

column (Sigma-Aldrich). From 1999 to 2006, samples prepared by

the Pylypiw method were analyzed using a model 6890 plus GC

equipped with dual 7683 series injectors, and a 7683 autosampler

(collectively known as an automatic liquid sampler [ALS]), a model

G2397A μECD system in the rear position, and a model 5973

MSD system in the front position (all from Agilent Technologies).

A programmable temperature vaporization (PTV) was installed on

the front inlet and a Merlin MicroSeal system (Merlin Instrument

Co.) on the rear inlet; dual 30 m × 250 μm, 0.25-μm df Supelco

MDN-12 fused-silica capillary columns (Sigma-Aldrich) were

used. Injections (2 μL) were made simultaneously onto both col-

umns. All data were collected and analyzed using MSD Productiv-

ity Chemstation software version B.02.00 (Agilent Technologies).

QuEChERS Extracts

Samples prepared by the QuEChERS method were analyzed by

GC–MS and LC–MS. For the GC analysis, a model 6890N GC

system equipped with a 7683 series ALS autosampler and a model

5975 MSD system were used (all from Agilent Technologies). The

inlet used a Merlin MicroSeal system with injections made onto

a 30 m × 250 μm, 0.25-μm df J&W Scientific DB-5MS+DG

column (Agilent Technologies). Data were collected and analyzed

using MSD Chemstation software version D.02.00.275 (Agilent

Technologies). The LC–MS analyses were made using a model

1100 LC system and a 150 mm × 2.1 mm, 5-μm dp Zorbax SB-C18

column (Agilent Technologies) with eluent flowing to a Thermo

Electron Finnigan LTQ ion-trap MS system through 2009. In

2010, the model 1100 LC system was replaced with a model 1200

Rapid Resolution LC system and a 50 mm × 4.6 mm, 1.8-μm

dp Zorbax XDB-C18 column (both from Agilent Technologies).

Data were collected and analyzed using Xcalibur software version

2.0 (Thermo Scientific). Beginning in 2010, confirmatory data

for violative samples were obtained using a model 1200 LC sys-

tem and a 150 mm × 2.1 mm, 5-μm dp Zorbax SB-C18 column

(both from Agilent Technologies) with eluent flowing to a Exac-

tive Orbitrap MS system (Thermo Scientific). Data analysis was

performed using ToxID version 2.1.2 and Xcalibur Qual Browser

version 2.1.0.1140 (both from Thermo Scientific).

2006 Blind Study Design

All samples were collected and homogenized as described above.

Subsamples of the homogenate were extracted and analyzed con-

currently by both the Pylypiw and QuEChERS protocols as out-

lined in Figure 1 by different laboratory personnel. The results


of the analyses performed were maintained

in separate databases by the laboratory per-

sonnel responsible for performing the indi-

vidual analysis. At the end of the one-year

study, the data obtained were reconciled

into a single database for statistical analysis.

Reproducibility of Results

All samples examined in this work were

individually homogenized, extracted,

and analyzed by GC and LC once. Inter-

and intralaboratory studies over a wide

range of pesticides, pesticide concentra-

tions, and matrices and its validation as

an Association of Analytical Communi-

ties (AOAC) method (4,8,9) have dem-

onstrated that a single, homogenized

extract is sufficient to obtain accurate

quantitation of pesticide residue con-

centrations. Anastassiades and cowork-

ers (8) have developed a database of

more than 150,000 recovery figures on

over 650 different pesticide residues. All

violative samples were re-extracted, ana-

lyzed, and quantified in duplicate using

portions of the original sample retained

from the homogenization step. One of

these duplicate samples was spiked with

the pesticides in question at a concen-

tration slightly above the originally

determined value. Quantitative values

of the pesticides in these extracts were

compared to the concentration found

in the original analysis. Beginning in

2010, exact MS data were also obtained

on those residues found to be violative.

Results and Discussion

In 2006, a comparison was made for 181

samples of fresh (140, 77.3%) and processed

Different active ingredients found

Residues/sample with pesticides

90Pre-QuEChERS QuEChERS

Secondary axis

3.5

2.5

1.5

3.0

2.0

1.0

0.5

0.0

50

40

30

20

10

02000 2002 2004 2006

Year

2008 2010

80

70

60

Percent sample with pesticides

Pri

mary

axis

Seco

nd

ary

axis

Average overall residue level (μg/g)

Figure 3: Impact of QuEChERS on the numbers and levels of detected pesticide residues found in Connecticut food samples.

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(41, 22.7%) samples between the Pylypiw

and QuEChERS extraction methods.

The 181 homogenized samples were each

divided, extracted, and analyzed as depicted

in Figure 1. There were a total of 362

extracts analyzed. It needs to be noted that

because of the extraction protocols used, the

Pylypiw extracts were 10 times more dilute

than the QuEChERS extracts (vide supra).

Of the 181 samples analyzed by the

Pylypiw protocol, 98 (54.2%) of the sam-

ples contained no detectable pesticide resi-

dues, 79 (43.6%) contained non-violative

residues (0.001–12.00 ppm), and 4 (2.2%)

contained violative residues (0.014–0.900

ppm). A total of 133 individual pesticide

residues were found on the 83 samples con-

taining pesticide residues.

By contrast, when the same 181 samples

were extracted by the QuEChERS method

and the extracts were analyzed by GC–MS

and LC–MS, 73 (40.3%) of the samples

were found to contain no detectable pes-

ticide residues (see Figure 2). The remain-

ing 108 samples contained 181 different

individual pesticide residues; 89 samples

(49.2%) contained nonviolative residues

(0.001–3.90 ppm) and 20 (11%) contained

violative residues (0.002–0.752 ppm). Of

these 181 residues, 42 were detected by GC

alone, 70 were detected by LC alone, and

69 residues were found by both instrumen-

tal methods of analysis.

The dramatic decrease in the number of

samples found to be pesticide residue free

when using the QuEChERS approach in

place of the Pylypiw protocol (40.3% vs.

54.2%) represents a major advancement

(13.9%) for our laboratory in the analy-

sis of pesticide residues in food. In 2006,

more violative residues (21 total; 11.6%)

were found than in any other year in our

survey. The Pylypiw method found only

one violation not found by the QuECh-

ERS approach, whereas the QuEChERS

method found 17 residues not found by the

Pylypiw method. It should be noted that

the number of 2006 violations was slightly

skewed owing to nine findings of atrazine.

A more thorough discussion about the

atrazine residues found has been presented

by Krol (11). Ultimately, it was found that

many of the residues were the result of

plant uptake when atrazine was applied in

a previous growing season. If the atrazine

violations are omitted from consideration,

there would have only been 12 violations

(6.6%) reported in 2006; and the violation

rate from the QuEChERS method would

have been nearly halved to 6.1% from the

11% reported above.

In 2006, it is clear that the Connecti-

cut Pylypiw results did not exactly match

those of the FDA study. Both studies how-

ever indicated that the majority of samples

tested, 98 (54.2%) and 3699 (67.1%)

respectively, were free from pesticide resi-

dues. The poor correlation is likely because

of a yearly fluctuation in the proportions

of residues present in the samples analyzed

over the course of this one year study.

These yearly fluctuations have been pres-

ent since 1990. The results of the QuECh-

ERS protocol in 2006 (Figure 2) indicate

that the majority of the food purchased for

consumption contain pesticide residues; in

fact 59.7% of the sampled food offered for

sale in Connecticut contains at least one

pesticide residue. The results obtained in

2006 more closely mimic those reported

in the PDP.

By way of comparison, in 2006 the

FDA reported (5) that it tested 5512 sam-

ples and found that 3699 (67.1%) of the

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samples contained no detectable pesticide residues; 1578 (28.6%)

contained nonviolative residues; 235 (4.3%) contained violative

residues. The USDA PDP (6) tested 9818 samples and found

that 3717 (37.9%) of the samples contained no detectable resi-

dues; 5767 (58.7%) contained nonviolative residues; 334 (3.4%)

contained violative residues.

To confirm the findings of our blind study and to address

potential yearly fluctuations in the data, we continued forward

with our work extracting all samples through the QuEChERS

protocol and analyzing the extract by both LC–MS and GC–MS.

We also continued with an indirect annual comparison of our

findings to those of the FDA pesticide monitoring program and

the PDP. Because of the fact that the LC–MS library used in the

QuEChERS screening needed to be established, the numbers of

different analytes detected has continually increased through the

course of the study.

As can be seen in Figure 2, beginning in 2006, the FDA

and Connecticut surveys dramatically diverge, and the results

of the Connecticut survey more closely matches those of the

PDP. On average, over the three years during 2006–2008, the

QuEChERS protocol in the Connecticut survey found that

36.3% of the samples contained no residues, compared to

66.9% reported by the FDA. During this same timeframe the

Connecticut violation rate was 6.9% as compared to the 3.9%

reported by the FDA.

A z-test comparison was made between the Connecticut data

obtained by the QuEChERS protocol (776 samples) and the

PDP data (40,726 samples) between 2006 and 2009. The pro-

portions of pesticide residue-free groups in the two studies were

found to be statistically significant (P = 0.783; z = 0.275).

