Journal of Chromatography A, 842 (1999) 351–371
Review
Applied gas chromatography coupled to isotope ratio mass
spectrometry
W. Meier-Augenstein*
Department of Anatomy and Physiology, University of Dundee, Small’ s Wynd, Dundee DD1 4 HN, Scotland, UK
Abstract
Compound-specific isotope analysis (CSIA) by isotope ratio mass spectrometry (IRMS) following on-line combustion (C)
of compounds separated by gas chromatography (GC) is a relatively young analytical method. Due to its ability to measure
isotope distribution at natural abundance level with great accuracy and high precision, GC–C–IRMS has increasingly
become the method of choice in authenticity control of foodstuffs and determination of origin in archaeology, geochemistry,
and environmental chemistry. In combination with stable isotope labelled compounds, GC–C–IRMS is also used more and
more in biochemical and biomedical application as it offers a reliable and risk-free alternative to the use of radioactive
tracers. The literature on these topics is reviewed from the advent of commercial GC–C–IRMS systems in 1990 up to the
beginning of 1998. Demands on sample preparation and quality of GC separation for GC–C–IRMS are discussed also.
 1999 Elsevier Science B.V. All rights reserved.
Keywords: Reviews; Food analysis; Mass spectrometry; Isotope analysis; Environmental analysis; Geochemistry; Detection,
GC
Contents
1. Introduction ............................................................................................................................................................................
2. Principles of IRMS and GC–C–IRMS ......................................................................................................................................
2.1. IRMS.............................................................................................................................................................................
2.2. GC–C–IRMS .................................................................................................................................................................
2.2.1. Sample preparation..............................................................................................................................................
2.2.1.1. Derivatization ..................................................................................................................................................
2.2.2. Isotopic calibration ..............................................................................................................................................
2.2.3. HRcGC ..............................................................................................................................................................
3. High-precision CSIA of 13 C isotopic abundance ........................................................................................................................
3.1. GC–C–IRMS at natural abundance level ..........................................................................................................................
3.1.1. Authenticity ........................................................................................................................................................
3.1.1.1. Flavours and fragrances ....................................................................................................................................
3.1.1.2. Wine, fruit juice and honey ...............................................................................................................................
3.1.1.3. Vegetable oils...................................................................................................................................................
*Fax: 144 1382 345514.
E-mail address: w.meieraugenstein@dundee.ac.uk (W. Meier-Augenstein)
0021-9673 / 99 / $ – see front matter  1999 Elsevier Science B.V. All rights reserved.
PII: S0021-9673( 98 )01057-7
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W. Meier-Augenstein / J. Chromatogr. A 842 (1999) 351 – 371
3.1.1.4. Drugs ..............................................................................................................................................................
3.1.2. Origin ................................................................................................................................................................
3.1.2.1. Geochemistry ...................................................................................................................................................
3.1.2.2. Archaeology ....................................................................................................................................................
3.1.2.3. Environmental chemistry ..................................................................................................................................
3.2. Tracer studies .................................................................................................................................................................
4. Compound-specific isotope analysis of 15 N isotopic abundance ..................................................................................................
5. Hyphenated techniques ............................................................................................................................................................
6. Conclusions ............................................................................................................................................................................
Acknowledgements ......................................................................................................................................................................
References ..................................................................................................................................................................................
1. Introduction
During the 14 years that followed the first publications reporting the coupling of gas chromatography
(GC) to on-line combustion of GC separated compounds to yield CO 2 and N 2 for isotope ratio
analysis by a single collector mass spectrometer
[1,2], hardly any work was published that made use
of this new technique. Drawing on this work, in
1984, Barrie et al. [3] coupled a dual collector mass
spectrometer to a GC via an on-line combustion
interface, thus permitting continuous recording of
(m11) /m isotope ratios by detecting two successive
masses at the same time. Their instrument was the
first genuine GC–combustion interfaced–isotope
ratio mass spectrometry (GC–C–IRMS) system and
produced isotope ratios that were an order of magnitude more precise than obtained from an optimised
single collector instrument.
However, it was not until 1990 that GC–C–IRMS
instruments became commercially available. Since
then, GC–C–IRMS instrumentation has experienced
several advances and its application has increased to
such an extent that it ‘‘could almost be considered a
conventional technique’’ [4].
Tom Brenna of Cornell University’s Division of
Nutritional Sciences, once said ‘‘IRMS is probably
the first form of analytical mass spectrometry’’ (see
Ref. [166]). IRMS is certainly a very sensitive
detector, able to yield highly precise measurements
of isotope ratios with a standard deviation in the
range of four to six significant figures. When coupled
to a GC system, it enables the analyst to conduct
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highly precise compound-specific isotope analysis
(CSIA), especially at natural isotopic abundance
level. High-precision CSIA at natural abundance
level can provide information on biogenetic relation
and origin of a given organic compound. Compared
with authentic reference data, subtle differences in
the isotopic abundance of 2 H, 13 C, 15 N, or 18 O can
thus help uncover adulteration of foodstuff or drug
abuse in sports to name but a few.
There is also an increasing interest in the application of high-precision CSIA in tracer studies. One
area of application is concerned with quantitative
studies of biochemical processes such as assimilation / incorporation of nutrients, turnover rates of
biologically important molecules, and quantitation of
protein synthesis. The other area aims to improve
detection limits of bio-organic molecules by using
labelled precursor compounds at high enrichment
levels.
In either case, high-resolution capillary gas chromatography (HRcGC) is a prerequisite for highprecision CSIA by on-line IRMS. Peak overlap and
peak distortion have a detrimental effect on both
accuracy and precision of isotope ratio measurements. It is therefore not surprising that scientists
working on high-precision CSIA by on-line IRMS
invariably employ HRcGC methods.
It is the aim of this article to provide a review over
the current spectrum of applied GC–C–IRMS in
conjunction with the HRcGC aspects involved. To
illustrate as to why the two are inextricably linked,
the first section of this review will deal with the
characteristics of IRMS and GC–C–IRMS.
W. Meier-Augenstein / J. Chromatogr. A 842 (1999) 351 – 371
2. Principles of IRMS and GC–C–IRMS
2.1. IRMS
In order to understand why HRcGC is quintessential for high precision CSIA one needs to appreciate
exactly how IRMS works. In contrast to so-called
organic mass spectrometers (MS) that yield structural information by scanning a mass range (typically
over several hundred amu) for characteristic fragment ions, IRMS instruments achieve highly precise
measurement of isotopic abundance at the expense of
the flexibility of scanning MS.
For isotope ratio measurement, the analyte must
be converted into a simple gas, isotopically representative of the original sample, before entering the
ion source of an IRMS. Continuous flow isotope
ratio measurements of 2 H / 1 H, 15 N / 14 N, 13 C / 12 C,
18
O / 16 O and 34 S / 32 S are performed on gases of H 2 ,
N 2 , CO 2 , CO and SO 2 , respectively, with 13 C
abundance measurements accounting for almost 70%
of all gas isotope ratio analyses made. One also has
to bear in mind that IRMS, in fact, determines the
difference in isotope ratio with great precision and
accuracy rather than the absolute isotope ratio. IRMS
measurements yield the information of isotopic
abundance of the analyte gas relative to the measured
isotope ratio of a standard or reference gas. This is
done to compensate for mass discriminating effects
that may fluctuate with time and from instrument to
instrument. In dual-inlet IRMS systems, sample gas
and standard gas are introduced into two separate gas
reservoirs (bellows) and a changeover valve array is
used to toggle bellow effluents between the ion
source and a waste line, thus maintaining constant
viscous flow.
