bioanalytical method validationfiles.alfresco.mjh.group/alfresco_images/pharma/2019/07/...2019/07/11...
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July 2019 | Volume 32 Number 7
www.chromatographyonline.com
PEER-REVIEWED ARTICLEDetermining sulphonamides in liver
LC TROUBLESHOOTINGMobile phase buffers in LC
GC CONNECTIONSTemperature programmed GC
Bioanalytical Method Validation
Quantifying endogenously present small molecules in biological samples
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FEATURES
344 Sample Treatment in Routine Analysis: A Case
Study: Sulphonamides in Liver
Leah Walker, Francesc Borrull, Eva Pocurull, and Núria
Fontanals
A case study related to the determination of
sulphonamides in liver is presented. Different
extraction strategies, including solid-liquid extraction
(SLE), ultrasound-assisted extraction (UAE), and
solid-phase extraction (SPE), were evaluated to
produce a simple and effective extraction method.
COLUMNS
364 LC TROUBLESHOOTING
Mobile Phase Buffers in LC: Effect of Buffer
Preparation Method on Retention Repeatability
Dwight R. Stoll and Devin M. Makey
For liquid chromatography (LC) methods where the
buffer pH and composition have an influence on
retention, which buffer preparation method will provide
the most repeatable results?
370 GC CONNECTIONS
Temperature Programmed GC: Why Are All Those
Peaks So Sharp?
Nicholas H. Snow
Temperature programming is used for most
separations in capillary GC today. Despite this, many
of the principles by which we understand temperature
programmed capillary column separations are
based on ideas developed using packed columns
and isothermal conditions. This instalment of “GC
Connections” dives into temperature programming.
DEPARTMENTS
378 Products
380 Events
381 The Applications Book
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COVER STORY
354 PHARMACEUTICAL PERSPECTIVES
Strategies for the Quantification of Endogenously
Present Small Molecules in Biological Samples
Maxim Nelis, Patrick Augustijns, and Deirdre Cabooter
The main objective of this review is to provide a
clear summary of the different methods that can
be used to quantify endogenous small molecules.
Practical recommendations to face this bioanalytical
challenge, in particular in terms of method
validation, will also be provided.
July | 2019
Volume 32 Number 7
342 LCGC Europe July 2019
CONTENTS
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Daniel W. Armstrong
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Günther K. Bonn
Institute of Analytical Chemistry and Radiochemistry, University of Innsbruck, Austria
Deirdre Cabooter
Department of Pharmaceutical and Pharmacological Sciences, University of Leuven, Belgium
Peter Carr
Department of Chemistry, University of Minnesota, Minneapolis, Minnesota, USA
Jean-Pierre Chervet
Antec Scientific, Zoeterwoude, The Netherlands
Jan H. Christensen
Department of Plant and Environmental Sciences, University of Copenhagen, Copenhagen, Denmark
Danilo Corradini
Istituto di Cromatografia del CNR, Rome, Italy
Gert Desmet
Transport Modelling and Analytical Separation Science, Vrije Universiteit, Brussels, Belgium
John W. Dolan
LC Resources, McMinnville, Oregon, USA
Anthony F. Fell
Pharmaceutical Chemistry, University of Bradford, Bradford, UK
Attila Felinger
Professor of Chemistry, Department of Analytical and Environmental Chemistry, University of Pécs, Pécs, Hungary
Francesco Gasparrini
Dipartimento di Studi di Chimica e Tecnologia delle Sostanze Biologicamente Attive, Università “La Sapienza”, Rome, Italy
Joseph L. Glajch
Momenta Pharmaceuticals, Cambridge, Massachusetts, USA
Davy Guillarme
School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland
Jun Haginaka
School of Pharmacy and Pharmaceutical Sciences, Mukogawa Women’s University, Nishinomiya, Japan
Javier Hernández-Borges
Department of Chemistry (Analytical Chemistry Division), University of La Laguna Canary Islands, Spain
John V. Hinshaw
Serveron Corp., Beaverton, Oregon, USA
Tuulia Hyötyläinen
VVT Technical Research of Finland, Finland
Hans-Gerd Janssen
Van’t Hoff Institute for the Molecular Sciences, Amsterdam, The Netherlands151 353 3601
Kiyokatsu Jinno
School of Materials Sciences, Toyohasi University of Technology, Japan
Huba Kalász
Semmelweis University of Medicine, Budapest, Hungary
Hian Kee Lee
National University of Singapore, Singapore
Wolfgang Lindner
Institute of Analytical Chemistry, University of Vienna, Austria
Henk Lingeman
Faculteit der Scheikunde, Free University, Amsterdam, The Netherlands
Tom Lynch
Analytical consultant, Newbury, UK
Ronald E. Majors
Analytical consultant, West Chester, Pennsylvania, USA
Debby Mangelings
Department of Analytical Chemistry and Pharmaceutical Technology, Vrije Universiteit, Brussels, Belgium
Phillip Marriot
Monash University, School of Chemistry, Victoria, Australia
David McCalley
Department of Applied Sciences, University of West of England, Bristol, UK
Robert D. McDowall
McDowall Consulting, Bromley, Kent, UK
Mary Ellen McNally
DuPont Crop Protection, Newark, Delaware, USA
Imre Molnár
Molnar Research Institute, Berlin, Germany
Luigi Mondello
Dipartimento Farmaco-chimico, Facoltà di Farmacia, Università di Messina, Messina, Italy
Peter Myers
Department of Chemistry, University of Liverpool, Liverpool, UK
Janusz Pawliszyn
Department of Chemistry, University of Waterloo, Ontario, Canada Colin Poole Wayne State University, Detroit, Michigan, USA
Fred E. Regnier
Department of Biochemistry, Purdue University, West Lafayette, Indiana, USA
Harald Ritchie
Advanced Materials Technology, Chester, UK
Koen Sandra
Research Institute for Chromatography, Kortrijk, Belgium
Pat Sandra
Research Institute for Chromatography, Kortrijk, Belgium
Peter Schoenmakers
Department of Chemical Engineering, Universiteit van Amsterdam, Amsterdam, The Netherlands
Robert Shellie
Deakin University, Melbourne, Australia
Yvan Vander Heyden
Vrije Universiteit Brussel, Brussels, Belgium
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One of the current roles of analytical scientists is to determine low concentrations of
different compounds in complex matrices. The ability to quantify low concentrations
of compounds in an analytical method depends on the instrumental technique as
well as the sample preparation. In recent years, the sensitivity of detection
techniques has been improved by the development of mass spectrometry
(MS)-based detectors (1). However, sample preparation still plays a crucial role in
the development of an analytical procedure and is considered the most challenging
and variable step (2,3). The choice of sample treatment depends largely on the
matrix type, the method, and the physicochemical features of the analytes. The
typical steps involved in sample preparation include sampling, extraction, clean-up,
and concentration, followed by the final analysis (4). In addition, the development of
cost-effective analytical methods is a crucial factor for achieving versatile and
reliable results while largely avoiding the use of many high-tech instrumentation
resources that lead to an increase in the cost of the analysis.
An ideal extraction technique should achieve selective total extraction of the
target compounds (complete recoveries) while limiting the extraction of matrix
impurities. In addition, compromises are needed to ensure the technique is fast,
easy, and cheap as well as reducing organic solvent consumption. For solid
samples, among the most used extraction techniques are pressurized liquid
extraction (PLE), microwave-assisted extraction (MAE), and supercritical fluid
extraction (SFE). However, these techniques are all instrument-based and as such
have higher associated costs and not all laboratories have these instruments (5,6).
Traditional solid-liquid extraction (SLE) techniques are thought to be
time-consuming, lack efficiency in extracting target compounds, and require large
volumes of solvents. Nevertheless, this technique is still widely used because of
its simplicity and it does not require expensive equipment (5,6). As a result of
these features, this is often the only affordable technique available in laboratories
where routine analytical methods are developed.
Sample Treatment in Routine Analysis: A Case Study: Sulphonamides in LiverLeah Walker, Francesc Borrull, Eva Pocurull, and Núria Fontanals, Department of Analytical Chemistry and Organic Chemistry,
Rovira i Virgili University, Marcel·lí Domingo 1, Campus Sescelades, Tarragona, Spain
Currently, sample treatment is still the bottleneck in the development of analytical methods to analyze
complex samples, especially for routine analysis where high-tech instruments are not always available.
Research on the evaluation of different sample treatments is needed to achieve the sensitivity and selectivity
required. This article presents a case study related to the determination of sulphonamides in liver. Different
extraction strategies, including solid-liquid extraction (SLE), ultrasound-assisted extraction (UAE), and
solid-phase extraction (SPE), were evaluated to produce a simple and effective extraction method.
KEY POINTS• The role of sample treatment
is important when dealing
with complex samples.
• -Simple extraction techniques
are good alternatives to costly
instrumentation for some complex
samples such as edible tissues.
• -Sample treatment is still the
bottleneck in the development
of analytical methods.
344 LCGC Europe July 2019
FONTANALS ET AL.
PEER REVIEW
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Developments in classical SLE, where shaking by hand
usually ensures the partitioning of the analytes between the
solid matrix and the organic solvent, include sonication,
which is generally preferred to aid this contact between the
two phases and promote the extraction efficiency. This
extraction technique is known as ultrasound-assisted
extraction (UAE) (3,7).
Nevertheless, some studies have reported on the use of SLE,
not only for its simplicity, but also because it provided the best
extraction results. For instance, four common methods
(solid-phase extraction [SPE], matrix solid-phase dispersion
[MSPD], quick, easy, cheap, effective, rugged, and safe
[QuEChERS], and SLE) were compared and the authors found
that SLE followed by a clean-up step based on SPE using a
hydrophilic-lipophilic balanced copolymer SPE cartridge was
the most suitable approach for the simultaneous extraction of
antibiotics from eggs (8). Similarly, SLE was the extraction
technique of choice to extract a similar group of antibiotics
from swine manures (9). In another example (10), PLE and SLE
were compared for the extraction of a group of mycotoxins
from rat faeces. In this study, although the extraction efficiency
of PLE was higher, the authors finally developed the method
using SLE because it also provided suitable results, but the
method simplicity was an advantage when a large number of
samples had to be analyzed.
Among the different analytical methods where sample
preparation is important, the determination of veterinary
residues in edible tissues, such as the liver, was focused on
in this article. Antibiotics are widely administrated for
therapeutic purposes, but they can also be used for growth
promotion in food-producing animals. The use of antibiotics
for animal growth is considered fraudulent in Europe
because the residues of these compounds can persist in
edible matrices (11). For this reason, the European
Commission regulates their use through the establishment of
maximum residue levels (MRL) in foodstuff of animal origin
(12). Therefore, the presence of antibiotics in edible tissues
needs to be controlled through the development of analytical
methods in routine laboratories to comply with these
regulations.
With regards to sample preparation when working with
animal tissues, one of the main difficulties is related to the
complexity associated with the high fat and protein content in
these matrices (3,4). Thus, the sample preparation step is
critical in the development of the analytical method. In this
study, the general issue of affordable sample preparation for
routine analysis is transferred to the particular case of the
determination of antibiotics in liver tissue by presenting
different sample preparation strategies; however, these
strategies could be extended to other compounds present in
other complex matrices. The only constraint that is applied is
that these strategies should be simple and affordable to be
widely applied in any laboratory.
Experimental
Materials and Standards: Sulfadiazine (SDZ),
sulfamethoxazole (SMX), sulfamethazine (SMT), sulfapyridine
(SPY), sulfathiazole (STZ), all with purity ≥95%, ammonium
hydroxide solution (NH4OH), and formic acid were all
obtained from Sigma-Aldrich. HPLC-grade ethyl acetate
(EtAc), methanol, acetonitrile, and acetic acid (HAc) were all
purchased from J.T. Baker. Ultra-pure water was obtained
from a water purification system (Veolia Water). Individual
stock solutions of 1000 mg/L for each sulphonamide were
prepared in methanol and stored at 4 ºC. A standard working
solution of 100 mg/L was prepared weekly by diluting the
stock solutions with ultra-pure water and stored at 4 ºC. A
hydrophilic-lipophilic balanced copolymer and a mixed-mode
strong-cation exchange (SCX) polymer SPE cartridges of
150 mg format were used.
Sampling and Sample Treatment: Ovine liver samples were
purchased from local markets in Tarragona, Spain, and were
chopped and blended with a domestic food blender.
Homogenized samples were stored at -20 ºC prior to use. A
scheme highlighting the entire sample treatment and analysis
of the liver samples can be seen in Figure 1. Samples were
defrosted overnight at 4 ºC and a 1 g aliquot was transferred
into a 25-mL polypropylene centrifuge tube. The blank
FIGURE 1: Scheme of the optimized liver analysis procedure.
346 LCGC Europe July 2019
FONTANALS ET AL.
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samples were spiked when appropriate by adding the
working solution directly along with 10 mL of methanol. The
resulting solution was vortexed for 5 min using a vortex mixer
(Velp Scientifica) and then centrifuged at 1500 rpm for 10 min
using a Genevac miVac Duo Concentrator. The supernatant
was filtered using a 0.22-μm PTFE micro filter (Scharlab). The
resulting solution was made up to 10 mL again with methanol
and then diluted with water with 0.1% HAc to 50 mL. It was
then frozen at -20 ºC overnight to promote precipitation of
fats, lipids, and proteins. The solution was then centrifuged
for 25 min and filtered under gravity to obtain a clear solution
to submit to the mixed-mode SCX polymeric SPE cartridge for
clean-up and concentration of the sample. The SPE protocol
was as follows: the cartridge was conditioned with 5 mL of
methanol and then by 2 × 5 mL portions of ultrapure water.
The 50 mL sample solution was loaded and eluted with 10 mL
of 4.5% NH4OH in methanol. The solution was directly
injected into the LC–DAD system. During method validation,
the extract was evaporated to dryness using a Genevac
miVac Duo Concentrator, and reconstituted to 1 mL of mobile
phase.
LC–DAD Conditions: Chromatographic analysis was
performed using an Agilent 1100 HPLC system (Agilent
Technologies) equipped with a diode array detector (DAD), a
binary pump, and a 5 μL loop injector. Chromatographic
separation was performed using a 150 × 0.46 mm, 5-μm
Mediterranea Sea18 column (Technokroma). The mobile
phase was as follows: water with 0.1% HAc ~ pH 2.8 (A) and
acetonitrile (B). The flow rate was 1 mL/min, the monitoring
wavelength was 270 nm, and the column thermostat was set
at 30 ºC. The following gradient programme was used:
0–9 min from 10% to 55% (B); 9–11 min from 55% to 100%
(B); 11–15 min continued at 100% (B) and then returned to
initial conditions within 5 min.
Results and Discussion
Chromatographic Separation: Liquid chromatography (LC)
coupled to a DAD detector was used for chromatographic
separation, identification of analytes, and quantification. A DAD
detector can be used initially because, although it is not as
sensitive as MS, it is cheaper and more readily available, and if
necessary, the developed method can be easily transferred to
LC–MS because of the compatibility of the mobile phase. In
addition, sulphonamides absorb light in the UV–vis region and
the absorbance of the sulphonamides was evaluated from
220–360 nm. The monitoring wavelength was chosen to be
270 nm because this gave the strongest absorption for all
sulphonamides evaluated (13,14). The mobile phase chosen
was water with 0.1% HAc (A) and acetonitrile (B), which gave
adequate separation. To obtain sufficient retention and
separation, different elution gradients were tested and the
gradient that gave the best separation of compounds is
described in the experimental section. With this gradient all
compounds were eluted in 8 min.
When applied to liver samples, a high water content at the
beginning of the chromatographic run is needed to promote
the elution of interfering hydrophilic substances, co-extracted
from the complex liver matrix, in the first couple of minutes.
This avoids coelution with analytes and limits noise. Figure 2
shows a chromatogram where an extract from a liver sample,
spiked at 50 mg/kg with the analyte mixture, was injected. The
elution of various interferences can be seen in the first 5 min of
the chromatographic run. From 11–20 min after the gradient
had ended, the chromatographic run was continued at 100%
acetonitrile to clean the column and avoid a carry-over
phenomenon in the following chromatographic runs (15). While
this gradient provided the best possible separation, STZ and
SPY—the second and third eluted compounds—overlapped
slightly. However, resolution was calculated to be 1.87 ± 0.03
indicating they are sufficiently separated for this gradient
programme to be used.
Extraction Method: Animal tissues, particularly the liver, are
complex matrices that contain an abundance of interfering
components such as fats, proteins, and carbohydrates. However,
the liver is the site of detoxification and metabolism of
sulphonamides, which give a good indication of residue levels of
sulphonamides (16). To determine sulphonamides at low
concentrations, effective clean-up steps can be utilized rather than
increasing sensitivity through the use of expensive instrumentation.
