Comparison of human axillary odour profiles obtained by gas chromatography/mass spectrometry and skin microbial profiles obtained by denaturing gradient gel electrophoresis using multivariate pattern recognition

Article: Mouse urinary biomarkers provide signatures of maturation, diet, stress level, and diurnal rhythm.
Michele L Schaefer, Kanet Wongravee, Maria E Holmboe, Nina M Heinrich, Sarah J Dixon, Julie E Zeskind, Heather M Kulaga, Richard G Brereton, Randall R Reed, Jose M Trevejo[Show abstract] [Hide abstract]
ABSTRACT: Body fluids such as urine potentially contain a wealth of information pertaining to age, sex, social and reproductive status, physiologic state, and genotype of the donor. To explore whether urine could encode information regarding environment, physiology, and development, we compared the volatile compositions of mouse urine using solidphase microextraction and gas chromatographymass spectrometry (SPMEGC/MS). Specifically, we identified volatile organic compounds (VOCs) in individual urine samples taken from inbred C57BL/6JH2(b) mice under several experimental conditionsmaturation state, diet, stress, and diurnal rhythms, designed to mimic natural variations. Approximately 1000 peaks (i.e., variables) were identified per comparison and of these many were identified as potential differential biomarkers. Consistent with previous findings, we found groups of compounds that vary significantly and consistently rather than a single unique compound to provide a robust signature. We identified over 49 new predictive compounds, in addition to identifying several published compounds, for maturation state, diet, stress, and timeofday. We found a considerable degree of overlap in the chemicals identified as (potential) biomarkers for each comparison. Chemometric methods indicate that the strong grouprelated patterns in VOCs provide sufficient information to identify several parameters of natural variations in this strain of mice including their maturation state, stress level, and diet.Chemical Senses 04/2010; 35(6):45971. · 3.22 Impact Factor  SourceAvailable from: Anna Cohuet
Article: Human Skin Volatiles: A Review.
[Show abstract] [Hide abstract]
ABSTRACT: Odors emitted by human skin are of great interest to biologists in many fields; applications range from forensic studies to diagnostic tools, the design of perfumes and deodorants, and the ecology of bloodsucking insect vectors of human disease. Numerous studies have investigated the chemical composition of skin odors, and various sampling methods have been used for this purpose. The literature shows that the chemical profile of skin volatiles varies greatly among studies, and the use of different sampling procedures is probably responsible for some of these variations. To our knowledge, this is the first review focused on human skin volatile compounds. We detail the different sampling techniques, each with its own set of advantages and disadvantages, which have been used for the collection of skin odors from different parts of the human body. We present the main skin volatile compounds found in these studies, with particular emphasis on the most frequently studied body regions, axillae, hands, and feet. We propose future directions for promising experimental studies on odors from human skin, particularly in relation to the chemical ecology of bloodsucking insects.Journal of Chemical Ecology 04/2013; · 2.46 Impact Factor  SourceAvailable from: Stefan SchulzNiels O Verhulst, Yu Tong Qiu, Hans Beijleveld, Chris Maliepaard, Dan Knights, Stefan Schulz, Donna BergLyons, Christian L Lauber, Willem Verduijn, Geert W Haasnoot, Roland Mumm, Harro J Bouwmeester, Frans H J Claas, Marcel Dicke, Joop J A van Loon, Willem Takken, Rob Knight, Renate C Smallegange[Show abstract] [Hide abstract]
ABSTRACT: The African malaria mosquito Anopheles gambiae sensu stricto continues to play an important role in malaria transmission, which is aggravated by its high degree of anthropophily, making it among the foremost vectors of this disease. In the current study we set out to unravel the strong association between this mosquito species and human beings, as it is determined by odorant cues derived from the human skin. Microbial communities on the skin play key roles in the production of human body odour. We demonstrate that the composition of the skin microbiota affects the degree of attractiveness of human beings to this mosquito species. Bacterial plate counts and 16S rRNA sequencing revealed that individuals that are highly attractive to An. gambiae s.s. have a significantly higher abundance, but lower diversity of bacteria on their skin than individuals that are poorly attractive. Bacterial genera that are correlated with the relative degree of attractiveness to mosquitoes were identified. The discovery of the connection between skin microbial populations and attractiveness to mosquitoes may lead to the development of new mosquito attractants and personalized methods for protection against vectors of malaria and other infectious diseases.PLoS ONE 01/2011; 6(12):e28991. · 3.73 Impact Factor
Page 1
Comparison of human axillary odour profiles obtained by gas
chromatography/mass spectrometry and skin microbial profiles
obtained by denaturing gradient gel electrophoresis using multivariate
pattern recognition
Yun Xu,aSarah J. Dixon,aRichard G. Brereton,a,* Helena A. Soini,bMilos V. Novotny,bKarlheinz Trebesius,c
Ingrid Bergmaier,cElisabeth Oberzaucher,dKarl Grammer,dand Dustin J. Penne
aCentre for Chemometrics, School of Chemistry, University of Bristol, Cantocks Close, Bristol, BS8 1TS, UK
bInstitute for Pheromone Research and Department of Chemistry, Indiana University, 800 E. Kirkwood Ave, Bloomington, IN, 47405, USA
cVermicon AG, EmmyNoetherStr. 2, 80992, Munich, Germany
dDepartment for Anthropology, Ludwig Boltzmann Institute for Urban Ethology, Althanstrasse 14, A1090, Vienna, Austria
eKonrad Lorenz Institute for Ethology, Austrian Academy of Sciences, Savoyenstr. 1a, A1160, Vienna, Austria
Received 19 November 2006; Accepted 22 March 2007
Several studies have shown that microbial action is responsible for many compounds responsible for human odour. In this
paper, we compare the pattern of microbial profiles and that of chemical profiles of human axillary odour by using multivariate
pattern matching techniques. Approximately 200 subjects from Carinthia, Austria, participated in the study. The microbial profiles
were represented by denaturing gradient gel electrophoresis (DGGE) analysis and the axillary odour profiles were determined in the
sweat samples collected by a stirbar sampling device and analysed by gas chromatography/mass spectrometry (GC/MS). Both
qualitative and quantitative distance metrics were used to construct dissimilarity matrices between samples which were then used to
represent the patterns of these two types of profiles. The distance matrices were then compared by using the Mantel test and the
Procrustean test. The results show that on the overall dataset there is no strong correlation between microbial and chemical profiles.
When the data are split into family groups, correlations vary according to family with a range of estimated p values from 0.00 to
0.90 that the null hypothesis (no correlation) holds. When 32 subjects who followed four basic rules of behaviour were selected, the
estimated pvalues are 0.00 using qualitative and <0.01 using quantitative distance metrics, suggesting excellent evidence that there
is a connection between the microbial and chemical signature.
