November 2014
Volume 17 Number 4
www.chromatographyonline.com
Fast UHPLC Method
Development
Using a Quality-by-Design Framework
PEER REVIEW
GC CONNECTIONS
COLUMN WATCH
Pyrolysis–GC–MS for the
identification of polymeric
materials
How does an electronic gas
control system for GC columns and
detectors work?
Detecting food adulteration
using HPLC
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ES516854_LCA1114_CV2_FP.pgs 10.17.2014 01:41
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November | 2014
COVER STORY
Volume 17 Number 4
6
Rapid UHPLC Method
Development for Omeprazole
Analysis in a Quality-by-Design
Framework and Transfer to
HPLC Using Chromatographic
Modelling
Alexander H. Schmidt and Mijo
Stanic
This article describes ways to apply
quality-by-design principles to build
in a more scientific and risk-based
multifactorial strategy in the
development of an ultrahigh-pressure
liquid chromatography (UHPLC)
method for omeprazole and its related
impurities.
Features
16
Application of Pyrolysis–Gas Chromatography–Mass
Spectrometry for the Identification of Polymeric Materials
Peter Kusch, Gerd Knapp, Wolfgang Fink, Dorothee
Schroeder-Obst, Volker Obst, and Johannes Steinhaus
The pyrolysis–GC–MS method enables direct analysis of solid or
liquid polymers without sample pretreatment, as illustrated here
for various materials, including a dental filling material and a car
wrapping foil.
Columns
Editorial Policy:
All articles submitted to LC•GC Asia Pacific
are subject to a peer-review process in association
with the magazine’s Editorial Advisory Board.
22
GC CONNECTIONS
Electronic Control of Carrier Gas Pressure, Flow, and Velocity
John V. Hinshaw
Have you wondered how your GC system sets and controls gas
pressures, flows, and carrier gas velocities electronically? Here, we
describe the requirements for and the operation of electronic gas
control systems for GC columns and detectors.
27
COLUMN WATCH
When Bad Things Happen to Good Food: Applications of
HPLC to Detect Food Adulteration
W. Jeffrey Hurst, Kendra Pfeifer, and Ronald E. Majors
In this instalment, guest authors Jeff Hurst and Kendra Pfeifer from
Hershey Foods explore high performance liquid chromatography
(HPLC), ultrahigh-pressure liquid chromatography (UHPLC), and
mass spectrometry (MS) approaches being adopted to keep ahead
of the food adulteration game.
Departments
32
34
Products
Application Notes
Cover:
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ES517103_LCA1114_004.pgs 10.20.2014 18:30
ADV
THE
PRESSES ON.
Sitting proudly and powerfully on top of your instrument
stack, this 14” X 26” X 8” small wonder ushers in a new
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ES516852_LCA1114_005_FP.pgs 10.17.2014 01:41
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Rapid UHPLC Method Development
for Omeprazole Analysis in a
Quality-by-Design Framework
and Transfer to HPLC Using
Chromatographic Modelling
Alexander H. Schmidt1,2 and Mijo Stanic1, 1Steiner & Co., Deutsche Arzneimittel GmbH & Co. KG, Berlin, Germany, 2Freie
Universität Berlin, Institute of Pharmacy, Berlin, Germany.
The aim of this study was to apply quality-by-design principles to build in a more scientific and risk-based
multifactorial strategy in the development of an ultrahigh-pressure liquid chromatography (UHPLC)
method for omeprazole and its related impurities.
6
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method because interactions between factors are not
considered.
Today, systematic concepts use experimental design
plans as an efficient and fast tool for method development.
In a full or fractional factorial design, a couple of
experiments are carried out in which one or more factors
are changed at the same time. By using statistical software
tools (for example, Design Expert from Stat-Ease, Inc.), the
effect of each factor on the separation can be calculated
and the data can be used to find the optimum separation
(4). In our laboratory, this concept is used when the
development of nonchromatographic methods is
necessary.
However, the easiest and fastest way of developing a
liquid chromatographic method is by using chromatography
modelling, especially in combination with ultrahigh-pressure
liquid chromatography (UHPLC) technology. Based on a
small number of experiments, these software applications
can predict the movement of peaks when parameters such
KEY POINTS
• A quality-by-design–based method development
strategy for a method to test the purity of omeprazole
has been developed.
• The method development strategy uses visual
chromatographic modelling as a fast and easy to use
development tool.
• All experiments were performed on a UHPLC system
and the final method was successfully transferred to
HPLC conditions.
Photo Credit: Bertlmann/Getty Images
The quality-by-design concept was outlined years ago
by Joseph M. Juran (1) and is used in many industries
to improve the quality of products and services simply
by planning quality from the beginning. Since the US
Food and Drug Administration (FDA) announced its
“Pharmaceutical Current Good Manufacturing Practices
(cGMPs) for the 21st Century” initiative (2) in 2002, a
quality-by-design approach has also been sought in the
pharmaceutical industry.
Through the International Conference on Harmonization
(ICH), this concept resulted in ICH guideline Q8(R2)
in which quality-by-design is defined as “a systematic
approach to development that begins with predefined
objectives and emphasizes product and process
understanding and process control, based on sound
science and quality risk management” (3).
Although ICH guideline Q8(R2) doesn’t explicitly
take analytical method development into account
and no other regulatory guideline has been issued,
the quality-by-design concept can be extended to a
systematic approach that includes the definition of the
methods goal, risk assessment, design of experiments,
developing a design space, verification of the design
space, implementing a control strategy, and continual
improvement to increase method robustness and
knowledge (4). The novelty and opportunity in this
approach is that working within the design space of a
specific method can be seen as an adjustment and not a
postapproval change (4).
A systematic approach should replace the still common
“screening”, also known as a trial-and-error approach,
in which one factor at a time (OFAT) is varied until
the best method is found. The OFAT approach is
time-consuming and often results in a nonrobust
LC•GC Asia Paciàc November 2014
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ES517468_LCA1114_007_FP.pgs 10.21.2014 01:37
ADV
Schmidt and Stanic
Figure 1: Chemical structures of omeprazole and its related
impurities.
Name
Omeprazole
Chemical Structure
H3C
OCH3
H
N
H
N
CH3
S
N
OCH3
Name
Chemical Structure
Impurity A (EP)
SH
N
O
N
OCH3
H3C
OCH3
H
N
CH3
S
N
OCH3
N
O
Impurity B (EP)
Impurity C (EP)
H3C
H
N
H 3C
N
OCH3
H
N
CH3
S
OCH3
Figure 3: Graphical description of the design of experiments
plan for the method development by using chromatographic
modelling: For each organic eluent, methanol and acetonitrile,
12 experiments have to be performed with low and high
values for T, tG, and pH.
CH3
S
N
O
OCH3
N
N
H3C
H
N
CH3
S
N
N
O
Impurity D (EP)
H3C
OCH3
H
N
S
N
OCH3
Impurity E (EP)
H3C
O
CH3
S
N
O
OCH3
N
N
O
O
H
N
CH3
S
OCH3
N
N
O
O
Impurity G (EP)
CH3
S
O
H3CO
N
N
N
N
CH3
CH3
N
N
Impurity H (EP)
H3C
O
S
N
CH3
N
O
O
O
CH3
S
N
OCH3
Cl
H
N
OCH3
H
N
N
H3C
OCH3
H 3C
CH3
S
N
Impurity I (EP)
Cl
H
N
OCH3
tG(min)
CH3
S
O
OH3C
pH
OCH3
H3C
Impurity F (EP)
T(ºC)
OCH3
H
N
CH3
N
O
Figure 4: Three-dimensional resolution cube (tG/T/pH model)
and the corresponding two-dimensional resolution map (tG/T
model) at pH 9.0 for methanol as the organic solvent in the
UHPLC gradient method. The red regions in the resolution
maps represent the design space, in which the performance
criteria are met.
Figure 2: Typical chromatogram of a selectivity standard
solution containing omeprazole and its related impurities
A–I by using the purity method published in the European
Pharmacopoeia. Column: 125 mm × 4.6 mm, 5-µm dp
Symmetry C8 column; mode: isocratic; eluent: 27 vol%
acetonitrile and 73 vol% disodium hydrogen phosphate
(1.4 g/L), adjusted with phosphoric acid to pH 7.6; flow
rate: 1 mL/min.
8.5
Imp.A
Imp.I
pH
9
Imp.B
Imp.D
Imp.F+G
2.20 60
Imp.E
Omeprazole
8
2.00
1.80
1.60
4
6
Time (min)
8
10
1.20
Imp.A
Imp.I
50
1.40
T (°C)
OCH3
1.00
40
0
Imp.H
10
20
Time (min)
Imp.C
0.60
Imp.FÐG
Imp.B
Imp.D
Imp.E
0.80
0.40
0.20
30
40
as eluent composition or pH, flow rate, column temperature,
column dimensions, and particle size are changed (5–11).
When necessary, the developed method can be transferred
(back) to high performance liquid chromatography (HPLC).
8
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0.00
30
5
tG (min)
10
In our laboratory we have been using visual
chromatographic modelling (software packages) for many
years now in HPLC and UHPLC method development and
it has resulted in very robust methods (4,12–14). The aim
of this study was to apply quality-by-design principles to
LC•GC Asia Paciàc November 2014
ES517118_LCA1114_008.pgs 10.20.2014 18:32
ADV
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ES516855_LCA1114_009_FP.pgs 10.17.2014 01:41
ADV
Schmidt and Stanic
Table 1: Verification study for the newly developed UHPLC method. A comparison of predicted and experimental retention
times of all components at the working point and six verification points are shown below and found to be excellent with a
correlation coefficient of R 2 = 0.999, which can also be seen in the corresponding graphical comparison (Figure 8[a]).
Working Point
Verification
Point 1
Verification
Point 2
Verification
Point 3
Verification
Point 4
Verification
Point 5
Verification
Point 6
Flow rate
(mL/min)
0.70
0.70
0.75
0.70
0.65
0.65
0.75
tG (min)
4.0
3.9
4.1
4.0
3.9
4.1
4.0
Temp. (°C)
35
37
33
33
35
35
37
8.75
8.75
8.75
9.00
9.00
8.50
8.50
%start
10
9
10
11
10
11
9
%end
60
60
61
60
61
59
59
pH
Retention
time (min)
Pred.
Exp.
Pred.
Exp.
Pred.
Exp.
Pred.
Exp.
Pred.
Exp.
Pred.
Exp.
Pred.
Exp.
Imp. A
1.06
1.14
1.13
1.18
1.04
1.09
0.96
1.08
1.09
1.15
1.07
1.21
1.10
1.19
Imp. I
1.45
1.48
1.50
1.52
1.37
1.41
1.30
1.32
1.43
1.46
1.54
1.61
1.53
1.62
Imp. E
1.71
1.74
1.75
1.77
1.65
1.68
1.57
1.59
1.69
1.73
1.81
1.86
1.77
1.85
Imp. D
1.97
2.00
2.01
2.02
1.91
1.93
1.79
1.83
1.90
1.91
2.14
2.18
2.07
2.16
Imp. B
2.17
2.21
2.20
2.21
2.11
2.14
2.06
2.08
2.15
2.18
2.30
2.32
2.22
2.30
Omeprazole
2.26
2.29
2.28
2.29
2.20
2.22
2.15
2.18
2.24
2.27
2.38
2.40
2.30
2.38
Imp. H
2.68
2.72
2.68
2.70
2.62
2.65
2.58
2.62
2.65
2.68
2.84
2.85
2.72
2.80
Imp. C
2.96
2.99
2.95
2.96
2.90
2.92
2.91
2.93
2.96
2.98
3.11
3.10
2.96
3.04
Imp. F
3.68
3.71
3.64
3.65
3.62
3.65
3.66
3.67
3.67
3.69
3.88
3.84
3.66
3.71
Imp. G
3.82
3.84
3.76
3.77
3.75
3.78
3.79
3.81
3.80
3.82
4.02
3.97
3.79
3.84
build in a more scientific and risk-based, multifactorial
strategy in the development of a new UHPLC method for
testing the purity of omeprazole.
Omeprazole belongs to the group of proton-pump
inhibitors and is one of the most widely prescribed
drugs. It suppresses gastric acid secretion by specific
inhibition of the enzyme hydrogen-potassium adenosine
triphosphatase (H+, K +−ATPase). Omeprazole
formulations are used to treat acid reflux, heartburn, ulcer
disease, and gastritis (15).
Omeprazole is described in the monograph of the
European Pharmacopeia (EP) (16). Purity testing for
omeprazole is accomplished by using HPLC with UV
detection on a 125 mm × 4.6 mm, 5-µm d p C8 column
in isocratic mode with an eluent consisting of 27 vol%
acetonitrile and 73 vol% disodium hydrogen phosphate
solution (pH 7.6) and a flow rate of 1.0 mL/min. On the
basis of the synthetic route, the monograph recommends
testing the impurities A, B, C, D, E, H, and I by HPLC, and
the impurities F and G have to be tested by a photometric
method (chemical structures are shown in Figure 1). A
typical chromatogram of a selectivity standard solution
containing omeprazole and its related impurities A–I
obtained using the EP method is given in Figure 2 and
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shows that the method was developed without any
chromatography knowledge. Some of the impurity peaks
show coelution, but the last three peaks are separated
from each other with a huge distance of 10 min each.
