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Applied and Environmental Microbiology, October 1999, p. 4484-4489, Vol. 65, No. 10
0099-2240/99/$04.00+0
Copyright © 1999, American Society for Microbiology. All rights reserved.
Registered Designation of Origin Areas of Fermented Food Products
Defined by Microbial Phenotypes and Artificial Neural
Networks
M. F. S.
Lopes,1,2
C. I.
Pereira,1,2
F. M. S.
Rodrigues,1,2
M. P.
Martins,3
M. C.
Mimoso,3
T. C.
Barros,1,2
J. J.
Figueiredo Marques,1,2,3
R. P.
Tenreiro,4
J.
S.
Almeida,2,5 and
M. T.
Barreto
Crespo1,2,*
Instituto de Biologia Experimental e
Tecnológica, 2781-901 Oeiras,1
Instituto de Tecnologia Química e Biológica,
Universidade Nova de Lisboa,2 and
Estação Agronómica Nacional, Instituto
Nacional de Investigação
Agrária,3 2780 Oeiras,
Departamento de Biologia Vegetal, Faculdade de Ciências e
Centro de Genética e Biologia Molecular, Universidade de
Lisboa, 1600 Lisbon,4 and
Departamento de Química, Faculdade de
Ciências e Tecnologia, Universidade Nova de Lisboa, 2825 Monte de Caparica,5 Portugal
Received 12 April 1999/Accepted 4 August 1999
 |
ABSTRACT |
Cheese produced from raw ewes' milk and
chouriço, a Portuguese dry fermented sausage, are
still produced in a traditional way in certain regions of Portugal by
relying on colonization by microbial populations associated with
the raw materials, equipment, and local environments. For the purpose
of describing the product origins and types of these fermented foods,
metabolic phenotypes can be used as descriptors of the product as well
as to determine the presence of compounds with organoleptic value. The
application of artificial neural networks to the metabolic profiles of
bacterial isolates was assayed and allowed the separation of products
from different regions. This method could then be used for the
Registered Designation of Origin certification process of food
products. Therefore, besides test panel results for these traditionally produced food products, another tool for validating products for the
marketplace is available to the producers. The method can be improved
for the detection of counterfeit products.
| |
INTRODUCTION |
Cheese produced from raw ewes' milk
and chouriço, a Portuguese dry fermented sausage, are
parts of the daily diet in rural areas of Portugal as well as
fashionable food products in urban centers. They are still produced in
a traditional way in certain regions of Portugal by relying on
colonization by microbial populations associated with the raw
materials, equipment, and local environments. Their specific
characteristics are related to the geographical area where they are
produced, and especially with cheese, they are produced inside defined
geographical areas designated Registered Designation of Origin (RDO) areas.
These traditional food products have their quality assured by sanitary
control of the raw materials and of the final product. A product's
type is evaluated by trained and experienced taste testers belonging to
the RDO area. New analytical tools have to be developed to ensure that
a product's characteristics are not lost by the introduction of new
technologies and to certify that the products are truly from the region.
Given the regional origins and the diversified technological stresses
associated with the production of traditional sausages and cheeses, for
the purpose of characterization, each kind can be considered a distinct
ecological entity, attaining maturity in the finished product.
The mobilization of genetic material, involving either transfer of
plasmids or transfer of chromosomal genes among related species
(22) or among distant groups (3), constitutes a
base for the formation of new recombinants that may survive and spread if environmental conditions are selectively favorable. In the specific
case of microbes associated with traditional food fermentation there is
also a geographic effect due to differences in the genetic backgrounds
of strains available to colonize a given substrate. These differences
are further increased by the different nonlethal stresses associated
with particular choices of substrates and manufacturing techniques.
Therefore, metabolic profiles of isolates will reflect both
environmental and genomic constraints. It is also worth considering
that communities associated with given ecological entities will be
capable of complementary activities. Because any given activity can be
implemented by different species, the pattern of metabolic capabilities
of community members offers a better description of the ecosystem than
the pattern of species distribution, also referred to as species in sample.
Lachance and Starmer (18) have already used the
physiological characteristics of isolates for purposes other than those of taxonomy. Those authors found that physiological profiles of the
yeast communities associated with trees are significant descriptors of
these trees. Ellis et al. (9) studied the metabolic profiles of microbial communities associated with plants to evaluate the perturbation of those communities when genetically modified bacteria were introduced into the community. Microbial typing of traditional food products from different geographical origins has also been previously performed (6, 7). For the purpose of describing product origins and types of fermented foods, metabolic phenotypes for
group isolates may offer the additional advantage of determining the
presence of compounds with organoleptic value.
