Detecting Sensory Textures with Rheological
Characteristics from Recipe Sharing Sites
Hiroshi Uehara

Daichi Mochihashi

Faculty of Data Science
Rissho University
Saitama, Japanuehara@ris.ac.jp

Department of Statistical Inference and Mathematics
The Institute of Statistical Mathematics
Tokyo, Japandaichi@ism.ac.jp

Abstract—This study tries to estimate texture of gel related
dishes based on both the data from recipe sharing sites, andresearch results of food science. Since most of recipes are not accompanied by sufficient information about what kind of textures
they realize, we propose a method to estimate characteristics oftextures for each recipe by applying a joint topic model to bridge
sensory texture terms in a recipe sharing site with corresponding
quantitative textures resulting from food science research. The
result shows that the estimated texture terms for dishes are
consistent with rheology, the quantitative textures provided byrelated food science research.
Index Terms—computational cooking, topic model, food science, rheology, food texture

I. I NTRODUCTION
Most of recipes posted on recipe sharing sites (hereafter,
referred to as posted recipes) are not accompanied by sufficient information of their cooked characteristics such as
taste, aroma, and texture. Therefore, users have to select theirfavorites with insufficient knowledge of these cooked results.
Especially among these cooked characteristics, texture is themost important attribute for solid foods: it occupies top 32.1%
of all the food attributes associated by subjects over a wide
variety of foods with words of attributes [1]. With this respect,texture has been addressed by a wide variety of studies ranging
from international standardization of textural attributes by ISO[2] to empirical texture evaluations in food science research[3]–[5]. Nevertheless, the findings of these studies are notlinked to specifying texture characteristics of posted recipes.
This study tries to detect texture information of gel related dishes from collective posted recipes. Gelling agentsare indispensable ingredients for controlling and preserving
food texture of a wide variety of foods including sweets,
sauce, dressing, and frozen foods. Our proposal enables the
texture information to represent not only texture terms, but also
the relationship between the texture terms and concentrationsof ingredients. Then, these concentrations enable linkages
between the texture terms and quantitative texture resulted
from food science research. As such, this study aims to provide
home cooking users with reliable information of texture,
thereby enabling to find their favorite recipes in more suitablemanner.
Related work is surveyed in Section II. Then, our proposal
is described in Section III followed by introduction of data forour experiments in Section IV. Subsequently, the results andevaluations are described in Section V. Section VI concludes
our study.
II. R ELATED W ORK
A wide variety of texture terms exist to express the sametextural perceptions, further, the variations are different depending on language by each country [7], [8]. These variations have been an inherent issue for objective evaluationin empirical textural studies. With this background, manyof studies have adopted instrumental evaluation instead of
sensory evaluations(i.e., perceptive questionnaire to subjects)
to represent texture as universal quantitative attributes.
However, these attributes are not intuitively interpretablein terms of users’ perceived texture. Very few quantitative
research in food science have tried to interpret the quantitative
results to perceived texture [13], [14] Nevertheless, properinterpretations of a wide variety of texture terms are still
ambiguous, because the results of these studies are acquired
from questionnaires to limited number of subjects.
Our study addresses a wide variety of texture terms from alarge amount of posted recipes and enabled them to be linked
to corresponding quantitative results of food science research.
The result of the linkage is validated by [10], comprehensive
allocations for Japanese texture terms.
Thus, our proposal bridges research on natural language
processing and quantitative research in food science. Although
research on computational cooking have analyzed a wide
variety of data including recipe texts, and images etc., data
concerning physical characteristics of food have not beenaddressed [12].
III. M ETHOD
In order to detect classified texture information from posted
recipes, we adopt joint topic model [11], a kind of latent
Dirichlet allocation algorithms (LDA). LDA identifies topics,
each of which represents classified pattern of distribution.
While conventional LDA represents topics by a single typeof data (e.g., distribution of texture terms), joint topic model
represents topics by multiple types of data. In our proposal,
each joint topic comprises two types of distribution, the one
represents a pattern of texture terms and the other represents
concentrations of ingredients.

Once the distribution of the concentrations is acquired,empirical texture studies in food science can be evaluated interms of the topics, because the setting in these studies are also
represented as the distribution of concentration of ingredients.
This method allows topics to be texture are allocated toempirical studies so that patterns of texture terms are linked
to quantitative texture. The validity of these linkages can beevaluated by the Texture Profile [10]. Followings elaborate theproposal.

