Emabalajes que evitan contaminacion (1).pdf
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Sensors and Actuators B 128 (2008) 435441
Self-organizing algorithm for classification of packaged fresh vegetablepotentially contaminated with foodborne pathogens
Ubonrat Siripatrawan
Department of Food Technology, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
Received 26 March 2007; received in revised form 25 June 2007; accepted 27 June 2007
Available online 1 July 2007
Abstract
A rapid method for identification of foodborne pathogens contamination in packaged fresh vegetable using electronic sensor array and Kohonenself-organizing map (SOM) algorithm was developed.Escherichia coli was used as thetarget microorganism because its presence in foods indicates
fecal contamination, and the presence of pathogenic microorganisms. E. coli was grown in the packaged fresh vegetable. The electronic sensors
was used to monitor changes in the composition of the package headspace gas phase relating to the biochemical products of E. coli volatile
metabolites. SOM algorithm was then used to classify the data output from the electronic sensor array. The SOM algorithm created a map from a
high dimensional input vector space onto a two-dimensional output lattice. When integrated with SOM algorithm, the electronic sensors proved to
have the ability to classify the packaged fresh vegetable potentially contaminated with pathogens.
2007 Elsevier B.V. All rights reserved.
Keywords: Self-organizing map; Kohonen algorithm; Neural network; Foodborne pathogens; Contamination; Metal oxide sensors
1. Introduction
Numerous outbreaks of foodborne diseases strengthen the
need for rapid and sensitive methods for detection of foodborne
pathogens. Classical methods for the identification and clas-
sification of microorganisms are based on their biochemical,
morphological serological and toxigenic characteristics. These
methods usually require intact viable organisms and a series
of tests requiring the incubation of the microorganisms [1,2].
Early pathogens detection is important to implement disease
control measures[3].Recently, research has focused on devel-
opment of rapid and accurate techniques to identify pathogens
in food products[46].Zhao et al.[7] developed a disposable
electrochemical immunosensor for detection of Vibrio para-
haemolyticus(VP) based on the screen-printed electrode (SPE)
coated with agarose/Nano-Au membrane and horseradish per-
oxidase (HRP) labeled VP antibody (HRP-anti-VP). Wu et al.
[8] applied QCM systemin thedetection of PCR-amplifiedDNA
from real samples ofEscherichia coliO157:H7. The piezoelec-
tric biosensor detected the presence ofE. coli O157:H7 when
Fax: +66 2 2544314.
E-mail address:[email protected].
the DNA strand was complementary to the immobilized probes
with synthetic oligonucleotides.Microorganismscan be characterized by identification of spe-
cific metabolites generated by specific biochemical pathways.
The selection of volatiles for use as incipient disease indicators
has been reviewed in terms of the composite rate of pathogenic
destruction within food products[911].This concept has been
actualized in electronicsensor arrayor electronic nose [12,13].
A considerable number of electronic sensor applications have
been reported, including classification of changes in milk result-
ing from a variety of heat treatments [14], evaluation of the
off-odor in wine[15],quality measurement of smoked salmon
[16]and detection ofSalmonellain nutrient media[17].Elec-
tronic sensor technology is usually based on a hybrid sensor
array system with different selectivity and sensitivity, with the
result being a powerful analytical instrument especially for com-
plex food analyses. However, analysis of volatile compounds
using electronic sensor array often generates large and com-
plicated data set and difficult to interpret if used directly. A
mathematical resolution of complex data is usually performed
in far less time than it takes to conduct physical or chemical
experiments[1719].
Linear methods which have been used to classify the
data include linear discriminant analysis (LDA) and princi-
0925-4005/$ see front matter 2007 Elsevier B.V. All rights reserved.
doi:10.1016/j.snb.2007.06.030
mailto:[email protected]://localhost/var/www/apps/conversion/tmp/scratch_7/dx.doi.org/10.1016/j.snb.2007.06.030http://localhost/var/www/apps/conversion/tmp/scratch_7/dx.doi.org/10.1016/j.snb.2007.06.030mailto:[email protected] -
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436 U. Siripatrawan / Sensors and Actuators B 128 (2008) 435441
pal component analysis (PCA). The LDA and PCA is a linear
transformation that is well suited for separating image/signal
data for different objects or class [20]. The main advantage
of linear transforms is that they are easy to design and typi-
cally have closed-form solutions. However, linear transforms
typically extract information from only the second-order corre-
lations in the data (covariance matrix) and ignore higher-order
correlations in the data. Many researchers have suggested that
many signals in the real world are inherently non-symmetric
[21].A number of nonlinear transformation methods for pattern
recognition exist. Artificial neural networks (ANNs) are among
the most commonly used nonlinear techniques.
