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J Sci Food Agric 1998, 78, 343È348
Use of Digital Aroma Technology and SPME
GC-MS to Compare Volatile Compounds
Produced by Bacteria Isolated from Processed
Judy W Arnold¤ and Samuel D Senter
United States Department of Agriculture, Richard B Russell Agricultural Research Center, PO Box 5677,
Athens, Georgia 30604, USA
(Received 2 March 1998 ; accepted 10 March 1998)
Abstract : Digital aroma technology, solid-phase micro-extraction (SPME) and
gas chromatographic mass spectral (GC-MS) analysis of the headspace volatile
organic compounds were used to compare bacterial species important for food
safety and common to bioÐlms in the poultry processing environment. The
instrument for digital aroma technology, called the electronic nose, measured
changes in resistance of polymer sensors caused by volatile gases from the headspace of samples. Graphical output by the Sammon mapping technique produced patterns of di†erences or similarities among the samples. ArtiÐcial neural
network software was used to model groups of samples and classify subsequent
unknowns. Compounds isolated from the headspace of sealed cultures using
polydimethylsiloxane SPME Ðbres and identiÐed by GC-MS analyses were predominantly alcohols and indole. These qualitative proÐles were repetitive for speciÐc organisms in relation to purity and repeatability of the cultures, di†ered by
species and were used as objective standards to compare the graphical outputs of
the electronic nose. ( 1998 Society of Chemical Industry.
J Sci Food Agric 78, 343È348 (1998)
Key words : bioÐlms ; poultry ; bacteria ; aroma ; electronic nose ; SPME ; GC-MS
volatile organic compounds (VOCs) as they cross an
array of sensors (Hodgins and Simmonds 1995). Rapid
reversibility of the volatile to sensor binding process
allows samples to be run in rapid succession. In early
models, sensor arrays usually were connected to
numerical modules for statistical analysis, ie semiconductor gas sensors responded to the aromas, pattern
recognition analysis di†erentiated the resulting data
matrices and cluster analysis was used to classify the
samples (Aishima 1991 ; Newman 1991 ; Persaud 1991).
A system for odour classiÐcation that was introduced
later included a sensor array, statistical analysis module
and an artiÐcial neural network. The network classiÐed
and stored the statistical data, then used the information to match subsequent unknown samples with previously encountered samples (Dodd et al 1991 ; Shurmer
and Gardner 1992 ; Davide et al 1994 ; Hines and
Gardner 1994).
Digital aroma technology, or the electronic nose, has
been developed in recent years as a quality assurance
tool to detect odours in food and Ñavour industries.
Intended to mimic biological olfactory functions of
human sensory panels, it functions by rapidly adsorbing
and desorbing volatiles at the surfaces of sensors,
causing changes in measured electrical resistance. While
analytical techniques, such as gas chromatography
(GC), separate headspace gases from samples into peaks
corresponding to individual compounds, the electronic
nose integrates measurements of the total headspace
* This article is a US Government work, and, as such, is in
the public domain in the United States of America. Reference
to a company name or product does not imply endorsement
by the US Department of Agriculture.
¤ To whom correspondence should be addressed.
( 1998 Society of Chemical Industry. J Sci Food Agric 0022È5142/98/$17.50.
Printed in Great Britain
J W Arnold, S D Senter
Historically, qualitative assessment of spoilage potential for foods and the by-products of spoilage has been
made by human panelists (Dalgaard 1995) who are presented serial dilutions of air samples for olfactory measurements (Hobbs et al 1995). Quantitative
characterisation was then made with model substrates
and measurement of speciÐc VOCs by analytical procedures that characterise the aromas or odours. Examples are the isolation and identiÐcation by GC mass
spectroscopy (GC-MS) of key VOCs that contribute to
the odour of stewed beef (Guth and Grosch 1994) and
the analyses that chemically proÐled the major odour
compounds of pig and chicken livestock wastes (Hobbs
et al 1995). In the latter study, an electronic nose was
used to discriminate odours through sensor response
patterns. Pattern recognition by the electronic nose also
has been used to determine food quality and di†erentiate between raw food materials such as meat, grains
and types of cheese (Pisanelli et al 1994 ; Brewer and
Vega 1995 ; Jonsson et al 1997 ; Muir et al 1997) and to
predict storage time and quality of ground beef
(Winquist et al 1993). Analysis of vapours produced by
microorganisms involved in sausage fermentation indicated that the method could be used to assess contamination of food products (Rossi et al 1995).