SPE is a well-developed procedure used for clean-up and
concentration and is the most commonly used technique for food
and environmental sample preparation to determine veterinary
residues (17). Clean-up steps are essential in all samples and
must be optimized before the extraction technique.
60
50
40
30
20
10
0
mA
U
0 2.5 7.5 10
Time (min)
1512.5 17.5
SD
ZST
ZSP
Y
SM
T
SM
Z
5
FIGURE 2: The chromatographic separation of an extract from
a 1 g liver sample spiked with the five target sulphonamides at
50 mg/kg.
348 LCGC Europe July 2019
FONTANALS ET AL.
UNITY LAB SERVICES
Solid-Phase Extraction: Two different SPE cartridges were
evaluated for this procedure to concentrate the samples and
clean up the complex liver matrix: a hydrophilic-lipophilic
balanced copolymer SPE cartridge (150 mg), and a mixed-mode
SCX polymeric SPE cartridge (150 mg). They were evaluated
using initial conditions of 10 mL of aqueous solution spiked at
15 mg/L and eluting with 10 mL methanol. All these cartridges
have been previously used to retain sulphonamides so that
effective sample clean-up could occur; the hydrophilic-lipophilic
balanced copolymer SPE cartridge is the most popular for food
and environmental sample preparation for veterinary residues
(17–20).
Under the initial conditions, both cartridges gave full
recoveries of 92–130% for the mixed-mode SCX polymeric SPE
cartridge and 109–125% for the hydrophilic-lipophilic balanced
copolymer SPE cartridge. Previously, a silica-based modified
with C18 SPE cartridge was also evaluated, which provided
lower recoveries (35–90%) and had very poor reproducibility
with relative standard deviations (RSD) of between 15–30%.
Further optimization of both cartridges was achieved by
increasing the sample volume and by decreasing the elution
volume to concentrate the sample. For both cartridges, 50 mL
was chosen as the optimal sample volume as increasing the
sample volume up to this point did not affect recoveries. Larger
volumes than 50 mL were not evaluated since the loading
volume of SPE came from the extract for SLE. Moreover, 10 mL
was chosen as the elution volume because decreasing it from
10 mL to 5 mL resulted in some compounds not completely
eluting, in particular SDZ whose recoveries decreased by 37%
in the mixed-mode SCX polymeric SPE cartridge and 25% in the
hydrophilic-lipophilic balanced copolymer SPE cartridge. Two
TABLE 1: The percentage recoveries (n = 3) for the target sulphonamides with the hydrophilic-lipophilic balanced copolymer SPE
cartridge and mixed-mode SCX polymeric SPE cartridge when different loading solvents were tested using standard solutions
% Recovery
Hydrophilic-Lipophilic
Balanced Copolymer SPE
Cartridge
Mixed-Mode SCX Polymeric SPE Cartridge
H2O
0.1%HAc
Methanol/
H2O 0.1%
HAc (25:75)
H2O 0.1%HAc
Methanol/
H2O 0.1%
HAc (25:75)
Methanol
0.1% HAcMethanol EtAc
SDZ 103 43 90 65 37 17 11
STZ 116 52 117 114 87 35 48
SPY 128 68 130 106 133 118 69
SMT 120 86 106 111 50 32 17
SMZ 113 109 108 103 19 16 6
FIGURE 3: The percentage recoveries for each sulphonamide
when the hydrophilic-lipophilic balanced copolymer SPE
cartridge and the mixed-mode SCX polymeric SPE cartridge
were compared in SPE clean-up in a procedure starting from 1 g
of liver tissue spiked with the analyte mixture and including SLE,
and their associated % error (n = 5). H-L = hydrophilic-lipophilic.
FIGURE 4: The percentage recoveries for the target sulphonamides
when liver sample size was increased in the range 0.5 g to 5 g.
350 LCGC Europe July 2019
FONTANALS ET AL.
different organic elution solvents were
tested, acetonitrile and methanol, and in
the case of the mixed-mode SCX
polymeric SPE cartridge, NH4OH was
added to displace ionic interactions. In
the mixed-mode SCX polymeric SPE
cartridge both organic solvents gave
similar recoveries, but the
hydrophilic-lipophilic balanced
copolymer SPE cartridge, methanol gave
increased (11–13%) recoveries for each
sulphonamide and was selected as the
extraction solvent in both cases.
As the loading solvent in SPE is the
extracting solvent in the previous SLE
step, solvents other than water were also
tested using standard solutions spiked at
15 mg/L for both SPE methods. The
mixed-mode SCX polymeric SPE
cartridge is a mixed mode sorbent
functionalized with sulfonic groups so
that it can establish cationic interactions
with the target analytes. Therefore,
organic solvent can be tested as the
loading solvent. In contrast for the
hydrophilic-lipophilic balanced
copolymer SPE cartridge, dilution of the
organic solvent with water or an
evaporation step must occur before SPE.
For this cartridge, dilution of methanol by
a factor of four was found to significantly
decrease recoveries, particularly for the
most hydrophilic compounds as shown
in Table 1. Therefore, an evaporation step
is needed after SLE to completely
remove the organic solvent and
reconstitute the sample in water to be
loaded into the hydrophilic-lipophilic
balanced copolymer SPE cartridge.
For the mixed-mode SCX polymeric
SPE cartridge, organic solvents
methanol, EtAc, methanol with 0.1%
HAc, and methanol–water with 0.1 HAc
(25:75) were tested to load analytes onto
the cartridge. Table 1 highlights that
EtAc, methanol, and methanol with 0.1%
HAc gave poor recoveries for SDZ, SMT,
and SMZ. Although water with 0.1% HAc
gave the highest percentage recoveries,
25:75 methanol–water with 0.1% HAc
was chosen as the loading solvent
because it gave similarly good
recoveries, bar slightly lower for SDZ,
and disregards the need for an
evaporation step of the extract from SLE.
The recoveries and standard error for
the two optimized SPE methods when
applied to a 1 g liver sample spiked at
150 mg/kg, including the SLE extraction
step of 10 mL methanol and 5 min
extraction time, can be seen in Figure 3.
For the hydrophilic-lipophilic balanced
copolymer SPE cartridge, the resulting
solution from SLE was evaporated to
dryness and reconstituted in 50 mL of
water. SPE clean-up was performed and
compounds were eluted with 10 mL of
methanol. For the mixed-mode SCX
polymeric SPE cartridge, the resulting
solution from SLE was diluted to 50 mL
with water with 0.1% HAc (final solution
was 25:75 methanol–water v/v) before
SPE clean-up where compounds were
eluted with 10 mL of methanol with 4.5%
NH4OH. The mixed-mode SCX
polymeric SPE cartridge was chosen
because recoveries were higher with the
exception of SDZ. In addition, this
procedure is less time consuming than
that of the hydrophilic-lipophilic
balanced copolymer SPE cartridge due
to the absence of an evaporation step.
Solid-Liquid Extraction: Although PLE
can be used to extract analytes from the
solid matrix, it is expensive and
unavailable in routine laboratories.
Therefore, SLE with UAE and manual
extraction were tested for the extraction
of compounds from the liver. As SPE
conditions were optimized before SLE
conditions, methanol was chosen as the
extraction solvent because it provided
the best recoveries in SPE; no other
organic solvents were tested. Thus, in
both cases, 10 mL of methanol was
added to 1 g of liver and the resulting
mixture was shaken or sonicated for
5 min. The resulting solution was
351www.chromatographyonline.com
FONTANALS ET AL.
UNITY LAB SERVICES
subjected to the optimized SPE clean-up procedure as the
sample is too complex to be analyzed at this stage. The
recoveries obtained are in general those depicted in Figure 3,
with values ranging from 82% to 105% for all the compounds,
with the exception of SDZ (56%).
Increasing the extraction time for longer than 5 min was
found to have no effect on recoveries. Decreasing methanol
volume from 10 mL to 5 mL resulted in lower recoveries (from
97% to 49%); therefore, methanol volume was kept at 10 mL.
Recoveries obtained for UAE and manual extraction aided
with vortex were similar, between 61–106%. As a result,
manual extraction was chosen as a more readily available
method as UAE equipment was deemed unnecessary.
To lower the limits of detection (LOD), sample size can be
increased. However, increasing sample size also increases
the amount of interfering matrix components. Liver sample
size was increased from 0.5 g to 5 g to test the optimal
amount. A 1 g sample of liver gave good recoveries
(60–100%); however, when the liver amount was increased to
1.5 g, the recoveries were lower (49–97%) as seen in
Figure 4. Therefore, 1 g of sample was selected.
After SLE extraction with methanol, the sample was diluted
with water prior to SPE extraction. However, this promoted
the precipitation of fats and proteins. “Freezing-out” of
interfering components was a simple and cheap method
utilized by freezing extracts overnight and centrifuging for
25 min to remove interfering fats and proteins and provide a
cleaner extract. Although this step is long, it allows many
samples to be treated simultaneously.
The final optimized method consisted of taking 1 g of liver,
adding 10 mL methanol, and vortexing for 5 min. The
solution was then centrifuged and the supernatant was
filtered with a 0.22-μm PTFE filter and made up to 10 mL
again with methanol before diluting to 50 mL with water with
0.1% HAc. This solution was frozen overnight, centrifuged for
25 min, and then filtered under gravity to obtain a clear
solution percolated in the mixed-mode SCX polymeric SPE
cartridge. The SPE protocol was as follows: the cartridge
was conditioned with 5 mL of methanol and then by 2 × 5 mL
portions of ultrapure water. The 50-mL sample solution was
loaded and eluted with 10 mL of 4.5% NH4OH in methanol.
The solution was directly injected into the LC–DAD system.
To further decrease the limits of detection, an evaporation
step can occur after elution. This strategy was only used in
the method validation. However, during the method
optimization, samples were directly injected from the eluted
10 mL of 4.5% NH4OH in methanol because this is quicker
and more straightforward than when it needed to be
evaporated.
Method Validation: The final method was validated to
demonstrate its performance by assessing linearity,
percentage recoveries, repeatability, reproducibility, LODs,
and limits of quantification (LOQ).
Instrumental calibration curves were first constructed from
standard solution and good linearity from 0.05 or 0.1 mg/L to
30 mg/L was found for all analytes, with the value for the
coefficient of determination (R2) being above 0.996 in all
cases.
The final percentage recoveries were calculated by spiking
the 1 g liver samples at 1 mg/kg and using the optimized
procedure. Table 2 shows recovery values ranging from 78%
to 99% with the exception of SDZ (64%). All RSDs were
below 10% showing highly reproducible results. It should be
mentioned that a non-spiked liver sample was first analyzed,
and peaks at the same retention time to the target analytes
were subtracted.
Method limit of quantification (MQLs) were calculated by
applying the percentage recoveries as well as the
concentration factor to the lowest point on the calibration
curve. Method limit of detection (MDLs) were calculated by
dividing this value by three and then subsequently spiking
samples to obtain this final concentration to show that it is
identifiable. Signal-to-noise ratio (S/N) criterion higher than
three and 10 was used for MDLs and MQLs, respectively. All
these results are displayed in Table 2.
The repeatability and reproducibility of the method were
evaluated using five replicate extractions of 1 g of liver
sample spiked at 2×MQL and performed on the same day or
on different days, respectively. Both were expressed in terms
of %RSD, which was 8–18% for repeatability and 9–29% for
reproducibility.
As the MDL values obtained were 0.01–0.05 mg/kg, which
is below the total MRL of 0.1 mg/kg for all sulphonamides
issued by the EU Commission, this method can be used to
TABLE 2: Validation parameters for each sulphonamide when
applied to 1 g liver samples
% Ra MQL (mg/kg) MDL (mg/kg)
SDZ 64 0.1 0.05
STZ 83 0.1 0.02
SPY 99 0.1 0.02
SMT 78 0.05 0.01
SMZ 79 0.05 0.01
a %R calculated when 1 g of liver sample was spiked at 1 mg/kg with the mixture
of the target sulphonamides. See the text for the experimental conditions. RDS (n
= 5) < 10%.
352 LCGC Europe July 2019
FONTANALS ET AL.
confirm EU regulations are conformed
to and it can also easily be transferred
to LC–MS to lower MDLs.
Conclusions
Although a more sensitive technique,
such as tandem mass spectrometry
(MS/MS), can be used to obtain lower
MDLs, this technology is not always
available in routine laboratories. With
respect to sample extraction, in this
case it was found that cost could be
cut by using more basic techniques,
such as UAE and conventional SLE
methods, which gave good recoveries,
rather than methods using more
expensive instrumentation, such as
PLE and MAE.
It is evident that the complexity of the
animal matrix is still creating a huge
problem to produce effective methods
to routinely comply with EU regulations.
Finding effective sample clean-up and
extraction techniques to reduce
interfering matrix components is still an
area open for improvement and
exploration.
Acknowledgements
The authors would like to thank the
Ministerio de Economía y
Competitividad and the European
Regional Development Fund (ERDF)
(Project: CTQ2017-84373-R and
CTQ2017-88548-P) for the financial
support given.
References1) J. Aceña, S. Stampachiacchiere, S. Pérez, and D.
Barceló, Anal. Bioanal. Chem. 407, 6289–6299
(2015).
2) L. Ramos, J. Chromatogr. A 1221, 84–98 (2012).
3) M. Núñez, F. Borrull, N. Fontanals, and E.
Pocurull, Trends Anal. Chem. 97, 136–145
(2017).
4) M. Pérez-Rodríguez, R. Gereado Pellerano, L.
Pezza, and H. Redigolo-Pezza, Talanta 182,
1–18 (2018).
5) A. Larivière, S. Lissalde, M. Soubrand, and M.
Casellas-Français, Anal. Chem. 89, 453–465
(2017).
6) P. Vázquez-Roig and Y. Picó, Trends Anal. Chem.
71, 55–64 (2015).
7) B.K. Tiwari, Trends Anal. Chem. 71, 100–109
(2015).
8) A.G. Frenich, M.D. Aguilera-Luiz, J.L.M. Vidal,
and R. Romero-González, Anal. Chim. Acta 661,
150�160 (2010).
9) M. Solliec, A. Roy-Lachapelle, and S. Sauvé, Anal.
Chim. Acta 883, 415–424 (2015).
10) E. Miró-Abella, P. Herrero, N. Canela, L. Arola,
M.R. Ras, F. Borrull, and N. Fontanals, J.
Chromatogr. B 1105, 47–53 (2019).
11) A. Freitas, J. Barbosa, and F. Ramos, J.
Chromatogr. B 976, 49–54 (2015).
12) European Commission, O� . J. Eur. Union L15,
1–72 (2010).
13) Q. Zou, M. Xie, Y. Liu, J. Wang, J. Song, H. Gao,
and J. Han, J. Sep. Sci. 30, 2647–2655 (2007).
14) K. Deng, X. Lan, G. Sun L. Ji, and X. Zheng, Food
Anal. Methods 9, 3337–3344 (2016).
15) M.T. Martins, J. Me, F. Barreto, R.B. Ho� , L. Jank,
M.S. Bittencourt, J.B. Arsand, and E.E.S.
Schapoval, Talanta 129, 374–383 (2014).
16) H. Abdallah, C. Arnaudguilhem, F. Jaber, and R.
Lobinski, J. Chromatogr. A 1355, 61–72 (2014).
17) A. Andrade-Eiroa, M. Canle, V. Leroy-Cancellieri,
and V. Cerdà, Trends Anal. Chem. 80, 641–654
(2016).
18) M. Periša and S. BarbiÉ, J. Sep. Sci. 37,
1289–1296 (2014).
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Food Chem. 83, 601–608 (2003).
Leah Walker is currently completing
her MChem degree at the University
of Edinburgh.
Francesc Borrull is Full Professor
in analytical chemistry in the
Analytical and Organic Chemistry
Department at the Rovira i Virgili
University in Tarragona since 2003.
Eva Pocurull is Assistant Lecturer
in analytical chemistry in the
Analytical and Organic Chemistry
Department at the Rovira i Virgili
University in Tarragona since 2000.
Núria Fontanals is Senior
Researcher in the Analytical and
Organic Chemistry Department at
the Rovira i Virgili University in
Tarragona since 2007.
353www.chromatographyonline.com
FONTANALS ET AL.
UNITY LAB SERVICES
Strategies for the Quantifi cation of Endogenously Present Small Molecules in Biological SamplesMaxim Nelis, Patrick Augustijns, and Deirdre Cabooter, University of Leuven, Department of Pharmaceutical and Pharmacological
Sciences, Leuven, Belgium
The quantification of endogenously present compounds in biological samples demands appropriately validated
methods, in particular because increasing research efforts are aimed at studying the impact of such compounds
on human health and disease. International regulatory agencies, such as the Food and Drug Administration (FDA)
and the European Medicine Agency (EMA), have published a vast number of guidelines concerning bioanalytical
method validation over recent decades. Compared with the quantification of exogenous compounds, these
guidelines have remained rather vague and unclear when it comes to the quantification of endogenous compounds.