To achieve accurate and highly precise measurement of isotope ratios, obviously great care must be
taken to ensure that no part of the analyte data is
lost. In the case of CO 2 , the data comprise three ion
traces for the different isotopomers 12 C 16 O 2 , 13 C 16 O 2
and 12 C 18 O 16 O with their corresponding masses at
m /z 44, 45 and 46, respectively. The three ion beams
are registered simultaneously by a multiple Faraday
cup (FC) arrangement with a dedicated FC for each
isotopomer. The resulting ion currents are continuously monitored, subsequently digitised and trans-
353
ferred to the host computer. Here, the peak area for
each isotopomer is integrated quantitatively and the
corresponding ratios are calculated.
In both application areas of IRMS (low level
enrichment and natural abundance work), small
variations in very small amounts of the heavier
isotope are detected in the presence of large amounts
of the lighter isotope. The abundance A s of the
heavier isotope n 2 in a sample s, given in at.%, is
defined as:
A s 5 R s /(1 1 R s ) 3 100 (at.%)
(1)
where R s is the ratio n 2 /n 1 of the two isotopes for
the sample. The enrichment of an isotope in a sample
as compared to a standard value (A std ) is given in
at.% excess (APE):
APE 5 A s 2 A std
(2)
Since the small variations of the heavier isotope
habitually measured by IRMS are of the order of
0.001–0.05 at.%, the d -notation in units of per mil
(‰) has been adopted to report changes in isotopic
abundance as a per mil deviation compared to a
designated isotopic standard:
ds 5 (R s /R std 2 1) 3 1000 (‰)
(3)
where R s is the measured isotope ratio for the sample
and R std is the measured isotope ratio for the
standard.
2.2. GC–C–IRMS
From the above it is obvious that a GC cannot be
directly coupled to an IRMS. The need for sample
conversion into simple gases has prompted the
design of a combustion interface where the GC
effluent is fed into a combustion reactor (Fig. 1).
This reactor, either a quartz glass or ceramic tube, is
filled with CuO / Pt or CuO / NiO / Pt and maintained
at a temperature of approximately 820 or 9408C,
respectively [5,6]. The influence of combustion tube
packing on analytical performance of GC–C–IRMS
has been reported by Eakin et al. [7]. To remove
water vapour generated during combustion, a water
trap is required. Most instrument manufacturers
employ a Nafion tube for this purpose. Nafion is a
fluorinated polymer that acts as a semi-permeable
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W. Meier-Augenstein / J. Chromatogr. A 842 (1999) 351 – 371
Fig. 1. Set-up of an isotope ratio mass spectrometer coupled to a gas chromatograph via a combustion interface to measure 13 C / 12 C (carbon
mode) or 15 N / 14 N ratios (nitrogen mode). This schematic shows the reference gas set-up used for automated internal isotopic calibration
[24].
membrane through which water passes freely while
all the other combustion products are retained in the
carrier gas stream. Quantitative water removal prior
to admitting the combustion gases into the ion source
is essential because any water residue would lead to
protonation of CO 2 to produce HCO 21 , which interferes with analysis of 13 CO 2 (isobaric interference).
Very recently, a detailed study of this effect has been
reported by Lecktrone and Hayes [8].
In dual-inlet systems, the analyte gas comes from
a reservoir and only travels a short distance prior to
entering the ion source. For this reason, the gas
pulses result in rectangularly shaped signals. In
contrast, in continuous flow IRMS (CF–IRMS)
systems used for gas isotope analysis on-line gas
purification steps and overall interface length lead to
Gaussian-shaped signals. This is evidently even more
pronounced in GC–C–IRMS systems, where analyte
peaks eluting from the GC column are fed into an
on-line microchemical reactor to produce, e.g., CO 2
peaks. However, due to the chromatographic isotope
effect [9–11] the m /z 45 signal ( 13 CO 2 ) precedes the
m /z 44 signal ( 12 CO 2 ) by 150 ms on average (Fig.
2) [5], an effect not observed in ordinary CF–IRMS
systems. This time displacement depends on the
nature of the compound and on chromatographic
parameters such as polarity of the stationary phase,
column temperature and carrier gas flow [12]. There-
fore, loss of peak data due to unsuitably set time
windows for peak detection and, hence, partial peak
integration will severely compromise the quality of
the isotope ratio measurement by GC–C–IRMS, as
will traces of peak data from another sample compound due to close proximity resulting in peak
overlap with the sample peak to be analysed. Due to
the fact that isotope ratios cannot be determined
accurately from the partial examination of a GC
peak, HRcGC resulting in true baseline separation
for adjacent peaks is of paramount importance for
high-precision CSIA.
It should be noted, that the chromatographic
isotope effect is not caused by a vapour pressure
effect but is the result of different solute / stationary
phase interactions that are dominated by Van der
Waals dispersion forces leading to an earlier elution
of the heavier isotopomer [11]. This difference in
chromatographic solute / stationary phase interaction
is caused by lower molar volumes of the labelled,
and thus heavier, compounds. The reason for the
decrease in molar volume is the increased bond
strength and thus shortened bond length between
13
C–H and, to a lesser degree, 12 C– 13 C, and 12 C–H
and 12 C– 12 C, respectively. If the chromatographic
isotope effect would indeed be the result of a vapour
pressure effect, one would expect the lighter isotope
species of a given compound to elute more rapidly
W. Meier-Augenstein / J. Chromatogr. A 842 (1999) 351 – 371
355
standard) using combinations of exponentially modified Gaussian (E) and Harhoff / Van-der-Linde (H )
functions and were tested on up to 70% valley peak
overlap. When the adjacent peaks were of equal
abundance (leading peak:trailing peak, 1:1) combinations of HE and HH appeared to provide the best
recovery of isotope ratios. In the case of unequal
abundance in favour of the leading peak (10:1), the
HH combination gave the best accuracy. When the
abundance was reversed (1:10), the EH combination
provided the best accuracy but only for peak overlap
up to 40% valley. Despite these encouraging results,
curve-fitting algorithms for restoring lost accuracy
have not been incorporated into any commercial
IRMS data reduction software by IRMS manufacturers. It could be argued that the potential of curvefitting algorithms was only demonstrated on two
compounds, methyl tridecanoate and butylated hydroxytoluene, which were of almost identical carbon
isotope ratios and that any curve-fitting software
should also be able to extract accurate and precise
isotope ratios of two overlapping compound peaks
with different carbon isotope ratios. However, any
progress in this direction needs to be aided by full
evaluation of new algorithms for routine use (under
‘real life conditions’), thus requiring wide user
access to such algorithms which in turn depends on
the support from IRMS manufacturers.
Fig. 2. Illustration of the time displacement between 13 CO 2 and
12
CO 2 that causes the S-shaped 45 / 44 ratio signal [5]. From Ref.
[5], Intercept 1990.
from the column because of its higher vapour
pressure and, hence, lower boiling point as compared
to the heavier isotope species.
Although baseline separated peaks should be the
ultimate goal in GC–C–IRMS, there is many an
application where overlapping peaks simply cannot
be avoided. In addition, CO 2 and N 2 disperse more
freely within the carrier gas stream than their parent
organic compounds resulting in overlapping CO 2
peaks for barely baseline resolved GC peaks. To
extract the valuable information obscured by such
peak overlaps, Goodman and Brenna [13,14] suggested software algorithms for improved data processing. These algorithms were based on curve
fitting rather than the summation (the industrial
2.2.1. Sample preparation
To achieve high-precision CSIA by GC–C–IRMS
the following points must be considered:
(1) Every step of the sample preparation protocol
(collection, work up, derivatization) must be scrutinised for potential mass discriminatory effects to
avoid isotopic fractionation of the target compounds.