The FDA program (12) currently uses multiresidue methods

(MRMs) and single-residue methods (SRMs) as described in the

Pesticide Analytical Manual (7) to determine the approximately

400 pesticides with Environmental Protection Agency (EPA) tol-

erances (12,13). Alternatively, the PDP laboratories have modi-

fied their protocols to take advantage of more-recent technological

advancements for the identification and quantification of pesticide

residues (6).

The 2006 Connecticut study has provided convincing evi-

dence that the use of the QuEChERS sample preparation

procedure combined with the complementary analyses of the

extracts by both GC–MS and LC–MS is preferred for deter-

mining pesticide residues in food. There are three key factors

responsible for this finding. First, the ability of the QuECh-

ERS method to extract greater and broader numbers of pesti-

cide active ingredients with adequate recoveries as documented

by Anastassiades (4) and others (8) has increased the number

of analytes routinely tested for in our laboratory. From 1990 to

2005, we reported on 18 different active ingredients, on aver-

age. Owing partly to the addition of active ingredients to our

spectral libraries, since 2006 we reported on an average of 52

different active ingredients.

Second, the QuEChERS extracts are amenable to both LC and

GC analysis providing complementary coverage of pesticide resi-

dues which cannot be determined by one instrumental method or

the other. In addition, because of the greater sensitivity provided

by LC–MS, it is not surprising to observe that the number of resi-

dues seen by LC rapidly outpaced those seen by GC. Third, the

fact that the final stage of the QuEChERS protocol allows for a

concentration step (in our work by a factor of 5) leads to greater

overall method sensitivity. This has led to the overall detection

of more pesticide residues at lower levels in the samples analyzed

(Figure 3). Taken together, these factors have led to a more accu-

rate picture of the occurrence of pesticide residues in the Con-

necticut food supply.

Conclusions

The vast majority of the fruits and vegetables we consume, with

the exception of organically grown produce, have been treated

with pesticides during the course of their production. If the pes-

ticides used during the production of this food have been used

in accordance with the approved use of the pesticide product, the

levels resulting on the food will be below the EPA tolerance (13).

In the past, because of the sensitivity and selectivity of the instru-

ments used at the CAES and in other pesticide residue monitor-

ing programs at both the Federal and state level, many of the

residues have gone undetected. By changing the extraction and

analysis methodologies used in our work, the results obtained in

the Connecticut studies which once showed statistical similari-

ties to FDA data, now show those similarities to data generated

by the USDA in their PDP studies. Because of the increased sen-

sitivity of our instrumentation and to the QuEChERS sample

preparation approach, our program is detecting greater numbers

of pesticides at lower levels. The results of this work allow the

consumer to gain a better understanding of the prevalence and

levels of pesticide residues in the food they consume.

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Acknowledgments

We would like to thank the Food Division

of the Department of Consumer Protec-

tion for providing samples for this study.

We would especially like to thank Ellen

Sloan for her tireless devotion and dedica-

tion to collecting the vast majority of the

samples included in this work.

References

(1) H.M. Pylypiw, Jr., J. AOAC Int. 76, 1369–

1373 (1993).

(2) H.M. Pylypiw and L. Hankin, Bulletin 900

(Connecticut Agricultural Experiment Sta-

tion, New Haven, Connecticut, 2006), p. 2

and references cited therein.

(3) W.J. Krol, T. Arsenault, and M.J.I. Mattina,

Bulletin 1006 (Connecticut Agricultural

Experiment Station, New Haven, Connecti-

cut, 2006), pp. 1–12.

(4) M. Anastassiades, S.J. Lehotay, D. Stajnbaher,

and F.J. Schenck, J. AOAC Int. 86, 412–431

(2003).

(5) Food and Drug Administration, Residue

Monitoring Reports 1993–2008, http://www.

fda.gov/Food/FoodborneIllnessContami-

nants/Pesticides/UCM2006797.htm.

(6) United States Department of Agriculture Pes-

ticide Data Program, Databases and Annual

Summaries 1992–2009, http://www.ams.

usda.gov/AMSv1.0/ams.fetchTemplateData.

do?template=TemplateG&topNav=&leftNav

=ScienceandLaboratories&page=PDPDownl

oadData/Reports&description=Download+P

DP+Data/Reports&acct=pestcddataprg

(7) Pesticide Analytical Manual Volume I (3rd

Ed., 1994 and subsequent revisions), available

from FDAs website at http://www.fda.gov/

food/foodscienceresearch/laboratorymethods/

ucm2006955.htm, and Volume II (1971 and

subsequent revisions), available from National

Technical Information Service, Springfield,

Virginia. Food and Drug Administration,

Washington, DC.

(8) M. Anastassiades et al., DataPool of the EU

Reference Laboratories for Residues of Pesti-

cides, http://www.eurl-pesticides-datapool.eu/.

(9) Official Methods of Analysis of AOAC Inter-

national (2011) 18th Ed., online, Official

Method 2007.01. http://www.eoma.aoac.org/

(10) F.J. Schenck and J.W. Wong, “Two Modi-

fied QuEChERS Methods for the Mul-

tiresidue Determination of Pesticides in

Produce Samples” presented at the 45th

Annual Florida Pesticide Residue Work-

shop, St. Pete Beach, Florida, 2008, http://

f lworkshop.com/09documents/2009-Pre-

sentations/08_Schenck.pdf

(11) W.J. Krol, B.D. Eitzer, T. Arsenault, and

M.J.I. Mattina, Bulletin 1012 (Connecticut

Agricultural Experiment Station, New Haven,

Connecticut, 2007), pp. 1–13.

(12) ibid. 5, Pesticide Monitoring Program FY

2008, Analytical Methods, http://www.fda.

gov/Food/FoodborneIllnessContaminants/

Pesticides/ucm228867.htm#Analytical_

Methods_and_Pesticide_Coverage.

(13) e-CFR (Electronic Code of Federal

Regulations) (2011) Title 40, 24, Part

180. http://ecfr.gpoaccess.gov/cgi/t/

text/text-idx?&c=ecfr&tpl=/ecfrbrowse/

Title40/40tab_02.tpl.

Walter J. Krol, Brian D. Eitzer,

Terri Arsenault, Mary Jane

Incorvia Mattina, and Jason

C. White are with the Department of

Analytical Chemistry at the Connecticut

Agricultural Experiment Station in New

Haven, Connecticut. Direct correspondence to:

[email protected] ◾


please visit

www.chromatographyonline.com

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Rapid UHPLC Method Development for

Omeprazole Analysis in a Quality-by-Design

Framework and Transfer to HPLC Using

Chromatographic Modeling

The aim of this study was to apply quality-by-design principles to

build in a more scientific and risk-based multifactorial strategy in the

development of an ultrahigh-pressure liquid chromatography (UHPLC)

method for omeprazole and its related impurities.

The qua l it y-by-design con-

cept was outlined years ago

by Joseph M. Juran (1) and is

used in many industries to improve

the quality of products and services

simply by planning quality from

the beginning. Since the US Food

and Drug Administration (FDA)

announced its “Pharmaceutical Cur-

rent Good Manufacturing Practices

(cGMPs) for the 21st Century” initia-

tive (2) in 2002, a quality-by-design

approach has also been sought in the

pharmaceutical industry.

Through the International Confer-

ence on Harmonization (ICH), this

concept resulted in ICH guideline

Q8(R2) in which quality-by-design

is defined as “a systematic approach

to development that begins with pre-

def ined objectives and emphasizes

product and process understanding and

process control, based on sound science

and quality risk management” (3).

Although ICH guideline Q8(R2)

doesn’t explicit ly take ana ly t ica l

method development into account

and no other regulatory guideline has

been issued, the quality-by-design

concept can be extended to a sys-

tematic approach that includes the

def inition of the methods goal, risk

assessment, design of experiments,

developing a design space, verif ica-

tion of the design space, implement-

ing a control strategy, and continual

improvement to increase method

robustness and knowledge (4). The

novelty and opportunity in this

approach is that working within the

design space of a specific method can

be seen as an adjustment and not a

postapproval change (4).

A systemat ic approach should

replace the sti l l common “screen-

ing,” also known as a trial-and-error

approach, in which one factor at a

time (OFAT) is varied until the best

method is found. The OFAT approach

is time-consuming and often results

in a nonrobust method because

interactions between factors are not

considered.

Today, systematic concepts use

experimental design plans as an effi-

cient and fast tool for method develop-

ment. In a full or fractional, factorial

design, a couple experiments are car-

ried out in which one or more factors

are changed at the same time. By using

statistical software tools (for example,

Design Expert from Stat-Ease, Inc.),

the effect of each factor on the separa-

tion can be calculated and the data can

be used to find the optimum separa-

tion (4). In our laboratory, this concept

is used when the development of non-

chromatographic methods is necessary.

However, the easiest and fasted way

of developing a liquid chromatographic

method is by using chromatography

modeling, especially in combination

with ultrahigh-pressure liquid chro-

matography (UHPLC) technology.

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Based on a small number of experi-

ments, these software applications can

predict the movement of peaks when

parameters such as eluent composition

or pH, f low rate, column temperature,

column dimensions, and particle size

are changed (5–11). When necessary,

the developed method can be trans-

ferred (back) to high performance liq-

uid chromatography (HPLC).

In our laboratory we have been using

visual chromatographic modeling (soft-

ware packages) for many years now in

HPLC and UHPLC method develop-

ment and it has resulted in very robust

methods (4,12–14). The aim of this study

was to apply quality-by-design prin-

ciples to build in a more scientific and

risk-based, multifactorial strategy in the

development of a new UHPLC method

for testing the purity of omeprazole.