Nonetheless, the continuous expansion of studies devoted to the search for endogenous disease markers in the
human metabolome has incited the regulatory bodies to include endogenous compounds in a draft of an updated
International Conference on Harmonization (ICH) guideline on bioanalytical method validation. In light of these recent
developments, the main objective of this review article is to provide a clear summary of the different methods that
can be used to quantify endogenous small molecules. Because of the increased use of mass spectrometry (MS) in
the field of bioanalysis, a special focus will be placed on quantification by liquid chromatography (LC)–MS. Practical
recommendations to face this bioanalytical challenge, in particular in terms of method validation, will also be provided.
Endogenous analytes serve as important upstream
indicators and informative sources for downstream
biological processes (1). Apart from providing a platform
for the in-depth understanding of the underlying molecular
mechanism of a disease, metabolites might also play key
roles as “biomarkers” in the early diagnosis and prognosis
of diseases. The surge in high resolution and sensitive
mass spectrometry (MS) instruments has tremendously
propelled the realm of metabolomics and, hence, the
human metabolome has received increased attention as a
major source of information related to health and disease.
Nonetheless, a reliable quantification, in terms of accuracy
and precision, is hampered by the lack of a true blank
matrix, that is, a matrix without the analyte of interest, in
order to prepare matrix-matched calibrators (2). The use of
matrix-matched calibrators is recommended in the analysis of
biological samples, particularly when a mass spectrometer is
used. This is because matrix effects (MEs) are likely to occur
in the ionization source that might result in false negative and
false positive diagnostics (3,4,5). An additional challenge
lies in the absence of samples with a priori known quantities
of the compound of interest, which are required to check
for the accuracy of the method and the determination of the
quantitation limit. Even though the most recent Food and Drug
Administration (FDA) guidelines (2018) on bioanalytical method
validation address the challenge related to the quantification
of endogenous compounds, they merely provide some
general advice on the use of analyte-free matrix and the need
to evaluate parallelism (6). The Japanese Ministry of Health,
Labour, and Welfare (MHLW) recognizes the use of a surrogate
matrix on condition that its validity has been shown, but it does
not specify any further practicalities (7). The European Medicine
Agency (EMA), on the other hand, does not provide any
considerations on endogenous compounds in its guidelines (8).
Nonetheless, very recently, a joint effort of these three regulatory
bodies in the International Conference on Harmonization (ICH)
has resulted in a draft version of bioanalytical guidelines, which
now addresses the issue of endogenous compounds (9). The
respective regulatory agencies are accepting comments
until September 2019 before its formal implementation.
Over the years, three major strategies have been proposed
to quantify endogenous compounds: (i) the standard addition
method (SAM), (ii) quantification in a surrogate matrix, and (iii)
quantification with a surrogate analyte. As well as these, the
use of background subtraction has also been suggested. In
the latter, the calibration curve is created by spiking authentic
matrix with increasing concentrations of the compound of
interest. The resulting response curve is subsequently corrected
354 LCGC Europe July 2019
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for the endogenous (background) signal of the compound of
interest in the original matrix. Since the quantification limit is
confined by the endogenous levels and is consequently larger
than in other methods, the use of background subtraction has
remained limited (10). Nonetheless, correction of the resulting
peak area for background signal can serve as a viable way
to deal with the insufficient removal of the compound from the
matrix by, for instance, charcoal treatment (11). Each of the
approaches mentioned is associated with practical challenges,
advantages, and drawbacks, which are discussed in the
first part of this review. In the second part, further potential
issues related to the validation of the analytical method will be
addressed, including the determination of linearity, parallelism,
accuracy, matrix effects, and the limit of quantification.
Quantifi cation Strategies
Standard Addition Method: The standard addition method
(SAM) is recommended when no internal standards (IS) are
available to compensate for pronounced matrix effects. In
particular in multicomponent analysis, finding an adequate IS
for each analyte is not always possible. The standard addition
method comprises the addition of a reference standard of
the analyte of interest at increasing concentration levels into
multiple aliquots derived from the same biological sample
in which the endogenous concentration of the analyte has
to be determined. One aliquot remains unspiked. After
analysis, the detector response is plotted against the added
concentration and the endogenous concentration is derived
from the negative intersection with the x-axis (Figure 1). As
an alternative, the concentration can be plotted against the
detector response and the endogenous concentration can
be obtained from the intersection with the y-axis, which can
be immediately derived from the regression equation. This is
referred to as the reversed-axis method (Figure 1) (12,13).
SAM considers matrix effects by relying on the assumption
that all concentration levels—both endogenous and spiked—
experience a proportional ion enhancement or suppression
effect because each aliquot contains the same amount of
coeluting matrix compounds. As a new calibration line is
created for each individual sample, the method also considers
inter-individual differences in matrix effects. The use of SAM
has remained rather limited in the liquid chromatography–mass
spectrometry (LC–MS) analysis of biological samples because
matrix effects are mostly compensated by the use of an
appropriate IS (14) (see section “Surrogate Matrix Approach”).
The use of extrapolation presumes that the relationship
between the concentration and the response remains
unchanged outside the linear range. Therefore, the spiked
concentration range should be as close as possible to the
endogenous levels. The EU guideline on pesticide analysis,
a field where SAM is more commonplace, prescribes that
the added amounts should be within one half and five times
the endogenous concentration (15). However, this requires
information on the endogenous concentration levels, which
is, of course, the question of interest. To deal with this, an
explorative study needs to be conducted. One possible
method regards the estimation of these levels using an
external calibration curve created in solvent (16). Nevertheless,
it is doubtful whether the concentration obtained by this
method can serve as a trustworthy prediction because of
the occurrence of matrix effects, which, moreover, might also
vary between samples. As an alternative, a certain number
of samples can be spiked at a wide range of concentration
levels. After choosing an appropriate calibration model to fit
the data, the curve can be extrapolated to a tentative value
for which the subsequent spiking levels are selected, at for
instance 0.5, 1, and 2 times the tentative value. The remaining
spiking levels are omitted and the curve is again extrapolated
to zero response. Once the average expected endogenous
concentration is established, the remaining samples can be
spiked with a lower number of spiking levels, which cover
approximately 0.5, 1, and 2 times the expected level. This
approach was used by Olesti et al. (16) and resulted in values
that did not differ more than 15% from those obtained by
employing the surrogate matrix approach with the inclusion
of an IS (see section “Surrogate Matrix Approach”).
Even though the use of an IS is not required to compensate
for matrix effects, recent work by Hewavitharana et al. (2018)
has shown that the addition of an internal standard can
successfully correct for procedural errors and variations in
instrumental response (13). They showed that the IS can
even be used to correct for other compounds, provided
that the response of the internal standard is not influenced
by its unlabelled counterpart. This method was proven to
be superior to the use of an IS as surrogate analyte.
Surrogate Matrix Approach: The most widely adopted
way to quantify endogenous components in biological
FIGURE 1: The reversed-axis method (right) as an alternative
for the conventional way (left) to determine the endogenous
concentration by using SAM.
356 LCGC Europe July 2019
PHARMACEUTICAL PERSPECTIVES
samples is by using a surrogate matrix. This approach
is often called isotope dilution and involves the use of
the authentic analyte in an analyte-free matrix in order
to prepare the calibration standards. The endogenous
concentration is then simply calculated from the calibration
curve by interpolation. Surrogate matrices cover a broad
range of alternatives for the original matrix going from its
most simple form, a solvent or buffer, to more complex
alternatives including so-called stripped matrices in which
the analyte has been removed from the matrix (17–20).
Even though the method is simple and straightforward, it
is important to recognize that the matrix effects exercised
on the analyte might differ considerably in the authentic and
surrogate matrix. Even when the samples are subjected
to an extensive sample clean-up, such as solid-phase
extraction (SPE), matrix effects might still be pronounced
(5,21). Therefore, an IS can be added to both the calibrators
and the samples at a fixed concentration. The general idea is
that the IS experiences the same matrix effect as the analyte
of interest and, hence, their peak areas are affected to the
same degree. Regardless of any matrix effect, the analyte
to IS peak ratio will thus remain unaffected. For a given
concentration, the following equation (equation 1) then applies:
Analyte
Matrix Surrogate matrix
=IS
Area
Area
Analyte
IS
Area
Area
[1]
In order to experience the same matrix effect, the natural
analyte and its IS should have the same retention time and
similar physicochemical properties. A stable isotopically
labelled internal standard (SIL-IS) is considered the best
option due to its structural relatedness with the nonlabelled
analyte. The difference in mass generated by isotopic labelling
provides the option to differentiate the structural analogues
using MS. However, given the natural abundance of isotopes,
the SIL-IS should differ at least three mass units from its
nonlabelled analogue in order to minimize spectral overlap (22).
Despite its structural similarity, a SIL-IS does not always
guarantee successful compensation for the matrix effect
experienced by a target compound. The replacement of a
carbon-hydrogen bond by a carbon-deuterium bond, for
example, might decrease the polarity of the molecule and
affect its retention time. This phenomenon is known as the
isotope effect and can be circumvented by employing 13C or 15N labelled internal standards or by altering the
chromatographic conditions (23,24). Regarding which
concentration of IS to add, it is vital to investigate the influence
of the concentration of both IS and target analyte on each
other’s ionization signal in advance. A suitable IS concentration
should be selected to keep the product of the peak area
ratio (analyte/IS) and the inverse of the analyte concentration
(equation 2) constant across the entire linear range (25):
Constant = Response 1
xResponse [Analyte]IS
Analyte
[2]
Apart from compensating for matrix effects, the use of
SIL-ISs offers other advantages. It considers inter-individual
differences since the response ratios at a certain concentration
will not differ from sample to sample, even though the
matrix effect might be present to a different extent (21). A
SIL-IS can also compensate for other variable parameters
that might affect accurate and precise quantification, such
as procedural errors during sample clean-up (21,25,26).
Nevertheless, it is important to always assess whether analyte
and SIL-IS possess equal extraction recoveries (27–29).
Notwithstanding these advantages, the choice for
a SIL-IS might be restrained as a result of economic
reasons or commercial unavailability, particularly in
multicomponent analysis. In this case, the standard
addition method might be a viable alternative.
Surrogate Analyte Approach: In this approach, the calibration
curve is created in authentic matrix by using a surrogate
357www.chromatographyonline.com
PHARMACEUTICAL PERSPECTIVES
analyte possessing an identical ionization behaviour as the
naturally occurring compound. This is a stringent requirement
as both compounds should produce identical peak areas at
each concentration level to generate accurate results. For this
reason, a response factor (RF), expressing the difference in
peak area at the same concentration, is generally calculated
(equation 3) (28,30–31). Because of the endogenous nature
of the analyte, the RF can only be calculated in neat solution.
The RF should ideally be equal to 1 and can be optimized
by altering the collision energy in the MS instrument (28).
Alternatively, a correction factor reflecting the average RF at
different physiologically relevant concentration levels should be
included in the final calculation (equation 4) (17,31). The RF is
sometimes omitted in the final calculation of the endogenous
concentration if the difference in response is not greater
than 5% (28,32). The RF should be checked regularly after
optimization because changes in ionization efficiency might
occur, for example,after shutdown of the instrument (30).
RF =Response
Response
natural analyte
surrogate analyte
[3]
Regarding the choice of the surrogate analyte, a labelled
analogue offers the best option given its structural similarity
with its natural counterpart and, hence, similar ionization
behaviour. Moreover, since the surrogate analyte does not
consider matrix effects, an IS, correcting for the matrix effects
experienced by the natural as well as the surrogate analyte,
must be added as well. This triangular relationship demands a
strong compatibility between natural analyte, surrogate analyte,
and IS for which labelled analogues are the best choice. In this
case, the mass of all the analogues should differ sufficiently
to prevent overlap of their respective isotopic pattern.
The endogenous concentration is calculated
in accordance with the following formula:
[(Response
m
surrogate analyte/ IS) x RF ] –bConcentration =natural analyte
Response
[4]
in which m represents the slope of the calibration curve,
b its y-intercept, and RF the average response factor.
Validation
Regression Analysis and Parallelism: The calibration
range should comprise a physiologically relevant range
wherein at least six equally distributed concentration levels
are included (33,8). Although EMA and FDA do not compel
the replicate analysis of each calibrator, it is advised to
perform at least duplicate analysis at each level (6,8,34).
The choice of an appropriate regression model is necessary
to ensure accurate results. To fit the data to a linear curve, the
ordinary least squares regression (OLS) method is the simplest
approach in bioanalysis. The OLS method assumes that the
response variables have equal variances across the entire
concentration range, often referred to as homoscedasticity
(35). To verify this, the residuals can be plotted against the
concentration (34). In case of homoscedasticity, the residuals
will be homogenously distributed around the x-axis. Another
fast approach comprises the computation of the F-value,
which represents the ratio of the highest to the lowest variance
(36–38). This calculated F-value is compared against the critical
F-value to evaluate whether the assumption is violated or not.
In case of unequal variances, the number of calibration
points can be decreased because higher variances are often
observed at higher concentrations. This can only occur at the
cost of a reduction of the interpolation range (36). When SAM
is used, the highest spiked values can be eliminated, provided
that a sufficient amount of calibration points remain to assess
linearity. Alternatively, a weighed linear regression (WLS) model
can be applied (37). In bioanalysis, the following empirical
weighing factors are commonly used: 1/x, 1/x², 1/y, and 1/y². To
verify which weighing factor is appropriate, the back-calculated
FIGURE 3: Determining accuracy when the surrogate matrix or
surrogate analyte approach is used (left) and when SAM is used
(right). The arrow indicates the difference between CM and C
0
that should be compared with the nominal spiked concentration.
FIGURE 2: Calibration curve in authentic matrix with surrogate
analyte or in surrogate matrix with authentic analyte showing
parallelism with matrix 1, but not with matrix 2.
358 LCGC Europe July 2019
PHARMACEUTICAL PERSPECTIVES
concentrations of the calibrators should be compared with the
nominal values. The relative residual (equation 5) should be
within 15% for at least 75% of the calibration levels (6,8,16,37):
x 100%Relative residual (%) =
Conc –Concback–calculated
Concnorminal
nominal
[5]
If linear regression models are not sufficient to fit the data
across the desired concentration
range, polynomial regression
models can be considered. Note
that the addition of an increasing
number of independent variables will
automatically result in an increased
coefficient of determination (R²).
Even though it is often used as a
criterion to gauge the goodness
of fit of the calibrator data, an
increased R²-value might also be
the result of overfitting and, hence,
does not necessarily imply a higher
predictive power. Therefore, other
formal tests to assess whether an
increase in prediction parameters
improves the fitting are more
suitable, such as the lack-of-fit test
(LOF) or Mandel’s test (39–41). In
accordance with Occam’s razor,
the simplest model describing the
relationship is always preferred (6).
Because the detector
response might be different in
matrix and solvent, international
guidelines advocate the use of
matrix-matched calibrators. Strictly
speaking, the standard addition
method encompasses the use of
matrix-matched calibrators because
a calibration curve is created
in each individual sample (13).
Nonetheless, it is necessary to be
aware that SAM assumes a linear
relationship outside the calibration
range and as aforementioned, it is
for this reason required to evaluate
linearity as close as possible to the
endogenous concentration value.
The surrogate analyte approach
involves the use of authentic
matrix to create the calibration curve. However, it must
still be verified whether matrix effects experienced by
the authentic and surrogate analyte are equal. This
can be examined by showing so-called parallelism
(28,42). From a practical point of view, a matrix spiked
with authentic analyte at increasing levels is compared
with the same matrix spiked with increasing levels of
the surrogate analyte (Figure 2). The IS is added at a
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PHARMACEUTICAL PERSPECTIVES
fixed concentration level each time to correct the peak
area of authentic and surrogate analyte. If the obtained
slopes of the calibration curves do not differ significantly
from each other, parallelism has been demonstrated.
Significance can be traced by a formal t-test (43).
In contrast with SAM and the surrogate analyte
approach, the surrogate matrix approach does not involve
the creation of the calibration curve in authentic matrix. In
this case, demonstrating parallelism between the authentic
and surrogate matrix is necessary to ensure the valid use
of a surrogate matrix (43,44). Both authentic and surrogate
matrix are for this purpose spiked at increasing levels of
the analyte and at a fixed level of the IS. The regression
curves are compared as described above (Figure 2).