Several analytical procedures for the determination
of omeprazole and its related impurities have been
described. A review of the analytical methods for the
determination of omeprazole, mostly in plasma and
urine, was published in 2007 (17). Only some recent
publications focus on stability-indicating methods for
the analysis of impurities and degradation products in
omeprazole formulations (18–20). As far as we know,
no analytical method has been published that would
separate all synthesis impurities and degradation
products mentioned in the EP monograph. Therefore,
there is a need for a simple, fast, and reliable purity
method for the determination of omeprazole and its
related impurities in the active pharmaceutical ingredient
(API) and in pharmaceutical formulations.
Experimental
Chemicals: Methanol and acetonitrile were
HPLC-gradient grade (Sigma). All other chemicals were
at least analytical grade and were also purchased from
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Figure 7: Experimental UHPLC chromatogram of omeprazole
spiked with its related impurities A–I for conditions at the
working point (for details see text).
3.711 Imp. F
3.840 Imp.G
2.718 Imp. H
2.988 Imp. C
2.010 Imp. B
2.002 Imp. D
1.479 Imp. I
60
1.743 Imp. E
2.293 Omeprazole
1.144 Imp. A
Figure 5: Three-dimensional resolution cube (tG/T/pH model)
and the corresponding two-dimensional resolution map (tG/T
model) at pH 8.75 for acetonitrile as the organic solvent
in the UHPLC gradient method. The large red regions in
the resolution maps represent the design space, in which
performance criteria are met.
5
1.0
T (°C)
5.5
40
0.8
2.40
50
T (°C)
1.60
1.20
1.00
40
0.80
0.60
0.40
4.0
by Empower 2 C/S-software (Waters) was used. The dwell
volume of the system was 1.000 mL.
A 50 mm × 2.1 mm, 1.7-µm d p Acquity UPLC BEH C18
column (Waters) was used in the UHPLC study and the
equivalent 50 mm × 4.6 mm, 2.5-µm d p XBridge BEH C18
column (Waters) was used in the HPLC study.
All method development experiments were performed
on the UHPLC system in gradient mode. Eluent A was
10 mM ammonium bicarbonate buffer at different pH
values (adjusted with ammonia) and eluent B was
acetonitrile. Eluent C was methanol (for screening
60
2.00
1.40
3.0
6.9
4
tG (min)
2.20
1.80
2.0
Time (min)
30
0.20
0.00
5
tG (min)
Figure 6: Predicted UHPLC chromatogram for omeprazole
and its related impurities for conditions at the working point
(for details see text).
0
1.0
2.0
Time (min)
3.0
JAPAN 2 14
3.685 Imp.F
3.817 Imp.G
2.964 Imp.C
2.683 Imp.H
2.173 Imp.B
1.710 Imp.E
1.972 Imp.D
1.540 Imp.I
1.064 Imp.A
2.257
Omeprazole
4.0
Sigma. Ultrapure water was obtained using a TKA water
purification system (Thermo Fisher Scientific).
Equipment and Chromatographic Conditions: For
the UHPLC experiments, an Acquity UPLC H-class
system consisting of a quaternary solvent system with
a solvent-selection valve, a sample injection system,
column management system, and a photodiode-array
detector, all controlled by Empower 2 C/S-software
(Waters) was used. The dwell volume of the system was
0.400 mL.
For the HPLC experiments an Alliance 2695 XE system
with a model 2996 photodiode-array detector, controlled
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Table 2: Verification study after the method transfer to HPLC. A comparison of predicted and experimental retention times of all
components at the working point and six verification points are shown below and found to be excellent with a correlation coefficient
of R2 = 0.999, which can also be seen in the corresponding graphical comparison (Figure 8[b]).
Working Point
Verification
Point 1
Verification
Point 2
Verification
Point 3
Verification
Point 4
Verification
Point 5
Verification
Point 6
Flow rate
(mL/min)
1.9
1.9
2.0
1.9
1.8
1.8
2.0
tG (min)
7.0
6.8
7.2
7.0
6.8
7.2
6.8
Temp. (°C)
35
37
33
33
35
35
37
8.75
8.75
8.75
9.00
9.00
8.50
8.50
%start
pH
10
9
10
11
10
11
9
%end
60
61
61
60
61
59
59
Retention
time (min)
Pred.
Exp.
Pred.
Exp.
Pred.
Exp.
Pred.
Exp.
Pred.
Exp.
Pred.
Exp.
Pred.
Exp.
Imp. A
1.51
1.64
1.59
1.71
1.52
1.61
1.37
1.49
1.51
1.69
1.52
1.56
1.59
1.67
Imp. I
2.10
2.09
2.18
2.19
2.05
2.04
1.84
1.83
1.99
2.02
2.21
2.19
2.26
2.26
Imp. E
2.54
2.49
2.61
2.57
2.50
2.44
2.31
2.25
2.43
2.42
2.66
2.55
2.66
2.58
Imp. D
3.01
2.98
3.06
3.05
2.95
2.92
2.69
2.65
2.81
2.81
3.24
3.20
3.18
3.17
Imp. B
3.35
3.26
3.38
3.31
3.31
3.21
3.15
3.06
3.24
3.19
3.51
3.36
3.43
3.31
Omeprazole
3.50
3.40
3.52
3.44
3.45
3.35
3.31
3.21
3.39
3.32
3.66
3.50
3.56
3.44
Imp. H
4.24
4.13
4.23
4.14
4.20
4.09
4.06
3.94
4.10
4.03
4.46
4.28
4.28
4.15
Imp. C
4.73
4.59
4.69
4.58
4.70
4.55
4.64
4.51
4.65
4.55
4.92
4.72
4.70
4.54
Imp. F
6.00
5.84
5.90
5.77
5.97
5.81
5.95
5.77
5.89
5.78
6.27
6.05
5.90
5.73
Imp. G
6.23
6.08
6.12
5.99
6.20
6.04
6.18
6.03
6.10
6.00
6.52
6.29
6.12
5.95
experiments only). The flow rate was set to 0.7 mL/min
and the injection volume was 2 µL.
The temperature in the experiments was optimized
between 30 °C and 60 °C. The UV detection of the
compounds of interest was carried out at 303 nm and the
UV spectra were taken in the range of 200–400 nm.
Software: For chromatography modelling the DryLab
4.0 software package (Molnar-Institute) was used.
The software package includes PeakMatch and
3-D-Robustness modules.
Standard Preparation: A selectivity standard solution
containing 0.2 mg/mL omeprazole (in-house standard
substance) and approximately 0.002 mg/mL of each of
the nine impurities was prepared with a 2:8 (v/v) mixture
of acetonitrile and 10 mM ammonium bicarbonate buffer
as the solvent. The impurities A, B, C, E, H, and I were
obtained from LGC. Impurity D was purchased from
the European Directorate for the Quality of Medicines
(EDQM) and the impurities F and G were obtained from
the U.S. Pharmacopeial Convention (USP). The selectivity
standard solution was protected from light by using
amber glassware.
Results and Discussions
Development Strategy: Our development strategy (4)
follows quality-by-design principles and can be divided
into six steps as follows:
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Step 1: Definition of Method Goals: Our primary
goal was to develop a stability-indicating method that
separates the API from all impurities with a critical
resolution (R s,crit) of no less than 2.0. To speed up the
development process, UHPLC technology was used; the
final method was intended to be transferred to HPLC.
Step 2: Risk Assessment: Using a fishbone diagram,
an early risk assessment was identified and possible
risk factors associated with sample preparation as well
as the instrumental analysis were prioritized. The initial
list of potential parameters that can affect critical quality
attributes (CQAs) were ranked and prioritized using
failure mode and effects analysis (FMEA).
It was obvious that resolution is a CQA and the
selectivity term α in the general equation R s = 0.25N 1/2 [(α
− 1)/α][k/(1 + k)] has the greatest impact on the
resolution. Selectivity is influenced by the mobile phase
composition, column chemistry, and temperature (21),
and the influence should be investigated by design of
experiments (DoE).
Other CQAs that were taken into account include the
robustness of the method and the run time.
Step 3: Design of Experiments: For the critical
process parameters (CPPs), which have an impact
on the CQAs, experiments should be conducted to
determine acceptable ranges. As the result of the risk
assessment, the four parameters gradient time (t G),
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Table 3: Description of the final analytical procedure including the tolerance limits.
Chromatographic
Parameter
UHPLC Conditions
HPLC Conditions
Column
50 mm × 2.1 mm, 1.7-µm dp Acquity BEH C18 (Waters)
50 mm × 4.6 mm, 2.5-µm dp XBridge BEH C18 (Waters)
Eluent A
10 mM ammonium bicarbonate buffer, pH 8.75 (±0.1
pH units)
10 mM ammonium bicarbonate buffer, pH 8.75 (±0.1
pH units)
Eluent B
Acetonitrile
Acetonitrile
Gradient
Linear increase from 10% (±1%) to 60% (±1%) of eluent
B in 4.0 min (±0.05 min), followed by reequilibration
Linear increase from 10% (±1%) to 60% (±1%) of eluent
B in 7.0 min (±0.5 min), followed by re-equilibration
Stop time
5 min
8 min
Flow rate
0.70 mL/min (±0.05 mL/min)
1.90 mL/min (±0.05 mL/min)
Column temp.
35 °C (±2 °C)
35 °C (±2 °C)
Injection volume
2 µL
20 µL
Detection
UV absorbance at 303 nm
UV absorbance at 303 nm
Figure 8: Plots of experimental retention time versus
predicted retention time for (a) the UHPLC method and (b)
after method transfer to HPLC.
4.50
4.00
3.50
3.00
y=0.9813x + 0.0795
R2 = 0.999
2.50
60
2.00
1.50
40
1.00
N
Experimental retention time (min)
(a)
20
0.50
0.00
0.00
Figure 9: Frequency distribution of the Rs,crit values for
all 729 experiments of the robustness study on the UHPLC
system. The six parameters tG (4 min ± 0.1 min), T (35 °C
± 2 °C), pH (8.75 ± 0.1), flow rate (0.7 mL/min ± 0.05 mL/
min), and the %B start (10% ± 1%) and %B end (60% ±
1%) of the gradient were varied at three levels (+1, 0, -1). All
experiments fulfill the requirement for resolution Rs,crit no less
than 2.0. That means that the failure rate is 0, so there will
be no method-related out-of-specification (OOS) results and
production quality control will be smooth and robust.
1.00
2.00
3.00
4.00
5.00
Predicted retention time (min)
0
2.12
2.17
2.22
Rs, crit
2.27
2.32
2.37
(b) 7.00
Experimental retention time (min)
6.00
5.00
4.00
y=0.952x + 0.1024
R2 = 0.999
3.00
2.00
1.00
0.00
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
Predicted retention time (min)
temperature (T ), pH of the aqueous eluent A, and type
of the organic eluent B were screened and optimized
because of their strong known influential effects on
selectivity.
A set of 12 experiments was performed for each of the
two organic eluents methanol and acetonitrile under the
following conditions: gradient times: t G1 = 3 min and
t G 2 = 9 min; temperatures: T1 = 30 °C and T2 = 60 °C.
The pH values of the buffer were pH1: 8.0, pH2: 8.5, and
pH3: 9.0. Because of prior knowledge, a modern C18
column was used.
The ranges between these factors were large enough
to induce peak movements to discover hidden peaks (4).
A graphical description of the DoE plan can be seen in
Figure 3.
Step 4: Design Space: The retention times of all peaks
of interest in the 12 experiments were entered into the
chromatographic modelling software and matched in each
of the chromatograms by using the PeakMatch module.
Based on the limited set of only 12 experiments, the
modelling software builds a three-dimensional model
of the critical resolution (the so-called “knowledge
space”), in which the combined influence of the optimized
parameters are visualized. The modelling software
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Figure 10: Predicted HPLC chromatogram for omeprazole
and its related impurities for conditions after the transfer to
the HPLC system (for details see text).
1.0
3.0
4.0
Time (min)
2.0
5.996 Imp. F
6.226 Imp. G
4.9731 Imp. C
4.240 Imp. H
3.351 Imp. B
3.006 Imp. D
2.543 Imp. E
2.098 Imp. I
1.506 Imp. A
3.495 Omeprazole
5.0
6.0
Figure 12: Frequency of the distribution of the resolution
values Rs,crit for all 729 experiments of the robustness study
after the transfer to the HPLC system. The six parameters tG
(7 min ± 0.1 min), T (35 °C ± 2 °C), pH (8.75 ± 0.1), flow rate
(1.9 mL/min ± 0.1 mL/min), and the %B start (10% ± 1%)
and %B end (60% ± 1%) of the gradient were varied at three
levels (+1, 0, -1). All experiments still fulfill the requirement
for resolution Rs,crit of no less than 2.0. That means that the
failure is also 0.