The most important features of ANN are their learning and
adaptation abilities. According to their learning strategies, ANN
can be classified as supervised and unsupervised networks. In
supervised learning, each time the ANN is exposed to a train-
ing input, the related class information is required as well.
The multilayer perceptrons (MLP) neural network or the feed
forward ANN has been the most popular. The term unsuper-
vised means that the knowledge of environment is not learnedfrom the specific inputoutput examples. Self-organizing map
(SOM) is an unsupervised artificial neural network which is
frequently used for data partitioning and classification. SOM
can be used for grouping of complex sample data without
any strict assumption and without any priori knowledge of
the number of groups present[20]. Lin and Wang [22] com-
pared SOM with various hierachical cluster analysis methods.
The result shows that the performance of SOM in clustering
messy data is better than that of the other hierachical clustering
methods.
The principle of SOM is characterized by the formation of
a topographic map of the input patterns in which the spatiallocations of the neurons in the lattice are indicative of intrin-
sic statistical features contained in the input patterns[23].The
SOM can be considered as a grid with predefined nodes. Prior to
learning, a large unit area that surrounds the winner is selected
as a neighborhood region. During learning, the pattern of filling
the nodes is determined by the degree of similarity between the
data. If an input vector is presented to the SOM network, the
weight vector in the network that is closest to the input vec-
tor is selected as the best-matching (winner) node. The wining
mapping node is defined as that with the smallest Euclidean dis-
tance between the mapping node vector and the input vector
[2426].
Although various rapid methods for detection of microorgan-isms have been developed, no research has used electronic nose
coupled with SOM to classify the contamination of pathogens
directly from the packaged food products. Hence, this research
was aimed to develop a method to identify E. coli contamina-
tion in packaged fresh vegetable using electronic sensor array
coupled with Kohonen neural network. E. coli is a common
member of the normal flora of the large intestine. In this study,
E. coliwas used as the target microorganism in packaged alfalfa
sprouts because its presence in foods indicates fecal contamina-
tion, and the presence of pathogenic microorganisms. Alfalfa
sprouts were chosen as the product component because the
National Advisory Committee on Microbial Criteria for Foods
(NACMCF) [27] identified sprouts as a special problem due
to the potential for pathogen growth during production, while
there is increasing demand for sprouts due to their popularity as
a healthy food[28,29].The electronic nose was used to monitor
the volatiles produced byE. coli. SOM algorithm was used as an
experimental platform (in addition to the instrumental methods)
to identifyE. colicontamination.
2. Materials and methods
2.1. Preparation of inoculated vegetable
The alfalfa seeds (Natural Sprout Company, Springfield,MO)
were soaked in 20,000ppm of calcium hypochlorite prior to ger-
mination as advised by the U.S. Food and Drug Administration
[28]and NACMCF[27].Alfalfa sprouts were grown in a labo-
ratory environment at 20 C and 65% RH with indirect sunlight
and away from any possible contaminations. The sprouts were
harvested after 5 days (fully grown) when length is 3.84cm.
The sprouts were washed and drained several times before use.
The nonpathogenic strainE. coli ATCC 25922 obtained from
the American Type Culture Collection (ATCC, Rockville, MD)
was cultured in tryptic soy broth and incubated at 37 C for
8 h in a gyrotory shaker and centrifuged. Broth was poured
from the culture and the sedimented pellet was resuspended
in sterile Butterfields phosphate buffer which was used as a
dipping suspension. Preliminary experiments were conducted
to determine the population of E. coli necessary in the dip-
ping suspension to result in an initial population of105 CFU/g
on sprouts. Preliminary studies also showed that the electronic
nose was able to detect volatiles produced by E. coli when
the number ofE. coli was higher than 105 CFU/g. The sproutswere then placed in screened baskets, and submerged in the
suspension containingE. colifor 3 min. The uninoculated con-
trol was similarly treated except sterile phosphate buffer was
used in place of the inoculum. Fifty grams of sprouts were
then packed into commercial 1.5-mil, 15 cm 8 cm linear low-
density polyethylene (LDPE) bags and heat-sealed. The total
volume of thesprouts was200 ml which occupied about half of
the total bagvolume. Thesamples were incubated at 10 Cfor1
3 days.