Accuracy: Accuracy is a measure to describe the trueness
with which the concentration is determined in samples
(quality controls [QCs]) containing known amounts of
analyte, that is, the nominal amount. The concentration is
determined against the calibration curve and the accuracy
value is expressed as the ratio of the determined value
to the nominal value. The accepted deviation from the
nominal value is set at ± 20% for the LLOQ and ± 15% for
higher levels. However, as the endogenous concentration
is unknown, the accuracy of the method cannot be
assessed in this way for endogenous compounds. This
hiatus can be bypassed by using equation 6, in which
CM represents the measured concentration upon spiking,
C0 the measured basal concentration level, and C
s
the nominal spiked concentration (Figure 3) (45):
Accuracy (%) = x 100%C 0–CM
C S [6]
Both CM and C
0 are determined using either one of the
suggested methods in the ”Quantification Strategies” section.
Since the physiologically relevant range might be
surpassed by spiking the authentic matrix, the draft version
of the ICH bioanalytical guidelines recommends the use
of QC samples with the lowest endogenous concentration
(9). Besides stating that the spiked concentration levels
should be statistically different from the endogenous
concentration, it does not provide any further requirements
regarding spiking concentrations. However, it should be
recognized that spiking outside a physiologically relevant
range might undermine the actual accuracy of the method.
Therefore, spiking levels are suggested to not exceed
four times the expected endogenous concentration (46).
The determination of accuracy through spiking
might also enable to demonstrate the validity of the
utilization of the surrogate matrix or surrogate analyte
for quantification purposes as accuracy values within
predefined limits justify their usage (9,17,32,42).
Contrary to SAM and the surrogate matrix
approach, the surrogate analyte approach enables
the accuracy of the method below the natural
endogenous level to be monitored by using the
surrogate analyte to prepare QCs (30).
Matrix Effect: The matrix effect can be calculated in
multiple ways. EMA suggests the calculation of a matrix
factor (MF) (8). This parameter compares the response
of the analyte spiked at the same concentration level in
authentic matrix and in neat solution. In case the sample
undergoes a pretreatment step, the analyte is spiked
in the extracted matrix to differentiate the matrix factor
from recovery (47,48). The method is often referred to
as the post-extraction spike method (48). To circumvent
interfering responses arising from basal analyte levels,
the peak area of the unspiked matrix is subtracted from
the peak area of the spiked matrix (equation 7) (29,47):
MF =Analyte
Peak Area (Analyte Spiked in Matrix)– Peak Area (Endogenous Analyte in Matrix)
Peak Area (Analyte in Pure Solution)
[7]An MF < 1 indicates ion suppression and an
MF > 1 indicates ion enhancement with respect
to the response of the analyte in neat solution.
The IS should experience the same matrix effect as the
analyte of interest in order to function as an appropriate IS.
The matrix factor of the IS can be calculated without any
correction for endogenous levels (equation 8) (8,17,29).
MF =IS
Peak Area (IS in Matrix)
Peak Area (IS in Pure Solution) [8]
By dividing the MF of the analyte with the MF of the IS,
the IS-normalized MF is calculated. In spite of the absence
of any limits provided by the regulatory agencies, the
normalized MF should be as close to unity as possible.
The inter-individual variability of the IS-normalized MF,
often referred to as the relative matrix effect, should be
assessed by analyzing a minimum of six different samples.
The variability of the matrix effect among the samples
is expressed in terms of covariance and should not
exceed the limit of 15% (8). More stringent cut-off values
(CV < 5%) have, however, also been put forward (48,49).
Alternatively, the matrix effect can be assessed by
comparing the slopes of a standard addition curve in
matrix with a standard curve in neat solution, both spiked
at equal concentration levels posterior to the sample
preparation step (49,50). Slopes of the two curves can
subsequently be compared by using a two-tailed t-test at
360 LCGC Europe July 2019
PHARMACEUTICAL PERSPECTIVES
a predefined significance level (50). However, a p-value
does not provide any absolute numerical value that
characterizes the matrix effect. For this reason, it seems
more useful to calculate the matrix effect in the same way
as in the post-extraction method, namely by calculating the
percent ratio of the slopes of the standard addition curve
and the standard curve in neat solution (4,16,49,51,52).
Limit of Quantification: In accordance with EMA and
FDA guidelines on bioanalytical method validation, the
limit of quantification (LOQ) is defined as “the lowest
concentration of analyte in the sample which can be
quantified reliably, with an acceptable accuracy and
precision” (6,8). Acceptable refers to an accuracy value
of 20% from the nominal concentration, and a precision
value of less than 20%. Both guidelines further restrict
the definition of LOQ as the lowest concentration level
of the calibration curve that was created in authentic
matrix. Another common approach to determine
the LOQ is the determination of the concentration
corresponding to a signal-to-noise ratio of 10 (53).
Both definitions impose some issues when the
surrogate matrix approach is used. The EMA and
FDA expect that the LOQ is evaluated in authentic
matrix. When utilizing the surrogate analyte approach,
the surrogate analyte can be used to determine the
LOQ (30). Although the ICH draft guideline does not
encompass any guidance concerning the determination
of the LOQ when dealing with endogenous compounds,
the standard addition method and the surrogate
matrix approach demand an alternative approach.
In this context, it is interesting to discriminate between
the instrumental LOQ (iLOQ) and the method LOQ (mLOQ)
(54). While the iLOQ is a measure to characterize the
performance of an instrument itself, the mLOQ considers
the sensitivity of the actual method in the sample. The latter
thereby considers potential loss in response as a result of,
for example, procedural errors or matrix effects. The iLOQ
can be easily determined in solvent. In contrast, the mLOQ
is not as easily transferable to endogenously present
molecules when SAM or the surrogate matrix approach is
employed. There might be, for example, pronounced ion
suppression present in the matrix, and as a result, the LOQ
determined in solvent will differ from the LOQ in matrix.
For this reason, Tsikas suggested to define the LOQ as
“a representation of the lowest added analyte concentration
(CLOW+
), which, upon addition to the biological sample, can
be experimentally measured with acceptable accuracy
(i.e. max. ± 20% deviation of the nominal value) and
precision (max. RSD 20%)” (46). In the same way as for
accuracy, the CM and C
0, representing the concentrations
upon spiking and the basal concentration, respectively, are
determined by using either SAM or the surrogate matrix
approach. The accuracy is calculated in accordance
with the following formula, in which CLOW+
represents the
lowest concentration of the spiked analyte that generates
an accuracy value that falls within the limits (equation 9):
Accuracy (%) = 100%
C
xC
C–M
LOW+
0
[9]
Conclusion
The quantification of endogenous compounds imposes a
challenging task for the analyst because of the absence
of a true blank matrix, which is required by FDA and EMA
in the preparation of calibrators. Particularly in LC–MS,
it is important to consider the influence of the matrix on
the quantification as a result of matrix effects. Moreover,
the current description of the validation parameters
provided by the regulatory agencies provides a unique
focus on the quantification of exogenous compounds and
it should be appreciated that not all of them are directly
transferable to the context of endogenous compounds.
Very recently, the ICH has issued a new provisional
guideline that contains guidance on the validation of
methods used to quantify endogenous components (9).
A few ways are used to bridge the difficulties in the
quantification process. Among them, the surrogate matrix
approach offers the most straightforward way provided
that an IS is available. Nonetheless, the mere availability
of an IS does not guarantee instant success for accurate
quantification, for example, in case of differences in
elution time between the IS and the natural analyte. Its
validity should therefore be thoughtfully addressed, not
only in terms of coelution, but also in terms of mutual
suppression. The surrogate analyte approach, on the
other hand, offers the option to set the LOQ in the
actual matrix without additional spiking and to prepare
QC samples below the endogenous concentration
levels, but its use might be limited due to the need
for two ISs. In addition, in multicomponent analysis,
utilizing an IS for each compound of interest might be
impeded because of commercial unavailability. In the
latter case, SAM might provide a viable alternative.
Regarding method validation, the use of the surrogate
matrix or the surrogate analyte should be justified by
proving parallelism between the surrogate matrix and the
authentic matrix, or between the surrogate analyte and
the authentic analyte, respectively. For this purpose, the
361www.chromatographyonline.com
PHARMACEUTICAL PERSPECTIVES
comparison of slopes can be used as an alternative for
the determination of accuracy at different concentration
levels. As a result of the unavailability of a true blank
matrix, the assessment of accuracy can be determined
by comparing the difference between the concentration
found after spiking and the endogenous concentration
with the nominal spiked concentration. It is clear that
in this way, accuracy cannot be determined below the
endogenous concentration in the QC sample. In contrast,
the surrogate analyte approach offers the advantage of
evaluating accuracy below the endogenous concentration
of the natural analyte. In the same way, the LOQ can
be determined by simple use of the surrogate analyte
rather than, as is the case for the other approaches,
defining the lowest concentration that can be spiked to
generate an accuracy value within predefined limits.
Coping with the unavailability of a blank matrix in this
particular context might seem challenging at first sight.
However, even without the use of an IS, quantification
of endogenous concentration levels can still be
achieved through the standard addition method. Of
course, developing an adequate validation strategy is
key in the generation of reliable results. The constant
growth of the realm of metabolomics and biomarker
research has prompted the regulatory agencies to
provide a homogenous validation strategy regarding
the quantification of endogenous compounds.
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Maxim Nelis is a Ph.D. student at the University
of Leuven (KU Leuven) in Belgium. His current
research focuses on the identification and
quantification of small molecules in biological
specimens collected from IBS patients with
the objective to find a biomarker.
Patrick Augustijns is a professor in biopharmaceutics
at KU Leuven. His laboratory has developed
a unique technique to collect gastro-intestinal
samples. Upon quantification of drugs and
metabolites in these samples, this approach
allows the gastro-intestinal behaviour of drugs and
formulations to be linked to the systemic outcome.
Deirdre Cabooter is the editor of “Pharmaceutical
Perspectives”. She is a research professor at the
Department of Pharmaceutical and Pharmacological
Sciences of KU Leuven. Her research interests include
studying mass transfer in liquid chromatography,
analyzing complex samples in diverse fields of
application, retention modelling, and solutions for
automated method development. She is also a member
of LCGC Europe’s editorial advisory board. Direct
correspondence about this column to the editor-in-chief,
Alasdair Matheson, at [email protected]
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PHARMACEUTICAL PERSPECTIVES
Mobile Phase Buffers in LC:Effect of Buffer Preparation Method on Retention RepeatabilityDwight R. Stoll1 and Devin M. Makey2, 1LC Troubleshooting Editor, 2Gustavus Adolphus College, St. Peter, Minnesota, USA
For liquid chromatography (LC) methods where the buffer pH and composition have an influence on retention,
which buffer preparation method will provide the most repeatable results?
The measurement of pH, one of the most
common of all analytical measurements,
plays a major role in many chemical
processes, affecting everything from
the productivity of bioreactors in the
biopharmaceutical industry, to the
performance of separation methods
in liquid chromatography (LC) and
electrophoresis. Given that pH
measurement is so common, I think
we can be lulled into the perception
that it is also simple, and that the pH
reported by any benchtop pH meter
can be accepted at face value under all
circumstances. In my interactions working
with people at a variety of experience
levels over the years, I have often felt that
people preparing buffers for use in LC
are a little too trusting of the pH values
reported by pH meters under ordinary
circumstances. In preparation for this
month’s “LC Troubleshooting” column, my
student Devin Makey and I performed
some experiments to see if we could
move in the direction of getting answers
to questions around the topic of the “best”
way to prepare buffers for use in LC. What
follows here is a description of those
experiments, and the data we observed.
I believe that the results are interesting,
and can support best practices for
improving the reliability of LC methods.
I know they are certainly affecting the
way we operate in my laboratory, and I
hope you will find them useful as well.
Dwight Stoll
Introduction
The Role of Eluent pH in LC: The pH
of the eluent has a significant impact
on retention and peak shape in several
modes of LC separations. This is
well understood in reversed-phase
separations, where retention is strongly
dependent on the solubility of the analyte
in the organic-aqueous eluent. The pH
of the eluent affects the charge state
of various functional groups (COOH,
NH2, and so forth), and the charge
state of these functional groups has
a major impact on the solubility of the
analyte in water; this is the origin of the
primary influence of pH on retention. For
example, a simple weak organic acid like
benzoic acid will be neutral (uncharged)
in eluents buffered well below the pKa
(~4.1), because the carboxylic acid
functional group will be protonated.
However, in eluents buffered well above
the pKa, the carboxylic acid functional
group will be deprotonated, and carry a
negative charge. The strong interactions
between the negatively charged
carboxylate group and the highly dipolar
water molecules result in a much higher
water solubility of the benzoic acid
in the high pH eluent, and thus lower
reversed-phase retention under these
conditions. This is exemplified by the
experimental retention data shown in
Figure 1 for benzoic acid on a C18
reversed-phase column, where the
eluent was buffered at different pH levels.
The same chemistry is relevant
in hydrophilic interaction
(HILIC) separations, though the
dependence of retention on pH
is often more complicated than it
is in reversed-phase separations
because of the more influential role
of electrostatic interactions between
the analyte and the stationary phase
in HILIC. The influence of pH on other
LC modes, such as ion-exchange,
is even more evident because the
magnitude of electrostatic interactions
between the analyte and stationary
phase is the dominant factor
influencing retention. Thinking through
these examples, it is clear that pH
adjustment of buffered eluents is a
topic with broad implications in LC.
Current Perspectives on Buffer
Preparation: We also recognize that
buffer preparation and pH adjustment
is a pretty controversial topic in the LC
community. This topic has been covered
on multiple occasions in this column,
focusing on aspects including buffer
selection (1), preparation methods (2,3),
and the idea of solution pH when an
organic solvent is added to the mix (4). In
a recent article of our own, we discussed
the effects of different methods of
buffer preparation on results from HILIC
separations (5). In our preparation for
this instalment, we have found, through
discussions with a variety of people, that
they often have strongly held beliefs
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about what is right and wrong when it
comes to buffer preparation, but also
that these positions are not always
clearly supported by experimental
evidence. With this as a backdrop,
we set out to make some of our own
measurements with the hope that they
would add to understanding in this area.
The most commonly used approach to
buffer preparation for use in LC involves
adding a salt of a buffering agent to
water, then adding a small volume of
relatively concentrated acid or base
solution until a target pH is reached
(as indicated by a pH electrode), and
finally diluting to a specified volume. For
example, suppose we are interested
in making 1 L of phosphate buffer at
pH 6. Although there are many ways to
prepare this buffer, a commonly used
approach would be to first add sodium
hydrogen phosphate (Na2HPO
4) to
about 900 mL of water. The pH of this
initial solution will be about 9. Then, we
could add phosphoric or hydrochloric
acid to the solution slowly and watch
the pH meter, stopping the addition of
acid when the meter reads 6.00. We
could then transfer the solution to a 1 L
volumetric flask, and fill it to the mark
with water. The focus of this article is
really trying to answer the question, “If
we make this buffer ten times, will we
have added exactly the same amount
of acid when we have stopped at pH
6.00, according to the pH meter?” If the
answer is “yes”, then all is well, and we
should expect similar results from LC
separations involving these ten buffers.
We will show that more often than not
the answer is “no”, and that the extent
of variation of the acid added from one
buffer to the next is enough to cause
measurable variability in retention in
some cases. At this point you may be
asking yourself, “How can the answer
possibly be ‘no’?” That in itself is a
good question, and one that requires
many more words than we can fit in this
short article. We’ll come back to this
question at the end of our discussion,
and suggest some reading material
for those interested in really digging
into this more. For now, on to the data.
Experiments, Results, and
Discussion
Dependence of Retention on pH for
Some Probe Molecules: As a first
step in this work, we set out to identify
some simple probe molecules to use
under reversed-phase conditions, and
measure the dependence of their
retentions on eluent pH. We chose one
neutral molecule (ethylbenzene), one
weak acid (butylbenzoic acid, pKa ~4.2),
and two weak bases (4-hexylaniline,
pKa ~4.8; and 4-aminobiphenyl, pK
a
~4.3), and used uracil in our test mixture
as a column dead time marker. We
then prepared about 500 mL each of
nine potassium phosphate buffers with
expected aqueous pH values between
2.80 and 3.20, in increments of 0.05
units. The approach was to first add
potassium phosphate (the same amount
in each case, 30 millimoles), then add
different amounts of phosphoric acid
as needed to reach the target solution
pH, and finally add enough water to
reach a total mass of 500.0 g. These
amounts are shown in Table 1, and
were calculated by solving the charge
balance equation for this system for the
number of moles of phosphoric acid that
was required at each pH level. Activity
coefficients were calculated using the
extended Debye-Hückel equation (6).
Using each of these buffers as the
aqueous component of the eluent, we
measured the retention times of the four
probe compounds on a C18 column.
