RECENT DEVELOPMENTS IN HPLC AND UHPLCfiles.alfresco.mjh.group/alfresco_images/pharma/2019/01/... ·...

SUPPLEMENT TO

RECENT DEVELOPMENTS

IN HPLC AND UHPLC

Volume 33, Number s4 April 2015www.chromatographyonline.com

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HPLC and UHPLCHPLC and UHPLC

Recent Developments inRecent Developments in

4 RECENT DEVELOPMENTS IN HPLC AND UHPLC APRIL 2015

Articles

Recent Developments in HPLC and UHPLC . . . . . . . . . . . . . . . . . . . 8Mary Ellen McNally

A brief introduction to the articles — and ideas — presented in this supplement

The Simple Use of Statistical Overlap Theory in Chromatography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10Mark R. Schure and Joe M. Davis

How can statistical overlap theory be applied to chromatography in everyday usage?

Determination of Preservatives in Cosmetics and Personal Care Products by LC–MS-MS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16Emily A. Myers, Thomas H. Pritchett, and Thomas A. Brettell

The sample preparation for this LC–MS-MS method is short and simple, and the

method is capable of separating and identifying eight preservatives (including five

parabens) in <8 min, with excellent sensitivity.

Enhanced-Fluidity Liquid Chromatography: Connecting the Dots Between Supercritical Fluid Chromatography, Conventional Subcritical Fluid Chromatography, and HPLC . . . . . . . . . . . . . . . . 24Susan V. Olesik

In enhanced-fluidity LC (EFLC), a dissolved gas, such as carbon dioxide, is added

to the mobile phase. The resulting lower viscosity of the mobile phase and the

increased diffusivity decreases analysis time and often improves efficiency.

Selectivity and Sensitivity Improvements for Ionizable Analytes Using High-pH-Stable Superficially Porous Particles . . . . . . . . . . . 31William J. Long, Anne E. Mack, Xiaoli Wang, and William E. Barber

A novel approach to enhancing the selectivity of ionizable compounds using

superficially porous particles that are stable in a wider pH range is reported here.

Precision of Internal Standard and External Standard Methods in High Performance Liquid Chromatography . . . . . . . . . 40Karyn M. Usher, Steven W. Hansen, Jennifer S. Amoo, Allison P. Bernstein,

and Mary Ellen P. McNally

The internal standard method can significantly improve method precision, but attention

must be paid to the injection volume and the method by which the standard is added.

Cover images courtesy of Joe Zugcic, Joe Zugcic Photography; Tuomas Marttila/Pascal Broze/

Paul Tillinghast/Getty Images; Mark R. Schure and Joe M. Davis; Dan Ward

April 2015Volume 33 Number s4

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6 Recent Developments in Hplc anD UHplc April 2015 www.chromatographyonline.com

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www.chromatographyonline.com8 Recent Developments in Hplc anD UHplc APRIL 2015

FROM the Guest eDItOR

When I was asked to edit a supplement for LCGC again, I was delighted. My back-

ground in physical chemistry has always made me captivated by separation theory;

my career in industry has taught me to be practical in its use. My criterion to

choose contributors is the same as always: I ask fellow researchers who conduct work that I

find interesting. The selections here are quite varied, but they illustrate both the theory and

the practical perspectives of separation science.

Mark Schure and Joe Davis explain statistical overlap theory to a point that it can be

used in everyday chromatographic applications. This theory shakes the foundation for

many practicing chromatographers in that it statistically evaluates the probability of a peak

being pure. The results are not terrific. Simply, the authors show that for a moderately dif-

ficult separation the probability that a component of interest is resolved as a singlet peak

on a single column is only 14%. All is not lost however; multiple columns can be used to

increase this probability, although not as dramatically as I personally would like Ñ the

authors report the probability only increases to 52% for five columns in series. For those

of us who love the theory of chromatography, this is a fascinating article. For the rest who

know how important theory is to the practice of separations, this is an awakening and a reminder of what could be going

on inside our chromatographic systems. Chromatographers take heed!

Every morning when we reach inside our bathroom cabinets, we are probably not awake enough to think about the chemistry

behind the products we use. Brettell, Myers, and Pritchett, researchers at Cedar Crest College, have investigated liquid chroma-

tographyÐmass spectrometry (LCÐMS) for a series of parabens, BHT, and BHA. These compounds are used as preservatives in

everyday products such as toothpaste, hand lotion, deodorant, foundation, hand sanitizer, and lipstick. Methyl and ethyl parabens

were the most common preservatives and were found at the highest levels in deodorant and foundation samples. We should feel

safer knowing that there is a way to make sure preservatives can be accurately measured in the personal care products we use daily.

Supercritical fluids were first used in chromatography in 1962 by Ernst Klesper, and enhanced-fluidity chromatography (EFC)

was first examined by Susan Olesik and her group in 1991. Supercritical fluid chromatography (SFC) is when the mobile phase,

either a pure or modified gas, is operated above its critical point. Enhanced-fluidity solvents are solvents that have added dissolved

gases. Ironically, much of the literature using SFC with mixed solvents is mislabeled, as the temperature and pressures are too low

for criticality. In actuality, these supercritical separations are conducted with subcritical conditions. For the practicing chromatog-

rapher, Olesik explains the theory behind the appropriate choice of solvent and operating conditions given the variety of options

open to us: SFC, EFC, high performance liquid chromatography (HPLC), and even subcritical chromatography conditions.

At the frontier of column technology, there has always been a quest to control pH at extreme levels and adjust selectivity

while maintaining long column lifetimes. Long, Mack, Wang, and Barber showed that by keeping a gradient constant and

altering pH, the elution order of a group of eight acid, base, and neutral compounds could be dramatically changed and reso-

lution improved with a superficially porous column. Positive ion electrospray mass spectrometry of basic compounds using

high and low pH gradient HPLC showed improved peak shape, increased retention as well as signal and sensitivity increases.

With these new superficially porous particle technology columns, separation scientists can examine a wider range of method

development options. Here comes high efficiency, high speed, and durability, yeah!

With my coauthors Usher, Hansen, Bernstein, and Amoo, my laboratory has pursued the never-ending question of whether

or not better precision is obtained when an internal standard is used instead of an external standard. In all of our experiments,

the internal standard method significantly improved the precision. However, additional influencing factors on the precision

are the injection volume and the method by which the internal standard is added to the analyte. Does this definitively settle

this question? Only time will tell.

My hope is that you appreciate the articles in this supplement as much as I have taken pleasure in reading and editing them.

These are some of my favorite scientists, who have challenged and continue to challenge my own experimental design as well

as interpretation of results. I am confident that their advancements to the field of separation science will challenge you in your

laboratory. Thank you to the authors Ñ excellent work!

Recent Developments in HPLC and UHPLC

Mary Ellen McNally, PhDDuPont Crop Protection

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10 Recent Developments in Hplc anD UHplc APRIL 2015 www.chromatographyonline.com

1

0.8

0.6

0.4

k = 1

k = 2

k = 3

k = 4

k = 5

0.2

00 0.2 0.4 0.6 0.8 1.2 1.4 1.6 1.8 21α

s/m

Mark R. Schure

and Joe M. Davis

The Simple Use of Statistical Overlap Theory in Chromatography

The statistical overlap theory (SOT) of chromatography relates the

number of peaks that appear in a chromatogram to the number

of detectable components and the peak capacity. This theory

transformed chromatography in how it revealed that on a statistical

basis the number of peaks underestimates the number of components

present in the chromatogram. In this paper, we show how this

theory can be applied to chromatography in everyday usage.

The statistica l overlap theory

(SOT) is a useful theory that

gives the relationship between

the number of peaks observed in a

chromatogram, p, and the number

of detectable components, m. This

theory, originally devised by Davis

and Giddings (1), assumed that these

components were distributed using

Poisson statistics leading to peaks

being distributed randomly across

a chromatogram. The results were

rather sobering as one of the many

predictions of this theory states that

“a random chromatogram will never

contain more than about 37% of its

potential peaks and, worst of all from

an analytical point of view, 18% of its

potential single-component peaks” (1).

In other words, only 18% of the peaks

are from single, pure components and

only 37% or approximately one-third

of the components, show up as unique

peaks because of peak overlap. It is also

stated “that a chromatogram must be

approximately 95% vacant in order to

provide a 90% probability that a given

component of interest will appear as

an isolated peak.”

Simple Derivation

SOT shows that one of the most impor-

tant parameters in any chromato-

graphic separation is the peak satura-

tion, because it dictates how crowded

the separation is. The common label

for saturation is α, but it is not the

same as selectivity, which often has

the same label. For complex biological

samples α > 1 and for typical samples

with a few components α < 1. For a

separation of moderate complexity, α

≈ 1. The key to deciding whether to

proceed with a multidimensional sepa-

ration as opposed to a single column

separation is to understand the origin

and magnitude of α.

The treatment that follows is based

on time as the independent variable.

Space (as is the case for thin-layer chro-

matography, for example) and time are

equivalent in this treatment (1).

The peak saturation α is a metric of

peak crowding equal to

α = 4mσR*s/

1D [1]

where m is the number of detectable

single component peaks (SCPs) with

temporal standard deviation σ that

occupy a separation space of extent 1D. The term 1D is the time differ-

ence between the first and last peaks

in a chromatogram. A single component

peak is specif ically a peak in which

a pure component resides; in other

words, an SCP is a peak that is chemi-

cally pure, such as would be obtained

APRIL 2015 Recent Developments in Hplc anD UHplc 11www.chromatographyonline.com

on chromatographing a single com-

pound. The attribute R*s is the average

minimum resolution, which measures

the average smallest interval between

adjacent SCPs that are separated. R*s is

not a free parameter, but it depends on

the type of interpeak statistics func-

tion (for example, for random spacing

of SCPs or ordered, such as fractal

spacing of SCPs), the amount of peak

overlap, and the distribution of SCP

heights (2).

The attribute R*s differs from the

traditional resolution R s, which is

a parameter freely chosen by the

researcher. The traditional resolution

Rs is an important attribute of the peak

capacity that is defined (2) as the num-

ber of equi-spaced SCPs that fit within

a discrete time increment between t1

and tm so that

nc

−tm 1

4σR

Dt1

s 4σRs

= = [2]

By combining equations 1 and 2, one

obtains an alternate metric of peak

crowding, the effective saturation αe

= α / σ /= 4m = m / (ncRs)α D1R*

e s [3]

which depends only on m, σ, and 1D

(3). The effective saturation is a prac-

titioner-friendly metric for comparing

peak overlap in different separations,

because it is independent of R*s which

varies, as noted above, with saturation.

The predictions of SOT are derived

relative to α, but are more easily inter-

preted relative to αe.

Different approaches to SOT have

been proposed, including some based

on Fourier analysis (4,5) and pulse-

point statistics (6). Various reviews of

the different methods have been pub-

lished (7,8). In this article, we consider

only point-process statistics, in which

the distribution of intervals between

the retention times of successive SCPs

is considered. For simplicity, we con-

sider only cases where SCPs are spread

more or less equally throughout the

separation.

The simplest interpeak statistics

function in SOT is based on Poisson

statistics, which assume that SCPs are

distributed across the separation space

randomly. This assumption is well-

founded both empirically (1,9–12) and

theoretically (13,14) for a number of

mixtures. This random placement of

SCPs requires that the arrival times

of SCPs are governed by a Poisson

process based on exponential waiting

times (15):

P(t) = λe-λt [4]

where P(t) is the probability density of

finding the next SCP some time t after

the last SCP. This relationship gov-

erns such random processes as radio-

active decay and is called a renewal

process (16) in the probability litera-

ture. The quantity λ in equation 4 is

the component density or the number

of components per total separation

space so that λ = m/1D. The expecta-

tion (or average) value of the density

P(t) is E and is equal to 1/λ. This can

be generalized: E = 1D/m. Therefore,

the expectation value of the density

can be interpreted as the average sepa-

ration space between SCPs.

The probability that the interval

between two SCP centers exceeds

some time t′= 4σR *s, allowing these

SCPs to be resolved from each other is

Pr(t ≥ t')t'

∞

P(t)dt =exp [–λt']∫= [5]

Noting that α = λt′, one ultimately

finds

Pr(t ≥ t') p/m ≡γ=exp (–α)= [6]

where p is the number of peaks in the

chromatogram and the ratio γ = p/m

is the fraction of components that are

interpretable as peaks. Thus, this frac-

tion is a simple function of the satura-

tion α. For the appropriate choice of

R*s (17,18), p is the number of visible

maxima.

Another quantity of interest is the

fraction of components that are singlet

peaks (1):

12

P =exp (–2α)=γ [7]

In general, the fraction Pn of compo-

nents appearing in peaks containing n

components (for example, for doublets,

n = 2; for triplets, n = 3; and so on) is

as follows (1):

nn ne=P (1–γ) (1–e–α)=

2γn 1– n 1––2α [8]

Equations 6–8 are valid for different

interpeak statistics functions produc-

ing different renewal processes, as long

as the ratio γ = p/m is replaced by the

appropriate function of α (19).

Consequences of Overlap

We show the consequence of over-

lap using the random SCP approach

developed in the equations above in

Figure 1. Other renewal processes

besides the (random) Poisson process,

for example, two power-law (fractal)

1

0.8

0.6

0.4

0.2

0.5

Poisson

p/m

Gamma process

Fractal D = 0.2

Fractal D = 1.0

1.5α

2.5 3.53 4200 1

Figure 1: Plot of p/m, the fraction of peaks found in the chromatogram as a func-tion of the saturation α for four renewal processes: Poisson (random) process, solid line; power-law (fractal) process with D = 1.0, β = 10, dashed line, D = 0.2, β = 10, dotted line; and gamma process (P = 4, as explained in reference 19), dash-dotted line. The β parameter is explained in reference 2.


renewal processes (2) and a Γ process

based on the gamma distribution

(19), are a lso shown. While these

processes differ in the assumptions

of the statistical SCP spacing, the

trends are apparent. The dimension-

ality D used in Figure 1 and below

is explained in great detail in refer-

ence 2 and is a measure of the order-

ing of a chromatogram. At higher D

values ordering increases and at low

D values there are many gaps in the

chromatogram.

As the saturation α increases, the

fraction of components that appear as

peaks decreases rapidly. In the case of

SCPs that are more ordered, as found

in the fractal and Γ processes, the

decrease in peaks as α increases does

not fall as rapidly as a random order-

ing, at least at low saturations. How-

ever, Figure 1 also shows that as α

approaches one, ordering causes more

loss of peaks than a random filling of

the peak space.

The consequences of overlap are

shown in a complementary way in

Figure 2, where we use synthetic chro-

matograms comprising sums of Gauss-

ian peaks that are distributed randomly

throughout the retention time range

and have uniformly random heights.

The zone (or SCP) standard deviation

used here is obtained from the follow-

ing well-known equation (20):

t / N =—

√σ [9]

so that given a number N of theoreti-

cal plates and a retention time t the

zone standard deviation σ is deter-

mined. For Figure 2, the retention

time in equation 9 is that of the first

retained SCP, and a model of con-

stant zone width is assumed, which

is approximated in temperature-pro-

grammed gas chromatography (GC)

and gradient-elution liquid chroma-

tography (LC).

The four chromatograms in Figure

2 vary in efficiency (number of plates),

and this is ref lected in the Gaussian

zone standard deviation, σ, which in

turn affects αe and nc (as calculated

from equations 2 and 3, with Rs = 1).

The number of components, m, is 100.

The retention times are represented at

the bottom of Figure 2 by stick loca-

tions that show what the chromato-

gram would look like, except for the

distortion of peak heights, if the peaks

were infinitely narrow, that is, if σ = 0

and hence αe = 0 and nc = ∞.

As can be seen from Figure 2, at high

eff iciency with N = 100,000 plates,

94 peaks are present. This number

drops off to 87 when the plate count

is reduced to 50,000. At 10,000 plates,

75 peaks are present and at 5000 plates

only 66 peaks are present. Only a frac-

tion of these peaks are singlets; a good

number of these are fused doublets

and triplets (and even more complex

multiplets). Hence, as eff iciency is

reduced, as measured by increases of

αe, the number of peaks present drops

monotonically.

The consequences of this phenom-

enon are well known. Peak fusion

interferes with proper quantitation. It

also interferes with the identification

of specific components. Often times

this can be aided with mass spectrom-

etry (MS) detection. However, this is

not always the case as peak fusion can

lower ionization efficiency, and mass

spectrometry often cannot distinguish

between closely related compounds

with the same molecular weight (and

hence the same parent ion).

Chromatography is particularly

problematic for samples of biological

origin because of the multiplicity of

forms, called isoforms. These isoforms

are closely related in structure (but are

not the same) yet may have different

chromatographic retention. In addi-

tion, many biological molecules have

dynamic structure so that chromato-

graphic retention occurs with a mul-

tiplicity of different molecular con-

formations, all of which lead to zone

broadening and a lowering of the over-

all effective efficiency. These effects

reduce the apparent efficiency of the

chromatographic process and cause

an artif icial increase in α, making

chromatographic separation more dif-

ficult for biomolecules than in the case

of small molecules. This is why bio-

molecules are often denatured before

analysis in the hopes of minimizing

the conformational shifts during the

separation process.

Use of the SOT as a Ratio

Another useful view of SOT is to

express equation 6 as a ratio. In this

way, we can estimate what the gain or

loss of peaks will be by changing effi-

ciencies at constant sample, constant

selectivity, and constant relative sol-

vent program.