80
60
N
40
20
Figure 11: Experimental HPLC chromatogram of omeprazole
spiked with its related impurities A–I for conditions after the
transfer to the HPLC system (for details see text).
0
2.13
2.18
2.23
Rs, crit
2.28
2.33
1.0
2.0
3.0
4.0
Time (min)
5.840 Imp. F
6.067 Imp. G
4.591 Imp. C
4.130 Imp. H
3.260 Imp. B
2.979 Imp. D
2.490 Imp. E
2.090 Imp. I
1.632 Imp. A
3.401 Omeprazole
5.0
6.0
uses a colour code to represent the value of the critical
resolution: Warm, “red” colours show large resolution
values (R s > 2.0), and cold, “blue” colours show low
resolution values (R s < 0.5) corresponding to regions
of peak overlaps. The red geometric bodies within the
knowledge space, in which the performance criteria are
met, is called the design space. The ICH Q8 guideline
defines the design space as follows (3):
“The multidimensional combination and interaction of
input variables (e.g., material attributes) and process
parameters that have been demonstrated to provide
assurance of quality. Working within the design space
is not considered as a change. Movement out of the
design space is considered to be a change and would
normally initiate a regulatory post approval change
process.”
Figures 4 and 5 show the three-dimensional resolution
cubes for methanol and acetonitrile as the organic eluent in
the UHPLC gradient method. A visual inspection shows that
the design space in the methanol cube is much smaller than
the design space in the acetonitrile cube. That means that
the method with acetonitrile is more robust than the method
with methanol and all the peaks in the chromatogram are
well separated from each other (baseline resolution).
14
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Therefore, acetonitrile was chosen as the organic
eluent and, from the corresponding design space, the
working point was selected by visual examination. There
are several possible alternative working points within
the design space, but we looked for the highest critical
resolution (R s,crit) and best robustness of the method.
This working point was found in the cube at t G 4.0 min,
T 35 °C, and pH 8.75. The predicted and experimental
chromatograms for this working point are shown in
Figures 6 and 7.
A verification study comparing predicted and
experimental retention times for the working point and
six verification points around the working point, but
within the design space, was found to be excellent with
a correlation coefficient of 0.999, as shown in Table 1
and Figure 8(a). This is also in compliance to previous
reported data (4,22,23).
An important part of our method development strategy
is to perform robustness testing of the developed method
before the validation study. The ICH guideline Q2 (R1)
(24) defines robustness as follows:
“[. . .] the reliability of an analysis with respect to
deliberate variations in method parameters. The
robustness of an analytical procedure is a measure
of its capacity to remain unaffected by small, but
deliberate variations in method parameters and
provides an indication of its reliability during normal
usage.”
The robustness of the developed method was studied
using the robustness module of the chromatographic
modelling software. In a three-level, full-factorial design,
the module used the previously constructed and verified
design space for “in silico” robustness calculations (4).
The six parameters t G (4 min ± 0.1 min), T (35 °C ± 2
°C), pH (8.75 ± 0.1), flow rate (0.7 mL/min ± 0.05 mL/
min), and the %B start (10% ± 1%) and %B end
(60% ± 1%) of the gradient were varied at three levels
(+1, 0, −1).
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Figure 9 shows the frequency of the distribution of the
resolution values R s,crit for all 729 experiments. It can be
seen that the required resolution of 2.0 can be reached
in all experiments. Therefore, the developed method
is robust against small changes of chromatographic
parameters.
A formal validation study should be performed before
this new method can replace the existing method.
Step 5: Method Control Strategy: The ICH Q8
guideline defines the control strategy as “a planned set
of controls, derived from current product and process
understanding that ensures process performance and
product quality[. . .]” This means that the control strategy
should be implemented to ensure that the developed
method is performing as intended. Usually, this can be
done by using a system suitability test. In our method
development strategy, the resolution of the critical peak
pair (R s,crit), was chosen as a system suitability test
parameter and should not be less than 2.0.
Step 6: Continual Improvement: In this last step
further experiments can be planned and repeated to
try out better columns and eluents to further adjust or
improve the position of the working point. In addition,
business needs — for example, the transfer of the
developed UHPLC method (such as from the research
and development [R&D] laboratory) to HPLC conditions
(such as into the quality control [QC] laboratory) — can
be taken into account.
To transfer the UHPLC method to HPLC conditions,
the changed column dimensions, particle sizes, and
system dwell volumes were used to scale up the flow
rate and gradient time. This can be made by using free
available method transferring tools (such as the Acquity
Columns Calculator from Waters). A smart way is to use
the modelling software for the transfer and calculate
the gradient time and flow rate. At the same time, the
corresponding chromatograms can be visualized.
Small adjustments of the scaled conditions for flow
rate and gradient time had to be made to reduce the
back pressure in the HPLC system. The predicted and
experimental chromatograms for the up-scaled HPLC
method can be seen in Figures 10 and 11. A second
verification study for the working point on the HPLC
system and six verification points around the working
point confirmed the accuracy of the prediction (see
Table 2 and the corresponding graph in Figure 8[b]). In
addition, the robustness study after the transfer to the
HPLC system shows that the failure rate is still zero (see
Figure 12).
Table 3 summarizes the chromatographic parameters
and tolerances of the final method.
Conclusions
A quality-by-design–based method development
strategy for a method to test the purity of omeprazole
has been presented here. The scientific and risk-based
multifactorial method development strategy uses visual
chromatographic modelling as a fast and easy to use
development tool. To speed up the method development
process, all experiments were performed on a UHPLC
system. The final method was successfully transferred to
HPLC conditions. Verification studies between predicted
and experimental retention times confirm the accuracy of
the chromatographic modelling process.
All experiments, from the planning, performing on the
UHPLC system, verification and transfer to HPLC, to the
reporting, were made within one week.
References
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(18)
(19)
(20)
(21)
(22)
(23)
(24)
J.M. Juran, Juran on Quality by Design: The New Steps for
Planning Quality into Goods and Services (The Free Press, New
York, USA, 1992).
http://www.fda.gov/downloads/Drugs/
DevelopmentApprovalProcess/Manufacturing/
Questions andAnswersonCurrentGoodManufacturing
PracticescGMPforDrugs/UCM176374.pdf.
http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/
Guidelines/Quality/Q8_R1/Step4/Q8_R2_Guideline.pdf.
A.H. Schmidt and I. Molnár, J. Pharm. Biomed. Anal. 78–79,
65–74 (2013).
L.R. Snyder and J.L. Glajch, Computer-assisted Method
Development for High Performance Liquid Chromatography,
(Elsevier, Amsterdam, The Netherlands, 1990).
L.R. Snyder and J.L. Glajch, J. Chromatogr. A 485, 1– 675
(1989).
I. Molnár, J. Chromatogr. A 965, 175–194 (2002).
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3193–3200 (2010).
I. Molnár and K.E. Monks, Chromatographia 73(Suppl.1), 5–14
(2011).
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Chim. Acta 696, 116 –124 (2011).
K. Monks, I. Molnár, H.-J. Rieger, B. Bogáti, and E. Szabó, J.
Chromatogr. A 1232, 218–230 (2012).
A.H. Schmidt and I. Molnár, J. Chromatogr. 948, 51– 63 (2002).
A.H. Schmidt, J. Liq. Chromatogr. Relat. Technol. 28, 871–881
(2005).
A.H. Schmidt, M. Stanic, and I. Molnár, J. Pharm. Biomed. Anal.
91, 97–107 (2014).
Commentary of the European Pharmacopoeia (in German), 38
supplement, Deutscher Apotheker Verlag, Stuttgart, Germany,
(2011).
“Monograph Omeprazole” in the European Pharmacopoeia,
Seventh ed. (Deutscher Apotheker Verlag, Stuttgart, Germany,
2011).
M. Espinosa Bosch, A.J. Ruiz Sanchez, F. Sanchez Rojas, and
C. Bosch Ojeda, J. Pharm. Biomed. Anal. 44, 831–844 (2007).
C. Iuga, M. Bojita, and S.E. Leucuta, Farmacia 57, 534–541 (2009).
K.B. Borges, A.J.M. Sanchez, M.T. Pupo, P.S. Bonato, and I.G.
Collado, J. AOAC Int. 93, 1811–1820 (2010).
P. Venkata Rao, Ch.K. Sanjeeva Reddy, M. Ravi Kumar, and
Danta Durga Rao, J. Liq. Chromatogr. Relat. Technol. 35,
2322–2332 (2012).
L.R. Snyder, J.J. Kirkland, and J.L. Glajch, Practical HPLC
Method Development, 2nd ed. (Wiley-Interscience, New York,
USA, 1997).
M.R. Euerby, G. Schad, H.-J. Rieger, and I. Molnár, Chromatogr.
Today 3, 13–20 (2010).
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56, 874–879 (2011).
http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/
Guidelines/Quality/Q2_R1/Step4/Q2_R1_Guideline.pdf.
Alexander H. Schmidt is quality control director at Steiner
Pharmaceuticals in Berlin, Germany. He is also head of
analytical development of an R&D and contract analysis lab
and supervises 35 lab assistants and chemists. Over the years,
he has published numerous articles on HPLC and UHPLC
method development for pharmaceuticals and complex natural
compound mixtures. He is also a guest lecturer at the Beuth
University of Applied Sciences, in Berlin, Germany. In addition,
he is currently writing his doctoral thesis at the Institute of
Pharmacy at Freie Universität Berlin in Germany.
Mijo Stanic joined the development team at Steiner
Pharmaceuticals as a lab assistant and was promoted to deputy
lab manager in early 2013.
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Application of
Pyrolysis–Gas
Chromatography–
Mass Spectrometry
for the Identiàcation of
Polymeric Materials
Peter Kusch1, Gerd Knupp1, Wolfgang Fink1, Dorothee Schroeder-Obst1, Volker Obst2 , and Johannes Steinhaus1,
1Hochschule Bonn-Rhein-Sieg, University of Applied Sciences, Department of Applied Natural Sciences, Rheinbach,
Germany, 2Volker Obst, Dr. Obst Technische Werk-stoffe GmbH, Rheinbach, Germany.
The analytical pyrolysis technique hyphenated to gas chromatography–mass spectrometry (GC–MS) has
extended the range of possible tools for the characterization of synthetic polymers and copolymers. Pyrolysis
involves thermal fragmentation of the analytical sample at temperatures of 500–1400 °C. In the presence of an
inert gas, reproducible decomposition products characteristic for the original polymer or copolymer sample
are formed. The pyrolysis products are chromatographically separated using a fused-silica capillary column
and are subsequently identiàed by interpretation of the obtained mass spectra or by using mass spectra
libraries. The analytical technique eliminates the need for pretreatment by performing analyses directly on the
solid or liquid polymer sample. In this article, application examples of analytical pyrolysis hyphenated to
GC–MS for the identiàcation of different polymeric materials in the plastic and automotive industry,
dentistry, and occupational safety are demonstrated. For the àrst time, results of identiàcation of commercial
light-curing dental àlling material and a car wrapping foil by pyrolysis–GC–MS are presented.
Structural analysis and the study of degradation properties
are important to understand and improve performance
characteristics of synthetic polymers and copolymers in many
industrial applications. Traditional analytical techniques used for
characterization of polymers and copolymers such as thermal
analysis and Fourier transform infrared (FT–IR) spectroscopy
have limitations or are not sufficiently sensitive (1). Pyrolysis
techniques hyphenated to gas chromatography–mass
spectrometry (GC–MS) have extended the range of possible tools
for the characterization of synthetic polymers and copolymers.
Under controlled conditions, at elevated temperatures
(500–1400 °C) in the presence of an inert gas, reproducible
decomposition products characteristic for the original polymer
or copolymer sample are formed. The pyrolysis products are
chromatographically separated using a fused-silica capillary
column and subsequently identified by interpretation of the
obtained mass spectra or by using mass spectra libraries (such
as the National Institute of Standards and Technology [NIST] or
Wiley). Pyrolysis methods eliminate the need for pretreatment by
performing analyses directly on the solid polymer or copolymer
sample (1). (Please note that this article was presented at the XVII
European Conference on Analytical Chemistry, which was held in
Warsaw, Poland, on 25–29 August 2013).
Most of the thermal degradation results from free radical
reactions initiated by bond breaking and depends on the relative
strengths of the bonds that hold the molecules together. A large
molecule will break apart and rearrange in a characteristic
16
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way (2–4). If the energy transfer to the sample is controlled by
temperature, heating rate, and time, the fragmentation pattern
is reproducible and characteristic for the original polymer or
copolymer. Another sample of the same composition, heated at
the same rate to the same temperature for the same period of
time, will produce the same decomposition products. Therefore,
the essential requirements of the apparatus in analytical
pyrolysis are reproducibility of the final pyrolysis temperature,
rapid temperature rise, and accurate temperature control.