The purpose of the present work was to evaluate the
speciÐcity, sensitivity and reproducibility of the electronic nose when used to characterise and proÐle headspace VOCs from bacterial cultures isolated from
broiler carcasses and grown on deÐned media. The
species chosen were representative of potential pathogens commonly found in bioÐlms from the food processing environment. The inÑuence of parameters for
bacterial growth, such as medium, temperature and
phase of growth were considered and related to changes
in the concentration of VOCs as determined objectively
by solid-phase micro-extraction (SPME) GC-MS
Sample preparation
The source and names of bacterial species used in this
study are shown in Table 1. Field isolates were obtained
from whole carcases collected from a commercial
broiler processing plant after the New York rinse. Each
carcass was rinsed with 100 ml phosphate-bu†ered
saline. Aliquots of the rinse were diluted with trypticase
soy broth (TSB) in duplicate tenfold series, 1 ml of each
plated on plate count agar (PCA) and the plates incubated for 18 h at 37¡C. Isolates were selected for identiÐcation after multiple passages on PCA. For long-term
maintenance, aliquots of bacterial cultures were stored
at [30¡C in TSB containing 5% glycerol. To resuscitate, an aliquot was thawed and 200 ll was added to
10 ml TSB, then incubated at 35¡C for 18 h, unless
otherwise stated. Sensitivity tests of the instruments
were performed on tenfold dilution series of 18-h cultures, using TSB, phosphate-bu†ered saline (PBS) or
water as the diluent in separate experiments.
Sample analysis by digital aroma technology
A 5-ml aliquot of the incubated culture was transferred
to a 750-ml capacity, single-use, disposable pouch,
which was then Ðlled with air that had been Ðltered
through indicating Drierite, (WA Hammond Drierite
Bacterial species used in cultures to evaluate head space volatile organic compounds by the electronic nose and gas chromatography mass spectral analysis
% ConÐdencea
Cells ml~1 b
Salmonella enteritidis
Escherichia coli
L isteria monocytogenes
Klebsiella pneumoniae
Enterobacter cloacae
Pseudomonas aeruginosa
Acinetobacter calcoaceticus
Field isolated
Field isolate
Field isolate
Field isolate
2É91 ] 108
2É66 ] 108
1É08 ] 108
3É66 ] 108
4É99 ] 108
4É99 ] 108
3É53 ] 108
a Level of conÐdence for identiÐcation of experimental cultures, performed on a
Vitek instrument (bioMerieux, Inc, Hazelwood, MO, USA).
b Samples incubated in trypticase soy broth 18 h at 35¡C prior to testing. Each
concentration shown was the mean of four experiments.
c American Type Culture Collection (ATCC), Rockville, MD, USA. IdentiÐcation
of experimental cultures were veriÐed by Vitek.
d The method for selecting and maintaining Ðeld isolates is described in the
Experimental section.
Comparison of volatile compounds produced by bacteria from poultry
Co, Xenia, OH, USA). Pouches were stored in a 35¡C
conditioning cabinet for 5 min for temperature equilibration and then sampled with the AromaScanner
bench-top instrument (Foss Food Technology Corp,
Eden Prairie, MN, USA). Temperature and humidity
were monitored during sampling, and the relative
humidity of sample headspace and reference air were
controlled to 16% to allow consistent headspace generation. Between samples, the vapours from a wash bottle
containing approximately 20 ml of a 2% butanol solution were sampled for 10 s as a sample line wash. A
control of TSB was run as the Ðrst and last sample each
Data analysis for digital aroma technology
The combined responses from the sensors were used by
the AromaScanner software to generate a pattern characteristic of the controlled headspace from each sample.
The patterns were displayed as line graphs or histograms and stored in databases for later comparison.
Multiple discriminant analysis reduced the data for
each species into two dimensions viewed in a single
plot. The patterns, or proÐle data, were analysed by
cluster analysis (Sammon mapping), and then plotted as
a map. The axis of each map was in units of Euclidean
Distance (ED) (Persaud 1992), which is a measure of
di†erences between samples : the greater the ED, the
greater the di†erence. An ArtiÐcial Neural Network
(ANN) classiÐed and retained the data for comparison
with subsequent unknowns. Succeeding samples were
classiÐed by reference to the database of previously
encountered patterns, and correlation was displayed as
a percentage conÐdence level.