2.2. Microbiological analysis
The microbial cell count was determined on the date of inoc-
ulation and periodically throughout storage at days 13. Serial
dilutions were prepared from the stock suspension, and Petri
plates were inoculated with those dilutions expected to give
countable colonies. Inocula consisting of each of a dilution seri-
als were deposited on prepared plates in duplicate using 3M
Petrifilm Aerobic Count Plates (3M, St. Paul, MN) for deter-
mining aerobic bacteria and 3M PetrifilmE. coli/Coliform Count
Plates (3M, St. Paul, MN) containing Violet Red Bile nutrient
agar as an indicator of glucuronidase activity for E. coli. All
plates were incubated at 37 C for 48 h. Plate countsare recorded
as colony forming units (CFU/g).
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U. Siripatrawan / Sensors and Actuators B 128 (2008) 435441 437
2.3. Electronic nose analysis
A total of 120 samples, including non-inoculated alfalfa
sprouts (control) and alfalfa sprouts inoculated with E. coli at
time zero, were incubated for 13 days prior to analysis.
An electronic nose (Fox 3000, Alpha M.O.S., Hillsborough,
NJ) was used for monitoring changes in volatiles produced by
E. coli growing on the sprouts. The volatile analysis system
combines a measurement chamber for generating the volatile
compounds and a detection system made up of 12 metal oxide
sensors (SYLG, SYG, SYAA, SYGH, SYGCTI, SYGCT, T301,
P101, P102, P401, T702, and PA2). This instrument was linked
to an auto-sampler capable of analyzing a total of 64 samples.
Samples were placed in the HS100 auto-sampler in arbitrary
order. Five millilitre was collected from the headspace of pack-
aged alfalfa sprouts and injected into the electronic sensor. The
temperature of the injection syringe was 40 C. The delay time
between two injections was 300 s. Each injection was repeated,
with separate samples. The electronic signals from the sensors
were digitized and then transferred to the control computer.
2.4. Self-organizing neural network
Data were made up of 120 samples from8 subgroups(sprouts
(SP) and sprouts inoculated with E. coli (EC) in LDPE bags
on the first day of inoculation and incubated at 10 C for 13
days). Each subgroup had 15 replicate samples collected from
several cultivations. Each sample was analyzed using 12 metal
oxide sensors. The sensor responses of all 120 samples were
arrangedina120 12 matrix. Data classificationwas performed
using SOM algorithm. All calculations were carried out using
MATLAB 5.2 routines written by the authors, and making use ofthe toolbox provided by Mathworks (Mathworks, Inc., Natick,
MA).
3. Results and discussion
3.1. Microbial cell counts
The number of aerobic bacteria and E. coli on the alfalfa
seeds was determined. The number of aerobic bacteria was
101102 CFU/g, while noE. coli were found. The alfalfa seeds
were soaked in 20,000 ppm of calcium hypochlorite prior to ger-
mination as advised by NACMCF[27].This treatment has the
potential to substantially reduce microbial contamination whichcan be passedon to the growing sprouts.Gillet al. [30] suggested
that chemical disinfection can reduce the human risk for disease
posed by contaminated seed sprouts. The number of aerobic bac-
teria in alfalfa seeds increased from 101102 to 107 CFU/g
when the alfalfa sprouts were fully-grown. The conditions dur-
ing sprouting (e.g. time, temperature, water activity, pH, and
nutrient level) may have promoted the growth of microflora
[28,30],without affecting the smell, taste or appearance of the
sprouts. Thus, the risk of foodborne disease associated with
sprouts increases during sprouting[27].
The cell counts of aerobic bacteria andE. coli on fully-grown
sprouts with and withoutE. coliinoculation are shown inFig. 1.
Fig. 1. Growth of total aerobic bacteria and E. coliof uninoculated vegetable
(SP) and vegetable inoculated with E. coli(EC).