The different tests included in the metabolic profile are strongly
interdependent, reflecting their functional complementarity. The highly
nonlinear nature of the dependency precludes the use of
linear-regression methods as predictive tools and severely limits the
usefulness of breakpoint statistics for classification. In addition,
the complex nature of the dependency is a major obstacle to the
identification of mechanistic associations. Therefore, sample
classification based on the metabolic profile requires a method
that recognizes nonlinear relationships directly from the experimental
data. In the present work, artificial neural networks (ANN), an
artificial-intelligence technique that mimics learning from experience
(14), are used to disentangle the metabolic profiles of
Enterococcus and Lactobacillus, the two
predominant genera in traditional cheese and chouriço,
respectively, in order to infer geographic origin and organoleptic
type. This new technology is intended for certification of food
products from RDO areas.
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MATERIALS AND METHODS |
Sample preparation.
Chouriço samples were
processed as follows: 30 g of meat free of casing was homogenized
with 270 ml of a tryptone-salt solution for 30 s in a stomacher
(Masticator; IUL Instruments), decimal dilutions of this homogenate
were prepared with sterile water, and 100 µl of each dilution was
plated in Man Rogosa Sharpe (MRS) agar (Oxoid, Basingstoke, United
Kingdom) in order to isolate Lactobacillus strains.
For the cheese samples the procedure for isolation of
Enterococcus spp. was as follows: 10 g of each sample
was homogenized with 90 ml of a sterile solution of 2% sodium citrate
for 1 min in a stomacher (Masticator; IUL Instruments) and decimal
dilutions were prepared with sterile Ringer solution (Merck, Darmstadt, Germany) and plated on KF Streptococcus agar (Oxoid).
Isolation of Enterococcus from milk was performed as
follows: decimal dilutions of milk were prepared with a sterile Ringer solution and plated on KF Streptococcus agar.
Microorganisms.
The colonies selected from MRS agar and KF
Streptococcus agar were tested for purity in MRS agar and incubated for
2 days at 30°C for Lactobacillus spp. and at 37°C for
Enterococcus. All isolates used throughout this work were
kept at 
80°C.
Identification of microorganisms.
The identification of the
isolates at the genus level was performed according to methods
described in The Prokaryotes (8, 12).
Characterization of microorganisms.
Unless otherwise stated,
the medium used for cultivation of the Lactobacillus and
Enterococcus isolates was MRS broth with 1% glucose but
without acetate. The temperatures of incubation were, respectively, 30 and 37°C. For those strains unable to sustain growth in this medium.
All Purpose Tryptone broth (Difco, Detroit, Mich.) was used.
In the absence of any sound criterion for choosing characters, we
decided to use the results of the morphological, physiological,
biochemical, chemotaxonomic, and serological tests usually performed
for identification
purposes.
Cell morphology was observed with a phase-contrast microscope after
growth for 2 days in MRS broth. Gram reaction was tested
after 2 days
of incubation in MRS agar according to the method
of Buck
(
5).
The ability to sustain growth at 10, 15, and 45°C was tested by
incubating the isolates in liquid media for 10 days, 1 week,
and
48 h,
respectively.
The ability to sustain growth at a pH value of 9.6 was tested by
incubating the isolates in MRS broth, adjusted to pH 9.6,
for 48
h.
The ability to sustain growth with a high concentration of salt was
tested with MRS broth containing 6.5, 10, and 14% NaCl.
Incubations
were performed at 30°C for 3 days for
Lactobacillus and at
37°C for 3 days for
Enterococcus.
Catalase activity was tested on cells with H
2O
2
according to the method of Priest and Pleasants (
24).
Gas production from glucose was checked with a Durham tube inside the
test tube. A bubble formation, after incubation for
2 days, was
considered a positive
result.
The production of both
D-(
) and
L-(+)-lactic
acid isomers was detected spectrophotometrically in the culture
supernatants
of 24-h cultures by an enzymatic method using
D-(
) and
L-(+)-lactic
acid dehydrogenases
(Boehringer Mannheim, Mannheim,
Germany).
The production of ammonia from the hydrolysis of arginine was checked
according to the procedure of Tjandraamandja et al.
(
25).
Reduction of nitrate and nitrite in nutrient broth (Difco) supplemented
with 0.1% (wt/vol) KNO
3 was examined as reported by
Baird-Parker (
4).
