FFJ 2008 23 450-459 Quantificação e RRF

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FLAVOUR AND FRAGRANCE JOURNAL
Flavour Fragr. J. 2008; 23: 450–459
Published online 20 October 2008 in Wiley InterScience
(www.interscience.wiley.com) DOI: 10.1002/ffj.1906
Copyright © 2008 John Wiley & Sons, Ltd.
John Wiley & Sons, Ltd.Quantitation in gas chromatography: usual practices 
and performances of a response factor database
Esmeralda Cicchetti, Philippe Merle and Alain Chaintreau*
Firmenich SA, Corporate R&D Division, Route des Jeunes 1, CH-1211 Geneva 8, Switzerland
Received 7 May 2008; Revised 20 August 2008; Accepted 22 August 2008
ABSTRACT: In this study, usual methods commonly applied to report the quantitative composition of flavours,
fragrances and essential oils were compared. The MS determinations without response factors exhibited a lack of accuracy
and reproducibility. FID provided better results, but the determinations were still unsatisfactory. The best results were
obtained by true internal standardization in MS and FID, which is time-consuming for mixtures composed of many constituents.
The use of a response factor database was thus proposed and validated by testing the response dependence on GC parameters,
using an experimental design. Very low time variations were observed. Under standard GC conditions, the determination of
a model mixture with four different instruments exhibited a mean bias of < 3.4%. Copyright © 2008 John Wiley & Sons, Ltd.
KEY WORDS: GC; MS; FID; response factors; quanti¼cation; database; internal standardization
Introduction
In the scientific literature, raw area percentages resulting
from GC–FID or GC–MS analysis (FID% or MS%) are
often used to report the composition of essential oils,
flavours and fragrances (e.g. FID;1 MS2). So-called
‘semi-quantitation’ is also a frequent practice (e.g. FID;3
MS4): it consists of the normalization of peak areas to
those of an internal standard, assuming that all relative
response factors (RRFs) are equal to unity. Using an FID
is often considered as an acceptable approximation,5 but
to our knowledge the validity of MS% has never been
tested.
The quantitative composition of aromas and scents can
be used for various objectives: quality control of a pro-
duction batch, chemical taxonomy of plants, notifications
of plant extracts to the authorities, etc. For all these
applications, the published quantitative data must be
reliable and reproducible over time, with the lowest
possible variation from one instrument to another and
from one laboratory to another. A recent paper by L.
Mondello’s group, reporting the essential oil composition
of Tarchonanthus camphoratus L., has shown that the
balance between terpenes and oxygenated compounds is
significantly biased if only the raw FID% data are used
(45% and 38%, respectively) vs. the RRF-corrected
proportions (39% and 43%, respectively).6 As a more
critical example, the composition of an Açaï extract
resulting from raw GC% shows that major differences
are observed, depending on the means of detection
(Figure 1).
In a first step, the present study aims to clarify which
of these quantitative practices are valid, i.e. what is the
bias inherent in the different quantitation approaches,
using a model mixture. However, the determination of
response factors for all constituents of a mixture can
become a tedious task, depending on the complexity of
the mixture, and the availability of reference compounds.
To save time in the context of routine analyses, the use of
a database of response factors can be considered. Accord-
ing to our bibliographic survey, such an approach has
not been reported in the literature, whereas published
data suggest that it would be feasible. Some compounds
used by Dietz and co-workers exhibit RFs with a mean
relative standard deviation (RSD) of <6%, whereas
these studies were made over a period of 23 years.7–10
With the aim of building such a database, the present
work will investigate the variability of response factors
as a function of various parameters, and define the
experimental field that would validate its use in the
context of routine flavour and fragrance analyses,
within a given laboratory as well as between several
laboratories.
This paper focuses on FID because of its wide popularity
in the flavour and fragrance literature. The quantitative
performances of conductivity detectors would presumably
lead to similar results, but the construction of a dedicated
RRF database and its validation would have required
the same effort as the present work and so it was not
considered.
* Correspondence to: A. Chaintreau, Firmenich SA, Corporate R&D
Division, Route des Jeunes 1, CH-1211 Geneva 8, Switzerland. 
