jssc.200500509

J. Sep. Sci. 2006, 29, 2107 – 2125
Conor M. Delahunty
Graham Eyres
Jean-Pierre Dufour
Department of Food Science,
University of Otago, Dunedin,
New Zealand
C. M. Delahunty et al.
2107
Review
Gas chromatography-olfactometry
GC-olfactometry (GC-O) refers to the use of human assessors as a sensitive and selective detector for odour-active compounds. The aim of this technique is to determine
the odour activity of volatile compounds in a sample extract, and assign a relative
importance to each compound. Methods can be classified into three types: detection
frequency, dilution to threshold and direct intensity. Dilution to threshold methods
measure the potency of odour-active compounds by using a series of extract dilutions, whereas detection frequency and direct-intensity methods measure odouractive compound intensity, or relative importance, in a single concentrated extract.
Factors that should be considered to improve the value of GC-O analysis are the
extraction method, GC instrument conditions, including the design and operation
of the odour port, methods of recording GC-O data and controlling the potential for
human assessor bias using experimental design and a trained panel. Considerable
emphasis is placed on the requirement for multidimensional GC analysis, and on
best practice when using human assessors.
Keywords: Gas chromatography-olfactometry / Multidimensional GC-olfactometry / Methodologies / Sensory evaluation /
Received: December 19, 2005; revised: February 14, 2006; accepted: February 23, 2006
DOI 10.1002/jssc.200500509
1 Introduction
The perceived odour of any material is composed of one
or more volatile compounds that are present in concentrations above the sensitivity threshold. Study of the volatile composition of natural odours, including those of
plant and animal origin, has been ongoing since it was
first possible to do so. The development of GC has been
instrumental in advancing knowledge in this field, as it
has allowed chemists to separate, quantify and identify
the compounds which compose an odour. This knowledge is particularly important in the areas of food flavour quality and fragrance quality, as their odours
strongly influence behaviour towards them. However, in
order to understand the contribution of any volatile
compound to odour quality, it is not sufficient to just
know whether this compound is present or absent, one
must also have knowledge of how it is perceived at a
Correspondence: Dr. Conor M. Delahunty, Department of Food
Science, University of Otago, P.O. Box 56, Dunedin, New Zealand.
E-mail: [email protected].
Fax: +64-3-479-7567.
Abbreviations: AEDA, Aroma Extract Dilution Analysis; FD, flavour dilution; FID, flame ionisation detector; GC-O, GC-olfactometry; GC6GC, comprehensive 2-D gas chromatography; I, retention index; MDGC, multidimensional GC; MDSC-O, multidimensional GC-olfactometry; PCA, principal components analysis; SPME, solid-phase micro-extraction
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given concentration. Above what concentration will a
compound contribute directly to odour? As concentration of this compound continues to increase above
threshold, what will be its perceived intensity?
GC-olfactometry (GC-O) is the term used to describe techniques that use human assessors to detect and evaluate
volatile compounds eluting from a GC separation. The
human assessors take the place of a more conventional
detector, such as a flame ionisation detector (FID) or a
mass spectrometer (MS). Assessors sniff the eluate in
order to detect the presence of odour-active compounds
via a specifically designed odour port. GC-O was initially
described as a screening method to determine whether a
compound found in a sample had odour activity or not
[1]. However, applications of the technique have become
more advanced, and it is now common for investigators
to use the method to assign a relative importance to each
of the volatile compounds identified as being odour
active.
For each separated compound that emerges from the GC,
a human assessor has the potential to detect this compound (odour present or not), to measure the duration of
the odour activity (start to end), to describe the quality of
the odour perceived and to quantify the intensity of the
odour. Based upon this, various GC-O techniques have
been developed that can be classified into three types:
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detection frequency [2, 3], dilution to threshold [4, 5] and
direct intensity [6 – 9].
There have been very many applications of GC-O in
recent years. A search of Web of Science for the terms GCO’ or Gas Chromatography-Olfactometry’ or GC-Olfactometry’ from the year 2000 to 2005 revealed 290 hits. A
majority of applications reported are in food and beverage analysis, and in particular to improve understanding
of the contribution of specific volatile compounds to flavour, and to identify the cause of off-odours or taints.
There have also been numerous applications in the analysis of fragrances and essential oils. Sometimes the technique is used to understand better human sensitivity to
compounds, whereby using this technique the thresholds and psychometric functions of a relatively large
number of compounds can be practically measured [10].
,
,
,
There are a number of factors that determine the quality
of the data collected using GC-O, and also determine how
the data should be interpreted. The method that is used
to extract the volatile compounds from the sample of
interest will determine the composition of the extract,
and therefore the quality of the eluate available for perception. The set-up of the GC instrument and the separation conditions chosen for analysis will determine the
quality of chromatography, which will also influence the
response of the human detector. While these factors are
obviously important for any other GC technique, they
must also be considered in this specific context. In addition, the behaviour of the human detector is rather complex, and is too often overlooked. Investigators must
carefully consider all factors that affect the performance
or may bias the assessors used. It is important to consider
the design of the odour port, and the method of recording data. Then to reduce bias, one must carefully consider the data collection experimental design, including
order of presenting of sample extracts, and the scale
used to measure intensity. Finally, the data analysis
method and interpretation must take into consideration
the principle of the GC-O technique used.
2 Perception of odour
A complex food or natural fragrance may contain hundreds of volatile compounds. In fact, the number of compounds identified is increasing as analytical chemistry
technology improves. The human sense of smell is capable of distinguishing and recognising a wide range of
qualities attributable to volatile compounds. However,
researchers have yet to identify a good relationship
between a compound's smell and the compound's physical properties that cause that smell. Compounds that are
structurally very different from one another can smell
the same, whereas compounds almost identical in structure can have very different odour quality. For example,
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the optical isomers of carvone smell of spearmint (L-) and
caraway (D-) [11]. Of course this means that at present
there is insufficient knowledge that will enable an
instrument to be developed that will sufficiently elucidate the contribution of a volatile compound to a complex odour.
An individual volatile compound has essentially three
properties with regard to its odour potential or activity
for humans: absolute threshold, intensity as a function
of concentration and quality. The absolute threshold
refers to the minimum concentration of the compound
that can be detected, or the concentration above which it
can be perceived. The absolute thresholds of volatile compounds differ by many orders of magnitude (parts per trillion at the low extreme to odourless compounds). Therefore, in an odour composed of many different volatile
compounds, only a proportion of compounds present
will be above threshold and can therefore make a direct
contribution to the odour. Second, each compound has a
unique psychometric (concentration-response) function
(Fig. 1). In general, intensity of perception increases with
increase in the physical concentration of a compound,
and therefore the intensity of an odour-active compound
will depend on the extent to which its concentration
exceeds its threshold. Psychometric functions can best be
considered as sigmoidally shaped in a plot of log concentration against perceived intensity (Fig. 1a). There is an
initial function where response at concentration levels
below and just passing through threshold rises slowly
from baseline, an accelerating function when threshold
is passed and changes in intensity can be relatively easily
perceived, and finally a decelerating function that eventually becomes flat as human ability to perceive change
at high concentration diminishes. At this point, one
could consider the human detector to be saturated [12].
The first inflection point can be considered as an empirical definition of threshold [13]. Alternatively, when working in the dynamic range of response, i. e. between absolute threshold and diminishing ability at high concentrations, the Stevens power function [14] can be applied.
This forms a straight line in a log concentration – log
perceived intensity plot (Fig. 1b). The slope of the function (n), termed the power function exponent, indicates
nicely the relationship between concentration and perceived intensity. When one has determined the psychometric function of a compound, one can also calculate
the compound's differential threshold, which refers to
the just noticeable difference between two levels of concentration above the absolute threshold. The psychometric functions of individual compounds differ significantly, as do their differential thresholds. The lowest differential thresholds have been measured at approximately 15 – 20% change in concentration, but only under
very well controlled delivery of odour [15].
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Gas chromatography-olfactometry
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Figure 1. Theoretical dose-response curves
for two different compounds where (a) plots
log concentration vs. perceived odour intensity, and (b) plots log concentration vs. log
perceived odour intensity where n is the
slope of the line, termed the power function
exponent.