(2) If the potential of isotopic fractionation cannot
be ruled out conclusively, an internal standard, of a
similar chemical nature (but not requiring derivatization) and of known isotopic composition, should be
added to the sample prior to sample preparation.
(3) Signal size and isotopic composition of the
standard(s) must match those of the analyte(s) [15].
(4) The potential of all GC parameters (polarity of
stationary phase, carrier gas management, temperature programme) and techniques should be exploited
to their fullest to achieve HRcGC.
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W. Meier-Augenstein / J. Chromatogr. A 842 (1999) 351 – 371
2.2.1.1. Derivatization
Despite their importance for high-precision CSIA,
dedicated studies addressing issues of sample preparation are few and far between. Schumacher et al.
compared different sample preparation methods for
isotopic analysis of volatile organic compounds
(VOCs) from strawberries [16]. Khalfallah et al.
reported a correction method to compensate for 13 C
tracer dilution by carbon added during derivatization
[17], and a carbon balance equation was described
by Demmelmair and Schmidt to calculate d 13 C
values of free amino acids from d 13 C values of their
derivatives at natural abundance level [18]. Kinetic
isotope effects associated with derivatization reactions and resulting theoretical considerations for
calculating d 13 C values have been discussed by
Rieley [19].
Of course, one way of avoiding the problems with
derivatization is not to derivatize the sample at all.
This approach involves the use of moderately polar
to polar stationary phases and high-temperature GC.
However, not all polar compounds are amenable to
these techniques (e.g., amino acids) and high-temperature capability of polar stationary phases is
limited even when oxygen free helium is used as
carrier gas.
In addition to changes in 13 C isotopic signature by
derivatization, its effects on GC separation and
sample conversion into CO 2 and N 2 have to be
considered. Derivatization by silylation might
hamper GC separation as the apolar nature of
trimethylsilyl (TMS) and tert.-butyldimethylsilyl
(tBDMS) derivatives can obscure compound characteristics that could otherwise be chromatographically
exploited. Furthermore, an excessive carbon load
introduced by derivatization might result in incomplete combustion thus compromising accurate isotopic analysis. For reasons of non-quantitative sample conversion, the use of trifluoroacetates (TFA) or
heptafluorobutyrates (HFB) is not advisable as
fluorine forms extremely stable fluorides with Cu and
Ni, thus irreversibly reducing combustion efficacy of
the CuO / NiO system. In addition, fluorine poisons
the combustion catalyst platinum. Experiments with
N-TFA, O-propylates of alanine and leucine have
shown that only 50% of the expected CO 2 yield was
produced [20].
2.2.2. Isotopic calibration
For reasons mentioned before, it is not possible in
GC–C–IRMS to calibrate target compounds against
a standard of known isotopic composition, introducing the standard in exactly the same way as the
analyte. There are only three feasible means of
introducing a standard: (a) addition of reference
compounds to the sample, (b) introduction of reference gas pulses to the carrier gas stream, or (c)
introduction of reference gas pulses directly into the
ion source.
Caimi et al. comprehensively listed all the desirable properties internal reference compounds should
possess: (1) high chemical stability; (2) conveniently
available in high purity; (3) readily soluble in highpurity solvents; (4) low vapour pressure at room
temperature and atmospheric pressure; (5) environmentally rare; (6) ideally useful for GC and liquid
chromatography (LC) techniques; and (7) sufficiently different chromatographic characteristics to avoid
partial or complete co-elution with sample analytes
[21].
The results of an extensive study into methods of
isotopic calibration by Merritt et al. emphasised
these demands [22]. Comparing the use of internal
reference compounds with the introduction of reference gas pulses directly in the ion source of the
IRMS, Merritt et al. found an offset of .2‰
between the two methods in the case of incomplete
combustion and other systematic errors affecting
only the analytes. These systematic errors affected
both the analytes and the co-injected reference
compounds but were not reflected by the external
reference gas pulses. Similar observations were made
by other groups interested in isotopic calibration
[12,21,23]. In the absence of such systematic errors,
Merritt et al. found that both methods of isotopic
calibration gave consistent results as long as multiple
reference peaks were used to permit drift correction.
Only one reference peak for isotopic calibration,
albeit from an internal reference compound, is not
enough to compensate for the influence of GC
parameters, such as analyte / stationary phase interaction, column temperature on measured isotope
ratios [12].
Within the GC–C–IRMS system, seven potential
sources for mass discrimination and, hence, sys-
W. Meier-Augenstein / J. Chromatogr. A 842 (1999) 351 – 371
tematic errors can be identified: (1) isotopic fractionation during sample injection (which can be
overcome by on-column or time programmed splitless injection); (2) chromatographic isotope effect;
(3) chromatographic peak distortion (leading and
trailing peak tail); (4) combustion process; (5) peak
distortion of N 2 / CO 2 gas peak during passage of the
combustion interface; (6) changing flow conditions
at the open split prior to the IRMS; and (7) the
IRMS itself. Obviously, the external reference gas
pulses only compensate for item (7), whereas internal reference compounds reflect all of the aforementioned. Recently, a method for isotopic calibration was reported that, provided a combustible
gas was used, could reflect the systematic errors
caused by items (4–7) [24]. This method combines
the convenience and practicability of external reference gas calibration with the advantage of reflecting
the majority of physical influences to which analytes
are subjected in a GC–C–IRMS system.
2.2.3. HRcGC
As pointed out earlier, baseline separated gas
chromatographic peaks are the basis for high-precision CSIA. To achieve this goal, in the first instance,
basic gas chromatographic rules must be observed:
(1) the polarity of the stationary phase should meet
the polarity of the analytes; (2) column head pressure and, hence, carrier gas velocity, should be set to
suit column diameter; and (3) temperature gradients
should be chosen to exploit the maximum of the
357
column length (the longer the column, the slower the
temperature rise per minute; cf. Table 1) [25].
Further to these principles, HRcGC techniques
such as multi-dimensional capillary GC (MDcGC),
enantio-selective GC, porous layer open tubular
(PLOT) column GC for analysis of VOCs and hightemperature capillary GC (HTcGC) are powerful
tools for high-precision CSIA when used in combination with GC–C–IRMS. Nitz et al. were the first
to report the advantages of using MDcGC in GC–C–
IRMS [26]. MDcGC is now, often in combination
with enantioselective GC, almost exclusively used in
authenticity control of flavours and fragrances by
CSIA [27,28]. In a similar fashion, HTcGC is
strongly associated with CSIA of steroids and longchain fatty acids (e.g., Ref. [29]).
Regrettably, the achievements of HRcGC in terms
of well-defined peak shape and baseline separation
are likely to be impaired during combustion and the
subsequent passage through the interface. Changes in
tubing diameter and frequent use of unions to
connect the various parts of tubing lead to a loss in
peak definition (peak broadening; peak distortion)
and even to partial peak overlap, all of which have a
detrimental effect on accuracy and precision of
isotope ratio measurement [12]. Very recently, Goodman reported a single-capillary interface design
(SCID) which he developed to overcome these
problems [30]. As the name suggests, a single
capillary was used to connect the GC column to the
open-split in front of the IRMS. This capillary was
threaded through a furnace and accommodated two
Table 1
Recommended values for carrier gas velocity and temperature gradient according to column length when using helium as carrier gas a
Column length (m)
Elution of methane (s)b
Temperature gradient (8C / min)
10
15
20
25
30
40
50
35
53
70
88
105
140
175
2.5
1.65
1.25
1.05
0.84
0.63
0.5
a
Based on working directions given by Grob [25].