Omeprazole belongs to the group

of proton-pump inhibitors and is one

of the most widely prescribed drugs. It

suppresses gastric acid secretion by spe-

cific inhibition of the enzyme hydrogen-

potassium adenosine triphosphatase (H+,

K +-ATPase). Omeprazole formulations

are used to treat acid reflux, heartburn,

ulcer disease, and gastritis (15).

Omeprazole is described in the mono-

graph of the European Pharmacopeia (EP)

(16). Purity testing for omeprazole is

accomplished by using HPLC with UV

detection on a 125 mm × 4.6 mm, 5-μm

NameOmeprazole Impurity A (EP)

Impurity C (EP)Impurity B (EP)

Impurity D (EP)

Impurity F (EP) Impurity G (EP)

Impurity I (EP)Impurity H (EP)

Impurity E (EP)

OCH3

CH3

CH3

CH3

CH3

CH3

CH3

Cl

Cl

CH3

CH3

CH3

CH3

CH3

CH3

CH3

CH3

CH3

OCH3

OCH3

SH

OCH3

OCH3

OCH3

OCH3

OCH3

OCH3

OCH3

OCH3

H3CO

H3C

OCH3

OCH3

H3C

H3C

H3C

H3C H

3C

H3C

H3C

H3C

HN

N

HN

N

HN

N

HN

N

HN

N

N

N

N

N

HN

N

N

N

N

N

N

N

N

N

N

S

S

S

S

S

S

S

S

S

S

O

O

O

O

O O O

O

O

OO

OS

O

O

O

O

O

HN

N

HN

N

HN

N

N

HN

N

N

NH

N

N

NH

NS

S

N

OCH3

OCH3

OCH3

OCH3

OCH3

OCH3

OH3C

H3C

H3C

Chemical Structure Name Chemical Structure

Figure 1: Chemical structures of omeprazole and its related impurities.

Omeprazole

Time (min)

Time (min)

4 6 8 10

403020100

Imp

.H

Imp

.D

Imp

.D

Imp

.B

Imp

.B

Imp

.F–G

Imp

.F+G

Imp

.E

Imp

.E

Imp

.AIm

p.I

Imp

.AIm

p.I

Imp

.C

Figure 2: Typical chromatogram of a selectivity standard solution containing omeprazole and its related impurities A–I by using the purity method published in the European Phar-macopoeia. Column: 125 mm × 4.6 mm, 5-μm dp Symmetry C8 column; mode: isocratic; eluent: 27 vol% acetonitrile and 73 vol% disodium hydrogen phosphate [1.4 g/L], adjusted with phosphoric acid to pH 7.6; flow rate: 1 mL/min.

T(ºC)

tG(min)

pH

Figure 3: Graphical description of the design of experiments plan for the method development by using chro-matographic modeling: For each organ-ic eluent, methanol and acetonitrile, 12 experiments have to be performed with low and high values for T, tG, and pH.

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dp C8 column in isocratic mode with an

eluent consisting of 27 vol% acetonitrile

and 73 vol% disodium hydrogen phos-

phate solution (pH 7.6) and a flow rate

of 1.0 mL/min. On the basis of the syn-

thetic route, the monograph recommends

testing the impurities A, B, C, D, E, H,

and I by HPLC, and the impurities F

and G have to be tested by a photometric

method (chemical structures are shown

in Figure 1). A typical chromatogram of

a selectivity standard solution containing

omeprazole and its related impurities A–I

obtained using the EP method is given

in Figure 2 and shows that the method

was developed without any chromatog-

raphy knowledge. Some of the impurity

peaks show coelution, but the last three

peaks are separated from each other with

a huge distance of 10 min each.

Several analytical procedures for the

determination of omeprazole and its

related impurities have been described.

A review of the analytical methodolo-

gies for the determination of omepra-

zole, mostly in plasma and urine, was

published in 2007 (17). Only some

recent publications focus on stability-

indicating methods for the analysis of

impurities and degradation products in

omeprazole formulations (18–20). As

far as we know, no analytical method

has been published that would separate

all synthesis impurities and degradation

products mentioned in the EP mono-

graph. Therefore, there is a need for a

simple, fast, and reliable purity method

for the determination of omeprazole

and its related impurities in the active

pharmaceutical ingredient (API) and in

pharmaceutical formulations.

Experimental

Chemicals

Methanol and acetonitrile were HPLC-

gradient grade (Sigma). All other chemi-

cals were at least analytical grade and

were also purchased from Sigma. Ultra-

pure water was obtained using a TKA

water purification system (Thermo

Fisher Scientific).

Equipment and Chromatographic

Conditions

For the UHPLC experiments, an

Acquity UPLC H-class system consist-

ing of a quaternary solvent system with a

solvent-selection valve, a sample injection

Figure 4: Three-dimensional resolution cube (tG/T/pH model) and the corresponding two-dimensional resolution map (tG/T model) at pH 9.0 for methanol as the organic solvent in the UHPLC gradient method. The red regions in the resolution maps repre-sent the design space, in which the performance criteria are met.

Figure 5: Three-dimensional resolution cube (tG/T/pH model) and the corresponding two-dimensional resolution map (tG/T model) at pH 8.75 for acetonitrile as the organic solvent in the UHPLC gradient method. The large red regions in the resolution maps represent the design space, in which performance criteria are met.

8.5

pH

2.20

2.00

1.80

1.60

1.40

1.20

1.00

0.80

0.60

0.40

0.20

0.00 5 10tG (min)

30

40

50

60

T (

°C)

9

8

2.40

T (°C)

0.8

5.5

5

40

60

46.9

tG (min)

60

50

40

30

2.20

2.00

1.80

1.60

1.40

1.20

1.00

0.80

0.60

0.40

0.20

0.00 5

T (

°C)

tG (min)

● Ability to separate difficult isomers of Amino

Acid related compounds like GABA


system, column management system, and a photodiode-array

detector, all controlled by Empower 2 C/S-software (Waters)

was used. The dwell volume of the system was 0.400 mL.

For the HPLC experiments an Alliance 2695 XE system

with a model 2996 photodiode-array detector, controlled by

Empower 2 C/S-software (Waters) was used. The dwell vol-

ume of the system was 1.000 mL.

A 50 mm × 2.1 mm, 1.7-μm dp Acquity UPLC BEH

C18 column (Waters) was used in the UHPLC study and

the equivalent 50 mm × 4.6 mm, 2.5-μm dp XBridge BEH

Table I: Verification study for the newly developed UHPLC method. A comparison of predicted and experimental

retention times of all components at the working point and six verification points are shown below and found

to be excellent with a correlation coefficient of R2 = 0.999, which can also be seen in the corresponding graphical

comparison (Figure 8a).

Working Point Verification

Point 1

Verification

Point 2

Verification

Point 3

Verification

Point 4

Verification

Point 5

Verification

Point 6

Flow rate (mL/min)

0.70 0.70 0.75 0.70 0.65 0.65 0.75

tG (min) 4.0 3.9 4.1 4.0 3.9 4.1 4.0

Temp. (°C) 35 37 33 33 35 35 37

pH 8.75 8.75 8.75 9.00 9.00 8.50 8.50

%start 10 9 10 11 10 11 9

%end 60 60 61 60 61 59 59

Retention time (min)

Pred. Exp. Pred. Exp. Pred. Exp. Pred. Exp. Pred. Exp. Pred. Exp. Pred. Exp.

Imp. A 1.06 1.14 1.13 1.18 1.04 1.09 0.96 1.08 1.09 1.15 1.07 1.21 1.10 1.19

Imp. I 1.45 1.48 1.50 1.52 1.37 1.41 1.30 1.32 1.43 1.46 1.54 1.61 1.53 1.62

Imp. E 1.71 1.74 1.75 1.77 1.65 1.68 1.57 1.59 1.69 1.73 1.81 1.86 1.77 1.85

Imp. D 1.97 2.00 2.01 2.02 1.91 1.93 1.79 1.83 1.90 1.91 2.14 2.18 2.07 2.16

Imp. B 2.17 2.21 2.20 2.21 2.11 2.14 2.06 2.08 2.15 2.18 2.30 2.32 2.22 2.30

Omeprazole 2.26 2.29 2.28 2.29 2.20 2.22 2.15 2.18 2.24 2.27 2.38 2.40 2.30 2.38

Imp. H 2.68 2.72 2.68 2.70 2.62 2.65 2.58 2.62 2.65 2.68 2.84 2.85 2.72 2.80

Imp. C 2.96 2.99 2.95 2.96 2.90 2.92 2.91 2.93 2.96 2.98 3.11 3.10 2.96 3.04

Imp. F 3.68 3.71 3.64 3.65 3.62 3.65 3.66 3.67 3.67 3.69 3.88 3.84 3.66 3.71

Imp. G 3.82 3.84 3.76 3.77 3.75 3.78 3.79 3.81 3.80 3.82 4.02 3.97 3.79 3.84

Time (min)

2.257Omeprazole

1.0

64 Im

p.A 1.5

40 Im

p.I

1.7

10 Im

p.E

1.9

72 Im

p.D

2.1

73 Im

p.B

2.6

83 Im

p.H

2.9

64 Im

p.C

3.6

85 Im

p.F

3.8

17 Im

p.G

3.0 4.02.01.00

Figure 6: Predicted UHPLC chromatogram for omeprazole and its related impurities for conditions at the working point (for details see text).