With regard to perceived quality, it is the case that at
absolute threshold, an odour sensation may not have any
discernable quality, but as concentration increases, quality becomes more defined. The odour quality of a compound may also change as concentration continues to
increase.
It should be noted that we rarely ever encounter single
odour compounds occurring naturally in everyday life.
Odours that we perceive in nature – in foods and fragrances – as unitary, are in fact complex mixtures of
many volatile compounds. In fact, the sense of smell is
limited in identifying individual odours even in the simplest of mixtures, and in deciding whether a stimulus is
a single odorant or a mixture. In addition, predicting the
outcome of mixing odours is very difficult. Most often,
odours of different quality tend to mask or suppress one
another, or may remain distinct, whereas odours of similar quality tend to blend to produce a third unitary
odour quality. In addition, the perception of the mixture
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is determined by the odour thresholds of the compounds
that are mixed and their individual psychometric functions. It is also possible that subthreshold addition or
synergy can occur between volatile compounds, so that a
number of compounds present at concentrations that
are below threshold, or possess no odour activity when
assessed individually, may in fact contribute or possess
odour activity when mixed [16].
Analysis using GC enables the resolution and identification of a majority of volatile compounds that are present
in an odour. However, because of the large variation in
thresholds and psychometric functions of odour-active
compounds, a physical GC detector response will not be
representative of odour activity. For example, the largest
FID peak is not necessarily the most important odorant.
This is demonstrated in Fig. 2, which shows both an FID
chromatogram and an aromagram generated using
CharmAnalysisTM for a hop essential oil sample. Clearly,
there are compounds with a high odour potency that
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Figure 2. Comparison of (a) the GC-O
aromagram generated using CharmAnalysisTM with (b) the FID chromatogram
for a hop essential oil sample.
show low FID response (e. g. I = 870; I = retention index),
whereas there are also compounds with a low odour
potency that show high FID response (e. g. I = 1440). In
fact, the compound with the highest FID response has
very low odour potency (I = 1480). It is also well documented that the human nose is often more sensitive to many
odour-active compounds than physical detectors [10, 17].
Therefore, to determine which compounds are odour
active in a sample requires a specific detector. The
obvious choice is to use a human assessor to sniff the
resolved compounds as they elute from a GC. In this way,
GC-O provides the selectivity (specific for odour activity)
to determine the extent that the compounds in an
extract are above absolute threshold, their intensity at a
given concentration and their odour quality at that concentration.
3 GC-O hardware
The concept of sniffing the GC column eluate probably
originated soon after the invention of GC itself in 1952
[17, 18]. However, Fuller et al. [18] published the first
paper on GC-sniffing in 1964 using expert perfumers as
assessors. GC-O is carried out on a standard GC that has
been equipped with an odour port in place of, or in addition to, the conventional detector. When an FID or MS
detector is also used to record, the eluate is most often
split in equal proportion to both the conventional detector and the odour port. The odour port is effectively a
nose-cone, to where the eluting volatiles are directed via
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a connecting transfer line. The nose-cone is typically positioned a short length (perhaps 30 – 60 cm) away from the
instrument. Because the transfer line extends from the
oven, it must be heated to ensure that late eluting compounds with high boiling points do not condense [19].
Originally, a mask was used to facilitate sniffing [20], but
modern sniff ports typically use a glass cone or a Teflon
sleeve. It has been suggested that the design of the nose
piece may influence the flow characteristics of the gas
and therefore the sensory perception, but this has not
been investigated [21].
The position of the nose-cone must be such that the assessor is in a comfortable sitting position. This is particularly important when considering that GC-sniff runs may
be longer than 30 min. To achieve this end, the transfer
line should be adjustable or flexible. In addition, to avoid
discomfort the assessor should be far enough away from
the top of the hot GC oven, where the smell of hot metal
can interfere. To achieve best comfort and performance,
it is preferable that the odour port extends from the side
of the GC rather than its top.
Early in the development of the GC-O technique, it was
recognised that the carrier gas eluting from a GC is typically hot and dry, effectively drying out the nose, causing
considerable discomfort for the assessors and as such
affecting sensitivity. This issue was addressed by using
humidified air as a make-up carrier gas to deliver the
odorants to the human assessors [22]. In modern odour
ports, volatiles are typically carried to the nose in a
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stream of heated and humidified air (50 – 75% relative
humidity (RH)).
It is now more common that GCs specifically set-up for
GC-O have 2, 3 or 4 odour ports, and often in addition to
either an FID or MS detector [23, 24]. Splitting column
flow between the olfactory port and a mass spectral
detector provides simultaneous identification of odouractive compounds [24, 25]. Most investigators choose to
split the gas flow in equal proportions to all odour ports
and detectors. Another variation is to use an in-line, nondestructive detector such as a thermal conductivity
detector (TCD) [26] or photo-ionisation detector (PID) [25].
It would appear that if a relatively wide bore column
(0.32 or 0.53 mm id) is chosen for separation, then a sufficiently concentrated extract can be analysed to obtain
good olfactometry results from 25% of the eluate.
Gas chromatography-olfactometry
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lene lined cardboard cartons using ten assessors. However, the principle and details of the method were not
discussed. van Ruth and Roozen [34] and van Ruth et al.
[35] used the same method but also did not discuss methodology or results in detail. The detection frequency
method was further developed and formalised by Pollien
et al. [3]. The authors’ objectives were to develop a method
that did not require training a panel and produced repeatable results with the minimum required number of
GC runs.
4 GC-O methodologies
As described by Pollien et al. [3], each assessor records the
duration for every odour detected together with a taperecorded description. The individual responses are combined and normalised to produce an aromagram (Fig. 3).
The peak height corresponds to the number of assessors
to detect that odour, and was described by the authors as
the nasal impact frequency (NIF). Peak areas (% frequency6duration (s)) were described as surface of NIF (SNIF).
Using peak area results in greater discrimination (resolution) between compounds than using the detection frequency alone [36]. However, this can also alter the final
results. For example, the importance of partially co-eluting peaks may be overestimated due to a broader peak
width [36]. In comparison, early eluting compounds with
narrow peaks may be underestimated. Petersen et al. [36]
reported that E-2-hexenal was ranked the seventh most
important peak in a sample even though all ten assessors
detected it. The same study using a posterior intensity
method ranked E-2-hexenal as the most important peak.
Various methods of GC-O analysis have been developed
to determine the relative importance of the odorants in a
sample extract. These methods are based upon different
principles and can be placed into one of the three categories: (i) detection frequency [2, 3], (ii) dilution to threshold (CharmAnalysisTM [17, 29] and aroma extract dilution
analysis (AEDA) [5]) and (iii) direct intensity (posterior
intensity method [6], Osme [7, 28, 30] and the finger span
method [8, 31, 32]).
Pollien et al. investigated the minimum number of assessors required by looking at the simulated variation of
detection frequency for two compounds. The results of
21 assessors were randomly ordered to simulate the
results of 200 variously sized panels. It was concluded
that 8 – 10 assessors were a good compromise between
low variation and analysis time. In addition, column eluate is typically split between an FID and two or three sniff
ports to reduce analysis time [24, 37].
Commercial sniff ports for GC-O are available from DATU
[4, 20, 27] (Geneva, NY, USA), SGE (ODO I and ODO II; SGE
International, Ringwood, Australia), Atas (Phaser; ATAS
GL International B.V., Veldhoven, The Netherlands) and
Gerstel (Olfactory Detector Port – ODP2; Gerstel, Mhlheim an der Ruhr, Germany). Examples of homemade
systems that incorporate the components detailed above
are also common [7, 19, 24, 28]. A critical comparison of
the sensitivity of these different systems would be valuable.
4.1 Detection frequency
A panel of assessors (6 – 12 participants) each carry out
GC-O on the same extract. The proportion of the panel
that is able to detect an odorant at a particular retention
time is counted [3, 23, 33]. Compounds that are detected
more frequently are concluded to have a greater relative
importance and this is assumed to be related to actual
odour intensity perceived at the concentration of compound present in the extract [32]. In addition, the duration of the odour occurrence can be measured, and this
can be used to calculate a peak area when multiplied by
the number of odour detections [3].
