Metabolic pro¢ling as a tool for revealing Saccharomyces
interactions during wine fermentation
Kate S. Howell1,2, Daniel Cozzolino2,3, Eveline J. Bartowsky2, Graham H. Fleet1 & Paul A. Henschke2
1
Food Science and Technology, School of Chemical Engineering and Industrial Chemistry, University of New South Wales, Sydney, NSW, Australia;
The Australian Wine Research Institute, Glen Osmond, Adelaide, SA, Australia; and 3The Cooperative Research Centre for Viticulture, Glen Osmond,
Adelaide, SA, Australia
2
Correspondence: Paul A. Henschke, The
Australian Wine Research Institute, PO Box
197, Glen Osmond, Adelaide SA 5064,
Australia. Tel.: 161 8 8303 6600; fax: 161 8
8303 6601; e-mail:
[email protected]
Received 8 March 2005; revised 18 July 2005;
accepted 28 July 2005.
First published online 6 October 2005.
doi:10.1111/j.1567-1364.2005.00010.x
Editor: Lex Scheffers
Keywords
wine fermentation; principal-component
analysis; ecology; aroma; metabolic profiling;
Saccharomyces bayanus; Saccharomyces
cerevisiae.
Abstract
The multi-yeast strain composition of wine fermentations has been well established. However, the effect of multiple strains of Saccharomyces spp. on wine
flavour is unknown. Here, we demonstrate that multiple strains of Saccharomyces
grown together in grape juice can affect the profile of aroma compounds that
accumulate during fermentation. A metabolic footprint of each yeast in monoculture, mixed cultures or blended wines was derived by gas chromatography –
mass spectrometry measurement of volatiles accumulated during fermentation.
The resultant ion spectrograms were transformed and compared by principalcomponent analysis. The principal-component analysis showed that the profiles of
compounds present in wines made by mixed-culture fermentation were different
from those where yeasts were grown in monoculture fermentation, and these
differences could not be produced by blending wines. Blending of monoculture
wines to mimic the population composition of mixed-culture wines showed that
yeast metabolic interactions could account for these differences. Additionally, the
yeast strain contribution of volatiles to a mixed fermentation cannot be predicted
by the population of that yeast. This study provides a novel way to measure the
population status of wine fermentations by metabolic footprinting.
Introduction
The fermentation of grape juice into wine is carried by
yeasts. The population dynamics and diversity of yeast
species associated with the fermentation are quite complex
and variable (Fleet, 2003). Generally, various species of
Hanseniaspora, Candida, Torulaspora, Metschnikowia, Kluyveromyces and Saccharomyces, which originate from the
grape berry and winery environment, grow during the first
stages of fermentation (Fleet et al., 1984; Fleet & Heard,
1993). As the ethanol concentration increases, the nonSaccharomyces yeasts die off, leaving Saccharomyces cerevisiae and Saccharomyces bayanus to dominate and complete
the fermentation (Fleet & Heard, 1993; Loureiro & Querol,
1999). The predominance of S. cerevisiae in fermentations
has led to its recognition as the principal wine yeast, and
various strains of S. cerevisiae have been commercialized as
starter cultures for wine production (Fleet, 2003). Grape
juice is not sterilized or pasteurized, and the added starter
FEMS Yeast Res 6 (2006) 91–101
culture must therefore compete with indigenous yeasts.
Consequently, most wines are the products of fermentation
with mixtures of yeast species (Fleet & Heard, 1993; Fleet,
2003). It is now realized that the ecological complexity and
variability of these fermentations extend beyond the species
level. Within the one fermentation, several strains of each
species may be involved. Over 100 genetically distinct strains
of S. cerevisiae have been reported in some fermentations
(Pramateftaki et al., 2000; Torija et al., 2001), but in one
study, only one strain was found to persist (Frezier &
Dubourdieu, 1992).
The impact of the yeasts upon wine flavour is largely
determined by the array of volatile substances (e.g. higher
alcohols, acids, esters, carbonyls, thiols) produced by the
metabolism of grape juice components (Berry & Watson,
1987; Dumont & Dulau, 1997; Lambrechts & Pretorius,
2000). The profile of these flavour-active volatiles varies
with the yeast species and strains contributing to the
fermentation (reviewed by Suomalainen & Lehtonen, 1979;
c
2005 The Australian Wine Research Institute
92
Soles et al., 1982; Moreno et al., 1991; Longo et al., 1992;
Lema et al., 1996; Henick-Kling et al., 1998; Antonelli et al.,
1999; Heard, 1999; Marais, 2001; Garcia et al., 2002).
With the current understanding of the yeast ecology of
wine fermentation, winemakers are seeking to enhance the
flavour diversity and appeal of wines by controlled fermentation with multiple species or strains of yeasts (Lambrechts
& Pretorius, 2000; Fleet, 2003). Several studies have already
described fermentation with mixtures of non-Saccharomyces
yeasts and S. cerevisiae (Moreno et al., 1991; Soden et al.,
2000; Garcia et al., 2002), and one study has examined
fermentation with a mixture of different strains of S.
cerevisiae (Cheraiti et al., 2005). It is apparent from the
chemical data reported in these studies that the profile of
volatile aroma substances produced in mixed culture ferments can differ from the profile produced by the simple
addition of substances expected of the constituent, single
cultures. It could be concluded that, in mixed-culture
ferments, one species or strain may impact on the metabolic
behaviour of others (Cheraiti et al., 2005). The concept of
such metabolic interactions is new in the field of wine
science and requires more precise description and understanding to enable practical development and application.
Yeasts produce many metabolites (over 60) that are
known to have an impact on wine flavour (Berry & Watson,
1987; Dumont & Dulau, 1997; reviewed by Lambrechts &
Pretorius, 2000). Generally, these are analysed by gas chromatography–mass spectrometry (GC–MS) (Ebeler, 2001).
The large number of peaks and fragments that are found in
the analysis of one sample of wine presents a logistical
challenge when comparisons between several wine samples
are required, for example between mixed-culture and singleculture ferments of grape juice. Metabolomic footprinting
has been defined as gaining ‘enough information to unravel
(otherwise hidden) metabolic alterations, without aiming to
get quantitative data for all biochemical pathways’ (Jolliffe,
1986). Because analytical footprinting techniques (for example GC–MS) generate a large amount of information,
chemometric methods are generally employed to reduce the
size of the data. Both supervised and unsupervised multivariate analysis, such as cluster analysis, principal-component analysis (PCA) or discriminant partial least squares
(DPLS) (Martens & Martens, 2000), have been used to
generate biochemical footprints from samples ranging from
olive oil and cocoa butter to tomatoes and various plants.