KEY WORDS: multivariate pattern comparison; human odour profile; human microbial profile; gas chromatography/mass
spectrometry; denaturing gradient gel electrophoresis.
1. Introduction
It is well known that axillary microflora play very
important roles in human odour production. Previous
studies have shown that the population density of
certain microorganisms has strong association with the
intensity of odour (Rennie et al., 1990, 1991). Certain
areas of the human body possess a unique odour which
is produced partially by microbial action, especially the
human axillary odour is a combination of secretions
from eccrine, apocrine, apoeccrine and sebaceous glands
together with the local microflora (Sastry et al., 1980;
Sato et al., 1987). Studies have also shown that axillary
odour is absent if Grampositive bacteria are eliminated
from the skin (Marples, 1969; Gower et al., 1985).
In this study, we compare the pattern of microbial
profiles obtained by denaturing gradient gel electropho
resis (DGGE) with the pattern of chemical human
axillary odour obtained by gas chromatography/mass
spectrometry (GC/MS) and explore the common trends
between these two types of profile. Samples were taken
from approximately 200 test subjects. Most subjects were
sampled twice for the microbial signature, and five times
for the chemical signature, each sample taken over dif
ferent fortnights. Our previous studies have shown that
there is strong statistical evidence that both in GC/MS
and DGGE data, reproducible signals exist for individ
ual characterisation (Penn et al., 2007; Xu et al., in
preparation). In this paper, pairwise dissimilarity
matrices (both qualitative and quantitative distance
metrics) were calculated on both GC/MS and DGGE
profiles to determine the patterns of these two types of
data. The similarity of these two patterns was evaluated
by the permuted Mantel test. We also transformed the
dissimilarity matrices to samplelatent variable matrices
using Principal Coordinates Analysis and then com
pared the patterns of these two matrices by using a test
based on Procrustes analysis.
*To whom correspondence should be addressed.
Email: r.g.brereton@bris.ac.uk.
Metabolomics, Vol. 3, No. 4, December 2007 (? 2007)
DOI: 10.1007/s1130600700546427
15733882/07/12000427/0 ? 2007 Springer ScienceþBusiness Media, Inc.
Page 2
2. Experimental
2.1. Test subjects
A total number of 196 subjects participated in this
study. These individuals are from an isolated population
in Carinthia, Southern Austria, belonging to 17 different
families, coded by a single letter (e.g. A). The samples
were taken from July to August, 2005, the odour sam
ples were collected five times for each subject, once per
fortnight while microbial samples were collected twice,
(on the third and fourth fortnights). In this paper,
we only consider the fortnights when the microbial
and odour samples were collected simultaneously. The
subjects were also asked to fill in a survey when the
samples were taken which contains information about
their living habits, such as when was the last washing of
armpit, and when was the last usage of deodorant as
discussed in greater detail in Section 4. Due to experi
mental reasons, some subjects only provided one sample
over fortnights 3 and 4 and they were excluded from the
analysis, making the number of subjects reported in this
paper less than the full population. A total of P = 177
subjects are analysed in this study, each sampled twice
to give 2P samples, each with a corresponding GC/MS
and microbial profile.
2.2. Human axillary odour samples collection and
GC/MS analysis
The volatile and semivolatile compounds from arm
pits were collected by using a newly designed skin roller
device. A stir bar is attached to the device and rolled
over the skin to collect chemical compounds. The stir
bar was then subjected to the GC/MS analysis. Stir bar
sampling on the skin surface is likely to favour com
pounds which bind in the oily layer present on the
human skin: in fact, the relatively long storage stability
time of 20 days under cooled conditions supports the
hypothesis. Compounds with high volatility and of very
hydrophilic characteristics
against using this sampling approach. During the same
day, reproducibility for typical skin compounds taken
from five individuals was 3–25% (RSD, relative stan
dard deviation, three samples per individual). The long
term RSD of the internal standard is 14.3%. More
detailed information about the characteristics and the
reproducibility of both the sampling device and the GC/
MS experiments are given elsewhere (Soini et al., 2006).
The GC equipment for quantitative analysis consisted
of an Agilent 6890N gas chromatograph connected to a
5973i MSD mass spectrometer (Agilent Technologies,
Palo Alto, CA) with a Thermal Desorption Autosampler
(TDSA,Gerstel,Mu ¨ lheimanderRuhr).Positiveelectron
ionisation (EI) mode at 70 eV was used with a scanning
rate of 4.51 scans/s over the mass range of m/z 35–350.
The ion source and quadrupole temperatures were set at
230 ?Cand150 ?C,respectively.The separation capillary
wouldbe discriminated
was DB5MS (20 m·0.18 mm, i.d., 0.18 lm film thick
ness) from Agilent Technologies (Wilmington, DE).
Samples were thermally desorbed in a TDSA automated
system, followed by injection into the column with a
CooledInjectionSystem,CIS4.TheTDSAoperatedina
splitless mode. The temperature program for desorption
was 20 ?C (0.5 min), then 60 ?C/min to 250 ?C (3 min).
Thetemperatureofthetransferlinewassetat280 ?C.The
CIS was cooled with liquid nitrogen to )80 ?C. After
desorption and cryotrapping, the CIS was heated at
12 ?C/s to 280 ?C with the hold time of 10 min. The CIS
inlet was operated in the solvent vent mode, with a vent
pressure of 14 psi, a vent flow of 50 mL/min, and a purge
flow of 50 mL/min. The temperature program in the GC
operation was 50 ?C for 1 min, then increasing to 160 ?C
attherateof5 ?C/min,followedbythesecondrampatthe
rateof3 ?C/minto200 ?C(holdtime16 min).Thecarrier
gas head pressure was 14 psi (flow rate, 0.7 mL/min at
constant flow mode). The GC temperature program
lasted for 52.33 min, with mass spectrometric detection
commencingafteradeadtimeof1.88 min(solventdelay).
To increase throughput, two instruments of identical
specifications were used to analyse the samples. The
configurationofbothinstrumentswasthesame,andtests
had been done to ensure reference samples analysed on
eachinstrumentwereofacceptablesimilarity.Therewasa
slight difference in scan rates between the two instru
ments, with instrument 1 sampling 13,481 scans over the
analysis period, and instrument 2 sampling 13,460 due to
slightly different software versions. In the mass spec
trometry software (Agilent ChemStation) a parameter is
set, below which a scan will be recorded as having zero
intensity.Thisisthedetectionlimitoftheinstrument,and
canbe setby theinstrument operator.In this work, it was
set to 300: this number is essentially arbitrary and will
dependonthespecificinstrument,butinthecontextofthe
current study the smallest peaks which were visible above
the noise were of height around 600 units. Typical peak
heights in a mass channel range from 1000 to 100,000
(varyingfromsampletosample).Themassresolutionwas
reduced to unity before analysis. During the analysis,
test runs using quality control samples were performed
on a regular basis to ensure that the instruments were
performing acceptably.
