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Appl Environ Microbiol, June 1998, p. 2207-2214, Vol. 64, No. 6
0099-2240/98/$04.00+0
Copyright © 1998, American Society for Microbiology. All rights reserved.
Rapid and Reliable Identification of Food-Borne
Yeasts by Fourier-Transform Infrared Spectroscopy
Michael
Kümmerle,
Siegfried
Scherer, and
Herbert
Seiler*
Institut für Mikrobiologie,
Forschungszentrum für Milch und Lebensmittel Weihenstephan,
Technische Universität München, D-85354 Freising, Germany
Received 13 August 1997/Accepted 9 March 1998
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ABSTRACT |
Computer-based Fourier-transform infrared spectroscopy (FT-IR) was
used to identify food-borne, predominantly fermentative yeasts. Dried
yeast suspensions provided the films suitable for FT-IR measurement.
Informative windows in the spectrum were selected and combined to
achieve optimal results. A reference spectrum library was
assembled, based on 332 defined yeast strains from international yeast
collections and our own isolates. All strains were identified with
conventional methods using physiological and morphological
characteristics. In order to assess identification quality, another 722 unknown yeast isolates not included in the reference spectrum library
were identified both by classical methods and by comparison of their
FT-IR spectra with those of the reference spectrum library.
Ninety-seven and one-half percent of these isolates were identified
correctly by FT-IR. Easy handling, rapid identification within 24 h when starting from a single colony, and a high differentiation capacity thus render FT-IR technology clearly superior to other routine
methods for the identification of yeasts.
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INTRODUCTION |
Yeasts not only provided humans with
the first biotechnologically produced food such as wine, bread, and
fermented milk products but are also responsible for food spoilage
(19), and some species are of medical importance. Therefore,
a reliable method of yeast identification is economically
significant (40). Furthermore, until now about 700 yeast species have been described. Since only a few habitats have
been investigated in detail so far, a wide range of yeasts is likely to
be discovered in the future (6). Exploration of new species
includes the identification of a large number of isolates in order to
eliminate duplicates and to discover unusual forms. For such tasks, a
rapid, simple, low-cost identification method is needed. Conventional
differentiation systems using morphological characters as well as
patterns of the assimilation and fermentation of carbon sources
(4, 22, 35) do not fulfil these requirements (9, 33,
38, 40). They are tedious and time-consuming, and, quite often,
their capacity is limited since many species are distinguished from one
another by a single physiological reaction which is often controlled by
only one mutable marker (4, 20).
Alternative methods such as fatty acid analysis (1, 31),
electrophoretic karyotyping (10), restriction fragment
length polymorphism, and DNA fingerprinting (26, 37) have
already been evaluated (8). Restriction enzyme analysis of
PCR-amplified rDNA (2), randomly amplified polymorphic DNA
(3, 27), and nucleic acid hybridization with oligonucleotide
probes (21, 24) have also been used. While some of these
techniques do provide satisfactory results, molecular methods in
general are still difficult to perform on a routine basis in
laboratories of the food industry.
Fourier-transform infrared (FT-IR) spectroscopy is used for the
identification of substances in chemical analyses (14). The
wavelength of infrared radiation ranges from 1 µm to 1 mm (32). In general, the wave number 
, the reciprocal of the
wavelength, is used as a physical unit for FT-IR spectroscopy. Infrared
radiation is divided into near ( = 12,500 to 4,000 cm
1), middle ( = 4,000 to 200 cm1), and
far ( = 200 to 10 cm1) infrared. In this work, only
the middle infrared section was used. FT-IR spectroscopy involves the
observation of vibrations of molecules that are excited by an infrared
beam. Molecules are able to absorb the energy of distinct light quanta
and start a rocking or rotation movement. The FT-IR spectrum uses only
vibrations that lead to a change in the dipole moment (14).
An infrared spectrum represents a fingerprint which is characteristic
for any chemical substance.