Set GC oven temperature to 308C. Set split ratio to about 1:30, inject a few ml of natural gas (or lighter gas) and measure elution time of the
first peak (FID signal). Adjust column head pressure to match recommended elution time.
b
W. Meier-Augenstein / J. Chromatogr. A 842 (1999) 351 – 371
358
CuO wires positioned thus as to coincide with the
furnace dimensions. So far, this design has been
tested for 13 C isotopic abundance analysis of nalkanes.
3. High-precision CSIA of
abundance
13
C isotopic
3.1. GC–C–IRMS at natural abundance level
High-precision CSIA of 13 C isotopic abundance at
both natural abundance (NA) and low enrichment
level can yield measurements of d 13 C values with a
precision of 0.3‰ on average. Thanks to this high
precision, even small changes in 13 C isotopic abundance of 1‰ can be reliably detected. For this reason
GC–C–IRMS has become the method of choice to
determine the origin of a given organic compound by
measuring its characteristic isotope ‘finger print’.
In contrast to the generally held opinion, the
natural abundance of stable isotopes is not a fixed
constant but displays a considerable, yet subtle,
degree of variation. The variation on the natural
abundance of 13 C can be as high as 0.1 at.% (Fig. 3).
This wide range reflects the varying degree of mass
Fig. 3. Some typical examples of natural d 13 C values grouped according to origin along the scale of
13
C natural abundance.
W. Meier-Augenstein / J. Chromatogr. A 842 (1999) 351 – 371
discrimination associated with the different pathways
of carbon assimilation and fixation. To give an
example, in terms of 13 C isotopic abundance, beet
sugar is not the same as cane sugar. In sugar beet
CO 2 fixation results in the formation of a C 3 body,
3-phosphoglycerate (3-PGA). This pathway of CO 2
fixation is known as the Calvin cycle. Plants using
the 3-PGA pathway for CO 2 fixation are commonly
called C 3 plants. However, some plants, of which
sugar cane is one, make use of a different pathway.
Here, CO 2 fixation yields a C 4 -dicarboxylic acid,
oxalo acetate, hence the term C 4 plants (the C 4 dicarboxylic acid pathway is also known as the
Hatch–Slack cycle). The products of these two
pathways are characterised by their different 13 C
abundance. Glucose derived from C 3 plants has a
d 13 C value of about 225‰, whereas glucose derived from C 4 plants exhibits a more positive d 13 C
value of about 211‰ indicating a mass discriminatory bias towards 13 CO 2 of the Hatch–Slack cycle.
Differences in d 13 C values were reported for total
leaf tissue, total surface lipid extracts, and individual
n-alkanes isolated from plants utilising either C 3 , C 4 ,
and crassulacean acid metabolism (CAM) pathways
for carbon fixation [31,32]. The average d 13 C values
obtained from C 3 plant material were between 10
and 15‰ lower compared to the corresponding d 13 C
values obtained from C 4 and CAM plant material.
Measurement of the isotopic abundance of 13 C for
lutein isolated from marigold (C 3 ) and maize (C 4 )
yielded 229.9060.20 and 219.7760.27‰, respectively, showing a similar difference of .10‰ between the two photosynthetic pathways [33].
Although the vast majority of plants belong the C 3
group (.300 000) with bulk d 13 C values of
, 224‰, differences in the rate of photosynthesis
(mainly caused by differences in climate and geographical location) and in enzyme kinetics of biochemical pathways result in subtle variation of 13 C /
12
C ratios that can be detected by GC–C–IRMS.
Lockheart et al. were able to detect inter- and intraspecific differences in d 13 C values for n-alkanes and
alcohols in sun and shade leaves from oak and beech
ranging from 0.7 to 3.0‰ [34]. These subtle differences in d 13 C values can be used to determine origin
of an organic compound and, thus, the authenticity of
a sample.
359
3.1.1. Authenticity
3.1.1.1. Flavours and fragrances
Bernreuther et al. measured 13 C / 12 C isotope ratios
of natural and nature-identical g-decalactone and
reported significant differences in d 13 C values that
were source dependent although all plants belonged
to the C 3 group [35]. For natural g-decalactone from
stone fruit (apricot and peach) they found d 13 C
values ranging from 238.0 to 240.8‰, respectively, that were significantly different from d 13 C values
of g-decalactone extracted from soft fruit (strawberry) which were 229.2‰ on average. In contrast,
d 13 C values of artificial (nature-identical) g-decalactone ranged from 224.4 to 226.9‰, whereas gdecalactone of biotechnological origin showed d 13 C
values of 230.8‰ on average.
In an independent study, Mosandl et al. demonstrated the additional advantage to be gained from
enantioselective GC–C–IRMS on samples of synthetic g-decalactone (racemate RAC; 4R:4S550:50),
g-decalactone of biotechnological origin (BIO; 4R.
99% ee), and a mixture of both (BIO: RAC560:40)
[36]. They reported for the enantiomerically pure
(4R)-g-decalactone of biotechnological origin a d 13 C
value of 230.1260.14‰ which is in good agreement with the d 13 C values measured by Bernreuther
et al. The separated enantiomers of the nature-identical product showed, as could be expected, identical
d 13 C values within the observed standard deviation
(4R, 228.3260.36‰; 4S, 228.1860.39‰). Mixing
60% of BIO with 40% of RAC yielded an enantiomeric distribution of 4R:4S580:20, with 75% of the
4R-configured g-decalactone being of biotechnological origin. The measured d 13 C value of the 4R
enantiomer in this mixture was 229.6260.33‰
which was in good agreement with the theoretically
expected value of 229.67‰. The d 13 C value of the
4S enantiomer in this mixture was of course the
same as for the 4S enantiomer in the pure synthetic
sample (228.0861.30‰).
This early work suggested that two phenomena of
biosynthetic pathways, enantioselectivity and mass
discrimination (kinetic isotope effects), might serve
as compound-specific parameters to establish origin
and, hence, to control authenticity of natural flavours
and fragrances. However, when focused on indi-
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vidual compounds the application of stable isotope
abundance measurement is only of limited use as
most plants cultivated for human consumption are C 3
plants whose 13 C isotopic signatures partially overlap
with those of synthetic compounds derived from
fossil sources or those of compounds produced by
biotechnological methods.
This problem was soon recognised and Mosandl’s
group suggested the use of genuine internal isotopic
standards and formulated the following recommendations [37,38]:
(1) the compound selected as internal isotopic
standard should be a genuine characteristic compound of lesser sensorial relevance;
(2) the compound must be available in the sample
in sufficient amounts and must not be susceptible to
mass discrimination during sample preparation;
(3) the selected compound must be biogenetically
related to the compounds under investigation;
(4) chemical inertness of the compound during
storage and / or technical processes is mandatory;
(5) the compound selected as internal isotopic
standard must not be a legally allowed additive.
The obvious advantage of using an internal standard, biogenetically related to characteristic sample
compounds, is the elimination of variations in the
d 13 C-signature resulting from, e.g., climate-dependent variations of the photosynthetic rate and the
kinetic isotope effect associated with the CO 2 fixation step. Therefore, only the characteristic kinetic
isotope effects caused by enzymatic reactions during
secondary biogenetic pathways are investigated, and
the resulting relative d 13 C values can be used as a
sample-specific finger print (cf. Fig. 4).
