Egan, B.

Hulzebos, J.L.

Koenderink, N.J.J.P.

Roller, S.

Top, J.L.

Wageningen Yield 

Antimicrobials On-line: Concept and Application for Multidisciplinary
Knowledge Exchange in the Food Domain
Nicole J.J.P. Koenderink*, J. Lars Hulzebos*, Sibel Roller**,
Bernadette Egan**, and Jan L. Top***
*Agrotechnology and Food Innovations B.V. (A&F)
P.O. Box 17, NL-6700 AA Wageningen, The Netherlands
e-mail: {nicole.koenderink, lars.hulzebos}@wur.nl
http://www.agrotechnologyandfood.wur.nl
**Thames Valley University, Faculty of Health and Human Sciences
32-38 Uxbridge Road, London W5 2BS, United Kingdom
e-mail:  {Sibel.Roller, Bernadette.Egan}@tvu.ac.uk
http://www.wolfson.tvu.ac.uk/humansciences/content/index.htm
***Vrije Universiteit Amsterdam
De Boelelaan 1081, 1081 HV Amsterdam
e-mail: jltop@cs.vu.nl
http://www.cs.vu.nl/bioinf/index-en.html
Keywords: ontology, preservatives, associative search methods, knowledge modelling,
semantic network, food experiments.
Abstract: Industrial food developers need reliable, comprehensive, and complete information
on the application of food preservative agents. To this end, we have collected and integrated
knowledge from food technologists, microbiologists and food specialists in universities,
research institutes, and industry. This knowledge has been implemented in a web-based
expert knowledge system. Presently it holds the properties and practical applications of five
natural antimicrobial preservatives and their relation to a large number of microbes and food
types. Experiments described in literature are available at a user-specified level of detail. For
example, given a type of microbe and food, users can search the system automatically to find
the antimicrobial compound that guarantees a required shelf life time. Scientists can search
the same system for detailed information on the degree in which certain strains of microbes
are sensitive to a certain combination of anti-microbial compounds.
The technical objective behind the design of the system was to support associative search
methods, which yield a higher recall and relevance for the search results than traditional
database queries. Therefore, the food domain knowledge was modelled using ontologies,
which enabled us to store context knowledge in a semantic network. The ontologies developed
were implemented and made accessible using a conceptual query language (SeRQL) and a
metadatabase (Sesame). This has resulted in a highly flexible and easy to maintain knowledge
base. A second advantage of structuring the food domain knowledge in this way is that in
future project the already gathered knowledge can easily be reused and extended.
1 INTRODUCTION
In western society, food safety is a high priority issue. The consumer is to be protected
against food induced risks, and extensive research efforts are devoted to eliminate or
minimise the presence of pathogens in foodstuffs. In addition to standard preservation
techniques, such as drying, cooling, etc, new technologies are being developed such as ultra-
high pressure processing, pulsed electric field, etc. Traditional preservatives are replaced by
or supplemented by chemical compounds that protect food products against microbial
activity. In the work we present here, we focus on preserving additives, in particular those that
have a natural origin, such as nisin, natamycin, carvacrol, lactic acid, etc. However, our
framework is open for future extension towards other preservatives, preserving technologies,
and combinations of these (hurdle approach).
The main aim of the service is to provide structured information on the properties and
applications of antimicrobial compounds through a web-based knowledge system for
industrial and scientific users. In order to develop such a system, a co-operation between
knowledge modellers and food specialists has been set up. The problem in applying pre-
servatives in practical food design is that often the effectiveness of these compounds in
specific circumstances is unknown. Moreover, restrictions may apply due to legislation. This
makes it difficult for industrial users to decide which preservative could be applied for their
products and for which specific markets this would be allowed. Much knowledge about anti-
microbial compounds is available in academic research through scientific and semi-scientific
publications. However, this knowledge is not ready to use for practical food developers, since
most articles deal with the use of anti-microbials in laboratory media under idealised
circumstances. In these cases, it is not clear how effective the compounds are in non-ideal,
commercial food products. Furthermore, the information is scattered and unstructured and
therefore difficult to locate and apply.  The ambition of A/OL, Antimicrobials Online, is to
assist industrial (small scale and large scale) users of preservatives. Therefore, the information
provided is interpreted and summarised by domain experts, assessed with respect to its
practical use and structured according to the preferences of the user.
A/OL does not stand alone if we consider computer-based support for food preservation.
An example of a related application is Combase [1]. Combase provides a framework and
database for modelling and simulation of microbial growth. Its approach is complementary to