Fig. 1. A graphical model for topic of gel related texture

A. Constructing dataset
Our dataset comprises a sequence of texture terms and a
distribution of concentration of ingredients on each posted
recipe. In order to identify texture terms from posted recipes,
we make use of Comprehensive Japanese Texture Terms1
as a dictionary, which is constructed based on studies on
Japanese texture terms including [10]. Here, each texture term
is annotated by categories representing quantitative characteristics such as hardness and adhesiveness. We construct thedictionary by extracting all the texture terms belonging to
the categories of hardness, cohesiveness, and adhesiveness inComprehensive Japanese Texture Terms, in order to compare
texture terms with the quantitative attributes described later.
As the result, the dictionary includes 288 texture terms. Then,
all the texture terms appeared in the descriptions of posted
recipes are extracted by referring to the dictionary.
Meanwhile, we focus on characteristics of gel related texture
as a trial so that unrelated texture to gel is excluded byword2vec. All the descriptions of retrieved posted recipes aretrained by word2vec. Then, if similar words to the extracted
texture terms include ingredient terms unrelated to gel, thetexture terms are excluded. For example, a recipe of mousse
with topping of nuts might create texture terms representing
crispy. In such case, nuts appear in similar words to the textureterms of crispy.
On the other hand, the quantity of each ingredient is
described in different manner depending on each recipe, suchas “spoonful of”, “two cups of”, “g”, “cc”. We convert all thevariations to weight (grams). In case the unit of quantity is
described as a volume (i.e., “spoonful of”, “cc” etc.), a specificweight against water is taken into account to convert to grams.
Measuring spoons for cooking are standardized by nations. For
example, the capacity of a small measuring spoon is 5 mL
in Japan. We make use of these standard for the conversion.
Then, concentrations of all the ingredients in each recipe arederived as the ratios of each weight against total weight in each
recipe. Then, these feature x of concentrations are converted
to information quantity − log(x) because x represents a ratio
whose small difference will affect considerable difference of
textures.

ingredients’ concentrations. In each topic, texture terms forma categorical distribution of its own, whereas corresponding
ingredients’ concentrations form a multi-normal distribution.
Here, an expectation of each random variable of the multinormal distribution represents an average concentration valueof each ingredient. According to the studies of gel related
dishes in food science, small amount of gel related ingredients(i.e., gelatin, agar etc.) principally affect the resulting texture
with subordinate effects by large mixing amount of emulsions2such as milk and cream [19].
Reflecting these matters, the graphical model for the proposed joint topic model is designed as in Fig.1. In Fig.1, ϕrepresents the topic of texture terms, while µ and Λ represent
the topic of gel ingredient concentrations. Similarly, m andL represent the topic of emulsion ingredient concentrations.
All these parameters are generated through the generative
procedure of the latent topics, z and y, where z is a latent
topic for a texture word w in each recipe, and y is alatent topic for g, a vector of gel ingredient concentrations
in each recipe. Both of z and y are subject to latent topic
distribution,θ so that both the latent topic of texture terms andgel concentrations are linked with each other. On the other
hand, the topic of emulsions is subordinate to the latent topic
of gel concentrations, y. The equation (1) corresponds to thegenerative model given in Figure 1.
p (W, Z, θ, ϕ, g, y, µ, Λ, e, L) =Y
Dir (θ|α) Dir (ϕ|γ)
Mult (zdn |θd ) Mult (wdn |∅k , zdn )nY× N W (µk , Λk |µ0 , β g , ν g , Sg )Mult (yd |θd ) N (gd |µk , Λk , yd )d× N W (mk , Lk |m0 , β e , ν e , Se )Y

N (ed |mk , Lk , yd )(1)dJoint topic model is applied for acquiring topics comprisedof a pattern of texture terms accompanied by corresponding

Then, the generative process for the model is as follows.
1) For k ∈ 1, ..., K:a) ϕk ∼ Dir (γ)b) µk , Λk ∼ N W(µg0 , β g , ν g , Sg )c) mk , Lk ∼ N W(µe0 , β e , ν e , Se )2) For d ∈ 1, ..., D:a) Draw θd ∼ Dir (α)b) For n ∈ 1, ..., Nd :i) Draw zdn ∼ Mult (θd )ii) Draw wdn ∼ Mult (ϕzdn )1 Constructed by The National Agriculture and Food Research Organizationin Japan https://www.naro.affrc.go.jp/org/nfri/yakudachi/terms/texture.html