E-mail: alain.chaintreau@firmenich.com
Copyright © 2008 John Wiley & Sons, Ltd. Flavour Fragr. J. 2008; 23: 450–459
DOI: 10.1002/ffj
QUANTITATION IN GC: RESPONSE FACTOR DATABASE PERFORMANCE 451
Materials and Methods
Natural Extract
An alcoholic extract of Euterpe precatoria Mart., a
member of the family Arecaceae commonly called.
Açaï, was purchased from the RECA cooperative
(Rondônia State, Brazil).
Chemicals and Solvents
Methyl octanoate, benzyl benzoate, naphthalene and heptane
(purity >99%) were purchased from Acros Organic (Geel,
Belgium). Other chemicals came from the Firmenich
collection. The standard solutions were prepared in ethanol,
dichloromethane or acetonitrile (analytical grade) pur-
chased from Carlo Erba (Val de Reuil, France). Methyl
octanoate was chosen as internal standard (ISTD)
because it elutes on both DB-1MS and DB-Wax columns
at a retention time that differs from most of the off-
flavour ingredients. In the model mixtures, the analyte
concentrations (around 5% w/w) were close to those of
the ISTD.
GC–FID
Four different instruments were used, two Model 6890
(Agilent, Wilmington, DE, USA), one HRGC 5300
Megaseries (Carlo Erba, Italy) and one CP3800
Chrompack (Varian, Walnut Creek, USA), equipped with
either a DB-1 or a DB-1MS column (30 m × 0.25 mm
i.d., 0.25 μm film thickness) from J&W Scientific
(Agilent); injector and FID temperatures, 250°C; test
solutions (1 μl) were injected with a split ratio of 1:100;
carrier gas, helium; oven programme, 50°C for 5 min, then
increased at 5°C/min to 250°C and maintained for 45 min;
hydrogen:air ratio, 10%; air flow, set at 450 ml/min.
The database was built using the first Agilent 6890
model. The polar RRF database was made using a DB-
Wax column (Agilent) of dimensions 30 m × 0.25 mm
i.d., 0.25 μm film thickness. For the experimental design,
a DB-17 column (30 m × 0.25 mm i.d., 0.25 μm film
Figure 1. Relative peak areas of an Açaï extract and relative area differences using FID and MS
Copyright © 2008 John Wiley & Sons, Ltd. Flavour Fragr. J. 2008; 23: 450–459
DOI: 10.1002/ffj
452 E. CICCHETTI ET AL.
thickness) was also used. Both columns were used with the
same standard conditions as used for the non-polar column.
GC–MS
A HP 6890 gas chromatograph connected to a HP 5973
mass spectrometer, both from Agilent, was used, with the
GC column outlet directly coupled to the EI source;
injector and MS source temperatures, 250°C and 230°C,
respectively. The GC was equipped with a Combipal
autosampler (CTC Analytics, Swingen, Switzerland) and
a programmable thermal vapourizer (PTV). The acquisi-
tions were run in scan mode, under an ionization energy
of 70 eV. The column (DB1-MS) and the chromatographic
conditions were the same as for the GC–FID analyses.
Computation
The RRFs were calculated as:
 (1)
where mcompound and areacompound are the mass and corre-
sponding GC peak area of the analyte, mISTD and areaISTD
are the mass and corresponding GC peak area of the
internal standard. This RRF is the inverse of Dietz’
response factor (RFD)
7
 and is correlated to the inverse
of the effective carbon number (ECN) introduced by
Sternberg in 196211 (equation 2):
(2)
The Euclidian distance D between two sets of parameters, xi
and yi, was computed as:
(3)
Experimental Design
The screening plan was generated using NemrodW software,
version 2000-D (LPRAI,Marseille, France). Its results were
analysed using Statistica software (Statsoft, Tulsa, USA).