2.3. Microbial sampling and DGGE analysis
2.3.1. Fixation of samples
Microbial samples were taken in Greifenburg (Carinthia,
Austria) from the armpit of different subjects. Sampling
of the axillary microflora of the armpit was performed by
the washingscrub method of Williamson and Kligman
(1965). A plastic cylinder open at both ends was placed
on the armpit. About 1.5 mL detergent solution was
filled into the cylinder. A glass stirrer was moved with
constant pressure over the skin to detach the micro
organisms. The solution was removed and transferred to
Y. Xu et al./Comparison of human axillary odour profiles 428
Page 3
a sterilised reaction tube and the procedure was repeated.
The obtained solutions were fixed with ethanol at a ratio
1:1. The total sample volume of 6 mL consisted of 3 mL
sampling buffer containing the microbes (sample) and
3 mL of 96% ethanol.
2.3.2. DNA extraction
About 1.3 mL of the sample was centrifuged (10 min,
14,000 rpm) and the supernatant was discarded. This
step was repeated once, by adding 1.3 mL sample to the
pellet and an additional centrifugation step (10 min,
14,000 rpm). The obtained pellet was washed in 200 lL
1· phosphatebuffered saline (PBS). After centrifugation
(10 min, 14,000 rpm) the pellet was resuspended in
100 lL 6% Chelex? 100 solution (BioRad, Munich,
Germany) according to Rodrı`guezLa ´ zaro et al. (2004),
and incubated at 56 ?C for 20 min. The sample was then
thoroughly mixed and incubated further at 100 ?C for
8 min. Subsequently, the sample was mixed and cooled
for 5 min on ice. Next a centrifugation step (10 min,
14,000 rpm) was performed. The supernatant contain
ing the DNA was removed.
2.3.3. PCR amplification of target DNA for DGGE
The extracted genomic DNA was amplified using the
forward primer 341FGC (Muyzer et al., 1993) with a
GC clamp 5¢CGC CCG CCG CGC GCG GCG GGC
GGG GCG GGG GCA CGG GGG GCC TAC GGG
AGG CAG CAG 3¢ and the reverse primer 518R 5¢
ATT ACC GCG GCT GCT GG3¢. The final 50 lL
reaction mixture contained: 2 lL template DNA,
25 pmol primers each, 2.5 U of Taq DNA polymerase
(Promega, Mannheim, Germany), 1fold PCR buffer
(Promega), 75 mM MgCl2(Promega), 10 mM dNTPs,
(Promega). The PCR protocol included a 5 min initial
denaturation at 94 ?C, 30 cycles of 94 ?C for 0.5 min,
44 ?C for 1 min, 72 ?C for 1.5 min followed by 10 min
at 72 ?C for final extension in a Primus 96 thermocycler
(MWG, Ebersberg, Germany). PCR products were
stored at )20?C until further use.
2.3.4. DGGE analysis
DGGE analysis was performed on DcodeTMSystem
(BioRad). Samples were loaded onto a 8% (w/v)
acrylamide gel (37.5:1 acrylamidebisacrylamide) in 1·
TAE buffer with a denaturant gradient ranging from
20% to 60% prepared in accordance with Muyzer et al.
(1995). (100% denaturant contains 7 M urea and
volume ratio of 40% formamide.)
To standardise DGGE gels, reference standards were
applied to each gel. The reference standard consisted of
a mixture of PCR products of 11 different bacterial
species which are commonly found in human skin
samples (Trebesius et al., in preparation). The banding
pattern resulted from PCR products obtained by the
same primer pair as described above.
The electrophoresis was performed at 60 ?C, initially
at 25 V for 15 min following at 130 V for 4 h. The gel
was silver stained based on Sanguinetti et al. (1994) by
the following procedure. About 150 mL of fixing solu
tion (10% ethanol, 0.5% acetic acid) was applied to the
gel and shook gently for 3 min. Subsequently, the
gel was incubated for 10 min at room temperature in a
silver nitrate solution (0.2% AgNO3, 10% ethanol,
0.5% acetic acid). After discharging the silver nitrate
solution a washing step in distilled H2O for 2 min
followed. Hereafter, 150 mL of ‘‘developer solution’’
was applied to the gel (3% NaOH containing, 300 lL of
37% formaldehyde) for 5 min, while shaking gently.
The staining procedure was stopped by incubating the
gel for 5 min in 10% ethanol, 0.5% acetic acid solution.
A typical example of a DGGE gel is presented in
figure 1.
2.4. Software
The GC/MS instrument was controlled by Agilent
ChemStation software versions D.01.00 (system 1) and
D.01.02 (system 2). The GC/MS data was exported to
AIA/netCDF (network Common Data Format) format
and then imported into MATLAB (The Mathworks,
Inc., Natick, MA) using a freely available conversion
tool (available at http:// mexcdf.sourceforge.net/). The
stained gel was transferred on an overhead transparency
sheet and documented on a SnapScan 1236 scanner
(Agfa, Ridgefield Park, NJ). The scanning mode was
transparent, 300 dpi and 24 Bit colour. The resultant
pictures were converted to TIFF files for processing and
imported into MATLAB by using Image Processing
Toolbox. All data processing was performed using
MATLAB version 7.0.4.365, Release 14, Service Pack 2.
Figure 1. A typical DGGE gel.
Y. Xu et al./Comparison of human axillary odour profiles 429
Page 4
3. Data analysis methods
3.1. Data preparation
3.1.1. DGGE data preparation
The images of scanned DGGE gels were processed by an
inhouse image digitisation software. For each lane
(sample) on the gel, the bands were detected and their
positions recorded. The position of each band was then
corrected with respect to the positions of reference
standards to cope with different separation behaviour
between gels. The detailed description is given in a
separate paper (Xu et al., in preparation) and not
repeated here for brevity.
The main difficulty in this study is the band
assignment. DGGE is a 1D technique hence the only
useful information for the band assignment is the
position information and there is no spectroscopic or
other information which can help in identifying which
band is which. When there were many lanes and gels, it
is often hard to assign some bands unambiguously, i.e.
when two bands come from different lanes/gels with
slightly different positions, it is difficult to decide
whether these two bands originate from the same
microorganism or two different ones. However, data
analysis methods such as PCA (Wold et al., 1987;
Brereton, 2003) based on a samplefeature matrix
require each variable to originate from the same
source, otherwise the results will be influenced by the
incorrect assignments of variables. To overcome this
problem, we used a pairwise dissimilarity matrix
between sample profiles rather than a full sample
variable matrix since it is much easier to measure the
dissimilarity between two samples as the number of
ambiguous assignment of the bands is small when
comparing two lanes, rather than using global band
assignments. Previously we developed a fuzzy distance
metric to measure this dissimilarity. The metric is
weighted by a fuzzy function and the value of this
function depends on the difference of the positions of
two bands, so it does not require an accurate assign
ment of each band and tolerates slight imprecision of
the positional information. We briefly summarise the
method here which will be described elsewhere (Xu
et al., in preparation): in this paper we use the square
root of the intensities rather than the raw intensities to
be comparable to the GC/MS data, otherwise the
method is identical.