Time (min)

3.0 4.02.01.0

1.1

44 Im

p. A

1.4

79 Im

p. I

1.7

43 Im

p. E

2.0

02 Im

p. D

2.0

10 Im

p. B

2.7

18 Im

p. H

2.9

88 Im

p. C

3.7

11 Im

p. F

3.8

40 Im

p.G

2.293 Omeprazole

Figure 7: Experimental UHPLC chromatogram of omeprazole spiked with its related impurities A–I for conditions at the working point (for details see text).

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C18 column (Waters) was used in the

HPLC study.

All method development experi-

ment s were per formed on the

UHPLC system in gradient mode.

Eluent A was 10 mM ammonium

bicarbonate buffer at dif ferent pH

values (adjusted with ammonia) and

eluent B was acetonitrile. Eluent C

was methanol (for screening experi-

ments only). The f low rate was set to

0.7 mL/min and the injection volume

was 2 μL.

The temperature in the experi-

ments was optimized between 30 °C

and 60 °C. The UV detection of the

compounds of interest was carried out

at 303 nm and the UV spectra were

taken in the range of 200–400 nm.

Software

For chromatography modeling the

DryLab 4.0 software package (Mol-

nar-Institute) was used. The software

package includes PeakMatch and

3-D-Robustness modules.

Standard Preparation

A selectivity standard solution con-

ta in ing 0.2 mg/mL omepra zole

(in-house standard substance) and

approximately 0.002 mg/mL of each

of the nine impurities was prepared

with a 2:8 (v/v) mixture of acetoni-

trile and 10 mM ammonium bicar-

bonate buffer as the solvent. The

impurities A, B, C, E, H, and I were

obta ined from LGC. Impurity D

was purchased from the European

Directorate for the Quality of Medi-

cines (EDQM) and the impurities F

and G were obtained from the U.S.

Pharmacopeia l Convention (USP).

The selectivity standard solution was

protected from light by using amber

glassware.

Results and Discussions

Development Strategy

Our development strategy (4) follows

quality-by-design principles and can

be divided into six steps as follows:

Step 1: Definition of Method Goals

Our primary goal was to develop

a stability-indicating method that

separates the API from all impurities

Figure 8: Plots of experimental retention time versus predicted retention time for (a) the UHPLC method and (b) after method transfer to HPLC.

4.50(a)

4.00

3.50

3.00

2.50

2.00

1.50

1.00

0.50

0.000.00 1.00 2.00

Predicted retention time (min)

y=0.9813x + 0.0795R2 = 0.999

Exp

eri

me

nta

l re

ten

tio

n t

ime

(m

in)

3.00 4.00 5.00

7.00

Exp

eri

me

nta

l re

ten

tio

n t

ime

(m

in)

6.00

5.00

4.00

3.00

2.00

1.00

0.000.00 1.00

Predicted retention time (min)

2.00 3.00 4.00 5.00 6.00 7.00

y=0.952x + 0.1024R2 = 0.999

(b)

Triazole Bonded Stationary Phase

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with a critical resolution (R s,crit) of no

less than 2.0. To speed up the devel-

opment process, UHPLC technol-

ogy was used; the f inal method was

intended to be transferred to HPLC.

Step 2: Risk Assessment

Using a fishbone diagram, an early risk

assessment was identified and possible

risk factors associated with sample

preparation as well as the instrumen-

tal analysis were prioritized. The ini-

tial list of potential parameters that

can affect critical quality attributes

(CQAs) were ranked and priori-

tized using failure mode and effects

analysis (FMEA).

It was obvious that resolution is a

CQA and the selectivity term α in

the general equation Rs = 0.25N1/2[(α

- 1)/α][k/(1 + k)] has the greatest

impact on the resolution. Selectiv-

ity is inf luenced by the mobile phase

composition, column chemistry, and

temperature (21), and the inf luence

should be investigated by design of

experiments (DoE).

Other CQAs that were taken into

account include the robustness of the

method and the run time.

Step 3: Design of Experiments

For the critica l process parameters

(CPPs), which have an impact on

the CQAs, experiments should be

conducted to determine accept-

able ranges. As the result of the risk

assessment, the four parameters gra-

dient time (tG), temperature (T ), pH

of the aqueous eluent A, and type of

the organic eluent B were screened

and opt imized because of their

strong known inf luential effects on

selectivity.

A set of 12 experiments was per-

formed for each of the two organic

eluents methanol and acetonitri le

under the following conditions: gra-

dient times: tG1 = 3 min and tG2 = 9

min; temperatures: T1 = 30 °C and

T2 = 60 °C. The pH values of the

buffer were pH1: 8.0, pH2: 8.5, and

pH3: 9.0. Because of prior knowl-

edge, a modern C18 column was used.

The ranges between these factors

were large enough to induce peak

This QbD

strategy can

be divided

into six steps,

from defining

the goals of

the method

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movements to discover hidden peaks

(4). A graphical description of the

DoE plan can be seen in Figure 3.

Step 4: Design Space

The retention times of all peaks of

interest in the 12 experiments were

entered into the chromatographic

modeling software and matched in

each of the chromatograms by using

the PeakMatch module.

Based on the limited set of only 12

experiments, the modeling software

builds a three-dimensional model

of the critica l resolution (the so-

called “knowledge space”), in which

the combined inf luence of the opti-

mized parameters are visualized. The

modeling software uses a color code

to represent the value of the critical

resolution: Warm, “red” colors show

large resolution values (Rs > 2.0), and

cold, “blue” colors show low resolu-

tion values (R s < 0.5) corresponding

to regions of peak overlaps. The red

geometric bodies within the knowl-

edge space, in which the performance

criteria are met, is called the design

space. The ICH Q8 guideline defines

the design space as follows (3):

“The multidimensional combination

and interaction of input variables

(e.g., material attributes) and pro-

cess parameters that have been dem-

onstrated to provide assurance of

quality. Working within the design

space is not considered as a change.

Movement out of the design space is

considered to be a change and would

normally initiate a regulatory post

approval change process.”

Figures 4 and 5 show the three-

dimensiona l resolution cubes for

methanol and acetonitri le as the

organic eluent in the UHPLC gradi-

ent method. A visual inspection shows

that the design space in the methanol

cube is much smaller than the design

space in the acetonitrile cube. That

means that the method with acetoni-

trile is more robust than the method

with methanol and the all peaks in the

chromatogram are well separated from

each other (baseline resolution).

An important

part of the

method

development

strategy is

to perform

robustness

testing of

the method

before the

validation

study.

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Therefore, acetonitri le was cho-

sen as the organic eluent and, from

the corresponding design space, the

working point was selected by visual

examination. There are several possi-

ble alternative working points within

the design space, but we looked for

the highest critical resolution (R s,crit)

and best robustness of the method.

This working point was found in the

cube at tG 4.0 min, T 35 °C, and pH

8.75. The predicted and experimen-

tal chromatograms for this working

point are shown in Figures 6 and 7.

A verif ication study comparing

predicted and experimental retention

times for the working point and six

verif ication points around the work-

ing point, but within the design space,

was found to be excellent with a cor-

relation coefficient of 0.999, as shown

in Table I and Figure 8a. This is also

in compliance to previous reported

data (4,22,23).

An important part of our method

development strategy is to perform

robustness testing of the developed

method before the validation study.

The ICH guideline Q2 (R1) (24)

defines robustness as follows:

“[. . .] the reliability of an analysis

with respect to deliberate variations

in method parameters. The robust-

ness of an analytical procedure is

a measure of its capacity to remain

unaffected by small, but deliberate

variations in method parameters

and provides an indication of its re-

liability during normal usage.”

The robustne s s of the deve l-

oped method was stud ied using

the robustness module of the chro-

matographic modeling software. In

a three-level, ful l-factoria l design,

the module used the previously con-

structed and verif ied design space

for “ in si l ico” robustness ca lcula-

tions (4). The six parameters tG (4

min ± 0.1 min), T (35 °C ± 2 °C),

pH (8.75 ± 0.1), f low rate (0.7 mL/

min ± 0.05 mL/min), and the %B

start (10% ± 1%) and %B end (60%

± 1%) of the gradient were varied at

three levels (+1, 0, -1).

Figure 9 shows the frequency of the

distribution of the resolution values

Rs,crit for all 729 experiments. It can

be seen that the required resolution of

2.0 can be reached in all experiments.

Therefore, the developed method is

robust against small changes of chro-

matographic parameters.

A formal validation study should

be performed before this new method

can replace the existing method.

Step 5: Method Control Strategy

The ICH Q8 guideline def ines the

control strategy as “a planned set of

controls, derived from current prod-

uct and process understanding that

ensures process performance and

product quality[. . .]” This means

that the control strategy should

be implemented to ensure that the

developed method is performing as

intended. Usually, this can be done

by using a system suitability test. In

our method development strategy,

the resolution of the critical peak pair

(R s,crit), was chosen as a system suit-

ability test parameter and should not

be less than 2.0.

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Step 6: Continual Improvement

In this last step further experiments can be planned and

repeated to try out better columns and eluents to further

Table II: Verification study after the method transfer to HPLC. A comparison of predicted and experimental

retention times of all components at the working point and six verification points are shown below and

found to be excellent with a correlation coefficient of R2 = 0.999, which can also be seen in the corresponding

graphical comparison.