Results and Discussion
Biases with Usual Quantification Practices
To test the performances of the usual quantitation methods,
a model mixture was prepared that included various
chemicals commonly used in the flavour and fragrance
industry, i.e. alcohols, phenols, aldehydes, ketones and
esters. This mixture was analysed according to four dif-
ferent quantification methods:
1. FID ‘Semi-quantification’ (normalization assuming
that all factors are equal to 1) (FID–SQ).
2. MS ‘Semi-quantification’ (MS–SQ).
3. FID internal standardization (FID–ISTD).
4. MS–ISTD.
The raw area percentages were not tested, as they
would give the same results as the semi-quantification
because the model mixture did not contain any non-
quantified ingredient (e.g. non-volatile compounds). If that
had been the case, the absence of internal standard would
have given rise to an over-estimation of volatile analytes
compared to the semi-quantification and the real mixture
composition.
The determinations resulting from the four methods are
reported in Table 1. The mean biases were calculated as
the Euclidian distance between the percentages obtained
from a given technique and those of the real composition
(w/w). The MS semi-quantification clearly gives the
worst results. Although many papers report the composition
of essential oils using MS-SQ, it is not based on any
published justification. The MS areas account for the sum
of ions resulting from the ionization and fragmentation of
a given compound, and there is no reason to support the
notion that this process would be comparable from one
compound to another. Indeed, the ionization potential
varies from one compound to another, and intensities
depend on many parameters, such as the cleanliness and
temperature of the source, the filament conditions, the
repeller voltage, etc.12 These limitations mean that a
MS semi-quantification is unlikely to be reproducible,
and it is then inappropriate to report the composition of a
mixture.
The ionization mechanism of a FID is reported to
mainly give rise to the formation of HCO+ species,13,14
hence the response of this detector is roughly a function
of the number of carbons in a compound. Many RRFs are
close to unity,7–10 which has led many authors to justify
the use of FID semi-quantification to report the composition
of essential oils, flavours and fragrances. In the present
study, the mean bias of FID–SQ was better than that of
MS–SQ, but some compounds were significantly mis-
evaluated (Table 1; bias > 20%). These deviations could
have even been greater, as RRFs may differ by a factor of
up to six, according to Dietz’ compilation.7 This suggests
that FID–SQ is unsatisfactory for reporting the composition
of a mixture, particularly when these relative amounts are
intended to be used as a reference, e.g. for a taxonomic
purpose.
The internal standardization gave the best results using
both detection methods, with mean biases of 1% or less.
Whatever the detector response, the peak areas were
RRF
m area
m area
compound ISTD
ISTD compound
=
RRF
M ECN
M ECN
compound ISTD
ISTD compound
=
D x yi i
i
n
= −
=
∑( )2
1
Copyright © 2008 John Wiley & Sons, Ltd. Flavour Fragr. J. 2008; 23: 450–459
DOI: 10.1002/ffj
QUANTITATION IN GC: RESPONSE FACTOR DATABASE PERFORMANCE 453
corrected with the RRFs. Nevertheless, the composition
of a flavour, fragrance or essential oil may include tens or
even hundreds of constituents and the RRF determination
for all of them can be very tedious. If the RRFs of the
most common compounds could be stored in a database,
and remain valid for later analyses, this would save a
huge amount of time. This validity is checked hereafter
by investigating the RRF variation as a function of the
different experimental parameters.
Variability of Response Factors
The influence of experimental conditions on the FID
response has already been mentioned by some authors.
The analyte concentration, the ISTD choice, may play a
role,8,11,15–17 as well as GC conditions, such as the carrier
gas, the injector and column temperature, the injection
mode17,18 and the FID conditions (hydrogen:air ratio,
flow rate, detector temperature, instrument sup-
plier)8,11,16,17,19,20. Nevertheless, there is no comprehensive
investigation reporting the influence of all these parameters,
and indicating which of them play a significant role on
RRF values. Therefore, a screening plan based on 10
parameters was set up to simultaneously investigate their
role (detector and injector temperatures, initial oven tem-
perature, temperature programme, air flow, split ratio,
solvent dilution, sample and internal standard concentra-
tions in the solvent, hydrogen:air ratio in the flame). For
this test, limonene and benzyl benzoate were used because
they are common flavour and fragrance ingredients and
significantly differ from each other in terms of volatility
and polarity. Under our standard conditions, the repeata-
bility of their RRFs was excellent (Table 2).