All samples had a high number of total aerobic bacteria. How-ever,E. coliwas not found in the control samples. The numbers
ofE. coli in the inoculated samples increased from105 CFU/g
on the first day of inoculation to 107 CFU/g after 3 days incu-
bation.
3.2. Electronic sensor array
Each sensor element changes in resistance (max) when
exposed to volatile compounds. The information from electronic
sensor array analysis was extracted from the series of sensor
resistances. In order to produce consistent data for the classifica-
tion, the sensor response was presented with a volatile chemical
relative to the base resistance in air, which is the maximum
change in the sensors electrical resistance divided by the initial
resistance, as follows
Relative resistance change =max 0
0(1)
wheremaxis the maximum change in the sensors electrical
resistance and 0 is the initial baseline resistance of the sen-
sor. The relative resistance change was used for data evaluation
because it gives the most stable result, and is more robust against
sensor baseline variation.
The data matrix comprised 120 samples from 8 subgroups
(SP-D0, SP-D1, SP-D2, SP-D3, EC-D0, EC-D1, EC-D2, andEC-D3) as analyzed using the 12 sensors (SYLG, SYG, SYAA,
SYGH, SYGCTI, SYGCT, T301, P101, P102, P401, T702, and
PA2).Fig. 2shows the average responses of all samples in 8
subgroups to the 12 metal oxide sensors. In Fig. 2, the sensi-
tivities of all samples are compared. These values express the
average sensor responses of each sensor in the range of mea-
surement. Since the sensor outputs from the 12 different sensors
are not homogeneous, a direct comparison of sensitivities is not
adequate to interpret the information from the samples. The sen-
sor responses from an array of nonspecific metal oxide sensors
are generally insufficient to discriminate between a series of
samples.
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Fig. 2. Electronic sensor responses form the headspace of packaged samples
labeled using thefollowing scheme: SP-D0,SP-D1, SP-D2and SP-D3 arepack-
aged vegetable kept at 10 C for 03 days, respectively; EC-D0, EC-D1, EC-D2
and EC-D3 are packaged vegetable inoculated with E. coli on the first day of
inoculation and after stored at 10 C for 13 days, respectively.
In this study, unsupervised SOM was used for analysis of
electronic sensor array data. Although, the supervised MLP neu-
ral network has been the most popular to model the complex
data, it requires prior training pairs (input vectors and corre-
sponding target vectors) to make training possible. Therefore,
the MLP may be unable to provide a real-time response to detect
the contaminated samples. Self-organizing map is an unsuper-
vised neural network which does not need any class information
for learning, but acquires that knowledge by itself during the
training phase through cluster formation. For SOM, the only
input is needed to construct an output. The SOM algorithm cre-ates a mapping from a high dimensional input vector space onto
a two-dimensional output lattice. The SOM network is basically
composed of a single, two-dimensional layer of neurons which
helps provide a visual presentation of data.
3.3. Self-organizing neural network
The unsupervised SOM was used to classify electronic
voltametric response outputs by replacement of the actual
data points using topographic map reference vectors. A two-
dimensional Kohonen output layer was used to help provide a
visual presentation. According to Lee et al. [31],selecting theappropriate number of output nodes is quite difficult and this
is usually experiment-dependent. There is no consensus among
researchers about the subject. To obtain good mapping results,
the number of output nodes in the Kohonen neural network
should be at least 1020% of training vectors. However, using
too few output nodes may cause the congestion of input vectors
over an output node, which may make it difficult to distinguish
the characteristics of the output space.
A Kohonen network consisting of 55 nodes was employed
for classification of the 8 subgroups from the input data matrix
(12120). The predefined neuron number (grid size) in the
Kohonen outputlayerwas chosenbecauseit wassufficient to dis-
tinguish different sample groups. In SOM process, the mapping
nodes are first initialized with random numbers. The SOM is
initialized by assigning small random values to all of the weight
vector elements. The algorithm responsible for the formation
of the SOM proceeds first by initializing the synaptic weight
in the network. Once the network has been properly initialized,
there are three essential processes involved in the application
of the algorithm including sampling, similarity matching, and
updating[25,32,33].