Working Point Verification

Point 1

Verification

Point 2

Verification

Point 3

Verification

Point 4

Verification

Point 5

Verification

Point 6

Flow rate (mL/min)

1.9 1.9 2.0 1.9 1.8 1.8 2.0

tG (min) 7.0 6.8 7.2 7.0 6.8 7.2 6.8

Temp. (°C) 35 37 33 33 35 35 37

pH 8.75 8.75 8.75 9.00 9.00 8.50 8.50

%start 10 9 10 11 10 11 9

%end 60 61 61 60 61 59 59

Retention time (min)

Pred. Exp. Pred. Exp. Pred. Exp. Pred. Exp. Pred. Exp. Pred. Exp. Pred. Exp.

Imp. A 1.51 1.64 1.59 1.71 1.52 1.61 1.37 1.49 1.51 1.69 1.52 1.56 1.59 1.67

Imp. I 2.10 2.09 2.18 2.19 2.05 2.04 1.84 1.83 1.99 2.02 2.21 2.19 2.26 2.26

Imp. E 2.54 2.49 2.61 2.57 2.50 2.44 2.31 2.25 2.43 2.42 2.66 2.55 2.66 2.58

Imp. D 3.01 2.98 3.06 3.05 2.95 2.92 2.69 2.65 2.81 2.81 3.24 3.20 3.18 3.17

Imp. B 3.35 3.26 3.38 3.31 3.31 3.21 3.15 3.06 3.24 3.19 3.51 3.36 3.43 3.31

Omeprazole 3.50 3.40 3.52 3.44 3.45 3.35 3.31 3.21 3.39 3.32 3.66 3.50 3.56 3.44

Imp. H 4.24 4.13 4.23 4.14 4.20 4.09 4.06 3.94 4.10 4.03 4.46 4.28 4.28 4.15

Imp. C 4.73 4.59 4.69 4.58 4.70 4.55 4.64 4.51 4.65 4.55 4.92 4.72 4.70 4.54

Imp. F 6.00 5.84 5.90 5.77 5.97 5.81 5.95 5.77 5.89 5.78 6.27 6.05 5.90 5.73

Imp. G 6.23 6.08 6.12 5.99 6.20 6.04 6.18 6.03 6.10 6.00 6.52 6.29 6.12 5.95

60

40

N

20

2.12 2.17 2.22 2.27

Rs, crit

2.32 2.370

Figure 9: Frequency distribution of the Rs,crit values for all 729 experiments of the robustness study on the UHPLC system. The six parameters tG (4 min ± 0.1 min), T (35 °C ± 2 °C), pH (8.75 ± 0.1), flow rate (0.7 mL/min ± 0.05 mL/min), and the %B start (10% ± 1%) and %B end (60% ± 1%) of the gradient were var-ied at three levels (+1, 0, -1). All experiments fulfill the require-ment for resolution Rs,crit no less than 2.0. That means that the failure rate is 0, so there will be no method-related out-of-specification (OOS) results and production quality control will be smooth and robust.

Time (min)

3.0 4.0 5.0 6.02.01.0

1.5

06 Im

p. A

2.0

98 Im

p. I

2.5

43 Im

p. E

3.0

06 Im

p. D

3.3

51 Im

p. B

4.2

40 Im

p. H

4.9

731 Im

p. C

5.9

96 Im

p. F

6.2

26 Im

p. G

3.495 Omeprazole

Figure 10: Predicted HPLC chromatogram for omeprazole and its related impurities for conditions after the transfer to the HPLC system (for details see text).

adjust or improve the position of the working point. In

addition, business needs — for example, the transfer of

the developed UHPLC method (such as from the research

and development [R&D] laboratory) to HPLC conditions

(such as into the quality control [QC] laboratory) — can

be taken into account.

To transfer the UHPLC method to HPLC conditions,

the changed column dimensions, particle sizes, and system

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dwell volumes were used to scale up

the f low rate and gradient time. This

can be made by using free available

method transferring tools (such as the

Acquity Columns Calculator from

Waters). A smart way is to use the

modeling software for the transfer

and calculate the gradient time and

f low rate. At the same time, the cor-

responding chromatograms can be

visualized.

Small adjustments of the scaled

conditions for f low rate and gradient

time had to be made to reduce the

back pressure in the HPLC system.

The predicted and experimental chro-

matograms for the up-scaled HPLC

method can be seen in Figures 10 and

11. A second verification study for the

working point on the HPLC system

and six verification points around the

working point conf irmed the accu-

racy of the prediction (see Table II

and the corresponding graph in Fig-

ure 8b). In addition, the robustness

study after the transfer to the HPLC

system shows that the failure rate is

still zero (see Figure 12).

Table III summarizes the chromato-

graphic parameters and tolerances of

the final method.

Conclusions

A quality-by-design–based method

development strategy for a method

to test the purity of omeprazole has

been presented here. The scientif ic

and risk-based multifactorial method

development strategy uses visua l

chromatographic modeling as a fast

and easy to use development tool.

To speed up the method develop-

ment process, all experiments were

performed on a UHPLC system. The

final method was successfully trans-

ferred to HPLC conditions. Verifica-

tion studies between predicted and

experimental retention times confirm

the accuracy of the chromatographic

modeling process.

All experiments, from the plan-

ning, performing on the UHPLC

system, verif ication and transfer to

HPLC, to the reporting, were made

within one week.

References

(1) J.M. Juran, Juran on Quality by Design:

The New Steps for Planning Quality into

Goods and Services (The Free Press, New

York, 1992).

(2) http://www.fda.gov/downloads/Drugs/

Development Approva lProcess/Manu-

facturing/QuestionsandAnswersonCur-

rent Good Ma nu f ac tu r ingPr ac t ic e s c -

GMPforDrugs/UCM176374.pdf.

(3) http://www.ich.org/f i leadmin/Public_

Web_ Site/ICH_Products/Guidel ines/

Quality/Q8_R1/Step4/Q8_R2_Guide-

line.pdf.

Table III: Description of the final analytical procedure including the tolerance limits

Chromatographic

Parameter

UHPLC Condition HPLC Condition

Column50 mm × 2.1 mm, 1.7-μm dp Acquity BEH C18 (Waters)

50 mm × 4.6 mm, 2.5-μm dp

XBridge BEH C18 (Waters)

Eluent A10 mM ammonium bicarbonate buffer, pH 8.75 (±0.1 pH units)

10 mM ammonium bicarbonate buffer, pH 8.75 (±0.1 pH units)

Eluent B Acetonitrile Acetonitrile

Gradient

Linear increase from 10% (±1%) to 60% (±1%) of eluent B in 4.0 min (±0.05 min), followed by reequilibration

Linear increase from 10% (±1%) to 60% (±1%) of eluent B in 7.0 min (±0.5 min), followed by re-equilibration

Stop time 5 min 8 min

Flow rate 0.70 mL/min (±0.05 mL/min) 1.90 mL/min (±0.05 mL/min)

Column temp. 35 °C (±2 °C) 35 °C (±2 °C)

Injection volume 2 μL 20 μL

Detection UV absorbance at 303 nm UV absorbance at 303 nm

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(4) A.H. Schmidt and I. Molnár, J. Pharm.

Biomed. Anal. 78–79, 65–74 (2013).

(5) L.R. Snyder and J.L. Glajch, Computer-

assisted Method Development for High

Performance Liquid Chromatography,

(Elsevier, Amsterdam, 1990).

(6) L.R. Snyder and J.L. Glajch, J. Chro-

matogr. A 485, 1–675 (1989).

(7) I. Molnár, J. Chromatogr. A 965, 175–

194 (2002).

(8) I. Molnár, H.-J. Rieger, and K.E. Monks,

J. Chromatogr. A 1217, 3193–3200 (2010).

(9) I. Molnár and K.E. Monks, Chromato-

graphia 73(Suppl.1), 5–14 (2011).

(10) K. Jayaraman, A.J. Alexander, Y. Hu,

and F.P. Tomasella, Anal . Chim. Acta

696, 116–124 (2011).

(11) K. Monks, I. Molnár, H.-J. Rieger, B.

Bogáti, and E. Szabó, J. Chromatogr. A

1232, 218–230 (2012).

(12) A.H. Schmidt and I. Molnár, J. Chromatogr.

948, 51–63 (2002).

(13) A.H. Schmidt, J. Liq. Chromatogr. Relat.

Technol. 28, 871–881 (2005).

(14) A.H. Schmidt, M. Stanic, and I. Molnár, J.

Pharm. Biomed. Anal., 91, 97–107 (2014).

(15) Commentary of the European Pharmacopoeia

(in German), 38 supplement, Deutscher

Apotheker Verlag, Stuttgart (2011).

(16) “Monograph Omeprazole” in the European

Pharmacopoeia, Seventh ed. (Deutscher

Apotheker Verlag, Stuttgart, 2011).

(17) M. Espinosa Bosch, A.J. Ruiz Sanchez,

F. Sanchez Rojas, and C. Bosch Ojeda, J.

Pharm. Biomed. Anal. 44, 831–844 (2007).

(18) C. Iuga, M. Bojita, and S.E. Leucuta,

Farmacia 57, 534–541 (2009).