A/OL in the sense that A/OL provides information on growth limiting compounds and
Combase on microbial growth. In the related domain of flavour components, FlavourWorks
exists, which is a tool in which information on flavour compounds can be found.
2 USING ONTOLOGIES
Presently, knowledge
modelling is leaving its academic
playground in artificial intelligence
to become a standard activity in
‘real world’ application
development. This is due to the fact
that for the next generation of
Internet, the Semantic Web [ref],
search methods will be based on
meaning of text rather than simple Figure 1: Structure of system architecture
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string pattern matching. This will significantly enhance the usefulness of the web as a world-
wide knowledge and information source. To give meaning to information, this information
has to be viewed in the light of its knowledge domain. The concepts and relations between
these concepts in the knowledge domain are formally described in knowledge models. These
models are also known as ontologies. The thing that makes an ontology special from
knowledge modelling in general is that an ontology is an agreed explicit structure in a certain
domain, which can also exist outside the application for which it was developed. Ontologies
can be reused by other applications and maintained for a particular knowledge domain. The
knowledge modelling for databases and software applications usually are drawn from scratch
and are only used for that certain application, implicating poor maintenance and reusability.
For the A/OL project we have developed ontologies for the various knowledge domains in
OntoEdit [ref]. The ontologies are expressed in RDF format and imported in Sesame [ref].
With the query language SeRQL [ref], an enhanced version of RQL, the ontologies and their
instances are questioned and the results are displayed in the user interface (see Figure 1).
3 MICROBES, ANTIMICROBIALS AND FOOD
The basic concepts in A/OL are antimicrobials, microbes, and food products. The  service
essentially provides information on experimental studies concerning the effectiveness of
antimicrobial compounds in laboratory circumstances. The antimicrobial knowledge in A/OL
contains information on physical and chemical properties, like boiling point, solubility,
stability, toxicity, etc. The microbial information is stored in a taxonomy, in which relations
between families of microbes are indicated. In A/OL three main types of media are
distinguished: real food, food models and laboratory media.
Besides knowledge on these three components, the largest amount of data in A/OL
consists of the inhibitory working of anti-microbials against microbes in certain environments
(food, food model or laboratory medium). Typical properties in these experiments are pH,
temperature, duration, etc. Experiment descriptions can be found, as well as results and
conclusions. All these data have been assessed by experts in the field of food preservation.
Figure 2: Search screen for experimental information
4 USER INTERACTION
In the web-based user-interface, we distinguish several user roles, like food scientist,
marketeer, etc, where each role has its own preferences of search criteria. We discern four
different categories of search criteria: (1) activity, in which information on the actual working
of preservatives is contained, (2) permitted usage, in which information on the legal status of
food preservatives in certain countries can be found, (3) literature, in which articles are
presented for detailed information on anti-microbial compounds, and (4) suppliers, where
information can be found on sellers of preservatives. In Figures 2 and 3, an overview of the
search steps in the user interface are given. In Figure 2, the screen is shown in which the user
can select on which criteria the data have to be searched. Figure 3 displays a list of the
resulting experiments. Clicking on of these experiments, results in detailed information.
The described user interface is accessible via the Internet. The web-address is:
http://www.atoapps.nl/AOLKnowledge, where one can apply for a guest account.
5 CONCLUSIONS
A/OL is becoming an informative system with reliable data on the activity of
antimicrobial compounds. Because of the ontological knowledge structure, the system is
highly flexible. The ontology-based searching enables interesting access capabilities of the
data. In future, the system will be extended to other antimicrobial compounds, and a consumer
front end will be added.
6 REFERENCES
[1] Baranyi J., Tamplin M., “ComBase: A Common Database on Microbial Responses to
Food Environments” J. Food Prot. (2002).
[2] Y. Sure, S. Staab, J. Angele. “OntoEdit: Guiding Ontology Development by
Methodology and Inferencing”, Proceedings of the International Conference on
Ontologies, Databases and Applications of Semantics (ODBASE 2002).
[3] Jeen Broekstra, Arjohn Kampman, Frank van Harmelen. “Sesame: An Architecture for
Storing and Querying RDF and RDF Schema”, Proceedings of the First International
Semantic Web Conference (ISWC 2002).
Figure 3: Part of the list of resulting experiments for the inserted query.


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