The composition of biological material and, thus, of its FT-IR
spectrum, is exceedingly complex, representing a characteristic fingerprint. Some years ago, Naumann and coworkers suggested
identifying microorganisms by FT-IR spectroscopy
(28-30). In principle, a reference spectrum
library is assembled based on well-characterized strains and
species. The FT-IR spectrum of any unidentified isolate is then
measured under the same conditions as those used for the reference spectra and is compared to spectra in the reference spectrum library. If the library contains an identical or a
very similar spectrum, an identification is possible. The success
of the method is, therefore, directly dependent on the
complexity of the reference spectrum library. The application of
FT-IR spectroscopy has been reported for some species of the genera
Lactobacillus (7), Actinomyces
(15), Listeria (18),
Streptococcus (13), and Clostridium
(11). There are two reports which present preliminary data
indicating that eukaryotic microorganisms such as yeasts may also be
identified by FT-IR (17, 36). However, all these studies are
based on a very limited number of species and isolates. For
verification of the method only a few strains, which often were part of
the reference spectrum library as well, were used. It was, therefore,
still unclear whether FT-IR spectroscopy indeed was a competitive
identification method.
The aim of this study was to develop a standardized sample preparation
procedure for yeasts (suitable for the normal laboratory), to select
the most significant spectral windows for efficient identification, and
to assemble a spectral reference library of sufficient complexity.
Last, the identification of a great variety of unknown yeast isolates
by FT-IR spectroscopy and conventional techniques had to be done in
order to verify the method.
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MATERIALS AND METHODS |
Yeast strains.
One hundred and seventy-four strains from
international yeast culture collections provided the reference
material. This collection was supplemented by 158 isolates from the
Weihenstephan Yeast Collection (housed at our institute), representing
a wide variety of habitats. All strains were identified by using
miniature test samples in microtiter plates and conventional methods
(35, 39), which test for about 100 physiological and
morphological characters. The computer program of Barnett
(5) was used to evaluate the results. The 332 reference
yeasts of the resulting spectrum library represent 74 species of 18 genera.
Sample preparation.
A single colony of yeast cells was
transferred to an agar plate with a platinum loop, distributed with a
Drigalski spatula, and incubated for 24 ± 1 h at 27 ± 2°C on YGCA (standard agar for yeasts in the food industry; Merck,
Darmstadt, Germany) containing 5.0 g of yeast extract, 20.0 g
of glucose, 0.1 g of chloramphenicol, and 14.9 g of agar per
liter. For sample preparation, one loopful (1-mm-diameter platinum
loop) of yeast cells scraped from this confluent lawn was suspended in
100 µl of distilled water. An aliquot of 35 µl was transferred to a
ZnSe optical plate (sample holder) and dried at 42 ± 2°C for
1 h to yield transparent films, which were used directly for FT-IR
spectroscopy. One sample holder accommodates 15 different samples.
Measurement and comparison of the spectrum with the reference spectrum
library containing spectra of defined strains take less than 2 min. In
total, starting from a single yeast colony, identification is completed
within 24 to 26 h.
FT-IR spectroscopy.
All spectra between wave numbers 4,000 and 500 cm1 were recorded with an IFS-28B FT-IR
spectrometer (Bruker, Karlsruhe, Germany). For data processing, the
software OPUS, version 2.2, for microbiological identification (Bruker)
was used. The adjustment of instrument parameters was done according to
the suggestions of the FT-IR workgroup of the Robert-Koch-Institut,
Berlin, Germany (12). To diminish the difficulties arising
from unavoidable baseline shifts and to improve the resolution of
complex bands, the digitized original spectra were smoothed by the
second derivation (16).
In principle, the five spectral windows W1 to W5 shown in Fig.
1 are potentially informative (16,
29, 30). Ranges of wave numbers can sometimes be associated with
special chemical bonds. W1 is the so-called fatty acid region (3,050 to
2,800 cm1), where peaks mark the vibrations of the CH2
and CH3 groups of fatty acids. The W2 region is the amide section
(1,750 to 1,500 cm1), where protein and peptide bands
dominate. W3, which ranges from 1,500 to 1,200 cm1, is a
mixed region containing vibrations of fatty acids, proteins, and
polysaccharide. W4 (1,200 to 900 cm1) is dominated by
polysaccharide peaks. Until now an exact correlation between peaks and
molecules in this section was not possible. The so-called fingerprint
region W5 ranges from 900 to 700 cm1. This window
contains bands which are most characteristic at the species level.
Again, just a few peaks can be assigned to the vibrations of special
substances.