The 25 experiments proposed by the NemrodW software
are reported in Table 3. The overall standard deviations
calculated from these different experimental conditions
were almost 30 times that of the standard conditions
(Table 2), which indicates a RRF dependence on some of
these parameters.
From Pareto’s charts (not shown), six parameters
appeared to significantly influence the RRFs (p < 5%):
(a) the column phase (for both compounds); (b) the detector
temperature (for both compounds); (c) the injector
temperature (for benzyl benzoate); (d) the split ratio (for
benzyl benzoate); (e) the hydrogen:air ratio (for limonene);
and (6) the air flow rate (for limonene) (Figure 2).
Column phase
As the phase influence shown in Figure 2 resulted from a
simultaneous change of all parameters, this factor was
investigated alone, using three different polarities while
all other parameters were kept under the standard condi-
tions. The RRF of both compounds were little affected as
a function of the phase polarity (Figure 3). However, this
observation was made under the standard conditions
and could again result from the combination of several
parameters. During the oven temperature programme,
Table 1. Individual deviations and mean biases of the model mixture composition resulting from a semi-quantification
and a full internal standardization in MS (MS–SQ and MS–ISTD) and FID (FID–SQ and FID–ISTD)
Compound MS–SQ (%) FID–SQ (%) MS–ISTD (%) FID–ISTD (%)
Isoamyl acetate 41.85 19.74 4.02 0.44
Eucalyptol 0.30 8.73 4.78 1.55
Linalool 9.75 12.36 3.06 0.50
4-Methylacetophenone 1.48 11.15 4.14 5.49
Anisaldehyde 8.08 14.60 1.01 0.90
Citronellol 15.03 7.51 3.16 0.91
Eugenol 17.20 3.90 3.72 1.88
Coumarin 25.52 2.79 4.38 7.34
Ethyldecanoate 14.09 0.28 0.51 0.21
β-Caryophyllene 41.66 41.79 7.35 4.93
Methylisoeugenol 23.56 7.48 3.90 1.92
Pentadecane 18.66 29.07 0.22 1.20
Hedione® 23.01 12.43 3.30 2.33
Mean bias 6.98 4.88 1.03 0.71
Table 2. Mean RRF values and relative standard devi-
ations of limonene and benzyl benzoate under stan-
dard and screening plan conditions (ISTD = methyl
octanoate)
Compound Standard conditionsa Screening planb
Mean RRF RSD (%) Mean RRF RSD (%)
Limonene 0.71 0.25 0.76 6
Benzyl benzoate 0.88 0.43 0.76 12
a Three replicates.
b 25 replicates.
Copyright © 2008 John Wiley & Sons, Ltd. Flavour Fragr. J. 2008; 23: 450–459
DOI: 10.1002/ffj
454 E. CICCHETTI ET AL.
both analytes elute at different retention times, i.e. at
different elution temperatures, which could interfere with
the phase polarity effect.
A series of isothermal elutions of both compounds on
the DB1 and DB-WAX phases confirmed that both
parameters, the phase polarity and the elution temperature,
played a role on the final RRF (Figure 4). A temperature
dependence was already noticed by Kállai when deter-
mining ECNs, but no interpretation was proposed.17 As a
consequence of building up the RRF database, the thermal
conditionsshould be standardized to allow reproducible
determinations with a given phase.
Injector temperature
Surprisingly, the injector temperature influenced the RRF
(Figure 2), whereas Kállai observed constant values in
the split mode.17 When only the injector temperature was
increased while all other parameters were kept constant,
the RRF of limonene remained unchanged and that of
benzyl benzoate increased again (Figure 5). The reverse
situation was observed in splitless mode, i.e. a RRF loss
of 0.18 from 250°C to 350°C (data not shown), which
suggests that the amount of benzylbenzoate entering the
column was lower than expected. This would be consistent
with Grob’s observations reporting the occurrence of a
discrimination in the injection syringe needle against
high-boiling compounds. Increasing the injector temperature
minimizes this discrimination without fully eliminating
it.21 In the split mode, the RRF increase could result from
the combined effects of the injector temperature and the
split ratio on low-volatile compounds. This will be further
discussed below.