Depending on the heating mechanism, pyrolysis systems have
been classified into two groups: the continuous-mode pyrolyzer
(furnace pyrolyzer) and pulse-mode pyrolyzer (flash pyrolyzer,
such as the heated filament, Curie-point, and laser pyrolyzer). The
pyrolysis unit is directly connected to the injector port of a gas
KEY POINTS
• Pyrolysis–GC–MS is a valuable technique for the analysis
and identification of synthetic polymers and copolymers.
• The technique described allows the direct analysis
of very small sample amounts (5–200 μg) without the
need for time-consuming sample preparation.
• Commercial light-curing dental filling material and car
wrapping foil were identified using this method.
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Kusch et al.
Table 1: Pyrolysis products and identified materials in plastic
Figure 1: Schematic view of the furnace pyrolyzer used in
particles from industrial filter fins.
this study.
Sample injector
Septum injector
Carrier gas
Septum purge
Quartz furnace liner
Furnace assembly
Adaptor ftting
Transfer tube
existing GC injector port
Figure 2: Pyrolysis–GC–MS chromatogram of plastic particles
from industrial filter fins at 700 °C obtained with apparatus 1.
Fused-silica GC capillary column: 60 m × 0.25 mm, 0.25-µm
df Elite-5ms. GC conditions: programmed column temperature:
60 °C for 1 min, then 60–100 °C at 2.5 °C/min and then
100–280 °C at 10 °C/min (20 min hold at 280 °C); split–
splitless injector temperature: 250 °C; split flow: 50 cm³/
min; helium programmed pressure: 70 kPa for 1 min, then
70–110 kPa at 1 kPa/min (hold at 110 kPa to the end of
analysis). For peak identification, see Table 1.
100
5.44
95
90
85
10.51
80
75
Relative abundance
70
65
60
5.86
55
50
45
40
35
40.40
30
25
6.13
20
15
31.40
6.227.58 9.72
5
0
32.08
30.81
11.81
7.43
10
7.77
13.33
0.521.25 2.90 3.87
0
2
4
6
8
10
12
14.39
14
16.65
18.52 20.26
17.47
16
18
20
32.22
28.15
20.99
22
24.82 25.30
24
26
27.40 29.11
28
30
32.52
32
37.66
34.8935.36
34
36
38
37.86
40
42.19 42.64 45.34 46.52 48.1949.31 50.49 51.89 53.13
42
44
46
48
50
52
54
Time (min)
chromatograph. A flow of an inert carrier gas, such as helium,
flushes the pyrolyzates into the fused-silica capillary column.
Figure 1 shows the schematic view of the furnace pyrolyzer used
in our investigation. The detection technique of the separated
compounds is typically MS, but other GC detectors have also
been used depending on the intentions of the analysis (1,4).
18
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Retention
Time t R
(min)
Pyrolysis Product Matching
at 700 °C
Factor
Identified Material
5.44
Propylene
820
Polypropylene glycol
5.58
1-Butene/1,3butadiene
840
Styrene–butadiene
rubber (SBR)
5.86
Acetone
850
Polypropylene glycol
6.13
Pentadiene
885
SBR
7.43
Benzene
954
SBR
9.72
Toluene
863
SBR
10.51
Cyclopentanone
933
Poly(hexamethylene
adipamide) (nylon 6-6)
11.81
2-Cyclopenten1-one
906
Poly(hexamethylene
adipamide) (nylon 6-6)
14.39
Styrene
851
SBR
28.15
4-Isopropylphenol
944
Polycarbonate or
bisphenol A epoxy resin
40.40
N-Phenyl-1naphthalen-amine
948
Antioxidant
The applications of analytical pyrolysis–GC–MS range
from research and development of new materials, quality
control, characterization and competitor product evaluation,
medicine, biology and biotechnology, geology, airspace, and
environmental analysis to forensic purposes or conservation and
restoration of cultural heritage. These applications cover analysis
and identification of polymers, copolymers, and additives in
components of automobiles, tyres, packaging materials, textile
fibres, coatings, half-finished products for electronics, paints
or varnishes, lacquers, leather, paper or wood products, food,
pharmaceuticals, surfactants, and fragrances.
Our earlier publications (1,5–12) presented the analysis and
identification of degradation products of commercially available
synthetic polymers and copolymers by using analytical pyrolysis
hyphenated to gas chromatography with flame ionization
detection (GC–FID) and GC–MS. In this work, new examples of
applications of this analytical technique for the identification of
different polymeric materials are demonstrated.
Experimental
Samples: Plastic particles from industrial filter fins, a car
wrapping foil, unknown fibres, and commercial light-curing dental
filling material were used in the investigation.
Instrumentation and Analytical Conditions: Approximately
100–200 µg of solid sample was cut out with a scalpel and
inserted without any further preparation into the bore of the
pyrolysis solids-injector and then placed with the plunger on the
quartz wool of the quartz tube of the furnace pyrolyzer Pyrojector
II (SGE Analytical Science). Three spots on each sample were
analyzed in duplicate. The pyrolyzer was operated at a constant
temperature of 550, 600, 700, or 900 °C. The pressure of helium
carrier gas at the inlet to the furnace was 95 kPa.
Pyrolysis–GC–MS measurements were made using two
apparatus. In the first apparatus (1), the pyrolyzer was connected
to a Trace 2000 gas chromatograph (ThermoQuest, CE
Instruments) with a quadrupole mass spectrometer Voyager
(ThermoQuest, Finnigan, MassLab Group) operated in electron
ionization (EI) mode. A 60 m × 0.25 mm, 0.25-µm Elite-5ms
fused-silica GC capillary column (PerkinElmer Instruments)
was used. The GC conditions were as follows: programmed
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Kusch et al.
follows: programmed column temperature: 60 °C for 1 min, then
60–280 °C at 7 °C/min (hold at 280 °C to the end of analysis);
programmed helium pressure: 122.2 kPa for 1 min, then 122.2–
212.9 kPa at 7 kPa/min (hold at 212.9 kPa to the end of analysis).
Second set of GC conditions: programmed column temperature:
75 °C for 1 min, then 75–280 °C at 7 °C/min (hold at 280 °C to
the end of analysis); programmed helium pressure: 122.2 kPa for
1 min, then 122.2–212.9 kPa at 7 kPa/min (hold at 212.9 kPa to
the end of analysis).
The temperature of the split–splitless injector was 250 °C
and the split ratio was 50:1. The transfer line temperature was
280 °C. The MS EI ion source temperature was kept at 230 °C.
The ionization occurred with a kinetic energy of the impacting
electrons of 70 eV. The quadrupole temperature was 150 °C.
Mass spectra and reconstructed chromatograms (total ion
current) were obtained by automatic scanning in the mass range
m/z 35–750 u. Pyrolysis–GC–MS data were processed with the
ChemStation software (Agilent Technologies) and the NIST 05
mass spectra library.
Figure 3: Pyrolysis–GC–MS chromatogram of a car wrapping
material at 600 °C obtained with apparatus 1. Fused-silica GC
capillary column 60 m × 0.25 mm, 0.25-µm df Elite-5ms. GC
conditions: programmed column temperature: 60 °C for 1 min,
then 60–100 °C at 2.5 °C/min and then 100–280 °C at 10 °C/
min (20 min hold at 280 °C); split–splitless injector temperature:
250 °C; split flow: 50 cm³/min; helium programmed pressure:
70 kPa for 1 min, then 70–110 kPa at 1 kPa/min (hold at 110 kPa
to the end of analysis). For peak identification, see Table 2.
5.49
100
95
90
85
80
75
Relative abundance
70
65
60
55
50
45
10.29
40
5.64
35
30
20.57
10.46
36.69
7.48
25
6.99
20
10.60
10.93
6.01
15
10
8.20
19.99
9.78
5
0
2
4
12.64
9.16
0.07 1.23 2.37 3.53
0
6
8
10
12
14.24 15.15 17.33
18.27
14
16
18
20
21.43
32.52
22.99 24.06
25.9326.52 28.95
33.60
34.33
24.85
29.56 31.93
22
24
26
28
30
32
34
36
37.72 39.6740.12
41.86
38
40
42
45.23
42.68
46.2747.33
44
46
48
50.05
50
50.49 52.23
52
54
Time (min)
Figure 4: Pyrolysis–GC–MS chromatogram of polyaramid
fibers at 900 °C obtained with apparatus 2. Fused-silica GC
capillary column: 59 m × 0.25 mm, 0.25-µm df DB-5ms. GC
conditions: programmed column temperature: 75 °C for 1 min,
then 75–280 °C at 7 °C/min (hold to the end of analysis);
programmed pressure of helium carrier gas: 122.2 kPa for
1 min, then 122.2–212.9 kPa at 7 kPa/min (hold at 212.9 kPa
to the end of analysis). For peak identification, see Table 3.
8
11.808
Abundance (X106)
3.0
3
7.562
2.5
2.0
1.5
7
11.613
1
6.519
1.0
0.5
2
3.592
6.00
4
8.497
8.00
11
19.599
6
11.297
5
10.198
9 10
14.033 15.612
20.640
12
15
14
26.188 13
30.007
23.791
26.644 28.307
24.678 25.821
10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00 28.00 30.00 32.00 34.00 36.00 38.00 40.00
Time (min)
column temperature: 60 °C for 1 min, then 60–100 °C at 2.5 °C/
min, 100–280 °C at 10 °C/min (20-min hold at 280 °C). The
temperature of the split–splitless injector was 250 °C and the split
flow was 50 cm³/min. Helium, grade 5.0 (Westfalen AG), was
used as a carrier gas. The helium programmed pressure was
70 kPa for 1 min, then 70–110 kPa at 1 kPa/min (hold at 110 kPa to
the end of analysis) was used. The transfer line temperature was
280 °C. The MS EI ion source temperature was kept at 250 °C.
The ionization occurred with a kinetic energy of the impacting
electrons of 70 eV. The current emission of the rhenium filament
was 150 µA. The MS detector voltage was 350 V. Mass spectra
and reconstructed chromatograms (total ion current [TIC]) were
obtained by automatic scanning in the mass range m/z 35–450 u.
Pyrolysis–GC–MS data were processed with the Xcalibur
software (ThermoQuest) and the NIST 05 mass spectra library.
In the second apparatus (2), the pyrolyzer was connected to a
7890A gas chromatograph with a series 5975C quadrupole mass
spectrometer (Agilent Technologies Inc.) operated in EI mode. A
59 m × 0.25 mm, 0.25-µm df DB-5ms fused-silica GC capillary
column (J&W Scientific) was used. Helium, grade 5.0 (Westfalen
AG), was used as a carrier gas. The GC conditions were as
Results and Discussion
Pyrolysis–GC–MS of Plastic Particles from Industrial Filter
Fins: A sample of plastic particles from industrial filter fins
was pyrolyzed at 700 °C to identify its composition. Figure 2
shows the obtained pyrolysis–GC–MS chromatogram of the
sample. Based on the decomposition products summarized
in Table 1, the plastic particles were identified as a mixture of
poly(hexamethylene adipamide) (nylon 6-6) and polypropylene
glycol with a small amount of styrene–butadiene rubber (SBR).
The peaks of propylene and acetone indicate the presence of
polypropylene glycol. The main decomposition product of nylon
6-6 is cyclopentanone (retention time [tR] = 10.51 min). Other
peaks in Figure 2, like butene/1,3-butadiene (tR = 5.58 min),
benzene (tR = 7.43 min), toluene (tR = 9.72 min), and styrene
(tR = 14.39 min), are typical pyrolysis products of SBR (1,2,5,6,18).
The small peak of 4-isopropylphenol (tR = 28.15 min) may be a
clue to the presence of polycarbonate or bisphenol A epoxy resin
(5,6). All of the pyrolysis products and the materials identified from
pyrolysis products in filter fins are summarized in Table 1.
Pyrolysis–GC–MS of a Car Wrapping Foil: The next object
of identification was a car wrapping foil pyrolyzed at 600 °C.
Figure 3 shows the obtained pyrolysis–GC–MS chromatogram
of the car wrapping foil. Based on the decomposition products
summarized in Table 2, the plastic material was identified as a
mixture of flexible poly(vinyl chloride) (PVC) with bis(2-ethylhexyl)
phthalate (BEHP) plasticizer and poly(hexamethylene adipamide)
(nylon 6-6). The chromatogram in Figure 3 shows the typical
pyrolysis products of PVC, like hydrogen chloride (tR = 5.49 min),
benzene (tR = 7.48 min), and naphthalene (tR = 25.93 min). This
is the result of the formation of double bonds by the elimination of
hydrogen chloride from the poly(vinyl chloride) macromolecules,
followed by the breaking of the carbon chain with or without
cyclization reaction (2). The detected cyclopentanone
(tR = 10.46 min) is generally known as a characteristic pyrolysis
product of nylon 6-6 (2,3,6). Methyl methacrylate (tR = 8.20 min)
identified in pyrolyzate is formed from poly(methyl methacrylate)
(6) and most likely comes from the adhesive film. Thus, the
identified 3,3-diphenylacrylonitrile (tR = 36.69 min) may be from
the adhesive layer of the foil.