(19) K.B. Borges, A.J.M. Sanchez, M.T.

Pupo, P.S. Bonato, and I.G. Collado, J.

AOAC Int. 93, 1811–1820 (2010).

(20) P. Venkata Rao, Ch.K. Sanjeeva Reddy,

M. Ravi Kumar, and Danta Durga Rao,

J. Liq. Chromatogr. Relat. Technol . 35,

2322–2332 (2012).

(21) L .R. Snyder, J.J. Kirk land, and J.L .

Glajch, Practical HPLC Method Develop-

ment, 2nd ed. (Wiley-Interscience, New

York, 1997).

(22) M.R. Euerby, G. Schad, H.-J. Rieger,

and I. Molnár, Chromatogr. Today 3,

13–20 (2010).

(23) K.E. Monks, H.-J. Rieger, and I. Mol-

nár, J. Pharm. Biomed. Anal. 56, 874–

879 (2011).

Time (min)

3.0 4.0 5.0 6.02.01.0

1.6

32 Im

p. A

2.4

90 Im

p. E

2.0

90 Im

p. I

2.9

79 Im

p. D

3.2

60 Im

p. B

4.1

30 Im

p. H

4.5

91 Im

p. C

5.8

40 Im

p. F

6.0

67 Im

p. G

3.401 Omeprazole

Figure 11: Experimental HPLC chromatogram of omeprazole spiked with its relat-ed impurities A–I for conditions after the transfer to the HPLC system (for details see text).

Contact Us if You Want to Try Our Products

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(24) http://www.ich.org/f i leadmin/Public_

Web_ Site/ICH_Products/Guidel ines/

Quality/Q2_R1/Step4/Q2_R1_Guide-

line.pdf.

Alexander H. Schmidt is quality

control director at Steiner Pharmaceuticals

in Berlin, Germany. He is also head of

analytical development of an R&D and

contract analysis lab and supervises 35 lab

assistants and chemists. Over the years,

he has published numerous articles on

HPLC and UHPLC method development

for pharmaceuticals and complex natural

compound mixtures. He is also a guest

lecturer at the Beuth University of Applied

Sciences, in Berlin, Germany. In addition,

he is currently writing his doctoral thesis

at the Institute of Pharmacy at Freie

Universität Berlin in Germany.

Mijo Stanic joined the development

team at Steiner Pharmaceuticals as a lab

assistant and was promoted to deputy

lab manager in early 2013.

Direct correspondence to:

[email protected] ◾


please visit

www.chromatographyonline.com

80

60

N 40

20

02.13 2.18 2.23 2.28

Rs, crit

2.33

Figure 12: Frequency of the distribution of the resolution values Rs,crit for all 729 experiments of the robustness study after the transfer to the HPLC system. The six parameters tG (7 min ± 0.1 min), T (35 °C ± 2 °C), pH (8.75 ± 0.1), flow rate (1.9 mL/min ± 0.1 mL/min), and the %B start (10% ± 1%) and %B end (60% ± 1%) of the gradient were varied at three levels (+1, 0, -1). All experiments still fulfill the requirement for resolution Rs,crit of no less than 2.0. That means that the failure is also 0.


PITTCON PRODUCT SHOWCASE

Autosampler syringesHamilton’s CTC PAL autos-ampler syringes for gas chromatography and liquid chromatography applications are designed to comple-ment the company’s exist-ing autosampler syringes. According to the company, the design of the plunger and flange of its C-Line Syringe ensures reli-able installation, and its X-Type syringes, developed with CTC Analytics for HPLC applications, have near-zero carryover. Hamilton Company,

Reno, NV. www.hamiltoncompany.com

SPE systemThe EconoTrace automated solid-phase extraction sys-tem from FMS is designed to increase laboratory sam-ple throughput. According to the company, the system is modular and expandable, uses any SPE cartridge format, and can be used to isolate analytes from liquid matrices such as urine, blood, water, milk, and beverages. FMS, Inc.,

Watertown, MA. www.fms-inc.com

GC autosamplerThe Flex Series autosampler from EST Analytical is designed for GC and GC–MS laboratory end users and OEM custom-ers. According to the company, the autosampler has liquid injection capabil-ity with an upgrade path to headspace or SPME analysis. The system reportedly was designed with machine-to-machine industrial wireless technology and is expandable. EST Analytical,

Fairfield, OH. www.estanalytical.com

Ceramic ion source for GC–NPDThe TID-10 ceramic ion source from DETector Engineering is designed to produce catalytic combustion ioniza-tion of compounds containing chains of methylene functional groups. According to the company, the ceramic ion source replaces the ion source used in GC–NPD equipment, and expands the use of that equipment to selective detection of paraffins, isopar-affins, olefins, FAMEs, and triglycerides in complex petroleum and biological samples. DETector Engineering & Technology, Walnut Creek, CA. www.det-gc.com

Report development and generation softwareBruker Dash Reporting soft-ware, from Bruker Chemi-cal and Applied Markets is designed to provide custom-ized reporting that centers on Dash Designer, a purpose-built standalone application that allows users to position and closely format report ele-ments, and preview reports with relevant data. According to the com-pany, individual elements can be sorted, filtered, resized, and formatted with common editing operations and advanced functions. Bruker Corporation, Fremont, CA. www.bruker.com

UHPLC sealThe Enduris UHPLC seal from Bal Seal Engineering is designed to provide con-sistent, long-term performance in liquid chromatography pumps at pressures of 22,000 psi and higher. According to the company, the seal is machined from precision-formulated blends of ultra-high-molecular-weight polyethylene or PTFE and uses a Bal Seal Canted Coil Spring energizer to promote uniform wear and longer service life. Bal Seal Engineering, Inc.,

Foothill Ranch, CA. www.balseal.com

2D-LC systemThe model 1290 2D-LC sys-tem from Agilent Technologies is designed with peak-triggered operation, “shifted gradients,” and valve technology for com-prehensive or heart-cutting 2D-LC analysis. According to the company, the system is beneficial for the analysis of complex samples because it performs a single ultrahigh peak capacity 2D-LC run instead of many conventional separations. Agilent Technologies, Santa Clara, CA. www.agilent.com

Mass detectorThe Acquity QDa mass detector from Waters is designed to provide mass spectral data for chromato-graphic separations. According to the company, the detector is no larger than a photodiode-array detec-tor and, when paired with the company’s UPLC, LC, or SFC system, it generates the mass spectral data expected of a single-quadrupole mass spectrometer. Waters,

Milford, MA.www.waters.com


GC system optionsNew options for Thermo Scientific’s Trace 1300 Series GC system are designed to conserve helium, permit use of multiple detectors simulta-neously, automate sampling gas workflows, and perform flame photometric detection. According to the company, the options include the Instant-Connect Helium Saver module; the Trace 1310 high-capacity auxiliary oven with multivalve, multicolumn capacity; the Dedicated Instant Con-nect flame photometric detector; and the Instant Connect gas sampling valve module. Thermo Fisher Scientific, Waltham, MA. www.thermoscientific.com/trace1300

SorbentsSupelco’s Supel QuE Z-Sep, Z-Sep/C18, and Z-Sep+ sorbents are designed to provide robust LC–MS and GC–MS methods for a variety of analytes in difficult matrices. According to the company, the Z-Sep family of sorbents is available in 2-mL and 12-mL tub formats for QuEChERS. Supelco/Sigma-Aldrich,

Bellefonte, PA. www.sigma-aldrich.com/zsep

HPLC columnsThe IC YS-50 polymer-based ion chromatography column from Shodex is designed for cation analysis applicable to both suppressor and non-suppressor methods. According to the company, the column can support the analysis of alkylamines such as adrenaline, dopamine, noradrenaline, ace-tylcholine, and choline. Applica-tions reportedly are available from the company’s database.Showa Denko America, Inc.,

New York, NY. www.shodex.net

Photodiode-array detectorShimadzu’s SPD-M30A photo-diode-array detector is designed for a variety of HPLC and UHPLC conditions and report-edly can be used for a range of analyses without replacing its capillary cell. According to the company, the detector’s capil-lary cell allows the peak from the principal component and a 0.005% infinitesimal peak to be quantified simultaneously. Shimadzu Scientific Instruments,

Columbia, MD. www.ssi.shimadzu.com

M icrochannel platesA long-life, low noise performance (L3N) option is available from Photonis for its microchannel plate (MCP) formats. According to the company, the option provides a 100-fold reduction in background noise when compared to traditional long-life MCPs, and any MCP made by Photonis can be ordered with the low-noise performance option. Photonis USA,

Sturbridge, MA.www.photonis.com

LC–MS nitrogen generatorThe Parker Balston NitroFlow 60 LC–MS membrane nitrogen generator from Parker Hannifin is designed to produce up to 60 slpm of pure LC–MS-grade nitrogen at pressures as high as 110 psig. According to the com-pany, the output flow produced by the generator is equivalent to using one cylinder of com-pressed gas every 2 h. Parker Hannifin

Corporation,

Haverhill, MA. www.parker.com

Ion chromatography systemMetrohm’s 940 Professional IC Vario modular, self-monitoring ion chromatography system is designed with options that include sequen-tial, chemical, or no suppression; conductivity, UV–vis, or ampero-metric detection; high-pressure, low-pressure, and dose-in gradients; and columns of any base material, selectivity, capacity, and dimen-sion. According to the company, the instruments have a three-year warranty, a 10-year suppressor warranty, and a 10-year spare parts guarantee.Metrohm, Riverview, FL. www.metrohmuse.com