For the sampling process, a sample xfrom the input space
was drawn with a certain probability. Let xdenote an input vec-
tor selected randomly from the input data space and m denotes
the dimension of the input space. The vector x represents the
activation pattern that is applied to the lattice.
x = [x1, x2, . . . , xm]T (2)
In the similarity matching step, the best matching, winning
neuron i(x) attime step twas determined by using the minimum-
distance Euclidean criterion:
i(x) = arg min||x(t) wj(t)|| (3)
wherewjdenotes the synaptic weight vector of neuronj. Ifan m-
dimensional input vector is presented to the SOM network, then
the weight vector in the network that is closest to the input vector
is selected as the best-matching node. The particular neuron i
which is the best matching is called the winning neuron for the
input vectorx.
For the updating process, the synaptic weight vectors of all
neurons were adjusted using the update formula:
wj(t+ 1) = wj(t) + (t)hj,i(x)(xi(t) wj(t)) (4)
hj,i(x) = exp
d2j,i
22
(5)
wherex(t) is the input to node iat timetandwj(t) is the weight
from input node i to output node j at timet.(t) is the learning
rate parameter, hj,i(x)(t) is the neighborhood function centered
around the winning neuroni(x),d2j,iis the distance between the
winning neuron i and the adjacent neuronj, and is the width of
the topological neighborhood [32]. Thewinning node is selected
as the center of a neighborhood, in order to reduce the Euclidean
distance. At timet, the cell learns this input signal. During the
next time t + 1, it hasan information processing ability ofwj(t+
1), which is close to the input signal. The neighboring units thatsurround i(x) also learn the input vector x(t) by following the
same equation[33].
For training, the data vectors are first arranged in random
order and then presented in this order to the neural network for
training. In SOM, the neurons adaptively tend to learn the prop-
erties of the underlying distribution of the space in which they
operate. Additionally, they also tend to learn their places topo-
logically. The training consists of finding the winning neuron,
which is the one whose pattern has the best match andmodifying
the winning node and its closest neighbors in the neuron map
by moving their associated feature vectors closer to the input
vectors[25,32].
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U. Siripatrawan / Sensors and Actuators B 128 (2008) 435441 439
For the training cycle decision, there is no definitive stopping
point.A huristic is to useenough training cycles so that a network
approaches a stable state [20].As the learning progresses, the
mapbecomes more andmore structured. Iterations arecontinued
until all of the weight vectors are stabilized, and hence cluster
regions for each candidate target are properly established on the
SOM output map. At the end of the learning process, the feature
map is spanned over all input values. The network was trained
for 10,000 epochswith patterns selected randomly as opposed to
sequentially. During the training phase the map unfolds to form
a mesh. The neurons are mapped in the correct order at the end of
this phase. The SOM is configured on a two-dimensional feature
space consisting of discrete lattice nodes (the set of discrete
lattice nodes is denoted as the neuron map). Each node in the
lattice hasa feature vector. Thenodesare initialized with random
patterns and the SOM is subsequently trained iteratively.
After training, the algorithm organizes the sensor response
topologically on a two-dimensional grid, in which each node
corresponds to a weight vector. The algorithm constructs these
responsesby nonlinearinterpolation of the responsesin the train-ing set. SOM learns both the distribution and topology of the
input vectors they are trained on. The weight vector most closely
approximates that specific input vector, is chosen as the win-
ning neuron. The SOM feature map usually adjusts its weights
quickly to their inputs. Fig. 3 shows the plot of weight vectors of
winning neurons of each sample class. The SOM visualization
consists of running the same input file against the trained map
and reporting the map grid location that is closest in Euclidean
distance to each input. By labeling each neuron on the map
with the appropriate subgroup terms (Fig. 4),the clustering of
natural groups can be discovered from the electronic response
data. The performance of the SOM was measured as percent ofmisclassification. The SOM gave only 2.5% misclassification.
The SOM pattern provides information on sample classifi-
cation. For instance, node 4 had a weight pattern most similar
Fig. 3. Weight vectors of the trained network labeled using the following
scheme: SP-D0,SP-D1, SP-D2 andSP-D3are packaged vegetable kept at 10 C
for03 days, respectively;EC-D0,EC-D1, EC-D2and EC-D3are packagedveg-
etable inoculated withE. colion the first day of inoculation and after stored at
10
C for 13 days, respectively.