The thermal decomposition of the plasticizer bis(2-ethylhexyl)
phthalate identified in car wrapping foil leads to the formation
at 600 °C of 2-ethyl-1-hexene (tR = 10.29 min), 2-ethylhexanal
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Kusch et al.
Table 2: Pyrolysis products and identified materials in car
wrapping foil.
Retention
time tR
(min)
Pyrolysis product at
700 °C
Figure 5: Chemical structure of poly(p-phenylene
terephthalamide) (polyaramid).
Matching Identified material
factor
O
5.49
Hydrogen chloride
945
Poly(vinyl chloride)
(PVC)
H
5.58
Methyl chloride
800
PVC
N
5.64
1-Butene
938
PVC
6.01
1,3-Pentadiene
921
PVC
H
OH
O
N
6.99
Tetrahydrofuran
769
Solvent
7.29
1,4-Cyclohexadiene
923
PVC
7.48
Benzene
945
PVC
8.20
Methyl methacrylate
750
Poly(methyl
methacrylate)
(PMMA)
9.78
Toluene
904
PVC
10.29
2-Ethyl-1-hexene
890
Bis(2-ethylhexyl)
phthalate
(plasticizer)
10.46
Cyclopentanone
912
Poly(hexamethylene
adipamide) (nylon
6-6)
5
10.20
Styrene
924
10.53
1-Octene
907
PVC
6
11.30
Isocyanatobenzene
947
14.47
Styrene
934
PVC
7
11.61
Aniline
959
8
11.81
Benzonitrile
967
856
Bis(2-ethylhexyl)
phthalate
(plasticizer)
17.33
20.57
2-Ethylhexanal
2-Ethyl-1-hexanol
916
Bis(2-ethylhexyl)
phthalate
(plasticizer)
21.07
o-Methylstyrene
888
PVC
21.43
Indene
870
PVC
22.99
p-tert-Butyltoluene
856
2,6-Bis-(1,1dimethylethyl)-4methylphenol (BHT)
(antioxidant) (?)
25.93
Naphthalene
920
PVC
28.46
2-Methylnaphthalene
862
PVC
28.63
Phthalic anhydride
906
Bis(2-ethylhexyl)
phthalate
(plasticizer)
28.79
1-Methylnaphthalene
875
PVC
36.69
3,3-Diphenylacrylonitrile
937
Adhesive layer
(tR = 17.33 min), 2-ethyl-1-hexanol (tR = 20.57 min), and phthalic
anhydride (tR = 28.63 min) (1,7). In the car wrapping material,
the rest of the tetrahydrofuran solvent (tR = 6.99 min) was
also detected. Table 2 shows the identified ingredients of the
pyrolyzed car wrapping foil.
Identiàcation of Unknown Plastic Fibres: A sample of unknown
plastic fibres was pyrolyzed at 700 °C and 900 °C, respectively,
to identify its composition. Figure 4 shows the pyrolysis–GC–MS
chromatogram of the sample pyrolyzed at 900 °C. Based on the
decomposition products summarized in Table 3, the fibres were
identified as polyaramid [poly(p-phenylene terephthalamide)]
(Figure 5). The main identified degradation products of polyaramid
at 900 °C are benzene (tR = 7.56), aniline (tR = 11.61 min), and
benzonitrile (tR = 11.81 min). Currently, polyaramid fibres have only
been characterized in a few publications using thermal analysis
20
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H
n
Table 3: Pyrolysis products of polyaramid fibres.
Peak
Number
Retention
Time t R (min)
Pyrolysis Product at
900 °C
Matching
Factor
1
6.52
Carbon dioxide
957
2
6.70
Acrylonitrile
896
3
7.56
Benzene
968
4
8.50
Toluene
918
9
16.98
1,2-Benzodinitrile
959
10
17.02
1,4-Benzodiamine
905
11
19.60
Biphenyl
957
12
26.19
Acridine
931
13
26.64
1,1´-Biphenyl-4-amine
960
14
28.31
Carbazole
928
15
30.01
N-Phenylbenzamide
916
(thermogravimetry, derivative thermogravimetry, and differential
thermal analysis), infrared spectroscopy techniques (13–15), and
pyrolysis–GC–MS (2,16–19).
Polyaramid fibres are a class of heat-resistant, strong synthetic
fibres. They are used in aerospace and military applications for
ballistic-rated body armour, fabric, ballistic composites, and fire
fighters protective clothing as well as in bicycle tyres and as an
asbestos substitute.
Identiàcation of Commercial Light-Curing Dental Filling
Material: A number of dental filling materials are presently
available for tooth restorations. The four main groups of these
materials, which dentists have used for about 35 years, are the
conventional glass-ionomer cements, resin-based composites,
resin-modified glass-ionomer cements, and polyacid-modified
resinous composites (20). Light-curing glass-ionomer
cements contain polyacrylic acid, chemically or photo-curing
monomers (multifunctional methacrylates, like triethylene glycol
dimethacrylate or 2-hydroxyethyl methacrylate), an ion-leaching
glass, and additives (initiators, inhibitors, stabilizers, and others)
(20). Resin-modified glass-ionomer cements are now widely
used in dentistry as direct filling materials, liners, bases, luting
cements, and fissure sealants (21). These materials mainly consist
of polymer matrix and glass-ionomer parts. The polymer matrix
is based on a monomer system and different multifunctional
methacrylates with additives (21). Methacrylic monomers, such
as bisphenol A glycidyl methacrylate (Bis-GMA), urethane
LC•GC Asia Paciàc November 2014
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Kusch et al.
Figure 6: Pyrolysis–GC–MS chromatogram of commercial
light-curing dental filling material at 550 °C obtained with apparatus
2. Fused-silica GC capillary column: 59 m × 0.25 mm, 0.25-µm df
DB-5ms. GC conditions: programmed column temperature: 60 °C
for 1 min, then 60–280 °C at 7 °C/min (hold at 280 °C to the end
of analysis); programmed helium pressure: 122.2 kPa for 1 min,
then 122.2–212.9 kPa at 7 kPa/min (hold at 212.9 kPa to the end of
analysis). For peak identification, see Table 4.
11
5.0
Abundance (X106)
4.5
4.0
4
3.5
9
7
3.0
2.5
The carbon dioxide (tR = 6.85 min) identified in pyrolyzate is
formed from polyacrylic acid (2,18). The identified substances
2-hydroxyethyl methacrylate (HEMA) (tR = 13.65 min), ethylene
glycol dimethacrylate (EGDMA) (tR = 19.48 min), and triethylene
glycol dimethacrylate (TEDMA) (tR = 28.72 min) are known
as standard composites of dental filling materials (1). Other
compounds in Table 4, such as bisphenol A (tR = 33.10 min)
or bisphenol A diglycidyl ether (tR = 42.42 min), are probably
formed by thermal degradation of bisphenol A diglycidyl monoor dimethacrylates. The presence of the additives, such as the
antioxidant 2,6-bis(1,1-dimethylethyl)-4-methylphenol (BHT) (tR
= 23.17 min) or the UV-absorber drometrizol (tR = 31.95 min) was
also confirmed. The triphenylantimony (tR = 34.55 min) identified in
pyrolyzate is used as catalyst in the UV-induced polymerization (1).
Conclusion
2.0
Table 4: Pyrolysis products of commercial light-curing dental
filling material.
Analytical pyrolysis–GC–MS has been proven as a valuable
technique for the analysis and identification of organic polymeric
materials in the plastic and rubber industry. For the first time
pyrolysis–GC–MS was used for the identification of commercial
light-curing dental filling material and for the identification of a
car wrapping foil. This technique allows the direct analysis of
very small sample amounts (5–200 µg) without the need for
time-consuming sample preparation.
Peak
Number
Retention
Time tR
(min)
References
1
6.85
Carbon dioxide
999
2
9.62
Methacrylic acid
936
3
12.96
909
(3)
4
13.65
918
(4)
5
19.40
928
(5)
(6)
6
19.48
7
23.00
8
23.17
9
10
23.65
23.89
11
28.72
12
31.95
13
33.10
14
34.55
15
35.25
16
36.98
17
42.42
Phenol
2-Hydroxyethyl methacrylate
(HEMA)
4-Isopropenylphenol
Ethylene glycol
dimethacrylate (EGDMA)
Not identified
2,6-Bis-(1,1-dimethylethyl)-4methylphenol (BHT)
Not identified
Not identified
Triethylene glycol
dimethacrylate (TEDMA)
Drometrizol (Tinuvin-P)
4,4´-Dihydroxy-2,2diphenylpropane (bisphenol A)
Triphenylantimony
Tetraethylene glycol
dimethacrylate
Not identified
Bisphenol A diglycidyl ether
(BADGE)
1.5
6
1.0
0.5 1
2
3
10.00
15.00
10
5
8
20.00
25.00
30.00
Time (min)
12 13 14 15
16
17
35.00
Pyrolysis Product at 550 °C
40.00
Matching
Factor
(1)
(2)
915
(7)
(8)
923
(9)
(10)
958
(11)
(12)
938
(13)
924
(14)
911
(15)
791
(16)
(17)
(18)
839
dimethacrylate (UDMA), triethylene glycol dimethacrylate
(TEGDMA), and 2-hydroxyethyl methacrylate (HEMA), are the
main components of resin-based dental filling materials. The
presence of additives such as initiators, activators, inhibitors, and
plasticizers in uncured dental material mixture is necessary (21).
Figure 6 shows the total ion current pyrolysis–GC–MS
chromatogram of commercial light-curing dental filling material
pyrolyzed at 550 °C. The pyrolysis products identified by using
mass spectra library NIST 05 are summarized in Table 4.
(19)
(20)
(21)
P. Kusch, in Advanced Gas Chromatography – Progress in Agricultural,
Biomedical and Industrial Applications, M.A. Mohd, Ed. (InTech, Rijeka,
Croatia, 2012), pp. 343–362.
S.C. Moldoveanu, Analytical Pyrolysis of Synthetic Organic Polymers
(Elsevier, Amsterdam, The Netherlands, 2005).
T.P. Wampler, Applied Pyrolysis Handbook, 2nd Ed. (CRC Press, Boca
Raton, Florida, USA, 2007).
K.L. Sobeih, M. Baron, and J. Gonzales-Rodrigues, J. Chromatogr. A
1186, 51–66 (2008).
P. Kusch, Chem. Anal. (Warsaw) 41, 241–252 (1996).
P. Kusch, G. Knupp, and A. Morrisson, in Horizons in Polymer Research,
R.K. Bregg, Ed. (Nova Science Publishers, New York, New York, USA,
2005), pp. 141–191.
P. Kusch, LCGC North Am. 31(3), 248–254 (2013).
P. Kusch, V. Obst, D. Schroeder-Obst, W. Fink, G. Knupp, and J.
Steinhaus, Eng. Fail. Anal. 35, 114–124 (2013).
P. Kusch and G. Knupp, Nachr. Chem. 57, 682–685 (2009).
P. Kusch, V. Obst, D. Schroeder-Obst, G. Knupp, and W. Fink, LCGC
AdS, July/August, 5–11 (2008).
P. Kusch and G. Knupp, LCGC AdS, June, 28–34 (2007).
P. Kusch, W. Fink, D. Schroeder-Obst, and V. Obst, Aluminium
(Isernhagen, Germany) 84(4), 76–79 (2008).
S. Villar-Rodil, A. Martínez-Alonso, and J.M.D. Tascón, J. Anal. Appl.
Pyrol. 58–59, 105–115 (2001).
F. Suárez-García, A. Martínez-Alonso, and J.M.D. Tascón, Carbon 42,
1419–1426 (2004).
S. Villar-Rodil, A. Martínez-Alonso, and J.M.D. Tascón, J. Therm. Anal.
Calorim. 79, 529–532 (2005).
J.R. Brown and A.J. Power, Polym. Degrad. Stab. 4(5), 379–392 (1982).
H.-R. Schulten, B. Plage, H. Ohtani, and S. Tsuge, Angew. Makromol.
Chem. 155, 1–20 (1987).
S. Tsuge, H. Ohtani, and C. Watanabe, Pyrolysis-GC/MS Data Book of
Synthetic Polymers (Elsevier, Amsterdam, The Netherlands, 2011).
Zs. Czégény and M. Blazsó, J. Anal. Appl. Pyrol. 58–59, 95–104 (2001).
R. Rogalewicz, A. Voelkel, and I. Kownacki, J. Environ. Monit. 8, 377–383
(2006).
R. Rogalewicz, K. Batko, and A. Voelkel, J. Environ. Monit. 8, 750–758
(2006).
Peter Kusch, Gerd Knupp, Wolfgang Fink, Dorothee
Schroeder-Obst, and Johannes Steinhaus are with Hochschule
Bonn-Rhein-Sieg, University of Applied Sciences in the Department of
Applied Natural Sciences, in Rheinbach, Germany.
Volker Obst is with Dr. Obst Technische Werkstoffe GmbH, in
Rheinbach, Germany.
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GC CONNECTIONS
Electronic Control of Carrier
Gas Pressure, Flow, and
Velocity
John V. Hinshaw, Serveron Corporation, Oregon, USA.