A common shortcoming in SOT cal-

culations is the failure to distinguish

between the freely chosen traditional

resolution Rs and the average mini-

mum resolution R*s. The assumption

that they are the same introduces

αe = 0.769

αe = 0.544

αe = 0.243

αe = 0.172

σ (s) = 2.68

σ (s) = 1.89

σ (s) = 0.847

σ (s) = 0.599

N = 5000

N = 10000

N = 50000

N = 100000

0 10 155Time (min)

Re

lati

ve

in

ten

sity

2520 30

nc = 130

nc = 184

nc = 411

nc = 581

p = 66

p = 75

p = 87

p = 94

Figure 2: The effect of varying effciency (number of plates) on the number of visible peaks. The numbers of peaks detected (p) are 94, 87, 75, and 66 for four different eff-ciency scenarios given 100 components. The symbols are effective saturation, αe, number of plates, N, peak capacity, nc, Gaussian standard deviation zone width in s, σ, and the number of visible peaks, p.


error, and the distinction must be kept

in rigorous work. However, it is con-

venient to identify them to simplify

matters and evaluate trends. We do

so here for simplicity’s sake, but the

results obtained must be interpreted as

only guidelines.

Consider a case in which the dura-

tion 1D of two separations is the same

but the SCP standard deviations

therein are different. Evaluating the

ratio of equation 6 with fixed m (con-

stant sample component number), one

f inds, with the subscripts denoting

two different columns:

( )

=

p1p2

= = =

e 1

1 1

–α1–α

e 2–αe e

m2

nc 1nc

2α

–

4mR s { }exp [ ]D

1 2σ 1σ– [10]

where α1 and α2 are two different satu-

rations, nc1 and nc2 are two different

peak capacities, and σ1 and σ2 are two

different SCP standard deviations.

Using equation 10 and H = L/N,

where H is the plate height, L is the

column length, and the nondimen-

sional retention parameter k′ = (t/t0)–1,

with t0 equaling the void time, one can

show that

4mRs {=exp [ }]p

1p

2

dL

1+ h2 h1–k'kmax'• •

[11]

where d is the particle diameter, k′max

is the maximum k′ used in the analy-

sis, and h is the nondimensional plate

height, H/d. As an example in LC,

consider the chromatographic values

of Rs = 1, k′ = 5, k′max = 20, L = 15 cm,

d = 2.7 µm, h2 = 1.5, and h1 = 1.0. For

these parameters, the ratio in equa-

tion 11 for a 200 component mixture

(m = 200) is equal to 1.25, indicating

that 25% more peaks would appear

in a chromatogram using a column

that was extremely high in efficiency

where h = 1.0, as compared to a more

conventional very high performance

column, for example a core–shell par-

ticle where h = 1.5. This number sug-

gests that the pursuit of even higher

performance column technology is a

most desirable goal in increasing the

number of detectable peaks. Further-

more, it is known that even if this level

of performance is not warranted, the

speed of separation can be increased

when high efficiency column technol-

ogy is utilized.

For situations where zones are ordered,

using fractal statistics, the ratio approach

is powerful. It can be shown (2) that

under limiting conditions the ratio of

the number of peaks found is related to

the two plate counts, N1 and N2, and the

fractal dimension D, such that

=

p1

p2

D/2N 1N 2( ) [12]

Multidimensional

Separation by k Columns

The results presented earlier show

that the limited separation space of

one column, even those of very high

eff iciency, stil l has limited separa-

tions capabilities. Of historic inter-

est is the use of multiple columns to

increase the likelihood that a given

compound is separated by at least one

column. The probability of success

was f irst addressed by Connors (21)

and subsequently reexamined (22).

For k separations (that is, columns)

of the same mixture, with the separa-

tions having the same saturation but

independent separation mechanisms,

the probability s/m that a component

appears as a singlet peak on at least

one column is

(1–γ ) –2αs/m 2 k k= =–1 (1–e )–1 [13]

where the last equality applies to a

Poisson distribution of SCPs. Figure

3 is a graph of the f inal expression

in equation 13 for k values between

1 and 5. The k = 1 graph represents

separation by a single column. As k

increases, the likelihood of separation

increases. For a saturation α equal to 1,

corresponding to a separation of mod-

erate difficulty, the likelihood that a

component of interest is resolved as a

singlet peak on a single column (k =

1) is only 14%. However, this number

increases to 25%, then 35%, then 44%,

and finally 52% as the number of col-

umns is increased from two, to three,

to four, and finally to five.

Other types of multidimensional

separations (k ⩾ 2) can be considered.

A classic method is column switch-

ing, in which a subsection of the

entire chromatogram is transferred

to another column. Two-dimensional

chromatography (k = 2) attempts to

increase the peak capacity by provid-

ing a separation area rather than a

line, and this is needed for very com-

plex mixtures. A few cases of separa-

tions in higher dimensions (for exam-

ple, k = 3) have been reported. SOTs

have been developed for all of these

methods (23–26).

Conclusions

SOT started in the early 1980s, and a

glance at the references below shows

that many were published long ago.

What is the relevance of SOT today?

The hope of early researchers that

1

0.8

0.6

0.4

k = 1

k = 2

k = 3

k = 4

k = 5

0.2

00 0.2 0.4 0.6 0.8 1.2 1.4 1.6 1.8 21α

s/m

Figure 3: Graph of the probability that a given component appears as a singlet peak, s/m, versus the saturation of k independent columns.


SOT could be used routinely to esti-

mate the number of components in

mixtures is largely unfulf illed. The

reason is that too many unknowns

exist in rea l chromatograms, per-

haps most importantly the type of

interpeak statistics function (and its

possible variation over the separa-

tion) governing the spacing between

SCPs. The large variation of SOT

predictions for different functions is

shown in Figure 1. Various functions

have been proposed over the years

(1,2,4,19), with assessments based

on the quality of their description of

experimental data. Nevertheless, an

inf inite number of such functions

can be proposed (based on known or

empirical statistical laws), and unless

one knows on a physicochemica l

basis what functions are favored for a

given mixture and column, one is left

with uncertainty.

However, SOT does serve two pur-

poses. First, it provides the basis for

semiquantitative to quantitative pre-

dictions of the expected outcome of

the completeness of a separation in a

chromatogram, and how much effort

is required to improve that chromato-

gram. This is especially true for a

single column, where the validity of

the assumption of Poisson statistics

is often justif ied. Several such pre-

dictions were presented here. Second,

theory can be used to model attributes

of interest in chromatograms, even

when the assumptions of SOT do not

apply. For example, the correlation of

retention times in many two-dimen-

sional chromatograms inva lidates

the assumption of SCP randomness.

Nevertheless, two-dimensional SOT

has been used on model systems to

understand the undersampling of

f irst-dimension peaks in comprehen-

sive two-dimensional chromatography

(27), the improvement of resolution

therein by the use of multivariate

selectivity (28), and the comparison

of one- and two-dimensional chroma-

tography (29).

SOT offers powerful, yet practical

insight into the statistical mechanics

of separation. As with many areas of

separation science, the development

of SOT is an interdisciplinary task,

in this case between chemistry and

applied probability theory. The ini-

tia l mathematical dif f iculties have

been overcome by a continuous

ref inement of SOT conducted by

a multitude of authors involved in

developing and ref ining chromato-

graphic theory. The results and pre-

dictions are meaningful and make

very practical guides to experimental

methods development.

Biomedical areas of research such

as the search for biomarkers, metab-

olomics ana lysis, and proteomics

research a l l dea l with saturation

issues in chromatography. The con-

sequences of saturation include an

undeniable loss in unique identif ica-

tion in single channel detectors. The

instrumenta l development of the

chemical analysis process requires

coupling high resolution columns,

perhaps even multiple separation

stages, together with multichannel

detectors such as mass spectrometers

and multiple MS stages. Sometimes

zones can be resolved with unique

ion identif ication schemes, and some-

times zones have mixtures that are

not resolvable by MS. This coupling

and its ref inement towards reach-

ing reliable molecular identif ication

needs to be understood quantitatively

in the context of chromatography by

the extension of SOT.

References

(1) J.M. Davis and J.C. Giddings, Anal .

Chem. 55, 418–424 (1983).

(2) M.R. Schure and J.M. Davis, J. Chro-

matogr. A 1218, 9297–9306 (2011).

(3) J.M. Davis and P.W. Carr, Anal. Chem.

81, 1198–1207 (2009).

(4) A. Felinger, L. Pasti, and F. Dondi, Anal.

Chem. 62, 1846–1853 (1990).

(5) M.C. Pietrogrande, M.G. Zampolli, and

F. Dondi, Anal. Chem. 78, 2579–2592

(2006).

(6) F. Dondi, A. Bassi, A. Cavazzini, and

M.C. Pietrogrande, Anal . Chem. 70,

766–773 (1998).

(7) M.C. Pietrogrande, A. Cavazzini, and F.

Dondi, Rev. Anal. Chem. 19, 123–156

(2000).

(8) A. Felinger and M.C. Pietrogrande, Anal.

Chem. 73, 619A–626A (2001).

(9) D.P. Herman, M.-F. Gonnord, and G.

Guiochon, Anal. Chem. 56, 995–1003

(1984).

(10) M. Martin, D.P. Herman, and G. Gui-

ochon, Anal . Chem . 58, 2000–2007

(1986).

(11) C. Samuel and J.M. Davis, J. Chromatogr.

A 842, 65–77 (1999).

(12) C. Samuel and J.M. Davis, J. Microcol.

Sep. 12, 211–225 (2000).

(13) A. Felinger, Anal. Chem. 67, 2078–2087

(1995).

(14) J.M. Davis, M. Pompe, and C. Samuel,

Anal. Chem. 72, 5700–5713 (2000).

(15) J.F.C. Kingman, Poisson Processes (Oxford

University Press, 2002).

(16) D.R. Cox, Renewal Theory (Methuen &

Co. 1967).

(17) A. Felinger, Anal. Chem. 69, 2976–2979

(1997).

(18) J.M. Davis, Anal. Chem. 69, 3796–3805

(1997).

(19) M.C. Pietrogrande, F. Dondi, A. Felinger,

and J.M. Davis, Chemom. Intell. Lab. Sys.

28, 239–258 (1995).

(20) J.C. Giddings, Unified Separation Science,

(Wiley, 1991).

(21) K.A. Connors, Anal. Chem. 46, 53–58

(1974).

(22) J.M. Davis and L.M. Blumberg, J. Chro-

matogr. A 1096, 28–39 (2005).

(23) J.M. Davis, Anal. Chem. 65, 2014–2023

(1993).

(24) M. Martin, Fresenius’ J. Anal. Chem. 352,

625–632 (1995).

(25) C. Samuel and J.M. Davis, Anal. Chem.

74, 2293–2305 (2002).

(26) S. Liu and J.M. Davis, J. Chromatogr. A

1126, 244–256 (2006).

(27) J.M. Davis, D.R. Stoll, and P.W. Carr,

Anal. Chem. 80, 461–473 (2008).

(28) J.M. Davis, S.C. Rutan, and P.W. Carr, J.

Chromatogr. A 1218, 5819–5828 (2011).

(29) J.M. Davis, Talanta 83, 1068–1073

(2011).

Mark R. Schure is with Kroungold

Analytical, Inc. in Blue Bell, Pennsylvania.

Joe M. Davis is with the Department of Chemistry and Biochemistry at Southern Illinois University at Carbondale in Carbondale, Illinois. Direct correspondence to: [email protected] ◾

For more information on this topic,

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Emily A. Myers, Thomas H. Pritchett, and Thomas A. Brettell

Determination of Preservatives in Cosmetics and Personal Care Products by LC–MS-MS

A liquid chromatography–electrospray ionization tandem mass

spectrometry (LC–ESI-MS-MS) method has been developed to determine

multiple preservatives in cosmetics and personal care products.

Cosmetic and personal care products

that contain water require protec-

tion against the growth of microor-

ganisms to ensure product safety. Preserva-

tives are natural or synthetic ingredients

added to products to prevent spoilage,

microbial growth, undesirable chemical

changes, or to extend the product’s shelf

life (1). The use of preservatives in per-

sonal care products is important because

not only do they prevent product damage

caused by microorganisms but they also

help protect the product from inadvertent

contamination by the consumer during

use. Without the addition of preservatives,

the product may become contaminated,

which can lead to product degradation

and, in the case of cosmetic foundations,

ultimately increase the risk of irritation or

even infection. Preservatives are added to

personal care products at relatively low lev-

els to ensure products remain safe and per-

form as intended over their lifetime. The

determination of preservatives in these

products is important for quality control

to prevent allergic reactions and other

health issues.

The most widely used preservatives

in cosmetic products are a class of com-

pounds generally referred to as parabens

(1). These compounds are alkyl esters of

p-hydroxybenzoic acid (Figure 1). They

are used for their preservative properties

in cosmetic and personal care products

because of their antimicrobial activities,

low toxicity, and low production cost (2).

Methylparaben (MeP) is found in nearly

all cosmetics and many pharmaceuticals

(3). The use of parabens in personal care

products has caused concern because of

their potential adverse effects, including

proliferation of breast cancer (4–8) and

reduction of sperm count and testosterone

levels (9–12). The United States Food and

Drug Administration (FDA) finds that

although parabens can mimic estrogen,

the levels found in these products are at

such low levels that their activity on the

body does not cause cancer in any higher

incidence than naturally occurring estro-

gen despite contrary belief (13). However,

when counterfeit products make it to

market, they are unregulated and may

contain preservative levels that may pose a

health risk to the user. Therefore, accurate

methods to determine the levels of these

compounds need to be available for moni-

toring preservative concentrations in cos-

metic and personal care products.

Current analytical methods for the

determination of preservatives in cos-

metic and personal care products include

high performance liquid chromatogra-

phy (HPLC) (14,15), ultrahigh-pressure

liquid chromatography (UHPLC) (16),

UHPLC–tandem mass spectrometry

(MS-MS) (17), gas chromatography–mass

spectrometry (GC–MS) (18), GC (19,20),

solid-phase microextraction (SPME)-GC–

MS-MS (21), and micellar electrokinetic

chromatography (MEKC) (22).

Most methods have previously deter-

mined single preservatives in pharmaceu-

tical or personal products. The methods

that have been published have focused on

a small group of preservatives, but only a

few methods have included the simultane-

ous measurement of multiple preservatives.

Methods for the preservative analysis of cos-

metic products have mainly focused on the


determination of parabens. The analysis of

more than one class of preservatives is still a

field under development. We have included

not only parabens in this study, but also

compounds such as DL-α-tocopherol ace-

tate (Toco) and butylated hydroxytoluene

(BHT). In previous chromatographic meth-

ods the sample preparation has focused on

specific product categories; most are GC

methods requiring derivatization. Cur-

rently, there is no universally accepted

sample preparation or analytical method

for different types of sample matrices such

as pastes, liquids, creams, and ointments.

In this study, we have developed a simple

sample preparation method using liquid

chromatography–electrospray ionization

tandem mass spectrometry (LC–ESI-MS-

MS) to analyze preservatives in cosmetic

and personal care products using a relatively

small sample, 100 mg. Experimental condi-

tions were optimized for sample preparation

and analysis to achieve maximum sensitiv-

ity and accuracy. The optimized method

was used to analyze the following preserva-

tives: methylparaben, ethylparaben, propyl-

paraben, isopropylparaben, benzylparaben,

O

O

HO

Name

Methylparaben (MeP)

Ethylparaben (EtP)

n-Propylparaben (PrP)

n-Butylparaben (BuP)

Benzylparaben (BzP)

R

–CH3

–CH2CH3

–CH2CH2CH3

–CH2CH2CH2CH3

R

CH2

Figure 1: Chemical structures of parabens.

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butylparaben, triclosan, DL-α-tocopherol

acetate, butylated hydroxyanisole (BHA),

and butylated hydroxytoluene (BHT).

The procedure can simultaneously ana-

lyze these preservatives in a single chro-

matographic analysis of various kinds of

sample matrices from cosmetic and per-

sonal care products.

Experimental

Sample Preparation

Cosmetic and personal care samples were

purchased from local stores. Products

analyzed included lipstick, foundations,

deodorant, hand lotion, hand soap, and

toothpaste. Standard preservative sample

and cosmetic and personal care prod-

uct preparation was as follows: 100 mg

sample was placed into 5 mL of 1:1 (v/v)

methanol–acetonitrile. This solution was

then sonicated for approximately 10 min

and then centrifuged for 5 min at 800g.

The supernatant was then filtered using

a 0.2-µm Millipore filter. Then, 1 mL of

the filtered supernatant was placed into an

autosampler vial along with 60 µL of 100

ppm internal standard (BHA).

The following reagents were purchased

from VWR: HPLC-grade methanol,

HPLC-grade water, formic acid, and ace-

tonitrile.

Preservative Standards

The following preservatives were purchased

from Sigma Aldrich: ethyl 4-hydoxybenzo-

ate (ethylparaben) (lot STBC0530V), pro-

pyl 4-hydroxybenzoate (propylparaben)

(lot BCBK9343V), methyl 4-hydroxyben-

zoate (methylparaben) (lot MKBG5184V),

butyl 4-hydroxybenzoate (butylparaben)

(lot MKBR1951V), benzyl 4-hydroxyben-

zoate (benzylparaben) (lot MKBL1242V),

triclosan (lot LRAA1072), and butylated

hydroxyanisol (BHA) (lot MKBJ4456V).