The first step is to correct the position of bands in
each lane according to a reference lane consisting of
extracts from 11 microbes, the corrected position relates
to how close the bands are to the microbial extracts in
the nearest reference lane, for example a band that is
detected half way between bands 3 and 4 in the reference
lane will be given a value of 3.5. These positions relative
to the reference bands are then used for subsequent
analysis as follows.
The next step is to determine a dissimilarity measure
between the reference band corrected profiles in each
lane. Suppose lane i has Nibands and lane j has Njbands,
and also assume Ni£Nj. A Ni· Njposition difference
matrix is constructed; each row representing the posi
tional difference of one band in lane i to all the bands in
lane j. The minimum value in each row will be the nearest
band. For each dissimilarity metric calculation, there can
be Nicomparisons at most. However, sometimes two or
more bands in lane i share the same nearest neighbour in
lane j. In such case, we only consider the closest pair and
the others will be discarded (i.e. consider them as unique
bands in lane i). We denote the number of band pairs k
that have been identified as b (b£Ni).
Both quantitative and qualitative (presence/absence
criterion) fuzzy weighted distance metrics are used to
measure the dissimilarity between two DGGE samples.
The quantitative metric takes into account band inten
sities as well as uncertainties in position using the fol
lowing equation:
dði;jÞ ¼ 1 ?
P
b
k¼1
s
ffiffiffiffiffiffiffiffiffiffiffiffiffi
P
xik:xjk
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
n¼1
p
? wk
??
Ni
xin
P
Nj
n¼1
xjn
ð1Þ
where b is the number of pairs being considered (see
above) each matched pair being denoted by k, where xik
and xjkare the integrated intensities of the band pair k in
lanes i and j. The qualitative distance metric is the
square root of the fuzzy weighted Jaccard distance
(Jaccard, 1908) metric defined as
dði;jÞ ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1 ?
P
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1 ?
Niþ Nj? b
P
b
k¼1
wk
p
k¼1
wkþ Niþ Nj? b ?P
P
p
k¼1
wk
v
u
v
t
u
u
u
u
u
u
u
t
u
¼
b
k¼1
wk
ð2Þ
The fuzzy weight function wk= H(Ddk) is determined
by the absolute difference in the corrected position (Ddk)
of the band pair k (=1 to p) defined by
wk¼ HðDdkÞ ¼1
2? erfcðA ? Ddk? SÞð3Þ
where function erfc(x) is the complementary error
function(Zwillinger, 1997),
2
xe?t2dt and t is an integration factor, from x to
+¥. S and A are two adjustable free parameters which
control the sensitivity to the positional difference of the
definedbyefrcðxÞ ¼
p?R1
Y. Xu et al./Comparison of human axillary odour profiles 430
Page 5
weight function. With an appropriate setting of S and A
this function has the following properties.
(a) When the difference is small enough, the output is 1
or very close to 1,
(b) when the difference is moderate, the output is
between 0 and 1 and decays exponentially with
increasing Dd,
(c) when the difference is large enough, the output is 0
or very close to 0.
In this paper, S is set to 5 and A to 30. If the corrected
positional difference is closer than about 0.1 they are
assumed to be a perfect match, if differing by more than
0.2 they are assumed not to match at all.
3.1.2. GC/MS data preparation
A peak table was constructed based on all the GC/MS
total ion chromatograms which is a matrix whose rows
correspond to samples and whose columns correspond
to summed peak areas over all significant masses of
the corresponding compounds. The method for peak
identification and alignment has been described in detail
in Dixon et al. (2006). The peaks corresponding to
known background compounds such as siloxanes orig
inating from stir bars or vial septa were identified based
on their mass spectra and removed from the peak table.
Next, peaks that occur in 4 or less samples in the overall
dataset are removed as they are unlikely to have any
diagnostic significance. A further reduction in the size of
the peak table is performed to consider only those peaks
that are detected in at least 4 out of 5 fortnights in at
least one subject leaving NGCMS =373 peaks in total
over all subjects: this is so as to retain peaks that are
likely to relate to stable biological signatures which are
detected in the majority of samples from one or more
individuals. Finally peak areas were scaled as follows:
the square root of the peak areas were computed, these
peak areas were summed to a constant total of 1 in each
chromatogram. The reason we square root peak areas is
that there can be large peaks in some chromatograms
that could dominate the profile, hence distorting data
after normalisation; a common alternative of log scaling
is not suitable in this study because many peaks are not
detected in chromatograms so a large number of zero
numbers would need to be replaced, square root scaling
is a common alternative for reducing contrasts. More
detailed discussion about these data transformation
methods can be found elsewhere (Box and Cox, 1964;
Huber et al., 2002).
In order to compare the patterns of the GC/MS and
DGGE data, we constructed two pairwise distance
matrices for each data set, one using qualitative dis
tance metric and another using quantitative distance
metric. For the quantitative distance between GC/MS i
and j we use
dði;jÞ ¼ 1 ?
P
NGCMS
k¼1
½ðxik? xjkÞ?
xi
k k ? xj
????
ð4Þ
where xikis the normalised square root intensity of peak
k in chromatogram i and xiis the vector containing all
NGCMS(=373) intensities of this chromatogram. For
the qualitative distance we use the square root of the
Jaccard distance (Jaccard, 1908)
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
where a are the number of peaks that are common to
both samples, and b and c the number of peaks unique
to one of the two samples.
The reason we square root the distances will be
explained in Section 3.2.2.
dði;jÞ ¼
1 ?
a
a þ b þ c
r
ð5Þ
3.2. Comparison of multivariate patterns
3.2.1. Mantel test
To compare two distance matrices directly, the Mantel
test (Mantel, 1967) is a commonly used and effective
method. Given two distance matrices A and B with
dimensions of N·N, the lower (or upper) triangular
parts of the matrices are unfolded into two vectors:
a ¼ fa11;a21;...aN1;a22;...aN2;...;aNNg
b ¼ fb11;b21;...bN1;b22;...bN2;...;bNNg where amnand
bmnare the mth row and nth column of the elements in
matrices A and B respectively. The correlation coeffi
cient r between a and b is then calculated.