SPME extraction, GC analysis of headspace volatiles
For each species evaluated, two culture tubes
(150 ] 16 mm), each containing 10 ml TSB, were inoculated with 200 ll of the speciÐc culture and incubated
for 18 h at 35¡C. Two control tubes of TSB were prepared and incubated similarly. After incubation, aliquants from one tube were added to the second to bring
the depth of the culture to 110 mm, which resulted in
consistent headspace volume among samples. Thorough
mixing was succeeded by capping with a 20-mm silicone
septa, which was covered with ParaÐlm to assure adequate sealing. The tubes were then placed in a 37¡C
water-bath for 30 min before sampling for headspace
VOC equilibration.
A 100-lm polydimethylsiloxane (PDMS), manually
operated SPME Ðbre and holder system (Supelco Inc,
Belfonte, PA, USA) was used for headspace sampling.
These Ðbres are a recent innovation that have been used
for the extraction of VOC from the headspace of such
diverse biological materials as Ñavour volatiles from
fruit beverages (Penton 1996), forensic and pharmaceutical samples (Mindrup 1995), volatiles from soil
and water samples (Schumacher 1997), organic compounds from environmental samples (Zhang and Pawliszyn 1993) and the volatile compounds from human
breath (Grote and Pawliszyn 1997). The PDMS Ðbre
was inserted through the spectrum of the sample tube
and allowed to equilibrate with the headspace volatiles
for 30 min. The Ðbre was then retracted into the barrel
of the syringe and immediately inserted into the injector
of the GC for 1 min desorption of the entrapped VOCs.
Chromatographic Ñame ionization detection (GCFID) analyses of the VOCs were performed with a
Perkin-Elmer Autosystem XL gas chromatograph
(Perkin-Elmer, Norwalk, CT, USA) that was equipped
with a 60 m ] 0É25 mm (id), 0É25-lm Ðlm thickness and
DB-1 column (J & W ScientiÐc, Folson, CA, USA).
Helium was the carrier gas at 30 psig, which resulted in
a linear velocity of 30 cm s~1 with the column at 40¡C.
Chromatograph operating conditions were as follows :
initial oven temperature 40¡C with no hold time, then
programmed to 275¡C at 8¡C min~1 and held for
5 min ; injector temperature 250¡C ; detector temperature 310¡C. The injector was held in the splitless
mode for 1 min after insertion of the Ðbre ; the purge
was then automatically engaged.
GC-MS analyses
Qualitative analysis of the extracted volatiles was performed with a Finnigan MAT GCQ mass spectrometer
(Finnegan, San Jose, CA, USA) operated in the
electron-ionisation (EI) mode. Electron energy was
70 eV, multiplier voltage 1100 V, source temperature
200¡C and transfer line 240¡C. Spectral data was
acquired over a mass range of 28È200 amu at a scan
rate of 0É6 s scan~1. Chromatographic conditions for
the interfaced GC were as follows : column
60 m ] 0É25 cm (id), 0É25-lm Ðlm thickness DB-1 (J &
W ScientiÐc) using He carrier gas at 28 cm s~1 linear
velocity at 40¡C ; oven temperature initially 40¡C with
1 min hold, then programmed to 275¡C at 8¡C min~1
and held for 5 min ; injector temperature 225¡C. The
SPME absorption and desorption time was the same as
in the preceding chromatographic conditions, with the
injector (0É75-mm liner) operated in the splitless mode
for 1 min, then in the purge mode for the remainder of
the run.
Analysis of the VOCs in the headspace of samples
tested by digital aroma technology detected the presence of each of the bacterial species tested : Salmonella
J W Arnold, S D Senter
enteritidis, Escherichia coli, L isteria monocytogenes,
Klebsiella pneumoniae, Enterobacter cloacae, Pseudomonas aeruginosa and Acinetobacter calcoaceticus
(Table 1). After data was collected for multiple samples
of each species tested, the electronic nose was able to
distinguish each of the cultured species from each other
in subsequent analysis. Responses to the di†erent
samples and mapped data are shown in Fig 1. Di†erences or similarities between the bacterial species are
shown by the relative spatial separation of the data
point clusters.
Fig 1. Mapping of the response patterns generated by the
electronic nose for bacterial species. Samples were incubated
in TSB, 18 h, 35¡C prior to testing. Each point represents the
data taken from separate experiments performed on nine different days : (=) Salmonella enteritidis ; (…) Escherichia coli ;
(>) Enterobacter cloacae ; (@) Pseudomonas aeruginosa. For
each sample, the Sammon mapping technique reduced the
responses from the 32 sensors to a single data point and compared the points by non-linear dimensional analysis.
Sample response patterns were reproducible over time.