Chemometric methods have been successfully used to
obtain biochemical fingerprints of fruits and beverages
(Jellum et al., 1991; Munck et al., 1998; Arvantoyannis
et al., 1999; Brereton, 2000; Sunesson et al., 2001; Cordella
et al., 2002; Eide et al., 2002; Fiehn, 2002; Fiehn & Weckwerth, 2003; Johnson et al., 2004), as well as metabolic
footprints of yeasts (Raamsdonk et al., 2001; Eglinton et al.,
2002; Allen et al., 2003).
c
2005 The Australian Wine Research Institute
K. S. Howell et al.
In this study, we use chemometric analysis of GC–MS
data to elucidate differences in the volatile components of
wines fermented with different strains of S. cerevisiae and S.
bayanus. To investigate potential metabolic interactions
between the yeasts, wines made using each yeast strain
grown in monoculture were blended and compared with
wines made with a mixed inoculum of the strains.
Materials and methods
Yeast strains and culture preparation
Saccharomyces cerevisiae strains AWRI 796, AWRI 838,
AWRI 835 and AWRI 1434 were obtained from The Australian Wine Research Institute (Adelaide, Australia). Strain
AWRI 796 is a commercial yeast from AB Mauri Yeast
(Australia). Strain AWRI 838 was isolated from the commercial preparation Lalvin EC1118 (Lallemand, Adelaide,
Australia). Saccharomyces cerevisiae strain AWRI 1434 was
isolated from the commercial preparation Zymaflore VL3
from Laffort Oenologie (Australia). Saccharomyces cerevisiae
strains ICV D47 and QA23 were provided by Lallemand,
and are available as commercial preparations. All yeast
strains have the killer phenotype. Saccharomyces bayanus
strain AWRI 1176 was isolated from a spontaneously
fermented cold stored grape juice (Eglinton et al., 2000).
Yeasts were maintained by culture on plates of YPD agar
(28 1C) and stored at 4 1C. YPD consisted of yeast extract
(10 g L 1), peptone (20 g L 1) and glucose (20 g L 1) with
the inclusion of agar (20 g L 1) for solid media.
Yeast colonies were taken from YPD plates, inoculated
into liquid YPD and incubated with shaking at 25 1C overnight. The overnight culture was sub-cultured in grape juice
diluted with double-distilled water (1 : 1) and incubated in
cotton wool-plugged conical flasks at 25 1C, with agitation.
When the culture reached 1–2 108 cells mL 1 (as determined by haemocytometer counts), yeast cells were inoculated into fermentors to give an initial density of
approximately 1 106 cells mL 1. Mixed cultures were inoculated with about 3.3 105 cells mL 1 for each strain of
yeast.
Wine fermentations
Mixed culture fermentations (coded M1, M2, M3 and M4)
were conducted using four different mixtures of strains of S.
cerevisiae or S. bayanus, as indicated in Table 1. Two
combinations of mixed fermentations were examined in this
study. The first combination examined the effect of varying
a single strain in a mixture of S. cerevisiae strains. These
fermentations were inoculated with two constant S. cerevisiae strains (AWRI 796 and ICV D47) and a third, variable
strain which was QA23 for M1, AWRI 838 for M2 and AWRI
FEMS Yeast Res 6 (2006) 91–101
93
Saccharomyces interactions during wine fermentation
Table 1. Strain composition of yeast cultures used in fermentation trials.
Each number represents a strain of Saccharomyces cerevisiae, except
AWRI 1176, which is a strain of Saccharomyces bayanus
Fermentation trial
Combination of yeast strains
M1
M2
M3
M4
AWRI 796. ICV D47, QA23
AWRI 796, ICV D47, AWRI 838
AWRI 796, ICV D47, AWRI 835
AWRI 1434, AWRI 1176, AWRI 838
835 for M3 (Table 1). The second combination examined
the effect of varying the Saccharomyces species in mixed
cultures. Saccharomyces bayanus strain AWRI 1176 was used
in conjunction with two S. cerevisiae strains, AWRI 1434 and
AWRI 838, to make M4 (Table 1). Monoculture ferments of
each of the seven yeast strains were performed in either
duplicate or quadruplicate. Fermentations were conducted
in 3-L batches of grape juice contained in Bellco fermentors
at 15 1C. Fermentors were fitted with a magnetically driven
stirrer, water air-lock and sampling and filling ports, and
were sterilized prior to use. The grape juice was a gift from
The Hardy Wine Company (Australia) and had an initial
sugar concentration of 230 g L 1 and a pH of 3.2. The
Chardonnay juice was sterilized by cross-flow membrane
filtration (0.2 mm, S & F Fabrications, Victor Harbour, South
Australia), stored at 4 1C and transferred aseptically to the
fermentors using a peristaltic pump. Fermentations were
conducted in duplicate (M1, M2 and M3) or quadruplicate
(M4), including the respective monoculture fermentations.
Monitoring offermentation
The progress of fermentation was followed by analysis
for sugar utilization and yeast populations. Glucose and
fructose were assayed using an enzymatic kit (Roche,
Mannheim, Germany). Wine samples (1 mL) were taken
aseptically throughout fermentation, using nitrogen flushing to prevent ingress of air. A portion of the sample was
immediately used for determination of yeast population and
the remainder was stored at
20 1C until analysis. Yeast
viability was determined by surface plating on WL Nutrient
Agar (Amyl Media, Sydney, Australia). Samples were appropriately diluted in 0.1% peptone (Amyl) and then 0.1 mL
was inoculated onto the plates. Plates were incubated at
27 1C for 3 days and colonies were examined for species and
strain diversity. Species differentiation was determined on
the basis of colony morphology. Colonies of S. bayanus
stained green, and S. cerevisiae colonies were green with a
white margin. The validity of this species differentiation
technique was confirmed by plating mixed cultures on WL
Nutrient agar plates, and identifying colonies by morphology and PCR amplification of the MET2 gene (Masneuf
et al., 1996).
FEMS Yeast Res 6 (2006) 91–101
For S. cerevisiae strain differentiation, PCR amplification
of the SC9182X locus was performed (Howell et al., 2004).