The significance of the correlation is determined
using a Monte Carlo simulation. The order of the
elements in one vector is permuted while the other
remains unchanged and the correlation coefficient
between these two vectors is calculated. This process is
repeated sufficiently large number of times and the
correlation coefficients obtained by these permutations
are used to form an empirical null distribution. The
significance of the correlation coefficient r calculated
above is computed to provide an indication as to
whether this belongs to the null distribution or not.
For an estimate of the significance of the correlation
coefficient we can calculate the proportion of times the
Monte Carlo simulation results in a correlation coeffi
cient greater than the observed value r (one tailed test)
or the absolute value is greater than r (two tailed test).
These approximate to probabilities and are denoted by
p values below. In this paper we use the two sided
Mantel test.
and
3.2.2. Multidimensional scaling and the Procrustean test
A major drawback of the Mantel test is that it is not
possible to visualise the data structure as it is based on a
Y. Xu et al./Comparison of human axillary odour profiles 431
Page 6
single number (the correlation coefficient), between two
sets of data. Multidimensional scaling (MDS) is a set of
methods that can transform the pattern represented by a
pairwise distance matrix back to a matrix of samples
and latent variables. MDS constructs a new sample
latent variable matrix and the relative configuration of
the data points has been preserved and can be visualised,
if appropriate, by using 2D or 3D scatter plots. We
used a common MDS method called Principal Coordi
nate Analysis (PCO) (Gower, 1966) to transform the
distance matrices to samplefeature matrices. A brief
description of PCO is given below.
Given an N·N symmetric distance matrix D, each
element dijin D represents the distance from sample i to
sample j.
(1)
A ¼ ðaijÞ= ?1
2? d2
ij
??
ð6Þ
(2) Calculate Gower?s centred matrix G using
G ¼
I ?1
N1 ? 10
??
? A ?
I ?1
N1 ? 10
??
ð7Þ
where I is the identity matrix and 1 is a column of 1s of
length N.
(3) Perform eigendecomposition on matrix G so that
G ¼ Q ? K ? Q0
ð8Þ
where K is a diagonal matrix with eigenvalues on the
diagonal in descending order. Q is a square matrix
whose columns are corresponding orthonormal eigen
vectors. It is important to point out that the eigen
values and eigenvectors here are defined by linear
algebra: each pair of eigenvalues k and corresponding
eigenvectorsq of the square
G ? q ¼ k ? q. The definition of an eigenvalue often used
in the chemical PCA literature (Wold et al., 1987;
Brereton, 2003), the sum of squares of scores, has a
similar property to the eigenvalues as defined above:
however unlike eigenvalues in PCA literature, linear
algebra defined eigenvalues cannot be guaranteed to be
nonnegative for all types of matrices. A matrix G
having all diagonal elements of K positive is called
positive definite ; if one or a few diagonal elements of
K are 0 and others are all positive G is denoted a
positive semidefinite matrix (p.s.d). In context of PCO,
nonnegative eigenvalues can only be guaranteed when
the Euclidean distance has been used for determining
dissimilarities (Gower, 1966).
(4) If l dimensions are desired for modelling the data,
retain the first l columns in Q and first l rows and l
columns in K, the PCO scores matrix T can be
matrixGsatisfies
obtained by T = QÆK1/2with imaginary elements if
there are negative eigenvalues.
It is important to pay special attention to the values of
the diagonal elements of K. If a nonEuclidean distance
is used to construct the distance matrix, it is possible
that one or a few eigenvalues in K are negative. In such
case, the original distance matrix cannot be perfectly
embedded into Euclidean space because some PCs exist
in imaginary space. However, if the magnitude of the
negative eigenvalues is relatively small compared to that
of positive eigenvalues, the negative part is usually
indicative of noise. The rationale behind such method
ology is given by the WielandtHoffman theorem
(Golub and Loan, 1996): the best approximation of a
matrix G whose eigenvalues K contain negative elements
is by setting all negative elements in K to zero to give a
matrix^K and then reconstructing^G using Q and the new
matrix^K. This method can be justified as noise reduc
tion so long as the negative eigenvalues are small relative
to the positive ones and has been extensively used in
PCO and kernel learning related literature (Krzanowski
and Marrior, 1994; Graepel et al., 1998; PeresNeto and
Jackson, 2001; Pekalska et al., 2002). In our study, the
quantitative dissimilarity matrices given by equations (3)
and (4) contain only a few very small negative eigen
values but Jaccard distance matrices often result in some
very large negative eigenvalues. Fortunately, as dem
onstrated by Gower and Legendre (1986), although the
Jaccard distance itself is nonEuclidean, the square
rooted Jaccard distance has Euclidean properties and
hence can be perfectly embedded into Euclidean dis
tance space. The square rooted fuzzy weighted Jaccard
distance sometimes still has some negative eigenvalues
but such imperfection has been largely reduced, reflected
by the ratios of the sum of absolute negative eigenvalues
over that of positive eigenvalues are significantly lower
than before and never exceed 10%, hence we just ignore
all PCs with negative eigenvalues and keep those with
positive eigenvalues. Hence in equations (2) and (5) we
use the square root of the Jaccard dissimilarity metric.
Once the pairwise distance matrices are transformed
to samplelatent variable matrices using PCO there are
various methods available to measure the correlation
between these matrices. We employ the Procrustean
Test (Jackson,1995) to measure
between the two types of data inputs (GC/MS and
DGGE). The most attractive aspect of this method is
that it is possible to superimpose two patterns on the
same scatter plot and visualise the matching. The test
involves two steps.
the correlation
(1) The Procrustean transformation (Rohlf and Slice,
1990) is performed on one data matrix and the other
used as the target. The transformed matrix as well as
the matching error when it is compared to the target
matrix is calculated as follows. Given two matrices
A and B to be compared, we set matrix A as the
Y. Xu et al./Comparison of human axillary odour profiles 432
Page 7
target and transform B to match A as closely as
possible. The Procrustean transformation involves
three basic operations:
(a) Translation. Column mean centre both A and B and
scale both matrices so that the sum of squares of all
the elements in each matrix equals 1. The mean
vector and the root sum of squares of all elements in
A has is computed for further use, as discussed be
low. We denote the matrices after the translation as
Ascland Bscl.
(b) Rotation. The rotation matrix R is obtained by
performing Singular Value Decomposition (Gentle,
1998) on the matrix A¢sclBsclto get A0sclBscl¼ USV0,
R = VU¢ and in this application all N nonzero
components are retained. Note that SVD, unlike
eigendecomposition, always results in a positive
diagonal matrix S.