L C columnsACE UltraCore solid-core LC columns from Advanced Chromatography Technologies are designed to provide a low column back pressure and are available in SuperC18 and Super-PhenyHexyl bonding. According to the company, both phases feature proprietary encapsulated bonding technology for peak shape and phase stability across a pH range of 1.5 to 11.0. Advanced Chromatography

Technologies Ltd, Aberdeen, Scotland. www.ace-hplc.com


High-temperature GPC instrumentThe EcoSEC High Tempera-ture GPC system from Tosoh Bioscience is designed to provide researchers with an all-in-one temperature-controlled system that has a comprehensive temperature range of 40 °C to 220 °C. According to the company, the system comes equipped with a dual-flow refractive index detector. Tosoh Bioscience, LLC,

King of Prussia, PA. www.tosohbioscience.com

PEEK fittingsVICI Valco’s Cheminert high-pressure PEEK fittings are designed to permit direct connection of 360-μm o.d. fused silica, PEEK, stainless, or electroformed nickel tubing without hav-ing to use liners. According to the company, the 360-μm fittings in PEEK can be used safely at pressures as high as 10,000 psi. Valco Instruments Co.,

Inc., Houston, TX. www.vici.com

96-well plateUCT’s FASt 96-well plate is designed to pair with the company’s 96-well positive pressure manifold. Accord-ing to the company, the well material uses a simple “filter and shoot” prepara-tion and is effective at cleaning up urine samples for more than 50 different drugs and metabolites. UCT, Inc.,

Bristol, PA. www.unitedchem.com

Irregular silica gelsSiliaFlash irregular silica gels from SiliCycle are designed for use in flash and gravity chro-matography columns and for analytical and preparative chro-matography columns. According to the company, the gels have a narrow pore-size distribution, the absence of fines, and a long shelf life.SiliCycle,

Quebec City, Canada. www.SiliCycle.com

2nd Annual

CHROMATOGRAPHY

COMMUNITY MIXER

Buddy Guy’s

Legends Chicago

Tuesday March 4

5:30 – 8:30 p.m., 700 South Wabash Ave.Buffet, open bar, music and chromatographers galore.

Tickets required for entry

Register for tickets through your regional chromatography discussion group or chromatography vendor.

For further inquiry contact:Jonathan Edelman, PresidentWashington DC Chromatography DG(215) 850-8748 [email protected]

! Buddy Guy’s invites attendees to stay

for the evening show.

!"Help us revive the Chicago

Chromatography Discussion Group.

Join colleagues from around the world

Conversation, community, collaboration


HPLC columnsFlare HPLC columns from Diamond Analytics are designed as diamond-based columns that allow for the exploration of novel chemistries. According to the company, the columns are pH stable, can run at elevated temperatures, and can be regenerated for repeat use. Diamond Analytics,

Orem, UT. www.diamond-analytics.com

Capillary tubingPolymicro’s flexible fused-silica capillary tubing is designed with an outer diameter of 1/32 in. According to the company, the tubing mates with existing 1/32-in. fittings and is available in a range of internal diameters from 50 μm to 500 μm.Polymicro

Technologies,

Phoenix, AZ.www.polymicro.com

Sample cleanup workstationThe Freestyle workstation from Pickering Laboratories is designed for automated sample cleanup work flow. The instrument is based on a suspended rack design, with an XY robot arm for liquid handling. The workstation reportedly is able to handle multiple flask shapes with volumes ranging from 1 mL to 1 L, and the instrument’s software enables users to program multiple sample parameters and to pre-pare graphical reports and audit logs. SPE, GPC, and evaporation and solvent exchange modules are available. Pickering Laboratories, Inc.,

Mountain View, CA. www.pickeringlabs.com

Quick-connect systemThe Opti-Lynx II system from Opti-mize Technologies is designed as a combination of quick-connect hold-ers with a selection of packed-bed cartridges. According to the com-pany, accessing and changing the insert takes a quarter turn and the connection is rated up to 6000 psi. Optimize Technologies,

Oregon City, OR.www.optimizetech.com

Olfactory portThe GC SNFR olfactory port from PerkinElmer is designed to perform aroma characterization in the food, beverage, and fragrance applications. Users reportedly can capture a comprehensive sensory evaluation and correlate it with analytical data from a GC–MS system.PerkinElmer,

Waltham, MA. www.perkinelmer.com

SEC–MALS detectorThe Dawn Heleos-II multiangle light scat-tering (MALS) detector from Wyatt Technology is designed for absolute molecular weight and size determinations of poly-mers and biopolymers in solution. According to the company, the detector may be connected in series to any chromatographic system to determine absolute molar masses with-out the use of reference standards or column calibration. Wyatt Technology Corp., Santa Barbara, CA.www.wyatt.com

Water and soil sample processorOI Analytical’s model 4100 water and soil sample proces-sor is designed to automate the handling and processing of samples in 40-mL VOA vials for purge-and-trap analy-sis of volatile organic com-pounds in accordance with US EPA methods. According to the company, the instru-ment processes up to 100 drinking water, wastewater, or soil samples and operates with the company’s Eclipse 4660 purge-and-trap instruments. OI Analytical, College Station, TX. www.oico.com

S ample preparation automation systemThe AutoMate-Q40 system from Teledyne Tekmar is designed to automate the QuEChERS sample prepara-tion workflow. According to the company, the system is configured “out of the box” to conduct two QuEChERS sample preparation meth-ods: AOAC2007.01 and EN 15662.2008.Teledyne Tekmar,

Mason, OH.www.teledynetekmar.com/AutoMateQ40

EDITORS’ SERIES

PART I: Key Learning Objectives

■ What new chromatographic advances are available for the characterization of biopharmaceuticals?

■ What type of mobile phases and stationary phases should be used in reversed-phase LC for biopharmaceutical characterization?

■ What are the constraints when working with large proteins under very high pressure or temperature?

PART II: Key Learning Objectives

■ What is the classical MS-based workflow for biopharmaceutical characterization?

■ What are the benefits of using emerging approaches like native MS or ion mobility for biopharmaceutical characterization?

■ What can high resolution MS bring for biopharmaceutical characterization?

EVENT OVERVIEW:

Proteins, monoclonal antibodies (mAbs) and antibody-drug

conjugates (ADCs) are powerful therapeutic agents. Because of

their high molecular complexity, a panel of separation techniques

based on both liquid chromatography and electrophoresis has

been used for their characterization and comparability studies. In

terms of detection, mass spectrometry (MS) plays a pivotal role in

the structural elucidation of biopharmaceuticals, because it offers

an additional degree of separation by mass/charge ratio, greatly

facilitating the characterization of variants. This two-part web

seminar will discuss possibilities and limitations of chromatographic

techniques and mass spectrometry detection for the physico-

chemical characterization of biopharmaceutical compounds. Part I

will focus on chromatographic techniques, with some discussion of

ion exchange and size-exclusion methods, but primarily focusing

on reversed-phase LC, which is more compatible with MS. Part II

will focus on mass spectrometry techniques, including classical MS

analysis (intact mAb mass measurements, LC-MS analyses in reducing

conditions or after deglycosylation treatment, and peptide mapping),

enzymatic treatments for the analysis of mAb fragments, and newer

trends such as native MS, ion mobility–MS, and high resolution native

MS analysis.

Who Should Attend:

Analysts from industry, government

and academic laboratories who:■ Currently carry out the physico-chemical

characterization of biopharmaceuticals

■ Plan to work in the field of biopharmaceuticals in the near future

■ Want to learn more about recent trends in LC and MS for the analysis of biomolecules

Presenter Part I

Davy Guillarme, PhD

School of Pharmaceutical Sciences

University of Geneva

University of Lausanne

Switzerland HTMLH

Presenter Part II

Sarah Cianférani

BioOrganic Mass Spectrometry Laboratory,

Hubert Curien Pluridisciplinary Institute

University of Strasbourg,

Strasbourg, France

Moderator

Laura Bush

Editorial Director,

LCGC

Analytical Tools for the Characterization of Biopharmaceuticals

For questions, contact Kristen Moore at [email protected]

Sponsored by

Presented by

Part I: Chromatographic Methods Register free at:

www.chromatographyonline.com/methods_1

Part II: Mass Spectrometry Detection Register free at:

www.chromatographyonline.com/detection_1

ON-DEMAND WEBCASTS


Literature

CHANGING THE WAY YOU THINK ABOUT HPLC

HILIC application notesThe COSMOSIL HILIC Application Notebook from Nacalai reportedly contains close to 200 chromato-grams for the separation of polar compounds using the company’s HILIC column. According to the company, the publication also describes how mobile-phase condi-tions such as buffer pH and salt concentration influence separation in HILIC mode. Nacalai USA, San Diego, CA. www.nacalaiusa.com

Chemical ionization sourceFour application notes from LECO discuss the use of a chemical ion-ization (HR-CI) source for the com-pany’s Pegasus GC-high resolution time-of-flight mass spectrometer. According to the company, applica-tion note topics include jet fuel analysis, metabolite profiling, poly-mer extracts, and drug residues. LECO Corporation, St. Joseph, MN. www.leco.com/registration