2 A mixture of two or more liquids that are normally unmixable such as oilin water.

B. Topic modelingc) Draw yd ∼ Mult (θd )d) Draw gd ∼ N (µyd , Λyd )e) Draw ed ∼ N (myd ,Lyd )Here,

TABLE IE MPIRICAL DATA FROM RESEARCH IN FOOD SCIENCE

Gels

ϕk : Parameters of word distribution in each topic, k, withhyper parameters, γµyd , Λyd : Parameters of gel distribution in k, with hyper
parameters, µg0 , β g , ν g , Sg
myd , Lyd : Parameters of emulsion distribution in kθd : Parameters of topic distribution in d(each recipe)
α : Hyper parameters of θdzdn : A latent topic for a texture word, wdn , appeared in nthplace in d
yd : A latent topic for gd , gel concentrations in d
ed : Emulsion concentrations in d

C. Inference of topics
Given the graphical model above, the latent topics areinferred by Gibbs sampling. And the topics concerning texture terms accompanied by concentration vectors of gels are
acquired through the sampling. (2)(3)(4) are the sampling
formulas.
1) Sampling zdn :p zdn = k W, Z−dn , α, γ, Y, X, µ, Λ, µg0 , β g , ν g , Sg
−dn

∝ (N dk + Mdk + α)

−dn
Nk,w
+γ
dn

Nk−dn + γV

(2)

Here,
Ndk : The number of texture words belonging to k in d
Nk : The number of texture words belonging to k
Nd : The number of texture words in each recipe
Nkw : The number of each texture term,w, belonging to k
Md : The number of gel ingredient vectors in d
Mdk : The number of gel ingredient vectors belonging to k
in d
−dn
Ndk
: The number of texture words belonging to k in d
except dn. (Hereafter, “-” of upper suffix represents similar
exceptions)2) Sampling yd :p(y d = k|W, Z, α, γ, Y−d , G, µ, Λ, µg0 , β g , ν g , Sg )∝−d
Ndk + Mdk
+ αk
P× N (ed |µyd , Λyd )Nd + Md − 1 + k αk(3)3) Inferring topic parameters :The topic parameters, µk and Λk for gel ingredientconcentrations, are sampled along with the sampling of
the latent topics above. The formula is as follows. mk
and Lk are subject to the same formula.
p µk , Λk Y, G, µ−k , Λ−k , µ0 , β, ν g , Sg
∝ N µk µc , (β g Λc )−1 W(Λk |νcg , Sgc )
(4)
Here,
W(Λk |νcg , Sgc ) : Wishart distribution with hyperparameters νcg
and Sgc
P(Sgc )−1 = (Sg )−1 + gd ∈topic k (gd − ḡ)(xgd − ḡ)T +Nk βg(ḡ − µ0 )(ḡ − µ0 )T
Nk +β gḡ = N1gk

Data Gelain Kanten Agar
1
2
3
4
5
6
7
8
8
10
11
12
13

0.018
0.02
0.025
0.03
0.03
0
0
0
0
0
0
0
0

0
0
0
0
0
0.008
0.01
0.012
0.02
0
0
0
0

Λc = (N gk + β g )Λk , νcg = ν g + Nkg

Meanwhile, ϕk , the topic of texture terms is derived by
the following formula after the convergence of Gibbs
Sampling, together with θd , the topic distribution in each
recipe.
Nkv + γNdk + MdkPϕkv =, θdk =
(5)
Nk + γV
Nd + Md + α4) Making linkage between topic and quantitative texture :Once the topics are acquired, patterns of texture terms
are accompanied by corresponding vectors of ingredientconcentrations. By comparing the vectors and the empirical settings of gel concerning research in food science[3]–[5], [15]–[17], the quantitative results of texture areaccompanied by the relevant topics. Kullback–Leibler
divergence is applied for deriving most similar topic tothe settings of the research. Then, the quantitative texture
is linked to corresponding texture terms, qualitative onesin the topics. And the linkages are validated by referringto the dictionary introduced in Section II, A. Constructing
dataset, where each texture term is annotated by thecategory representing quantitative attributes. As described
before, the gel ingredient concentrations primarily determine the resulting texture so that only the gel ingredientconcentrations are used for the comparison.
IV. DATA
Gel related posted recipes are collected from Cookpad3 , thelargest recipe sharing site Japan. And empirical research on gel
related texture are collected from the journals of food science.
Followings are the details of the data.