The Voges-Proskauer test was performed after the isolates were grown in
nutrient broth according to the instructions of the
manufacturer
(BioMérieux, Marcy l'Etoile,
France).
The ability to utilize organic substrates as carbon sources was also
tested. The following substrates were used for
Lactobacillus isolates:
D-(
)-ribose,
L-(+)-arabinose,
D-(+)-xylose,
,
L-rhamnose,
D-(
)-mannitol,
D-(
)-sorbitol, ribitol,
glycogen, glycerol,
D-(
)-fructose,
D-(+)-mannose,
D-(+)-galactose,
D-(+)-glucose, lactose, maltose,
sucrose,
D-(+)-trehalose,
D-(+)-cellobiose,
D-(+)-raffinose, melibiose,
D-(+)-melezitose, salicin,
D-gluconate,
-gentiobiose, and
D-amygdalin.
The substrates used
for
Enterococcus were the ones stated above
and also
D-(+)-turanose, esculin, inulin, arbutin,
D-(+)-fucose,
and dextrin. All the substrates were
purchased from Sigma (St.
Louis, Mo.). These tests were carried out as
described by Gurakan
et al. (
10).
The presence of 19 enzymes was tested with the API Zym system
(BioMerieux) according to the manufacturer's instructions. The
enzymes detected were alkaline phosphatase (Zym 2), esterase (C4)
(Zym
3), esterase-lipase (C8) (Zym 4), lipase (C14) (Zym 5), leucine
arylamidase (Zym 6), valine arylamidase (Zym 7), cystine arylamidase
(Zym 8), trypsin (Zym 9), chymotrypsin (Zym 10), acid phosphatase
(Zym 11), naphthol-AS-BI-phosphohydrolase (Zym 12),
-galactosidase
(Zym 13),
-galactosidase (Zym 14),
-glucuronidase (Zym 15),
-glucosidase (Zym 16),
-glucosidase (Zym 17),
N-acetyl-
-glucosaminidase
(Zym
18),
-mannosidase (Zym 19), and
-fucosidase (Zym
20).
The type of hemolysis reaction was tested for
Enterococcus
with Columbia agar supplemented with 5% sheep blood (BioMérieux)
according to the method of Lányí (
19).
Agglutination by antiserum (detection of group D) was performed with a
Streptococcal Grouping Kit
(Oxoid).
The presence of diaminopimelic acid in the cell walls of
Lactobacillus isolates was detected as described by Komagata
and
Suzuki (
17).
Data analysis. (i) Principal-component analysis (PCA).
Multivariate statistical analysis was performed with Statistica Version
4.2 (Statsoft, Inc.). The extraction of principal components was
applied directly to the characteristic profiles of the isolates. The
characteristic profile is the binary vector containing the results of
the physiological, biochemical, chemotaxonomic, and serological tests
described above. Plotting factor scores of a set of isolates provided a
general evaluation of data structure, i.e., whether the characteristic
profile reflects a geographic or genomic origin.
(ii) (ANN).
ANN were implemented with Brain Cell version 2.3 (Promised Land Technologies Inc.), which uses Excel (Microsoft Inc.) as
an interface. Only feedforward topologies were used with a single hidden layer (Fig. 1). The number of
hidden nodes was iteratively selected by searching for the minimum
cross-validated error (21). Neural networks emulate natural
learning by using experimental information (14) to associate
the vector of input measures (metabolic profile) with the corresponding
geographical origin. Cross-validation was performed with a validation
data set, consisting on input and output patterns randomly excluded
from the training data set. Ten percent of all available data was put
aside to validate the neural network trained with the remaining 90%.
The ANN connection weights were optimized by backpercolation
(16), a variant of the standard error backpropagation
algorithm (13).
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FIG. 1.
Schematic representation of a feedforward ANN with five
input nodes (metabolic profile), one hidden layer with three nodes, and
two output nodes (geographic region).
|
|
It must be noted that, unlike PCA and cluster analysis, ANN takes into
account the fact that the elements of the characteristic
profile are
interrelated. That is to say, if geographic origin
is reflected in the
interdependency between characteristic elements
rather than in the
values of the elements themselves, a successfully
trained ANN will be
able to predict geographic origin by capturing
that relationship
(
1,
2,
11,
23).
(iii) Statistical analysis of classification results.
The
different classes (geographic origins) were compared by analyzing the
frequency of misclassifications. The underlying rationale was that a
profile is more likely to be wrongly assigned to a similar region.