Linssen et al. [2] originally used detection frequency to
identify taints in mineral water packaged in polyethy-
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The main advantage of the detection frequency-based
methods is their simplicity, and as a result assessors do
not require much training. In theory, the method
accounts for the variable sensitivities of assessors, is
repeatable and the results are applicable to a population
[3, 32, 38].
A limitation in the detection frequency method relates
to the scale of measurement. It is assumed that detection
frequency is related to actual odour intensity perceived
[23]. The basis of this relationship is that individual detection thresholds in a population display a normal distribution and at a given concentration, a certain percentage of the population are able to detect the compound
[3, 32]. However, it is the case that many odour-active
volatile compounds in foods and other natural materials
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Figure 3. Comparison of results simulated for six compounds in the same extract using four different GC-O methods: (a) AEDA;
(b) CharmAnalysisTM; (c) detection frequency; and (d) direct intensity (Osme).
(start and end) and generates chromatographic peaks
(Fig. 3). The peak areas are expressed as unitless Charm’
values [17, 29]. Charm values are calculated using an
algorithm so that they are proportional to the amount of
compound in the extract and inversely proportional to
the odour detection threshold [29, 39]. The Charm calculation is given by Eq. (1)
,
(and therefore in representative extracts) are above
threshold for all of the population (except those with a
specific anosmia). Therefore, at a particular concentration and odour intensity, a compound may be perceived
by all assessors (100% above threshold). As concentration
increases, odour intensity may continue to increase;
however, detection frequency cannot increase [32].
dv ¼ Fn1 di
4.2 Dilution to threshold
These methods are used to quantify the odour potency of
a compound, based upon the ratio of its concentration to
its odour threshold in air [17]. A dilution series of an
extract is prepared (usually by a factor of 2 or 3) and each
dilution (usually between 8 and 12 in total) is assessed by
GC-O [29, 39]. Assessors record when they detect an odour
and usually record an odour description. Odour potency
is equivalent to the concept of aroma values’ [40], odour
units’ [41], odour values’ [42], flavour units’ [43] and
odour activity values’ (OAVs) [29, 44]. The most frequently reported dilution methods are CharmAnalysisTM
[17, 29, 39] and AEDA [5, 45]. It should be noted that these
methods do not measure odour intensity at any of the
concentrations evaluated.
,
,
,
,
,
AEDA measures the maximum dilution of an extract that
an odour is perceived in, and reports this as the flavour
dilution (FD) factor [5, 44, 45] (Fig. 3). In comparison,
CharmAnalysisTM records the duration of the odours
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Charm ¼
Z
dv
ð1Þ
peak
where dv is the dilution value, F is the dilution factor and
n is the number of coincident odour responses detected
at a single retention index, di. The peak area is integrated
from the duration of retention indices to yield the
Charm value [39].
AEDA only reports the maximum dilution value, which
is equivalent to the height of the Charm peak. On the
other hand, CharmAnalysisTM takes the peak width and
peak shape into account. For example, a short and broad
peak may have the same Charm value as a tall and narrow peak that is perceived at a higher dilution [39]. This
gives CharmAnalysisTM more discriminating power than
AEDA, but it also results in greater variation than AEDA
[19]. The dilution factor used in AEDA or CharmAnalysisTM will also determine the precision of the method.
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A disadvantage of dilution methods is the length of time
required to complete the analyses on each dilution for a
single extract. This also precludes the use of multiple
assessors if the study is to be practical, and therefore only
one or two assessors are usually used. However, using a
low number of assessors makes the results highly susceptible to the large variation in individual's sensitivities.
Splitting the column flow to multiple sniff ports would
obviously decrease the analysis time required [23, 24].
4.3 Direct intensity
Dilution to threshold methods have been criticised
because they do not measure the intensity of the perceived stimulus (i. e. odour intensity), and are therefore
not a psychophysical measurement [30, 33, 46]. Using
direct-intensity methods, assessors are required to use a
scale to measure the perceived intensity of the compound as it elutes.
This can be a single time-averaged measure (e. g. posterior
intensity [6]), or can be dynamic, whereby the onset, maximum intensity and decay of the eluting odour is
recorded continuously (e. g. Osme [7]). Using the dynamic
method a peak area can be calculated for each odouractive compound.
Using the posterior intensity method, assessors only rate
the maximum odour intensity once the compound has
eluted [6, 47]. Best results will be obtained if a panel of
assessors is used, and the average panel result is treated
as one signal. This follows the principles of sensory evaluation. van Ruth [37] concluded that compared to a
detection frequency method, the discrimination
between compounds was better for the posterior intensity method. However, the results from the two methods
were found to be strongly correlated [23, 33, 37]. Log-linear and logistic relationships between concentration
and posterior odour intensity have been demonstrated
[23, 48]. Odour intensity responses recorded were also
found to correlate reasonably well with odour intensities
determined using sensory evaluation [23]. Tønder et al.
[49] used another time-averaged intensity method called
GC odour profiling’ on fresh and stored orange juice,
using five assessors. Only the retention time at the start
of each odour was recorded along with an odour intensity rated on a five point scale. Results were not convincing and appeared to be limited by the intensity scale
used and inconsistencies in how assessors used the scale,
most likely due to a lack of assessor training.
,
A dynamic time-intensity GC-O method was developed by
McDaniel et al. [7, 28, 30]. The authors named the method
Osme‘, after the Greek word for smell [28]. Odour intensity was assessed by a small panel of four assessors who
continuously recorded the intensity using a horizontal
slide bar on a 16 point structured scale (15 cm) ranging
,
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from 0 (none) to 15 (extreme) [30]. A computerised signal
was recorded using a variable resistor. Verbal odour
descriptions were also recorded [7]. Each sample was
replicated four times by every assessor at a single concentration to produce an individual Osmegram‘, where
peak intensities detected in at least two replicates (out of
the 4) were averaged. This produces odour intensity
peaks analogous to an FID chromatogram plotting odour
intensity as a function of retention time or retention
index. The intensities for each peak that was detected by
at least three assessors were averaged to produce a consensus Osmegram [7, 28, 30]. The height of the peak corresponds to maximum odour intensity, the peak width
describes the stimulus duration and the integrated peak
area is a measure of the total response [30]. It could be
the case that one assessor is more sensitive to a compound than others, consistently capable of detecting and
rating an odour that other panel members do not. To
treat this assessor's response as noise would be an oversight. da Silva et al. [30] investigated the Osme response to
a range of five concentrations of six reference compounds. Significant psychometric functions (concentration-intensity and concentration-peak area) could be
modelled by the assessors. It was demonstrated that the
responses of the assessors were reliable, and that intensity and peak areas varied with the stimulus according to
psychophysical relationships [30].
,
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A variation of the Osme method was used by Delahunty
et al. [9], who recorded perceived intensity of odour elution continuously during the GC run using a 100 mm
sliding scale controlled by computer mouse, and subsequently by a pressure-resistant button [50]. Aromagrams
were produced when data were analysed by noncentred
Principal Components Analysis. tivant et al. [31, 32]
developed a method of recording time-intensity GC-O
data by using crossmodality matching with an assessors
finger span. This indirect means of scaling intensity
response is theoretically instinctive, and therefore
should result in improved use of scale. In both methods,
verbal odour descriptions were tape recorded during run
time.
One potential drawback of direct-intensity methods is
the substantial amount of training that assessors require
in order to obtain individual reproducibility and agreement with one another. However, while training is initially time-consuming, once an experienced panel is
formed, it can be used to rapidly characterise the aroma
profiles of samples and the precision of data collected
will be excellent.
5 Methods of recording data
A binary signal (0/1; presence/absence) may be recorded
using appropriate software by either pushing a button
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C. M. Delahunty et al.
on a computer keyboard, clicking the mouse or another
push-button device. An odour description may also be
recorded simultaneously by selecting from a predetermined list (e. g. CharmAnalysisTM), or by registering a verbal response using either tape recorders or voice recognition software [51]. If a descriptor list is used, it is necessary for the assessors to be familiar with the terms. It is
expected that experience and/or training should
improve this familiarity and consistency between assessors.