The resulting chromatograms for five of
the buffers are shown in Figure 2, and
a plot of the retention factors of the
three ionizable probes relative to the
retention factor of ethylbenzene is shown
in Figure 3. At this point we make two
observations. First, Figure 2 shows that
the retention of ethylbenzene is nominally
independent of pH, as expected, allowing
us to normalize the retention of the
other three probe compounds to the
retention of ethylbenzene to minimize
20
15
10
5
0
4 5 6 7 8
Eluent pH (Aqueous Phase)
Rete
nti
on
Fact
or
3
1600
1
2
34 5
1200
800
400
Time (min)
pH = 3.15
pH = 3.05
Sig
na
l (m
AU
, 2
54
nm
)
pH = 2.95
pH = 2.85
0
0 1 2 3 4 5
FIGURE 1: Effect of eluent pH on the
retention of benzoic acid under
reversed-phase conditions.
Chromatographic conditions: column,
SB-C18; eluent, 10:90 acetonitrile–
phosphate buffer; temperature, 40 °C.
FIGURE 2: Effect of eluent pH on the
retention of probe compounds (2)
4-aminobiphenyl, (3) n-butylbenzoic
acid, (4) 4-hexylaniline, and (5)
ethylbenzene. Uracil (1) was used as
a dead time marker. Chromatographic
conditions: column, StableBond C18
(50 mm × 4.6 mm, 3.5-μm); flow rate,
2.0 mL/min.; eluent, 40:60 acetonitrile–
buffer; temperature, 40 °C.
366 LCGC Europe July 2019
LC TROUBLESHOOTING
the effects of other variables such as
temperature and organic to water ratio
in the eluent on these measurements.
On the other hand, the retention of the
other three probes all exhibit some
dependence of retention on pH, with the
hexylaniline being the most sensitive
of the three by far. Second, Figure 3
shows that the observed retention of
each of the three ionizable probes is
a smooth function of the calculated
pH. Although the exact dependence
of retention on pH is unknown for
these conditions, we would at least
expect it to be a smooth relationship.
Comparison of pH Meter-Directed
and Gravimetric Methods of Buffer
Preparation: Now, let’s return to our
1.0
0.8
0.6
k/k
eth
ylb
en
ze
ne
0.4
0.2
0.0
2.8 3.0 3.1 3.2
pH
2.9
FIGURE 3: Retention of the three
ionizable probes relative to the
retention of ethylbenzene. Conditions
are as in Figure 2. Key: O hexylaniline,
O butylbenzoic acid, O aminobiphenyl.
TABLE 1: Reagents added to make 0.5 L potassium phosphate buffers solutions in the
range of pH 2.8 to 3.2a.
Mass
KH2PO
4 (g)
Mass 85% H3PO
4(g) Expected PH Measured pH
4.083 0.75 2.80 2.84
4.083 0.67 2.85 2.84
4.083 0.60 2.90 2.92
4.083 0.54 2.95 2.97
4.083 0.47 3.00 2.94
4.083 0.43 3.05 3.04
4.083 0.37 3.10 3.12
4.083 0.33 3.15 3.17
4.083 0.30 3.20 3.12
aThe pHs of all solutions used in this work were measured using a low sodium error glass
electrode (Orion 8101BNWP ROSS Half-Cell Electrode, from Thermo Scientific (Waltham, MA,
USA), calibrated using pH 1.68 and 4.00 standards from VWR (West Chester, PA, USA).
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LC TROUBLESHOOTING
question above: “If we make this buffer
ten times, will we have added exactly
the same amount of acid when we have
stopped at pH 6.00, according to the pH
meter?” We prepared three replicates of
a nominal pH 3 buffer as described in
Table 1 by using two different methods:
A) pH meter-directed: In this case,
we use the pH meter to decide when
to stop adding phosphoric acid (for
example, when the meter reads 3.00).
B) Gravimetric: In this case, we
calculate the amounts of each reagent
ahead of time, and repeat that recipe
each time, only measuring the pH of the
solution when the buffer is complete.
The nominal procedures for the
two methods, and the amounts of
reagents added for the six buffers
used to obtain the data shown in
Figure 4, are shown in Table 2.
Figure 4 shows the mean relative
retention of hexylaniline measured for six
different buffers prepared by the same
analyst, three by the pH meter-directed
method (all using the same meter and
electrode), and three by the gravimetric
method. The results are quite clear. They
show that the buffers prepared using
the gravimetric method lead to much
better repeatability of retention time in
different buffers, relative to the repeatability
observed for different buffers made
using the pH meter-directed approach.
These results are evidence that the
answer to our question posed earlier in
this article is “no”. In other words, the
pH values reported by the meter are not
sufficiently repeatable to guide preparation
of the buffer when buffers of highly
repeatable composition are needed.
Having settled on the protocol shown in
Table 2 for the gravimetric method, three
other analysts from our laboratory each
prepared three replicate buffers using
the pH meter-directed approach, and
three using the gravimetric approach.
The results are shown in Table 3, where
we see that all four analysts were
able to produce buffers that led to
highly repeatable retention time using
the gravimetric approach, whereas
the buffers prepared using the pH
meter-directed approach always led to
much more variable retention times.
Closing Thoughts
Clearly not all work involving buffered
solutions requires the level of repeatability
in pH that we explored in this work.
However, we believe these results show
that, when working with analytes that
0.680 (a) (b)
kh
ex
yla
nil
ine/k
eth
ylb
en
ze
ne
kh
ex
yla
nil
ine/k
eth
ylb
en
ze
ne
0.695
0.690
0.685
0.680
0.6751 2 31 2 3
0.675
0.670
0.665
0.660
Buffer# Buffer#
FIGURE 4: Relative retention of hexylaniline (normalized to ethylbenzene) for six
buffers prepared by the same analyst; (a) for pH meter-directed approach, and (b) for
gravimetric approach. Chromatographic conditions are as in Figure 2. Details of the
buffer preparations are given in Table 2. Error bars represent one standard deviation
for ten replicate injections of the probe compound with a given buffer.
TABLE 2: Buffer preparation steps and amounts of reagents added for three different
replicate buffers made using the pH meter-directed and gravimetric approaches.
Buffer Number
Step pH Meter-Directed Approach 1 2 3
1 Weighed 4.0827 g KH2PO
4 in a weighing boat 4.0827 g 4.0827 g 4.0827 g
2 Transferred salt to 500 mL beaker
3 Rinsed weighing vessel 4× with H2O
4 Added 450 g H2O
5 Titrated to pH 3.00 with 0.85% H3PO
429.3 mL 26.9 mL 24.9 mL
6Transferred solution to 500 mL volumetric
flask and filled to line with H2O
Buffer Number
Step Gravimetric Approach 1 2 3
1Weighed 4.0827 g KH
2PO
4 in a weighing
boat4.0827 g 4.0828 g 4.0827 g
2 Weighed 28.179 g 0.85% H3PO
4in a beaker 28.1727 g 28.1788 g 28.1734 g
3 Transferred salt and acid to solvent bottle
4 Rinsed weighing vessels 4× with H2O
5 Diluted up to 500 g with H2O 500.03 g 500.02 g 500.00 g
Measured pH 2.98 2.97 2.96
TABLE 3: Percent relative standard
deviation (% RSD) of relative retention of
hexylaniline measured using buffers (n(( = 3
in each case) prepared by four different
analysts and two different methods.
Analyst
Approach
pH Meter-
DirectedGravimetric
1 2.72 0.06
2 2.23 0.10
3 2.34 0.02
4 1.25 0.003
368 LCGC Europe July 2019
LC TROUBLESHOOTING
have a significant retention dependence
on pH of the eluent, the gravimetric
approach to buffer preparation is worth
considering seriously. Simply put, in most
cases weighing reagents using a balance
is a simpler operation than measuring pH
using a glass electrode, and can be done
with extraordinary precision compared
to most other analytical methods. When
the recipe for a particular buffer is
known, and repeating the preparation of
the buffer in a precise way is desirable,
then the gravimetric approach is most
precise. Readers interested in learning
more about factors that influence
the accuracy and precision of pH
measurement at this level are referred
to Bates’ book on the topic (7). Finally,
readers interested in tools for calculation
of buffer recipes that can be used with
a gravimetric approach are referred to
free web-based tools developed by
Professor Rob Beynon (https://www.
liverpool.ac.uk/pfg/Research/Tools/
BuffferCalc/Buffer.html), and Professor
Peter Carr and Aosheng Wang (http://
zirchrom.com/Buffer.asp) It is important
to recognize that the latter tool does not
correct pH calculations to account for
activity effects, which affect calculated
pHs of solutions of high ionic strength
and multiply charged buffer components
(for example, phosphate at pH 7).
Acknowledgements
We’d like to acknowledge the effort of
Hayley Lhotka, Alex Florea, and Gabriel
Leme and their willingness to participate
in the experiments described here. We
also thank Professor Peter Carr and Dr.
William Tindall for their willingness to share
their knowledge of this subject with us.
DM was supported by a grant from the
Camille and Henry Dreyfus Foundation.
References1) G.W. Tindall, LCGC North Am. 20, 1114–1118
(2002).
2) J.W. Dolan, LCGC Europe 28(1), 40–44 (2015).
3) G.W. Tindall, LCGC North Am. 21, 28–32 (2003).
4) G.W. Tindall, LCGC North Am. 20, 1028–1032
(2002).
5) D.R. Stoll and C. Seidl, LCGC Europe 31(3),
144–148 (2018).
6) L.W. Potts, Quantitative Analysis: Theory and
Practice (Harper & Row, New York, New York,
USA, 1987).
7) R.G. Bates, Determination of pH: theory and
Practice (John Wiley and Sons, New York, New
York, USA, 2nd ed.,1973).
Dwight R. Stoll is the editor of “LC
Troubleshooting” and professor and
co-chair at Gustavus Adolphus College.
Devin Makey is an undergraduate
student in his fourth year of
study in chemistry at Gustavus
Adolphus College.
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369www.chromatographyonline.com
LC TROUBLESHOOTING
Temperature Programmed GC: Why Are All Those Peaks So Sharp?Nicholas H. Snow, Department of Chemistry and Biochemistry, Seton Hall University, New Jersey, USA
Temperature programming is used for most separations in capillary gas chromatography (GC) today. Despite
this, many of the principles by which we understand temperature-programmed capillary column separations
are based on ideas developed using packed columns and isothermal conditions. This instalment of “GC
Connections” dives into temperature programming. First, the differences in peak widths and retention times
between temperature programmed and isothermal chromatograms are examined. Why are all the peaks so
sharp in temperature programmed GC, yet they get broader (and shorter) in isothermal GC? Next, we explore
some early ideas about temperature programming and peak broadening that explain why the peaks are so
sharp in temperature-programmed GC, and why the peak spacing is different from isothermal GC. Finally,
we examine an important consequence of our ability to program temperature: the need for temperature
programming in splitless and other injections that use “solvent effects” and other peak focusing mechanisms.
These points are illustrated using several historical figures and chromatograms from the early days of GC.
Nearly every student of gas
chromatography (GC) has seen
chromatograms like the ones shown in
Figure 1. These chromatograms were
originally published in 1959, in one of
the first papers describing an apparatus
for temperature programming (1).
Although developed on a handmade
packed column with firebrick as the
stationary phase, this work shows the
same comparison and contrast between
isothermal and temperature-programmed
GC seen today. Starting from the
bottom, Figure 1(c) shows an isothermal
separation of normal alkanes. Notice
that as the retention times get longer, the
peaks get broader, and the last peak
appears to exhibit fronting. Also notice
that the retention time difference between
each peak appears to nearly double with
each successive alkane. The difference
between C9 and C10 (the last two peaks)
is about twice the difference between C8
and C9. Notice also that nearly 30 min
is required to separate the six alkanes.
This chromatogram illustrates the main
limitations of isothermal GC. First, the
range of analytes that can be separated
in a reasonable time is relatively small.
Second, as the retention times get
longer, the peaks get significantly
broader (band broadening), and, as a
result, they get shorter and harder to
detect. If the peak area is constant, as a
peak becomes broader, it must become
shorter, limiting sensitivity. Third, the
fronting of the later peaks is caused by
the column temperature being too low
for effective adsorption on the surface of
the stationary phase. The liquid analyte
condenses on the surface, causing some
to be evaporated into the mobile phase
more quickly and to therefore elute too
soon. This is a form of column overload.
Moving up, Figure 1(b) shows a
temperature-programmed separation
of the same mixture of n-alkanes with a
temperature programming rate of 5 °C
per min and Figure 1(a) shows the same
separation with a rate of 30 °C per min.
Note the significant differences from
the isothermal separation. First, the run
time is reduced from 30 min to 10 and
5 min, respectively. Second, the peaks
are spaced evenly. The retention time
difference between each successive
alkane is about the same. Finally, all of
the peaks are sharper (remember this
was a handmade packed column); they
appear to have about the same peak
width and as a result all have about the
same peak height, while the isothermal
peaks get broader and shorter.
Figure 1 raises two critical questions:
1. Why are the retention times
evenly spaced in the temperature
programmed separation, while
the spacing doubles from peak
to peak in the isothermal run?
2. Why are all the peaks sharp in the
temperature-programmed separation,
while the later peaks get significantly
broader in the isothermal separation?
We will address these questions by
drawing some simple pictures of the
chromatographic process, followed
by descriptions based on theories
for both isothermal and temperature-
programmed GC that were developed
in the first 10 years of GC.
For an analyte to move along a GC
column, it must have a vapour pressure
of at least a few torr at the operating
temperature. Remember that this
vapour pressure is affected by both
the normal vapour pressure and any
change resulting from interactions with
370 LCGC Europe July 2019
GC CONNECTIONS
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the stationary phase. Figure 2 shows
a simplified picture of the inside of
a capillary column, with the analyte
molecules represented as dots (2). This
compound has nine dots in the stationary
phase and three in the mobile phase,
giving a retention factor (k) of 3. A higher
value of k indicates that more of the dots,
a larger mass of the analyte, will be in
the stationary phase, causing the analyte
to be retained longer. The carrier gas
forces the three dots in the mobile phase
to move along the column. When they
encounter fresh stationary phase, their
attraction to the stationary phase and
low (but finite) vapour pressure cause
them to condense onto and dissolve in
this new region of stationary phase. In
isothermal GC, the thermodynamics of
the partitioning process between the
mobile phase and the stationary phase is
governed by the enthalpy of vaporization
(the change in heat content) for the
analyte from the stationary phase into
the mobile phase. For the alkanes seen
in Figure 1, the enthalpy of vaporization
increases linearly as each -CH2- unit is
added to the carbon chain. Through the
Gibbs equation, which relates enthalpy
and temperature, this results in an
exponential increase in K and k, leading
to an exponential increase in retention
time. The full theory and thermodynamics
are discussed elsewhere in more detail
with the relevant equations (3–5).
In temperature programming, the
column temperature usually increases
linearly with time as the separation
proceeds. This has the effect of
increasing the vapour pressure and
decreasing k of the analytes with
time. From general and physical
chemistry courses, we know that vapour
pressure increases exponentially
with temperature, with the linearized
form of the relationship expressed by
the Clausius-Clapeyron equation:
[1]
where Pvap
is the vapour pressure, ΔHvap
is the enthalpy of vaporization, R is
the gas constant, T is the temperature
in degrees Kelvin, and β is ΔS/R. Gas
chromatographers often use a similar
expression, the van’t Hoff equation,
which relates the equilibrium constant
for a reaction and the temperature,
assuming a constant change in
enthalpy ΔH, and entropy ΔS:
In K =–∆H
R+ ( )
∆S
R
1
T [2]
where K is the partition coefficient,
which in GC is related to the retention
factor (k) by the phase ratio (β), the ratio
of the volume of the stationary phase
to the volume of the mobile phase. In
1963, less than 10 years after the initial
inception of GC, Giddings provided a
model for temperature programming,
depicted in Figure 3, based on the
Clausius-Clapeyron equation, modified
for the specific case of GC and on
relationships derived previously by Dal
Nogare and Harris and Habgood (6–9).
One equation for describing temperature
programming and relating it to the same
(a)
0
0
0
2
21
3
3
6
4
8
8 16 24 32
2 MV.
4 MV.
5
10DETEC
TO
RR
ESPO
NSE
(b)
(c)
FIGURE 1: Temperature-programmed and isothermal chromatograms of a C5–C
10 alkane
mixture. The temperature program in (a) is 30 °C per minute starting at 40 °C and in (b) is
5 °C per minute starting at 40 °C, and (c) is isothermal at 75 °C. Reproduced with
permission from reference 1, American Chemical Society (copyright 1959).