Detector temperature
According to the experimental design, changing the
detector temperature led to a significant RRF variation
for both compounds (Figure 2). Varying only this parameter
under standard conditions did not confirm an influence
on limonene’s RRF, whereas benzyl benzoate’s RRF was
lowered by a temperature increase (Figure 6). To justify a
similar observation, Kállai suggested a possible partial
condensation in the detector when its temperature was
lower than the analyte boiling point.17 As it was the case
for benzyl benzoate (b.p. = 320°C) and not for the internal
standard (b.p. = 194–195°C), too low a temperature
tends to lower the area of the former for a same injected
amount, and then the resulting RRF is enhanced.
Split ratio
Only the RRF of benzyl benzoate varied with the split
ratio in the experimental design (Figure 2). Keeping all
other parameters constant (standard conditions) confirmed
this observation (Figure 7). This seems to be in agreement
with the possible role of the analyte boiling point when it
is higher than the injector temperature. Limonene, as a
Table 3. Ten-factor experimental design, generated by NemrodW software
Solvent Oven 
temp. (°C)
Injector 
temp. (°C)
Split Detector 
temp. (°C)
H2 (%) Air flow 
(ml/min)
Oven rate 
(°C/min)
ISTD:compound 
concentration ratio
Column
Ethanol 50 200 10 270 8 300 2 1 DB1
Dichloromethane 50 200 10 250 8 500 5 0.5 DB17MS
Acetonitrile 50 200 10 270 12 500 10 2 DB1
Dichloromethane 50 200 10 250 12 300 5 0.5 DB17MS
Ethanol 50 200 10 270 8 300 2 1 DB1
Acetonitrile 100 200 100 270 8 300 2 0.5 DB17MS
Dichloromethane 100 200 100 270 8 500 2 1 DB1
Ethanol 100 200 100 270 12 500 5 1 DB17MS
Ethanol 100 200 100 250 12 300 10 0.5 DB1
Dichloromethane 100 200 100 270 8 300 5 2 DB1
Ethanol 50 250 100 270 8 300 5 0.5 DB1
Ethanol 50 250 100 250 8 300 10 2 DB17MS
Dichloromethane 50 250 100 270 12 500 2 0.5 DB1
Acetonitrile 50 250 100 250 12 300 5 1 DB1
Dichloromethane 50 250 100 270 8 300 10 1 DB17MS
Dichloromethane 100 250 10 270 8 300 10 1 DB17MS
Acetonitrile 100 250 10 250 8 500 5 1 DB1
Dichloromethane 100 250 10 270 12 500 2 0.5 DB1
Ethanol 100 250 10 250 12 300 2 2 DB17MS
Ethanol 100 250 10 270 8 300 5 0.5 DB1
Dichloromethane 50 200 10 270 8 300 5 2 DB1
Ethanol 50 200 10 250 8 500 10 0.5 DB1
Ethanol 50 200 10 270 12 500 5 1 DB17MS
Dichloromethane 50 200 10 250 12 300 2 1 DB1
Acetonitrile 50 200 10 270 8 300 2 0.5 DB17MS
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DOI: 10.1002/ffj
QUANTITATION IN GC: RESPONSE FACTOR DATABASE PERFORMANCE 455
low-boiling compound that itself refers to a low-boiling
ISTD, had a constant RRF. In the benzyl benzoate case, a
greater RRF implies that a depletion of its area occurred,
and suggests that a long vapourization time combined
with a high split ratio favours the rejection of the analyte
in the split vent.