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FIG. 1.
Original spectrum as well as first and second
derivations of an FT-IR measurement of an S. cerevisiae
strain. Potentially informative spectral windows (W1 to W5) are
indicated.
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Selection of spectral windows.
Cluster analysis was used to
identify optimal windows (Fig. 1). For this purpose, windows were
varied systematically until FT-IR identification was in accordance with
the results of conventional microbiological identification. This
variation included window size, the number of windows used, and the
weighting factors imposed on each window. Best results were obtained
with windows in the 3,030 to 2,830, 1,350 to 1,200, and 900 to 700 cm1 ranges (all weighting factors were 1), and this
configuration was therefore used as the standard. To cope with
distances caused by unavoidable physical and biological variations such
as slightly different growth in different batches due to medium
preparation and variation in the dry microorganism film on the sample
holder, each strain was measured at least three times in independent
assays using different growth medium preparations of the standard agar. Then, an average spectrum was calculated and added to the reference spectrum library.
Cluster analysis and hit list identification.
The spectral
distance (also called the d value) is a measure of the similarity of
the spectra of two isolates and reflects the size of nonoverlapping
areas (29) of both spectra (for an example, see Fig.
2). d values between all spectra were
calculated. The resulting distance matrix provided the basis for
cluster analysis (construction of dendrograms by average linkage). By
using identification analysis, the spectrum of an unknown isolate was
compared to all spectra of the library. A hit list of 10 strains
exhibiting the closest spectral distances was printed along with the d
values. In Table 1, three identifications
of different Saccharomyces cerevisiae strains are given.
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FIG. 2.
Comparison of the fingerprint regions of two normalized
FT-IR spectra. The d value is equivalent to the area which is covered
by only one of the spectra.
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TABLE 1.
Examples of hit lists of different identification quality
for FT-IR identification of three Saccharomyces isolates
preidentified by conventional methodsa
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RESULTS AND DISCUSSION |
Reproducibility of measurements.
FT-IR spectra are influenced
by variation of plating methods, growth temperature, incubation time,
and even the drying method of the microorganism suspension located on
the sample holder. For a high level of reproducibility it was necessary
to develop the standardized preparation procedure given in the sample
preparation section above. The significance level of spectral distances
is given by the d values observed for multiple, independent
measurements of one strain. This level was always characterized by a d
value less than 0.3 and turned out to be species dependent. In some cases, the d value was as low as 0.1.
Changes of the agar medium used had pronounced influences on the
spectra. Any newly purchased charge of an identical medium
produced
slightly different spectra and had to be verified by
recording the
spectra of a standard set of seven yeast strains
which had been
shown to be especially sensitive to medium variations.
A new
charge of medium can only be considered suitable for FT-IR
measurements
if distances between the new spectra and the reference
spectra, both
taken from the standard species test set, are below
0.3. While this
procedure can be performed easily in any research
laboratory, "FT-IR
grade" standard media have to be commercially
available if the FT-IR
technique is to be adopted for routine
microbiological analysis.
FT-IR spectroscopy as a general taxonomic tool?
A dendrogram
of 332 well-characterized reference yeasts calculated from FT-IR
spectra is shown in Fig. 3. It provides a
graphic impression of the distances dealt with in Table
2, where the isolates and
cluster levels are listed in detail. Three arbitrary cluster levels
were defined in order to divide the dendrogram into spectrally related
groups. The 22 groups (A to V) of level 1 are separated by spectral
distances of 2.0 to 2.5. The subclusters of those groups (level 2) have
d values of 1.25 to 1.75, while level 3 is characterized by d values of
0.5 to 0.75. It is not possible to assign taxonomic interpretations to
these levels. The first clear result emerging from the data presented
in Fig. 3 and Table 2 is that different species of the same genus
generally did not cluster. This is most obvious for the genera
Pichia and Candida, for which a variety of
species were available. Other algorithms for cluster analysis also do
not cluster all species of a single genus.
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FIG. 3.
Dendrogram of the mean spectra of the 332 yeast strains
forming the reference spectrum library used for identification of
unknown isolates. The dendrogram was calculated by an average-linkage
algorithm and is divided into 22 major clusters (A to V). Each of those
is further subdivided into second-order (1 to 9) and third-order (a to
h; not listed) clusters. In Table 2, this nomenclature is also employed
and can be used to identify individual strains.