FIGURE 2: Simplified picture of analyte partitioning in a capillary GC column. Analyte
molecules are represented by dots. There are nine dots in the stationary phase and
three in the mobile phase, giving k = 3. Reprinted from reference 2 with permission of
the author.
372 LCGC Europe July 2019
GC CONNECTIONS
thermodynamic quantities as seen in
isothermal GC and derived by Harris
and Habgood is seen in equation 3.
T
∫ αe
β
R dTRate=
To tM(T) 1+ ( ( ) )∆H/RT
[3]
where To is the initial temperature
of the temperature program, TR is the
elution temperature of the analyte, tM(T)
is the gas hold-up time at temperature
T, α = ΔS/R, and β is the column phase
ratio. Rate is the slope (°C/min) of the
linear temperature program. Equation 3
must be solved numerically for the
second integration constant, which
provides the elution temperature of the
analyte (easily translated into retention
time using the starting temperature
and programming rate), and provides
the basis for several of the computer
simulation programs for GC that have
been developed over the years (10,11).
For computer simulations of GC, ΔH
and ΔS are easily measured and
have been termed thermodynamic
retention indices (12). With knowledge
of these for a given analyte and
stationary phase, and equation 3, it is
possible to predict the retention time
of any analyte on that same stationary
phase under any conditions.
In Figure 3, the exponential curve
describes the rate of zone or peak
migration as the column temperature
in increased. This exponential curve
resembles a vapour pressure curve, and
can be approximated as such, with the
addition that acceleration of the analyte
along the column is faster than predicted
by vapour pressure alone, due to
expansion of the carrier gas as it travels
from the higher pressure at the column
inlet to the lower pressure at the outlet.
This demonstrates that, as the column
temperature is increased, the peak
accelerates as it simultaneously travels
along the column because its vapour
pressure increases and the carrier
gas is expanding inside the column
as it flows from the inlet to the outlet.
In order to simplify the model, the
exponential curve is broken up into
six 30 °C steps, a so-called “step
approximation” for temperature
programming. As seen in Figure 3,
with temperature programming
conditions, in contrast to isothermal
conditions, analytes move slowly
when first injected, and accelerate
exponentially as the temperature is
increased and the chromatographic
run proceeds. This exponential
acceleration has the practical effect
of linearizing the relationship between
carbon chain length and retention time
for the n-alkanes, as seen in Figure 1.
As an example of using the step
approximation, Giddings described a
temperature program from 85 to 265 °C,
with the steps being six 30 °C intervals.
He demonstrated that 50% of a peak’s
migration down the column occurs in the
final 30-degree segment. In short, the
peak travels about half of the column
length in the last 1/6 of the retention
time, and about 3/4 of the column length
0.15
0.10
ACTUAL
INCREASE
STEP FUNCTION
ELUTIONTEMPERATURE
T
APPROXIMATION
0.05
0100 130 160 190 220 250 265
RELA
TIV
E M
IGR
ATIO
N R
ATE
85
FIGURE 3: Step function
approximation for the rate of zone
migration in temperature-programmed
gas chromatography. Reprinted with
permission from reference 6, American
Chemical Society (copyright 1962).
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GC CONNECTIONS
in the last 1/3 of the total retention time.
Likewise, at the beginning of the run, the
peak travels about 1/64 of the column
length in the first 1/6 of the retention time,
and about 3/32 of the column length
in the first 1/3 of the retention time.
From this discussion, we
see that retention times in
temperature-programmed GC are
based on the same thermodynamic
quantities as in isothermal GC. However,
in temperature programming, in
contrast to isothermal conditions, the
relationship between the enthalpy of
vaporization and the retention time
becomes linear, due to the linear
increase in temperature giving rise to an
exponential increase in vapour pressure,
as seen in the Clausius-Clapeyron
equation, or in K as seen in the van’t
Hoff equation. This explains the first
aspect of the temperature programmed
chromatogram seen in Figure 1:
alkane peaks are evenly spaced.
Turning to the second observation
about Figure 1, that all of the peaks in
the temperature-programmed run are
of similar width, the step approximation
can help explain that, as well. Giddings
developed the step approximation so
that the six segments could be further
approximated as isothermal segments
at the mean temperature of the segment.
This allows us to think about each
segment as a single isothermal portion
of the run, and to apply the Golay
equation, shown below in abbreviated
form, to the conditions in each segment:
H =B +(C
s + C
M)
uu [4]
where H is the height equivalent to a
theoretical plate, B is related to solute
diffusion rates in the mobile phase, CS
and CM are related to mass transfer rates
in the stationary and mobile phases,
respectively, and � is the average velocity
of the carrier gas. A full explanation of
the Golay equation and the principles
related to the kinetics of band
broadening is provided in references
4 and 5. The Golay equation reminds
us that the rate of band broadening
(expressed by H, the height equivalent
to a theoretical plate) is a consequence
of diffusion in the mobile phase, mass
transfer in the mobile phase, and mass
transfer in the stationary phase, as well.
Simplified views of diffusion and mass
transfer are shown below in Figure 4.
From the Golay equation, we see that
band broadening caused by diffusion
in the mobile phase, illustrated in
Figure 4(a), is inversely related to the
average linear carrier gas velocity. Using
the step model, we see that, in the first
segments immediately following the
injection, the bulk of analyte molecules
reside in the stationary phase, so they
are not affected much by mobile phase
diffusion. As the separation proceeds in
the later segments, the bulk of analyte
molecules are in the mobile phase, and
move very rapidly as they approach
the column outlet. This minimizes band
broadening due to molecular diffusion
because of the inverse relationship
with the carrier gas velocity.
Next, we turn to band broadening
due to mass transfer, which is somewhat
more complicated, but can be explained
using similar logic. In capillary columns,
there are terms related to mass transfer
in both the mobile and stationary
phases. In this case, the rate of band
broadening is directly proportional to the
average velocity of the carrier gas, and
is related to k and the respective mobile
phase and stationary phase diffusion
constants, illustrated in Figure 4(b). On
(a)
Flow
Mobile
Phase
Stationary
Phase
(b)
FIGURE 4: Diffusion and mass transfer in a capillary column: (a) molecular diffusion
occurring in the mobile phase and relating to the B term in the Golay equation; (b)
mass transfer occurring in both the mobile and stationary phases and referring to the
C terms in the Golay equation.
374 LCGC Europe July 2019
GC CONNECTIONS
the left side of the figure, a symmetrical
peak with k = 3 is shown, with its relative
portions in both phases. On the right
side of Figure 4(b), the peak is seen to
distort, or spread, caused by the mass
in the mobile phase being shifted down
the column (to the right in the figure),
followed by the resulting evaporation
of new analyte on the left of the figure.
The step approximation is useful
here, as well. In the early steps, with
the bulk of analyte molecules in the
stationary phase, mass transfer is limited
by the low temperature at the start of
the temperature program. The analyte
band is essentially “frozen” in place. In
later segments, when the bulk of the
analyte molecules are in the mobile
phase, the time spent to traverse 50%
of the column is short—1/6 of the total
retention time—limiting the effect of mass
transfer in the mobile phase. For mass
transfer in the stationary phase, k gets
smaller as the temperature increases,
pushing the analyte more and more out
of the stationary phase into the mobile
phase, also limiting stationary phase
mass transfer. In short, the narrow initial
band, once it starts to move, accelerates
and moves very quickly along most
of the column length, minimizing the
time for significant mass transfer as it is
moving. The Golay equation and the step
approximation together explain why the
peaks are all sharp and about the same
width in temperature-programmed GC.
The step approximation also provides
a useful explanation for many practical
aspects of temperature-programmed
GC beyond the appearance of the
chromatogram in Figure 1. Best practice
in performing splitless injections provides
a good example. The basics of splitless
injection were recently reviewed in
this column, and are the subject of an
excellent review and book by Konrad
Grob, so they will not be reviewed
again in detail here (13–15). For this
discussion, the important principles in
splitless injections are that the injection
process itself may require up to 60 s
to complete, and splitless injection
is always used in combination with
temperature programming. Despite
the long time required for the injection
process, the peaks seen in separations
that employ splitless injection are
often very sharp. The step model
of temperature programming can
help to explain this phenomenon.
In a splitless injection, there are
two peak focusing mechanisms at
work once the sample reaches the
375www.chromatographyonline.com
GC CONNECTIONS
column: solvent effects, and thermal
focusing or “cold trapping”. The step
approximation explains how both can
work in combination with temperature
programming to generate sharp peaks.
First, assume a splitless injection
in combination with a temperature
program that starts at a temperature
well below the boiling point of the
sample solvent, and even further below
the boiling points of the analytes. For
example, if using hexane as solvent,
which has a normal boiling point of
68 °C, I start my temperature programs
at 40 °C. As the sample and solvent
transfer into the column, the low
initial column temperature causes the
solvent to condense as a long plug
of liquid at the head of the column.
Analytes with higher boiling points
or strong affinity for the stationary
phase will be strongly retained by the
stationary phase. This is cold trapping.
Analytes with lower boiling points or
stronger affinity to the solvent will be
initially retained in the solvent plug,
followed by retention as a narrow
initial band on the stationary phase
as the solvent evaporates. This is the
solvent effect. A detailed description
of solvent effects is provided in the
text and article by Grob (14,15)
The step approximation applies
to splitless injections whether the
peaks are refocused by solvent effects
or by cold trapping. All the peaks are
broadened as the splitless injection
process proceeds during the initial
purge-off time, with the peak width
determined by the length of the
purge-off time. The cold initial column
temperature effectively condenses
the analytes into a narrow band at the
column head. As the column is heated,
the analytes begin to move down the
column one by one, determined by their
heat of vaporization from the stationary
phase to the mobile phase. The
process is similar for solvent effects,
except that the initial bands
are focused by evaporation of
the solvent plug during the early
stages of the separation. As an
example, picture two analytes, one with
a retention time of 12 min, and
one with a retention time of 18 min.
When the first analyte elutes after
12 min, the second analyte will have
travelled about 1/4 of the column
length. It will travel the final 3/4 of the
column in the remaining 6 min. As
discussed above, this process of
refocusing, either by cold trapping or
solvent effects, followed by temperature
programming, keeps all of the peaks
in a splitless injection sharp.
Temperature-programmed GC has
been in common use for about six
decades, and continues to be among
the most powerful, yet easy to use,
high resolution separation methods
available. However, much of the theory
of GC was developed assuming
isothermal conditions, and continues
to be discussed on that basis. The
theory of temperature-programmed
GC is much more complex than for
isothermal GC, but is still based on
the same fundamental thermodynamic
and kinetic principles. In temperature
programmed GC, retention time
relates linearly to the enthalpy of
vaporization, while in isothermal GC the
relationship is exponential. Combined
with the high temperature stability of
columns, this allows a wide range of
analytes to be separated in a single
run. The temperature-program step
approximation provides a simple means
for understanding how the peaks in
temperature programmed GC remain
sharp throughout the run, based on
acceleration of the migration rate along
the column as the temperature program
proceeds. These principles make
temperature-programmed capillary GC
still the most powerful chromatographic
separation technique available today.
References1) S. Dal Nogare and J.C. Harden, Anal. Chem.
31(11), 1829–1832 (1959).
2) N.H. Snow, LCGC Europe 31(11), 616–623 (2018).
3) N.H. Snow, J. Chem. Educ. 73(7), 592–597 (1996).
4) L.M. Blumberg, Gas Chromatography, C.F. Poole,
Ed. (Elsevier, Amsterdam, The Netherlands,
2012), pp. 19–78.
5) H.M. McNair, and J.M. Miller, Basic Gas
Chromatography (John Wiley and Sons, New
York, New York, USA, 2nd ed., 2008), pp. 29–52.
6) J.C. Giddings, J. Chem. Educ. 39, 569–573 (1962).
7) H.W. Habgood and W.E. Harris, Anal. Chem. 32,
450–453 (1960).
8) H.W. Habgood and W.E. Harris, Anal. Chem. 32,
1206 (1960).
9) S. Dal Nogare, Anal. Chem. 35, 19R–25R (1960).
10) N.H. Snow and H.M. McNair, J. Chromatogr. Sci.
30, 271–275 (1992).
11) Pro EZGC Chromatogram Modeler https://www.
restek.com/proezgc (Accessed May 16, 2019).
12) E. Dose, Anal. Chem. 59, 2414–2419 (1987).
13) N.H. Snow, LCGC Europe 31(7), 378–384 (2018).
14) K. Grob, Split and Splitless Injection for Quantitative
Gas Chromatography: Concepts, Processes, Practical
Guidelines, Sources of Error (John Wiley and Sons,
New York, New York, 4th. ed., 2008).
15) K. Grob, Anal. Chem. 66(20) 1009A–1019A (1994).
Nicholas H. Snow is the
Founding Endowed Professor in
the Department of Chemistry and
Biochemistry at Seton Hall University,
and an Adjunct Professor of Medical
Science. He is interested in the
fundamentals and applications of
separation science, especially gas
chromatography, sampling, and
sample preparation for chemical
analysis. His research group is very
active, with ongoing projects using
GC, GC–MS, two-dimensional GC,
and extraction methods including
headspace, liquid–liquid extraction,
and solid-phase microextraction.
“GC Connections” editor John V.
Hinshaw is a Senior Scientist at
Serveron Corporation in Beaverton,
Oregon, USA, and a member of
LCGC’s editorial advisory board.
Direct correspondence about this
column to the author via e-mail:
376 LCGC Europe July 2019
GC CONNECTIONS
CF200
IS YOUR LAB SOLVENT SAFE?
Report: R1961-01-A | BS EN61010-1 | BS EN61010-2-020
To learn more about our robotic centrifuge
get in touch at [email protected]
Mass SpectrometerShimadzu’s LCMS-9030 research-grade mass spectrometer is
designed to deliver high-resolution, accurate mass detection
with fast data acquisition rates, allowing scientists to identify and
quantify more compounds with greater confidence, according
to the company. It utilizes the same engineering as Shimadzu’s
rugged, high-performance triple quadrupole (LC–MS/MS)
platform and integrates this with TOF architecture.
www.shimadzu.eu
Shimadzu Europa GmbH, Duisburg, Germany.
HILIC ColumnsHilicon offers a broad range of HILIC products to separate polar
compounds. Three column chemistries in UHPLC and HPLC,
iHILIC-Fusion, iHILIC-Fusion(+), and iHILIC-Fusion(P), provide
customized and complementary selectivity, excellent durability,
and very low column bleeding, according to the company. The
columns are suitable for the analysis of polar compounds in “omics” research,
food and beverage analysis, pharma discovery, and clinical diagnostics.
www.hilicon.com
Hilicon AB, Umeå, Sweden.
(U)HPLC ColumnsYMC-Triart Bio C4 is a wide-pore phase for (U)HPLC
based on the established hybrid-silica particle, YMC-Triart.
As a result of its 300 Å pore size, this column is designed
for peptide, protein, or monoclonal antibody separations.
High temperature (up to 90 °C) and pH (1–10) stability is
provided. Excellent column-to-column, as well as lot-to-lot
reproducibility, is offered, according to the company.
https://ymc.de/rp-bioseparation.html
YMC Europe GmbH, Dinslaken, Germany.
Mass SpectrometerThe Thermo Scientific Orbitrap Exploris 480 benchtop
mass spectrometer combines proven technology,
advanced capabilities, and intelligence-driven data
acquisition strategies for rigorous, high-throughput
protein identification, quantitation, and structural
characterization of biotherapeutics and translational
biomarkers, according to the company.
www.thermofisher.com
Thermo Fisher Scientific, San Jose, California, USA.
Trapping ColumnPharmaFluidics has launched
a μPAC Trapping column with
identical stationary phase
support morphology compared
to the analytical μPAC columns.
By effectively desalting and
preconcentrating the analytes
of interest onto the trap column,
analytical column lifetime and
workflow throughput can be
improved, according to the
company. Dilute samples can be
loaded in a bidirectional way at high
flow rates and with minimal loss of
chromatographic performance.
www.pharmafluidics.com
PharmaFluidics, Ghent, Belgium.
Triple DetectionPostnova has introduced the Triple
Detection for thermal field-flow
fractionation (FFF) and GPC/SEC.
Triple Detection is the combination
of multi-angle light scattering
(MALS), viscosity detection,
refractive index detection, and UV
detection. In a single separation
experiment, Triple Detection
provides molar mass distribution,
molecular size distribution, and
molecular structure (branching,
composition) of polymers,
biopolymers, polysaccharides,
proteins, and antibodies.
www.postnova.com
Postnova Analytics GmbH,
Landberg, Germany.