Applying the aforementioned recommendations,
Mosandl and co-workers, who have to be regarded as
the leaders in the field of authenticity control, studied
a wide range of commercially relevant flavours and
fragrances and assessed authenticity of allegedly
natural samples from commercial sources. The materials studied included lemon oil [37], balm oil [39],
citronella oil [39], lemongrass oil [39], coriander oil
[40], bergamot oil [41], orange oil [42], mandarin oil
[43,44], peppermint oil [45], volatile components
from strawberries [16] and apples [46] and a- as well
as b-ionone from raspberries [47]. Using self-prepared authentic samples, sample-specific finger prints
of six to eight biogenetically related compounds
were established based on d 13 C values relative to an
internal standard (either limonene, g-terpinene or
neryl acetate). With the help of these finger prints,
samples made up entirely of synthetic compounds
and even samples containing mixtures of authentic
(natural) material and synthetic compounds could be
reliably identified. In cases where chiral compounds
such as linalol occur naturally as enantiomeric
mixtures (in coriander oil, R:S520:80), enantioselective GC–C–IRMS could even detect nonauthentic samples imitating the natural enantiomeric
ratio by measuring the d 13 C values of each enantiomer [40,48].
3.1.1.2. Wine, fruit juice and honey
In the early days of GC–C–IRMS, detection of
added sugar in fruit juice and wine was fairly simple
as mainly cheap corn syrup (maize: C 4 plant) was
predominantly used to boost sugar and / or ethanol
content, respectively. Measuring d 13 C values of
glucose or bulk carbon was sufficient to prove
adulteration. However, addition of small amounts of
C 4 plant sugars (#10%) to C 3 plant products such
as wine, fruit juice and honey, or the addition of
sugars from other C 3 plants (sugar beet; concentrated
and deflavourized grape juice) could no longer be
detected by these measurements [49]. Schmidt and
co-workers studied several methods based on highprecision CSIA to determine authenticity of wine,
fruit juices and honey, and to prove fraudulent
addition of sugars and even vitamin C from other
sources. Their research revealed that in authentic
fruit juices biogenetically related compounds such as
L-ascorbic acid [50,51], L-malic acid [52] and Ltartaric acid [51] showed d 13 C values strongly
correlating with those of their corresponding sugars.
For instance, the d 13 C value of L-ascorbic acid is
14.8‰ higher than that of its precursor glucose.
This enrichment is mainly located in the C-1 position
of L-ascorbic acid of authentic origin and the result
of plant-specific kinetic isotope effects during biosynthesis, whereas L-ascorbic acid of biotechnological origin preserves the 13 C pattern of glucose [51].
In glycerol originating from natural sources,
Weber et al. found a 13 C depletion position specific
for C-1 and they suggested this unique feature might
be used as a means to test for illegal addition of
synthetic glycerol to wines [53]. They also discov-
W. Meier-Augenstein / J. Chromatogr. A 842 (1999) 351 – 371
361
Fig. 4. d 13 C fingerprint of biogenetically related compounds in lemon oils of different geographical origin (top graph). The graph at the
bottom shows the Dd 13 C fingerprint of the samples obtained when using neryl acetate (8) as internal isotopic standard [38]. From [38],
Marcel Dekker, 1995.
ered a constant Dd 13 C correlation between ethanol
and citric acid (12.4‰) in addition to the known
Dd 13 C correlation between fermented sugar and
ethanol (21.7‰) [54]. Dennis et al. suggested the
use of d 13 C values of sorbitol as a further means for
authenticity control of wines [55].
3.1.1.3. Vegetable oils
High-quality, single-source vegetable oils are
another target for fraudulent adulteration, i.e., partial
or total substitution of minor quality and, hence,
cheaper oils for the high quality product. In a blind
study, Woodbury et al. were able to detect the
adulteration of maize germ oil with oils of C 3 plant
origin down to a level of 5% (w / w) [56]. They found
the saturated 16:0 fatty acid in maize oil to be more
depleted in 13 C than the corresponding unsaturated
fatty acids 18:1 and 18:2. In addition, consistent
differences were observed for d 13 C values of vegetable oils from different geographical regions. In a
subsequent study, Woodbury et al. determined fatty
acid composition and d 13 C values of the major fatty
acids of more than 150 vegetable oils [57], thus
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W. Meier-Augenstein / J. Chromatogr. A 842 (1999) 351 – 371
establishing a database that provides isotopic information for authenticity control of vegetable oils.
Variability in d 13 C values could be related to geographical origin, year of harvest, and the particular
variety of oil. Their findings suggest that ultimately
d 13 C values of fatty acids are determined by a
combination of environmental and genetic factors.
Kelly et al. investigated authenticity of single-seed
vegetable oils of C 3 plant origin such as groundnut,
palm, rape seed and sunflower oils [58]. They found
that the d 13 C values for the authentic vegetable oil
fatty acids fell within a narrow range of 227.6 to
232.1‰. Employing canonical discriminant analysis, 13 C data from sunflower oil could be separated
from other oils, exploiting small, yet significant,
differences in d 13 C values within the oil varieties.
To detect adulteration of olive oils, Angerosa et al.
compared d 13 C values of the aliphatic alcoholic oil
fractions and found those of the adulterant pomace
oil to be significantly more negative than those of
virgin and refined olive oils [59]. Furthermore, they
studied isoprenoids and methylsterols isolated from
each grade of olive oil and showed the better the
olive oil grade, the more positive (i.e., less negative)
the d 13 C values of these compounds became.
3.1.1.4. Drugs
Measuring 13 C isotopic abundance of heroin to
trace the origin of heroin samples in narcotic drug
abuse, showed some evidence of variation in d 13 C
values of heroin depending on its geographical site
of production [60]. A preliminary study to trace the
origin of different batches of confiscated 3,4(methyldioxy)methylamphetamine (MDMA, Ecstasy) tablets by GC–C–IRMS allowed the discrimination of four different groups of MDMA tablets
based on variations in their NA d 13 C values [61].
The same study showed that further discrimination
could be obtained when using d 15 N values of
MDMA.
Prompted by the uncertainty associated with the
T / E.6 test that measures the ratio of testosterone
(T) and epitestosterone (E) and the high public
interest in alleged doping in athletes, several studies
have been carried out to use metabolic pathway
related 13 C isotope patterns to differentiate between
endogenous human testosterone and exogenous testosterone [62]. In the human body, testosterone is
synthesised from cholesterol via dehydroepiandrosterone and is then further metabolised to androstanediol. The group around Aguilera et al. found that
the averaged d 13 C values for endogenous 5a- and
5b-androstanediol dropped from 226.52‰ before
synthetic testosterone administration (natural background or baseline value) to 232.44‰ during
testosterone administration [63]. Independent work
by Shackleton et al. obtained a similar baseline d 13 C
value of 226.87‰ and a drop down to 230.21‰ on
average for androstanediol samples after a bolus
administration of 250 mg exogenous testosterone
[64,65]. This drop of about 22‰ lasted for up to 10
days before d 13 C values of androstanediol returned
to their former baseline value. They also reported a
narrow range of 229.15 to 230.41‰ for five
synthetic testosterone samples manufactured in five
different countries. Based on the results reported by
Aguilera et al. [63] and their own, Shackleton et al.
suggested a conservative cut-off value of 229.0‰
for androstanediol to identify unambiguously testosterone abuse for up to 7 days after administration.