Have you wondered how your gas chromatography (GC) system sets and controls gas pressures, flows,
and carrier gas velocities electronically? Here, we describe the requirements for and the operation of
electronic gas control systems for GC columns and detectors.
Computerized or electronic
pneumatic control (EPC) systems for
carrier gas, split flow control, and
detector gases abound in modern
gas chromatographs (GC), as well as
in headspace samplers and column
switching systems. The accuracy
and repeatability of EPC are superior
to that of manual adjustment, and
the improved control of instrument
parameters greatly reduces the
possibility for making gas-related
mistakes. Computerized pneumatics
excel at controlling column
pressure drop or detector gas flow
rates. An EPC system generally
relieves operators from having to
make repetitive adjustments and
measurements with a flow meter
and stopwatch. However, running
an EPC system blindfolded, so to
speak, by never cross-checking
actual gas behaviour with selected
set-points, only invites trouble. Like
any computer system, the results can
only be as good as the column and
gas parameters that a user enters.
Thus, a good working understanding
of how an EPC system works and
what goals are to be accomplished
is essential for obtaining the best
possible results.
How It Works
In an EPC system, gas flows from
the gas supply input, through a
metering valve, into a pressure or
flow transducer, and then out to the
device — inlet, detector, or other
GC component — that consumes
22
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the gas. The GC system sends a
set-point value to the EPC controller,
which returns the measured flow or
pressure value from its transducer.
The set-point and actual values
are compared in the EPC system,
which adjusts the metering valve
as required to maintain the desired
set point. The EPC controller
incorporates column and carrier
gas characteristics to determine
the necessary pressure drop at any
given moment. Atmospheric and
gas supply pressures plus controller
temperatures are included as
required to compensate for drift and
instabilities.
In the simplest configurations an
EPC channel acts as a carrier gas
flow controller for a packed column
or controls a detector gas, such as
air or hydrogen, for flame ionization
detection (FID). Two controllers —
one pressure and one flow — can
provide the split flow and inlet
pressure for a capillary column
inlet splitter. Other more complex
applications include pressure or flow
controllers for auxiliary devices such
as purge-and-trap or headspace
samplers, or pressure-switching
controllers for multidimensional
column systems.
Computer-controlled pneumatics
cannot prevent operators from
selecting inappropriate column
pressures or split flow rates. An
operator may easily establish
incorrect conditions and become
misled as to the reasons for a
problem. Because of their complexity
and flexibility, computerized
pneumatic systems offer analysts
more opportunities for errors.
Capillary Column Control
One of the most common
applications for EPC is the control
of capillary (open-tubular) column
carrier gas. The column flow rate
and the carrier gas linear velocity
are complex functions of the column
dimensions, oven temperature, and
type of carrier gas. The mathematical
relationships between GC column
dimensions, temperature, and flow,
pressure, and linear velocity are well
understood. Fortunately for today’s
GC users, all of this is incorporated
into the EPC system, which acts as a
kind of gas calculator.
A wide-bore 30 m × 530 µm
column is a good example to help
understand the ins and outs of
EPC control. At 50 °C with helium
carrier gas this column will require
about 4.1 psig of inlet pressure to
maintain a flow of 5.6 mL/min or an
average carrier gas linear velocity
of 40.0 cm/s. I know this because I
used the EPC “calculator” in my GC
system to compute the pressure,
flow, and velocity by first entering
the column dimensions, carrier gas
type, oven temperature, and desired
velocity of 40 cm/s. Because I am
using a split inlet, I entered the split
ratio as well. With this column, a
desired split ratio of 20:1 results in a
split-flow set point of 112 mL/min.
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GC CONNECTIONS
Figure 1: Carrier gas viscosity as a function of temperature (adapted from reference
2). The dots correspond to published measurements; the solid lines represent
interpolated data and the dashed lines extrapolated data.
35
Vis cos ity (P a.s x 10-6)
30
25
Helium
Nitrogen
20
15
10
Hydrogen
5
-200
-100
0
100
200
300
Temperature (°C )
Figure 2: Theoretical effect of column temperature on (a) corrected carrier gas flow
and (b) average linear velocity with constant pressure drop. Column dimensions:
30 m × 0.530 mm; pressure drop: 4.1 psig; carrier gas: helium; column outlet
pressure: 1 atm.
Corrected outlet fow (sccm)
40
5
35
4
(b)
30
25
3
(a)
20
15
2
10
1
5
0
0
50
100
150
200
250
0
300
Average linear velocity (cm/s)
45
6
Column temperature (°C)
would cause the actual 530-µm
column flow to increase to 41.9 mL/
min with an average linear velocity
of 190 cm/s! The split ratio of 20:1
would still call for a split flow rate of
112 mL/min, and so the actual split
ratio would be more like 2.67:1.
These erroneous operating
parameters are within the capabilities
of the split inlet pressure and flow
controllers, so the GC system would
not report an error even though the
column was operating very far from
its desired set point. A subsequent
injection might alert the operator to a
problem; the peaks would be eluted
too rapidly and they would be much
larger than expected. If not identified
immediately, this type of problem
could cause serious difficulties
later on. Of course, if the retention
times were known under the correct
conditions then this situation would
be evident after one injection.
Rather than wait until after a run
is recorded to discover such errors,
the operator can double-check flow
rates or unretained peak times after
a column has been installed and
the preliminary setup completed.
Time an unretained peak or measure
the flow at the column outlet and
compare that to what the EPC system
says they should be. Bear in mind
that direct flow measurement is more
difficult with smaller internal diameter
columns, and if necessary use an
electronic flow meter that is rated for
low flow rates.
Small deviations in the actual
column internal diameter or length
will give rise to relatively small errors
in actual velocities or flows, as
discussed in more detail in another
“GC Connections” instalment (1). For
the best accuracy, it’s a good idea
to include the stationary phase film
thickness, if greater than about 1 µm,
with the column dimensions entered
into the GC system.
Pneumatic Programming
Garbage In, Garbage Out: An alert
reader will realize at this point that
they could have entered an incorrect
column length or internal diameter,
or perhaps failed to correct an
existing set of parameters from the
previously installed column. This
type of error would, for the most
part, go unnoticed by the GC system
itself unless a required pressure or
flow could not be attained by the
controller. Suppose that the column
internal diameter was mistakenly
set at 320 µm. Entering a velocity of
40 cm/s would result in a pressure
drop of 20.7 psig. This is achievable
by the EPC controller, which upon
increasing the pressure to that level
The pressure drop that’s required for
a particular column flow depends
on the oven temperature as well as
the column dimensions and carrier
gas, so the operator must specify
the temperature at which the desired
flow is to be achieved — this is
nearly always the same as the initial
oven temperature. So far so good,
but what happens when the column
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GC CONNECTIONS
Figure 3: Theoretical effect of column temperature on (a) carrier gas pressure
70
10
Inlet pressure (psig)
9
60
8
50
7
6
(b)
40
(a)
30
5
4
3
20
2
10
1
0
0
50
100
150
200
250
0
300
Average linear velocity (cm/s)
drop and (b) average linear velocity while maintaining constant corrected column
flow. Column dimensions: 30 m × 0.530 mm; column flow: 6.0 mL/min; carrier gas:
helium; column outlet pressure: 1 atm.
Column temperature (°C)
temperature increases during oven
temperature programming? The
column flow rate depends on the
oven temperature, as well as many
other factors, so the flow and velocity
will change during temperature
programming. The EPC split inlet
controller maintains a set pressure
level and does not directly control
flow or velocity — it must instead
calculate the pressure required to
establish a desired flow or velocity.
What are the effects of choosing
these different pneumatic control
modes?
Constant Column Pressure Drop:
As temperatures increase so does
the carrier gas viscosity, which
causes the flow and velocity to
decrease at higher temperatures
if the pressure is kept constant.
Figure 1 shows the relationships of
temperature and gas viscosity for
three common GC carrier gases.
EPC systems use these relationships
to calculate carrier-gas viscosity
dynamically as the oven temperature
changes. Figure 1 gives a good idea
of how large this viscosity effect can
be. Going from 50 °C to 250 °C, for
example, causes helium viscosity to
increase by about 40%. The other
carrier gases undergo viscosity
changes of a similar magnitude.
With a constant inlet pressure drop,
the column flow decreases during
temperature programming. Figure 2
24
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illustrates the effect on carrier gas
flow and average linear velocity of
changing the oven temperature while
holding the pressure drop constant,
using our example wide-bore column
with a constant 4.1 psig of helium
carrier gas. The column flow rate of
5.6 mL/min at 50 °C decreases to
2.5 mL/min at 250 °C, a 55 % loss.
Across the same temperature range,
the average linear velocity decreases
from 40 cm/s at 50 °C to 28 cm/s at
250 °C, a 30% loss.
When using a constant inlet
pressure with temperature
programming, the reduction in flow
rate at elevated temperatures can
cause unwanted peak broadening
as the carrier gas velocity departs
from optimum, and it also can extend
elution times unnecessarily. Usually
in this situation, choosing a different
pneumatic operating mode will
produce better results.
Constant Column Flow Rate: An
EPC system can be programmed
to increase the column pressure
drop sufficiently to maintain a
constant carrier-gas flow rate as
the temperature increases. Figure 3
shows how this choice affects the
pressure drop and the average
linear velocity. The EPC controller
maintains a constant column flow
of 6.0 mL/min by increasing the
pressure drop from 4.5 psig at 50 °C
to 9.1 psig at 250 °C. Note that the
average linear velocity increases as
the oven temperature goes up, which
perhaps runs counter to intuition.
But with a constant flow rate the
linear velocity can remain in a more
efficient region somewhat higher
than optimum, which will largely
avoid column efficiency losses.
Peaks will be eluted sooner and at
lower temperatures, reducing the
total run time compared to constant
pressure operation.
Detector Effects: Large shifts in
column flow rate can affect detector
operation. Constant column flow is
desirable for consistent detector
function during temperature
programming. Mass spectrometry
(MS) detection solute ionization
fragmentation patterns depend
somewhat on the source pressure,
which in turn depends on the
incoming flow rate, especially with
higher column flows. Granted, the
530-µm column example discussed
here is not the best choice for direct
interfacing to a bench-top mass
spectrometer, but the benefits can
be significant in terms of consistent
spectra and library searches for
those narrower i.d. columns that can
be connected directly.
When FID is used with hydrogen
carrier, it’s a good idea to keep
the total hydrogen flow through
the detector constant. This can be
accomplished in two different ways,
either by maintaining a constant
column flow rate or by programming
the detector hydrogen flow to
compensate for changes in the
column flow, that is, by keeping the
sum of the column and detector
hydrogen flows constant.
Flow Control Versus Pressure
Control: Ultimately, the most
important consideration may be
peak resolution and not necessarily
detector performance, especially in
situations where the separation is
marginal. Deciding whether constant
pressure or constant flow will yield
a better temperature-programmed
separation, let alone the effects
of changing the temperature
programme itself, is a more complex
consideration that doesn’t give itself
over very well to purely theoretical
modelling. Relative peak positions
in a chromatogram depend heavily
on the thermodynamic relationships
between the stationary phase and
LC•GC Asia Paciàc November 2014
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GC CONNECTIONS
Figure 4: Series of chromatograms at increasing oven temperature programme
rates with constant pressure or constant flow controlled pneumatics. Temperature
programming from 50 °C, hold 2 min, then to 250 °C at: (a) 3 °C/min; (b) 6 °C/min; (c)
12 °C/min; (d) 24 °C/min. Constant pressure at 4.1 psig (P) or constant flow at 6.0 mL/
min (F). Column: 30 m × 0.530 mm, 1.3-µm df EC-Wax. 1.0 µL manual injections split
20:1 at 200 °C. Peaks: 1 = n-C10, 2 = n-C11, 3 = n-C12, 4 = n-C13, 5 = 2-octanone,
6 = 1-octanol, 7 = 2,6-dimethylaniline, 8 = 2,4-dimethylphenol.
(a)
1
3
2
45
7
6
8
P
F
0.0
10.0
30.0
20.0
40.0
(b)
P
F
0.0
5.0
10.0
15.0
20.0
25.0
(c)
P
F
0.0
5.0
10.0
15.0
(d)
P
F
0.0
5.0
Time (min)
10.0
a solute’s chemical characteristics.
As a result, individual peaks move in
different ways relative to each other
as the temperature changes.
In general, the more the chemical
natures of a pair of peaks differ, the
greater the effect of changing the
column temperature or temperature
programme will be. At higher
flows peaks will be eluted earlier
in a temperature programme, and
therefore at somewhat lower column
temperatures. But this change of
elution temperature may or may not
improve the separation of a pair of
peaks if both are not affected in the
same way. This effect is illustrated
in the series of test chromatograms
shown in Figure 4. Here, a test
mixture was injected with either
constant pressure or constant flow
programmed pneumatics, with the
initial pressure drop the same in
either case. The programme rate was
doubled in each of four consecutive
runs, from 3 °C/min to 6 °C/min,
12 °C/min, and finally 24 °C/min.
