=Paper=
{{Paper
|id=Vol-2180/paper-46
|storemode=property
|title=Tripartite Flavour Model: Food Phenotype, Sensory and Interpretative Matrices
|pdfUrl=https://ceur-ws.org/Vol-2180/paper-46.pdf
|volume=Vol-2180
|authors=Tarini Naravane,Matthew Lange
|dblpUrl=https://dblp.org/rec/conf/semweb/NaravaneL18
}}
==Tripartite Flavour Model: Food Phenotype, Sensory and Interpretative Matrices==
https://ceur-ws.org/Vol-2180/paper-46.pdf
Tripartite Flavour Model: Food Phenotype,
Sensory and Interpretative
Matrices
Tarini Naravane1, Matthew Lange2
1 Biological and Agricultural Engineering, University of California, Davis
California 95616, USA
tnaravane@ucdavis.edu
2 Food Science and Technology, University of California, Davis
California 95616, USA
mclange@ucdavis.edu
Abstract. Among all sensory sciences, flavour remains a wicked problem while sight, sound,
and touch have all been digitized. While the biological basis for food consumption is primarily
to nourish bodily functions, it fulfills a greater second function of sensory pleasure. Flavor,
and the pleasure it engenders, is the primary driver of food choice. Moving toward a semantic
web of food that enables personalization of food and flavor experiences requires an
interoperable ontological model of flavor. This paper proposes a framework of several
ontologies to model a comprehensive view of flavor, by partitioning it into three interoperable
matrices of interacting variables: objective characteristics of food, subjective sensory
experience, and interpretive communication of that experience. Together these three matrices
represent an initial ontological model for the flavor and sensory experience portion of the
emerging semantic web of food.
1 Introduction
In 1973, two social scientists, Horst Rittel and Melvin Webber defined a class of problems they
called “wicked problems”.[1] Wicked problems are messy, ill-defined, more complex than we fully
grasp, and open to multiple interpretations based on one’s point of view. [2] Flavour among all
sensory neurosciences remains a wicked problem. While many researchers have proposed methods
for digital replication of specific tastes and aromas [3], to date there exist no semantic or
ontological models for operating over food flavor and the sensory experience.
Selection of food for nourishment in animals is an evolutionary process, influenced by habitat
and ecological conditions, whereby recognition of tastants and especially aromants are associated
with (dis)pleasurable eating and post-prandial experiences, and highly influence future food
choices. Learned consequences of ingested foods cross five sensory modalities of taste, aroma,
texture/mouthfeel, colour and sound and this complex sensation is called Flavour. [4–6] Challenges
for designing computational flavor systems are effectively highlighted by comparison to more
developed computational neuroscience systems of vision and sound, where scientific research and
technology successfully mapped physical properties of stimuli to their perceptual characteristics
due to the continuous nature of their data. In vision, wavelength translates into a digital model
color; in audition, frequency and wavelength translates into amplitude/pitch model. [3] This
information digitisation provides unambiguous identification of colour and sound, without
influence of perception or hedonic response. The separation of objective and subjective
perspectives is our proposed solution to the wicked flavour problem, albeit the dimensionality of
flavour is orders of magnitude greater than for sound or colour. Since the scope of this Flavour
“model” is so vast, it requires a top-down modular approach. The Ontology of Nutrition Studies
adopts the similar approach to curate heterogeneous nutrition data into ontologies and accordingly
involve researchers from different nutrition-related fields (health sciences, agricultural sciences,
food technology) where the same term has different semantics [7]. Another purpose of such a
top-down approach is for better integration of ontologies by creating clear distinctions between
high level domain ontologies and in depth ontologies.
2 Tripartite Flavour Model
The model in Figure 1 shows the three matrices. The first matrix enclosed by a curve dashed line
represents the Food Phenotype Matrix, unbiased by individual response. The second matrix is
enclosed in the human body boundary, represents the sensory capture and modulating factors in
decoding the ingested food. The third layer still partly enclosed in the human boundary is the
interpretation of the experience which is finally communicated.
2.1 Objective Properties
A food is composed of biological components and chemical components. Biological components
includes bacteria and morphological features of the food, like germ, in a grain. Chemical
components are all the molecules. Biological properties are the bioactivity roles. Chemical
properties characterize the reactability. Physical properties include Rheological, Acoustic,
Volumetric properties to name a few. Organoleptic properties felt as the sensation of touch, sight,
smell, taste, sound, inflammation, and lacrimation is relevant to the consumption of food. The Food
Phenotype Ontology in Fig 2 ,is designed to characterise a single-molecule food like table sugar, or
a flour with several different molecule types as shown in Figure 3. Future development of the
ontology will also consider modelling the structure of foods like lasagna. The top section of Figure
1 illustrates the transformation of a food by adding another food and/or the effect of time, process
and environment.
2.2 Sensory Matrix
The sensory apparatus and neural processing is a highly-nuanced combination of psychological and
physiological factors. The olfactory apparatus of 400 odorant receptors, [3] has variations across
ancestry, age, and gender for over 70% of the explainable variance for some odors (guaiacol,
diacetyl, and nonyl aldehyde) and less than half of the explainable variance for others[8]. The taste
papillae in the tongue vary in density [9, 10] and these are some responses to tastes comparing high
and low density groups; sucrose (196%), NaCl (135%) ,PROP (142%), Citric acid (118%) and
quinine HCl (110%) [11]. Anosmia and hyposmia, the inability or decreased ability to smell, is
estimated to afflict 3–20% of the population and is linked to old age,chronic sinonasal diseases or
neurodegenerative diseases. [12] On the psychological front, stress caused changes in
neuroendocrine balance (high cortisol and insulin) can lead to non-homeostatic eating patterns.
2.3 Interpretative Matrix
Folksonomies are the varied taxonomies across socio-cultural demographics. The origin is rooted
in the communication theory of social constructionism; that human beings rationalize their
experience by creating models of their social and cognitive processes and reify these models
through language.[13] Research studies found that differences in expression that can be divided
into three groups: sensory descriptors (hard, red, noisy); symbolic descriptors (interesting,
expensive, modern); and affective descriptors (pleasant, beautiful)[14].
3 Conclusion
The semantic web of food critically depends on digital models of flavour for enabling predictive
outcomes from food production, transformation, and ingredient combination processes relative to
flavors, bioactive/nutritional properties, and ultimately health/behavioral outcomes. Modularization
of the flavor model, as illustrated with bread example, considers the (future) role of measurements
to support reasoning and decision making in any food processing sequence toward a desired
food-phenotype outcome. The Food Phenotype model applied toward quality/grading standards of
commodities like wheat, by virtue of characterizing the bread organoleptic properties, provides
basis for price premium by consistent quality attributes. Finally, this framework while focussed on
flavour and processing, enables connection to connection of flavor outcomes with
production/transformation process energy usage, effluent production, and ultimately sustainability
outcomes with specific flavor desires.
Fig. 2. Phenotype Ontology base classes
Fig. 1. Tripartite Flavour Model.
Boundary lines separate three matrices.
Fig. 3. Bread as an example of the Objective layer. Fig 3a(top) gives details of the Phenotype
Model. Note on terminology - Parameter is a set of variables; Fig 3b lists variables. (Not all
Parameters are defined yet.) Fig 3c explains sequence of ingredient and process
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