In either dilution to threshold methods, or detection frequency methods, measuring the duration of the odour
response creates results based upon peak areas as
opposed to just peak heights, which allows greater discrimination between peaks. However, recording the start of
an odour peak has been shown to be much more reproducible than the end [19, 24]. This is true for both within
an individual and between individuals. This could be due
to assessor physiology, adaptation or could be affected by
the sniff port design (delivery of the odorants) [24].
Because of the difficulty in determining the end of an
odour, peak areas are generally more variable than peak
heights [19, 24, 52].
Direct-intensity measurement is a more complex task
than the binary signal measurement, but also provides
more information. Direct-intensity data have been
recorded using a variable resistor [7, 28], a variable rheostat activated by finger span [8, 31, 32] or an on-screen
scale using a mouse [8]. Use of a pressure-resistant device
or button has also been used [50]. Pollien et al. [3] believed
that it would be difficult for assessors to concentrate on
detecting odours, continuously evaluating intensity and
assigning descriptions at the same time. However, a
somewhat similarly complex task is performed very
effectively in descriptive sensory analysis [53], although
the assessors have received a considerable amount of
training. If one was concerned, then descriptions of quality could easily be assigned in separate sniffing sessions
without intensity measurements. Whatever method is
used, it is important for the assessor to be able to focus
on the most important task, which is actually sniffing
the odorants. As the complexity of the recording task
increases, the risk of missing odorants due to distraction
increases.
In addition to the physical apparatus and/or software
used to collect data, consideration should also be given
to the unit of measurement or the measurement scale. In
the case of the detection frequency and dilution to
threshold methods, each assessor simply indicates presence or absence of odour. In each method, when the
duration of an odour is taken into account, this is measured as time in seconds between the start and end of the
odour signal. In data analysis, these single unit responses
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J. Sep. Sci. 2006, 29, 2107 – 2125
are added together to develop a measure of compound
intensity or potency. The detection frequency method
purports to represent a measure of intensity; however,
this is clearly not the case.
Posterior intensity and direct intensity methods do measure perceived intensity, and therefore require a measurement scale. Many different scale types have been
used, including category scales and unstructured line
scales. Category scales have ranged from simple three
point scales [48] to five point [49] and nine point scales
[23, 37]. Guichard et al. [8] compared a sliding 60 cm scale
and a finger span scale. In the finger span method, assessors estimate the maximum intensity of an eluting
odour using their own finger span (distance between
thumb and middle or index finger) as a scale. The thumb
is held stationary and the intensity signal is generated by
moving the finger along a 195 mm rheostat. Delahunty
et al. [9] used an unstructured sliding scale to record both
odour intensity and odour duration dynamically (1 datapoint per second during the GC run). In addition, a
spring-loaded button (i. e. incorporating a renewable
resistance to pressure) has been used [50]. This device
allowed each assessor to relate perceived odour intensity
to the physical stimulus of hand pressure, thereby
improving the reliability of recorded odour intensity
data. The pressure applied could be visualised by the
assessor on a 100-mm unstructured sliding scale. The finger span and pressure-resistant device allowed a more
instinctive, and less distracting, measure of intensity by
using another sensory modality to effectively match perceived odour intensity [32, 50].
The type of scale used can influence the assessor's ability
to differentiate between differing intensity levels adequately and can therefore determine the ability to construct accurate psychometric functions for compounds
[12]. For example, a three point scale is likely to suffer in
this way as one should expect that intensity will vary
over a relatively wide range of concentration when
different samples are compared. In addition, the number
of points of difference a scale provides will determine
the subsequent ability to statistically analyse data. Parametric statistics (e. g. t-test, ANOVA and Pearson's correlation coefficient) can only be used when it is assumed that
data are normally distributed. If there is only a small
spread of scores possible, then there will not be enough
of them to be normally distributed. Values measured on
a three point scale are hardly normally distributed as
there will be only three points of difference. In theory,
one needs approximately ten points of difference to
achieve a normal distribution. The level of training an
assessor has received is also important. Untrained assessors may not be capable of using a scale with many points
of difference; however, a highly trained assessor should
be capable of precise discrimination. It can be the case
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6 Comparison of different GC-O methods
Various authors having critically compared the different
GC-O methods, using either mixtures of reference standards or real systems [8, 19, 23, 24, 36 – 38]. Table 1 allows
a rapid comparison of the main characteristics of these
methods. As can be observed, and has been explained
above, the methods differ quite considerably in each of
their primary factors. Figure 3 demonstrates, hypothetically, the differences in the results that one obtains
when each of these methods is applied to analyse the
same extract. Discrepancies in results exist because the
methods are based on different principles. Compound
properties that are important include peak width and
shape (dependent on chromatography), absolute threshold, psychometric functions (Fig. 1) and the impact of
variable sensitivity of assessors.
In Fig. 3, peak 1 represents an early eluting compound
with a narrow peak width. Peak 2 has a greater peak
width than peak 1, which usually is observed at greater
elution times but is plotted next to peak 1 for easy comparison. Both compounds have a high odour threshold,
but a steep psychometric function and high maximum
intensity. These compounds could be depicted by compound A in Fig. 1. Peak 3 represents a very broad peak
such as could occur for a fatty acid on a nonpolar stationary phase. Peak 4 represents a compound with a low
odour threshold, but a flat psychometric slope and a low
maximum intensity, as depicted by compound B in
Fig. 1. Peak 5 demonstrates a compound that is present
at a concentration close to threshold for all assessors, or
for which some assessors are insensitive (i. e. specific anosmia). Peak 6 represents a compound that has been
affected by a gap in the coincident responses in dilution
analysis.
Peak widths can vary considerably in a GC analysis
depending on elution temperature and compound functionality. GC-O methods that do not record odour duration cannot account for this. For example, compounds
represented by peaks 1 and 2 have the same FD factors
(AEDA) and detection frequencies, but different Charm
values and areas calculated by detection frequency. Conversely, peaks 2 and 3 have different FD factors but simi-
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2115
lar Charm values due to the broader peak width of compound 3.
It is well established that the slope of the psychometric
function of a compound varies markedly between different compounds [36, 37] (Fig. 1). Due to the low odour
threshold and flat psychometric function of compound
4, its odour will persist in higher dilutions resulting in a
large response when evaluated using AEDA and CharmAnalysisTM (Fig. 3). However, when evaluated using directintensity methods at a single concentration, its relative
importance is less due to its relatively low odour intensity. In comparison, peaks 1 and 2 exhibit the behaviour
of compound A with steep psychometric functions and a
higher odour threshold. They demonstrate high odour
intensity and detection frequency responses, but low FD
factors and Charm values using the dilution-based methods.
A potential problem with the detection frequency
method is an end-of-scale’ effect described above, where
at a particular concentration a compound may be perceived by all assessors [23]. If the concentration is
increased, the odour intensity is also likely to increase
but the detection frequency cannot. For example, using
the direct-intensity method, the odour intensity of compound 2 is perceived to be higher than that of compound
1, despite having the same detection frequency.
,
that even 100 mm unstructured line scales may suffer
end effects, giving the impression that response to concentration increase has reached a plateau, whereas in
fact assessors have run out of available scale to use. This
effect will be influenced by the scale anchors used, and
also by the extent of assessor training. However, it is now
often recommended that the Labelled Magnitude Scale
[54] be used in psychophysical studies, particularly
where maximum perceived intensity of some compounds within an experiment is likely to be high.
Gas chromatography-olfactometry
The danger of only using a few assessors for GC-O is that
specific anosmia has a serious impact for underestimating the importance of an odour. This is especially important for dilution to threshold methods (AEDA and
CharmAnalysisTM), which are particularly time-consuming and typically only use one or two assessors. This is
demonstrated by peak 5, where the compound is not
detected in AEDA or CharmAnalysisTM, but is found by
30% of assessors by detection frequency, and has a low
direct-intensity measure.