Therefore, a measure of dissimilarity (Euclidean distance) was
developed with the frequency of crossed identifications with the
following equation:
|
(1)
|
where
i,
j, and
k are
origin
1, origin
2, ... ,
origin
n,
n is the number of regions
of origin,
Pi,j is the number of
isolates of region
i identified as coming from region
j, and
Ci,j is the
correlation coeficient (
Pi,k,
Pj,k).
Overlapping between predicted origins will lead to a smaller Euclidean
distance between the vectors of predicted origins.
The unweighted pair
group average was used as an amalgamation
rule to perform
clustering.
| |
RESULTS |
First study case: milk and cheese.
Six hundred five
Enterococcus isolates were isolated from milk and cheese
samples obtained from four different Portuguese RDO areas: Serra da
Estrela, Nisa, Castelo Branco, and the Azeitão (Fig.
2). The four RDO areas produce cheeses
with distinct organoleptic characteristics and manufacturing procedures
that are certified by trade organizations and associations of
producers.
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FIG. 2.
Map of Portugal with geographic regions of origin of
cheese, milk, and chouriço isolates. In the upper
central portion of the figure, cheese and milk isolates are indicated
with the letters "Q" and "L", respectively, followed by the
first letter of the indicated region (e.g., QS is cheese from Serra da
Estrela).
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|
Each isolate was tested for the following: growth at 10 and 45°C,
growth in 6.5% NaCl, growth at pH 9.6, production of
D-
and/or
L-lactic acid, hydrolysis of arginine, reaction by
the
Vosges-Proskauer test, and utilization of the carbon sources
mentioned
in Materials and Methods. The type of hemolysis reaction as
well
as the detection of group D antigen was also recorded. All these
data were used even if they gave the same results for all the
isolates
studied. Cell morphology data were not used because of
a strong
dependence on medium composition, culture age, and incubation
conditions (
20). The results were coded as 0 for negative
and
1 for positive, with data missing also being
accepted.
The data was first analyzed by standard multivariate statistics,
namely, PCA. Reduction of dimensionality of characteristic
profiles by
PCA did not allow discrimination of geographic origin,
as shown in Fig.
3, where factor loading for milk isolates
from
Azeitão and Castelo Branco are plotted. This figure
illustrates
the fact that linear decomposition analyses such as PCA are
unable
to discriminate geographic origins. Not only are isolates from
the two regions mixed together but in addition only 58% of the
total
variance is represented by the first two principal components.
Multilinear regression was also attempted (results not shown),
but
again, discrimination of geographic origin was not significant.
Similar
results were obtained for all other regions and for isolates
from milk,
cheese, and
chouriço (second case study).
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FIG. 3.
Principal-component extraction for isolates from milk
samples from Azeitão (LA) and Castelo Branco (LC).
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|
The profile was then analyzed by multilayer feedforward ANN with the
goal of predicting geographic origin. As detailed in
Materials and
Methods, the available profiles of cheese isolates
from four regions
were randomly divided in two sets, a training
set (293 isolates)
and a smaller validation set (48 isolates).
The latter was kept aside
to evaluate the accuracy of the trained
ANN. This procedure evaluates
the generality of the classification
and enables the use of
cross-validation to prevent overfitting
(i.e., interpolation of
experimental results by ANN predictions).
The results obtained are
presented in Table
1.
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TABLE 1.
Actual and predicted geographic regions of origin
for 341 isolates from cheese samples collected in four
different regions (Fig. 2)a
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|
Error analysis should be interpreted by taking into account the fact
that the probability of obtaining a correct positive
identification by
chance alone is proportional to the frequency
of obtaining correct
positive identifications. For example, of
29 isolates from
Azeitão (training set) (Table
1), only 1 was
not recognized,
which corresponds to an error of 1/29. On the
other hand, four isolates
from other regions were incorrectly
assigned to Azeitão, which
corresponds to an error rate of 4/(293
29) or 1/66. Similarly
low deviations were observed for the
other regions, which allows us to
conclude that the characteristic
profiles of microbial isolates from
cheese were successfully associated
with the samples' origins. This
conclusion was validated by repeating
the analysis for profiles not
used to develop the ANN and obtaining
the same predictive accuracy
(Table
1). It should be noted that
the ANN may assign a sample
metabolic profile to more than one
region (more than one positive
identification) or may not even
assign it to any region (no positive
identification). The extent
of false-positive and false-negative
identifications can be extracted
from Table
1 by recalling that the
values in the central diagonal
axes (upper left to lower right)
correspond to the samples that
were correctly assigned to their true
origins. The false positives
from a particular origin are the total
number of positives found
in the corresponding column that are recorded
in other positions
than on the diagonal axis (e.g., there were 11 false
positives
for cheese from Serra da Estrela). Similarly, the number of
false
negatives from a particular origin will be the difference between
the diagonal value and the total number of samples from that origin
(e.g., there were nine false negatives for cheese from Serra da
Estrela).