(c) Scaling. Thescaling
s ¼P
for all the elements in matrix A. The transformed
matrix obtained from B can be calculated by
Btrans¼ s ? Bscl? R þ 1 ? a where a is the row vector of
means of matrix A and 1 is column vector of 1s. The
matching error e is given by e = Btrans)A.
(2) Evaluate the significance level of the matching error
using a Monte Carlo simulation procedure. Like the
Mantel test, a null distribution of the matching error
is derived by permuting the order of samples in one
factorisobtained by
N
n¼1
snn A
k k, where A is the root sum of square
data matrix (the order of the variables remains the
same) while keeping the other unchanged. Procrus
tean transformation is performed on this new pair of
matrices and the matching error computed as de
scribed above. This process is repeated a large
number of times and the matching errors form the
null distribution. The null distribution is then used
to assess the significance level of the observed
matching error and to give p values as described
above for the Mantel test.
Since the matching error can never be negative, the
Procrustean test is always a onetailed test. One can also
visualise the matching of two most significant compo
nents of the data matrices by superimposing the
Procrustean transformed matrix with the target matrix
in a 2D or 3D scatter plot.
It is necessary to note that the p values of both the
Mantel test and the Procrustean test are estimated
from empirical null distributions which are formed by
using Monte Carlo simulations. If none of the
randomised resampling experiments produced a higher
(or lower) value than the true observed value, the
estimated p value is reported as 0. Such a case sug
gests that the observed value is significantly different
from the null distribution. However this does not
necessarily mean that the probability of the observed
value derived from the null distribution is actually 0,
i.e. that the null hypothesis can be rejected at 100%
confidence level.
0.10.05
Correlation Coefficient
0 0.050.1
0
200
400
600
800
s t i
H
f o
. o
N
0.03 0.02 0.0100.01 0.020.03
0
200
400
600
800
Correlation Coefficient
s t i
H
f o
. o
N
0.68 0.6850.690.695 0.7
0
200
400
600
800
Matching Error
s t i
H
f o
. o
N
0.9650.970.975
Matching Error
0.98 0.985 0.99
0
200
400
600
800
s t i
H
f o
. o
N
Null DistributionObserved Value
p=0.0914
p=0.3396
p=0.0774
p=0.3483
(a)(b)
(d) (c)
Figure 2. Mantel test and Procrustean test applied to the full data set of 354 samples: (a) Mantel test on qualitative distance matrices; (b) Mantel
test on quantitative distance matrices; (c) Protest on qualitative distance matrices and (d) Protest on quantitative distance matrices. A total of
10,000 permutations are performed and for the purpose of the bar chart, data are divided into 50 equally spaced bins between the lowest and light
values on the graph, the number of hits is the number of time the relevant parameter falls within a bin.
Y. Xu et al./Comparison of human axillary odour profiles433
Page 8
4. Results
The first step was to compare the patterns of the GC/
MS and DGGE profiles by using all 2P (=354 pairs of
samples). The null distributions for the Mantel and
Procrustean test were obtained using 10,000 permuta
tions. Both the twosided Mantel test and Procrustean
test suggest that there is a weak correlation (p<0.1)
between these two data blocks when qualitative distance
metrics were used while no significant correlation can be
found when quantitative distance metrics were used. The
results are presented in figure 2, using 50 equally spaced
bins as illustrated.
We then performed such a comparison on each of
the family groups separately. At first sight, the results
for each family give apparently inconsistent conclu
sions. Some families showed very significant correlation
between two data blocks while some families do not
show any significant correlation at all. When the
qualitative distance metrics were used, the strongest
correlation between the DGGE and GC/MS signals
come from family D (11 members, 22 samples), both
the Mantel test and Procrustean test result in a value of
p = 0, i.e. not a single case in 10,000 random permu
tations results in such high correlation and the
observed value of correlation coefficient or matching
error of the Procrustean transformation is also far
away from the corresponding null distribution. Family
A (17 members, 34 samples) also shows very strong
correlation at the level of p = 0 but the observed
values are closer to the null distributions compared to
family D. The superimposed PCO scores plots of two
data blocks of these two families are shown in figure 3.
To interpret these graphs, it is necessary to observe
how close the corresponding DGGE and GC/MS
samples appear. Each family member has a unique
identifying number, and the scores of the two samples
from each individual are averaged after the Procrustes
transformation, for simplicity. Note, for example in
figure 3(b) that the two samples for individual 86
appear in the top right corner, 83 in the bottom left
corner, and 91 and 88 are in the middle. Had there
been no correlation we would expect these paired
samples to appear more or less randomly. Families G,
P and Q also show strong correlations (p<0.05);
families B, C, J show weak correlations (p<0.1) but
correlations between the two blocks for families E, H,
L, M, N, O, R, S and U are consistent with the ran
dom model for the null distribution at a level of
p>0.1. When the quantitative distance metrics were
used, only three families, G, P and Q show correlations
whose p values varied from 0.01 to 0.07. The results
from all 17 families are summarised in table 1.
However it is important to consider that human
odour can be significantly influenced by many environ
mental factors, so we examined the behaviour surveys
which the subjects filled in when the samples were taken.
We found that there are considerable variations in
human behaviour, such as the time elapsed from the last
armpit washing, the time from the last use of deodorant
or soap and whether the suggested Tshirt (chemical
free) was worn. This variation was suspected to influ
ence the GC/MS volatile compound profiles. We expect
that such variations can impact subject?s odour, as well
as their microbial profiles. Hence we defined four subject
screening rules to reduce possible influence from envi
ronmental factors.
(1) The time of the last armpit washing must be between
12 and 48 h.
(2) The time of the last usage of deodorant must be no
less than 48 h.
(3) The suggested Tshirt must be worn before the
sampling.
(4) The suggested soap must be used in the last armpit
washing.
0.250.20.150.10.0500.05 0.10.15 0.2
0.25
0.2
0.15
0.1
0.05
0
0.05
0.1
0.15
0.2
1
2
4
5
7
9
10
15
17
18
20
24
25
27
28
30
31
1
2
4
5
7
9
10
15
17
18
20
24
25
27
28
30
31
GC/MS
DGGE
0.4 0.3 0.2 0.10 0.10.2 0.3
0.2
0.15
0.1
0.05
0
0.05
0.1
0.15
0.2
0.25
83
84
85
86
86
87
88
89
90
91
92
93
83
84
85
87
88
89
90
91
92
93
GC/MS
DGGE
(a)
(b)
Figure 3. Superimposed scatter plots of PCO scores of the first two
components of the GC/MS data and Procrustean transformed DGGE
data on families A and D. (a) Family A; (b) Family D. Each number in
the figure represents one test subject; the PCO scores of two repeats of
one testing subject have been averaged to avoid making the plots too
crowded.