The amplified PCR products were separated on precast 12%
polyacrylamide gels (Gradipore, New York, NY), in 0.5 Tris borate EDTA buffer (100 V for 1 h 30 min), stained with
ethidium bromide, visualized with ultraviolet light and
photographed (Sambrook & Russell, 2001). Strain-specific
banding profiles were then tallied. For each treatment
replicate, 45 colonies were analysed per sample point
(beginning, middle and end of fermentation). The proportion of each yeast strain was calculated as a percentage of the
total colonies counted. The midpoint of fermentation was
considered to be when 50% of the initial sugar had been
metabolized.
Postfermentation wine handling and adjustment
When the sugar concentration in each fermentor was less
than 2 g L 1, the temperature of the culture was decreased
to 4 1C and the yeast lees allowed to settle for 5 days.
Excluding air by nitrogen gas cover and solid carbon dioxide
additions, the wine was racked into 5-L carboys, and
sulphur dioxide (as potassium metabisulphite, AnalaR,
Gibbstown, NJ) was added incrementally to achieve a
concentration of 10–15 mg L 1 free sulphur dioxide (Iland
et al., 2000). Next, the wine was filtered under nitrogen
pressure through a glass prefilter (Millipore, Bedford, MA),
and a 0.2-mm membrane filter (Millipore) in a 2-L capacity
dead-end filter apparatus (Sartorious, Göttingen, Germany). The wine was placed into glass bottles (750 mL),
crown-sealed, and stored at 15–20 1C for up to 10 weeks
until chemical analysis.
Wine blending
To investigate the effects of yeast co-fermentation, blended
wines were made. Blended wines corresponding to mixed
fermentations M1, M2 and M3 were named B1, B2 and B3.
These wines consisted of equal proportions of monoculture
wines corresponding to the yeast which fermented them. For
example, blended wine B1 contained equal proportions of
monoculture wines prepared with strains AWRI 796, ICV
D47 and QA23 (see Table 1). The volatile profile of the
blended wines will be different from the corresponding
mixed culture wine (for example B1 vs. M1) if the constituent yeasts interact metabolically.
A second blending experiment was carried out based on
the population of each yeast strain found at the endpoint of
fermentation. Here, proportionately blended wines (B1P,
B2P and B3P) were constructed using a ratio obtained by
reference to Fig. 2. For example, blended wine B1P was
prepared using 18% AWRI 796, 75% ICV D47 and 7% QA23
(Fig. 2). If a yeast dominates the fermentation numerically,
c
2005 The Australian Wine Research Institute
94
there may be a flavour impact on that wine which may not
be reflected in the proportionately blended wines. The
proportionately blended wines allow comparisons with the
mixed-culture wines, taking into consideration potential
flavour impact by the numerically dominating yeast.
Gas chromatography--mass spectrometry
An HP 6890 gas chromatographer (GC) coupled to an HP
5973 mass spectrometer (MS) (Agilent, Forest Hill, Victoria,
Australia) was used to determine the concentrations of
volatile components in wines. Immediately upon opening
the bottle, 5 mL of wine was extracted at 20–25 1C into an
equal volume of pentane : ether after addition of a mixture
of standards (ethyl nonanoate, octanol and nonanoic acid).
The GC was fitted with a 30 m 0.25 mm fused silica
capillary column DB-1701, film thickness 0.25 mm (J&W
Scientific, Folsom, CA). The oven temperature was 50 1C
and was held at this temperature for 1 min before being
increased by 10 1C per min to 250 1C and then kept at that
temperature for a further 20 min. The injector was held at
220 1C and the transfer line at 280 1C throughout the
analysis. The sample volume injected was 2 mL. The carrier
gas was helium, with a flow rate of 1.2 mL min 1. The inlet
was in pulse splitless mode. Positive ion impact spectra at
70 eV were recorded in the range m/z 50–350 for scan runs.
Data analysis and interpretation
Multivariate models of GC–MS data were constructed to
describe compositional changes that occurred in the samples
due to the different yeast mixtures. All the GC–MS data files
(nonprocessed, CSV format) were exported to THE UNSCRAMBLER software (version 7.5, CAMO ASA, Oslo, Norway) for
chemometric analysis. Before performing PCA, GC–MS
data were pre-processed in order to avoid baseline influence,
retention time drifts, variations in peak shapes and differences in recovery between the analysed samples (Jonsson
et al., 2004).
In this study, smoothing (moving average on each of
seven data points) and normalization (mean normalization)
provided by THE UNSCRAMBLER software were used as preprocessing methods. The moving average reduced the noise
and made it easier to observe the start and end peaks
(Jonsson et al., 2004). Mean normalization consisted of
dividing each row of a data matrix by its average, thus
neutralizing the influence of any hidden factor. It is equivalent to replacing the original variables by a profile centred on
one. Only the relative values of the variables were used to
describe the sample, and the information carried by their
absolute level was dropped. This is indicated in the specific
case where all variables are measured in the same unit, and
their values assumed to be proportional to a factor, which
c
2005 The Australian Wine Research Institute
K. S. Howell et al.
cannot be directly taken into account in the analysis. Such
transformation is required to express the results in the same
units for all samples (Jonsson et al., 2004).
PCA was used for reducing the dimensionality of data,
detecting the number of components and visualizing the
outliers (Martens & Naes, 1989; Jonsson et al., 2004). It is a
mathematical procedure for resolving sets of data into
orthogonal components whose linear combinations approximate the original data to a desired degree of accuracy. PCA
was used to derive the first 20 principal components from
the GC–MS data and arrange samples into groups. DPLS is a
supervised pattern recognition technique, used to locate
differences between members of different logical groups by
searching for structural information that can discriminate
between the groups. DPLS models were developed using a
dummy variable. With this technique, each sample in the
calibration set is assigned a dummy variable as a reference
value, which is an arbitrary number. Samples of mixed
fermentations (M) were assigned a numeric value of 1 and
blended fermentations (B) a value of 2. The DPLS model
was then developed by regressing the GC–MS data against
the assigned dummy value.
In empirical modelling, it is essential to determine the
correct complexity of the model. With numerous and
correlated GC–MS variables, there is a substantial risk of
over-fitting, where a well-fitting model has little or no
predictive power (Martens & Martens, 2000). Hence, a strict
test of the predictive significance of each PCA or PLS
component is necessary. Cross-validation was performed by
arranging the data into four groups and then developing
a number of parallel models from the reduced data when
one of the groups had been deleted.