In AEDA, FD factors are usually defined as the maximum
dilution at which an odour is perceived and ideally correspond to the height of CharmAnalysisTM peaks. However,
if there are gaps in coincident responses during CharmAnalysisTM, the peak height and Charm value is reduced,
underestimating a compound's relative importance and
causing a discrepancy with AEDA. This is demonstrated
using peak 6. Gaps in coincident responses occur when
an odorant is not perceived at a particular dilution but
then is detected again at a higher dilution. How these
gaps are treated in the data analysis is important, particularly for AEDA. Obviously, different results will be
obtained for the FD value depending on whether the
dilution value is taken as: (a) the highest dilution that
the compound is detected, (b) the number of dilutions
with correct responses, (c) the number of continuous
responses [19] or (d) as the geometric mean of the maxiwww.jss-journal.com
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J. Sep. Sci. 2006, 29, 2107 – 2125
Table 1. Comparison of the principles and typical parameters used for different GC-O methodologies based on the literature
reviewed
GC-O methodologies
Dilution to threshold
Signal recorded
Binary (0/1)
Odour durationa)
Principle based on
Potency – ratio of concentration to detection threshold
Unit of odour measurementb) FD factor (height)
Charm value (area)
Number of assessors
1–3
Dilutions per sample
10 – 12
Repetitions per sample
1
Analyses required per samplec) 10 – 36
Suitable for statistical analysis No
a)
b)
c)
d)
Direct intensity
Binary (0/1)
Odour durationa)
Proportion of panel to detect
an odour
Frequency (height)
Frequency6duration (area)
6 – 12
1
1–2
6 – 24
Yes
Continuous (intensity scale)
Perception of odour intensity
Maximum intensity (height)
Intensity6duration (area)
3 – 10
1
1–4
6 – 30d)
Yes
The duration of the odour perception is recorded in some methods but not others (see text).
Methods differ whether they report the height of odour peaks or the peak areas including duration.
Based on the combination of the number of dilutions6number of assessors6number of repetitions.
When a low number of assessors are used for direct-intensity methods, the number of repetitions per assessor is usually
greater and vice versa.
mum FD value obtained when a number of different
assessors sniff [55]. Debonneville et al. [24] developed an
algorithm that alters the Charm calculation (1) by applying a correction factor in order to minimise this error. It
should also be noted that the probability of missing an
intense odour is less than the probability of missing a
weak odour.
The number of analyses required, which is reported in
Table 1, is based upon the combination of number of
dilutions, assessors and repetitions. This is reported
assuming the use of a single odour port. If the column
eluate is split into multiple sniff ports (usually the case
for detection frequency methods), then obviously the
number of individual GC runs decreases considerably.
Detection frequency methods are usually the fastest, followed by direct-intensity methods, with dilution-based
methods being the most time-consuming. The detection
frequency is also the easiest method to employ, followed
by dilution methods, whereas direct-intensity methods
are the most complex [38].
Dilution to threshold methods have been criticised
because they do not adhere to a psychophysical relationship between concentration and perceived intensity [36,
46]. However, this was not the method's original purpose.
In reality, these methods are measuring an entirely
different principle, that of odour potency as opposed to
stimulus intensity [4, 29, 56, 57]. As long as this distinction is considered, dilution methods can provide reliable
results for odour potency values. This is due to the simplicity of the task and that results for each assessor are validated by the fact that each compound is detected in multiple dilutions [31].
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2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
7 Performance of the human detector
Because human beings are used as the detector, any additional factors that may influence the performance of the
human assessor individually and that can influence how
the data collected by a panel of assessors should be interpreted must also be given very careful consideration. We
strongly emphasise that GC-O is essentially a sensory evaluation method and should be founded upon well-established psychophysical principals that must be known. If
the individual assessors, and the panel overall, are effectively used, then measures of absolute threshold and of
intensity at a given concentration can be accurately
obtained.
7.1 Testing environment
The environment in which the assessors are situated during GC-O is of great importance. The assessor’s comfort
and ability to sniff free of distraction is paramount to
allow for maximum attention [19, 58]. This requirement
generally means that the GC-O instrument should be
located in a dedicated laboratory. Laboratory air should
be filtered to remove any unwanted odours, and pumped
into the room to maintain positive pressure. The laboratory should also be temperature controlled for assessor
comfort as well as to ensure instrument reproducibility.
As referred to above, the odour port should be easily
accessible for an assessor seated in a comfortable position, and the eluate that is sniffed should be humidified
to prevent drying and discomfort in the nose. Some
investigators ask assessors to listen to music through
soundproof headphones to prevent distraction from
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noise; however, most will simply ensure that the laboratory is quiet and the laboratory door is closed during
analysis.
7.2 Experimental bias
There are a number of experimental design and context
factors that can bias the response of the assessor. Many of
these have been well documented in the sensory literature [12].
In GC-O, odours are not presented to assessors in containers, but rather are only present for a few seconds at
undefined’ intervals over a relatively long period
(30 min or more). Therefore, GC-O is very different from
typical sensory analysis or conventional olfactometry
where one can be certain that the judge will have opportunity to perceive the odour. It could be argued that classical psychological errors that rely on anticipation, habituation, prior assumptions, etc. are far less likely in GC-O
than in typical sensory analysis where the assessor is presented directly with samples to evaluate. However, it
would be nave to believe that assessors will not quickly
learn the point where an odour-active compound will
elute as it is likely that each will smell an extract of the
same sample type many times during one study.
,
The most obvious bias that can occur is an error of anticipation, which can occur if a nonrandom sequence of
samples is presented for analysis (e. g. in dilution to
threshold methods where a descending sequence is presented), and an assessor responds in anticipation of an
odour occurring, when perhaps they should perceive no
odour. An error of this type can also occur in detection
frequency and direct-intensity measures as the same
extract is likely to be evaluated many times by an assessor, and retention times where odour-active compounds
elute will be learned. This bias can be overcome by randomising the sequence of samples presented, and by including blanks or unknown samples intermittently to monitor performance [17, 33, 47].
In the case of GC-O, it can occur that the assessor will not
perceive the odour because they have simply missed
their opportunity to do so due to a lack of concentration,
or due to their breathing cycle [59]. Therefore, it could
also be argued that it is advantageous to provide elution
times, allowing an assessor to increase concentration at
these key times in a long run time. This method will be
effective if anticipation bias is offset by randomisation
and by including blanks as suggested above.
There are also biases associated with scale use [12]. The
most important include simple contrast effects, range
effects, frequency effects, centring bias and transfer bias.
There can also be a time-order error, where evaluation of
extracts in one order can give a different result to judging them in another order. The effects of these can be
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lessened by again randomising or balancing the order of
presentation of samples, and also by training assessors in
use of scale. The most effective training will ensure that
assessors are very familiar with reference standards that
clearly indicate the lower and upper ends of the intensity
scale, thereby reducing the likely range, frequency, centring and transfer bias.
The GC run time, and therefore as a consequence, the
GC-O sniff duration, can influence performance when
assessors become increasingly fatigued. It would be
expected that alertness is better at the start than at the
end of a run, and a maximum sniff time of 25 min has
been recommended [39]. However, van Ruth and O'Connor [47] showed that fatigue was not a factor when using
the detection frequency method. It has also been noticed
that alertness will be of most concern when only a small
number of odour-active compounds are present in an
extract [33].
Assessors should be asked to refrain from smoking, eating or drinking strongly flavoured foods for 1 h prior to
performing GC-O and not to wear aftershave, perfume or
strong deodorants on the day of assessment [31].
7.3 Assessor sensitivity
It is well documented that the sensitivity of the human
olfactory system is superior than any chemical detector
for odour-active compounds [17]. For example, Reineccius hypothesised that the human nose has a theoretical
odour detection limit of 10 – 19 mol [60]. It has been
reported that 1 pg of b-damascenone is perceived by
most individuals during GC-O and that sensitive people
can detect 50 – 500 fg (10 – 15 g) [10, 17].
However, there are very significant differences in olfactory ability between humans, and it is vital to recognise
that these are present and account for them. Odour
thresholds can vary significantly, both within and
between people, and some people with an otherwise normal sense of smell are unable to detect families of similar
smelling compounds [61, 62]. This condition is termed
specific anosmia, and can be defined as an odour threshold for an individual compound more than two standard
deviations (SDs) above the population mean. It is also the
case that sensitivity to individual compounds varies by
many orders of magnitude even in the case where specific anosmia does not exist, and that there is poor correlation between compounds when sensitivity within a
group of assessors is compared [63]. When the performances of different GC-O assessors have been compared
in the literature, specific anosmia has frequently been
observed [8, 10, 37]. This is probably due to the natural
diversity of expression of specific olfactory receptor
genes [64]. In addition, the olfactory response of an individual is known to vary over time, even during the course
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C. M. Delahunty et al.
of a single day [65]. Sensitivity may fluctuate due to
health status and mood [10]. Response to a perceived
odour may also change with levels of concentration,
experience and increasing familiarity with specific compounds and odours.