Y. Xu et al./Comparison of human axillary odour profiles434
Page 9
By applying these four rules, only 32 of the 177
subjects meet all the requirements. The Mantel test and
Protest were applied to these subjects using a null dis
tribution based on 64 rather than 354 samples. The
results show very strong correlation between two data
blocks at the level of p = 0 with the qualitative distance
metrics and p<0.01 with the quantitative distance
metrics (see figure 4). For comparison purpose, we also
randomlyselected another
remaining 145 subjects that had not obeyed these
behavioural rules (excluding the 32 individuals above)
and did the same experiment and repeat the computa
tions 10 times with different random selections of sub
jects. The results showed that 1 experiment resulted in
strong correlation at the level of p<0.01 and 1 exper
iment resulted in weak correlation at the level of p<0.1
using a qualitative distance metric; 1 experiment resulted
in strong correlation at the level of p<0.05 with the
quantitative distance metric. The results are summarised
in table 2.
We also compared the null distributions for the
qualitative and quantitative distance metrics generated
by Monte Carlo simulation both for the overall popu
lation and for each family separately. Although the null
distribution generation procedures are exactly the same
and the only difference involves using different distance
metrics, the characteristics of the null distributions are
very different. For the Mantel test, both null distribu
tions of qualitative and quantitative distances are cen
tred around 0 as expected (as a correlation coefficient of
0 implies that there is no link between two blocks of
data) but the distributions of the qualitative distance
metrics always appeared to have much larger standard
32subjectsfrom the
0.40.2
Correlation Coefficient
0 0.20.4
0
200
400
600
800
s t i
H
f o
. o
N
0.20.1
Correlation coefficient
0 0.1 0.2
0
200
400
600
800
s t i
H
f o
. o
N
0.4 0.42
Matching Error
0.440.46
0
200
400
600
800
s t i
H
f o
. o
N
0.7 0.750.80.85
0
200
400
600
800
Matching Error
s t i
H
f o
. o
N
p=0
p=0.0082
p=0
p=0.0054
(a)
(c)
(b)
(d)
Figure 4. Mantel test and Procrustean test applied to the selected data set of 32 subjects (64 samples) that obeyed four behavioural rules over the
period of sampling. (a) Mantel test on qualitative distance matrices; (b) Mantel test on quantitative distance matrices; (c) Protest on qualitative
distance matrices and (d) Protest on quantitative distance matrices. A total of 10,000 permutations are performed and for the purpose of the bar
chart, data are divided into 50 equally spaced bins between the lowest and light values on the graph, the number of hits are the number of time the
relevant parameter falls within a bin.
Table 1
The results of Mantel test and Protest on all families
Family
Number
of
subjects
pvalue of Mantel testpvalue of Protest
Qualitative
distance
metric
Quantitative
distance
metric
Qualitative
distance
metric
Quantitative
distance
metric
A
B
C
D
E
G
H
J
L
M
N
O
P
Q
R
S
U
All
17
15
12
11
8
9
11
10
15
8
10
5
9
10
10
9
8
177
0
0.0571
0.0641
0
0.4075
0.0423
0.3114
0.0912
0.2088
0.5007
0.128
0.1763
0.0016
0.0095
0.4331
0.4053
0.2989
0.0914
0.1676
0.2895
0.2764
0.1457
0.4806
0.067
0.355
0.3181
0.1112
0.5592
0.4324
0.2691
0.0185
0.0139
0.3271
0.4513
0.3587
0.3396
0
0.0615
0.0607
0
0.5837
0.0431
0.2451
0.0634
0.4714
0.5884
0.1076
0.1136
0
0.0032
0.4174
0.4187
0.2985
0.0774
0.1231
0.137
0.2514
0.1512
0.8977
0.045
0.3055
0.2181
0.4755
0.5983
0.3478
0.2919
0.0112
0.0103
0.6425
0.519
0.3056
0.3483
All pvalues less than 0.1 are in italic and bold.
Y. Xu et al./Comparison of human axillary odour profiles 435
Page 10
deviation compared to those of the quantitative
distances, i.e. the histogram of the p.d.f. (probability
distribution function) of the null distribution for the
qualitative distance metric is much broader. It may
suggest that for qualitative distance matrices, the per
mutation process has much higher influence on the
calculated correlation coefficient for each simulation.
For the Procrustes test, matching errors are used to
form the null distribution. The most obvious difference
is that the null distribution for the qualitative distance
always has much lower average (centre of the p.d.f.)
compared to that of the quantitative distance. It sug
gests that when the qualitative distance metric is used,
the matching error calculated from Procrustes analysis
could be low even if there is in fact no real correlation,
hence should be interpreted only in the light of Monte
Carlo simulations.
5. Conclusion
Based on the results of the Mantel test and the
Procrustean test, it appears that there is no strong cor
relation for the overall population between microbial
and chemical odour profiles. However when divided into
individualfamilies,some
revealed and it is probable that correlations are over
whelmed by environmental factors such as the personal
habits and living conditions of the subjects. When we
select individuals whose behaviour follows certain rules,
there is indeed a significant correlation between the
microbial and chemical odour profiles: the differences
between the families could also be interpreted in terms
of the different personal behaviour patterns in certain
family groups. We also notice that in most cases, the
correlation between human odour profiles and microbial
profiles is usually stronger when the qualitative distance
metrics were used compared to quantitative distance
strongcorrelations are
metrics. This is interpreted in part as the amount of
sweat sampled not being precisely controlled and the
method for DGGE gels is only semiquantitative so
quantitative dissimilarity metrics are more sensitive to
such variations, whereas qualitative patterns based on
presence/absence of microbes and chemicals are more
robust.
Acknowledgements
Alexandra Katzer is thanked for her superb organisa
tional skills. Hejun Duan of the Centre for Chemo
metrics is thanked for helping organise the GC/MS
data, and Fan Gong for preliminary help in the micro
bial analysis. This work was sponsored by ARO Con
tract DAAD190310215. Opinions, interpretations,
conclusions, and recommendations are those of the
authors and are not necessarily endorsed by the United
States Government.
References
Brereton, R.G. (2003). Chemometrics: Data Analysis for the Labora
tory and Chemical Plant. Wiley, Chichester.
Box, G.E.P. and Cox, D.R. (1964). An analysis of transformations.
J. R. Stat. Soc. B 26, 211–252.
Dixon, S.J., Brereton, R.G., Soini, H.A., Novotny, M.V. and Penn
D.J. (2006). An automated method for peak detection and
alignment in gas chromatographymass spectrometry as applied
to a large metabolomic dataset from human sweat. J. Chemomet.
in press.
Gentle, J.E. (1998). Numerical Linear Algebra for Applications in
Statistics. SpringerVerlag, Berlin.