Hydrocortisone abuse in horse racing and other
equine sports can be confirmed on the basis of
significantly different 13 C isotope patterns between
endogenous urinary hydrocortisone and synthetic
material. This method, proposed by Aguilera et al.,
employs conversion of urinary hydrocortisone into
its bismethylenedioxy derivative to improve its gas
chromatographic properties [66].
3.1.2. Origin
3.1.2.1. Geochemistry
The desire to study 13 C isotope abundance of
sedimentary hydrocarbons on a molecular level was
one of the driving forces behind the development of
GC–C–IRMS. Geochemists and archaeologists
wanted to extract all possible information contained
in fossil biomarkers such as sedimentary long-chain
alcohols and sterols [67], triterpene-derived hydrocarbons [68], neutral monosaccharides [69], longchain alkanes [70–74], alkanes and isoprenoids
[75,76], polycyclic aromatic hydrocarbons (PAHs)
[77], amino acids [78–80] and phenolic acids [81].
Freeman et al. measured hydrocarbons from sediments deposited in the Messel shale and found a
wide spectrum of d 13 C values ranging from 220
W. Meier-Augenstein / J. Chromatogr. A 842 (1999) 351 – 371
down to 275‰, thus proving the equally wide
spectrum of origins [82]. The extreme negative
values were thought to indicate the activity of
methanotrophic bacteria, as d 13 C values of , 245‰
thus far had only been observed for methane but not
for larger molecules. This and others studies
[68,69,73,83] demonstrated that sedimentary organic
compounds contain contributions of bacterial origin
rather than being solely of plant origin. Using GC–
C–IRMS to measure 13 C isotope abundance in
hydrocarbons from sedimentary rocks across the
Precambrian–Cambrian boundary, Logan et al. [84]
were able to shed some light on the development of
multicellular life during the so-called ‘Cambrian
explosion’. Based on the isotopic data, they could
show a transition from an environment dominated by
sulphate-reducing bacteria to one dominated by
photosynthetic organisms, thus transforming the
hitherto anaerobic ocean into an aerobic ocean.
3.1.2.2. Archaeology
Measuring 13 C / 12 C isotope ratios has become an
increasingly important tool to glean information on
prehistoric diet and lifestyle from organic residues
preserved in archaeological artefacts. Employing
both HTcGC–MS and HTcGC–C–IRMS to identify
chemical structures and measure d 13 C values, respectively, Evershed et al. showed that lipid extracts
(C 25 –C 33 alkanes and a C 29 ketone, nonacosain-15one) from organic residues found in archaeological
potsheds were derived from Brassica species (wildtype cabbage) [85]. Later work on organic residues
from archaeological pottery vessels found C 31 , C 33
and C 35 ketones with d 13 C values that were up to
10‰ higher than those found for the C 29 ketones
from wild-type Brassica species. Based on HTcGC–
MS and HTcGC–C–IRMS data, Evershed and coworkers formed the hypothesis that a precursor /
product relationship may exist between C 35 ketones
and fatty acids, and corresponding triacylglycerols
such as tripalmitin and tristearin from animal fats
[86]. Studying pyrolysis reactions of acyl lipids and
monitoring their products by HTcGC–MS and
HTcGC–C–IRMS, they could confirm that C 31 , C 33
and C 35 mid-chain ketones found in archaeological
pottery vessels were indeed derived from a mixture
of free fatty acids [87]. Comparing carbon number
distributions of triacylglycerols and d 13 C values of
363
16:0 and 18:0 acyl moieties of lipid compounds from
extracts of neolithic vessels with modern reference
animal fats, Evershed et al. could identify animal fat
residues found in vessels dated circa 4200 BP as
being close to reference pig adipose fats, whereas
residues found in vessels dated circa 4500 BP were
closer to reference ruminant fat [88].
O’Donoghue et al. reported d 13 C values in the
range of 225.4 to 229.2‰ for the principal fatty
acids (16:0 to 24:1) of radish seed found in a 6th
century AD storage vessel [89]. Composition and
d 13 C values of fatty acids found in the ancient radish
seeds matched closely those found in modern radish
seeds.
Stott et al. measured d 13 C values of cholesterol
and 3b-hydroxycholest-5-en-7-one from fossil whale
bones [90] and archaeological human bones and
teeth [91], and showed that their 13 C content could
be used as an important new source of palaeodietary
information. Another insight into prehistoric life was
gleaned from the 13 C analysis of adsorbed lipids
preserved in the fabric of Minoan lamps and conical
cups. The d 13 C values together with HTcGC–MS
profiles identified beeswax as the illuminant burned
in prehistoric Aegean lamps rather than olive oil as
hitherto supposed [92].
3.1.2.3. Environmental chemistry
Monitoring environmental and climate changes by
measuring d 13 C values of atmospheric gases such as
methane, carbon monoxide and carbon dioxide has
traditionally been carried out using dual-inlet IRMS
systems. Their measurements however, required
time-consuming sample preparation of large sample
volumes. The high abundance sensitivity of GC–C–
IRMS together with the use of PLOT fused-silica
capillary columns for routine GC analysis of highly
volatile organic compounds (HVOCs) has now become the method of choice for scientists as air
samples between 50 ml and 5 ml can be analysed
on-line without any prior sample preparation [93–
95].
High-precision CSIA by GC–C–IRMS is also
used to determine origin and identify sources of oil
spills and oil pollution [96–98], ocean-transported
bitumen [99], characterisation of refractory wastes at
heavy-oil contaminated sites [100,101] and to trace
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W. Meier-Augenstein / J. Chromatogr. A 842 (1999) 351 – 371
the sources of PAHs in the environment [102–104].
Bird et al. used GC–C–IRMS to record the d 13 C
values of individual biomarker compounds (n-alkanes) to assess vegetation changes [105], whereas
Naraoka et al. measured differences in d 13 C values
of long-chain fatty acids (C 20 –C 30 ) that had been
extracted from terrestrial and marine sediment [106].
3.2. Tracer studies
The high abundance sensitivity of GC–C–IRMS
has also been increasingly exploited in metabolic
studies investigating turnover, incorporation and
synthesis processes in vivo that could previously
only be investigated either with stable isotope tracers
at high enrichment level using GC–MS, or not at all.
The latter usually for reasons of excessive tracer
dilution in the various metabolic pools and / or low
incorporation rates. Although in these cases radioactive tracers can provide an alternative, their use is
associated with certain risks which are nowadays
regarded as unacceptable especially when dealing
with members of the paediatric age group.
GC–C–IRMS proved to be a decisive tool in
determining if and to what extent neonates and
premature infants of very low birth weight could
synthesise arachidonic acid, which is essential for
their growing tissues, from dietary fatty acids. Demmelmair et al. could show that in neonates fed on a
phenylalanine-free diet, on average 23% of free
plasma arachidonic acid on study day 4 originated
from infantile linoleic acid conversion [107]. They
determined 13 C content of linoleic acid and arachidonic acid in 0.25–0.5 ml serum before and for 4
days after the infants’ diet contained corn oil.