The preservatives DL-α-tocopherol acetate

(lot SLBB9917V) and 2,6-di-tert-butyl-4-

methylphenol (BHT) (lot 10156687) were

purchased from Alfa Aesar. Isopropylpara-

ben (lot S5QHD-CE) was purchased from

Santa Cruz Biotechnology.

Liquid Chromatography

Liquid chromatography was performed

on a Shimadzu LC-20 Prominence sys-

tem equipped with two Shimadzu LC-20

AD prominence liquid chromatography

binary pumps, a Shimadzu DGO-20A3

Prominence degasser, and a Shimadzu

SIL-20AC Prominence autosampler. A 50

mm × 3.0 mm, 3.0-µm Ultra Biphenyl

column (Restek) was used for all analyses.

A binary mobile phase was used: the weak

mobile phase (A) was 0.1% (v/v) formic

acid in HPLC-grade water and the strong

mobile phase (B) was 0.1% (v/v) formic

acid in 2-propanol. The flow rate was 0.3

mL/min. Before running the method on

the preservative standards and samples,

the lines and the column were flushed

using the mobile phase to elute any

compounds that may have been present.

Pumps A and B were also purged before

any experimental run to eliminate any

cross contamination. The LC oven tem-

perature was held constant at 25 °C. To

m/z

Inte

nsi

ty (

cps)

4.2e7139.1

167.2

95.1

121.1

4.0e7

3.8e7

3.6e7

3.4e7

3.2e7

3.0e7

2.8e7

2.6e7

2.4e7

2.2e7

2.0e7

1.8e7

1.6e7

1.4e7

1.2e7

1.0e7

8.0e6

6.0e6

4.0e6

2.0e6

50 55 60 65 70 75 80 55 90 55 100 105 110 115 120 125 130 135 140 145 150 155 160 165 170

Figure 3: Enhanced product ion 10 eV ethylparaben spectrum (Q1 mass: 167 amu; Q3 masses: 95, 139 amu).

100

80

60

40

20

00.0 5.0

Time (min)

%B

10.6

Figure 2: Gradient profle of %B versus time.


obtain optimal separation the following

gradient was used: start with 50% B and

hold for 2.5 min; from 2.50 to 3.00 min

linearly increase the concentration of B to

95%; hold the concentration of B at 95%

to 7.5 min. After completion of the data

acquisition, the concentration of B was

dropped back to 50% and the column was

allowed to reequilibrate for 3 min. The

gradient profile can be seen in Figure 2.

The autosampler injection volume was set

constant at 2 µL for each sample.

Mass Spectrometry

MS analysis of all samples was performed

on an AB Sciex 3200 QTRAP triple-

quadrupole mass spectrometer equipped

with an ESI interface. Electrospray ioniza-

tion was carried out in positive-ion mode.

Q1 and Q3 were both operated with unit

resolution. The source temperature was

500 °C and the ionization voltage was

4500 V. The preservatives were quantified

in multiple reaction monitoring (MRM)

mode with a dwell time of 100 ms. Opti-

mized parameters for MS-MS analysis are

listed in Table I. The collision energy (CE)

and declustering potential (DP) for each

preservative analyte are listed in Table II.

Results and Discussion

Using 100 ppm preservative stock solutions,

the enhanced product ions (EPIs) for each

preservative were determined using ESI-

MS-MS with the exception of BHT, whose

EPI spectrum was determined using a 1000

ppm solution. The data obtained are listed

in Table II, and an example EPI spectrum

of ethylparaben can be seen in Figure 3. The

LC gradient conditions were optimized to

achieve the best separation of a mixture of

standard preservatives. The chromatogram

of a standard preservative mixture (30.0 ppm

each) can be seen in Figure 4. The respective

retention times of the standard preservatives

in the mixture are listed in Table III. The

relative responses of the parabens were con-

sistent. However, BHT showed poor ioniza-

tion efficiency and did not give enough of a

response to give detection limits that were

acceptable enough to detect it in some sam-

ples. On the other hand, DL-α-tocopherol

acetate gave a larger response than the para-

bens. We attempted to include triclosan in

the method, but it was only detectable in the

negative ion mode so this analyte was not

included in the procedure.

The following calibrator solutions were

made by dilution with HPLC-grade

methanol of the stock preservative mixture

solution: 0.1, 0.5, 1.0, 5.0, 30.0, 70.0, and

100.0 ppm. Calibration curves were gener-

ated by analyzing samples in triplicate over

six days. As an example, the calibration

curve for ethylparaben can be seen Figure

5. Figures of merit were obtained from the

calibration curve data. The figures of merit

data for all of the preservatives including

Table II: The enhanced product ion data for standard preservatives

Preservative Q1 Mass (amu) Q3 Mass (amu) CE (V) DP (V)

Methylparaben 153 121 20 29

Methylparaben 153 65 45 29

Ethylparaben 167 139 15.5 25

Ethylparaben 167 95 24 25

Ethylparaben 167 121 28 25

Propylparaben 181 139 15.5 21.5

Propylparaben 181 95 26 21.5

Propylparaben 181 121 29.5 21.5

Tocopherol acetate 473 207 28 85

Tocopherol acetate 473 165 55 85

BHT 220 205 23 39

BHT 220 145 40 39

BHA 180 165 21 37

BHA 180 137 33 37

BHA 181 166 22 37

BHA 181 138 32 37

Butylparaben 195 139 15.5 26

Butylparaben 195 121 31 26

Butylparaben 195 95 25.5 26

Benzylparaben 229 91 22 26

Benzylparaben 229 65 50 26

Isopropylparaben 181 139 15.5 21.5

Isopropylparaben 181 121 30 21.5

Isopropylparaben 181 95 25 21.5

Table I: Optimized MS-MS parameters for the determination of standard preservatives

Parameter Optimized Value

Source temperature (°C) 500

Ionization voltage (V) 4500

Ion source (GS1) settings 40

Ion source (GS2) settings 40

Curtain gas settings 40

CAD gas settings 4

Declustering potential (DP) See Table II for individual analytes

Entrance potential (V) 10

Collision energy (CE) See Table II for individual analytes

Collision cell exit potential (V) 2.3


the equation for regression, R2 values, limit

of detection (LOD), and limit of quanti-

fication (LOQ) are listed in Table IV. All

analytes produced linear data plots with

R2 values >0.99. The LOD and LOQ were

determined by first plotting the calibration

curves for each standard preservative. After

they were plotted, the following equation

was used to calculate the LOD:

LOD = (3 × SEINTERCEPT)/S [1]

and LOQ was calculated using the follow-

ing equation:

LOQ = (10 × SEINTERCEPT)/S [2]

The LOD and LOQ values from each

individual preservative resulted in an over-

all LOD range of 0.91–4.19 ppm and an

LOQ range of 3.03–14.00 ppm.

Using the sample preparation procedure

described above, the chromatographic

method was applied to the following cos-

metics and personal care products: founda-

tions, lipstick, deodorant, hand lotion, hand

sanitizer, and toothpaste. Sample prepara-

tion was identical to that used to create the

standard preservative solutions. For example,

the chromatogram of a toothpaste sample

can be seen in Figure 6. Methylparaben

and ethylparaben were both detected in this

sample. Figure 7 shows the chromatogram

from a foundation sample. The preserva-

tive peaks methylparaben, ethylparaben,

and propylparaben as well as the peak for

the internal standard, BHA, can easily be

seen. Although the peaks for BHT and

DL-α-tocopherol acetate cannot be readily

observed on the chromatogram since they

were present in relatively low concentrations,

they were detected and quantified. The per-

cent concentrations for the samples analyzed

are listed in Table V. BHT, methylparaben,

and ethylparaben were detected in most of

the samples. Benzylparaben and butylpara-

ben were not detected in any of the samples

tested. Figure 8 is a bar graph comparing

the relative quantities of the different pre-

servatives in the sample products tested. It

is interesting to note that the deodorant

sample tested and one of the foundation

samples (F4) had relatively larger quantities

of parabens compared to the other products.

Specifically, methylparaben and ethylpara-

ben were present in larger concentrations.

Propylparaben was also detected in the one

foundation sample (F4).

Concentration (µg/mL)

y = 0.1999x + 0.1586R2 = 0.99952

LOD: 1.17 µg/mLLOQ: 3.89 µg/mL

Tota

l avera

ge p

eak r

ati

os

2.50E+01

2.00E+01

1.50E+01

1.00E+01

5.00E+00

0.00E+000 20 40 60 80 100 120

Figure 5: Calibration curve for ethylparaben.

Table III: Retention times for preservatives in the 30.0 ppm mixture solution

Preservative Retention Time (min)

Methylparaben 1.60

Ethylparaben 1.83

Propylparaben 2.21

Butylparaben 2.70

BHA (internal standard) 2.84

Benzylparaben 3.59

BHT 5.37

Tocopherol acetate 6.11

Time (min)

Inte

nsi

ty (

cps)

BuP

BzP

BHT

6.11

Toco

PrP

EtP

MeP

8.0e4

7.5e4

7.0e4

6.5e4

6.0e4

5.5e4

5.0e4

4.5e4

4.0e4

3.5e4

3.0e7

2.5e4

2.0e4

1.5e4

1.0e4

5000.0

0.0

8.5e4

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5

Figure 4: Chromatogram of standard preservative mixture (methylparaben = MeP; ethylparaben = EtP; propylparaben = PrP; butylparaben = BuP; benzylparaben = BzP; butylated hydroxytoluene = BHT; tocopherol acetate = Toco).


It was noticed that some of the prod-

ucts analyzed contained peaks belonging

to preservatives that were not listed on the

product label ingredient list. For example,

in the foundation sample (Figure 7) the

chromatogram shows peaks consistent

with methylparaben, ethylparaben, and

propylparaben; however, these preserva-

tives were not listed on the ingredient

list of the product label. The analysis of

the hand lotion sample also resulted in

an ethylparaben peak, but ethylparaben

is not listed on the product label ingredi-

ent list. Similarly, the toothpaste sample

contained methylparaben and ethylpara-

ben peaks yet they were not listed on the

product ingredient list. Unfortunately, the

threshold values are unclear for preserva-

tives in cosmetics and personal care prod-

ucts sold in the United States. Because of

this, the FDA may not require companies

to list certain preservatives if they fall

below a certain cutoff value. However,

the European Union has a set maximum

concentration of preservatives allowed in

cosmetics, as follows: 0.4% one single

ester, 0.8% ester mixtures of parabens,

0.5% benzoic-salicylic acid, 0.6% sorbic

acid, and 1.0% phenoxyethanol (15).

Conclusion

An LC–ESI-MS-MS method has been

developed to determine multiple pre-

servatives in cosmetic and personal care

products. The sample preparation is short

and simple and when combined with the

optimal chromatographic conditions, the

method allows for a quick analysis time.

In under 8 min, the developed method is

capable of separating and identifying eight

preservatives (including five parabens) in a

100-mg sample of cosmetic and personal

care product with an LOD ranging from

0.91 to 4.19 µg/mL and an LOQ ranging

from 3.03 to 14.00 µg/mL. Compared

to other literary references, this method

combines a simple and cheap sample

preparation procedure along with a short

analysis time while providing similar if not

improved separation and sensitivity.

Acknowledgments

This research was supported by the Foren-

sic Science Program in the Chemical and

Physical Sciences Department of Cedar

Crest College and the 2014 Carol DeFor-

est Research Grant, Northeastern Associa-

tion of Forensic Scientists.

Time (min)

Inte

nsi

ty (

cps)

5180

5000

4800

4600

4400

4200

4000

3800

3600

3400

3200

3000

2800

2400

2200

2000

1800

1600

1400

1200

1000

800

600

400

200

00.5 1.5 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0

6.22

6.5 7.0

MP

EP

BHA(internal standard)

Figure 6: Chromatogram of toothpaste sample. Preservative peaks (left to right): methyl-paraben, ethylparaben, and BHA (internal standard).

Table V: Product analysis

Compound Concentration of Preservative in Sample (% [w/w])

Product Tocopherol EtP MeP BHT PrP BuP BzP

Deodorant 0.00 0.35 1.60 0.07 0.00 0.00 0.00

Foundation (F1) 0.06 0.04 0.00 0.00 0.00 0.00 0.00

Foundation (F4) 0.01 1.04 0.55 0.01 0.51 0.00 0.00

Toothpaste 0.00 0.04 0.14 0.02 0.00 0.00 0.00

Hand sanitizer 0.02 0.00 0.00 0.02 0.00 0.00 0.00

Lipstick 0.03 0.00 0.03 0.06 0.00 0.00 0.00

Hand lotion 0.00 0.03 0.05 0.02 0.00 0.00 0.00

Table IV: Figures of merit determined from calibration curves

Compound IonEquation for Regression

R2 LOD (µg/mL)

LOQ (µg/mL)

LDR (µg/mL)

Butyl 139 y = 0.3499x + 0.3565 0.9993 1.37 4.56 4.56–100

Butyl 95 y = 0.1625x + 0.1932 0.9988 1.84 6.15 6.15–100

Benzyl 91 y = 0.3417x + 0.6639 0.9975 2.69 8.96 8.96–100

Benzyl 65 y = 0.06x + 0.1105 0.9972 2.84 9.45 9.45–100

Propyl 139 y = 0.2983x + 0.088 0.9995 1.17 3.91 3.91–100

Propyl 95 y = 0.1584x + 0.0877 0.9995 1.14 3.79 3.79–100

Ethyl 139 y = 0.1999x + 0.1586 0.9995 1.17 3.89 3.89–100

Ethyl 95 y = 0.138x + 0.1674 0.9985 2.06 6.87 6.87–100

Methyl 121 y = 0.0488x + 0.061 0.9986 1.99 6.65 6.65–100

Methyl 65 y = 0.0193x + 0.0.0447 0.9977 2.55 8.50 8.50–100

BHT 205 y = 0.0053x + 0.0024 0.9943 4.02 13.40 13.40–100

BHT 145 y = 0.0014x + 0.0137 0.9939 4.19 14.00 14.00–100

Toco 207 y = 0.3161x + 0.5548 0.9997 0.91 3.03 3.03–100

Toco 165 y = 0.215x + 0.4146 0.9995 1.21 4.02 4.02–100


References

(1) http://www.cosmeticsinfo.org/HBI/6 (last

accessed January 8, 2014).

(2) M. Soni, I. Carabin, and G. Burdock, Food

Chem. Toxicol. 43, 985 (2005).

(3) I Branowska, I. Wojciechowska, N. Solarz, and

E. Krutysza, J. Chromatogr. Sci. 52, 88–94 (2014).

(4) Q. Zhang, M. Lian, L. Liu, and H. Cui,

Anal. Chim. Acta 537, 31–39 (2005).

(5) J. Byford, L. Shaw, M. Drew, G. Pope, M.

Sauer, and P. Darbre, J. Steroid Biochem.

Mol. Biol. 80, 49 (2002).

(6) P. Darbre, J. Byford, L. Shaw, R. Horton, G. Pope,

and M. Sauer, J. Appl. Toxicol. 22, 219 (2002).

(7) P. Darbre, J. Byford, L.Shaw, S. Hall, N.

Coldham, and M. Sauer, J. Appl. Toxicol.

23, 43 (2003).

(8) P. Darebre, A. Aljarrah, W. Miller, N. Cold-

ham, M. Sauer, and G. Pope, J. Appl. Toxi-

col. 24, 5 (2004).

(9) S. Oishi, Toxicol . Ind. Health 17, 31

(2001).

(10) S. Oishi, Arch. Toxicol. 76, 423 (2002).

(11) D. Oishi, Food Chem. Toxicol. 40, 1807

(2002).

(12) X.Q. Li et al., Anal. Chim. Acta 608, 165–

177 (2008).

(13) http://www.truthinaging.com/ingredients/

ethylparaben-2 (last accessed January 6,

2014).

(14) P. Perez-Lozano, E. Garcia-Montoya, A.

Orriols, M. Minarro, J.R. Tico, and J.M.

Sune Negre, J. of Pharmaceutical and

Biomed. Anal. 39, 920–927 (2005).

(15) A. Aoyama, T. Doi, T. Tagami, and

K.J. Kajimura, Chromatogr. Sci. 51, 1–6

(2013).

(16) T. Wu, C. Wang, X. Wang, and Q. Ma, J.

Cosmetic Sci. 30, 367–372 (2008).

(17) M. Pedrouzo, F. Borrull R.M. Marce, and

E. Pocurull, J. Chromatogr. A 1216, 6994–

7000, (2009).

(18) A.M.C. Ferreira, M. Moder, and M.E.F.

Laepada, J. Chromatogr. A 1218, 3837–

3844 (2011).

(19) M. Abbasghorbani, A. Attaran, and M.

Payehghadr, J. Sep. Sci. 36, 311–319

(2013).

(20) H. Wei, J. Yang, H. Zhang, and Y. Shi, J.

Sep Sci. 37, 2349–2356 (2014).

(21) G. Alvarez-Rivera, M. Vila, M. Lores, C.

Garcia-Jares, and M. Llompart, J. Chro-

matogr. A 1339, 13–25 (2014).

(22) F. Han, Y.Z He, and C.Z. Yu, Talanta 74,

1371–1377 (2008).

Emily A. Myers, Thomas H. Pritchett, and Thomas A. Brettell are with the Forensic Science Program in the Department of Chemistry and Physical Sciences at Cedar Crest College in Allentown, Pennsylvania. Direct correspondence to: [email protected] ◾

BzP

BuP

PrP

BHT

MeP

EtP

Tocopherol

Co

nce

ntr

ati

on

(%

w/w

)

2.50%

2.00%

1.50%

1.00%

0.50%

0.00%

Deo

dorant

Foundat

ion (F

1)

Foundat

ion (F

4)

Tooth

paste

Han

d saniti

zer

Lipst

ick

Han

d lotio

n

Figure 8: Relative quantities of preservatives in cosmetic and personal care products analyzed.