This raises the issue of how many assessors should one
use for GC-O. The risk of using one or just a few assessors
is that specific anosmia, and less obvious but also significant differences in sensitivity, have a serious impact for
underestimating the importance of an odour. This is
especially important for dilution to threshold methods
[8], which are particularly time-consuming and typically
only use one or two assessors. In the descriptive analysis
method routinely used in sensory analysis, between 8
and 12 assessors are generally used. Results are based
upon panel mean scores [53]. In addition, using a panel
allows results to be statistically evaluated and therefore
samples to be compared. It could also be argued that the
incidence of specific anosmia should be taken into
account when making an inference about the importance of an odorant to a population. For example, bionone, which has a violet character, is present at concentrations that exceed absolute threshold in many
plants and wine, and is likely to contribute to the overall
odour quality [66 – 68]. However, it is estimated that 1/3
of the population have a specific anosmia to b-ionone
(unpublished data) [69].
An assessor's breathing cycle may also influence detection or sensitivity in GC-O. While an assessor is breathing
out (expiration), there is no perception of odour. The
time taken to breathe out is potentially long enough for
an assessor to miss a compound with a narrow peak
width [59]. Hanaoka et al. [59] used a piezo-electric pressure sensor to investigate how an assessor's breathing
rate affects detection frequency and intensity perception
of compounds. However, the authors could only conclude that the breathing rate only partially explains the
lapses in detection. The distribution of compound peak
height relative to the breathing cycle may also have an
influence on intensity rating [59]. For instance, if inspiration peaks during the onset or decay of a GC-peak, then
intensity may be perceived as lower than in the instance
where the inspiration termination and maximum peak
height coincide.
Adaptation can be defined as the decrement in intensity
or sensitivity to a compound under constant stimulation
by this compound. Adaptation results in a higher threshold or a reduced perception of intensity. Adaptation to
the same compound is not a problem in GC-O, like it is in
typical sensory testing, as opportunity to sniff the same
compound is only presented after a considerable period
of time has elapsed (i. e. in the next GC-run). However,
compounds of the same quality tend to crossadapt, and
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J. Sep. Sci. 2006, 29, 2107 – 2125
therefore the ability to perceive two odours of similar
quality in close succession can be limited. It can also be
the case that the ability to perceive an odour of different
quality can be significantly influenced if the interval
between eluting odours is very short [70].
7.4 Assessor selection and training
The quality of the GC-O results depends upon the ability
of the assessors and/or the panel. In a majority of published GC-O studies, there is no detail given about
whether assessors have been trained or not, and then
when it is said that assessors are experienced, the nature
of this experience is not given. There is no reason why
one should prefer untrained assessors above selected and
trained assessors for GC-O, in particular, if GC-O is a
method that is applied regularly by a laboratory. Careful
selection and training of assessors will improve assessor
performance, and therefore improve the accuracy and
precision of the data collected. It is suggested that potential assessors should be screened for sensitivity, motivation, ability to concentrate, and ability to recall and
recognise odour qualities [10]. Prescreening of assessors
could also include determining age, smoking, sinusrelated conditions, allergies, denture wearing and medication use [10]. Ethics should also dictate that assessors
be told what hazardous chemicals they could come into
contact with, and that the solvent peak should not be
sniffed [17, 71]. Assessors with general anosmia or low
sensitivity could be screened using a standard test mixture [65, 72].
Formal training would involve presentation and assessment of reference standards, and then subsequent feedback to facilitate response adjustment if necessary. In
addition, when rating intensity, reference standards
should be used to indicate relative intensity to assessors,
and in particular the limits of the intensity scale [30].
Where training has been reported [73, 74], it has been
observed that assessor and panel performance improves.
Of course, this is well-recognised in sensory science. van
Ruth and O'Connor [47] found that detection frequencies
for a standard mixture of eight compounds were not significantly affected by training. However, the noise level
(number of artefact peaks) significantly decreased,
thereby increasing the S/N. However, in effect this panel
was not formally trained; they were simply more experienced by having assessed more samples between
training’ test intervals. Experience and training are not
the same. It will also be best practice to interpret a panel
GC-O results as single detector (rather than overly interpret individual assessor results). When this has been
done good reproducibility has been reported [37, 52].
This is common practice in sensory evaluation methods,
as variation between assessors is expected.
,
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Training is also essential with regard to description of
odour quality. Without training, it is most likely that
different assessors will describe the same sensation in
different ways. Through training, a fixed terminology
can be agreed upon. Terminologies are likely to work better on a product-by-product basis (e. g. for wine, beer and
other product categories) than across product categories.
A noncentred Principal Components Analysis (PCA) has
been used to obtain an interpretable overview of timeintensity GC-O data [50, 73]. PCA provides a principal aromagram that represents the weighted average of each
individual assessor's time-intensity data, and accounts
for most of the variation within and between individual
assessors. One significant advantage of this data analysis
method is that the PCA loadings quantify the relative
contribution of each individual assessor to the principal
aromagram, and in this way the performance of each can
be monitored. This method can also be used during panel
training.
8 Methods of extraction
There are several good reviews of the extraction of volatile compounds from natural materials that one can
refer to as this is not the subject of this paper [1, 75 – 77].
However, as GC-O is a method that aims to link volatile
chemistry to perceived odour quality, relevant factors
that impact upon the interpretation of GC-O data will be
discussed.
GC presents odorants that are completely volatilised in
air [4, 44, 57]. This does not take into account how a compound is held or released from a food matrix, where only
a proportion of the compounds present will be volatile in
the gas phase (available for perception), depending upon
the solubility and binding to nonvolatile components.
Other compositional variables, such as sugars and proteins can adsorb, trap or bind volatile compounds. Fats
will act as a strong solvent depending upon the relative
hydrophobicity of volatile compounds. The method one
uses to obtain a volatile compound extract from a material will therefore determine its qualitative and quantitative composition. It is important that this is taken into
account when interpreting GC-O data.
Solvent extracts or distillation methods yield total
extracts of a sample that do not necessarily represent the
proportion of compounds that are perceived by a subject
when smelling (orthonasal perception) or eating a sample (retronasal perception) [56]. In addition, highly volatile compounds may contribute to the top-note of an original food, plant, flower or sample. These may be lost
during solvent extraction, distillation and concentration
procedures and could result in an extract that is not
representative of the original sample [78]. These compounds could also coelute with the solvent peak and so
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2119
not be evaluated by GC-O [78]. A potential solution to
these problems is to use headspace sampling, using
either static or dynamic methods [56, 78]. However, headspace methods may yield relatively low concentrations
making identification more difficult. Headspace volatiles
can be concentrated using adsorbent traps (e. g. Tenax) [3]
or using solid-phase micro-extraction (SPME) [79]. Alternatively, once the important odours have been determined using headspace GC-O, the odorants could be
identified from a concentrated extract [71]. Static headspace sampling has been applied to AEDA by injecting
decreasing volumes into the GC [80 – 82]. Dilution analysis has also been performed using SPME by varying the
thickness and length of the fibre exposed [56], and by
altering the split flow at the inlet [83]. Adsorbent traps
(e. g. Tenax) can be eluted with solvent, which can then be
diluted further [9].
O'Riordan and Delahunty [50] compared vacuum distillates and Buccal Headspace Analysis (BHA) extracts of
Cheddar cheese using GC-O. Vacuum distillation is an
almost total extraction method, whereas the BHA
method displaces volatile compounds during consumption of the cheese, and therefore only contains those that
could physically be released in the short time that the
cheese was present in the mouth [50]. The resulting data
were very significantly different, demonstrating clearly
how a GC-O analysis determines the relative potency and
intensity of compounds present in the extract, and not
their contribution to odour of the food unless the extraction technique is specifically chosen to reflect this.