Golub, G.H. and Loan, C.F.V. (1996). Matrix Computations. The
Johns Hopkins University Press, London.
Gower, J.C. (1966). Some distance properties of latent root and vector
methods used in multivariate analysis. Biometrika 53, 325–338.
Gower, D.B., Bird, S., Sharma, P. and House, F.R. (1985). Axillary
5aandrost16en3one in men and women: Relationships with
olfactory activity to odorous 16androstenes. Cell. Mol. Life Sci.
41, 1134–1136.
Gower, J.C. and Legendre, P. (1986). Metric and Euclidean properties
of dissimilarity coefficients. J. Classif. 3, 5–48.
Graepel, T., Herbrich, R., BollmannSdorra, P. and Obermayer, K.
(1998). Classification on pairwise proximity data in Jordan, MI,
Kearns, MJ and Solla, SA (Eds), Proceedings of the 1998 Con
ference on Advances in Neural Information Processing Systems.
MIT Press, Cambridge, MA, pp. 438–444.
Huber, W., von Heydebrek, A., Su ¨ ltmann, H., Poustka, A. and
Vingron, M. (2002). Variance stabilization applied to micro
array data calibration and to the quantification of differential
expression. Bioinformatics 18(suppl. 1), S96–S104.
Jaccard, P. (1908). Bull. Soc. Vaud. Sci. Nat. 44, 223–270.
Jackson, D.A. (1995). Protest: A Procrustean randomization test of
community environment concordance. Ecoscience 2, 297–303.
Krzanowski, W.J. and Marrior, F.H.C. (1994). Multivariate Analysis,
Part I. Distributions, Ordination and Inference. Arnold, London.
Mantel, N.A. (1967). The detection of disease clustering and a gener
alized regression approach. Can. Res. 27, 209–220.
Marples, M.J. (1969). Life on the human skin. Sci. Am. 220, 108–115.
Muyzer, G., de Waal, E.C. and Uitterlinden, A.G. (1993). Profiling of
complex microbial populations by denaturing gradient gel
Table 2
The results of Mantel test and Protest on randomly selected sets of 32
subjects chosen among the 145 subjects that had not obeyed the four
basic behavioural rules
Run
pvalue of Mantel test pvalue of Protest
Qualitative
distance
metric
Quantitative
distance
metric
Qualitative
distance
metric
Quantitative
distance
metric
1
2
3
4
5
6
7
8
9
10
0.2145
0.3388
0.0092
0.3919
0.1987
0.3014
0.4001
0.1687
0.0821
0.2687
0.5412
0.4178
0.0412
0.4082
0.3024
0.3954
0.4413
0.3011
0.1498
0.3541
0.1947
0.4408
0.0088
0.6147
0.1471
0.4157
0.7041
0.1577
0.0714
0.2514
0.4922
0.7998
0.0359
0.7912
0.2921
0.6955
0.8589
0.4998
0.1501
0.6562
All pvalues less than 0.1 are in italic and bold.
Y. Xu et al./Comparison of human axillary odour profiles436
Page 11
electrophoresis analysis of polymerase chain reactionamplified
genes encoding for 16S rRNA. Appl. Environ. Microbiol. 59,
695–700.
Muyzer, G., Hottentra ¨ ger, S., Teske, A. and Wawer, C. (1995).
Denaturing gradient gel electrophoresis of PCRamplified 16S
rRNA: A new molecular approach to analyse the genetic
diversity of mixed microbial communities in Akkermans, A.D.,
Elsas, J.D.van and Bruijin, F.J.de (Eds), Molecular Microbial
Ecology Manual. Kluwer Academic Publishers, Dordrecht, The
Netherlands, pp. 1–23.
Pekalska, E., Paclik, P. and Duin, R.P.W. (2002). A generalized Kernel
approach to dissimilaritybased classification. J. Mach. Learn.
Res. 2, 175–221.
Penn, D.J., Oberzaucher, E., Grammer, K., Fischer, G., Soini, H.A.,
Wiesler, D., Novotny, M.V., Dixon, S.J., Xu, Y. and Brereton,
R.G. (2007). Individual and gender fingerprints in body odour.
J. R. Soc.: Interface 4, 331–340.
PeresNeto, P.R. and Jackson, D.A. (2001). How well do multivariate
data sets match? The advantages of a Procrustean superimpo
sition approach over the Mantel test. Oecologia 129, 169–178.
Rennie, P.J., Gower, D.B., Holland, K.T., Mallet, A.I. and Watkins,
W.J. (1990). The skin microflora and the formation of human
axillary odour. Int. J. Cosmet. Sci. 12, 197–208.
Rennie, P.J., Gower, D.B. and Holland, K.T. (1991). Invitro and
invivo studies of human axillary odour and the cutaneous
microflora. Br. J. Dermatol. 124, 596–602.
Rodrı`guezLa ´ zaro, D.A., Jofre ´ , T., Aymerich, M., Hugas, M. and Pla,
M. (2004). Rapid quantitative detection of Listeria monocytog
enes by RealTime PCR. Appl. Environ. Microbiol. 70, 6299–
6301.
Rohlf, F.J. and Slice, D.E. (1990). Extensions of the Procrustes
method of the optimal superimposition of landmarks. Syst.
Zool. 39, 40–59.
Sanguinetti, C.J., Dias Neto, E. and Simpson, A.J.G. (1994). Rapid
silver staining and recovery of PCR products separated on
polyacrylamide gels. Biotechniques 17, 915–919.
Sastry, S.D., Buck, K.T., Janak, J., Dressler, M. and Preti, G. (1980).
Volatiles emitted by humans in Waller, G.R. and Dermer, O.C.
(Eds), Biochemical Applications of Mass Spectrometry. John
Wiley & Sons, New York, pp. 1085–1129.
Sato, K., Leidal, R. and Sato, F. (1987). Morphology and development
of an apoeccrine sweat gland in human axillae. Am. J. Physiol.
Regul. Integr. Comp. Physiol. 252, R166–180.
Soini, H.A., Bruce, K.E., Klouckova, I., Brereton, R.G., Penn, D.J.
and Novotny, M.V. (2006). Insitu surface sampling of biolog
ical objects and preconcentration of their volatiles for chro
matographic analysis. Anal. Chem. 78, 7161–7168.
Wold, S., Esbensen, K. and Geladi, P. (1987). Principal component
analysis. Chemom. Intell. Lab. Syst. 2, 37–52.
Williamson, P. and Kligman, A.M. (1965). A new method for the
quantitative investigation of cutaneous bacteria. J. Invest.
Dermatol. 45, 498–503.
Zwillinger, D. (1997). Handbook of Differential Equations. (third ed.).
Academic Press, Boston.
Y. Xu et al./Comparison of human axillary odour profiles437