The loadings correspond to the total ion chromatograph
(as a numeral) measured in one scan by the mass spectrometer. They do not correspond to a single compound, but to
the total ions produced by all the compounds detected in
that scan. Therefore, sample grouping was based on the
score plots and the loadings after each PCA model. The
procedure does not aim to identify GC–MS peaks, but to
provide groupings of samples.
Results
Mixed yeast culture fermentation dynamics
The progress of fermentations with mixed cultures is shown
in Fig. 1. The end of fermentation was taken as when less
than 2 g L 1 of glucose plus fructose remained in the wine.
The length of fermentation varied from 9 to 11 days for M1,
M2 and M3, and 17 days for M4. Monoculture fermentations of the corresponding yeast strains used in mixtures
M1, M2 and M3 required 8–15 days to complete, whilst the
strains constituting M4 needed 15–18 days to complete
FEMS Yeast Res 6 (2006) 91–101
95
M2
150
M3
100
M4
(a)
50
0
0
2
4
6
8
10
12
Days after inoculation
14
16
18
Fig. 1. Utilization of sugars during mixed-culture fermentations with
wine yeasts. Each point is the average of duplicate determinations of
samples from duplicate or quadruplicate fermentations. Error bars
indicate the standard deviation, where it was possible to be resolved on
the graph.
(data not shown). However, the monoculture fermentation
with strain AWRI 1176 did not give final sugars below
20 g L 1, despite attempts to restart the fermentation using
air sparging and nitrogen addition (Bisson, 1999; data not
shown).
Change in relative proportions of yeast strains
during fermentation
Total yeast populations reached 5–9 107 cells mL 1 within
5 days for M1, M2, M3 and associated monoculture
fermentations, and 5–6 107 cells mL 1 within 4 days for
fermentation M4 and associated monocultures (data not
shown). The viability of the total yeast population did not
decline over the course of fermentation (data not shown).
The data presented in Fig. 2 show shifts in the proportions
of each yeast strain in the four mixed culture fermentations.
The proportion of each yeast in the mixed culture at
inoculation ranged from 25% to 50% for each strain,
although an optimal inoculation density of 33% for each
strain was desired. For mixtures M1, M2 and M3, the
proportion of the three yeast strains at inoculation did not
change until after the midpoint of fermentation, whereas
M4 showed a larger proportion of AWRI 1434 when 50% of
the sugar was utilized. For all mixed cultures, dominance by
one strain was evident at the end of fermentation. M1, M2
and M3 were dominated by AWRI 796, whereas M4 was
dominated by AWRI 1434. Interestingly, the two strains
(AWRI 796 and ICV D47) that were used across the three
ferments (M1, M2 and M3) had similar relative proportions
at the end of fermentation, irrespective of the third strain
used in these mixtures (QA23, AWRI 838 or AWRI 835). For
M1, M2 and M3, strain AWRI 796 dominated from inoculation through to the completion of fermentation. M4 was
dominated by strain AWRI 1434, which was the yeast
present in the lowest concentration at the beginning of
fermentation. Although S. bayanus AWRI 1176 represented
45% of the yeast population at the beginning, it had
decreased to 18% by midfermentation and was not detected
at the end of fermentation in M4.
FEMS Yeast Res 6 (2006) 91–101
Percent of colonies
assayed (%)
M1
200
(b)
Percent of colonies
assayed (%)
250
(c)
Percent of colonies
assayed (%)
Glucose and
fructose (g L )
Saccharomyces interactions during wine fermentation
100
90
80
70
60
50
40
30
20
10
0
M1
M2
M3
M4
M1
M2
M3
M4
M1
M2
M3
M4
100
90
80
70
60
50
40
30
20
10
0
100
90
80
70
60
50
40
30
20
10
0
Fig. 2. Proportions of each yeast strain at the beginning (a), middle (b)
and end (c) of fermentation. Yeast strain mixtures are the same as in
Table 1. Error bars indicate the standard error between the duplicate
(M1, M2 and M3) and quadruplicate (M4) fermentations. , AWRI 796;
, ICV D47; , QA23; , AWRI 838; , AWRI 835; , AWRI 1434; ,
AWRI 1176.
The volatile composition of mixed-culture wines
differs from that in blended wines
Volatile wine components were measured by GC–MS.
Although there are more sensitive methods to measure
individual compounds, such as stable-isotope dilution analysis with GC–MS, (Kotseridis et al., 2000; Steinhaus et al.,
2003), the approach taken here can examine more compounds irrespective of whether they can be identified (Allen
et al., 2003; Jonsson et al., 2004). The volatiles were
extracted, and injected into a GC–MS, which collected data
from the total ion chromatograph during the scan interval.
The collated ions, and therefore the volatiles measured by
GC–MS, were able to differentiate mixed-culture ferments
(M) and blended (B) wines (Fig. 3a). The PCA plot shows
that 97% of the variation is explained by the first three
principal components, and that replicate fermentation measurements group together. The spread grouping of the
replicates could be due to inherent biological variation
among three fermentation replicates (see error bars, Fig. 2).
c
2005 The Australian Wine Research Institute
96
(a)
K. S. Howell et al.
8
6
(b)
B2P
B3
B
1
2
B2
0
M2
–2
PC2 (6%)
B3P
PC2 (14%)
M
2
4
B
BB
0
M3
–6
B
M
B
B
M
B
–1
M
–2
–4
–8
–15
3
B
–3
M
–4
–10
–5
0
5
10
15
20
25
–6
–4
–2
0
0.20
0.25
0.15
0.20
0.10
0.15
0.05
0.00
– 0.05
– 0.10
2
4
6
8
10
12
PC1 (89%)
Eigenvectors
Eigenvector
PC1 (80%)
0.10
0.05
0.00
– 0.05
– 0.15
– 0.10
– 0.20
100 200 300 400 500 600 700 800 900 1000 1100 12001300
Scan number
400
500
600
700
800
Scan number
900
1000
1100
Fig. 3. Principal-component analysis (PCA) score plots and corresponding loadings of volatiles measured by gas chromatography–mass spectrometry
(GC–MS). Wines were made by inoculating Saccharomyces cerevisiae strains (a) or Saccharomyces cerevisiae with Saccharomyces bayanus strains (b).