Hydrogen and air flow rate
The absolute detector response is known to exhibit a
maximum as a function of hydrogen and air flows.16
However, the optimal hydrogen:air ratio slightly varies
from one compound to another, and the response ratio
Figure 2. Most significant RRF changes (p > 0.05) observed from the screening plan
Copyright © 2008 John Wiley & Sons, Ltd. Flavour Fragr. J. 2008; 23: 450–459
DOI: 10.1002/ffj
456 E. CICCHETTI ET AL.
between two compounds also varies, depending on the
hydrogen:air ratio. Our observations (Figure 2) are con-
sistent with these previous studies, and obtaining universal
hydrogen:air flow conditions does not seem feasible. The
flame conditions are normally set according to manufac-
turers’ recommendations and are rarely changed. In addition,
GC manufacturers generally propose a similar optimum
hydrogen:air ratio of ca. 1:10.
Performances of the RF Database
In 1994, Dressler underlined a dependence of both relative
and absolute responses from one FID supplier to another
and even between FIDs from the same supplier.19 This
variation was not stable in time and he suggested a
dependence on the FID’s ‘life’. No hypothesis was for-
mulated to explain these observations.
From the previous experimental design, the RRFs
should be stable if standard conditions are used, in particular
concerning the phase polarity, the injector and detector
temperatures. Two databases were then built, using the
RRF values obtained from 400 common flavour and fra-
grance compounds, using a DB-1-MS and a DB-Wax
column and FID detection. The injector temperature of
250°C was maintained as a compromise, because a higher
Figure 3. RRF dependence on the phase polarity
(standard conditions)
Figure 4. RRFs of limonene and benzyl benzoate
under isothermal conditions and two different phase
polarities (ISTD = methyl octanoate)
Figure 5. RRFs of limonene and benzyl benzoate as a
function of the injector temperature (DB 17 MS column
under standard conditions; ISTD = methyl octanoate)
Figure 6. RRFs of limonene and benzyl benzoate as a
function of the detector temperature (DB 17 MS
column under standard conditions; ISTD = methyl
octanoate)
Copyright © 2008 John Wiley & Sons, Ltd. Flavour Fragr. J. 2008; 23: 450–459
DOI: 10.1002/ffj
QUANTITATION IN GC: RESPONSE FACTOR DATABASE PERFORMANCE 457
temperature may also undergo thermal isomerizations of
sensitive analytes.
Comparison with literature data and 
time-dependence of RRFs
The comparison between previously published RRFs
mentioned in the introduction suggests that a relationship
should also occur between these values and our RRFs.
After having corrected Dietz, Jorgensen and Kállai’s
RRFs7,8,22,23 using the relative response factor of their
internal standard in our database, their values appear to
be highly correlated with ours (r2 = 0.969, 0.953 and
0.807, respectively), with slopes close to unity (Figure 8).
This suggests that RRFs would be stable over time and
reproducible from one instrument to another. This will be
further investigated below.
In contrast, Musumara and Katritzky’s9,10 values were
poorly correlated with ours, and exhibited slopes far from
unity. However, these authors used an injection temperature
50°C lower than our standard conditions and, as shown
in this study, RRFs are dependent on this parameter.
Phase dependence of the database
In contrast to the above results on the phase influence,
90% of RRFs were similar when using either a DB-1 or a
DB-Wax column (difference <5%) and only 3.5% of the
400 database compounds exhibited a difference of >10%.
These latter differences could not be clearly correlated to
the compound boiling point (taken as the apolar retention
index on a DB-1 column) or to their polarity (taken as the
difference of the polar retention index on DB-Wax minus
that of the non-polar index on DB-1).
Interlaboratory use of the same RRF database
To extend the possible use of a RRF compilation outside
the laboratory it was created implies investigating
whether an instrument dependence exists.A test mixture
was analysed, first with the instrument used to determine
the RRFs of the database, then with a second instrument
of the same model from the same supplier and with two
instruments from other GC suppliers. The intra-model
variability of RRFs was low (mean RSD = 4.8%), whereas
the global variability (mean RSD = 5.7%) remained satis-
factory whatever the instrument (Table 4). The model
mixture was quantified using the four instruments and the
RRFs determined using the first one (‘Agilent-1’). The
Euclidian distances between the resulting amounts and
the real mixture composition were 0.83%, 2.55%, 3.07%
and 3.41%, using Agilent-1 and -2, Varian and Carlo
Erba instruments, respectively. These biases are higher
than those of a true internal standardization but better
than a semi-quantification.