It is important to take into consideration that in GC-O
compounds are (ideally) presented to the assessor individually without co-elution. This does not consider any
interaction with other compounds in a mixture, where
synergistic, antagonistic (suppression) or additive effects
may occur. One can evaluate how well the extract represents the original sample using sensory analysis [84 – 86].
However, this is difficult if the extraction uses a solvent
that is not suitable for direct sensory evaluation, or if the
extract is obtained using a headspace method (e. g. SPME).
One innovative solution for headspace analysis using
SPME has been proposed, whereby direct-GC-O (D-GC-O) is
carried out on a GC instrument fitted with an uncoated
column, and therefore compounds are not separated
prior to sniffing [87]. It may also be important to authenticate the conclusions of GC-O experiments by evaluating
the similarity of synthetic models (created using important odorants identified by GC-O) with the original food
product [45, 65, 78, 88]. This is relatively straightforward
for homogenous, liquid foods but can be very difficult
for solid foods [78]. Importance of the individual odorants and their interactions with other compounds in a
model system can then be evaluated using omission
experiments [78].
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C. M. Delahunty et al.
9 Impact of GC separation conditions
When deciding upon an injection method for an extract,
it is important to consider discrimination and degradation of labile compounds in the hot injector [1]. On-column injection is usually considered the best when the
sample is clean’, i. e. free of nonvolatiles and moisture.
Splitless injection produces good results as long as a low
volume liner and an adequate carrier gas velocity are
used in order to eliminate discrimination and degradation due to long residence times. According to Ferriera et
al. [86], split injection should not be used unless the
study is focussed on a narrow range of odorants with
close boiling points’.
,
,
To prevent assessor fatigue during a GC-O analysis, it is
recommended that a sniff run should be less than
25 min [39]. To facilitate an assessor profiling an entire
sample, relatively fast oven temperature programs are
often used (4 – 108C/min). Nonpolar stationary phases are
robust and allow odour-active volatiles to elute at the
lowest possible temperature so that compounds that
elute with a retention index less than 1800 can be
assessed in the 25 min period [39]. However, very polar
molecules, such as fatty acids, result in poor peak shapes
using nonpolar phases. In addition, polar phases demonstrate greater selectivity [86], although the overall quality of the separation will depend upon the composition
of the sample. An alternative is to use slower temperature programmes and split the analysis between two
assessors [3], or have an individual assessor test part 1
and part 2 during separate GC-runs. This has the added
advantage of maintaining the resolution between peaks.
The use of two stationary phases has been recommended
to improve the resolution of the compounds of interest
and to improve the strength of identification of odorants
[1, 86]. Matching the retention index and odour quality
of an unknown compound to a reference standard provides sufficient evidence for an identification, particularly when this is performed on two different stationary
phases [1, 17, 56, 57]. Blank [1] has presented results indicating that the choice of stationary phase considerably
changes the odour thresholds determined for compounds. However, the author did not discuss reasons for
this observation. This could be due to the ability of the
human assessor to detect compounds eluting with different peak shapes or due to altered elution order or co-elution of compounds. Using two stationary phases alters
elution order and allows evaluation of crossadaptation,
where a strong odour may affect the odour intensity of a
close eluting odour [58].
Peak shape has been found to affect the perception of
odour intensity and calculation of detection thresholds
[20]. It is expected that short, broad peaks will produce a
lower intensity response than tall, narrow peaks. To our
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J. Sep. Sci. 2006, 29, 2107 – 2125
knowledge, this has not been comprehensively studied
for a group of compounds over a range of analytical conditions. As mentioned previously, the peak width also
affects the measurement of GC-O values and the ranking
of relative importance. This may lead to underestimation
of the importance of narrow, early eluting peaks. Proper
optimisation of temperature programmes and selection
of an appropriate stationary phase can correct for this.
Stability of labile compounds during GC analysis should
also be considered as losses can occur due to adsorption
or degradation and impact upon the results of GC-O [89].
The low volumetric flow rates (1 – 2 mL/min) and high
velocity (25 – 35 cm/s) of carrier gas in capillary GC columns complicates sniff port design. To maintain the
resolution achieved with the GC during the delivery of
the volatiles to the nose, the velocity of the make-up gas
should be greater than or equal to the carrier gas eluting
from the column [17]. Therefore, it is expected that the
optimum volumetric flow rate of the make-up gas will
vary depending upon the sniff port design and the diameter of the sniff port transfer line. Make-up gas is typically combined with column eluate in one of two configurations; either perpendicular to column flow in a venturi tube at high flow rates [17, 20, 27], or in parallel to
column flow using a concentric arrangement at low flow
rates (e. g. ODO II, SGE International). The optimal flow
rate of make-up gas for each system is a subject of importance [21]. For olfactometers, it has been reported that
increasing the flow rate to the nose decreases the odour
detection threshold [30]. This can be attributed to
enhancing odour transport and concentration at the
receptor level’ [30].
,
2120
Hanaoka et al. [21] investigated the effect of make-up gas
flow rate (25 – 500 mL/min) using a static direct-intensity
method. Air flow rate had a significant effect on the
detection frequency and intensity measurements for
each compound investigated. Hanaoka et al. [21] also
investigated the humidification of the make-up air and
found no significant effect on assessor performance, or
their level of comfort and nasal dryness over the 15 min
session. van Ruth and O'Connor [47] studied make-up gas
flow rate using the ODO system (SGE International), and
the optimal flow was determined to be 6 mL/min. Other
researchers have used flow rates ranging from 25 mL/
min [19] to 100 mL/min [31]. The DATU system (DATU)
designed by Acree [39] and associates uses a venturi tube
design and a 1 cm diameter stainless-steel pipe to deliver
the volatiles to the assessor. With such a wide diameter,
Acree states that the volumetric flow rate required is
between 3 and 5 L/min. The system described by Miranda-Lopez et al. [28] for Osme used a 60 cm long transfer
tube constructed from glass and coated with silicone.
Like the DATU system, it also had a diameter of 1 cm and
was operated with a humidified air flow (60% RH) of 11 L/
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J. Sep. Sci. 2006, 29, 2107 – 2125
10 Multidimensional GC-olfactometry
(MDGC-O)
The distribution of compounds eluting from a GC column is neither even nor random, with peaks eluting in
clusters according to their retention mechanism on the
column employed [90]. Compounds that share similar
chemistry have a high probability of co-elution. Therefore, using single-dimensional GC, there is a high probability that compounds will co-elute. Using relatively
rapid GC conditions to facilitate short GC-O runs
increases the frequency of co-elution, making identification of the compound responsible for an odour challenging. For example, a trace odour-active compound may be
masked by a larger odourless peak. Alternatively, co-eluting odour-active compounds results in the perception of
odour clusters’ [68]. A potential solution to resolve coeluting compounds is to change the stationary phase of
the analysis. However, in complex samples this may only
succeed in altering the combination of co-eluting peaks
[91]. A more powerful solution is to use MDGC-O to
resolve discrete regions of co-eluting compounds.
,
MDGC apparatus and methods have been comprehensively reviewed [90, 92 – 94] and are not the main purpose
of this contribution. However, there are some points relevant to GC-O that can be considered here. Recent
advances in comprehensive 2-D methods (GC6GC) [90,
95, 96] are not suitable for olfactory detection using
human subjects. Regular modulation during GC6GC
(e. g. every 5 s) creates multiple slices for each peak, and
zone compression (e. g. cryogenic trapping) produces narrow peaks (100 – 400 ms) that are too short for the typical
breathing cycle of a human assessor (3 – 4 s). However,
using a traditional MDGC system, co-eluting compounds
in an odour-active region can be heart-cut to a second
orthogonal column (different stationary phase) and
resolved.
The least sophisticated technique to perform multidimensional GC analysis is using an off-line system, where
a particular region eluting from the column is collected
using an adsorbent trap. The trap is then reintroduced
into a second system with a different column and
equipped with a sniff port [97, 98].
On-line systems require an interface to selectively transfer an odour-active region from the first analytical column to the second dimension. This may be a mechanical
valve [99, 100] or a pneumatic switching system such as a
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2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
2121
Deans Switch [101, 102]. These methods increase the
complexity and expense of multidimensional systems,
requiring extra equipment, but offer greater resolving
power and flexibility. It has been demonstrated that
labile compounds (e. g. sulphur compounds) can degrade
or adsorb to active surfaces in mechanical valves [91].