The value of the principal component is given as a percentage in each of the dimensions. The data points correspond to a mixed-culture (M) wine or a
blended wine of monocultures (B or BP). The loading plots show the magnitude of the eigenvector used to construct the PCA, by the scan number of the
GC–MS data. The data points at each scan number are the sum of the total ion chromatograph total at that point.
The PCA scores for both B2 and M2 wines plotted in
different quadrants. The basis for the observed separation is
the diverse PCA loadings (Fig. 3a). Further, the blended
wine made with the proportions of monoculture wines
corresponding to the yeast ratios at the endpoint of fermentation (B2P) was separated from both B2 and M2. A similar
pattern was observed for B3, M3 and B3P, where blended
wines were clearly different from the corresponding mixed
culture (Fig. 3a). The PCA plots for M1 and the corresponding blended wines are not shown due to the poor data
resolution from GC–MS.
The preliminary hypothesis that blending wines proportionately would imprint the wine with an aroma profile
related to the dominating yeast in the mixed cultures was
investigated. However, as proportionately blended wines
were different from the mixed-culture wines and the equalproportion blended wines, this hypothesis did not appear to
apply (Fig. 3a).
c
2005 The Australian Wine Research Institute
When a different species of yeast (S. bayanus) was
included in the fermentations (M4), wines from mixedculture fermentation and the corresponding blended
wines (B4) were separated into opposite quadrants of the
PCA plot (Fig. 3b). By investigating the loadings for the
PCA, Fig. 3b shows that more ions produced by the GC–MS
are responsible for separating the S. cerevisiae/S. bayanus
wines than wines produced only with strains of S. cerevisiae.
In addition, the results show that the volatile yeast
products in a mixed-culture wine differ from those produced in a monoculture wine (Fig. 4a), as well as in the
blended monoculture wines.
Wines made with AWRI 835 have volatile profiles
different from those in other wines
The degree of difference between wines from mixed-culture
and blended monoculture wines prompted further analysis
FEMS Yeast Res 6 (2006) 91–101
97
Saccharomyces interactions during wine fermentation
(a)
(b)
12
10
10
AWRI 835
M2
8
12
QA 23
8
6
4
PC2 (13%)
PC2 (16%)
6
M3
2
QA 23
0
–2
AWRI 796
–4
ICV D47
–8
0
–10
0
–5
5
10
15
AWRI 838
AWRI 796
–4
AWRI 838
–15
AWRI 835
2
–2
–6
–20
4
–6
–15
20
ICV D47
–10
–5
PC1 (76%)
0
5
10
15
20
25
PC1 (77%)
0.20
0.12
0.15
Eigenvectors
Eigenvectors
0.06
0.00
0.10
0.05
0.00
– 0.06
– 0.05
– 0.12
200
400
600
800
1000
1200
Retention time
200
400
600
800
1000
1200
1400
Retention time
Fig. 4. Principal-component analysis (PCA) score plots and corresponding loadings of volatiles measured by gas chromatography–mass spectrometry
(GC–MS). Wines made using a monoculture of Saccharomyces cerevisiae were compared to mixed-culture wines (M2 and M3) (a) or the monocultures
alone (b). The value of the principal component is given as a percentage in each of the dimensions. The data points correspond to a monoculture (strain
name) or mixed-culture inoculum wine (for composition see Table 1). The loading plots show magnitude of the eigenvector used to construct the PCA,
by the scan number of the GC–MS data. The data points at each scan number are the sum of the ion chromatograph total at that point.
of the volatile profiles of the monoculture fermentations.
A PCA plot compared the differences between the
monoculture and the mixed wines for M2 and M3 only
(Fig. 4a). The score plot confirms that monoculture wines
are different from mixed-culture wines. The loadings show
that the separation could be related to several variables
(ions) not observed when mixed-culture or blended wines
were analysed (cf. Figs 3b and 4b). Monoculture fermentation data were re-analysed to distinguish the grouping of
monocultures seen in Fig. 4a. As shown in Fig. 4b, wines
made with strain AWRI 835 are different from the other
monocultures. The wines made with QA23, although scattered, group together, and wines made with strains AWRI
838, AWRI 796 and ICV D47 are close to one another (Fig.
4b). The latter combination of yeasts (AWRI 838, AWRI 796
and ICV D47) was used for mixed-culture fermentation M2
(Table 1). The M2 inoculum consists of yeasts which, when
grown in monoculture, provide a similar GC–MS volatile
profile. Despite the similarity in volatile production between
the yeast strains, the data presented in Fig. 4a show that M2
FEMS Yeast Res 6 (2006) 91–101
separates from the group of monoculture wines. Interactions between these yeasts provide a fermentation outcome
dissimilar to that predicted by the monocultures.
Confirmation of the PCA modelling analysis by
DPLS
Figure 5 shows the PLS score plots for the B4 and M4
cultures. The explained variation in the X matrix (GC–MS
data) is around 87%, and the explained variation in the Y
matrix (dummy values) is 92%. To validate the DPLS
models, samples not included in the calibration model were
used to test the predictive ability. Blended samples were all
correctly (100%) classified, whereas only 66% of the samples
belonging to the mixes were correctly classified. The patterns
in the loadings for the DPLS models were similar to those
observed for the PCA models. These observations show that
wines made by mixed cultures were more complex than the
corresponding blended wines. Additionally, the results show
that differences in the GC–MS measurements (replicates)
c
2005 The Australian Wine Research Institute
98
K. S. Howell et al.
6
M
M
PC2 (8%)
4
B
2
B
0
–2
M
M
B B
B B
B
B
M
M
–4
–10
–5
0
5
10
15
PC1 (80%)
Fig. 5. Discriminant partial least squares (DPLS) score plots for the mixed
culture (M) and blended culture (b) for fermentations containing Saccharomyces cerevisiae and Saccharomyces bayanus (M4 and B4). For
strain composition see Table 1. Samples not included in the calibration
model were used to test the predictive ability as validation of the model.
Loadings as described previously.
did not explain the variation observed in the monocultures,
meaning that real differences separated the samples.