GC postersFree GC poster packages from Restek provide wall charts paired with companion guides. According to the company, one package focuses on GC columns and includes two posters, one with tips on simplifying GC column selection, the other with solutions for GC troubleshooting. Another package offers information about GC liner selection including packing options, geometries, and dimensions, plus types of injections and how they relate to liner choice. Restek Corporation, Bellefonte, PA. www.restek.com/posters

HPLC columns product bulletinA 12-page product bulletin from Advanced Materials Technology describes its HALO-5 HPLC col-umns. According to the company, the bulletin includes charts and figures that describe the per-formance of the columns, and provides descriptions of the seven available phases (C18, C8, Phenyl-Hexyl, PFP, ES-CN, Penta-HILIC, HILIC). Advanced Materials Technology, Wilmington, DE. www.advanced-materials-tech.com

SPE cartridgesSupelMIP SPE – Patulin SPE cartridges from Supelco are designed with a molecu-larly imprinted polymer to provide sample prepara-tion for the analysis of the mycotoxin patulin in fruit matrices. According to the company, the cartridges consist of highly cross-linked polymers that are engineered to extract a single analyte of interest or a class of structurally related analytes of interest with an extremely high degree of selectivity. Supelco/Sigma-Aldrich, Bellefonte, PA. www.sigma-aldrich.com

Analytical reference materials websiteChem Service has updated its website. According to the company, on-line ordering of its analytical reference materials is now available, and customers who place an order will receive an e-mail message or a phone call from one of its sales representatives to confirm the order.Chem Service,

West Chester, PA.www.chemservice.com

Peptide mapping columnsAdvanceBio peptide map-ping columns from Neta Scientific are designed for resolution and iden-tification of amino acid modifications in primary structure. According to the company, the columns fea-ture a 120-Å pore size with superficially porous 2.7-μm particles. Neta Scientific,

Hainesport, NJ.www.netascientific.com

Food safety analysis virtual conference EMD Millipore’s Lab Solutions Virtual Conference on Food Safety Analysis reportedly includes three webinars: “ISO Standardization in Food Micro-biology and Quality of Media,” “The Importance of Water Quality in Food Analyses,” and “Monolith Chromolith Columns – an Ideal Tool for the Analysis of Food Samples with Complex ‘Dirty’ Matrices.” Registration for on-demand viewing is available at https://engage.vevent.com/index.jsp?eid=2484&seid=425 EMD Millipore, Billerica, MA. www.millipore.com

Multi-Analyte Analysis of 390 Pesticide Residues, Mycotoxins, and Pyrrolizidine Alkaloids in Phytopharmaceuticals, and Herbal Food Supplements with UHPLC–HRAM–MS/MS

EVENT OVERVIEW:

Herbal food supplements and phytopharmaceuticals have

become an important part of consumers’ health care in

recent years and their popularity is constantly growing. But

the safety control and analysis of such a difficult and largely

variable matrix is a challenging task. Besides heavy metals

and pesticide residues, occurrence of “natural” contaminants

such as mycotoxins and some toxic plant species has been

reported in various commercial products. In this presentation,

a multi-analyte method for target analysis of 323 pesticide

residues, 56 mycotoxins, and 11 pyrrolizidine alkaloids using

QuEChERS extraction method and UHPLC–HRAM- MS/MS will

be presented along with:

■ An advanced data-dependent MS/MS algorithm used for

simultaneous screening, quantitation, and confirmation of

all analytes within a single analytical run.

■ The method performance characteristics and validation

■ Analytical and economical improvements resulting from

the use of high resolution and accurate mass spectrometry

Sponsored by

Presented by

Key Learning Objectives:

■ How to achieve the benefits of

UHPLC-high resolution accurate

mass spectrometry in modern

residue analysis

■ Learn a unified sample preparation

method for multi-analyte

determination in a heavy matrix

■ Understand the benefits of the

HRAM approach in routine testing

with the emphasis on higher

throughput and lower costs per

sample

Who Should Attend:

■ Laboratory technicians, managers,

and analytical technology experts

focused on routine residue analysis

ON-DEMAND WEBCAST

Register Free at http://www.chromatographyonline.com/_analysis

Presenter

Zbynek Dzuman

Mass Spectrometry Specialist

Institute of Chemical Technology

Prague, Czech Republic (MSc.)

Moderator

Laura Bush

Editorial Director

LC/GC

For questions, contact Kristen Moore at [email protected]


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THE ESSENTIALS Excerpts from LCGC’s professional development platform, CHROMacademy.com

More Online:

There are only a few topics in gas chro-

matography (GC) that are responsible

for a large number of problems posed

to the “Ask the Expert” function in Chrom

Academy (www.ChromAcademy.com).

This month’s installment presents a sum-

mary of some of these topics relating to

poor peak shape and rising baselines, to act

as a guide when setting up or troubleshoot-

ing a method.

Problems with split and shouldered peaks

are typically caused by a disruption to the

way the sample band is introduced into

the GC column. Check that the column

is correctly installed into the instrument

— usually the depth of insertion into the

inlet is critical in this respect. The column

cut is absolutely critical in determining peak

shape, and one should ensure that the cut is

at 90° to the column wall and that it is clean

(not jagged or rough). The exposure of high

numbers of silanol groups and the formation

of turbulent eddies at the head of a roughly

cut column can cause major peak shape

issues. Always inspect the column cut with

a magnifier or lower-power microscope —

we cannot overemphasize the importance

of good quality column cuts at both the

inlet and detector ends of the column. The

homogeneity of the analyte band can also

be disturbed by problems with the internal

column surface at the head of the GC col-

umn. If stationary phase has been stripped,

exposing silanol groups, or if nonvolatile

sample matrix has been deposited on the

surface, the analyte band will interact differ-

ently with these areas than with the bonded

phase, causing peak splitting or shouldering.

Typically, these issues can be solved by trim-

ming a few centimeters from the head of the

column. Occasionally it may be necessary

to trim the column by up to 10% of the

total column length to solve the problem;

however, note that peak retention times will

decrease and peak identification windows

may need to be altered in your data system.

Further peak shape issues are more spe-

cific to splitless injection modes, but again

all relate to the homogeneity (contiguous

nature) of the sample band as it enters the

GC column. One should ensure, especially

in splitless injection, that the polarity of the

sample diluent solvent matches that of the

stationary-phase chemistry. Further, the

initial oven temperature should be at least

10 °C (preferably 20 °C) below the boiling

point of the sample solvent, which will act

to condense the analyte as it slowly evolves

from the inlet and focus each analyte band to

give sharp peaks within the chromatogram.

These factors combined, ensure that the

sample vapors condense as contiguous bands

within the GC column before they revolatil-

ize as the oven temperature is raised.

We are often asked about the causes of

rising baselines within GC separations —

and these typically fall into three categories.

If operating a temperature programmed

separation, with constant carrier gas head

pressure, the flow rate (and linear velocity)

of the carrier gas will decrease because gas

viscosity increases as a function of tempera-

ture. If one is using a mass- or flow-sensi-

tive detector (a flame ionization detector,

for example), which responds not only to

the amount of analyte but also the rate at

which analyte passes through the detector,

then the baseline position will naturally

rise. The solution to this issue is to oper-

ate in a constant flow mode in which the

instrument increases the carrier gas head

pressure to maintain a constant flow (or

linear velocity with some instruments) dur-

ing the whole of the temperature program.

Note that in switching operating modes,

retention times, especially of later-eluted

compounds, will change.

Baseline rise can also be caused by an

increase in column bleed with temperature.

Ensure that columns are properly condi-

tioned before use, which will involve a short

time at room temperature with carrier gas

flowing (this step is very important), fol-

lowed by no more than 30 min at 10 °C

higher than the upper operating tempera-

ture of the analytical method. Remember

that more-polar and thicker-film GC col-

umns will show greater bleed and to set a

bleed specification beyond which the col-

umn will not be used.

The third common cause of rising base-

lines is an improperly optimized splitless

injection. Although the initial phase of a

splitless injection should be carried out with

the split valve closed, the split should then

be initiated to remove excess solvent and

sample vapors from the inlet. This “split-

less” or “purge” time needs to be carefully

optimized; too short and sample will be lost

resulting in poor quantitation, too long and

a large tailing solvent peak with rising base-

line will result. Typically, the purge time is

optimized by choosing a time value (usually

in seconds) at which repeated injection of

the sample gives reproducible analyte peak

areas, but which results in the narrowest

solvent peak width.

Finally, we are very often asked about

peaks that tail badly in capillary GC, that

is, peaks whose asymmetry or United States

Pharmacopeia (USP) tailing factor is greater

than one. Most often, peak tailing occurs

because a certain proportion of the analyte

molecules are being subjected to a second-

ary mechanism of retention compared to the

rest and this is usually some type of silanol

interaction with analyte polar functional

groups. The silanol groups are present on

the surface of your quartz glass inlet sleeve or

liner, glass wool used for liner packing, and

in the silica from which the wall coated cap-

illary columns are manufactured. To avoid

peak tailing one should use only profession-

ally deactivated inlet liners and glass wool

packing, ensure good column cuts, trim the

inlet end of the column to remove exposed

silanol groups because of phase stripping,

and, lastly, consider derivatizing analytes to

“cap” or “mask” polar functional groups.

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

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Troubleshooting Real GC Problems

Valves, fittings, and much more for chromatography and liquid handling

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