gP

gd ∈topick gk ,µc =Nk ḡ+β g µ0g
Nk +β g

0
0
0
0
0.03
0
0
0
0
0.008
0.01
0.012
0.03

Quantitative texture(rheological unit)
Hardness Cohesive- Adhesiveness
ness
0.20
0.6
0.1
0.3
0.59
0.04
0.72
0.17
0.57
2.78
0.31
0.42
3.01
0.35
12.6
2.2
0.12
0
3.5
0.1
0
5.0
0.8
0
5.67
0.03
0
1.0
0.48
0
1.5
0.33
0.01
2.7
0.28
0.02
2.21
0.20
1.95

3 https://cookpad.com/

A. Collecting recipes from a recipe sharing site
Among a variety of gels, gelatin, kanten (Japanese agar),and agar are widely used for home cooked dishes in Japan sothat the number of the recipes in Cookpad is 45,000, 15,000
and 3,000, respectively. Although the total recipes amount to
63,000, much smaller amount of them, approx.10,000 recipes
are accompanied by the descriptions including texture terms inthe dictionary. All these 10,000 recipes include gels, either ofgelatin, kanten, or agar, or the mixture of them with a varietyof the concentrations. Then, each recipe is converted to threekinds of features, a sequence of texture terms, a vector of gel
ingredient concentrations, and a vector of emulsion ingredientconcentrations. The features are constructed as below.
Sequence of texture terms
Term frequency by each texture term extracted by Section
II, A. Constructing dataset.
Vector of gel ingredient concentrations
Information quantity − log(x) of concentration x by eachgel (i.e., gelatin, kanten, and agar). Concentrations arederived based on Section II, A. Constructing dataset.
Vector of emulsion ingredient concentrations
Information quantity − log(x) of concentration x by each
emulsion. Here, emulsions consist of six types: sugar, egg
albumen, egg yolk, raw cream, milk, and yogurt.
Out of these recipes, the ones including large portions ofingredients unrelated to gels and emulsions are excluded. For
example, if fruits occupy a large portion, the recipes mightreflect texture unrelated to gels. To focus on the gel related
texture in our trial, the recipes occupied by more than 10
percent of unrelated ingredients are excluded. As the result,approx.3,000 recipes become the dataset, which include 41
texture terms out of 288 terms in the dictionary.

Fig. 2. Quantitative measurement by a rheometer

Hardness
The peak force of the first compression (F1), which
represents perceived hardness at the first bite.
CohesivenessThe ratio of the force for the second compression to theone for the first compression, c/a. Small cohesiveness
represents perceived easiness of swallowing, because thesmall cohesiveness indicates collapsed piece of foodbecomes smaller by fewer bites.
Adhesiveness
Cumulative forces during the first ascending action(b),which represents perceived stickiness.
The unit of measurements for these attributes are different
depending on the research, because the unit is not necessarily
standardized among the products of rheometers. So, we converted all the values of the measurement to the unit of RU(rheological unit), which is the most popular one adopted byrelated research.
V. R ESULTS AND EVALUATIONS

B. Collecting empirical data from research in food science

A. Linkage between texture terms and the quantitative attributes

Table I shows the empirical settings and the quantitative
results collected from six research [3]–[5], [15]–[17] concerning gel related ingredients. The settings are the concentration
vectors of the three types of gels, the same form of data as thegel ingredient vector in the topics. Corresponding quantitative
results comprise three attributes, hardness, cohesiveness, andadhesiveness measured by rheometers, the instruments forfood texture proposed by [9].
Figure 2 illustrates how rheometers work. Rheometers
imitate chewing actions in a mouth by descending/ascending
the disc-like probe onto a food sample. Positive forces forcompressing the samples are measured during the descending
actions, and negative ones are measured during the ascending actions, which represent the stickiness. F1 in Figure 2
indicates a peak force during a compression. If compressing
force becomes larger than the resilience of a food sample,
the food shape begins to collapse, and the force for thecompression becomes smaller than F1. The area of negative
force in b appears during ascending. Based on these principles,the quantitative attributes are defined as follows.