Furthermore, the large thermal mass of the valve can
generate a cold-spot’ in the oven causing problems when
temperature programmes are used [25, 91]. Pneumatic
switching systems avoid these problems, but require auxiliary electronic pressure control and more complex
method development. A simplified method has also been
suggested using a Longitudinally Modulated Cryogenic
System (LMCS; Chromatography Concepts, Doncaster,
Victoria, Australia) originally developed for GC6GC
used in what is described as the target mode [91, 103].
,
min. Supplying such a large volume of odourless air ideally requires a compressor and a purification system to
ensure that all contaminants are filtered out. In summary, inconsistencies in the make-up gas flow rate used,
and in results of the few studies which have investigated
this, demonstrate that further investigation is required.
Gas chromatography-olfactometry
Traditional MDGC generates discrete peaks with widths
broad enough to permit olfactory assessment using the
human nose. Cryogenic trapping may also be used to
refocus peaks eluting from the first column and to
increase the S/N [25, 91]. Cryogenic traps can also provide
additional flexibility by allowing peaks to be retained in
the trap until previous peaks have eluted and the assessor is ready [91]. When cryogenic trapping is used, either
a relatively long column (30 m) or a thick film of stationary phase (0.5 – 1.0 lm) may be necessary to produce a
wide enough peak to sniff.
MDGC-O methods can also be applied to chiral separations to evaluate the odour intensity, potency and quality of enantiomers [91, 97]. Begnaud and Chaintreau [91]
were able to separate enantiomers using a single oven
system and the LMCS as the MDGC interface. The cryogenic trap allowed the chromatographic band to be
retained while the oven temperature was decreased so
that the second dimension chiral separation would be
optimised [91].
Applications where MDGC-O has been used include ginger [97], off-flavour compounds in beer [98], kiwifruit
puree [104], orange oil [105] and malt whiskey [106].
Finally, hyphenating MDGC to a mass spectral detector
as well as an olfactory port provides a very powerful identification tool [25, 102, 105, 106].
11 Applications of GC-O
As mentioned in the introduction, a simple search of the
recent scientific literature reveals many GC-O references,
indicating that the application of GC-O for the identification of character-impact odorants is becoming routine.
The vast number of research articles applying GC-O
makes reviewing the specific applications prohibitive.
However, there are a number of general applications
focusing on various objectives that may be discussed.
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C. M. Delahunty et al.
J. Sep. Sci. 2006, 29, 2107 – 2125
The most common objective of GC-O is to characterise
the odour profile of a sample or extract, quantify the relative importance of odour-active compounds and then
make an inference about their significance in the original product. As many hundreds of different volatile compounds have been identified in many foods and natural
materials, knowledge of the contribution that each compound makes to odour ensures that this task is more
straightforward. In other words, this focuses chemical
analysis on the volatile compounds that can be perceived. A second related objective is to discover new
potent odorants present at trace levels [81]. For example,
GC-O led to the discovery of 1-p-menthene-8-thiol as the
impact odorant of grapefruit juice. This compound is
typically present at sub-ppb levels but has an extremely
low odour threshold [1, 107]. A great deal of commercial
research by flavour houses and perfume companies has
been performed looking at identifying novel odorants
with interesting odour characters from nature [108, 109].
This includes looking at unique plants and fruits from
exotic places, such as in tropical rainforests (e. g. Madagascar) [108]. The generation of flavours that closely
match natural products is also of great commercial interest [109].
GC-O is valuable in quality control to identify the compounds responsible for foreign taints or off-flavours and
to determine their cause [45]. This is especially important
when the off-flavour or taint is a very potent odorant present at trace levels or even below the detection limits of
chemical detectors. For example, the skunky’ or lightstruck’ odour of beer is caused by 3-methyl-2-buten-1thiol, which has an average detection threshold of
0.05 ppb [109]. Another potent defect is caused by 2,4,6trichloroanisole, which is responsible for the musty, papery taint found in corked’ wine and has an extremely
low average detection threshold of approximately
0.05 ppt [110].
,
,
,
For research purposes, GC-O can be used to study the
variability of peoples' sensitivity to odours and investigate the incidence of specific anosmia [10]. Dilution
methods are also able to measure odour detection
thresholds in air (the vapour phase) relatively easily and
accurately [5, 20, 86, 111]. The advantages of using GC-O
are that the amount of odorant delivered to an assessor is
known and can be accurately controlled. The resolution
obtained using GC means that compounds are assessed
individually. This means that compounds do not have to
be synthesised or extracted and purified before threshold
testing. Furthermore, the odour threshold of unidentified compounds can also be investigated. Methods that
directly measure odour intensity may also be used to
determine compounds psychometric functions, relating
concentration to odour intensity [30, 32, 36].
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2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
12 Future trends
To improve the quality and value of GC-O data, there are
some innovations in application and in methodology
that should be considered. Innovations can consider the
extract analysed (thereby the value of the GC-O results),
and the means of analysing this effectively using GC-O,
the type of chromatography performed and therefore
the compound resolution achieved, the experimental
design, data collection methods and panel experience
issues, which influence the precision and repeatability of
the human detector, and finally the best methods for
analysis and interpretation of the GC-O data collected.
In recent years, odour extracts from food have been
taken in vivo during consumption [76]. This is a major
innovation in flavour chemistry research. Extracts taken
during consumption are considered to be the most representative of the aroma perceived by a consumer, which is
most important in food quality. Only one study has used
an extract taken in this way for GC-O, and has shown
very significant differences between this extract and one
from the same sample taken by vacuum distillation [50].
Further investigation of in vivo extracts is warranted.
A move towards MDGC-O is likely, in particular, for complex extracts where co-elution of odour-active compounds is a significant problem. The development of a
GC instrument that allows this to be achieved in one run
is very much needed. Combination with GC6GC-TOFMS
provides excellent analytical power to identify the
resolved odorants.
There is very much known in sensory science, and in the
science of measuring olfaction, that can be taken on
board in improving the performance of assessors in GCO, and as a result obtaining better data. This is particularly the case where direct-intensity methods are used.
This existing knowledge can be used to reduce the influence of bias and individual differences between assessors
on the quality and potential for interpretation of the
data generated. There is no reason, apart from perhaps
that of a time limitation, why GC-O cannot be used to
comprehensively determine both the absolute threshold
and psychometric function of all odour-active compounds present in a complex natural odour. In fact, this
knowledge is very much needed as part of the improvement of our understanding of how individual compounds combine to produce the natural odours that we
are familiar with.
Intensity data collected using line scales that yield interval or ratio data can be analysed using parametric statistical techniques, such as the t-test, or ANOVA. This will
enable investigators not only to go beyond simply ranking odours in terms of relative importance, but also to
consider differences between samples (extracts). PCA has
also been used to analyse GC-O data [50], allowing a rapid
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J. Sep. Sci. 2006, 29, 2107 – 2125
analysis of time-intensity data, and the contribution of
individual assessors to the principal aromagram to be
quantified.
13 Conclusions
Using human assessors as a GC detector presents many
challenges not considered in typical GC analysis. GC-O
has been effectively applied for over 40 years, in particular as a screening method to distinguish between compounds in a sample that have odour activity and those
that do not. More recently, investigators have used GC-O
to quantify the relative contribution of individual volatile compounds to a sample's overall odour profile. There
has been considerable emphasis placed on the optimal
GC conditions needed to obtain good compound resolution for GC-O, and on the design of GC-O hardware. Some
investigators have compared GC-O methods, and recognised differences in data quality and value. However, the
considerable knowledge on best use of human assessors
in measuring stimulus, including use of a panel, and
effective panel training, has not been taken on board
very well. This is an area that should be addressed in the
future. Based upon this, more fundamental research is
required to effectively compare existing GC-O methodologies, and to develop a robust methodology that takes
the best of each into account. The ability to carry out
MDGC-O to heart-cut and resolve co-eluting compounds
is very much required.
The authors would like to thank Professor Philip J. Marriott for
reading a draft of this review and providing useful suggestions
that have improved it.
Gas chromatography-olfactometry
2123
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