Discussion
It is now widely accepted that many wines, whether produced with or without inoculated yeasts, are the outcome of
a mixed fermentation that involves contributions from
many species and strains (Henschke, 1997; De Vos, 2001;
Fleet, 2003). However, research investigating winemaking
practices is generally based on laboratory experiments where
a sterile or near-sterile juice or must is used (see for example
Heard & Fleet, 1988; Ciani & Maccarelli, 1998; Eglinton
et al., 2000; Soden et al., 2000). The final flavour of the wine
is determined in part by the composite of volatile aroma
compounds produced by the mixed-culture reaction (Lambrechts & Pretorius, 2000; Fleet, 2003). Building on this
knowledge, winemakers may now seek greater control over
yeast contribution to wine flavours and its predictability by
conducting fermentation with defined mixtures of yeast
species and strains. Such a mixed-culture product has
recently been proposed (Grossmann et al., 1996). For this
aspiration to become a practical reality, more information is
needed to understand how particular species and strains of
wine yeasts grow in mixed-culture, and how such culture
impacts on their production of aroma volatiles.
In a previous publication (Howell et al., 2004), we have
reported a convenient molecular method of yeast strain
differentiation that can be used to monitor the population
dynamics of strains of wine yeasts during fermentation.
Here, we have combined this method with several chemo-
c
2005 The Australian Wine Research Institute
metric methods to demonstrate that mixed cultures of
Saccharomyces wine yeasts give a combination of volatile
aroma substances distinctly different from wines made by
blending together monoculture wines made with the same
component yeast strains. These results indicate metabolic
interaction between component strain and species. The
profile of volatile aroma compounds produced by yeasts
could be considered as a partial or volatile-fraction ‘metabolome’. Processing of these data by the methods of Jonsson
et al. (2004) has facilitated the comparative analysis and
interpretation of these large sets of data. This method could
be further developed as a quality control tool to monitor
rapidly the aroma footprint given by yeasts during fermentation.
In the first series of trials, M1, M2 and M3, the potential
interaction of yeast strains was investigated. As monocultures, yeasts AWRI 796, ICV D47, QA23, AWRI 838 and
AWRI 835 gave profiles of aroma components that were
clearly differentiated from those in mixed culture (Fig. 4a).
This was evident despite the clear dominance of yeast strain
AWRI 796 in all three mixed-culture ferments.
This dominance might be due to its having the highest
population at the beginning of fermentation. Although the
intention was to inoculate the fermentations with equal
proportions of each strain, this was difficult to achieve in
practice. Inoculum populations were determined by microscopic counts, but subsequent monitoring of the fermentations was done by viable plate counts. It seems that,
compared with other strains, inoculum cultures of AWRI
796 always had a higher proportion of viable to non-viable
cells. The reason for this was not investigated. Apart from
the inoculum influence, the dominance of AWRI 796
throughout fermentation could be due to other factors such
as faster growth favoured by the cultural conditions used in
this study.
Wines made solely with AWRI 835, or with a mixedculture that contained AWRI 835 (M3), were distinctly
different from wines made with the other strains studied
(Fig. 4a). All mixed-culture inocula (M1, M2 and M3) had
the two strains AWRI 796 and ICV D47 in common, whereas
the third strain was varied (Table 1). Although the proportion of AWRI 835 in the M3 total population was small, the
aroma profile of the M3 mixed-culture wine was different
from that of the other mixed-culture wines that also
included AWRI 796 and ICV D47. Relative to the monoculture wines made with ICV D47 or AWRI 796, the AWRI
835 monoculture wine had a very different composition
(Fig. 4b). As yeasts AWRI 796 and ICV D47 produced wines
with similar profiles, we suggest that the wines made with
AWRI 835 contain a different profile of aroma compounds,
or at least some compounds are present in significantly
different concentrations. Indeed, wines made with AWRI
835 have been shown to display distinctive aroma and
FEMS Yeast Res 6 (2006) 91–101
99
Saccharomyces interactions during wine fermentation
sensory profiles (Rankine & Lloyd, 1963; Monk, 1982; Jane
et al., 1996), suggesting that the distinctive metabolic
activity of composite strains of a wine fermentation can
affect the metabolic outcome. Therefore, the volatile contribution of a yeast strain to mixed-strain fermentation
cannot be predicted by the population of that yeast alone.
The accepted view is that the most numerous strain of wine
yeast dominates the fermentation outcome (Fleet & Heard,
1993; Lambrechts & Pretorius, 2000; Fleet, 2003). Sensory
studies to examine the contribution of less populous strains
in mixed culture will show whether aroma modifications are
detectable.
Saccharomyces bayanus is another species of the genus
Saccharomyces that can ferment grape juice to completion
(Eglinton et al., 2000). This yeast was included in the mixed
culture M4 to examine yeast interactions at a species level.
However, populations of S. bayanus were not detected at the
end of fermentation (Fig. 1). The mechanism of S. bayanus
elimination was not determined, but killer interaction can be
dismissed as this strain is killer neutral (data not shown). The
metabolic activity of S. bayanus AWRI 1176, nonetheless,
affected the aroma profile of the wine when grown in mixed
culture with S. cerevisiae AWRI 1434 and AWRI 838, as
shown in Fig. 3b. The wines made by mixed and blended
cultures of this yeast combination were clearly distinguishable, and represent another example of a significant contribution to the metabolic profile by a numerically inferior yeast.
We hypothesize that yeasts, which metabolically interact
with one another during fermentation will give a mixedculture wine with a composition different from that made
by blending the monoculture wines in equal proportions.
The mixed-culture wines presented here were different and
distinguishable from the corresponding blended wines (Figs
3a and b). Further, as some yeasts dominated the fermentation population, a proportionate blend of wine was prepared in which wines were blended in the ratios of the yeast
population present at the end of sugar utilization (Fig. 3a).
These results add further weight to our hypothesis that
yeasts can modify the products of fermentation when grown
in mixed culture.
When a yeast strain produced a compound, it could be
taken up and used by other yeasts present. In this way, yeast
interaction and sharing of metabolites could occur. A recent
study by Cheraiti et al. (2005) has demonstrated that redox
interactions can occur between yeasts in co-culture and that
acetaldehyde produced by one yeast is metabolized by the
other. This observation provides an explanation as to why
modulation of wine flavour in mixed culture cannot be
replicated by blending wines together, as the modification
arises from complex, largely unknown, interactions between
wine yeasts.
This study has examined the hypothesis that different
yeasts growing together in wine fermentations interact to
FEMS Yeast Res 6 (2006) 91–101
change the volatile outcome, and that this interaction is
measurable by investigating the metabolome. This research
has important outcomes for mixed-culture research in
winemaking, and indeed in other fermentations as well.