Table II(a) shows topics derived by Section II, C in our
method. Each topic comprises gel concentrations (the column“gels:concentrations”) and corresponding texture terms (thecolumn “Texture terms”). The texture terms are sorted by
the probability of each term (the value in the parentheses).
The column “# Recipes” represents the number of recipes in
Cookpad belonging to each topic. A topic for each recipe is
specified by the maximum probability in θd , which represents
topic distributions for each recipe as in the formula 5.
The last column of Table II(a) indicates similarities to
empirical results in Table I derived by Section II, D in
our method. For example, topic 8 is similar to the research
results 1 and 2 in terms of gels concentrations. As such,the same topic could be corresponding to multiple research
results, because the concentrations of topics are not completelyclassified in accordance with the ones of the research results.
These similarities approximate the coincidence between thequantitative texture in Table I and the texture terms in the
topics. For example, both the gelatins, 0.018 and 0.02 in
Table I are characterized by “furufuru”(softness) in the topic

TABLE II
T OPIC ASSIGNMENT TO EMPIRICAL DATA OF FOOD SCIENCE RESEARCH(a) Acquired topics by the joint topic model and the assignment to the empirical data in Table I
Topic
7
4
0
8
3

5
2

6
1

9

Gels:concentration Texture terms(probability)[Japanese texture terms] ordered by probability# Recipes
gelain:0.005
Soft, tender(0.877)[yawarakai] Light, soft and fluffy(0.123)[howat]
73
gelain:0.007
Light, short(0.93)[karui] Soft swollen,and fluffy(0.07)[fukkura]
74
gelain:0.012
Sticky, elastic and chewy(1.0)[mochimochi]
152
gelain:0.014
Soft and slightly wobbly, easy to break(1.0) [furufuru]
300
gelain:0.054
Hard, firm, stiff, tough, rigid(0.307)[katai] Resilient, firm and slightly
38
sticky(0.245)[muchimuchi] Mushy; having lost its original shape(0.129)[gucha]
Thick, resistant to flow(0.089)[potteri] Elastic and slightly wobbly(0.062)[burunburun]
Dry, crumbly and not compact(0.06)[bosoboso] Thick and heavy, resistant toflow(0.055)[botet] Crisp; material is cut off or shear off easily(0.029)[shakusyaku]
Elastic and slightly wobbly(0.022)[buruburu]
agar(0.009)
Soft elastic and slightly sticky, slightly wobbly(1.0)[purupuru]
1046
gelain(0.009)
agar(0.016)
Sticky, viscous and thick(0.445)[nettori] Crispy, sound emitted by biting slightly hard62
foods(0.255)[purit] Thick and viscous, resistant to flow(0.21)[mottari] Crumbly andsoft(0.08)[horohoro] Very sticky and viscous(0.01)[necchiri]
gelain:0.003
Soft and fluffy(1.0)[fuwafuwa]
1200
kanten:0.002
kanten:0.004
Thin, loose, easy to deform(0.487)[yuruyuru ] Sticky, viscous and watery(0.432)
60[bechat] Soft, swollen and somewhat elastic(0.027)[fukahuka] Firm and resilient(0.027)[burit ]kanten:0.021
Heavy, dense(0.27)[dossiri] Slippery, smooth and wet surface(0.165)[churuchuru]
31
Soft elastic and slightly sticky(0.1)[punipuni]: Soft, not taut(0.074)[kutat] Firm andresilient(0.069)[burinburin] Crunchy(0.064)[korit] Thick, heavy(0.057)[daradara] Dry
and crispy(0.055)[karat] Cracking open, fizzy(0.055)[hajikeru] Heavy(0.054)[omoi]

Table I

1,2
3,4

5
10,11,12,13

6,7,8,9(b) Empirical research results of Bavarois and Milk jelly, and the topic assignment
Quantitative texture Gel concentrations
Emulsion concentrations
Dish
Hardness Cohesive- Adhesive- Gelain Kanten Agar Sugar Egg
Egg
Raw
Milk
ness
ness
albumenyolk
cream
Bavaois3.860 0.809 0.095 0.025 0
0
0
0
0.08
0.2
0.4
Milk jelly
1.83
0.27
0.44
0.025 000.032 000
0.787
Data 3 in Table I 0.72
0.17
0.57
0.025 0
0
0
0
0
0
0