T r i p l e D e t e c t i o nOnline Coupling to FFF and SEC
FFFSEC
PN3621 MALS Detector
Particle SizeRg / Molar Mass
PN3150 RI Detector
Concentration
PN3310 ViscometerDetector
Intrinsic ViscosityBranching
cosity
+
+
on
e
378 LCGC Europe July 2019
PRODUCTS
Multi-Angle Static Light ScatteringIntroducing the next generation DAWN
multi-angle static light scattering (MALS)
detector for absolute characterization of the
molar mass and size of macromolecules and
nanoparticles in solution. DAWN offers high
sensitivity, a wide range of molecular weight,
size, and concentration, and a large selection
of configurations and optional modules for enhanced capabilities.
www.wyatt.com/dawn
Wyatt Technology, Goleta, California, USA.
GPC/SEC Validation KitPSS EasyValid is a system suitability test
that evaluates the entire GPC/SEC system.
According to the company, it is ideal for various
aspects of quality assurance qualification,
whether mandated by stringent requirements
(GLP, DIN, ISO 9000× certifications) or good management practices. It
comprises a validation column, calibration standards, and European reference
materials and is available for organic solvents or aqueous systems.
www.pss-polymer.com
PSS GmbH, Mainz, Germany.
Crimping StationThe CR-5000S is a crimping station for 2- to 100-mL vials and
can be used for all types of caps. It is equipped with a rotation
table for a higher rate and screen touch. It is crimping force
adjustable and head height adjustable according to the type
of vial. It is reportedly ideal for pharmaceutical conditioning
and suitable for clean room. According to the company, the
machine is easy to use and has an average rate of 750 vials/h.
www.sertir.fr
Action Europe, Sausheim, France.
Hydrogen GeneratorThe Precision Hydrogen Trace generator supplies GC
carrier gas and GC fuel gas for GC detectors. A safe
alternative to a helium cylinder, this generator offers a
solution for those looking to reduce their reliance on
an increasingly scarce helium supply. According to
the company, the generator can produce up to
1.2 L/min of 99.9999% pure hydrogen.
https://bit.ly/2KY0QgM
Peak Scientific, Scotland, UK
Sample AutomationMarkes’ new Centri
multitechnique platform is an
advance in sample automation
and concentration for
GC–MS, according to the
company, and offers four
sampling modes: HiSorb
high-capacity sorptive extraction,
headspace, SPME, and thermal
desorption. The company
reports analyte focusing allows
increased sensitivity in all
modes, state-of the-art robotics
increases sample throughput,
and sample re-collection allows
repeat analysis without having to
repeat lengthy sample extraction
procedures.
http://chem.markes.
com/Centri
Markes
International Ltd.,
Llantrisant, UK.
Thermal Desorption
SystemThe MPS TD is a dedicated
sampler for automated thermal
desorption, thermal extraction,
and dynamic headspace (DHS)
analysis. MPS TD is compatible
with Gerstel TDU, TD 3.5+,
and DHS processing up to 240
samples. The complete system
including GC–MS is operated with
one integrated method and one
sequence table.
www.gerstel.com
Gerstel GmbH & C0. KG,
Mülheim an de Ruhr, Germany.
379www.chromatographyonline.com
PRODUCTS
11–13 SEPTEMBER 2019The 12th Balton Symposium on
High-Performance Separation
Methods
Siófok, Hungary
W: www.balaton.mett.hu
15–18 SEPTEMBER 2019The 30th International Symposium
on Pharmaceutical and Biomedical
Analysis (PBA 2019)
Tel Aviv, Israel
W: www.pba2019.org
29 SEPTEMBER–1 OCTOBER
2019
SFC 2019
Philadelphia, Pennsylvania, USA
W: www.greenchemistrygroup.org/
current-conference/sfc-2019
14–16 OCTOBER 2019The 11th Conference of The
World Mycotoxin Forum and the
XVth IUPAC International
Symposium on Mycotoxins
(WMFmeetsIUPAC)
Belfast, Northern Ireland
E: WMF@bastiaanse-communication.
com
W: www.worldmycotoxinforum.org
5–8 NOVEMBER 2019Recent Advances in Food Analysis
(RAFA 2019)
Prague, Czech Republic
W: www.rafa2019.eu
29–31 JANUARY 2020The 16th International Symposium
on Hyphenated Techniques in
Chromatography and Separation
Techniques
Ghent, Belgium
W: https://kuleuvencongres.be/htc16
Please send any upcoming event
information to Lewis Botcherby at
SWEMSA 2019—Non-Target Screening Embedded in (Open
Access) Platforms and Multi-Disciplinary Applications
The international, interdisciplinary workshop
Solutions and Workflows in (Environmental)
Molecular Screening and Analysis
(SWEMSA 2019) will be held 21–23 October
2019 in the City Hall in Erding, Germany. This
is the second time that this workshop will
be organized close to Munich, which has
one of the largest international airports in Germany. The symposium intends to
continue the exciting international dialogue that started in 2016 at the Non-Target
Conference 2016 in Ascona, Switzerland, and SWEMSA 16 in Garching, Germany,
and has been ongoing in many meetings, workshops, seminars, and conferences.
This workshop will extend the discussion on non-target screening (NTS) with the
following topics:
• Computational mass spectrometry
• NTS in forensics
• NTS in food(omics)
• NTS in metabolomics
• NTS in commercial solutions
• NTS (guideline) in water analysis
• NTS in environmental analysis.
This workshop will bring together leading international scientists from various
consortia. It is the ideal location for industrial and academic researchers
to exchange information with other colleagues from all over the world.
SWEMSA intends to inform, combine, and harmonize the NTS strategies and
workflows from each single discipline to extend the NTS horizon and to offer
the opportunity to “look over the edge”. Participants of various disciplines,
such as chemistry, environment, food, forensic, informatics, metabolomics,
water, and instrumental analysis will discuss the latest developments. The
programme will feature a solution-focused discussion strategy, including
overview talks and panel discussions in each slot. Each panel discussion—a
SWEMSA speciality—is guided and strongly integrates the participants.
The overall aim of this meeting is to condense and harmonize various
common aspects of NTS, to extend the use and understanding of software and
workflow strategies, and to learn about the potential of NTS applied in various
disciplines. The organizers encourage the active participation of younger
scientists in the workshop, and a great amount of effort has been made to
guarantee low participation costs (with registration until 31 August 2019).
A rich scientific and social programme awaits participants with scientific
presentations and discussions and two evening events, respectively. The evening
events will also bring participants closer to the Bavarian lifestyle and offer exciting
culinary delights. Erding offers a wide range of accommodation for all budgets,
and with its international airport it is easily accessible by plane, or by car or train.
The organizers look forward to welcoming delegates in Erding in October.
E: [email protected] W: www.swemsa.eu
Image c
redit:
pure
-life
-pic
ture
s/s
tock
.adobe.c
om
380 LCGC Europe July 2019
EVENTS
Th
e A
pp
lica
tio
ns B
oo
k
July 2019 | Volume 32 Number 7
www.chromatographyonline.com
FOOD AND BEVERAGE
383 Analysis of Active Cannabis Compounds in Edible
Food Products: Gummy Bears and Brownies
Olga Shimelis1, Kathy Stenerson1, and Margaret Wesley2, 1MilliporeSigma, 2Pennsylvania State University
MEDICAL/BIOLOGICAL
384 Analysis of Fentanyl and Its Analogues in Human
Urine by LC–MS/MS
Shun-Hsin Liang and Frances Carroll, Restek Corporation
386 Kinase Fragments Dimerize Without Oligomerization
Domains, Shown by SEC-MALS
Thomas Huber, University of Zürich, Department of
Biochemistry
GENERAL
387 Determination of Psilocin and Psilocybin in Magic
Mushrooms Using iHILIC®-Fusion and MS
Tibor Veress1, Norbert Rácz2, Júlia Nagy1, and Wen Jiang3, 1Department of Drug Investigation, Hungarian Institute for
Forensic Sciences, Budapest, Hungary, 2Department of
Inorganic and Analytical Chemistry, Budapest University of
Technology and Economics, Budapest, Hungary, 3HILICON
AB
July | 2019
Volume 32 Number 7
The Applications Book
Image credits: Kwanchaift/stock.adobe.com
Image credits: kenkistler1/stock.adobe.com
Image credits: Pinhead Studio/stock.adobe.com
Image credits: juliasudnitskaya/stock.adobe.com
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Image credits: volgariver/stock.adobe.com
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Image credits: pro500/stock.adobe.com
382 LCGC Europe July 2019
CONTENTS
THE APPLICATIONS BOOK – JULY 2019 383
FOOD AND BEVERAGE
Potency testing in marijuana-infused edibles is a problematic task due
to the complexity of the matrices. The concentration of active ingredients
in edibles can range from a few ppm to 3.5% (1). In this application,
active cannabinoid compounds were extracted from gummy bears (and
also brownies, results not shown), followed by HPLC analysis.
Experimental
Cannabinoid standards (Cerilliant®) used: cannabidivarinic acid
(CBDVA), cannabidivarin (CBDV), cannabigerolic acid (CBGA),
cannabigerol (CBG), cannabidiolic acid (CBDA), cannabidiol
(CBD), tetrahydrocannabivarin (THCV), cannabinol (CBN),
(-)-Δ9-Tetrahydrocannabinol (Δ9-THC), (-)-Δ8-Tetrahydrocannabinol
(Δ8-THC), and (-)-Δ9-Tetrahydrocannabinolic acid A (THCAA). Even
though acidic cannabinoids are not commonly found in edibles, these
were included to demonstrate the ability of the HPLC method to resolve
them from neutral forms, and the method’s potential for testing cannabis
fl owers (which contain acidic cannabinoids).
An Ascentis® Express Biphenyl (2.7 μm particles) HPLC column was
used for separation of all 11 compounds in under 13 min on a standard
pressure HPLC system.
Analysis of Active Cannabis Compounds in Edible Food Products: Gummy Bears and BrowniesOlga Shimelis1, Kathy Stenerson1, and Margaret Wesley2, 1MilliporeSigma, 2Pennsylvania State University
Figure 1: HPLC of orange gummy bear extract at (a) 220 nm and
(b) 280 nm. Peak IDs in Table 1.
Table 1: Recoveries from
spiked gummy bears
Peak
No.Compound
Average
(RSD)
1 CBDVA 91% (2%)
2 CBDV 98% (3%)
3 THCV 90% (3%)
4 CBDA 94% (3%)
5 CBGA 91% (4%)
6 CBD 97% (3%)
7 CBG 96% (4%)
8 CBN 95% (6%)
9 Delta-9-THC 97% (3%)
10 Delta-8-THC 95% (3%)
11 THCA-A 92% (7%)
MerckFrankfurter Strasse 250
Darmstadt, 64293, Germany
Website: www.sigmaaldrich.com
The life science business of Merck
KGaA, Darmstadt, Germany, operates as
MilliporeSigma in the U.S. and Canada
Sample Preparation
Four different bear colours were
tested—orange, yellow, red, and
green. One gummy bear, nonspiked,
(2.3 g) was dissolved in 20 mL of
warm water. This solution was
spiked with cannabinoid (45 ppm
each), and then transferred to a
50-mL QuEChERS extraction tube.
Acetonitrile (10 mL) was added,
followed by 1-min shaking. Supel™
QuE nonbuffered salts (55295-U)
were added, and the samples were
shaken for 5 min followed by 5 min
centrifugation (5000 rpm). The
supernatant was then injected into
the HPLC system (Figure 1).
Results
For all bears, detection was at 220 nm, except for CBDVA due to an
interference from the orange bear; detection was at 280 nm for that
orange bear sample. Excellent recovery values of above 90% were
achieved with good accuracies (Table 1).
Column: Ascentis Express Biphenyl, 10 cm × 2.1 mm i.d., 2.7 μm
Mobile phase: (A) 0.1% TFA in water; (B) 0.1% TFA in acetonitrile
Gradient: 47% B, to 50% B in 13 min, to 100% B in 0.1 min,
100% B for 3 min, to 47% B in 0.1 min, at 47% B
for 2.5 min
Flow rate: 0.70 mL/min
Temp.: 35 °C
Detector: UV, 220 nm & 280 nm (280 nm)
Injection: 5 μL
Pressure: 340 bar
Instrument: Agilent® 1200, with UV detector
Conclusion
The developed HPLC method showed good resolution and can also
be used for analysis of commodities containing acidic cannabinoids;
specifi cally, cannabis fl ower.
Reference
(1) http://analytical360.com/m/archived/216628 (accessed July 2016).
Read the full article at SigmaAldrich.com/ar (Issue 4)
384 THE APPLICATIONS BOOK – JULY 2019
MEDICAL/BIOLOGICAL
Analysis of Fentanyl and Its Analogues in Human Urine
by LC–MS/MS
Shun-Hsin Liang and Frances Carroll, Restek Corporation
Abuse of synthetic opioid prescription painkillers
such as fentanyl, along with a rapidly growing list of
illicit analogues, is a signifi cant public health problem.
In this study, we developed a simple dilute-and-shoot
method that provides a fast 3.5-min analysis of
fentanyl and related compounds (norfentanyl, acetyl
fentanyl, alfentanil, butyryl fentanyl, carfentanil,
remifentanil, and sufentanil) in human urine by
LC–MS/MS using a Raptor Biphenyl column.
In recent years, the illicit use of synthetic opioids has skyrocketed,
and communities worldwide are now dealing with an ongoing
epidemic. Of the thousands of synthetic opioid overdose deaths
per year, most are related to fentanyl and its analogues. With
their very high analgesic properties, synthetic opioid drugs such
as fentanyl, alfentanil, remifentanil, and sufentanil are potent
painkillers that have valid medical applications; however, they are
also extremely addictive and are targets for abuse. In addition
to abuse of these prescription drugs, the current opioid crisis
is fueled by a growing number of illicit analogues, such as
acetyl fentanyl and butyryl fentanyl, which have been designed
specifically to evade prosecution by drug enforcement agencies.
As the number of opioid drugs and deaths increases, so does
the need for a fast, accurate method for the simultaneous analysis
of fentanyl and its analogues. Therefore, we developed this
LC–MS/MS method for measuring fentanyl, six analogues, and one
metabolite (norfentanyl) in human urine. A simple dilute-and-shoot
sample preparation procedure was coupled with a fast (3.5 min)
chromatographic analysis using a Raptor Biphenyl column. This
method provides accurate, precise identifi cation and quantitation of
fentanyl and related compounds, making it suitable for a variety of
testing applications, including clinical toxicology, forensic analysis,
workplace drug testing, and pharmaceutical research.
Experimental Conditions
Sample Preparation: The analytes were fortifi ed into pooled human
urine. An 80 μL urine aliquot was mixed with 320 μL of 70:30
water–methanol solution (fi vefold dilution) and 10 μL of internal
0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80 2.00 2.20 2.40 2.60 2.80 3.00
Time (min)
Figure 1: The Raptor Biphenyl column effectively separated all target
compounds in urine with no observed matrix interferences. Peak elution
order: norfentanyl-D5, norfentanyl, remifentanil, acetyl fentanyl-13C
6,
acetyl fentanyl, alfentanil, fentanyl-D5, fentanyl, carfentanil-D
5,
carfentanil, butyryl fentanyl, sufentanil-D5, sufentanil.
Table 1: Analyte transitions
Analyte Precursor Ion Product Ion Quantifi er Product Ion Qualifi er Internal Standard
Norfentanyl 233.27 84.15 56.06 Norfentanyl-D5
Acetyl fentanyl 323.37 188.25 105.15 Acetyl fentanyl-13C6
Fentanyl 337.37 188.26 105.08 Fentanyl-D5
Butyryl fentanyl 351.43 188.20 105.15 Carfentanil-D5
Remifentanil 377.37 113.15 317.30 Norfentanyl-D5
Sufentanil 387.40 238.19 111.06 Sufentanil-D5
Carfentanil 395.40 113.14 335.35 Carfentanil-D5
Alfentanil 417.47 268.31 197.23 Acetyl fentanyl-13C6
Norfentanyl-D5
238.30 84.15 — —
Acetyl fentanyl-13C6
329.37 188.25 — —
Fentanyl-D5
342.47 188.27 — —
Sufentanil-D5
392.40 238.25 — —
Carfentanil-D5
400.40 340.41 — —
THE APPLICATIONS BOOK – JULY 2019 385
MEDICAL/BIOLOGICAL
standard (40 ng/mL in methanol) in a Thomson SINGLE StEP fi lter
vial (Restek cat. #25895). After fi ltering through the 0.2 μm PVDF
membrane, 5 μL was injected into the LC–MS/MS.