At first glance, it might look surprising that RRFs are
similar from one instrument to another, as detector
geometries and signal amplifiers presumably differ from
one supplier to another. However, as soon as the signal
linearity is obtained, and because RRFs represent a signal
ratio, their values only depend on the chemical reaction
occurring in the flame.
The finding that none of the instruments tested in this
work played a significant role on the RRF determination
strongly suggests that a database of RRFs is a valid quan-
titation tool for mixtures containing a large number of
constituents, if standard conditions are applied. However,
the number of instruments tested was not exhaustive and
the use of another GC model would require the same test
Figure 7. RRFs of limonene and benzyl benzoate as a
function of the split ratio (DB 17 MS column under
standard conditions; ISTD = methyl octanoate)
Figure 8. Comparison of literature RRFs with those of
the present database (using PDMS phases)
Copyright © 2008 John Wiley & Sons, Ltd. Flavour Fragr. J. 2008; 23: 450–459
DOI: 10.1002/ffj
458 E. CICCHETTI ET AL.
to be performed, to check whether the RRFs remain close
to the present ones.
Time-stability of RRFs
The similarity of RRFs with older values from the litera-
ture (see previous section) also shows that such a database
could be used over time, so long as the experimental
conditions remain similar. This RRF stability was tested
in the present study within a time interval of a few
months (Table 5). The long-term repeatability of MS was
very poor, which makes its unreliability worse than
observed in Table 1. As a consequence, compiling RRFs
from a MS is useless. In contrast, the RRF repeatability
using a FID was excellent over a period of 6 months,
therefore this long-term stability validates the interest of
a database.
Estimation of missing RRFs
As a database is never large enough to provide the ana-
lyst with all possible RRFs corresponding to the diversity of
compounds found in the flavours and fragrances, alternatives
should be found for these missing values. There are two
possible options. The first is based on mathematical modelling,
e.g. from other physical or structural properties. This is
investigated in a forthcoming paper [de Saint-Laumer JY,
Cicchetti E, Chaintreau A. Quantitation in gas chroma-
tography: prediction of flame ionization detector response
factors from molecular structures (in preparation)]. The
second option was recently used by Mondello’s group6
and consists of the use of the RRF of a similar compound.
Justifications of this approach already exist in the litera-
ture and will not be discussed here. As an example taken
from our database, average RRFs of monoterpenes (12
compounds) and monoterpene alcohols (6 compounds)
were 0.71 (RSD = 3.6%) and 0.82 (RSD = 1.5%),
respectively.
Conclusion
The present results indicate that the biases of GC quanti-
tative results decrease in the following order: MS–SQ,
FID–SQ, FID–database, MS–ISTD, FID–ISTD. If the
use of RRFs from a previously built database is a little
less accurate than a true ISTD, it allows a very satisfactory
analysis of complex mixtures when the RRF determination
of all their constituents would be too time-consuming.
Due to the dependence of RRFs on various GC parameters,
the use of the database implies that identical instrumental
settings are applied.
Acknowledgements—We gratefully acknowledge our colleagues who
provided help and suggestions or loaned their equipment: J. Murat, L.
Baroux, J. Egger, A. Trachsel, E. Giraud, J.-P. Larcinese and Dr O. Hae-
fliger, as well as Dr R. Snowden for a critical review of this manuscript.