However, further work is required to elucidate the molecular
mechanisms by which these interactions take place, and
could involve the use of gene arrays or cell-wide protein
assays to identify the key modified compounds with an
impact on the aroma.
Acknowledgements
The authors would like to thank Professors Peter Hj and
Sakkie Pretorius for support and advice during this study.
Ms Heather Smyth is thanked for advice on statistical
analysis and interesting discussions in the preparation of
this manuscript. Ms Dimi Capone and Dr Alan Pollnitz
provided assistance for the GC–MS instrumentation and
analysis. This project is supported by Australia’s grapegrowers and winemakers through their investment body,
the Grape and Wine Research Development Corporation,
with matching funds from the Australian Government.
K.S.H. is a recipient of an Australian Postgraduate Award
stipend and AWRI scholarship.
References
Allen J, Davey HM, Broadhurst D, Heald JK, Rowland JJ, Oliver
SG & Kell DB (2003) High-throughput classification of yeast
mutants for functional genomics using metabolic
footprinting. Nature Biotechnol 21: 692–696.
Antonelli A, Castellari L, Zambonelli C & Carnacini A (1999)
Yeast influence on volatile composition of wines. J Agric Food
Chem 47: 1139–1144.
Arvantoyannis I, Katsota MN, Psarra P, Soufleros E &
Kallinthraka S (1999) Application of quality control methods
for assessing wine authenticity: use of multivariate analysis
(chemometrics). Trends Food Sci Technol 10: 321–336.
Berry DR & Watson DC (1987) Production of organoleptic
compounds. Yeast Biotechnol (Berry DR, Russell I & Stewart
GG, eds), pp. 345–368. Allen & Unwin, London.
Bisson LF (1999) Stuck and sluggish fermentation. Am J Enol
Vitic 50: 107–119.
Brereton RG (2000) Introduction to multivariate calibration in
analytical chemistry. Analyst 125: 2125–2154.
Cheraiti N, Guezenec S & Salmon JM (2005) Redox interactions
between Saccharomyces cerevisiae and Saccharomyces uvarum
in mixed culture under enological conditions. Appl Environ
Microbiol 71: 255–260.
Ciani M & Maccarelli F (1998) Oenological properties of nonSaccharomyces yeasts associated with wine-making. World J
Microbiol Biotechnol 14: 199–203.
c
2005 The Australian Wine Research Institute
100
Cordella C, Moussa I, Martel A-C, Sbirrazzuoli N & LizzaniCuvelier L (2002) Recent developments in food
characterisation and adulteration detection: techniqueoriented perspectives. J Agric Food Chem 50: 1751–1764.
De Vos WM (2001) Advances in genomics for microbial food
fermentations and safety. Curr Opin Biotech 12: 493–498.
Dumont A & Dulau L (1997) The role of yeasts in the formation
of wine flavours. ASVO Seminar Advances in Juice Clarification
and Yeast Inoculation (Allen M, Leske P & Baldwin G, eds),
pp. 14–16. Winetitles, Adelaide, SA.
Ebeler SE (2001) Analytical chemistry: unlocking the secrets of
wine flavour. Food Rev Int 17: 45–64.
Eglinton JM, Heinrich AJ, Pollnitz AP, Langridge P, Henschke PA
& Lopes MD (2002) Decreasing acetic acid accumulation by
a glycerol overproducing strain of Saccharomyces cerevisiae
by deleting the ALD6 aldehyde dehydrogenase gene. Yeast
19: 295–301.
Eglinton JM, McWilliam SJ, Fogarty MW, Francis IL,
Kwiatkowski MJ, Hj PB & Henschke PA (2000) The effect of
Saccharomyces bayanus- mediated fermentation on the
chemical composition and aroma profile of Chardonnay wine.
Aust J Grape Wine Res 6: 190–196.
Eide I, Neverdal G, Thorvaldsen B, Grung B & Kvalheim OM
(2002) Toxicological evaluation of complex mixtures by
pattern recognition: correlating chemical fingerprints to
mutagenicity. Environ Health Perspect 6: 985–988.
Fiehn O (2002) Metabolomics – the link between genotypes and
phenotypes. Plant Mol Biol 48: 155–171.
Fiehn O & Weckwerth W (2003) Deciphering metabolic
networks. Eur J Biochem 270: 579–588.
Fleet GH (2003) Yeast interactions and wine flavour. Int J Food
Microbiol 86: 11–22.
Fleet GH & Heard GM (1993) Yeasts – growth during
fermentation. Wine Microbiology and Biotechnology (Fleet GH,
ed.), pp. 27–54. Harwood Academic, Chur, Switzerland.
Fleet GH, Lafon-Lafourcade S & Ribéreau-Gayon P (1984)
Evolution of yeasts and lactic acid bacteria during
fermentation and storage of Bordeaux wines. Appl Environ
Microbiol 48: 1034–1038.
Frezier V & Dubourdieu D (1992) Ecology of yeast strain
Saccharomyces cerevisiae during spontaneous fermentation in
a Bordeaux winery. Am J Enol Vitic 43: 375–380.
Garcia A, Carcel C, Dulau L, Samson A, Aguera E, Agosin E &
Gunata Z (2002) Influence of a mixed culture with
Debaryomyces vanriji and Saccharomyces cerevisiae on the
volatiles of a Muscat wine. J Food Sci 67: 1138–1143.
Grossmann M, Linsenmeyer H, Muno H & Rapp A (1996) Use of
olig-strain yeast cultures to increase complexity of wine aroma.
Vitic Enol Sci 51: 175–179.
Heard GM (1999) Novel yeasts in winemaking – looking to the
future. Food Aust 51: 347–352.
Heard GM & Fleet GH (1988) The effects of temperature and pH
on the growth of yeast species during the fermentation of
grape juice. J Appl Bact 65: 23–28.
c
2005 The Australian Wine Research Institute
K. S. Howell et al.
Henick-Kling T, Edinger W, Daniel P & Monk P (1998) Selective
effects of sulfur dioxide and yeast starter culture addition on
indigenous yeast populations and sensory characteristics of
wine. J Appl Microbiol 84: 865–876.
Henschke PA (1997) Wine yeast. Yeast Sugar Metabolism
(Zimmerman FK & Entian K-D, eds), pp. 527–560. Technomic
Publishing, Lancaster, PA.