MP

PP

EP

BHA(internal standard)

Time (min)

0.5 1.5 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0

6.21 6.55 6.74

6.5 7.0

Inte

nsi

ty (

cps)

7.0e4

6.5e4

6.0e4

5.5e4

5.0e4

4.5e4

4.0e4

3.5e4

3.0e4

2.5e4

2.0e4

1.5e4

1.0e4

5000.0

0.0

Figure 7: Chromatogram of foundation sample (F4). Preservative peaks (left to right): methylparaben, ethylparaben, propylparaben, and BHA (internal standard).


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mA

U

0 5 10 min

0

50

100

0

5000

10000

15000

20000

25000

0.4 0.9 1.4 1.9 2.4

Flow Rate, mL/min

Pla

te C

ount, N

( 3)

0.0

0.5

1.0

1.5

2.0

2.5

0.4 0.9 1.4 1.9 2.4

Flow Rate, mL/min

Resolu

tion, R

s(3

/2)

O

COCH2CH2CH2CH3HO

COCHHO

O

CH3

CH3HO

O

COCH2CH2CH3

O

HN

NH

O

150

butylparaben

isopropylparaben propylparabenuracil

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

0.4 0.9 1.4 1.9 2.4

Flow Rate, mL/min

Pre

ssure

(P

, M

Pa)

Pressure

Plate Count

Resolution

1.7um ODS

100 x 4.6 mm

1 mL/min

3um Cadenza CD-C18 HT

150 x 4.6 mm

1.5 mL/min

Rs(3/2) = 2.0

N(3) = 22000

P = 13 MPa

Rs(3/2) = 1.8

N(3) = 18600

P = 18 MPa


Vis

cosi

ty (

cP)

0.6

0.5

0.4

0.3

0.1

0

0.2

0 0.2 0.4 0.6 0.8 1

Carbon dioxide mole fraction

Susan V. Olesik

Enhanced-Fluidity Liquid Chromatography: Connecting the Dots Between Supercritical Fluid Chromatography, Conventional Subcritical Fluid Chromatography, and HPLC

Enhanced-fluidity liquids are organic solvents or organic–aqueous

solvents mixed with high proportions of liquefied gases, such as

carbon dioxide. These subcritical solvents share the positive attributes

of supercritical fluids (fast diffusion rates and low viscosities) and the

positive attributes of commonly used liquids (high solvent strength).

These solvent properties provide enhanced efficiency for reversed-

phase, conventional normal-phase, hydrophilic-interaction, and size-

exclusion chromatography. The capabilities of enhanced-fluidity liquid

chromatography for highly polar compounds is described and the value of

using the entire continuum of 0–100% carbon dioxide solvent systems is

discussed in terms of changes in mobile-phase properties and applications.

Enhanced-f luidity liquids (EFL)

are mixtures of conventional

l iquids to which dissolved

gases, such as carbon dioxide, are

added. Our group coined this term

in 1991 to explain the fact that these

solvents possess f luidity (inverse of

viscosity) that is markedly higher

than typical liquids (1). This was at

a time when supercritical f luid chro-

matography (SFC) was gaining inter-

est. These mixtures are described as

gas expanded liquids (GXLs) in the

chemical engineering literature (2),

but both terms are merely descriptive

of the physical phenomena involved

in producing the mixture. The addi-

tion of a liquefied gas to a conven-

tional liquid causes considerable vol-

ume expansion of the mixture, and

the f luidity of mixture increases sub-

stantially. Today, much of the work

in SFC is actually performed under

subcritical f luid conditions, meaning

the separations are being performed

below the critical point of the solvent.

Enhanced-f luidity liquids are indeed

subcritical solvents, but enhanced-f lu-

idity liquid chromatography (EFLC)

uses a smaller proportion of liquefied

gas in the mobile phase than in typi-

cal subcritical f luid chromatography,

which is 0Ð50% organic modifier. In

EFLC, conditions from 100% to 50%

conventional liquid combined with a

liquefied gas are used in the mixtures.

Therefore, by combining conventional

subcritical f luid chromatography with

EFLC, the entire solvent range of

0Ð100% organic solvent is spanned.

This solvent range is highly useful for

chromatographic applications. Like

in SFC, EFLC typically uses carbon

dioxide as the liquefied gas, but other

liquefied gases have also been evalu-

ated, such as f luoroform (3,4).

Optimization of

Chromatography

In high performance liquid chroma-

tography (HPLC), the fastest separa-

tions are achieved when working at

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the highest possible pressure of the

chromatographic instrument. Using

reduced (dimensionless) plate height,

h, and reduced velocity, ν, as defined

in equations 1 and 2, the Knox-Sal-

eem equation (equation 3) illustrates a

linear relationship between retention

separation time, to, and viscosity,

h = H/dp [1]

ν = udp/Dm [2]

to = Φh2

minN2

reqη/ΔP2

[3]

N/tR = Dm/d2

p(ν/h)(1/1 + k) [4]

where H is plate height, u is linear

velocity, dp is the particle size of the

packing, Dm is the diffusion coef-

f icient of the analyte, Nreq is the

required chromatographic eff iciency

for a given separation, to is the reten-

tion time (seconds), Φ is the dimen-

sionless f low resistance parameter, η

is the viscosity of the mobile phase,

ΔP is the pressure drop across the

column, and tR is the retention time

(5). Equation 3 assumes an optimized

chromatographic system in terms

of column length, particle size, and

f low rate. Assuming maintenance

of operating conditions near these

conditions, a substantial decrease in

time of analysis is observed by lower-

ing the viscosity of the mobile phase.

Finally, Guiochon and others illus-

trated that the separation power, N/

tR (6,7) can be increased by increasing

the mobile-phase diffusion coefficient

assuming the other parameters do not

vary much and the entire separation is

functioning at the optimum reduced

velocity where k is the retention factor.

N/tR = Dm/d2

p(ν/h)(1/1 + k) [4]

Properties of EFL Mixtures

Solvent Strength and

Dielectric Constant

An interesting attribute of enhanced-

f luidity liquid mixtures is that as much

as 60 wt% liquefied gas can be added

before considerable loss of solvent

strength occurs (1,4,8). Mixtures of

alcohols and carbon dioxide are robust

in their solvent strength, particularly

methanol–carbon dioxide mixtures.

Aida and Inomato (9) studied the

molecular structure of methanol-car-

bon dioxide mixtures using molecular

dynamics (MD) simulations. Their

study illustrated that up to 50 wt% car-

bon dioxide can be added to methanol

before impacting the hydrogen bond

(H-bond) network. However, below

1.00 0.65

0.55

0.45

0.35

0.25

0.15

0.80

0.60

0.40

0.20

0.000.00 0.20 0.40 0.60 0.80 1.00


∏*, α

β

20 40 60 80 100 120 140

Temperature (oC)

D1

2 (

cm2/s

)

0.25

0.3

0.4

0.25

0.2

0

0.20

0.15

0.05

0.00

Mole fraction

carbon dioxide

Figure 1: Variation of Kamlet-Taft solvatochromic parameters for methanol–water–carbon dioxide mixtures as a function of added carbon dioxide with the mole ratio of methanol–water held at 2.3 at 25 °C and 172 bar (⦁ = A, ♦ =E, ◾ = π*). Data adapted from reference 11.

Figure 2: Variation of the diffusion coefficient of benzene at 138 bar in 0.70:0.30 methanol–water (+), 0.56:0.24:0.20 (▲), 0.52:0.23:0.25 (⦁), 0.49:0.21:0.30 (♦), 0.42:0.18:0.40 (◾) methanol–water–carbon dioxide. Data adapted from reference 13.

APRIL 2015 aDvances in Hplc sYstems tecHnoloGY 27www.chromatographyonline.com

that percentage the H-bond network

degraded. Their data and the solvent

strength measurements correlate well.

The dielectric constant of methanol-

carbon dioxide mixtures (50 °C and

110 bar) decrease linearly with added

carbon dioxide until 0.45 mole fraction

methanol is added. Further addition of

carbon dioxide to methanol decreases

the dielectric constant, but at a slower

rate. The dielectric constant with 0.60

mole fraction methanol was nearly half

that of pure methanol (10).

Figure 1 shows the variation of sol-

vent strength when carbon dioxide

is added to a 0.70:0.30 mole ratio

methanol-water mixture at 25 °C and

172 bar (11). Similarly, carbon dioxide

can be added to other solvent systems

with minimal loss in solvent strength.

However, the unique attribute of these

data is that the hydrogen bond basic-

ity of the ternary mixtures appears to

increase with added carbon dioxide.

Buffers

When high proportions of carbon

dioxide are added to liquids, the sol-

vents become weakly acidic unless

other additives are included to con-

trol the pH. Buffers can be readily

produced in these mixtures. We pre-

viously illustrated that buffers could

be produced in methanol–water–car-

bon dioxide mixtures with pH values

from 2.2 to 6.8 (12). The addition of

carbon dioxide to methanol–water

mixtures with a mole ratio of 69:31

produces a buffer with pH varying

from 4.54 to 4.73 depending on the

proportion of carbon dioxide added.

The formation of carbonic acid and

the presence of dissolved carbon

dioxide provides the buffering com-

ponents of this system. This buffer

is very interesting because nonvola-

tile buffer additives are not neces-

sary. The other interesting aspect of

this study was that for the metha-

nol–water–carbon dioxide mixtures

studied, increasing the pressure from

120 to 207 bar did not significantly

impact the measured pH.

Diffusion Coefficients

Chromatographic band dispersion in

liquid chromatography is typically

highly inf luenced by the resistance

to mass transfer between the mobile

phase and the stationary phase, which

is inversely proportional to the diffu-

sion coefficient of the mobile phase.

Therefore, for low band dispersion,

high diffusion coefficients are desired.

Diffusion coefficients of solutes in a

number of enhanced f luidity liquids

were measured. For the methanol–

carbon dioxide mixtures at 25 °C and

172 bar, the diffusion coeff icient of

benzene increases by approximately

75% by adding carbon dioxide up to

50 mol% (1). With increasing carbon

dioxide above 50 mol% the diffusion

coefficients increase at a faster rate up

to that of pure carbon dioxide.

The variation of solute diffusion

coeff icients in methanol–water–car-

bon dioxide mixtures are also nonideal

(13). Temperatures in excess of 60 °C

were needed to increase the diffusion

coeff icient of benzene to the same

value as the addition of 0.30 mole frac-

tion carbon dioxide (Figure 2). How-

ever, by increasing the temperature and

adding carbon dioxide to this metha-

nol–water mixture, the greatest benefit

was observed. For a 0.70:0.30 mole

ratio methanol–water mixture, the

addition of 0.30 mole fraction carbon

dioxide and an increase in temperature

to 58 °C caused a ninefold increase in

the diffusion coefficient of benzene.

Viscosity

Foster’s group studied the change in

viscosity in both methanol–carbon

dioxide and ethanol–carbon dioxide

mixtures. For methanol–carbon diox-

ide mixtures for pressures ranging

from 12 bar to 78 bar and tempera-

tures varying from 25 °C to 40 °C, the

viscosity of the EFL decreased linearly

to approximately 50% of the original

viscosity from 0 to 50 mol% carbon

dioxide (14). The viscosity continued

to decrease with further addition of

carbon dioxide, but not at the same

rate. This change occurred at 50 mol%

carbon dioxide for the entire tempera-

ture range. Ethanol–carbon dioxide

mixtures were different; the viscosity

decreased substantially with added

carbon dioxide (15). However, the

composition where the rate of change

slowed varied with temperature. At

25 °C the reduction was linear up to

0.70 mole fraction carbon dioxide. For

30 °C, 35 °C, and 40 °C the rate of

viscosity reduction slowed at 0.60, 0.45,

and 0.25 mole fraction carbon dioxide,

respectively. These data clearly show

that the addition of carbon dioxide to

conventional solvents will substantially

impact the separation time.

Instrumentation

The instrumentation necessary to accom-

plish enhanced-fluidity chromatography

Vis

cosi

ty (

cP)

0.6

0.5

0.4

0.3

0.1

0

0.2

0 0.2 0.4 0.6 0.8 1


Figure 3: Variation of viscosity of a methanol–carbon dioxide mixture as a func-tion of added carbon dioxide at 25 °C (red curve, pressure increased from 0 bar to 56.7 bar with increasing carbon dioxide) and 40 °C (blue curve, pressure increased from 1 bar to 76.7 bar with increasing carbon dioxide). Data adapted from reference 14.


is the same as that used in SFC with

the condition that the software must

allow use of more than 50% modifier.

Alternatively, a conventional HPLC

system can be used to deliver the

organic solvents and a carbon diox-

ide compatible pump and mixer can

be added at the outlet of the HPLC

pump to allow for the use of carbon

dioxide in the mobile phase. With this

setup, a restrictor (small diameter tub-

ing) must be added at the exit of the

detector to control the pressure and

f low rate of the mobile phase. For

example, Sandra (16) used an Agilent

1200 HPLC system combined with

a separate carbon dioxide pump and

mixer at the outlet of the Agilent 1200

HPLC pumps. A stainless steel f low

restrictor (4 m × 0.12 mm i.d.) and a

needle valve were placed at the detec-

tor outlet to control the f low.

Commercial SFC instrumentation

can be used under subcritical condi-

tions all the way to EFLC conditions

as long as the pressure limit of the

instrument or the software for the

solvent programming doesn’t limit

the mobile-phase conditions. (Note:

These instruments use back-pressure

regulators to control f low instead

of f ixed restrictors, as an example,

an Agilent 1260 system can be used

without modification for EFLC sepa-

rations.)

Previous EFLC and a Range of

Chromatographic Mechanisms

EFLC has been used effectively for

reversed-phase, normal-phase, chiral,

and size-exclusion modes of chroma-

tography (17). In reversed-phase chro-

matography previous work has shown

the separation time can be reduced by

nearly half by adding carbon dioxide.

Using normal-phase conditions for

gradient polymer elution chromatog-

raphy, Kawai also showed that the use

of carbon dioxide in the mobile phase

provided the highest resolution for

poly(styrene-co-methyl acrylate) mix-

tures compared to the use of conven-

tional liquids (30). In addition, chiral

separations are typically faster and

more efficient than in HPLC or SFC

(17). As summarized by Guiochon

and Tarafder (18), higher mobile-

phase velocities, longer columns, and

finer particles can be used with EFLC

compared to HPLC with conventional

solvents. These attributes are shared

with high-temperature HPLC and

ultrahigh-pressure liquid chromatog-

raphy (UHPLC). The higher diffusion

coeff icients cause higher optimum

mobile-phase velocities compared to

HPLC and lower resistance to mass

transfer, which increases the overall

efficiency.

Separation of Polar

Compounds — Subcritical

Conditions Including

EFLC Conditions

There continues to be considerable

interest in providing fast and efficient

separations for highly polar com-

pounds. Taylor and coworkers (19)

have been studying the use of subcriti-

cal chromatography for the separation

of polar compounds with encouraging

results. For example, Zheng and col-

leagues (19) described the separation

of polypeptides up to 40-mers using a

2-ethylpyridine bonded silica station-

ary phase and carbon dioxide–metha-

nol mobile phases with trif luoroacetic

acid as an additive in the methanol

to suppress the deprotonation of car-

boxylic acid groups and protonate

the peptide amino acid groups on the

protein. A 5–50% methanol gradient

was used. Electrospray mass spectra

with high signal-to-noise ratios were

obtained with this technique and a

lower separation time was achieved as

well when comparing the optimized

protein separations under SFC and

HPLC conditions. Ashraf-Khorassani

and Taylor (20) also showed that alco-

hol–carbon dioxide mixtures with up

to 5% water using gradient conditions

Nu

mb

er

of

pla

tes/

m

Nu

mb

er

of

pla

tes/

m

AMP UMP CMP GMP ADP UDP CDP GDP

AMP UMP CMP GMP ADP UDP CDP

12,000 2500

2000

1500

1000

500

0

10,000

8000

6000

4000

2000

10,000

9000

1200

1000

800

600

400

200

0

8000

7000

6000

5000

4000

3000

2000

1000

0

0

Figure 4: Effect of different bases on the effciency of the nucleotides: (a) in LC and (b) with 0.1 mole fraction carbon dioxide added. Conditions: 5 mM of each base was added to a 75 mM ammonium phosphate solution, which was used to prepare the 90:10 (v/v) methanol–aqueous mobile phase. The y-axis on the left corresponds to the effciency values for the monophosphate compounds, the axis on the right corresponds to the values for the diphosphate compounds. No base (◾), TEA (◾), DABCO (◾), and DBN (◾). Data adapted from reference 24.

APRIL 2015 aDvances in Hplc sYstems tecHnoloGY 29www.chromatographyonline.com

up to 50% alcohol were effective in

separating the nucleobases, thymine,

uracil, adenine, and cytosine.

Guillarme and coworkers (21) illus-

trated the value of subcritical f luid

chromatography using a methanol–

carbon dioxide mixture with 2–40%

methanol at 40 °C and 150 bar with

a 2-ethylpyridine column (100 mm

× 3.0 mm, 1.7-µm dp Acquity UPC2

BEH-2-EP, Waters) or an Acquity

BEH Shield RP18 hybrid column (50

mm× 2.1 mm, 1.7-µm dp, Waters)

using 20 mM ammonium hydrox-

ide in the mobile phase at a f low

rate of 1.5 mL/min for the separa-

tion of highly polar compounds such

as nucleobases and carbohydrates. A

comparison of column types showed

that gradient subcritical f luid chroma-

tography could separate well at least

70% of the compounds studied with

enhanced mass spectral sensitivity.