Baseline d 13 C values for linoleic acid and arachidonic acid were 231.561.1 and 230.161.2‰,
respectively; after 4 days, changes in d 13 C values
over baseline were 112.760.7 and 12.760.7‰,
respectively. Carnielli et al. added linoleic acid and
linolenic acid, both 13 C-labelled, to the formula diet
which was administered continuously for 48 h (birth
weight, 1.1760.12 kg; gestational age, 28.461.3
weeks) [108]. They could show that both tracers
were rapidly incorporated into plasma phospholipids
and that their metabolic products including arachidonic acid and docosahexaenoic acid became highly
enriched with 13 C. Incorporation of 13 C-octanoic
acid into plasma triglycerides (10% of the enrichment of the diet), noticeably into myristic and
palmitic acid, by very low-weight preterm infants
was reported earlier by the same group [109].
A similar observation was made during a study
with an entirely different objective. Using GC–C–
IRMS, Koziet et al. could demonstrate that ethanol
itself may be used as a substrate for lipogenesis,
although only to a small extent [110]. They calculated that ,10% of fatty acids contained in verylow-density lipoproteins (VLDL) triglycerides were
derived from this pathway, with ethanol predominantly being incorporated into myristic and palmitic
acid.
The majority of 13 C tracer studies published have
dealt with various aspects of fatty acid biochemistry,
such as metabolism of triglycerides [111,112], and
transport and turnover of free saturated [113–115]
and unsaturated fatty acids [116–120]. While studying the metabolism of 13 C-labelled polyunsaturated
fatty acids by 13 C NMR, using GC–C–IRMS,
Cunnane et al. found low levels of 13 C-labelled
g-linolenic acid in brain phospholipids of suckling
rat pups that could not be detected by 13 C NMR
[121].
Employing 13 C tracers at low levels of enrichment, reliable data were obtained in studies of the
kinetics of glucose [122,123] and glycoprotein neutral sugars [124,125], cholesterol and lipoproteins
[126], phosphatidylcholine [127], urea [128,129],
branched-chain amino acid metabolism [130],
measuring protein fractional synthetic rates [131] and
protein synthesis in colorectal cancer cells [132,133].
Roscher et al. used L-rhamnose with two different
levels of 13 C abundance in parallel experiments to
determine if L-rhamnose serves as a carbon source
for furaneol [134].
Unlike GC–MS, where increasing the amount of
label has no general effect on detection limits, in
GC–C–IRMS, increasing label enrichment in precursor compounds produces significantly improved
detection limits. In a recent review of high-precision
CF–IRMS, Brenna et al. discussed theoretical and
practical considerations for this type of study [135].
High-precision CSIA in this type of tracer studies
has been shown to possess advantages over organic
GC–MS for stable isotopic tracer detection and to be
W. Meier-Augenstein / J. Chromatogr. A 842 (1999) 351 – 371
superior to radio-isotopic tracer methods in terms of
dose size and analysis efficiency [136]. Guo et al.
reported that GC–C–IRMS provides 15-fold lower
detection limits for [ 13 C 2 -C3,C4]cholesterol than
organic GC–MS [137]. Using [U- 13 C]a-linolenic
acid, Sheaff et al. were able demonstrate that conversion of a-linolenate into docosahexaenoate was not
depressed by high dietary levels of linoleic acid
[117]. An interconversion of saturated dietary fatty
acids (e.g., 18:0) into unsaturated fatty acids (e.g.,
18:1) in plasma of about 14% was reported by Rhee
et al. [138]. Menand et al. applied this technique to
measure carbon incorporation into plasma glutamine
[139]. Practical aspects of this technique have been
investigated by Dube et al. [140,141].
4. Compound-specific isotope analysis of
isotopic abundance
15
N
High precision CSIA of 15 N isotopic abundance
faces three major challenges. First, the relatively low
concentration of nitrogen in organic compounds (for
instance, amino acids contain two to 11 times more
carbon than nitrogen). Secondly, 15 N / 14 N isotope
ratios are measured on N 2 , which means the compound conversion step requires a reduction process
and, in the case of amino acids, the formation of one
mol equivalent of N 2 produces 4–22 molequivalents
of CO 2 (if we regard the underivatised case). Lastly,
even small amounts of atmospheric gas leaks into the
instrument result in a high N 2 background.
Due to the wide spectrum of applications that
would benefit from a system capable of analysing
15
N isotopic enrichment down to natural abundance
level, especially because there is no radioactive
alternative, these difficulties did not delay instrument
development for too long. The desire to study for
example nitrogen fixation in plants and micro-organisms and nitrogen metabolism in humans, as well as
the possibility to quantify gene expression by
measuring mRNA turnover, prompted two groups to
extend the scope of GC–C–IRMS. In 1994, Preston
and Slater presented a system with a conversion
interface comprising combustion furnace, liquid N 2
cold trap, to trap CO 2 and water, and a PLOT
column to resolve N 2 from any CO formed by poor
combustion [142]. If CO were permitted to enter the
365
ion source simultaneously with N 2 , this would result
in a serious isobaric interference at m /z 28. For d 15 N
values of tBDMS derivatives of amino acids, they
reported a precision of S.D. (d 15 N)55‰ at natural
abundance level.
In the same year, Merritt and Hayes presented a
similar system but for the addition of a reduction
furnace, loaded with Cu wires and maintained at a
temperature of 6008C, to reduce N-oxides to N 2 and
to scavenge O 2 emanating from the combustion
furnace [143]. Their system also included a cryogenic trap to remove CO 2 and water, and it produced
15
a precision of S.D. (d N)50.2‰.
This marked difference in precision was attributed
to different performances of the IRMS systems.
Ongoing investigations in our laboratory seem to
indicate that this difference might be caused by the
simultaneous presence of NO and N 2 in the ion
source. Placing a PORAPLOT Q capillary column of
0.32 mm internal diameter, maintained at 308C
between the water trap and the ion source, separates
N 2 from NO (Fig. 5). Comparing precision for d 15 N
analysis of identical aliquots from the same sample
with and without the PLOT column in place, we
15
found a drop in precision from S.D. (d N)50.15‰
15
to S.D. (d N)52‰, respectively. This phenomenon
cannot be explained by isobaric interference of NO
(m /z 30) with m /z 28 ( 14 N 2 ) or m /z 29 ( 15 N 14 N) but
might be caused by reaction of NO with N 2 in the
ion source.
To avoid potential isotopic fractionation due to
various degrees of nitrogen silylation when TMS
derivatives were formed, Hoffmann et al. prepared
tBDMS derivatives of amino acids from wheat
protein hydrolysate [144]. The same strategy was
followed by Segschneider et al. when investigating
15
N uptake from 15 N-labelled NO 2 by measuring
15
d N values of soluble amino acids in sunflower
leaves [145]. However, when preparing tBDMS
derivatives, one has to bear in mind that in the case
of, e.g., L-leucine, one adds 12 mol equivalent of
carbon to 1 mol equivalent of amino acid, thus
resulting in 36 mol equivalent of carbon for 1 mol
equivalent of N 2 , which might give rise to CO
formation due to poor combustion. Metges et al.
reported an alternative derivatisation protocol for
amino acids yielding N-pivaloyl, O-isopropylates,
and demonstrated their application by measuring NA
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W. Meier-Augenstein / J. Chromatogr. A 842 (1999) 351 – 371
Fig. 5. The N 1
2 mass traces obtained for alanine (as N-acetyl, O-propyl derivative) having passed post-combustion through a PORAPLOT Q
column, held at 308C, shows the presence of NO next to N 2 . The CO 2 peaks are caused by the formation of CO 1 from CO 2 in the ion
source.
d 15 N values of amino acids from plasma albumin
hydrolysate [146].