Howell KS, Bartowsky EJ, Fleet GH & Henschke PA (2004)
Microsatellite PCR profiling of Saccharomyces cerevisiae strains
during wine fermentation. Lett Appl Microbiol 38: 315–320.
Iland P, Ewart A, Sitters J, Markides A & Bruer N (2000)
Techniques for Chemical Analysis and Quality Monitoring
during Winemaking. Patrick Iland Wine Promotions,
Campbelltown, South Australia.
Jane TM, Gawel R & Henschke PA (1996) Yeast strain can affect
the profile of Chardonnay wine. Maintaining the Competitive
Edge: Proceedings of the Ninth Australian Wine Industry
Technical Conference; 16–19 July 1995 (Stockley CS, Sas AN,
Johnstone RS & Lee TH, eds), p. 223. Winetitles, Adelaide, SA.
Jellum E, Harboe M, Bjune G & Wold S (1991) Interpreting
complicated chromatographic patterns. J Pharm Biomed Anal
9: 663–669.
Johnson HE, Broadhurst D, Kell DB, Theodorou MK, Merry RJ &
Griffith GW (2004) High-throughput metabolic
fingerprinting of legume silage fermentations via Fourier
transform infrared spectroscopy and chemometrics. Appl
Environ Microbiol 70: 1583–1592.
Jolliffe IT (1986) Principal Component Analysis. Springer, New
York.
Jonsson P, Gullberg J, Nordström A, Kusano M, Kowalczyk M,
Sjöström M & Moritz T (2004) A strategy for identifying
differences in large series of metabolomic samples analyzed by
GC/MS. Anal Chem 76: 1738–1745.
Kotseridis Y, Razungles A, Bertrand A & Baumes R (2000)
Differentiation of the aromas of Merlot and Cabernet
Sauvignon wines using sensory and instrumental analysis.
J Agric Food Chem 48: 5383–5388.
Lambrechts MG & Pretorius IS (2000) Yeast and its importance
to wine aroma – a review. S Afric J Enol Vitic 21: 97–129.
Lema C, Garcia-Jares C, Orriols I & Angulo L (1996)
Contribution of Saccharomyces and non-Saccharomyces
populations to the production of some components of
Albariño wine aroma. Am J Enol Vitic 47: 206–216.
Longo E, Velázquez JB, Sieiro C, Cansado J, Calo P & Villa TG
(1992) Production of higher alcohols, ethyl acetate,
acetaldehyde and other compounds by 14 Saccharomyces
cerevisiae wine strains isolated form the same region (Salnés,
N. W. Spain). World J Microbiol Biotechnol 8: 539–541.
Loureiro V & Querol A (1999) The prevalence and control of
spoilage yeasts in foods and beverages. Trends Food Sci Technol
10: 356–365.
Marais J (2001) Effect of grape temperature and yeast strain on
Sauvignon blanc wine aroma composition and quality. S Afric
J Enol Vitic 22: 47–51.
FEMS Yeast Res 6 (2006) 91–101
101
Saccharomyces interactions during wine fermentation
Martens H & Martens M (2000) Multivariate Analysis of Quality:
An Introduction. John Wiley and Sons Ltd, Chichester, UK.
Martens H & Naes T (1989) Multivariate Calibration. John Wiley
& Sons Ltd, Chichester, UK.
Masneuf I, Aigle M & Dubourdieu D (1996) Development of a
polymerase chain reaction/restriction fragment length
polymorphism method for Saccharomyces cerevisiae and
Saccharomyces bayanus identification in enology. FEMS
Microbiol Lett 138: 239–244.
Monk PR (1982) Effect of nitrogen and vitamin supplements on
yeast growth and rate of fermentation of Rhine Riesling grape
juice. Food Tech Aust 34: 328–332.
Moreno JJ, Millan C, Ortega JM & Medina M (1991) Analytical
differentiation of wine fermentations using pure and mixed
yeast cultures. J Appl Microbiol 7: 181–189.
Munck L, Noragaard L, Engelsen SB, Bro R & Andersson CA
(1998) Chemometrics in food science: a demonstration of the
feasibility of a highly exploratory, inductive evaluation strategy
of fundamental scientific significance. Chemom Intel Lab Syst
44: 31–60.
Pramateftaki PV, Lanaridis P & Typas MA (2000) Molecular
identification of wine yeasts at species or strain level: a case
study with strains from two vine-growing areas of Greece.
J Appl Microbiol 89: 236–248.
Raamsdonk LM, Teusink B, Broadhurst D, et al. (2001) A functional
genomics strategy that uses metabolome data to reveal the
phenotype of silent mutations. Nature Biotechnol 19: 45–50.
FEMS Yeast Res 6 (2006) 91–101
Rankine BC & Lloyd B (1963) Quantitative assessment of
dominance of added yeast in wine fermentations. J Sci Food
Agric 11: 793–798.
Sambrook J & Russell DW (2001) Molecular Cloning; A
Laboratory Manual, Vol. 1. Cold Spring Harbor Laboratory
Press, Cold Spring Harbor, New York.
Soden A, Francis IL, Oakey H & Henschke PA (2000) Effects of
co-fermentation with Candida stellata and Saccharomyces
cerevisiae on the aroma and composition of Chardonnay wine.
Aust J Grape Res 6: 21–30.
Soles RM, Ough CS & Kunkee RE (1982) Ester concentration
differences in wine fermented by various species and strains of
yeasts. Am J Enol Vitic 33: 94–98.
Steinhaus M, Fritsch HT & Schieberle P (2003) Quantitation of
(R)- and (S)-linalool in beer using solid phase microextraction
(SPME) in combination with a stable isotope dilution assay
(SIDA). J Agric Food Chem 51: 7100–7105.
Sunesson AL, Gullberg J & Blomquist G (2001) Airborne
chemical compounds on dairy farms. J Environ Monit 3:
210–216.
Suomalainen H & Lehtonen M (1979) The production of aroma
compounds by yeast. J Inst Brew 85: 149–156.
Torija MJ, Rozes N, Poblet M, Guillamon JM & Mas A (2001)
Yeast population dynamics in spontaneous fermentations:
comparison between two different wine-producing areas
over a period of three years. Antonie van Leeuwenhoek 79:
345–352.
c
2005 The Australian Wine Research Institute