For chiral separations, Armstrong

(22) evaluated subcritical conditions

for chiral separations using macrocy-

clic glycopeptides. For the separation

of polar analytes such as native amino

acids using macrocyclic glycopeptides

stationary phases, a combination of

both acidic and basic modifiers and

subcritical conditions with 48% to

nearly 70% methanol were quite effec-

tive in providing excellent separations.

Sandra’s group (16) was the f irst

to study the possibility of using

EFLC conditions for hydrophilic-

interaction chromatography (HILIC).

In this study, using a silica column

(250 mm× 4.6 mm, 5-µm dp Zor-

bax Rx—SIL, Agilent Technologies)

the five nucleobases, thymine, uracil,

cytosine, guanine, and adenine were

separated using 95:5 (v/v) ethanol–20

mM ammonium formate buffer at pH

3.0 combined with carbon dioxide.

Increasing amounts of carbon diox-

ide increased the elution window and

the separations were similar to those

obtaining using acetonitrile–water

mobile phases. Plate counts of 16,200

and 19,000 were obtained for 3 mL/

min and 0.9 mL/min conditions.

Our group studied EFLC-HILIC

separation of RNA nucleosides (ade-

nosine [A], uridine [U], cytidine

[C], and guanosine [G]) using EFLC

mobile phases under isocratic condi-

tions (23). Using a 150 mm × 4.6

mm, 3-µm dp Tosoh amide column

and a 90:10 methanol–20 mM acetate

buffer mobile phase, the addition of

increasing proportions of carbon diox-

ide caused increased retention with a

slight increase in band dispersion for

the chromatographic bands. For exam-

ple, the addition of 0.2 mole fraction

carbon dioxide causes changes in the

retention factor for A, C, U, and G of

72%, 66%, 97%, and 138%, respec-

tively. With 20 mol% carbon dioxide,

a separation time of 15 min was pos-

sible with resolution values >4 for all

pairs. Alternatively, under optimized

HPLC conditions with the same col-

umn using a 90:10 acetonitrile–20

mM acetate buffer, a complete sepa-

ration was achieved in approximately

50 min.

To attempt separations of even more

polar compounds, the separation of

mono-, di-, and triphosphorylated

nucleosides were studied (24). These

compounds are typically separated

using aff inity chromatography, ion-

exchange, or electrophoresis (25–27).

A 150 mm × 4.6 mm, 3.5-µm dp

Waters X-bridge amide column was

used with 90:10 methanol–aqueous

mobile phases buffered with ammo-

nium phosphate. Often, phosphory-

lated compounds have poor peak

shapes because of strong tailing in

a broad range of chromatographic

retention mechanisms using silica as a

support. Phosphorylated compounds

are strong hydrogen bond bases that

interact with free silanols on a silica

column through very strong hydrogen

bonding interactions (28). The slow

kinetics of desorption are the likely

cause of the peak asymmetry. Strong

bases such as triethylamine (TEA)

are often added to the mobile phase

to compete with the interactions with

the free silanols (29). Two bases that

are widely used in organic synthesis

and described as superbases because of

their strong basicity in both aqueous

and organic solvents were also consid-

ered and compared to TEA: 1,4-diaz-

abicyclo [2.2.2] octane (DABCO) and

1,5- diazabicyclo [4.3.0] non-5-ene

(DBN).

Figure 4 compares the eff iciency

and the peak asymmetry for the

Time (min)

(a)

(b)

(c)

(d)

5+1

4+6

4+5

7+6+3

2 4

4

6

6

5

5

2

2

1

1

1

3

3

32

6

8

7

7

7

8

8

3.5 5.5 7.5 9.5 11.5 13.5 15.5 17.5 19.5 21.5

8

Figure 5: Separation of various nucleosides and nucleotides: (a) LC, (b) 0.15 mole fraction of carbon dioxide, (c) 0.20 mole fraction of carbon dioxide, and (d) 0.25 mole fraction of carbon dioxide. Mobile phase: 90:10 methanol–75 mM ammonium phosphate + 5 mM DBN. Peaks: 1 = adenosine, 2 = cytidine, 3 = uri-dine, 4 = guanosine, 5 = AMP, 6 = CMP, 7 = UMP, 8 = GMP. Data adapted from reference 24.


mono- and diphosphorylated nucleo-

sides obtained with TEA, DABCO,

and DBN in a mobile phase that con-

tained ammonium dihydrogen phos-

phate buffer under LC conditions

(Figure 4a) and with the addition

of 0.1 mole fraction carbon dioxide

to the mobile phase with a mobile-

phase velocity of 1 mL/min. When

compared to the data where no base

was added, DBN provides the best

results with a 12% to 18% increase

in eff iciency in LC for monophos-

phate nucleotides and 111% to 320%

for the diphosphates. In EFLC, the

eff iciency increased from 9% to 39%

for monophosphates, and from 67%

to 115% for diphosphates. For both

HPLC and EFLC, tailing was the

major contributor to low eff iciency

for the phosphorylated compounds;

the impact of base addition on the

asymmetry of the peak was studied

to better understand the impact of

each base on eff iciency. DBN pro-

vided both the greatest decrease in

peak asymmetry and increase in eff i-

ciency, followed by DABCO. Both

bases great ly improved the peak

shape while marginally affecting the

retention of the analytes; no signif i-

cant decrease in k was observed in

EFLC, and an average 10% decrease

in k was observed in LC. TEA either

increased the peak asymmetry or did

not impact it significantly.

Similar to other HILIC separations

of polar compounds, the addition of

a salt to the mobile phase was quite

important. Sodium chloride (0.02

M) was used to assist the formation

of the adsorbed water phase, and 75

mM ammonium phosphate was used

to decrease the retention of the phos-

phate groups. Figure 5 shows that

carbon dioxide expands the elution

window for the compounds.

Summary

Now that SFC chromatographic

instruments have a much larger range

of operating pressures with accompa-

nying precise temperature control,

it is time to consider using lique-

fied gases such as carbon dioxide in

mobile phases for a broader range of

chromatographies. The lower viscos-

ity of mobile phase and increased

dif fusivity for analyte separations

using 0–100% liquefied gas provides

decreased analysis time and often

improved eff iciency. The entire range

of this solvent continuum is valuable

for separation science. A continuous

change in viscosity, solvent strength,

diffusivity, and permittivity occurs

across this range of mobile-phase

compositions. In particular, EFLC

mobile phases are a lso providing

value for the separation of highly

polar compounds with similar oper-

ating parameters as in conventional

HPLC. While advantages in perfor-

mance are often noted when using

acetonitrile–water for reversed-phase

HPLC and HILIC compared to alco-

hol–water mixtures, the addition of

carbon dioxide to the alcohol–water

mixtures improves the chromato-

graphic performance signif icantly.

A lso, both a lcohol–carbon dioxide

and a lcohol–water–carbon dioxide

are environmentally friendly.

References

(1) Y. Cui and S.V. Olesik, Anal. Chem. 63,

1813–1819 (1991).

(2) C.J. Chang and A.D. Randolph, AIChE J.

36, 939–942 (1990).

(3) H. Yuan and S.V. Olesik, Anal. Chem. 70,

1595–1603 (1998).

(4) J. Zhao and S.V. Olesik, J. Chromatogr. A

923, 107–117 (2001).

(5) H. Chen and Cs. Horváth, J. Chromatogr.

A 705, 3–20 (1995).

(6) G. Guiochon, Optimization in Liquid

Chromatography in High Performance

Liquid Chromatography: Advances and

Perspectives, Vol . 2, C. Horvath, Ed.

(Academic Press, New York, 1980), pp.

1–56.

(7) F. Erni, J. Chromatogr. 282, 371–382

(1983).

(8) H. Yuan and S.V. Olesik, J. Chromatogr.

Sci. 35, 409–416 (1997).

(9) T. Aida and H. Inomata, Mol. Simulat.

30, 407–412( 2004).

(10) S.B. Lee, R.L. Smith, H. Inomata, and

K. Arai, Rev. Sci. Instr. 72, 4226–4230

(2000).

(11) Y. Cui and S.V. Olesik, J. Chromatogr. A

691, 151–162 (1995).

(12) D. Wen and S.V. Olesik, Anal. Chem. 72,

475–480 (2000).

(13) S.T. Lee and S.V. Olesik, Anal. Chem. 66,

4498–4506 (1994).

(14) R. Sih, F. Dehghani, and N.R. Foster, J.

Supercrit. Fluids 41, 148–157 (2007).

(15) R. Sih, M. Armenti, R. Mammucari, F.

Dehghani, and N.R. Foster, J. Supercrit.

Fluids 43, 460–468 (2008).

(16) A. dos Santos Pereira, AJ. Girón, E.

Admasu, and P. Sandra, J. Sep. Sci. 33,

834–837 (2010).

(17) S.V. Olesik, Adv. Chromatogr. 46, 424–

449 (2008).

(18) G. Guiochon, and A. Tarafder, J. Chro-

matogr. A 1218, 1037–1114 (2011).

(19) J. Zheng, J.D. Pinkston, P.H. Zouten-

dam and L.T. Taylor, Anal. Chem. 78,

1535–1545 (2006).

(20) M. Ashraf-Khorassani and L. Taylor, J.

Sep. Sci. 33, 1682–1690 (2010).

(21) A. Periat, A. Grand-Guillaume, and D.

Guillarme, J. Sep. Sci. 36, 3141–3151

(2013).

(22) Y. Liu, A. Berthod, C.R. Mitchell, T.L.

Xiao, B. Zhang, and D. Armstrong, J.

Chromatogr. A 978, 185–204 (2002).

(23) J.W. Treadway, G.S. Philibert, and S.V.

Olesik, J. Chromatogr. A 1218, 5897–

5902 (2011).

(24) G. Philibert and S.V. Olesik, J. Chro-

matogr. A 1218, 8222–8230 (2011).

(25) M. Hossel, M.G. Corneliu, J. vom

Brocke, and H.H. Schmeiser, Electropho-

resis 31, 299–302 (2010).

(26) N.J. Alves, S.D. Stimple, M.W. Hand-

logten, and J.D. Ashley, Anal. Chem. 84,

7721–7728 (2012).

(27) N. Tomiya, E. Ailor, S.M. Lawrence,

M.J. Betenbaugh, and Y.C. Lee, Anal.

Biochem. 293, 129–137 (2001).

(28) E.D. Davis, W.O. Gordon, A.R. Wilms-

meyer, D. Troya, and J.R. Morris, J. Phys.

Chem. Lett. 5, 1393–1399 (2014).

(29) D.V. McCalley, Adv. Chromatogr. 46,

305–350 (2008).

(30) E. Kawai, K. Shimoyama, K. Ogino, and

H. Sato, J. Chromatogr. A 991, 197–203

(2003).

Susan V. Olesik is the Dow

Professor and Chair in the Department

of Chemistry and Biochemistry at

Ohio State University in Columbus,

Ohio. Direct correspondence

to: [email protected] ◾


please visit



William J. Long, Anne E. Mack, Xiaoli Wang, and William E. Barber

Selectivity and Sensitivity Improvements for Ionizable Analytes Using High-pH-Stable Superficially Porous Particles

The most significant recent advancement in liquid chromatography

(LC) column technology is the new generation of superficially

porous silica particles. While chromatographers enjoy the

ultrahigh efficiency of these particles, they also desire more

selectivity options to facilitate method development. These

can be achieved with different bonded phases and different pH

mobile phases. However, the latter requires particles that can

withstand extremes in pH. Here, we report a novel approach

to enhancing the selectivity of ionizable compounds using

superficially porous particles that are stable in a wider pH range.

High performance liquid chro-

matography (HPLC) method

development for chemical and

pharmaceutica l ana lysis is a cha l-

lenging task. It involves screening a

range of chromatographic parameters

to generate robust separations with

sufficient resolution. While there are

many approaches to method develop-

ment, such as changing one factor

at a time and design of experiments

(DoE), the goals and factors used for

optimizing separations are the same.

They all involve changing columns or

mobile phase to increase the resolu-

tion between the desired analytes and

other compounds.

Resolution is affected by three fac-

tors: eff iciency (N ), retention (k ′),

and selectivity (α), as shown in the

resolution equation:

Rs4

. .=√N

∝

(∝−1) k'

k' +1[1]

It is well known that selectivity is the

most powerful factor that affects res-

olution. Selectivity can be controlled

through several factors including the

choice of stationary phase, the type

of organic modif ier, gradient slope,

f low rate, and temperature. For ion-

izable compounds, pH of the buffer

is a lso a powerful parameter. Opti-

mizing the separation of ionizable

compounds to f ind robust conditions

has become an important part of

method development in liquid chro-

matography (LC).

Most pharmaceutica l and bio-

logical compounds contain ionizable

moieties such as carboxylic or amino

groups. Because retention in reversed-

phase LC is strongly dependent on

the analyte charge, pH can be used to

make large changes in selectivity. At

pH values below their pKa, acids have

their maximum retention because

they are neutral, but bases have their

minimum retention because they

are fully charged. At pH levels more

basic than the pKa of the compound,

bases have their maximum retention

because they are neutral, and acids are

fully ionized and have their minimum

retention. For the best peak shape,

retention and sample loading of basic

analytes in reversed-phase LC, the

mobile-phase pH should be two units


higher than the pKa of the compound

of interest. In this work, the adjust-

ment of pH is used to control selec-

tivity using a high-pH-compatible

superficially porous particle C18 col-

umn that is designed to be stable over

a broad pH range, including high-pH

mobile phases.

Superficially porous particle (SPP)

technology is based on particles with

a solid core and a porous shell. The

most common particles consist of

a 1.7-μm solid core with a 0.5-μm

porous shell. In total, the particle size

is about 2.7 μm. The 2.7-μm SPPs

provide 40–50% lower back pressure

and 80–90% of the efficiency of sub-

2-μm totally porous particles (TPPs)

for small molecule separation. The

SPPs have a narrower particle size dis-

tribution than TPPs. This results in a

more homogeneous column bed and

reduces dispersion in the column. At

the same time, the thin porous shell

gives reduced longitudinal diffusion

and slightly lower resistance to mass

transfer for small molecules. The

result is minimal loss of efficiency at

higher f low rates (1–3).

Until recently, all SPP materials

were silica based and possessed lim-

ited lifetime in higher pH buffers,

including phosphate or bicarbonate

buffers. To achieve longer lifetimes, it

is necessary to protect the base silica

particle by either surface modif ica-

tion or special bonding modification.

The surface of newer high-pH-stable

SPP particles is chemically modi-

fied to form an organic layer that is

resistant to silica dissolution at high

pH conditions. The high-pH-stable

SPP particles are bonded with C18

and endcapped. The lifetime of these

new SPP C18 columns was compared

to the original 100% silica SPP col-

umns as well as another commercially

available column packed with totally

porous silica particles that have a

hybrid surface.

Experimental

A 1260 Infinity Binary LC (Agilent)

was used for this work. It consisted

of a binary pump, an autosampler, a

column thermostat, and a diode-array

detector equipped with a 10-mm

pathlength (1-μL) f low cell. OpenLab

110

100

90

80

Init

ial

eff

cie

ncy

(%

)

70

60

500 1000 2000 3000

Stress buffer (mL)

Silica SPP-C18

High-pH-stableSPP-C18

Figure 1: Lifetime of SPP columns in phosphate buffer, pH 8, at elevated temperature. Mobile phase: premixed 60% 30 mM sodium phosphate buffer at pH 8 and 40% methanol; fow rate 0.4 mL/min; UV absorbance 254 nm; 65 °C; column dimensions: 50 mm × 2.1 mm, 2.7 µm; analyte: naphthalene.

Figure 2: Column stability test under high-pH bicarbonate buffer conditions: (a) 50 mm × 2.1 mm HPH SPP-C18, (b) a commercially available high-pH-compatible 50 mm × 2.1 mm TPP C18. Mobile-phase A: 10 mM ammonium bicarbonate adjusted to pH 10.0 in water; mobile-phase B: acetonitrile; gradient: 5–95% B in 5 min, return to 5% in 1 min, hold 1 min at 5%; fow rate: 0.4 mL/min; detection: UV absorbance at 220 nm; temperature: 40 °C. Peaks: 1 = methyl salicylate, 2 = 4-chlorocinnamic acid, 3 = acetophenone, 4 = quinine, 5 = nortryptyline, 6 = heptanophenone, 7 = amitriptyline.

1

1.5

(a)

2.5 3.5 4.52 3 4

1.5 2.5 3.5 4.52 3 4

1.5 2.5 3.5 4.52 3 4

1.5 2.5 3.5Time (min)

4.52 3 4

23

4

56

7Injection 1

Injection 500

Injection 1000

Injection 2000

1

2 3

45 6

7

Injection 1

1.5 2.5 3.5 4.52 3 4

1.5 2.5 3.5 4.52 3 4

1.5 2.5 3.5 4.52 3 4

1.5 2.5 3.5 4.52 3 4Time (min)

Injection 2000

Injection 1000

Injection 500

(b)


Chromatography Data System (Agi-

lent) was used to control the HPLC

system and process the data. Columns

packed with 2.7-μm Poroshell HPH-

C18 (high-pH-stable SPP), or 2.7 μm

Poroshell EC-C18 (Agilent) in 50 mm

× 2.1 mm, 100 mm × 2.1 mm, or 50

mm × 4.6 mm dimensions were used.