The overall effects of constant
flow compared to constant
pressure operation are clear in
these chromatograms: The peaks
are all eluted sooner with constant
flow. The last peak in the run,
2,4-dimethylphenol (DMP), is eluted
at 24.85 min with constant pressure
and at 22.79 min with constant flow
at 6 °C/min. Similar reductions in
retention times hold for the other
peaks when the flow rate is kept
constant, across all of the selected
programming rates.
The effects on retention times
of increasing the temperature
programme rate far outweigh the
differences observed between
constant pressure and constant flow
pneumatic modes. The DMP peak
moves in from 40.66 min at 3 °C/
min (elution temperature = 166 °C)
to 11.1 min at 24 °C/min (elution
temperature = 250 °C). Again,
there are no surprises here — the
temperature effects are quite strong.
The overall effect on peak
resolution, however, is somewhat
unexpected. The two closest-eluted
peak pairs, n-decane–2-octanone
and 2,6-dimethlyaniline (DMA)–DMP,
show opposite trends. Resolution
increases with temperature
programme rate for the first pair
but decreases for the second
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GC CONNECTIONS
Figure 5: Peak resolution (RS) as a function of temperature programming rate and
pneumatic mode for (a) n-C13 and 2-octanone, and (b) 2,6-dimethylaniline and
2,4-dimethylphenol. P = constant pressure and F = constant flow. Conditions and
chromatograms from Figure 4.
(a)
2.5
2.0
P
F
Rs
1.5
1.0
0.5
0.0
0
5
10
15
20
25
30
Programme (°C/min)
(b)
Conclusion
16
14
12
Rs
10
8
6
P, F
4
2
0
0
5
10
15
20
25
30
Programme (°C/min)
pair, regardless of the pneumatic
programming mode. Interestingly, the
constant pressure mode gives slightly
better resolution than the constant
flow mode for the first pair, but makes
little difference for the later peak pair.
Figure 5 plots the resolution of these
two peak pairs as a function of the
temperature programming rate and
the pneumatic mode.
Although the resolution of DMA–
DMP is high in all cases — there is
no reason to be concerned with this
peak pair itself — if there were other
peaks between these two in a “real”
sample, then it would be interesting
to find an optimum set of conditions
for the overall chromatogram instead
of choosing the best case for
the first peak pair. A temperature
programming rate of 12 °C/min might
26
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the full advantage of the maximum
available resolution. Of course, other
solutions might include changing to a
narrow-bore column or investigating
the effects of a different stationary
phase composition.
Overall, these examples
demonstrate that there can be
significant differences in how peaks
behave relative to each other as
the column pneumatic and thermal
conditions are varied. Clearly, such
changes can affect resolution in
ways that may not be anticipated.
Chromatographers should always
validate and confirm that required
peak resolution and quantitation
performance levels are met
after making any changes to the
chromatography.
be a good choice because it keeps
the resolution of the first pair while
still utilizing a good portion of the
available resolving power between
the second pair.
A better choice might be to use a
two-ramp temperature programme
instead of a compromise single-ramp
programme. At the time that the
first peak pair is being eluted, the
later-eluted DMA–DMP pair has not
started to move along the column
much, so cutting the temperature
programming rate back to 6 °C/min
or even 3 °C/min halfway through
the run would make some sense.
That way, the later-eluted pair
would behave in much the same
way as found by running the entire
chromatogram at the lower rate,
while the earlier pair would get
Computerized pneumatic control adds
a very capable multipurpose tool to
the chromatographer’s tool belt. Such
systems make setting up a GC system
easier by automating some tasks. They
can give more repeatable results from
run to run and laboratory to laboratory.
Some detector performance
improvements can be obtained with
constant carrier-gas flow control.
And computerized pneumatics excel
in facilitating advanced separations
techniques. But when it comes to
chromatographic performance gains
in solute resolution, the choice of
constant flow or constant pressure
control can produce significant
differences for multiple peak pairs in
the same analysis.
References
(1)
(2)
J.V. Hinshaw, LCGC Europe 25(3),
148–153 (2012).
J.V. Hinshaw and L.S. Ettre,
Introduction to Open-Tubular Column
Gas Chromatography (Advanstar,
1994), p. 25.
John Hinshaw is a senior
scientist at Serveron Corporation
in Beaverton, Oregon, USA, and
is a member of the LCGC Asia
Pacific editorial advisory board.
Direct correspondence about this
column should be addressed to “GC
Connections”, LCGC Asia Pacific,
Honeycomb West, Chester Business
Park, Chester, CH4 9QH, UK, or
e-mail the editor-in-chief, Alasdair
Matheson, at amatheson@advanstar.
com
LC•GC Asia Paciàc November 2014
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COLUMN WATCH
When Bad Things Happen
to Good Food: Applications
of HPLC to Detect Food
Adulteration
W. Jeffrey Hurst1, Kendra Pfeifer1, and Ronald E. Majors2 , 1Hershey Company, USA, 2 Column Watch Editor.
Although it has been happening to some degree for centuries, food adulteration is increasingly
becoming a worldwide epidemic, as evidenced by the melamine scandal of 2008 and recent meat and
fish substitutions at major food chains. Analysts are applying more sophisticated chromatographic,
spectroscopic, and enzymatic analytical techniques to monitor and measure food adulterants, which
are often economically motivated. In this instalment, guest authors Jeff Hurst and Kendra Pfeifer from
Hershey Foods explore high performance liquid chromatography (HPLC), ultrahigh-pressure liquid
chromatography (UHPLC), and mass spectrometry (MS) approaches being adopted to keep ahead of the
food adulteration game.
Food adulteration, sometimes called
food contamination, is an intriguing
topic that can sometimes seem
daunting because it is in everyday
commerce and not just the analytical
laboratory. First, let’s discuss
a few general thoughts on food
adulteration.
The focus of this instalment is on
detecting food adulteration with high
performance liquid chromatography
(HPLC), but a veritable arsenal of
techniques are currently being used.
These include HPLC coupled with
a variety of detection methods
ranging from UV to mass
spectrometry (MS) and other
nonchromatographic techniques such
as immunoassay and mid‑infrared
(IR), near infrared (NIR), and Raman
spectroscopy.
Another interesting point of
contention is that there are some
who perceive scientists that are
involved in food adulteration from the
contaminations side as second‑rate
scientists. However, that seems to
be far from the truth because those
scientists exhibit not only a certain
level of scientific expertise, but also
ingenuity. One obviously doesn’t
condone these food adulteration
activities, but we should be aware.
Food fraud is seemingly large and
growing, with articles on the topic
appearing in publications ranging
from Chemical and Engineering
News to The Economist. In March
2014, The Economist published
an article titled “A la Cartel”,
indicating that organized crime is
diversifying into food and alcoholic
The Romans had laws that
focused on the adulteration
of wines because wine back
then tended to become bad
rather rapidly and a number
of items were added to
improve the flavour.
beverages (1). Several examples
are given in the article, including
the horsemeat scandal in 2013 and
another case in which nearly 2500
jars of honey were filled with sugar
syrup. There was also a report
on the seizure of 17,000 L of fake
vodka worth $1.7 million (around
€1.3 million). Finally, this article
contained information from Europol
that in the United Kingdom crooks
have switched from drugs to food
since everyone buys food and drink.
Despite the recent news coverage,
food fraud is nothing new (2). The
Romans had laws that focused on
the adulteration of wines because
wine back then tended to become
bad rather rapidly and a number
of items were added to improve
the flavour. One of the compounds
happened to be lead salts, which
sweetened the wine and likely
added to the lead load in the Roman
population. In the early 19th century,
Frederick Accum wrote a book titled
Treatise on the Adulteration of Food
and Culinary Poisons Exhibiting the
Fraudulent Sophistications of Bread,
Beer, Wine, Spirituous Liquors,
Tea, Coffee, Crème. Confectionary,
Vinegar, Mustard, Pepper, Cheese,
Olive Oils, Pickles and other Articles
Employed in Human Commerce (3).
In that time period, used tea and
coffee grounds could be purchased
inexpensively. The tea grounds
were then boiled with sheep dung
and ferrous sulphate and coloured
www.chromatographyonline.com
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COLUMN WATCH
Figure 1: Chromatogram of the separation of cyanuric acid and melamine and their
C13 analogues on a 50 mm × 2.1 mm, 2.6‑µm dp 100‑Å Kinetex HILIC LC column.
1,2
0
0.5
0.10
0.15
0.20
3,4
0.25
0.30
Time (min)
0.35
0.40
0.45
Figure 2: UHPLC chromatograms in Masslynx (Waters) format: Red = pure skim
milk powder (SMP), black = 99:1 (w/w) SMP–soy protein isolate (SPI), purple = 90:10
(w/w) SMP–SPI, and green = pure SPI. Detection: UV absorbance at 215 nm.
20.50
20.99
18.28
13.50
Absorbance (AU)
18.02
9.96
6.0e-2
24.24 26.15
19.50
22.31
36.43
15.92
16.79
5.0e-2
4.0e-2
3.0e-2
2.0e-2
1.0e-2
0.0
6.00
8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00 28.00 30.00 32.00 34.00 36.00 38.00 40.00
Time (min)
with a mixture of tannin, copper
acetate, and ferrrocyanide while
coffee grounds were mixed with
other roasted bean, gravel, sand,
and chicory. Burnt sugar was
used to add colour to coffee. In
the case of confectionary, lead,
copper, and mercury salts were
used to make bright colours that
were eye catching for children, but
toxic. Green vitrol, alum, and salt
were added to give beer a good
head because beer was sometimes
diluted.
The most likely event that brought
the topic of food adulteration to the
forefront today was the melamine
incident in 2008, which killed six
Chinese infants and sickened more
than 30,000. Another recent incident
28
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The top five foods
targeted for adulteration
are milk, olive oil, honey,
saffron, and seafood
23.30
12.30
8.25
25.09
exact definition for economically
motivated adulteration, the United
States Food and Drug Administration
(FDA) adopted a working definition
for EMA as the “Fraudulent,
intentional substitution or addition
of a substance in a product for the
purpose of increasing the apparent
value of the product or reducing
the cost of its production, that is,
for economic gain” (7). Common
types of EMAs include substitution
or dilution of an authentic ingredient
with a cheaper product (for
example, replacing extra virgin olive
oil with a cheaper oil), flavour or
colour enhancement using illicit or
unapproved substances (such as
unapproved dyes), and substitution
of one species with another (such as
fish species fraud).
occurred in the past month, in which
meats used by two very visible
restaurant chains was found to be
tainted — meat more than a year old
was mixed with fresh meat (5,6).
Top Five Food Targets for
Adulteration
According to Chemical and
Engineering News, the top five
foods targeted for adulteration are
milk, olive oil, honey, saffron, and
seafood (4). The adulterants can
be divided into three categories:
targeted, nontargeted, and
economically motivated (EMA). This
division recognizes that there is
a crossover effect and that HPLC
plays a key role in the detection of
adulterants. Although there is no
Milk: In the case of milk, many
individuals tend to focus on
melamine as the “poster compound”
for milk adulteration. Figure 1
shows a chromatogram of a
melamine‑contaminated milk with
the compounds cyanuric acid and
melamine identified by MS using an
isocratic mobile phase consisting of
acetonitrile and 100 mM ammonium
acetate. Peaks 1 and 2 are cyanuric
acid and its 13 C analogue and peaks
3 and 4 are melamine and its 13 C
analogue. While melamine is a high
visibility target, it is being monitored
and anecdotal information indicates
that it is now being replaced by
urea and even amino acids because
they are more difficult to identify. In
addition to melamine, soy protein,
corn syrup, whey, leather, and even
shampoo have been reported as
potential adulterants in milk. When
leather is added to milk, it can be
hydrolyzed to improve its solubility.
The use of this material can be
detected by the determination of the
amino acid hydroxyproline from the
hydrolysis of leather protein that is
not seen in milk protein (8). There is
an active group at the United States
Pharmacopeia called the Skim Milk
LC•GC Asia Paciàc November 2014
ES517160_LCA1114_028.pgs 10.20.2014 18:34
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COLUMN WATCH
Figure 3: (a) Total ion chromatogram (TIC) and (b) extracted ion chromatogram
(EIC) obtained from chloramphenicol with detection by MS in negative ESI mode.
Intensity (X 104)
1.5
(a)
1.0
0.5
1.0
0.8
0.6
0.4
0.2
0
(b)
1
2
3
4
Time (min)
Figure 4: Chromatogram of sudan dyes (see text for details).
1
Absorbance (mAU)
120
100
2
80
3
60
4
40
5
20
0
0
2
4
6
8
Time (min)
10
12
14
Figure 5: Chromatogram of myoglobin from different sources.
0.20
Mb
rse
Ho
0.30
0.20
O
Ch stric
ick
h
M
en
Mb b
Absorbance at 409 nm (––––)
0.40
Beef Mb
0.10
0.10
0.0
Advisory Group that is investigating
potential adulterants in skim milk,
one of which is soy. Figure 2
shows an example chromatogram
of various samples ranging from
skim milk powder to soy (9). Other
potential contaminants could be
different types of milk such as goat’s
milk, which can be detected at the
1% level by the determination of
beta‑lactoglobulin.