Calibration Standards and Quality Control Samples: The calibration
standards were prepared in pooled human urine at 0.05, 0.10,
0.25, 0.50, 1.00, 2.50, 5.00, 10.0, 25.0, and 50.0 ng/mL. Three
levels of QC samples (0.75, 4.0, and 20 ng/mL) were prepared in
urine for testing accuracy and precision with established calibration
standard curves. Recovery analyses were performed on three
different days. All standards and QC samples were subjected to the
sample preparation procedure described.
LC–MS/MS analysis of fentanyl and its analogues was performed
on an ACQUITY UPLC instrument coupled with a Waters Xevo TQ-S
mass spectrometer. Instrument conditions were as follows, and
analyte transitions are provided in Table 1.
Analytical column: Raptor Biphenyl (5 μm,
50 mm × 2.1 mm; cat. #9309552)
Guard column: Raptor Biphenyl EXP guard column
cartridge, (5 μm, 5 mm × 2.1 mm;
cat. #930950252)
Mobile phase A: 0.1% Formic acid in water
Mobile phase B: 0.1% Formic acid in methanol
Gradient Time (min) %B
0.00 30
2.50 70
2.51 30
3.50 30
Flow rate: 0.4 mL/min
Injection
volume: 5 μL
Column temp.: 40 °C
Ion mode: Positive ESI
Results
Chromatographic Performance: All eight analytes were well
separated within a 2.5-min gradient elution (3.5-min total analysis
time) on a Raptor Biphenyl column (Figure 1). No signifi cant matrix
interference was observed to negatively affect quantifi cation of the
fi vefold diluted urine samples. The 5-μm particle Raptor Biphenyl
column used here is a superfi cially porous particle (SPP) column.
It was selected for this method in part because it provides similar
performance to a smaller particle size fully porous particle (FPP)
column, but it generates less system back pressure.
Linearity: Linear responses were obtained for all compounds and
the calibration ranges encompassed typical concentration levels
monitored for both research and abuse. Using 1/x weighted linear
regression (1/x2 for butyryl fentanyl), calibration linearity ranged
from 0.05 to 50 ng/mL for fentanyl, alfentanil, acetyl fentanyl, butyryl
fentanyl, and sufentanil; from 0.10 to 50 ng/mL for remifentanil; and
from 0.25 to 50 ng/mL for norfentanyl and carfentanil. All analytes
showed acceptable linearity with r2 values of 0.996 or greater and
deviations of <12% (<20% for the lowest concentrated standard).
Accuracy and Precision: Based on three independent experiments
conducted on multiple days, method accuracy for the analysis of
fentanyl and its analogues was demonstrated by the %recovery
values, which were within 10% of the nominal concentration for
all compounds at all QC levels. The %RSD range was 0.5–8.3%
and 3.4–8.4% for intraday and interday comparisons, respectively,
indicating acceptable method precision (Table 2).
Conclusions
A simple dilute-and-shoot method was developed for the quantitative
analysis of fentanyl and its analogues in human urine. The analytical
method was demonstrated to be fast, rugged, and sensitive with
acceptable accuracy and precision for urine sample analysis. The
Raptor Biphenyl column is well suited for the analysis of these
synthetic opioid compounds and this method can be applied to
clinical toxicology, forensic analysis, workplace drug testing, and
pharmaceutical research.
Restek Corporation110 Benner Circle, Bellefonte, Pennsylvania 16823, USA
Tel. 1 (814) 353 1300
Website: www.restek.com
Table 2: Accuracy and precision results for fentanyl and related compounds in urine QC samples
Analyte
QC Level 1 (0.750 ng/mL) QC Level 2 (4.00 ng/mL) QC Level 3 (20.0 ng/mL)
Average Conc.
(ng/mL)
Average %
Accuracy %RSD
Average Conc.
(ng/mL)
Average %
Accuracy %RSD
Average Conc.
(ng/mL)
Average %
Accuracy %RSD
Acetyl fentanyl 0.761 102 1.54 3.99 99.7 2.08 19.9 99.3 0.856
Alfentanil 0.733 97.6 3.34 3.96 98.9 8.38 20.9 104 6.73
Butyryl fentanyl 0.741 98.9 6.29 3.77 94.3 6.01 20.8 104 4.95
Carfentanil 0.757 101 7.34 3.76 94.0 4.64 20.6 103 4.24
Fentanyl 0.761 102 1.98 3.96 99.1 2.31 19.9 99.6 1.04
Norfentanyl 0.768 103 6.50 4.04 101 1.84 20.1 101 2.55
Remifentanil 0.765 102 3.42 3.97 99.2 3.68 20.8 104 4.14
Sufentanil 0.752 100 1.67 3.93 98.3 1.28 20.1 100 0.943
386 THE APPLICATIONS BOOK – JULY 2019
MEDICAL/BIOLOGICAL
Kinase Fragments Dimerize Without Oligomerization Domains, Shown by SEC-MALSThomas Huber, University of Zürich,
Department of Biochemistry
Determination of oligomeric states is an important issue in protein
chemistry. For example, self-assembly via oligomerization domains
is crucial for the regulation of several protein kinases. Determination
of the oligomeric state of fragments of these kinases is a means of
verifying the involvement of each domain in self-assembly.
Analytical size-exclusion chromatography (SEC) is widely used for
determining molar mass and oligomeric state of proteins in solution,
but it exhibits some important limitations. For example, interactions
of proteins with column material can lead to delayed elution and
hence erroneous results when relying on column calibration. Since
even ideal elution occurs according to hydrodynamic size rather
than true molecular weight, there are no appropriate molar mass gel
fi ltration standards for analysis of proteins, fragments, or complexes
of non-globular structure that present a different size or molecular
weight dependence than globular proteins.
The use of size-exclusion chromatography in combination with
multi-angle light scattering (SEC-MALS) determines molecular weights
independently of elution time and conformation, overcoming the need
for standards and the errors inherent in analytical SEC. In SEC-MALS,
chromatography is used solely to separate the individual species so they
can be characterized by light scattering for biophysical properties such
as molar mass, size, conformation, and degree of conjugation.
This note describes the analysis of a kinase fragment lacking its
association domain in order to determine its oligomeric state in solution.
SEC-MALS revealed that the kinase moiety clearly remains dimeric in
solution, even in the absence of its purported oligomerization domain.
Experimental Conditions
An HP-SEC column was calibrated using bovine serum albumin (BSA)
monomer and dimer. The kinase fragment and alcohol dehydrogenase
(ADH, 38 kDa monomer and 150 kDa tetramer) were each run on the
column, and the elution times compared to those of BSA monomer
(66.4 kDa) and dimer (133 kDa). Absolute molar mass (MW) of the
proteins at each elution volume were determined by analysis of signals
from the multi-angle light scattering and refractive index detectors
(DAWN and Optilab, respectively) in ASTRA software. Chromatograms
were overlaid with molecular weight values calculated for each elution
time along the peaks, as seen in Figure 1.
Results and Discussion
The monomeric kinase fragment has a sequence molar mass of
53.5 kDa. The fragment (red trace) eluted at nearly the same volume
as the BSA dimer (blue trace), suggesting that its molar mass is
approximately 140 kDa, or trimeric. However, MALS determines an
absolute molar mass in solution of 108 kDa, revealing that the protein
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Figure 1: Molar mass, as determined by multi-angle light scattering,
vs. elution volume of kinase fragment (red), bovine serum albumin
(BSA, blue), and alcohol dehydrogenase (ADH, green). Molar masses
deduced from the elution volumes of kinase fragment and ADH are
shown to be misleading when compared with absolute molar masses
from SEC-MALS.
is actually a dimer. The molecular weight is absolutely uniform across
the peak, indicating a high degree of homogeneity. Such early elution is
indicative of a non-globular conformation.
ADH tetramer (green trace, 150 kDa) eluted between the monomer
and dimer of BSA, possibly because of ADH-column interactions that
caused it to elute late relative to its size. Comparison of the fragment’s
elution volume to ADH monomer and tetramer would mislead the
investigator to assume a tetrameric state, even further removed from
the truth than comparison to BSA.
Conclusions
SEC-MALS provides true solution molecular weight for proteins,
overcoming the inherent errors produced by reliance on column
calibration. Here we have shown by SEC-MALS that kinase
fragments are dimeric, even without the purported oligomerization
domain; but they are not trimeric or tetrameric as might have
been deduced via column calibration. The addition of a DAWN
MALS detector to standard SEC reveals the essential biophysical
properties of proteins, fragments, and complexes.
THE APPLICATIONS BOOK – JULY 2019 387
GENERAL
Determination of Psilocin and Psilocybin in Magic Mushrooms Using iHILIC®-Fusion and MSTibor Veress1, Norbert Rácz2, Júlia Nagy1, and Wen Jiang3, 1Department of Drug Investigation,
Hungarian Institute for Forensic Sciences, Budapest, Hungary, 2Department of Inorganic and Analytical
Chemistry, Budapest University of Technology and Economics, Budapest, Hungary, 3HILICON AB
Hallucinogenic mushrooms, known as magic mushrooms, contain
psychoactive compounds such as psilocin and psilocybin (Figure 1).
This hallucinogenic effect means they are constantly offered on
the black market. Therefore, the reliable quantifi cation of these
compounds is a particularly important task for forensic analysis
because their results have a signifi cant impact on the judgement
passed by the courts.
Although there are many analysis methods available in forensic
laboratories and in the scientific literature, the majority of them are
based on reversed-phase liquid chromatography (LC) separation
(1–6). Due to the highly hydrophilic nature of psilocybin and psilocin,
reversed-phase LC is not able to provide sufficient retention for them.
Moreover, it is crucial to develop new methods and techniques that
can improve the analysis detectability, selectivity, and productivity.
To fulfill these goals, the application of hydrophilic interaction liquid
chromatography (HILIC) and mass spectrometry (MS) is investigated.
In this study, we aimed to use a charge modulated iHILIC®-Fusion
HILIC column for the analysis of extracts from hallucinogenic
mushrooms and evaluate its potential for forensic application.
Experimental
LC–MS System: Agilent 1100 LC system and Bruker Esquire 6000
ion trap mass spectrometer, operated in positive ionization mode
(ESI+). Chromatographic data were acquired and evaluated with
ChemStation Rev. A. 10.02.
Column: 150 × 4.6 mm, 3.5-μm 100 Å iHILIC®-Fusion (P/N
110.154.0310, HILICON AB, Sweden)
Mobile Phase: 80:20 (v/v) acetonitrile–ammonium format (10 mM,
pH 3.5)
Flow Rate: 0.5 mL/min
Column Temperature: 12 °C
Sample Preparation: Quasi-counter current extraction with methanol
at 60 °C in a Shimadzu 10/A HPLC system. A 50-mg measure of
air-dried and homogenized hallucinogenic mushroom was fi lled in
the extractor chamber (an empty 250 × 4.6 mm HPLC column).
The standard solutions were 5 μg/mL and 500 μg/mL for psilocin
and psilocybin, respectively. Methanol was used as the solvent.
Injection Volume: 1 μL
Results and Discussion
In our previous study (2), the methanolic mushroom extract was
fi rst separated under the conditions within a designed experimental
space with a total of 18 model establishment points and two
approval points, considering the mobile phase composition, pH, and
temperature. The factors that affect the separation selectivity and
resolution on three iHILIC® columns were studied using DryLab®
and STATISTICA®. It was found that iHILIC®-Fusion provides
best separation regarding separation selectivity and effi ciency.
Figure 2 illustrates the separation of mushroom extract and also
HN
(a) (b)
HN
NN
OH
OHHO
OO
P
Figure 1: Chemical structures of (a) psilocin and (b) psilocybin.
1.1E+6
1.0E+6
9.0E+5
8.0E+5
7.0E+5
6.0E+5
5.0E+5
4.0E+5
3.0E+5
2.0E+5
1.0E+5
0.0E+0
0 1 2 3 4 5 6 7 8 9 10
Time (min)
11 12 13 14 15
0 1 2 3 4 5 6 7 8 9 10
Time (min)
11 12 13 14 15
0 1 2 3 4 5 6 7 8 9 10
Time (min)
11 12 13 14 15
TIC (Mushroom extract)
m/z 205 (Psilocin)
m/z 285 (Psilocybin)
Inte
nsi
ty
9.0E+5
8.0E+5
7.0E+5
6.0E+5
5.0E+5
4.0E+5
3.0E+5
2.0E+5
1.0E+5
0.0E+0
Inte
nsi
ty
9.0E+5
8.0E+5
7.0E+5
6.0E+5
5.0E+5
4.0E+5
3.0E+5
2.0E+5
1.0E+5
0.0E+0
Inte
nsi
ty
Figure 2: Total ion chromatogram (m/z 40–400) of the methanolic
mushroom extract and extracted ion chromatograms of m/z 205
(psilocin) and m/z 285 (psilocybin).
388 THE APPLICATIONS BOOK – JULY 2019
GENERAL
the extract ion chromatograms at m/z 205 (psilocin) and m/z 285
(psilocybin), respectively. It is clear that iHILIC®-Fusion was able to
separate psilocin and psilocybin from each other and also from the
major matrix compounds within 15 min. An unique feature is that
psilocybin elutes with a retention factor two times greater than that
of psilocin. In addition, the sample preparation consists of few steps
to minimize error sources and assure reliable results.
In the second step of this work, we separated the methanolic
solution of psilocin and psilocybin standards to confirm the detection
of these two alkaloids in the mushroom extract. As shown in Figure 3,
both psilocin and psilocybin have identical retention times to the
standards compared to those peaks from the mushroom extracts.
Therefore, the developed method is selective for the two target
compounds and can be used for the quantification as described in
our early work (1).
Conclusion
This work illustrates how to use an iHILIC®-Fusion column and
MS detection to separate and identify psilocin and psilocybin in
hallucinogenic mushrooms or ”magic mushroom” extracts. This
developed HILIC–MS method can be utilized in forensic and clinical
applications.
References
(1) J. Nagy and T. Veress, J. Forensic Res. 7, 356 (2016), DOI: 10.4172/2157-
7145.1000356.
(2) N. Rácz, J. Nagy, W. Jiang, and T. Veress, J. Chromatogr. Sci. 57, 230–237
(2019).
(3) M.W. Beug and J. Bigwood, Journal of Chromatography 207, 379–385
(1981).
(4) N. Anastos, S.W. Lewis, N.W. Barnett, and D.N. Sims, J. Forensic Sci. 51,
45–51 (2006).
(5) R. Kysilka and M. Wurst, Planta Med. 56, 327–328 (1990).
(6) V. Gambaro, G. Roda, G.L. Visconti, S. Arnoldi, and E. Casagni, J. Anal.
Bioanal. Tech. 6, 277 (2015).
HILICON ABTvistevägen 48, SE-90736, Umeå, Sweden
Tel.: +46 (90) 193469
E-mail: [email protected]
Website: www.hilicon.com
3.5E+6
2.8E+6
2.1E+6
1.4E+6
7.0E+5
0.0E+0
0 1 2 3 4 5 6 7 8 9 10
Time (min)
11 12 13 14 15
0 1 2 3 4 5 6 7 8 9 10
Time (min)
11 12 13 14 15
0 1 2 3 4 5 6 7 8 9 10
Time (min)
11 12 13 14 15
0 1 2 3 4 5 6 7 8 9 10
Time (min)
11 12 13 14 15
TIC (Psilocin)
TIC (Psilocybin)
Inte
nsi
ty
3.5E+6
2.8E+6
2.1E+6
1.4E+6
7.0E+5
0.0E+0
Inte
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3.0E+6
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1.0E+6
0.0E+0
Inte
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4.0E+5
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2.5E+5
2.0E+5
1.5E+5
1.0E+5
5.0E+4
0.0E+0
Inte
nsi
ty
m/z 205 (Psilocin)
m/z 285 (Psilocybin)
Figure 3: Total ion chromatogram (m/z 40–400) of psilocin and
psilocybin standards and extracted ion chromatograms of m/z 205
(psilocin) and m/z 285 (psilocybin).
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Detailed program, registration and information on www.itp2019.com
26th International Symposium on Electroseparation and Liquid Phase-Separation Techniques
2019September 1- 4I T P
� More than 60 oral communications
� 22 International Keynote Lectures
� 4 Plenary Lectures
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Main topics
Affinity Capillary Electrophoresis
Bioanalytical
CE/MS
Fundamentals
Liquid Chromatography
MS and Liquid Chromatography
Novelties in electrophoretic devices
Particles / Polymers analysis
PortASAP COST and Portable CE session
Young Session
& also an AFSEP session
Social programToulouse, with its artistic atmosphere, offers excellent opportunities for scientific, cultural and social experiences in a unique setting.� Excursion to Carcassonne (Tuesday afternoon)� Concert at the St Sernin Basilica (Monday evening)� Gala dinner in a wonderful place in Toulouse (Tuesday evening)
TOULOUSESeptember 1-4, 2019France
www.gerstel.com
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