A model 6140 single-quadrupole mass

spectrometer (Agilent) was added

to the instrument configuration to

determine the impact of pH on mass

spectrometry (MS) sensitivity of basic

compounds.

Stability of High-pH-Stable SPP

Column at Mid and High pH

HPLC column stability is a critical

factor impact ing method per for-

mance and has been widely stud-

ied (4,5). Column stability can be

a f fected by temperature, type of

aqueous buffer and their concentra-

tion, choice of organic solvents, addi-

tives, and mobile-phase pH. A column

that is not stable during method develop-

5.52.7-μm high-pH-stableSPP-C18

2.7-μm silica SPP-C185.0

4.5

4.0

3.0

3.5

2.5

1.5

1.0

0.50 2 4 6 8 10

Reduced interstitial linear velocity

Re

du

ced

pla

te h

eig

ht

12 14 16 18 20

2.0

Figure 3: Van Deemter curves of a silica SPP C18 column (50 mm × 4.6 mm) and a high-pH-stable SPP C18 column (HPH SPP-C18, 50 mm × 4.6 mm). Mobile phase: 60:40 acetonitrile–water; temperature: 25 °C; detection: UV absorbance at 254 nm, 80 Hz; analyte: naphthalene.

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30 distinctly different

phase chemistries.

Beyond reproducibility, YMC

delivers innovation … in

hybrid phases, core-shell

technology, chiral separations,

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ment leads to inaccurate results and

frustration. A robust HPLC method

using a durable column leads to suc-

cessful support of new clinical and

manufacturing projects.

Column degradation at elevated

pH and temperatures is caused by

si l ica d issolut ion, bonded-phase

removal, or through the exposure of

silanols through the loss of end cap-

ping (hydrolysis). Both dissolution

and hydrolysis of silica columns are

known to be related to pH and tem-

perature (increased degradation rate

at higher pH or temperatures).

One mobile phase that is f re-

quently used for lifetime testing at

mid-pH levels is pH 7 phosphate

buffer in methanol. However, most

silica columns lose eff iciency after

prolonged exposure to these condi-

tions. Kirkland and colleagues (6)

and Tindall and Perry (7) discussed

possible reasons for the reduced life-

time of silica columns in phosphate

buffer, but both agree that columns

do not last long.

A lthough phosphate buffers are

considered diff icult to use at pH 7

and above, they are commonly used

because of their clean UV baseline

during gradients. In a lifetime test

experiment, 50 mM sodium phos-

phate dibasic and sodium phosphate

monobasic buffer was made at pH

8 and diluted with methanol to a

60:40 buffer–methanol mixture with

a f inal buffer concentration of 30

mM. The column temperature was

raised to 65 °C. By elevating the tem-

perature and using a pH 8 phosphate

buffer, the rate of column degrada-

tion is signif icantly increased. Note

that this test was designed to accel-

erate the degradation of silica-based

columns and is not recommended as

an analytical method condition. A

sample containing naphthalene was

injected every 10 min. This method

was used to compare a standard

silica SPP C18 column and a high-

pH-stable SPP C18 column. The

high-pH-stable SPP particles were

synthesized by chemically modifying

the surface of the silica SPP particles

with an organic layer. As can be seen

in Figure 1, the standard silica SPP

C18 lasted approximately 200 mL

in this mobile phase before 10% of

eff iciency was lost. At 1000 mL, eff i-

ciency was reduced by 40%. When

the high-pH-stable SPP C18 column

was subjected to the same treat-

ment, no degradation was noted at

2000 mL and the column lost 10%

efficiency at 3000 mL.

For practitioners, a good criterion

for column stability under a given

pH is the ability to maintain stability

for ≥500 injections. This allows for

method development, as well as sub-

sequent use of the column with the

established method. We evaluated

the stability of a high-pH-stable SPP

C18 column with a gradient using

ammonium bicarbonate at pH 10 and

acetonitrile. A mixture of acidic, neu-

tral, and basic compounds was used

to probe a variety of possible ionic

and loss of hydrophobic interactions

caused by column degradation. As

can be seen in Figure 2a, the reten-

tion time of all compounds remained

stable throughout the 2000-injection

run with the exception of nortryp-

tyline. This compound, with a pKa

very close to the pH of the mobile

phase, moved slowly to longer reten-

tion times.

A second commercia lly available

column, packed with totally porous

particles designed for elevated pH

stability, was subjected to the same

experimental conditions. Most of the

analytes remained at the same reten-

tion time throughout the 2000 injec-

tions. Nortryptyline moved rapidly

to later elution times. Within 500

injections, nortryptyline began to

be coeluted with the next compound,

neutra l hexanophenone. The nor-

tryptyline peak continued to migrate

through this peak and was totally

coeluted by injection 2000. This

experiment revealed less degradation

of the high-pH-stable SPP C18 col-

umn than the other column. Note

that for both this and the previous

experiment with the high-pH-stable

SPP C18 column, the sample via l

solution was remade several times

during the lifetime study so that rela-

tive peak heights differed somewhat

throughout the study.

Efficiency of a High-pH-

Stable SPP C18 Column

A van Deemter study was done to

ensure that the surface modification

to make the high-pH-stable SPP C18

particles did not negatively impact

the eff iciency of the SPP particles.

To do this, we conducted f low stud-

ies on a high-pH-stable SPP C18

300 2

2

2

3

BasesAcids

Time (min)

3

3

4

5 6

4

4

5

5

6

68

8

7

7

7

8

3.532.51.50.50 1

1

2

3.532.51.50.50 1 2

3.532.51.50.50 1 2

10 mM HCO2NH

4 (pH 3)

10 mM NH4C

2H

3O

2 (pH 4.8)

10 mM NH4HCO

3 (pH 10)

1

1

250200150100

500

300

Ab

sorb

an

ce (

mA

U)

250200150100

500

300250200150100

500

Figure 4: Selectivity control by altering pH. Column: 50 mm × 4.6 mm, 2.7-µm HPH SPP-C18; mobile-phase A: 10 mM ammonium formate (pH 3), ammonium acetate (pH 4.8), or ammonium bicarbonate (pH 10.0) in water; mobile-phase B: acetonitrile, gradient: 10–90% B in 5 min, hold 2 min at 90%; flow rate: 2 mL/min; detection: UV absorbance at 254 nm; temperature: 30 °C. Peaks: 1 = pro-cainamide, 2 = caffeine, 3 = acetyl salicylic acid, 4 = hexanophenone degradant, 5 = dipyrimadole, 6 = diltiazem, 7 = diflunisal, 8 = hexanophenone.


column and a si lica SPP C18 col-

umn. The van Deemter curves are

shown in Figure 3. It is clear that the

two columns have very similar eff i-

ciency throughout the range of f low

rate studied with similar minimum

reduced plate height at the optimum

f low rate. Therefore, the kinetic per-

formance of the high-pH-stable SPP

C18 column is similar to the silica

SPP C18 column. All performance

features of the superf icially porous

particles are retained on the high-

pH-stable SPP C18, while the high-

pH stability is substantially improved.

Effect of Mobile-

Phase pH on Selectivity

With a SPP particle stable in high pH

mobile phases, we studied the effect

of pH on the selectivity of ionizable

analytes. Figure 2 depicts how the

elution order of a mixture consist-

ing of acidic, basic, and neutral com-

pounds changes as pH of the mobile

phase is changed. In this work, a

generic gradient was used with the

organic modif ier (acetonitrile) con-

centration changing from 10% to

90% over 4 min at 2 mL/min. Chro-

matograms at pH 3 (ammonium for-

mate), pH 4.8 (ammonium acetate),

and pH 10 (ammonium bicarbonate)

are shown. These are MS-compatible

buffers. The buffers were prepared by

dissolving suff icient ammonium for-

mate, ammonium acetate, or ammo-

nium bicarbonate in water to produce

10 mM solutions. The solutions were

adjusted to the desired pH with the

appropriate concentrated acid (for-

mic acid or acetic acid) or concen-

trated base (ammonium hydroxide).

The sample mixture included acids

(acetyl salicylic acid and dif lunisal),

bases (procainamide, dipyrimadole,

and dilt iazem), and neutra l com-

pounds (hexanophenone and impu-

rity, and valerophenone). Caffeine

was included in this work but its pKa

is outside of the range of pH studied,

so its ionization state does not change.

The three chromatograms in Figure

2 use the same organic gradient and

column so that hexanophenone (neu-

tral) and caffeine remain at the same

elution time. They are not affected

by the change in pH. As the mobile-

phase pH is increased from pH 3

to pH 4.8, the acidic compounds

become deprotonated (charged) and

their retention time decreases. This is

depicted by the red arrows in Figure

4. As the pH is increased further, the

retention times of the bases increase,

as shown with the blue arrows. The

peak elution order changes dramati-

cally as does the spacing. In all three

chromatograms the peak shape is

excellent. In this series of experi-

ments, the retention times of the

compounds were more evenly spaced

using the pH 10 buffer than either of

the other buffers.

Another way to look at selectivity is

by plotting retention time using two

dif ferent mobile-phase pH condi-

tions for a large group of acids, bases,

and neutral compounds. A list of the

compounds is provided in Table I. In

this case, 117 compounds were run


Table I: Compounds used in retention correlation

Sample Name Sample Name Sample Name Sample Name

1,2-Dimethoxybenzene Atenolol Esterone Procaine

1,2-Dinitrobenzene Atorvastatin Ethinylestradiol Progesterone

1,2,3-Trimethoxybenzene Beta estradiol Ethyl-4-hydroxybenzoate Promazine

1,2,4-Trimethoxybenzene Beclomethasone Fenprofen Propranolol

1,2,5-Trimethoxybenzene Benzocaine Fluoxetine Protriptyline

1,3-Dimethoxybenzene Benzoic acid Furazolidone Pyrimethamine

1,3-Dinitrobenzene Benzophenone Hesperidin Quinine

1,4-Dinitrobenzene Benzyl alcohol Hydrocortisone Resorcinol

2,3-Dimethylphenol Betamethasone Irganox 1330 Salicytic acid

2,4-Dichlorophenol Biphenyl (DMSO) Ketoprofen Salicylic acid

2,4-Dimethyl benzoic acid Butacaine Labetalol Sulfachloropyridazine

2,5-Dihydroxyl benzoic acid Butyl benzene m-Nitrophenol Sulfadiazine

2,5-Dimethyl phenol Butyl paraben Mefamic acid Sulfadimethoxine

2-Hydroxyhippuric acid Butylated hydroxy anisole Naldolol Sulfamerazine

2-Napthalene sulfonic acid Butylated hydroxy toluene Naproxen Sulfamethiazine

3,4-Dimethoxybenzoic acid Butyrophenone Nargingenin Sulfamethiazole

3-Nitrophenol Caffeine Nisoldipin Sulfamethoxazole

4-Hydrobenzaldehyde Catechol Norethindrone acetate Sulfamethoxypyridazine

4-Hydroxybenzoic acid Chloramphenicol Nortryptyline Sulfamonomethoxine

4-Nitrophenol Corticosterone p-Cresol Sulfaquinoxaline

5-Hydroxy-isophthalic acid Desimpramine p-Nitrophenol Sulfathiazole

8-Hydroxyquinoline Dexamethasone Pentachlorophenol Sulindac

Acebutolol Diclofenac Phenacetin Testosterone

Acetylsalicylic acid Diethyl phthalate Phenantranene Tetracaine

Alprenolol Difunisal Pindolol Tolemetin

Amitriptyline Diisopropyl phthalate Piperidine Triamcinalone

Andro Dioctyl phthalate Piroxicam Trimipramine

Antipyrin Dipropyl phthalate Pravastatin Ultranox 276

APAP Doxepim Prednisone Uracil

Valerophenone

using the high-pH-stable SPP C18

column with gradients of methanol

at pH 3 and 10 and acetonitrile at pH

3 and 10. The generic gradient was

0.42 mL/min, starting at 5% organic

and increasing to 95% organic over

4 min, followed by a hold at 95%

organic for 2 min. This methodology

was applied and discussed in previous

work where two highly similar col-

umns (similar phase chemistries on a

silica SPP and silica TPP) were com-

pared under similar chromatographic

conditions (8). The correlation coef-

f icient of retention times is a mea-

sure of the difference in selectivity

under two different pH conditions. A

highly correlated plot would indicate

that the chromatographic separations

are very similar. On the other hand,

a very low correlation value (close to

0.5 or lower) indicates a more orthog-

onal or dissimilar separation.

As can be seen in Figure 5a, the

overall correlation is quite low with

a correlation coefficient of 0.49. This

low correlation indicates that most of


the compounds were retained differently in the two mobile-

phase pHs. The group contains basic compounds that are

charged at pH 3. As they become deprotonated when the

pH is increased to 10, the retention time increases. Like-

wise, the acidic compounds, which are not charged at low

pH, become deprotonated (charged) as the pH is increased

and lose retention. It is easy to see a subgroup of com-

pounds that line up perfectly with a slope of 1. These are

neutrals or compounds that do not change ionization state

and their retention times are not affected by the pH of the

mobile phase, as expected. A second comparison is also

shown in Figure 5b, using low and high pH gradients with

acetonitrile as the organic modifier. In this case, the cor-

relation coefficient was slightly smaller than that in metha-

nol, but still indicates very different selectivities for acids

and bases at different pHs (9–11).

Improved LC–MS Sensitivity

for Basic Compounds at High pH

In another experiment, LC–MS of several bases was com-

pared at high and low pH using a generic gradient in posi-

tive ion mode electrospray ionization. Normally one expects

Figure 5: Selectivity comparison at low and high pH with (a) methanol and (b) acetonitrile as mobile-phase B. Col-umn: 50 mm × 2.1 mm, 2.7-µm HPH SPP-C18; mobile phase A: 10 mM ammonium formate (pH 3) or 10 mM ammonium bicarbonate (pH 10) in water; mobile-phase B: methanol or acetonitrile; gradient: 5–95% B in 4 min, hold at 95% for 2 min; flow rate: 0.42 mL/min; detection: UV absorbance at 220 nm; temperature: 30 °C.

7(a)

6

5

4

3

2

1

00 1 2 3

Retention time pH 3 methanol

Re

ten

tio

n t

ime

pH

10

me

tha

no

l

R2 = 0.49

4 5 6 7

7(b)

6

5

4

3

2

1

00 1 2 3

Retention time pH 3 acetonitrile

Re

ten

tio

n t

ime

pH

10

ace

ton

itri

le

R2 = 0.40

4 5 6 7

that the ionization state of analyte molecules is dependent

on the pH of the mobile phase, and that the ionization

efficiency in LC–MS with electrospray ionization in posi-

tive ion mode (ESI+) will be dramatically lowered in high-

pH mobile phases since the compounds become neutral.

However, many researchers investigating different types

of samples (including proteins, peptides, and amino acids)

have observed either insensitivity to an increase of mobile-

phase pH or even increases in sensitivity (12–17). High-pH

mobile phases do not suppress the ionization of basic com-

pounds in ESI+. Positive ions are formed abundantly and

analyte responses are often better in high pH compared

to acidic mobile phases. This finding is significant as it

extends the applicability of generic elution methods to the

analysis of polar basic compounds that were previously dif-

ficult to retain.

To test this finding, three bases with pKa values ranging

from 8.0 to 9.3 were chromatographed on a high-pH-stable

SPP C18 column at pH 3 (0.1% formic acid) and pH 10

(10 mM ammonium bicarbonate) with acetonitrile as the

organic modifier (Figures 6a–6c). The lower traces in each

figure show the samples analyzed at low pH and the upper

traces show the samples analyzed at high pH. In all three

cases, the analytes were less retained at pH 3 and the peaks

tailed. In contrast, at pH 10 (upper traces) the analytes were

more retained, had better peak shape, and were twice as tall.

At pH 10, the analytes were eluted in a mobile phase having

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a higher organic content. In general,

ionization in the more volatile organic

phase is more eff icient, leading to

higher signal intensity and, indeed, the

peak areas were also significantly larger

for the pH 10 compared to the pH 3

chromatograms. These results show

that the use of high-pH mobile phases

for the analysis of basic compounds

offers a good alternative to using low-

pH mobile phases in ESI+ LC–MS.

Conclusions

The chemical stability of surface mod-

if ied superficially porous particles is

evaluated by conducting column life-

time tests under different high-pH

buffers and high temperature condi-

tions. We determined that the surface

modif ication substantially improves

the high-pH stability of SPP while

preserving the performance features

of SPP columns. We showed that a

high-pH-stable SPP C18 column

could be used for extended periods

(over 2000 injections) with high-pH

mobile phases such as pH 10 ammo-

nium bicarbonate buffer. Therefore,

chromatographers can now explore a

wider range of pH in method devel-

opment using SPP technology, which

is being increasingly adopted because

of its high eff iciency and speed.

Control of pH can be used to adjust

selectivity without sacrif icing column

lifetime at elevated pH. This work

showed that by keeping a gradient

constant and altering pH, the elution

order of a group of eight acid, base,

and neutral compounds could be dra-

matically changed. Chromatographic

resolution was improved. In a second

experiment, the correlation coeff i-

cient of retention times in a generic

gradient was determined between

pH 3 and pH 10. Using R2 as a mea-

sure of orthogonality, we found that

the two conditions offered different

selectivity for acids and bases. There-

fore, using pH as a method develop-

ment tool is very effective, especially

when the sample contains ionizable

compounds.