Sodium chloride concentration (- - - -)
Buffalo Mb
Pig Mb
0
Olive Oil: The contamination of
olive oil with other oils including
corn, sunflower, safflower, and
sesame requires continual testing to
verify it is pure olive oil with
both polyphenols and triglycerides
used as marker compounds.
Furthermore, olive oil contains a
higher concentration of oleic acid
than other oils, but less linoleic and
linolenic.
In a similar vein, wines are
being adulterated by the addition
of polyphenols. Other things that
have been added to wine include
pigments and glycerol to give a
wine “body”. In a paper published
in Food Chemistry (10), the authors
described an HPLC method for the
anthocyanins in red wine, in which
elderberry extracts were added to
improve the colour. The method
determined that wine adulterated
with the elderberry contained an
extra peak attributed to cyanidin‑3‑
bubioside‑5‑glucoside.
Honey: The third food on the top five
list from Chemical and Engineering
News was honey. The dilution of
honey with less expensive materials,
such as corn syrup, has been
around for decades. The initial work
on this topic was done by White of
the United States Department of
Agriculture using nuclear magnetic
resonance (NMR) spectroscopy
(11), but as technology evolved
there have been a number of
HPLC applications to monitor this
phenomenon. In addition to the
EMA activities, there is also concern
about honey being contaminated
with the antibiotic chloramphenicol
used by beekeepers to treat
their hives against the crippling
foulbrood disease. Figure 3
provides a chromatogram from
a liquid chromatography–mass
spectrometry (LC–MS) method
developed for this application (12)
with either a 100 mm × 4.6 mm
RP‑18e column (EMD Millipore) or a
250 mm × 4.6 mm, 5‑µm d p Zorbax
XDB C18 (Agilent Technologies). For
both columns, the mobile phase was
45:55 (v/v) methanol–0.2% aqueous
ammonia acetate at a flow rate of
1 mL/min.
Saffron and Other Spices:
Because of its cost, saffron is fourth
on the list (according to Chemical
and Engineering News), but other
literature seems to be more inclusive
by indicating that spices, in general,
are targets for adulteration. Epicurean
Digest indicated seven spices of
concern: cayenne pepper, cumin,
coriander, pepper, saffron, turmeric,
and salt (13). Unscrupulous suppliers
can adulterate cayenne pepper
with sawdust and colours, cumin
with sawdust, turmeric with sawdust
and yellow colours, and saffron
www.chromatographyonline.com
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ES517161_LCA1114_029.pgs 10.20.2014 18:34
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COLUMN WATCH
with corn silk. Figure 4 provides a
chromatogram of Sudan Red I, II,
III, IIB, and IV dyes corresponding
to peaks 1–5, respectively, which
can be used to adulterate spices by
enhancing colour. The separation was
performed on a 150 mm × 4.6 mm,
4‑µm Synergi Polar‑RP 80A LC
column (Phenomenex) using
a 65:20:15 (v/v/v) methanol–
acetonitrile–water mobile phase.
Detection was UV absorbance at
480 nm.
Meat and Fish: As was indicated
in the introduction, earlier this year
there was an incident in China where
tainted meat was mixed with fresh
meat and provided to unsuspecting
organizations including KFC
and McDonald’s (6). Last year in
Europe, it was determined that
horsemeat was mixed with beef.
In addition, there is a lot of data
indicating that a large percentage
of fish is mislabelled, leading to a
cheaper fish being sold as a more
expensive one (14). This does
not include the issue with farmed
and wild caught salmon. Although
DNA‑based techniques have been
widely used, a number of HPLC
techniques including proteomics,
or “foodomics” as it is now called,
have been used to help solve this
quandary. Furthermore, information
about fish fraud indicates that a
substantial amount of fish sold in
outlets ranging from sushi bars
to the local fish market is not as
advertised. A recent report found
that fish samples purchased at
grocery stores, restaurants, and
sushi bars in major cities were often
mislabelled, including red snapper
(actually tilefish); white tuna and
butterfish (actually escolar); wild
Alaskan salmon (actually farmed
Atlantic salmon); caviar (actually
catfish roe); and monkfish (actually
puffer fish) (14).
A paper by Giaretta (15)
described an ultrahigh‑pressure
liquid chromatography (UHPLC)
method using myoglobin as a
marker for meat adulteration with
an example given on detecting pork
in beef. Figure 5 is an example
chromatogram of a variety of
meat types. This method used
a Protein‑Pak Hi Res Q column
(Waters) with photodiode‑array
detection and a mobile‑phase
30
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black
system consisting of three buffers
in a discontinuous gradient. A
final example can be seen in a
recent study in which Chou and
coworkers (16) used HPLC with
electrochemical detection using
copper nanoparticle‑plated
electrodes to differentiate meat from
15 animal species.
(7)
(8)
(9)
Conclusion
This column instalment has provided
a modest snapshot on the use
of HPLC techniques to address
challenges in EMA of the food
supply. This issue will continue to
be a moving target with a need to
be vigilant to monitor developments
of EMA in parallel with the food
industry, instrument vendors, and
government labs. There is still the
need for simpler sample preparation
protocols and it would seem that an
expansion of the various “ambient”
LC–MS techniques in this area could
be helpful. One of the techniques
that is being touted by HPLC–MS
vendors is the application of exact‑
mass LC–MS, but, in our opinion, one
must ensure that peak identifications
are appropriate since sometimes
standards are not available.
Finally, as the adulterers become
more sophisticated, it seems like
we will need more sophisticated
HPLC‑based techniques such
as foodomics paired with other
instrumental techniques like
immunoassay and Fourier transform
infrared (FTIR), NIR, and Raman
spectroscopy.
References
(1)
(2)
(3)
(4)
(5)
(6)
“A la Cartel”, The Economist, March
2014.
B. Wilson, Swindled: The Dark History
of Food Fraud, from Poisoned Candy to
Counterfeit Coffee (Princeton University
Press, Princeton, New Jersey, USA,
2008).
F.C. Accum, Treatise on the Adulteration of
Food and Culinary Poisons Exhibiting the
Fraudulent Sophistications of Bread, Beer,
Wine, Spirituous Liquors, Tea, Coffee,
Crème. Confectionary, Vinegar, Mustard,
Pepper, Cheese, Olive Oils, Pickles
and other Articles Employed in Human
Commerce (Longman, Hurst, Rees, Orme
and Brown, London, UK, 1820).
Chem. Eng. News Online, “Food
Fraud”, 25 August 2014.
http://www.nbcnews.com/id/28787126/
ns/world_news‑asia_pacific/t/face‑
execution‑over‑china‑poison‑milk‑
scandal/
http://money.cnn.com/2014/07/21/news/
companies/kfc‑mcdonalds‑china/
(10)
(11)
(12)
(13)
(14)
(15)
(16)
http://foodfraud.msu.edu/wp‑content/
uploads/2014/01/CRS‑Food‑Fraud‑and‑
EMA‑2014‑R43358.pdf
“Dionex Solutions: Methods for
Detecting Leather Protein Adulteration
in Milk,” Dionex Corporation, http://
www.dionex.com/en‑us/markets/food‑
beverage/news‑articles/lp‑110606.html
J.E. Jablonski, C. Pardo, L.S. Jackson,
B. Rohrback, J. Moore, and M. Han,
“Chemometrics and UPLC‑UV to Detect
Adulteration of Skim Milk Powder with
Soy Protein Isolate,” poster from United
States Pharmacopeia Skim Milk Powder
Advisory Group.
P. Brindle and C. García‑Viguera, Food
Chemistry 55, 111 (1996).
J.W. White, Jr., J. Assoc. Off. Anal.
Chem. 63, 11 (1980).
C. Pan et al., Acta Chromatographia 16,
320 (2006).
Epicurean Digest, epicureandigest.com
http://oceana.org/en
N. Giaretta et al., Food Chemistry 141,
1814 (2013).
C.‑C. Chou et al., J. Chromatog. B 846,
203 (2007).
Kendra C. Pfeifer is a manager
of regulatory affairs in the quality
and regulatory affairs department
at the Hershey Company. She has
a bachelor’s degree in chemistry
and a master’s degree in food
science. Kendra has a wide variety of
experiences within the corporation with
a tenure in the analytical research and
services organization before joining
the regulatory affairs group. She was
active in the Association of Analytical
Communities (AOAC) methods
committee and implemented robotics
in the lab environment.
Jeff Hurst is a principal scientist
with the Hershey Company. He is
the author of a substantial number of
papers on HPLC and food analysis
and is a member of numerous
scientific organizations including the
American Chemical Society (ACS),
the Institute of Food Technologists
(IFT), the American Society for
Mass Spectrometry (ASMS), and the
American Association for Integrative
Medicine (AAIM). He indicates that
Hershey was his first “real job”.
“Column Watch” Editor Ronald E.
Majors is an analytical consultant
and is a member of LCGC Asia
Pacific’s editorial advisory board.
Direct correspondence about this
column should be addressed to
“Column Watch”, LCGC Asia Pacific,
Honeycomb West, Chester Business
Park, Wrexham Road, Chester, CH4
9QH, UK, or e‑mail the editor‑in‑chief,
Alasdair Matheson, at amatheson@
advanstar.com
LC•GC Asia Paciàc November 2014
ES517162_LCA1114_030.pgs 10.20.2014 18:34
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ES516868_LCA1114_033_FP.pgs 10.17.2014 01:42
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Antibody Drug Conjugate (ADC) Analysis with SEC–MALS
Wyatt Technology Corporation
Molar mass vs. time
2.0x10
Molar Mass (g/mol)
1.6x105
ADC1
ADC2
(■) Mw of complex
1.0x105
(+) Mw of antibody
(x) Mw of conjugated drug
1.0x104
9.0
9.5
10.0
Complex
10.5
Time (min)
Mw (kDa)
Antibody
11.0
11.5
12.0
DAR
Drug
ADC1
167.8 (±1.2%) 155.2 (±1.8%)
12.6
10.1
ADC2
163.7 (±1.2%) 155.6 (±1.2%)
8.1
6.5
Figure 2: Molar masses for the antibody and total appended drug are
calculated in the ASTRA software package based on prior knowledge
of each component’s extinction coeffcent and dn/dc, allowing
determination of DAR based on a nominal Mw of 1250 Da for an
individual drug.
masses of the antibody fractions are similar, which indicates that
the overall differences between the two formulations refect distinct
average DARs that are consistent with values obtained by orthogonal
techniques. Note that the molar mass traces for the conjugated
moiety represent the total amount of attached pendant groups; the
horizontal trends indicate that modifcation is uniform throughout
the population eluting in that peak.
ADC1
ADC2
5
1.8x105
Antibody-Drug Conjugate Analysis
Molar Mass (g/mol)
There has been a signifcant resurgence in the development of
antibody-drug conjugates (ADC) as target-directed therapeutic
agents for cancer treatment. Among the factors critical to effective
ADC design is the Drug Antibody Ratio (DAR). The DAR describes
the degree of drug addition that directly impacts both potency and
potential toxicity of the therapeutic, and can have signifcant effects
on properties such as stability and aggregation. Determination of
DAR is, therefore, of critical importance in the development of novel
ADC therapeutics.
DAR is typically assessed by mass spectrometry (MALDI–TOF or
ESI–MS) or UV spectroscopy. Calculations based on UV absorption
are often complicated by similarities in extinction coeffcients of the
antibody and small molecule. Mass spectrometry, though a powerful
tool for Mw determination, depends on uniform ionization and
recovery between compounds — which is not always the case for
ADCs.
Here we present a method for DAR determination based on
SEC–MALS in conjunction with UV absorption and differential
refractive index detection. Figure 1 shows UV traces for two model
ADCs; molecular weights of the entire ADC complexes are determined
directly from light scattering data.
Component analysis is automated within the ASTRA 6 software
package by using the differential refractive index increments (dn/dc)
and extinction coeffcients, which are empirically determined for each
species or mined from the literature, to calculate the molar mass of
the entire complex as well as for each component of the complex.
In this example an antibody has been alkylated with a compound
having a nominal molecular weight of 1250 Da (Figure 2). Molar
167.8 kDa
163.7 kDa
1.4x105
1.2x105
1.0x105
8.0x104
9.0
9.5
10.0
10.5
11.0
Time (min)
11.5
12.0
Figure 1: Molar masses for two distinct ADC formulations are
determined using SEC–MALS analysis.
34
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Wyatt Technology Corporation
6300 Hollister Avenue, Santa Barbara, California 93117, USA
Tel: +1 (805) 681 9009 fax: +1 (805) 681 0123
Website: www.wyatt.com
LC•GC Asia Pacific November 2014
ES517147_LCA1114_034.pgs 10.20.2014 18:34
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ES516857_LCA1114_CV3_FP.pgs 10.17.2014 01:42
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ES516870_LCA1114_CV4_FP.pgs 10.17.2014 01:42
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