CSIA of 15 N isotopic abundance by GC–C–IRMS
has been applied to measure the effect of ibuprofen
on protein synthesis using 15 N-labelled glycine as
tracer [147]. Williams et al. used 15 N-labelled urea to
demonstrate that Helicobactor pylori uses urea as a
nitrogen source for its synthesis of amino acids
[148]. A multidisciplinary group comprising environmental, archaeological, geological and nutritional
scientists showed that differences in d 15 N values
from soil amino acids could be used to indicate
differences in land use in Bronze Age, medieval and
early modern soils [149]. One intriguing observation
they made was the consistently low levels of 15 N
abundance (d 15 N,0.0‰ vs air) in the amino acids
threonine (Thr) and phenylalanine (Phe) from soils
of unmanured cereal production sites, whereas d 15 N
values of all the other amino acids were positive. We
thought this was intriguing because a similar pattern
was found by Metges and Petalec when monitoring
d 15 N values of free plasma amino acids from fasting
human subjects. With the exception of Thr and Phe,
all other amino acids showed positive d 15 N values
[150].
Mas et al. suggested that d 15 N values of MDMA
(ecstasy) could be used for batch discrimination of
ecstasy tablets [61], and Faulhaber et al. used d 15 N
values of methyl-N-methylanthranilate as a biomarker in the authenticity control of mandarin oils
[43]. Last, but not least, GC–IRMS was used for
measuring d 15 N values of N 2 and N 2 O, separated
from the same sample [151,152].
5. Hyphenated techniques
In recent years, the research efforts of different
groups working in the field of GC–IRMS have
focused on extending the scope of on-line CSIA
towards the measurement of organic 18 O / 16 O and
organic 2 H / 1 H isotope ratios. Consequently, research
W. Meier-Augenstein / J. Chromatogr. A 842 (1999) 351 – 371
was undertaken with the aim of high-precision
measurement of two different elemental isotope
ratios such as 2 H / 18 O, 13 C / 18 O and 13 C / 15 N, from
the same compound source in one analytical run.
By placing a PORAPLOT Q capillary column
between the combustion reactor and the IRMS
(which enabled us to separate N 2 from CO 2 by 100 s
15
14
13
12
baseline to baseline), N / N and C / C isotope
ratio from alanine, leucine and phenylalanine could
be measured in one single analysis [153]. The
excellent separation of N 2 from CO 2 provided ample
time to switch IRMS ion source parameters from N 2 to CO 2 -mode (Fig. 6).
In 1994, using a GC-based IRMS system, Brand et
al. showed that CSIA of 18 O / 16 O ratios was possible
by converting oxygen-containing organic compounds
on-line to CO by means of a pyrolytic reaction [154].
The on-line coupling of GC and IRMS via a
pyrolysis interface (GC–Py–IRMS) was used for the
simultaneous determination of d 13 C and d 18 O values
for vanilla from different origins [155]. Farquhar et
367
al. converted bulk plant matter into N 2 and CO by an
automated on-line pyrolysis-based reaction using
nickelized carbon at about 11008C and separating N 2
˚
from CO post-pyrolysis in a GC fitted with a 5-A
molecular sieve PLOT column [156].
Independently, Begley and Scrimgeour reported
on high-precision d 2 H and d 18 O measurement for
water and VOCs by using 20% nickelized carbon to
generate both H 2 and CO at temperatures of between
1050 and 11008C [157]. Precisions were S.D.
(d 2 H)52‰ and S.D. (d 18 O)50.3‰ for samples
ranging from urine, water and VOCs. Their pyrolysis
system was based on earlier work that was aimed at
simultaneous d 2 H and d 18 O determination from
small water and urine samples (0.5 ml) [158].
Common to both studies was the use of a novel
IRMS with the high dispersion necessary for separation of the 2 H 1 H 1 and 4 He 1 ion beams. This novel
high mass dispersion IRMS has been described in
detail by Prosser and Scrimgeour [159]. Very recently, this high mass dispersion IRMS was coupled to a
Fig. 6. Dual isotope measurement of d 15 N and d 13 C from alanine (as N-acetyl, O-propyl derivative) during the same analysis. The arrow
indicates when the ion source parameters were switched from nitrogen mode (m /z 28) to carbon dioxide mode (m /z 44). A 100-s baseline to
baseline separation of N 2 from CO 2 was achieved by passing the combustion products past-reduction through a PORAPLOT Q column, held
at 358C. The results obtained from dual-isotope analyses (d 15 N, 27.7860.10‰ vs air; d 13 C, 240.2260.14‰ vs PDB) were in good
agreement with those obtained from separate analyses (d 15 N, 27.8660.38‰ vs air; d 13 C, 240.2060.21‰ vs PDB).
368
W. Meier-Augenstein / J. Chromatogr. A 842 (1999) 351 – 371
˚ molecuGC via a pyrolysis interface including a 5-A
lar sieve PLOT column to achieve CSIA for 2 H of
fatty acids. Preliminary d 2 H values for 16:0 and 18:1
fatty acids (as methyl esters) from tuna oil given in a
Technical Brochure were 2148.564.1 and
2155.361.0‰ (vs. VSMOV), respectively [160].
A different approach to CSIA for H of organic
compounds such as ethyl benzene and cyclohexanone was published by Tobias and Brenna. Initially
using a two-stage reactor interface (CuO at 8508C
followed by Ni held at 9508C) [161] they found that
better precision for d 2 H was achieved by employing
an empty alumina tube held at about 11508C [162].
Because their IRMS was not capable of fully resolving analyte 2 H 1 H from excess 4 He carrier gas, they
used a heated Pd filter in conjunction with a make-up
pressure unit to prevent He from entering the IRMS
while selectively admitting only hydrogen through
the Pd foil membrane into the ion source [163].
The measurement of intramolecular variations in
isotopic abundance due to kinetic isotope effects
during biosynthesis is another recent development to
extend the scope of GC–IRMS. The group around
Schmidt employed 13 C isotope pattern analysis for
distinction of natural compounds from corresponding
synthetic products [53,164]. In 1997, an on-line
pyrolysis system for position-specific isotope analysis (PSIA) of selected compounds from a complex
mixture was described in detail by Corso and Brenna
[165]. They coupled a GC (GC-1) for sample
separation prior to pyrolysis to the GC (GC-2)
separating pyrolytic products of the selected sample
compound. Furthermore, they installed a valve into
GC-2 to permit separated pyrolysis fragments to be
admitted to an organic MS for structure analysis of
these fragments.
6. Conclusions
Despite recent advances, many fundamental challenges for improved instrumentation still remain,
most notably developments leading to (a) quantitative sample conversion to achieve high-precision
CSIA for nitrogen; (b) routine CSIA of hydrogen
isotopes after gas chromatographic separation; and
(c) the routine application of PSIA to detect intramolecular isotope patterns. Continuing improve-
ments in accuracy, precision and abundance sensitivity of GC–IRMS, accompanied by increased
user-friendliness, will ensure that this technique will
cement its role as an important and unique tool of
analytical mass spectrometry.
The sheer number of applications, as well as their
wide spectrum clearly demonstrates that state-of-theart GC–C–IRMS instruments are already powerful
tools providing quantitative and qualitative information that cannot be obtained by other means.
Acknowledgements
The author gratefully acknowledges financial support through an industrial grant awarded by Europa
Scientific Ltd (Crewe, UK). Many thanks are due to
Professor Michael J. Rennie for advise and helpful
discussions. The author is indebted to Dr Helen F
Kemp for critical reading of the manuscript.
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