Table 4. Comparison of RRFs obtained with four different instruments (DB-1 MS, standard conditions)
Compound Agilent-1 Agilent-2 Carlo Erba Varian RSD (%)
2-Methylpyrazine 1.21 1.08 1.17 1.15 5
Furfuryl alcohol 1.34 1.35 1.35 1.34 0
Heptanal 1.01 1.13 1 0.98 7
Limonene 0.71 0.69 0.72 0.74 3
p-Cresol 0.85 0.88 0.96 0.77 9
Dimethylsulphide 1.57 1.61 1.6 1.5 3
4-Methylacetophenone 0.77 0.84 0.8 0.76 5
α-Terpineol 0.86 0.87 0.71 0.79 9
Hydroxycitronellal 1.01 1.16 1.08 0.99 7
Anisyl formate 1.32 1.32 1.27 1.14 7
Diphenylmethane 0.67 0.69 0.62 0.61 6
β-Caryophyllene 0.69 0.69 0.7 0.68 1
Pentadecane 0.71 0.71 0.74 0.65 5
γ-Undecalactone 0.93 0.93 1.01 0.87 6
Table 5. RRF deviations over the time using MS and
FID, DB-1 MS, standard conditions
Compound MS deviation 
(2 months) (%)
FID deviation 
(Agilent-1, 6 
months) (%)
2-Methylpyrazine 1.97 0.16
Furfuryl alcohol 8.90 –4.22
Heptanal –30.77 0.11
Limonene 1.02 –1.00
p-Cresol –0.60 –1.26
Dimethylsulphide –5.65 0.64
4-Methylacetophenone –5.64 –6.72
α-Terpineol –4.15 –0.76
Hydroxycitronellal –38.89 –3.59
Anisyl formate –18.59 4.38
Diphenylmethane –21.62 –1.84
Caryophyllene –15.85 –5.90
Pentadecane –17.47 –0.42
γ-Undecalactone –47.68 0.54
Copyright © 2008 John Wiley & Sons, Ltd. Flavour Fragr. J. 2008; 23: 450–459
DOI: 10.1002/ffj
QUANTITATION IN GC: RESPONSE FACTOR DATABASE PERFORMANCE 459
References
1. Kurose K, Okamura D, Yatagai M. Flavour Fragr. J. 2007; 22:
10–20.
2. Garcia Vallejo MC, Moujir L, Burillo J et al. Flavour Fragr. J.
2006; 21: 277–281.
3. Benkaci-Ali F, Baaliouamer A, Meklati BY et al. Flavour Fragr.
J. 2007; 22: 148–153.
4. Diaz-Maroto MC, Castillo N, Castro-Vasquez L et al. Flavour
Fragr. J. 2007; 22: 114–118.
5. J. Agr. Food Chem.; Scope, Policy, and Instructions for Authors,
2008.
6. Costa R, d’Acampora Zellner B, Crupi ML et al. Flavour Fragr.
J. 2008; 23: 40–48.
7. Dietz WA. J. Gas Chromatogr. 1967; 5: 68–71.
8. Jorgensen AD. Anal. Chem. 1990; 62: 683–689.
9. Katritzky A, Ignatchenko ES, Barcock RA et al. Anal. Chem.
1994; 66: 1799–1807.
10. Musumarra G, Pisano D. Tetrahedr. Comput. Methodol. 1989; 2:
17–36.
11. Sternberg JC, Gallaway WS, Jones DTL. In Gas Chromatography.
Academic Press: New York, 1962; 231–267.
12. Millard BJ. Quantitative Mass Spectrometry. Gaillard: Great
Yarmouth, UK, 1978.
13. Nicholson AJC, Swingler DL. Combust. Flame 1980; 39: 43–52.
14. Gardner MP, Vinckier C, Bayes KD. Chem. Phys. Lett. 1975; 31:
318–320.
15. Blanco CG, Canga JS, Dominguez A et al. J. Chromatogr. 1992;
607: 295–302.
16. El-Naggar AY. Pet. Sci. Technol. 2006; 24: 41–50.
17. Kállai M, Máté V, Balla J. Chromatographia 2003; 57: 639–644.
18. Blades AT. J. Chromatogr. Sci. 1976; 14: 45–48.
19. Dressler M, Cigánek M. J. Chromatogr. A 1994; 679: 299–305.
20. Halász I, Schneider W. Anal. Chem. 1961; 33: 978–982.
21. Grob K. Split and Splitless Injection for Quantitative Gas Chro-
matography, 4th edn. Wiley–VCH: Weinheim, 1999.
22. Kállai M, Veres Z, Balla J. Chromatographia 2001; 24: 511–517.
23. Kállai M, Balla J. Chromatographia 2002; 56: 357–360.