We also investigated positive ion

electrospray MS of several basic com-

pounds using gradient reversed-phase

LC at high and low pH. The peak

shapes of basic compounds improved

Figure 6: Comparison of LC–MS of three basic compounds in positive ion electro-spray at low and high pH: (a) procainamide, (b) lidocaine, and (c) diltiazem. Column: 100 mm × 2.1 mm, 2.7-µm HPH SPP-C18; mobile-phase A: 0.1% formic acid (pH 2.8) or 10 mM ammonium bicarbonate (pH 10) in water; mobile-phase B: acetonitrile; gradient: 10–90% B in 10 min, hold at 90% 2 min; fow rate: 0.5 mL/min; system: single-quadrupole LC–MS; temperature: 30 °C.

1,000,000

0

2,000,000

3,000,000

4,000,000

5,000,000

1,000,000

02 4 6 8 10

2 4 6 8 10

Time (min)

0.1 % Formic acid–acetonitrile

10 mM Ammonium bicarbonate–acetonitrile

(pH 2.8)

(pH 10)

Area = 2.1 X 107

Area = 1.2 X 107

2,000,000

3,000,000

4,000,000

5,000,000

(a)

10 mM Ammonium bicarbonateÐ

acetonitrile (pH 10)

0.1 % Formic acidÐacetonitrile

(pH 2.8)Area = 3.9 X 106

Area = 5.2 X 106

6.1

33

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

0

2 4 6 8 10

2 4 6 8 10Time (min)

(b)

1,000,000

(c)

8,000,000

6,000,000

4,000,000

2,000,000

0

1,000,000

8,000,000

6,000,000

4,000,000

2,000,000

0

2 4 6 8 10

2 4 6 8 10

Area = 2.7 X 107

0.1 % Formic acid–acetonitrile(pH 2.8)

10 mM Ammonium bicarbonate–acetonitrile (pH 10)

Area = 2.9 X 107

6.4

97

4.1

51

Time (min)


and retention times increased with the

high pH eluents. We also observed a

signal increase as measured by the

peak area and sensitivity increase as

measured by peak height. The magni-

tude of the signal increase was not the

same in all cases and was likely to be

compound dependent. In no case was

a signal decrease observed for bases at

elevated pH.

References

(1) X. Wang, W.E. Barber, and W.J. Long, J.

Chromatogr. A 1228, 72–88 (2012).

(2) F. Gritti, A. Cavazzini, N. Marchetti,

and G. Guiochon, J. Chromatogr. A 1157,

289–303 (2007).

(3) S. Fekete, D. Guillarme, and M.W. Dong,

LCGC North Am. 32(6), 420–433 (2014).

(4) C. Ye, G. Terf loth, Y. Li, and A. Kord, J.

Pharm. Biomed. Anal. 50, 426–431 (2009).

(5) A.M. Faria, E. Tonhi, K.E. Collins, and

C.H. Collins, J. Sep. Sci. 30, 1844–1851

(2007).

(6) J.J. Kirkland, M.A. van Straten, and

H.A. Claessens, J. Chromatogr. A 797,

111–120 (1998).

(7) G.W. Tindall and R.L. Perry, J. Chro-

matogr A 988, 309–312 (2003).

(8) Transfer of Methods between Poroshell

120 EC-C18 and ZORBAX Eclipse Plus

C18 Columns, Agilent Technologies, Inc.

Technical Report 5990-6588EN (2011).

(9) L .R. Snyder, J.J. Kirk land, and J.W.

Dolan, Introduction to Modern Liquid

Chromatography, 3rd Ed. ( John Wiley &

Sons, Hoboken, New Jersey, 2010), p. 29.

(10) K. Croes, A. Steffens, D. Marchand, and

L. Snyder, J. Chromatogr. A 1098, 123–

130 (2005).

(11) W. Long and A. Mack, Agilent Tech-

nologies, Inc. Application Note 5990-

4711EN, (2009).

(12) R. Chirita-Tampu, C. West, L. Foug-

ere, and C. Elfakir, LCGC Europe 26(3),

128–140 (2013).

(13) H.P. Nguyen and K.A. Schug, J. Sep. Sci.

31(9), 1465–1480 (2008).

(14) S. Zhou and K.D. Cook, J. Am. Soc. Mass

Spectrom. 11 961–966 (2000).

(15) R.D. Ricker, Agilent Technologies, Inc.

Application Note 5989-0683EN (2004).

(16) F.E. Kuhlmann, A. Apffel, S.M. Fischer,

G. Goldberg, and P. Goodley, J. Am. Soc.

Mass Spectrom. 6, 1221–1225 (1995).

(17) C.R. Mallet, Z. Lu, and J.R. Mazzeo,

Rapid Commun. Mass Spectrom. 18,

49–56 (2004).

William J. Long, Anne E. Mack, Xiaoli Wang, and William E. Barber are with Agilent

Technologies, Inc., in Wilmington,

Delaware. Direct correspondence

to: [email protected] ◾


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Karyn M. Usher, Steven W. Hansen, Jennifer S. Amoo, Allison P. Bernstein, and Mary Ellen P. McNally

Precision of Internal Standard and External Standard Methods in High Performance Liquid Chromatography

Internal standard methods are used to improve the precision and

accuracy of results where volume errors are difficult to predict and

control. A systematic approach has been used to compare internal and

external standard methods in high performance liquid chromatography

(HPLC). The precision was determined at several different injection

volumes for HPLC and ultrahigh-pressure liquid chromatography

(UHPLC), with two analyte and internal standard combinations.

Precision using three methods of adding the internal standard to the

analyte before final dilution was examined. The internal standard

method outperformed external standard methods in all instances.

A systematic approach was used

to compare interna l stan-

dard (IS) and external stan-

dard (ESTD) methods used in high

performance liquid chromatography

(HPLC). The experiments described

were specif ically designed to exam-

ine the precision of the IS method as

compared to the ESTD method using

the last two generations of HPLC and

ultrahigh-pressure liquid chromatog-

raphy (UHPLC) systems. Two meth-

ods of introducing the IS were com-

pared; these methods involved either

weighing the amount of IS added as

a solid or an internal standard solu-

tion of known concentration. Along

with two types of instruments, HPLC

and UHPLC, we used three analytes

at different concentrations and injec-

tion volumes. A review of the literature

revealed a limited number of papers

that discussed the use of the internal

standard in HPLC. None of the ref-

erences used the approaches described

herein to evaluate the effect of using

an internal standard compared to the

external standard approach.

In an external standard calibration

method, the absolute analyte response

is plotted against the analyte concen-

tration to create the calibration curve.

An external standard method will not

provide acceptable results when con-

siderable volume errors are expected

because of sample preparation or

injection-to-injection variation. An IS

method, which is a method where a

carefully chosen compound different

from the analyte of interest is added

uniformly to every standard and sam-

ple, gives improved precision results in

quantitative chromatographic experi-

ments. The internal standard calibra-

tion curves plot the ratio of the ana-

lyte response to the internal standard

response (response factor) against the

ratio of the analyte amount to the

internal standard amount. The resul-

tant calibration curve is applied to the

ratio of the response of the analyte to

the response of the internal standard

in the samples and the amount of ana-

lyte present is determined.

Several approaches have been used

to determine the amount of internal

standard that should be used in pre-

paring the standards and the samples,

but none have illustrated definitive

results (1–4). For example, Haefelfin-


ger (1) reports that the IS peak height

or area must be similar to that of the

analyte of interest, but does not pres-

ent supporting data. Araujo and col-

leagues (2) show that experimental

design strategies can be used to deter-

mine the optimal amount of internal

standard used while Altria and Fabre

(3) show that the IS should be used in

the highest possible concentration.

Calculation of the response factor

assumes that the detector gives a linear

response for both the analyte and the

internal standard over the entire range

of the experiment. Since this is not

always the case, it is essential to under-

stand the behavior of the response fac-

tor as the concentration or amount

of analyte and internal standard are

varied. Knowing the behavior of the

response factor allows one to set limits

on the useful range of the chosen ana-

lyte or internal standard concentration

combinations.

The internal standard method is used

to improve the precision and accuracy

of results where volume errors are dif-

ficult to predict and control. Examples

of types of errors that are minimized

by the use of an internal standard are

those caused by evaporation of sol-

vents, injection errors, and complex

sample preparation involving transfers,

extractions, and dilutions. An internal

standard must be chosen properly and

a known amount added carefully to

both sample and standard solutions to

minimize error and be utilized to its

full advantage. The resulting internal

standard peak should be well resolved

from other components in the sample

and properly integrated. If all of these

conditions are not met, the use of an

internal standard may actually increase

the variability of the results. One

report suggests that whenever detec-

tor noise or integration errors are the

dominant sources of error, the use of

an internal standard will likely make

the results of the experiment worse (5).

A paper published by P. Haefelfin-

ger in the Journal of Chromatography

in 1981 (1) discussed some limitations

of the internal standard technique in

HPLC. Using the law of propagation of

errors, the paper showed conditions that

need to be met for the internal standard

procedure to improve results. In addi-

tion to the mathematical illustration,

Haefelfinger detailed practical exam-

ples where either internal or external

standard methods were advantageous.

The Journal of the Pharmaceutical

Society of Japan published a study in

Table I: Chromatographic conditions used for the analysis of indoxacarb. The HPLC method is an official DuPont technical assay method and the UHPLC method is a method developed for these experiments.

Indoxacarb* Chromato-graphic Conditions

HPLC UHPLC

Chromatographic columnZorbax SB-C8

250 mm × 3.0 mm, 5 µmZorbax SB-C8

75 mm × 4.6 mm, 3.5 µm

Mobile phase A 38% water 38% water

Mobile phase B 62% acetonitrile 62% acetonitrile

Flow rate 0.65 mL/min 2.0 mL/min

Column temperature 45 °C 45 °C

Injection volume 0.2–10 µL 0.2–5 µL

Wavelength 280 nm (bandwidth = 4 nm) 280 nm (bandwidth = 4 nm)

Reference wavelength 380 nm (bandwidth = 80 nm) 380 nm (bandwidth = 80 nm)

*Pure active enantiomer

Table II: Chromatographic conditions used for the analysis of diuron. The HPLC method is an official DuPont technical assay method and the UHPLC method is a method developed for these experiments.

Diuron (14740) Chro-matographic Conditions

HPLC UHPLC

Chromatographic columnZorbax XDB-C8

150 mm × 4.6 mm, 3.5 µm Zorbax XDB-C8

75 mm × 4.6 mm, 3.5 µm

Mobile phase A70% water (pH = 3.0 adj.

with H3PO4) 70% water (pH = 3.0 adj.

with H3PO4)

Mobile phase B 30% acetonitrile 30% acetonitrile

Flow rate 1.5 mL/min 1.5 mL/min

Column temperature 40 °C 40 °C

Injection volume 0.2–10 µL 0.2–2 µL

Wavelength 254 nm (bandwidth = 4 nm) 254 nm (bandwidth = 4 nm)

Reference wavelength 350 nm (bandwidth = 100 nm) 350 nm (bandwidth = 100 nm)

(c)

(d)

(a)

(b)

0.2

1.2

1.0

0.8

0.6

0.4

0.2

0.0

5.0

4.0

3.0

2.0

1.0

0.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0

0.5 1.0 2.0 4.0 5.0

Injection volume0.2

3.0

2.5

2.0

1.5

1.0

0.5

0.00.5 2.0 4.0 10.0

Injection volume

ESTD volume ESTD weight IS solution

HPLCUHPLC

HPLCUHPLC

0.2 0.5 1 2

Injection volume

0.2 0.5 1 2 10

Injection volume

Figure 1: Comparison of external and internal calibration methods: (a) indoxacarb with UHPLC, (b) diuron with UHPLC, (c) indoxacarb with HPLC, (d) diuron with HPLC. Each bar represents eight injections.


2003 (6) that found that the inter-

nal standard method did not offer an

improvement in precision with the

then current autosampler technology.

Interestingly, they also found that if

the peak of the internal standard was

small, the relative standard deviation

(RSD) was actually larger than the

RSD for the external standard method

(6). The limitation of this study was

that only one injection volume (10 µL)

was used to establish the conclusions.

In our work, a systematic approach

has been used to compare the inter-

nal to the external standard method

using two analytes and two internal

standards. The precision resulting

from both an internal and external

standard method were determined at

several injection volumes and on two

different instruments. Three methods

of adding the IS to the analyte before

final dilution have been compared. In

the first, a solid internal standard was

weighed directly into the glassware

containing the sample before dilution

with solvent. In the second, a solution

of a known concentration of the IS

was prepared and a known volume of

this solution was added to the sample

prior to dilution. In the third, the

IS was added in the same manner as

the second method, but the internal

standard solution was weighed and

the weight, not the volume, was used

in the IS calculations. We examined

the effect of weight of analyte and

internal standard on the precision of

the results. Initially, the weights of

the analyte were varied versus a con-

stant IS concentration, and then the

concentration of the internal standard

was varied versus a constant weight of

the analyte.

Standard deviation was chosen to

monitor precision. All possible errors

are ref lected in the standard deviations

of the final measurements, including

each step in the sample preparation,

sample transfer, and sample introduc-

tion into the HPLC or UHPLC sys-

tem, as well as the HPLC or UHPLC

Table III: Operating conditions for technical assay methods for experiments comparing the method of addition of the internal standard

Technical Assay

ColumnMobile-Phase A

Mobile-Phase BFlow Rate (mL/min)

Temp. (ºC)

Inj. Vol. (µL)

λ (nm) Reference

Indoxacarb*Zorbax SB-C8 Solvent Saver

25 cm × 3.0 mm, 5 µm62% acetoni-

trile38% water 0.65 45 4 280 380

Indoxacarb† Chiralcel OD 25 cm × 4.6 mm

85% hexane and 15%

isopropanol

50% isopropa-nol–50% hexane (post run fush)

1 40 5 310 450

FamoxadoneZorbax Rx C18

15 cm × 4.6 mm, 5 µm48% acetoni-

trile52% water 2.0 40 10 260 400

DiuronPartisil 5 RAC II ODS3

10 cm × 4.6 mm32% acetoni-

trile68% water 2 40 5 254 450

*Pure active enantiomer †75:25 racemic mixture of active and inactive enantiomers

Table IV: Regression results and correlation coefficients for injector linearity tests

Compound Instrument Slope y-intercept R2

Diuron UHPLC 3913.5 310.71 1.0000

3-Methyl-1,1-diphenylurea UHPLC 1763.4 93.166 1.0000

Indoxacarb HPLC 333.46 0.9806 1.0000

p-Terphenyl HPLC 477.48 2.1863 1.0000

Injection volume

Avera

ge s

tan

dard

devia

tio

n

DuPont technical assay

Doubleconcentration

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.00 2 4 6 8 10

Figure 2: Comparison of results obtained for the DuPont technical assay method when injections at two different volumes were made.


analyses themselves. Both external and

internal standard calibration methods

were used to calculate the percent

recoveries for comparison.

Experimental

Chemicals

The mobile phases were binary mix-

tures of acetonitrile and water (pH

adjusted with phosphoric acid). The

water was purified house water (EMD

Millipore Corp.) and the acetoni-

trile and phosphoric acid (EM Sci-

ence) were HPLC grade. Diuron and

indoxacarb standards were obtained

from DuPont Crop Protection. The

internal standards were p-terphenyl

(Aldrich Chemicals) and 3-methyl-1,1-

diphenylurea (Aldrich Chemicals). All

solutions were prepared as needed or

stored in the refrigerator.

Sample Preparation

For the comparison of calibration

methods, the official DuPont techni-

cal assay methods for indoxacarb and

diuron were adapted for use. All stan-

dards and samples were prepared in

acetonitrile. The indoxacarb standards

ranged in concentration from 0.15 to

0.7 mg/mL; samples were prepared at

a concentration of 0.5 mg/mL. The

internal standard was p-terphenyl at

a concentration of 0.08 mg/mL. The

diuron standards ranged in concentra-

tion from 0.75 to 1.25 mg/mL; sam-

ples were prepared at a concentration

of 1 mg/mL. The internal standard

was 3-methyl-1,1-diphenylurea at a

concentration of 1 mg/mL. Precision

data was calculated based on eight

individually prepared samples with

duplicate injections of each sample.

For the comparison of the method

of addition of the internal standard

experiments, three DuPont enforce-

ment methods for technical assay of

indoxacarb, famoxadone, and diuron

were used. Precision data was calcu-

lated based on eight individually pre-

pared samples with duplicate injec-

tions of each sample.

Instrumentation and

Chromatographic Conditions

Agilent 1100 and 1290 Infinity HPLC

systems (Agilent Technologies) were

used, each consisting of a binary pump,

an autosampler, a thermostated column

compartment, and a diode-array detector.

Instrument control and data collection

were performed using ChemStation soft-

ware. Chromatographic conditions are

given in Tables I, II, and III. The techni-

cal methods were adapted as needed; for

example, a method specifies the injection

volume, and we collected data using sev-

eral injection volumes for each compound.

Calibration Methods

Internal Standard Versus

External Standard Calibrations

A set of samples was prepared in such a

way that results could be calculated for

both the internal and external standard

methods. All samples were prepared

using class A volumetric glassware. Ini-

tially, the analyte was weighed directly

into the volumetric f lask. Next, the

internal standard was weighed into the

same f lask and acetonitrile was added

to dissolve the solids. The f lask was

then diluted to the mark and the mass

of the final solution was recorded. This

step allowed the results to be calculated

using the external standard method in

two ways, by using the nominal vol-

ume of the volumetric f lask and also

by using the mass of the solution to

calculate the concentrations. In both of

these cases, the internal standard added

was not included in the calculations.