A database, generated for each species, contained patterns from samples tested on each of nine di†erent days
(Fig 1). The responses to the sensors of each species
were stable over the course of the experiment. Multiple
classes or descriptor levels of bacterial odour proÐles
were processed and retained by the ANN for comparison of new samples with previously encountered
The sensitivity and discriminating ability of the ANN
increased as more data points were collected for the
standard samples. Sensitivity of the system was further
tested by a dilution experiment in which the bacterial
cultures were diluted in a tenfold series of TSB, PBS
and water. When data from the samples shown in Fig 1
were used to train the ANN and diluted samples were
compared, recognition was achieved.
Chromatographic analyses
Fig 2. Comparative gas chromatograms of volatile organic
compounds (VOCs) from the headspace of Escherichia coli
and Pseudomonas aeruginosa cultures using polydimethylsiloxane (PDMS), solid-phase micro-extraction (SPME) Ðbres
(Supelco Inc, Bellfonte, PA, USA) for extraction and concentration. B refers to the VOCs isolated from the culture broth,
and the numerals refer to the identiÐed bacteria VOCs listed
in Table 2.
Gas chromatograms of the VOCs isolated from the
headspace of E coli and P aeruginosa are presented in
Fig 2 and represent the diversity in the headspace
VOCs isolated from cultures of the eight species
exposed to the PDMS Ðbres. Compounds identiÐed
from the isolates are presented in Table 2 as area percentage of the chromatograms with threshold level of
the PE-Nelson Turbochrom-4 integration system
(PerkinÈElmer) set at 500 lV s~1. IdentiÐed compounds were predominantly alcohols, with ethanol
being the major constituent in all species except P aeruginosa and A calcoaceticus. In these species, components
characteristic of the other species were absent except for
P aeruginosa, which produced only 3-methyl-1-butanol
and phenethyl alcohol in measurable quantities. Species
producing the greater number of compounds, predominantly alcohols, were S enteritidis, E coli, K pneumoniae
and E cloacae. L isteria monocytogenes was distinguished
Comparison of volatile compounds produced by bacteria from poultry
Volatile organic compound isolatesa from the headspace of bacterial cultures from processed poultry by
polydimethylsiloxane, solid-phase micro-extraction (comparative areas of the chromatograms)
RT b
3 : 76
4 : 20
4 : 98
6 : 07
12 : 92
13 : 68
16 : 89
17 : 10
17 : 22
20 : 84
23 : 87
24 : 17
3-Methyl butanal
Phenethyl alcohol
Bacteria speciesc and percent aread
a IdentiÐcation by comparison of mass spectra and chromatographic retention times with authenic standards.
b Retention time of the individual compounds identiÐed.
c SE, Salmonella enteritidis ; EsC, Escherichia coli ; LM, L isteria monocytogenes ; KP, Klebsiella pneumoniae ; EnC, Enterobacter cloacae ; PA, Pseudomonas aeruginosa ; AC, Acinetobacter calcoaceticus bio anitratus.
d Percentage area of the chromatogram.
by the production of 3-methyl butanal and E coli by the
production of indole. Extractable volatiles from the
headspace of TSB were predominantly pyrazine derivatives and benzaldehyde.
Because traditional human evaluation of odour is very
subjective, digital aroma technology should lend itself
well to the development of a system based on objective
measurements. The use of human odour panels to
evaluate and control the quality of raw materials for
spoilage, or bacterial contamination of Ðnished products, is labour-intensive, time-consuming, expensive,
prone to errors and can be hazardous to human health.
Objective odour measurement for improving quality
control from raw material to Ðnished product could
provide real-time detection of process problems. Early
detection and measurement of hazardous or contaminated samples would have cost-saving beneÐts for raw
material quality control, new product development and
Ðnal product standardisation. Digital aroma technology
can alleviate these concerns.
Results showed that digital aroma technology was
sufficiently sensitive to measure the volatiles in the
headspace of bacterial samples, and the method was
reproducible within the limits of the analyses. Changes
in sensor resistance generated data to cluster and group
samples. The array of sensors had di†ering degrees of
selectivity towards di†erent volatile compounds, as evidenced by the GC-MS analyses where substantial diversity was observed by species. Samples were processed
rapidly, and data was output in graphical form and
stored to later compare and classify succeeding samples.
The sensitivity and discriminating ability of the instrument was improved as more data points were collected
and further processed. Neural network algorithms performed real-time recognition ; conÐdence levels were
reported for subsequent data validation.