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Since the taxonomy of yeasts is far from being finally settled and
relies largely on phenotypic characters, one might suppose
that
molecular data may turn out to be in agreement with FT-IR
spectroscopy.
Some sequences do not confirm this idea. For instance,
according to 18S
rRNA,
Candida tropicalis,
Candida parapsilosis,
and
Candida maltosa are very closely related (see the
phylogenetic
tree in reference
21). However,
C. tropicalis clusters far away
from the other two species
(cluster C6b versus clusters N1a and
N2a in Fig.
3). At this stage,
however, the relation between FT-IR
and molecular taxonomy cannot be
assessed conclusively, but we
doubt that FT-IR can be used as a general
taxonomic tool above
the species level.
Strains of the same species may appear in different clusters.
As is shown in Table 2, strains of many species cluster at level 3. However, there are a number of exceptions to this rule. For example,
strains of Issatchenkia orientalis and Issatchenkia occidentalis fell into different clusters (clusters A1, A2, and C2b). They appear together with, e.g., Pichia
membranaefaciens and Pichia norvegensis. It is
interesting that these species are also difficult to separate with
physiological markers (31). The same is true for
Kluyveromyces marxianus and Kluyveromyces lactis
(clusters I7b and -c), Hanseniaspora uvarum and
Hanseniaspora guilliermondii (clusters I8a and R4a and
-e) and Hanseniaspora vineae and Hanseniaspora
osmophila (clusters D1a and I9b). A clear identification of these
species was not always possible by physiological and morphological
characteristics (unpublished data; see also reference
40).
During the creation of the reference spectrum library, it often
happened that a new strain of a species clustered far away
from the
other strains already investigated. However, when more
strains were
included, it became clear that such an "aberrant"
strain just
represented the first example of a new cluster including
several
representatives. Therefore, species with many strains
often formed more
than one independent cluster. Typical examples
are
S. cerevisiae,
K. marxianus, and
Debaryomyces
hansenii. The
taxonomic significance of this finding has not been
studied in
detail so far and must be assessed in the future by using
molecular
taxonomic markers (compare references
3
and
20).
Optimization of spectral windows used for closely related yeast
groups.
There are several possible causes which may account for
the formation of different clusters by strains of the same species. First, the window combination used for FT-IR spectroscopy may have been
suboptimal in some cases. For instance, strains of
Zygosaccharomyces bisporus, Zygosaccharomyces
bailii, Zygosaccharomyces rouxii, and
Zygosaccharomyces mellis (cluster J1) do not form species clusters. By conventional methods it is often difficult to identify these species (33, 40). Growth rates in the presence
of 50 or 60% glucose and 1% acetic acid are the only criteria used to distinguish between these Zygosaccharomyces species
(4). Analysis of 18S rRNA reflected a very close
relationship between Z. bailii and Z. bisporus and a slightly greater distance between Z. rouxii and Z. mellis (20). Specific
problems such as the identification of Zygosaccharomyces
strains by FT-IR may be solved by optimizing the spectral window
combination. For example, the windows characterized by wave numbers of
1,710 to 1,690, 1,213 to 1,202, and 777 to 767 cm1
yielded the dendrogram shown in Fig. 4,
which corresponds exactly to the results of the 18S rRNA
analysis. This example demonstrates that a separation of yeasts which
are difficult to identify by the general yeast identification window
setting is possible by a stepwise optimization of spectral window
selection. However, to do so, the "real" relationship of the
isolates according to genomic DNA sequences, in this case 18S rRNA,
must be known beforehand.
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FIG. 4.
Cluster analysis (average linkage) of four
Zygosaccharomyces species. Spectral windows have been set at
the following wave number ranges: 1,710 to 1,690, 1,213 to 1,202, and
777 to 767 cm1. The pattern shown in this figure
corresponds closely to that derived from 18S rRNA sequences.
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Detection of novel species, subspecies, or mutants by
FT-IR?
Another reason why strains of the same species do
not cluster may be the existence of new subspecies or species.