8. On the contrary, gelatins more than 0.025 in Table I are
characterized by “katai”, “muchimuchi”(hardness) in the topic
3. Four variations of kanten in Table I are accompanied by thesame topic, because only two topics are acquired concerning
kanten, and the one is more similar to all the variations thanthe other. The result inclines to texture terms of hardness
such as “dossiri”, “burinburin”, “korit”, coincident with thehardness of kanten in Table I. Thus, the texture of gel related
recipes (i.e., the ones of empirical research in Table I) could
be interpreted to corresponding texture terms by estimating themost similar topic.
B. Texture terms and quantitative attributes of Bavarois andmilk jelly
As a trial, we try to interpret quantitative texture attributesof gels mixed with emulsions to texture terms. Table II(b)shows the attributes of the dishes, Bavarois and Milk Jally
both of which are research results of food science [20], [21].
The concentration of gels of both the research are very similar,
approx. 2.5 % of gelatin, which is the same as data id 3 inTable I. Despite of the same concentrations, the quantitative
attributes are different with each other, which might be thesubordinate effects of different concentrations of emulsions.

Yogurt Assigned
topic
0
3
0
3
0
3

The most similar topic for both the dishes is 3, derived
by Section II, D in our method. While overall texture termsof topic 3 are represented in Table II(a), individual recipe
belonging to the topic has its own texture terms. Amongthese recipes, we acquired similar ones to each dish bycomputing KL divergence of emulsion concentrations between
the recipes and the dish. Figure 3 shows the results. The
histograms show the number of recipes within topic 3 orderedby KL divergence. For example, bins of hardness represent the
number of texture terms belonging to the category of hardness
in Comprehensive Japanese Texture Terms as described in
Section II, A in our method. Similarly the bins of softness
represent the ones concerning softness.
Apparently the smaller the KL is, the more frequent thebins of hardness become (Figure 3(a)), which implies both
the dishes are likely to be harder recipes among the recipes in
topic 3. The result is coincident with hardness of quantitative
texture in Table II(b), where both the dishes are much harderthan pure gelatin (the third row). Meanwhile, the smaller
the KL is, the more frequent the bins of elastic in case ofBavarois, but not in the case of milk jelly (Figure 3(b)). This
contrast coincides with the gaps of quantitative cohesivenessin Table II(b), because strong elasticity leads to large valueof cohesiveness.

(a) The number of recipes classified into hard or soft by order of KL divergence of emulsion concentrations(b) The number of recipes classified into hard or soft by elastic or cohesive by order of KL divergence of emulsion concentrations
Fig. 3. Distribution of Cookpad recipes by order of similarities of emulsion concentration to Bavarois and Milk jelly in Table II(b)Fig. 4. Distribution of Cookpad recipes on hardness/cohesiveness axis classified by similarities of emulsion concentrations to Bavarois and Milk jelly

Here, all the recipes in the histogram can be represented
by two axis, Hardness, and Cohesiveness, because softness isnegative hardness, and also elasticity is negative cohesiveness.
Figure 4 is the scatter plot of the recipes by the consolidated
two axis. Colors of the plots represent KL divergence ofemulsion concentration, the same as Figure 3. The star markrepresents the result of the similar classification of textureterms for topic 3.
Red colored plots concentrate in the right area in both ofBavarois, and Milk jelly, indicating similar recipes to thesedishes tend to be harder than topic 3. In addition, Red colored
plots of Bavarois concentrate in the upper right area while Milk
jelly concentrate in the middle right. These plots coincide with
the quantitative texture in Table II(b) in that both the dishes
are much more harder than pure gelatin dish (The third row
in the table), and Bavarois indicates larger cohesiveness than
Milk jelly.
Thus, the results imply the coincidence of texture terms to
the quantitative characteristics in case of the gel mixed recipes,
though the samples are limited.
VI. C ONCLUSION
This study tried to acquire characteristics of texture concerning gel related recipes, as a form of texture terms com-bined by rheology, a quantitative texture. According to foodscience research, principal determinant of gel related texture
is concentrations of gel ingredients with subordinate effectsof emulsions. We designed a joint topic model to reflect thesefindings, and compared acquired topics with the findings offood science research. The results indicated coincidence withthe findings as follows.
Topics represent texture terms in accordance with typesof gels and their concentrations.
• Quantitative texture of simple gel dishes was properlyinterpreted to corresponding texture terms based on the
topics.
• Quantitative texture of emulsion-gel mixture dishes wasfound to be similar to texture terms reflecting the subordinate effects.
•

These results support the valid interpretation of gel related
recipes to texture. We will apply our method to a wider
variety of dishes to acquire more general validity in future.
Then, we will detect rules bridging between recipe information
including ingredient concentrations, cooking steps etc., andsensory textures of consumers. These rules will contribute todevelopment of new food in food industry.

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