These two methods will be denoted as

“ESTD nominal volume” and “ESTD

weight,” respectively. The internal

standard method, where the weighed

volume of the internal standard solu-

tion was recorded, will be denoted as

“IS solution.” Because the samples were

prepared in this manner, the results for

the three methods were calculated using

the same data files. The difference in

the calculated standard deviations in

this way is attributed to the calibration

method, and is independent of any dif-

ferences in sample preparation.

Comparison of Methods

of Addition of the Internal Standard

Two sets of samples were prepared for

each compound analyzed. The first set of

samples were prepared by weighing the

solid analyte and then weighing the solid

IS into the sample container and dilut-

ing. The second set of samples were pre-

pared by weighing the solid analyte into

the sample container and then adding

a specified volume of internal standard

solution, which was subsequently also

weighed. Standard deviations were cal-

culated for these two internal standard

introduction methods.

Results and Discussion

To determine if instruments were func-

tioning properly, eight replicate injections

of one prepared sample for each analyte

Table V: The nominal injection volumes, and exact masses and volumes of IS and analyte along with the resulting response factors

CompoundInjection Volume

(µL)

Avg. Mass (g)Avg. IS Volume

(solution) (mL)

Avg. Peak Areas Response

FactorIS Solid IS solution Analyte IS

Indoxacarb* 4.0 0.055 0.008 15.5 6000 4000 1.50

Indoxacarb* 4.0 0.050 0.010 15.3 3687 3627 1.02

Indoxacarb† 5.0 0.100 0.012 7.9 7100 1500 4.73

Famoxadone 10.0 0.125 0.040 7.9 4100 4300 0.95

Diuron 2.0 0.100 0.100 7.8 4230 1830 2.31

*Pure active enantiomer †75:25 racemic mixture of active and inactive enantiomers

Table VI: Comparison of results using different methods for the addition of the internal standard

Compound

Standard Deviation

Solid IS

Weighed IS

IS by volume

Indoxacarb* 3.592 0.391 0.293

Indoxacarb* 1.408 0.368 ‡

Indoxacarb† 2.041 0.190 0.172

Famoxadone 1.410 0.110 0.172

Diuron 0.444 0.162 ‡

*Pure active enantiomer† 75:25 racemic mixture of active and inactive enantiomers

‡ Dispensers were used rather than volu-metric pipettes, so this value could not be accurately calculated.


and internal standard were injected into

each instrument at different injection vol-

umes. The chromatographic conditions

are shown in Tables I and II. The injector

linearity was tested for both analytes and

both internal standards and the results

are given in Table IV. The range tested

for the standard HPLC and the UHPLC

instruments were 0.2–10.0 µL and 0.2–

5.0 µL, respectively. The injection pre-

cision at each flow rate was compared

to the manufacturer specifications, the

instruments performed at or better than

the manufacturer’s specifications.

There are other important factors to

consider when optimizing chromato-

graphic methods, some of which are car-

ryover, column back pressure, and wave-

length (7). Our experiments controlled

these parameters.

Comparison of Calibration Methods

In general, there was not a large difference

in the calculated standard deviations for

the two external standard methods. Any

differences seen did not suggest a trend,

and appear to be random. An expected

trend when using both external standard

methods was that standard deviations

became larger with decreased injection

volume.

The results calculated using the inter-

nal standard calibration method always

demonstrated improved precision over

the results calculated using an external

standard calibration. See Figure 1 for

precision results for diuron and indoxa-

carb using HPLC and UHPLC instru-

ments. The graphs in Figure 1 show that

at larger injection volumes the precision

for the IS method appears constant, but

at lower injection volumes the standard

deviation increases drastically. This phe-

nomenon does not occur at the same

injection volume for both compounds,

nor does it occur at the same injection

volume for either compound using HPLC

or UHPLC.

Logically, overall peak areas are smaller

with smaller injection volumes and loss of

precision is caused by integration errors.

Larger integration errors occur with smaller

areas being integrated and lead to larger

standard deviations calculated for the

percent error. To determine if this effect

of volume injected was the cause for the

increase in RSD for low peak areas, sam-

ples of diuron were prepared at twice the

concentration level of the original experi-

ment and two different volumes were

injected. If the loss of precision was solely

because of the smaller size of the peak, then

the standard deviation calculated using the

higher concentration samples should be

smaller than the standard deviation calcu-

lated for the original samples. This was not

the case; Figure 2 shows that the standard

deviations calculated when peaks were two

times as large as the original were not sig-

nificantly different from the original stan-

dard deviation. Again, the loss of precision

was not explained by the smaller absolute

size of the peak.

Figure 3 shows the peak areas corre-

sponding to different injection volumes for

diuron and indoxacarb standards and their

corresponding internal standards. With

diuron, the internal standard method did

not produce acceptable results at injec-

tion volumes lower than 1 µL; the internal

standard peak area was smaller than the

analyte peak area at all injection volumes.

The horizontal lines drawn in Figure 3

correspond to the peak area of the internal

standard, 3-methyl-1,1-diphenylurea, in

the diuron solutions. If the peak size was

completely responsible for loss of precision

at small injection volumes, then any results

calculated using peak areas below this

line at any injection volume should show

similar loss of precision. Correspondingly,

for indoxacarb, a similar loss of precision

would have been seen at all the chosen

injection volumes. Indoxacarb was not

consistent with this hypothesis. The loss

of precision is not completely explained by

the absolute size of the peak.

Peak Area Ratios

To further investigate this precision loss

when smaller injection volumes (0.2, 0.5,

and 1 µL) were used, two separate samples

of diuron and indoxacarb, each with IS,

were injected eight times using the con-

ditions described in Tables I and II. The

resulting peak area ratios (analyte peak

area/internal standard peak area) were

plotted against the injection number as

shown in Figure 4. At these smaller injec-

tion volumes, the responses are less precise

than at the larger injection volumes. The

exact injection volume where this is seen

varies from compound to compound, but

generally occurred at injection volumes

Table VII: Calculated standard devia-tions for IS added by two methods when analyte weight is varied

Analyte Weight (mg)

Standard Deviation

IS (Weighed Solid)

IS (Weighed Solution)

25 0.691 0.368

75 0.149 0.142

100 0.131 0.086

125 0.25 0.203

175 0.121 0.083

Table VIII: Calculated standard devia-tions for IS added by two methods and resulting in varying final con-centrations of IS while the analyte weight is kept constant

IS Weight (mg)

Standard Deviation

IS (Weighed Solid)

IS (Weighed Solution)

25 0.795 0.253

75 0.247 0.297

100 0.131 0.086

125 0.197 0.149

175 0.182 0.287

Peak a

rea

Peak a

rea

18,000

16,000

14,000

12,000

10,000

8000

3000

Diuron peak

Indoxacarb peak

Indoxacarb IS peakDiuron IS peak

Injection volume (µL) Injection volume (µL)

2500

2000

1500

1000

500

0

6000

4000

2000

0

0 1 2 3 4 5 0 1 2 3 4 5

(a) (b)

Figure 3: Graphs of peak area versus injection volume for (a) diuron and (b) indoxa-carb. The solid line corresponds to the peak area for the IS in the diuron method.


smaller than 2 µL. Figures 1 and 2 show

that on average, the peak area ratio is

changing as the injection volume changes

and is greater at smaller injection volumes.

Thus, confirming a calibration curve pre-

pared using one injection volume should

not be used with data resulting from a

different injection volume. The difference

in area ratio over the range of injection

volumes appears small, but is significant.

For the diuron analysis using UHPLC,

the percent recoveries calculated using

the highest and lowest calculated peak

area ratios shown in Figure 4 (0.2 µL and

5.0 µL, respectively), resulted in a differ-

ence of 0.86% overall recovery. For the

diuron analysis using HPLC data, percent

recoveries determined using the highest

and lowest calculated peak area ratios

(0.2 µL and 10.0 µL, respectively), resulted

in an overall recovery difference of 4.28%.

Small differences in the area ratios at dif-

ferent injection volumes can have a large

impact on the calculated recoveries.

Figure 4 shows that the peak area ratios

used for the IS method do not remain con-

stant over the range of injection volumes

examined. Some peak area ratios varied

by as much as 0.05 units. This change as

the injection volume is changed can cause a

systematic error in the calculated recoveries

that results from the use of an IS calibration

curve. As previously discussed, the error in

percent recovery because of the changing

value of the peak area ratios over the range

of injection volumes used was as small as

0.86% for the UHPLC analysis and as

large as 4.28% for the HPLC analysis. This

topic warrants further investigation.

Comparison of the Methods

of Internal Standard Addition

Three methods of internal standard addi-

tion were compared. In the first method,

the internal standard was added directly

as a solid. In the second method, a solu-

tion of the internal standard was prepared,

added, and weighed into the analyte solu-

tion before final dilution. Calculations

were then performed using the weight of

the added solution. For the third method,

the internal standard preparation and

introduction were the same as the second

method; however, the calculations were

performed using the nominal volume

from the Class A volumetric pipette. Table

V gives the injection volumes used in the

chromatographic methods, the masses of

the analyte and IS used, the volume of the

IS used, the average peak areas for both

the analyte and the IS, and the resulting

response factors. Table VI shows the stan-

dard deviations that were calculated when

the IS was added by these three different

methods. An F-test showed a significant

difference in the resulting standard devia-

tions between the first method (weigh-

ing the IS as a solid) and the other two

methods (introducing a solution of the

IS). There were small differences in the

standard deviations using the two sepa-

rate methods of introducing the internal

standard as a solution and calculating via

either the volume or weight; however, no

specific trend was obvious.

When the IS was weighed as a solid,

the precision was almost a factor of three

and 13 times larger, for diuron and

famoxadone, respectively, than when

the IS was added as a weighed solution

(see Table VI). These results suggest the

precision could potentially be limited by

the accuracy of the balance. Supporting

this, whenever the weight of either the

analyte or IS was less than 100 mg, the

standard deviation was large, generally

1.4%; conversely, when the weight was

100 mg or higher, the standard devia-

tion was less than 0.5%. Nearly a three-

fold improvement in standard deviation

was obtained by increasing the weights

being used to at least 100 mg, or add-

ing another significant digit to the mass

measurement. Overall, the standard

deviation was significantly smaller when

the internal standard was added as a solu-

tion rather than as a solid, attributed to

the larger mass of solution versus solid

being weighed. To confirm this, the

measured weights of the analyte and the

IS were varied separately using the diu-

ron enforcement method. This method

was chosen because it exhibited the low-

est inherent standard deviation. Table

VII shows the results where the mass of

the analyte was varied from 25 mg to

175 mg while the IS amount was held

constant. Both methods of internal stan-

dard introduction were used; the constant

amount of solid and internal standard

solution weighed into the analyte solu-

tion was 100 mg, and 7.8 g, respectively.

Table VII shows the standard deviations

for the varied amount of analyte, from

75 to 175 mg. These calculated standard

deviations are all 0.25% or less for both

IS introduction methods. Decreasing

the analyte mass to 25 mg, the standard

deviation quadruples to 0.69% for solid

introduction and 0.37% for the weighed

(a)

Injection number

Are

a r

ati

o

0 2 4 6 8

0.2 µL1.46

1.45

1.45

1.44

1.44

1.43

0.5 µL

1 µL

2 µL

4 µL

5 µL

(b)

Injection number

Are

a r

ati

o

0 2 4 6 8

0.2 µL2.39

2.38

2.37

2.36

2.35

2.34

2.33

0.5 µL

1 µL

2 µL

4 µL

5 µL

Figure 4: Comparison of peak area ratios at different injection volumes for (a) in-doxacarb and (b) diuron.


solution of internal standard. The stan-

dard deviations for the samples prepared

by adding the IS as a solution are always

lower than for those prepared by adding

the IS as a solid. Conversely, when the

mass of analyte was kept constant at 100

mg and the mass of the IS was varied,

similar results were obtained. Specifi-

cally, the calculated standard deviations

were less than 0.3% except when the IS

mass was 25 mg for the weighed solid

internal standard. For 25 mg of weighed

solid internal standard, the precision was

0.8%. These results are given in Table

VIII. Therefore, it can be concluded that

excellent precision in chromatographic

results is effected by the precision of the

balance, the weight of the internal stan-

dard, and the introduction method of

the internal standard.

Further analysis of the data disputes

some of the ideas regarding the internal

standard that were previously reported.

Haefelfinger (1) reported that the IS peak

area must be similar (response factor

close to 1) to that of the analyte of inter-

est. The data and results given in Tables

V and VI do not support this and do not

suggest any specific correlation between

the response factor and the standard

deviation. Altria and Fabre (3) state that

the IS should be used in the highest pos-

sible concentration. The results in Table

VIII elucidate the standard deviation for

some of the samples with lower concen-

trations of IS showing better precision

than some with higher concentrations

of IS. Our results illustrate that injection

volumes and the method of addition of

the internal standard are more impor-

tant than having a response factor close

to one or using high concentrations of IS.

Conclusions

When precision is an important factor,

the chromatographic instrument should

be tested before the start of any analy-

sis to ensure that it is working properly.

Injection-to-injection variation and the

injector linearity both have a pronounced

effect on precision at smaller injection

volumes, so it is important to confirm

that the instrument being used is capa-

ble of providing acceptable results at the

chosen injection volume. The internal

standard method corrects for different

sources of volume errors, including injec-

tion-to-injection variation, volume errors

in sample preparation, and accounts for

routine variations in the response of the

chromatographic system.

We have shown the internal standard

method outperformed external standard

methods in all experiments, regardless of

the analyte, choice of internal standard,

method of introduction of internal stan-

dard, and the injection volume. Even so,

at low injection volumes the resulting

precision, when using the internal stan-

dard method, was poor. For the com-

pounds used, this breakdown typically

occurred at injection volumes of less

than 2 µL and was dependent on the spe-

cific compound and IS being used, and

not the instrument. Loss of precision did

not coincide with a specific minimum

peak area, so poor precision cannot be

attributed to the smaller size of the peaks

at smaller injection volumes. The break-

down in precision was also not because

of larger injection variability at smaller

injection volumes. If that was the case,

the loss of precision would occur at the

same injection volume on each instru-

ment regardless of what compound was

being studied.

The results of this study show that

when poor precision occurs at injec-

tion volumes less than 2 µL, significant

improvement in results may be achieved

by simply increasing the injection vol-

ume without the need for developing a

new method. This is true whether an

external standard or an internal standard

method is being used.

With an internal standard method, the

precision of the experiment is affected by

how the internal standard is measured.

For solutions prepared to have the same

final concentration of analyte and IS,

there is a significant difference in the

precision when the internal standard is

added as a solid or a solution of known

concentration. For all the analyte and

IS combinations tested, the precision

was significantly better when a solution

of the IS was first prepared at a known

concentration then added to the analyte

before dilution.

Our chromatographic resultant preci-

sion was not limited by the precision of

the balance when the masses being used

were larger than 25 mg. There was no

direct correlation between the response

factors and the calculated standard devi-

ations. Our data also did not support the

common perception of an IS being used

in the highest concentration possible.

Overall, the results show that the inter-

nal standard method can significantly

improve the precision of a chromato-

graphic method. However, attention

must be paid to the injection volume and

the method by which the internal stan-

dard is added to the analyte. To achieve

better precision, increasing the injection

volume of the sample solution is effective.

Acknowledgments

The authors would like to acknowl-

edge Steve Platz for many contributions,

including being a safety mentor and

training on using the HPLC instrument.

We would also like to thank Jim Schmit-

tle, Peter Schtur, and Jennifer Llewelyn

for their support of this project.

References

(1) P. Haefelfinger, J. Chromatogr. 218, 73–81

(1981).

(2) P. Araujo, F. Couillard, E. Leirnes, K. Ask,

A. Bøkevoll, and L. Frøyland, J. Chromatogr.

A 1121, 99–105 (2006).

(3) K.D. Altria and H. Fabre, Chromatographia

40, 313–320 (1995).

(4) K. Baumann and H. Wätzig, Process Control

Qual. 10, 59–73 (1997).

(5) Y. Hayashi and R. Matsuda, Anal. Sci. 11,

389–400 (1995).

(6) R. Ohtaka, M. Maeda, T. Iwagami, T. Ueda,

Y. Kimura, K. Imai, C. Yomota, R. Matsuda,

and Y. Hayashi, J. Pharm. Soc. Jpn. 123(5),

349–355 (2003).

(7) K.M. Usher, S.W. Hansen, and M.P. McNal-

ley, LCGC North Am. 31(S4a), 50–54 (2013).

Steven W. Hansen, Jennifer S. Amoo, and Mary Ellen P. McNally are with DuPont Crop Protection at the Stine Haskell Research Center in

Newark, Delaware. Karyn M. Usher is an associate professor at Metropolitan State University, Minnesota. She contributed to this work as a visiting scientist at DuPont

Crop Protection. Allison P. Bernsteinis with DuPont Industrial Biosciences in Cedar Rapids, Iowa. Direct correspondence to: [email protected] ◾


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TSKgel Butyl-NPR, excellent for antibody-drug conjugates.

• Size Exclusion columns for proteins and polymers,

including the new TSKgel SW mAb line for monoclonal

antibody a nalysis.

• Anion and Cation Exchange columns for

a host of biomolecules.

Date post:	23-Aug-2020
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