If we assume that similarity between regions is reflected by a higher
probability of misidentifications, then the false-positive
identifications will measure similarity between regions. Therefore,
the
correlation between false-positive identifications for each
region can
be used to determine recognition similarity (see Materials
and Methods,
equation 1). Cluster analysis was applied to the
resulting vectors of
correlation coefficients by using the Euclidean
distance as a measure
of dissimilarity (equation 1). An unweighted
pair group average
amalgamation scheme was used to obtain the
cluster tree, represented in
Fig.
4a. It is important to note
that the
similarity between isolates corresponds exactly to the
geographic
proximity of their areas of origin (see the map in
Fig.
2).
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FIG. 4.
Cluster analysis of correlations between false-positive
identifications of the geographic regions of origin of cheese (a) and
milk (b) isolates. Q, cheese; L, milk; A, Azeitão; C, Castelo
Branco; N, Nisa; S, Serra da Estrela.
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The same analysis was applied to isolates from milk obtained from three
of the cheese-producing areas (Table
2;
Fig.
4b).
Similarly low predictive deviations were observed, extending
the
geographic specificity to the raw material. The overlapping between
false positives also seems to reflect geographic distance (compare
Fig.
4b with Fig.
2). It should be noted that the predictive deviations
refer to individual isolates, independently of their taxonomic
identification. The predictive error (standard deviation) if calculated
for multiple isolates from a given product will decrease proportionally
to the square root of the number of isolates.
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TABLE 2.
Actual and predicted geographic regions of origin for 264 isolates from milk obtained in three different cheese-producing
areas (Fig. 2)a
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Second study case: chouriço.
The same data
analysis that we used for cheese and milk was applied to the
characteristic profiles of 261 strains of Lactobacillus isolated from chouriço samples. The isolates were
tested for the following characteristics: gram reaction, growth at 15 and 45°C, growth in 10 and 14% NaCl, reduction of nitrate to
nitrite, production of D- and/or L-lactic acid,
hydrolysis of arginine, gas production from glucose, the presence of
meso-diaminopimelic acid in the cell wall, API ZYM system results, and
the utilization of the carbon sources mentioned in Materials and Methods.
Unlike with the two previous products, the geographic origins of
chouriço samples were not defined by organoleptic
characteristics,
as they were not subjected to the same strict
certification process
used in cheese production. Instead, we considered
the following
administrative regions (Fig.
2): the Azores islands (16 isolates),
Alto Alentejo (69 isolates), Beira Alta (41 isolates), Beira
Baixa
(63 isolates), Beira Litoral (11 isolates), and
Trás-os-Montes
(61 isolates), which corresponds with current
practice but may
constitute an unnatural sampling base due to
environmental and
technological
heterogeneities.
Geographic origins of characteristic profiles used to develop the
recognition ANN were predicted with an almost absolute accuracy
(Table
3). However, when the same ANN analysis
was applied to
the validation set (Table
3), the number of
false-positive identifications
amounted to 32% (and 6% false-negative
identifications). An analysis
of overlapping between false-positive
identifications (Materials
and Methods, equation 1) is presented in
Fig.
5. As with milk
and cheese isolates,
recognition similarity with characteristic
profiles of
chouriço reflected geographic distance between sample
origins (compare Fig.
5 with Fig.
2).
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TABLE 3.
Actual and predicted geographic regions of origin
for 261 isolates from chouriço obtained from
seven producing areas (Fig. 2)a
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FIG. 5.
Cluster analysis of correlations between false-positive
identifications of the geographic regions of origin of
chouriço isolates. TOM, Trás-os-Montes; BL,
Beira Litoral; AL, Alto Alentejo; BB, Beira Baixa; BA, Beira Alta; AC,
the Azores.
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DISCUSSION |
Phenotypic characteristics, meaning metabolic, morphological,
serological, and chemotaxonomic characteristics, are normally used for
taxonomic purposes and for identification of isolates. Throughout this
work, although we used the same types of characteristics, no attempt
was made to achieve species identification either for the genus
Lactobacillus or for the genus Enterococcus.