Fig. 4. Self-organizingmap of sensor responses on a 55 rectangular grid with
clusters indicating the sample subgroups labeled using the following scheme:
SP-D0, SP-D1, SP-D2 and SP-D3 are packaged vegetable kept at 10 C for 03
days, respectively; EC-D0, EC-D1, EC-D2 and EC-D3 are packaged vegetable
inoculated with E. coli on the first day of inoculation and after stored at 10 C
for 13 days, respectively.
to the input pattern for EC-D3 (vegetable inoculated with E.
coliand incubated for 3 days), and therefore, this node was rep-
resentative of this subgroup. The output patterns generated by
the Kohonen network for the different volatile fingerprints show
that the self-organizing pattern was able to distinguish different
subgroups with different number ofE. coli.However, samples
from the first day of inoculation (EC-D0) and the control on thefirst day of preparation (SP-D0) overlapped. This suggested that
the capability of the electronic sensors to detect the volatiles
produced by E. coli occurred when the number ofE. coli was
higher than 105 CFU/g. The SOM method can be considered
an alternative way to the classification schemes. Dimensional-
ity reduction from the input space to the network field was also
accomplished using this algorithm.
The volatile metabolites from the headspace of packaged
alfalfa sprouts inoculated withE. coliwere analyzed using elec-
tronic nose. The volatile compounds present in the headspace of
inoculated sprouts and absent in the headspace of uninoculated
sprouts can be used as possible indicators ofE. coli contami-
nation. The electronic nose was used to monitor changes in thecomposition of the gas phase of biochemical products from E.
colivolatile metabolites directly from packaged alfalfa sprouts
(without culturing in standard media). In this research, the elec-
tronic nose has shown potential to detect specificE. colivolatile
metabolites, even though the sprouts contained high aerobic
counts. The primary advantage of the electronic nose in a qual-
ity assurance method is in its speed of analysis, including data
acquisition and interpretation. Rapid, significant data interpreta-
tion is possible using unsupervised SOM neural network. SOM
is able to find relationship between data, grouping and mapping
them topologically. The developed method has potential in the
real-time detection ofE. coli. This method is easy to use and
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440 U. Siripatrawan / Sensors and Actuators B 128 (2008) 435441
enable continuous operation. However, its future lies in attain-
ing selectivities and sensitivities comparable to conventional
methods.
This system is not limited to theaforementionedapplications.
The system can be applied to other packaged food products or
incorporated into HACCP protocols or quality control systems
in the food industries.
4. Conclusion
The electronic nose has shown potential to detect specificE.
coli volatile metabolites, even though the sprouts contained high
aerobic counts. However, the capability of the electronic nose to
detect the volatiles produced byE. colioccurred when the num-
ber ofE. coliwas higher than 105 CFU/g. The algorithm based
on Kohonen self-organizing map was used to organize topolog-
ically sensor response inputs into clusters on a 5 5 rectangular
grid feature map. The SOM algorithm created a map from a high
dimensional input vector space onto a two-dimensional outputlattice. The location of the node in the grid provides information
about the different sample groups. SOM can be used for analyz-
ing multicomponent data, in order to get classification of data
from an analysis of the contribution that each sensor brings to
the whole array. The SOM neural network was shown to provide
patterns which were more easily distinguished than the original
sensor response patterns for similar types of interactions. This
provided the basis of an effective feature extraction method in
that distance values generated by the network allowed better dis-
crimination of a fingerprint. The SOM profiling method can
be considered an alternative way to the classification schemes.
Dimensionality reduction from the input space to the network
field was also accomplished using this algorithm. The sensorarray coupled with SOM has the potential to be a sensitive,
fast, one-step method to identify E. coli contamination in the
packaged samples.
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Biography
Ubonrat Siripatrawanobtained her PhD degree in packaging from Michigan
State University. She is an assistant professor in Department of Food Technol-
ogy, Faculty of Science, Chulalongkorn University. Her research interests coverelectronic sensors, GCMS and ANN for rapid detection of microorganisms in
packaged food products, food shelf life simulations and active packaging.
http://www.cdc.gov/ecoli/2006/september/http://www.cdc.gov/ecoli/2006/september/http://www.cdc.gov/ecoli/2006/september/http://www.cdc.gov/ecoli/2006/september/