It was not the intention of this work to discover new
VOCs produced by known species of bacteria in a food
processing environment, but to detect and proÐle headspace VOCs by a new analytical technique (the electronic nose) and conÐrm observed di†erences by a new
isolation technique (SPME) and standard analytical
procedures (GC-MS). Selectivity of the PDMS Ðbres
used was a limiting factor in the extraction of these
volatiles for GC-MS analyses ; however, the availability
of di†erent Ðbres has been improved since the completion of this study and may o†er an improved
approach to this type of analysis. These experiments
suggest that digital aroma technology may have potential for studying the VOCs of bacteria. However, the
aroma technology system needs further development to
improve reproducibility and sensitivity and to reduce
the time required for the experimental work and data
The preceding results acknowledge di†erences in the
headspace VOCs that constitute the complex odours
J W Arnold, S D Senter
generated by bacteria and shows that a sensory study
on the basis of digital aroma technology, static headspace analysis with SPME GC-MS analysis is a promising approach to detect the key compounds and
bacterial species implicated in food spoilage and is
important for food safety.
The authors wish to thank T Breedlove, P Mason and
K Tate for technical assistance.
Aishima T 1991 Discrimination of liquor aromas by pattern
recognition analysis of responses from a gas sensor array.
Anal Chim Acta 243 293È300.
Brewer M S, Vega J D 1995 Detectable odor thresholds of
selected lipid oxidation compounds in a meat model system.
J Food Sci 60 592È595.
Dalgaard P 1995 Qualitative and quantitative characterization of spoilage bacteria from packed Ðsh. Int J Food
Microbiol 26 319È333.
Davide FA M, Di Natale C, DÏAmico A 1994 Self-organizing
multi-sensor systems for odour classiÐcation : internal categorization, adaptation and drift rejection. Sens Actuators B
18 244È258.
Dodd G, Bartlett P, Gardner J 1991 Complex sensor systems :
odour detection by the sense of smell and by electronic
noses. Biochem Soc T rans 19 36È39.
Grote C, Pawliszyn J 1997 Solid-phase microextraction for
the analysis of human breath. Anal Chem 69 587È596.
Guth H, Grosch W 1994 IdentiÐcation of the character
impact odorants of stewed beef juice by instrumental
analyses and sensory studies. J Agric Food Chem 42 2862È
Hines E L, Gardner J W 1994 ArtiÐcial neural emulator for
an odour sensor array. Sens Actuators B 19 661È664.
Hobbs P J, Misselbrook T H, Pain B F 1995 Assessment of
odours from livestock wastes by a photoionization detector,
an electronic nose, olfactometry and gas chromatographyÈ
mass spectrometry. J Agric Eng Res 60 137È144.
Hodgins D, Simmonds D 1995 The electronic nose and its
application to the manufacture of food products. J Autom
Chem 17 179È185.
Jonsson A, Winquist F, Schnurer J, Sundgren H, Lundstrom I
1997 Electronic nose for microbial quality classiÐcation of
grains. Int J Food Microbiol 35 187È193.
Mindrup R F 1995 Solid phase microextraction simpliÐes
preparation of forensic, pharmaceutical, and food and beverage samples. T he Reporter (Supelco) 14 2È5.
Muir D D, Hunter E A, Banks J M 1997 Aroma of cheese. 2.
Contribution of aroma to the Ñavour of Cheddar cheese.
Milchwissensch Milk Sci Int 52 85È88.
Newman A R 1991 Electronic noses. Anal Chem 63 585AÈ
Penton Z 1996 Flavor volatiles in a fruit beverage with
automated SPME. Food T est Anal 2 16È18.
Persaud K C 1991 Odour detection using sensor arrays. Anal
Proc 28 339È341.
Persaud K C 1992 Electronic gas and odour detectors that
mimic chemoreception in animals. T rends Anal Chem 11
Pisanelli A M, Qutob A A, Travers P, Szyszko S, Persaud K
C 1994 Applications of multiarray polymer sensors to food
industries. L ife Chem Rep II 303È308.
Rossi V, Talon R, Berdague J L 1995 Rapid discrimination of
Micrococcaceae species using semiconductor gas sensors. J
Microbiol Meth 24 183È190.
Schumacher T L 1997 Fast screening of water and soil
samples using solid phase microextraction (SPME). T he
Reporter (Supelco) 16 8.
Shurmer H V, Gardner J W 1992 Odour discrimination with
an electric nose. Sens Actuators B 8 1È11.
Winquist F, Hornsten E G, Sundgren H, Lundstrom I 1993
Performance of an electronic nose for quality estimation of
ground meat. Meas Sci T echnol 4 1493È1500.
Zhang Z, Pawliszyn J 1993 A by a rapid, new analytical procedure (SPME analysis of organic compounds in environmental samples by headspace solid phase microextraction).
J High Res Chrom 16 689È692.
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