One of the objectives of the work was to determine whether an isolate
from a given genus, and from a defined fermented food product, had a
phenotypic profile that was characteristic of the geographic area from
which it was isolated. With milk and cheese, in which isolates of the
genus Enterococcus are used, the results indicate that this
genus is present in all the regions sampled, meaning that it is
ubiquitous in the manufacturing environments considered.
The capacity of ANN to correctly identify the region from which an
isolate comes gives an indication that Enterococcus must undergo local genetic and phenotypic differentiation, creating locally
differentiated members (15).
The choice of two genera from two different food products was done with
the purpose of validating the analysis by ANN. The results obtained
with cheese and milk were very consistent, but a consideration, already
mentioned in Results, must be stressed here, namely, that the four
types of cheeses used are already regulated in terms of the type of
milk that can be used and in terms of the general manufacturing
process. These cheeses, as final products, are tested by trained
members of test panels that accept or reject them as having the desired
organoleptic qualities before awarding the seal of the RDO area. This
means that the term geographical origin corresponds with a defined area
and a given set of technological stresses in terms of production, thus giving the products more uniformity. Beyond simply distinguishing the
geographical origin of a cheese or milk, ANN perceive the distances
between different regions (Fig. 4). For instance, Azeitão (milk
and cheese), in terms of Euclidean distances, is always more distant
from the other regions, which reflects a geographical reality (Fig. 2).
Cheeses and Nisa and Castelo Branco are more similar, which is not
surprising, considering the proximity of these areas. Analogous
considerations apply to milk results.
A second objective was to test the approach proposed for
Enterococcus from cheese with a different genus and a
different product, chouriço, that had, with regard to
the samples analyzed, no RDO area. As already mentioned in Results, the
regions of production of this meat product are separated only
administratively, and some of the samples from different regions come
from adjacent places at the boundaries of the regions, meaning that
they can be more similar to each other, due to the proximity of their
regions of origin, than to other isolates of the same administrative
region. The manufacturing differences overlooked by the administrative officials granting certification of origin for
chouriço can be seen in terms of the types of spices
or wine added or the sizes of meat pieces used (always pork).
Therefore, it is not surprising that the predictions of the regions of
origin for chouriço were less accurate. As suggested
for cheese and milk, the differences in cross-identification of
chouriço can be readily explained in terms of
geographic distance and manufacturing practices. Isolates from the
Atlantic islands the Azores are very distant from the ones obtained
from continental regions, whereas isolates from adjacent Beira Baixa
and Beira Alta are metabolically more similar. The similarity between
isolates from regions that are not contiguous can be due to
similarities in the manufacturing processes. Therefore, the proposed
method could then be used to define an RDO area for this type of product.
The application of ANN to the metabolic profiles of bacterial isolates
allows the separation of products from different regions. The practical
application of this methodology would allow the use of the phenotypic
characteristics for the certification of food products, in conjunction
with the utilization of commercial identification systems, without the
need for very sophisticated equipment and extensive training of
technicians. The analysis of metabolic profiles of microbial isolates
should be used in conjugation with traditional test panels to ensure
that the organoleptic properties will be maintained. The high accuracy
of ANN analysis of the metabolic profiles reported here suggests that
ANN should be adopted as a standard for certification of origin, with
periodic confirmation by test panelists. Since metabolic profiles are
easy to obtain, amenable to automation, and relatively inexpensive, they would be particularly useful for the detection of counterfeit food products.
| |
ACKNOWLEDGMENTS |
This work was supported by Fundação para a
Ciência e Tecnologia grant PBIC/C/AGR/1282/92 and Program PRAXIS
XXI grants 2/2.1/BIA/309/94 and 2/2.1/BIO/1121/95. C.I. Pereira thanks
the Fundação para a Ciência e Tecnologia for the
scholarship BIC 740. F.M.S. Rodrigues acknowledges Program PEDIP-2.
We acknowledge the technical assistance of Isaura Velez.
| |
FOOTNOTES |
*
Corresponding author. Mailing address: Instituto de
Biologia Experimental e Tecnológica, Apartado 12, 2781-901 Oeiras, Portugal. Phone: 351-1-4469 551. Fax: 351-1-442 1161. E-mail:
tcrespo{at}itqb.unl.pt.
| |
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Applied and Environmental Microbiology, October 1999, p. 4484-4489, Vol. 65, No. 10
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Copyright © 1999, American Society for Microbiology. All rights reserved.
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Abstract
Introduction