For instance, separate clusters for one species may be due to
"habitat variants" (34). Successful adaptation to
completely different habitats may result in the evolution of subspecies
which may have different FT-IR spectra. One example is D. hansenii: strains from clusters U1a to -c, U2a, and T2a and -b
were isolated from cheese and brine, while strains within cluster T1b
come from beer, cattle, and yogurt. A second example is provided by
strains of S. cerevisiae: those contained in clusters K3a to
-c and L1a (Table 2) are from beer, wine, and must, while the strains
combined in clusters P2a and R1a and -b were isolated from yogurt,
diseased nails, and other sources. It is well known that strains of
S. cerevisiae fall into different groups (3, 20,
25), but it is not clear whether these groups represent isolates
from certain habitats.
Another indication for the existence of different subspecies or even
species is extremely different degrees of homogeneity
in the spectral
distances between strains. This is, for instance,
the case when
clusters of
D. hansenii,
Pichia anomala, and
Torulaspora delbrueckii are compared. While the
P. anomala cluster and the
two
T. delbrueckii clusters
exhibit an internal spectral distance
of approximately 0.7, the
distances between
D. hansenii strains
within the three
species clusters were more than 1.7 (clusters
T1 and T2 and U1 and U2;
Table
2). Again, Kurtzman (
23) noted
that
D. hansenii is a heterogeneous species according to a taxonomy
based
on physiological markers.
While such data are in accordance with the hypothesis of
different taxonomic forms, molecular data are clearly
needed to clarify
the situation. In particular, there
might be other reasons for
single strains clustering independently,
e.g., slow-growing mutants,
strains with mutations of a biochemical
character such as slime
production, and strains with fast ascospore
formation. Such mutants
most probably would have significantly
different FT-IR spectra
due to major differences in cellular
composition (
11).
Validation of FT-IR identification.
It appears that the use of
FT-IR spectroscopy for taxonomic purposes is limited. This fact,
however, does not prevent it from being a powerful identification
system. To evaluate this potential, the method has been tested with 722 independent yeast isolates, which were obtained from different
habitats, mostly from the food industry. They were not included in the
reference spectrum library and constituted 36 yeast species belonging
to 11 genera (Table 3). All isolates were
identified in parallel by physiological and morphological characters.
An identification by FT-IR spectroscopy was considered to be successful
if the d value of the first recommended reference strain in the hit
list (Table 1) was below 1.5 and, in addition, either the next similar
hits were three strains of the same species or the distance between the
first hit and the next species was larger than 0.25. Table 1
shows example hit lists as a result of three identity tests with three
unknown isolates.
Five of 722 isolates were not identifiable by conventional methods.
These strains may be mixed cultures which are difficult
to purify,
defective mutants, or novel species. They have not
been investigated in
further detail. Twelve isolates (1.7% of
717 strains) could be
identified only when a subjective decision
based on personal experience
with yeast taxonomy (habitats and
morphology, etc.) was used to
evaluate the unclear FT-IR hit list.
Another 6 of 717 strains could not
be identified by FT-IR at all.
In summary, 699 of 717 strains were
identified correctly by FT-IR
spectroscopy, which corresponds to an
identification rate of 97.5%.
Conclusion.
With an identification time of 24 to 26 h
starting from a single colony and an identification rate of about 97%,
FT-IR spectroscopy provides a superior, rapid alternative
to conventional identification systems for food-borne yeasts,
which take several days. Identification is limited only by the
quality of the reference spectrum library, which can be improved
steadily by adding further yeast isolates to the database. The method
is easy to use, and we now routinely identify yeasts by FT-IR.
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ACKNOWLEDGMENTS |
This work was supported by the Bundesministerium für
Wirtschaft through the Arbeitsgemeinschaft industrieller
Forschungsvereinigungen "Otto von Guericke" e.V. (AiF), grant no.
AiF-FV-10768N.
The comments of three reviewers led to a significantly improved
presentation of the data.
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FOOTNOTES |
*
Corresponding author. Mailing address: Institut
für Mikrobiologie, Forschungszentrum für Milch und
Lebensmittel Weihenstephan, Technische Universität München,
Vöttingerstrasse 45, D-85354 Freising, Germany. Phone:
08161/713519. Fax: 08161/714492. E-mail: seiler{at}lrz.tu-muenchen.de.
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Abstract
Introduction
