JP2014167470A - Food texture evaluation system, food texture evaluation program, recording medium, and food texture evaluation method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a food texture evaluation system allowing an objective and numerical evaluating of a superficial impression.SOLUTION: A food texture evaluation system 1 includes a pressing device 2 which presses a sample 5, a pressure distribution sensor 3 which measures temporal change of pressure distribution received from the sample 5 when the sample 5 is pressed, and a control PC 4 which evaluates food texture of the sample 5 based on feature quantity obtained from the pressure distribution before and after fracture of the sample 5.

Description

  The present invention relates to a food texture evaluation technique, and more particularly, to a surface impression evaluation technique for gelled food.

  For elderly people and people with disabilities, gel foods such as nursing food jelly that can be crushed and eaten with the tongue and palate (tongue eating), and paste foods such as mixer food and porridge are being developed. Yes. In such a gel-like or paste-like food, it is desired that the safety of eating / swallowing and “taste” are compatible at a high level. “Taste” is not only the chemical properties of food such as taste and odor, but also the mechanical properties of food, that is, textures resulting from food force response and morphological changes during eating and swallowing (tongue touch, melting in the mouth, over the throat, etc.) ) Strongly depends on.

  Conventionally, texture is defined by sensory evaluation based on human sensory characteristics. Sensory evaluation has the merit that human senses are directly digitized, but for highly reproducible evaluation, it is necessary to train a large number of trained evaluators (panels), which is costly and time consuming. .

  Then, the technique which evaluates food texture using a mechanical testing machine is proposed (for example, patent documents 1-3, nonpatent literature 1). In the technique described in Patent Literature 1, a food product is pressed to obtain a break curve, and a specific peak on a desired frequency region of the power spectrum of the break curve is calculated as a texture evaluation value of the test food. In the technique described in Patent Document 2, the load and strain rate during pressing are continuously measured, and based on the values of the load and strain rate, the change of the curve portion before reaching the maximum value in the strain rate-load curve is changed. The slope of the tangent at the inflection point is used as an index representing the hardness of the food when chewing the food. In the technique described in Non-Patent Document 1, food pressing is repeated twice, and the texture such as “hardness”, “adhesiveness”, and “cohesiveness (cohesiveness)” is evaluated from the maximum stress and negative stress. (Texture profile analysis: TPA). Furthermore, in the technique described in Patent Document 3, the quality (components and physical properties) of the gel-forming food is determined by a spectroscopic measurement system that uses light in a specific wavelength region from visible light to infrared light.

JP 2001-133374 A JP 2010-223737 Japanese Patent Laid-Open No. 2003-106995

Bourne, M. C. 2002. Food Texture and Viscosity 2nd Edition. 182-184 pages. Academic Press, New York

  However, the food texture index obtained by the techniques described in Patent Documents 1 and 2 merely represents the “hardness” (mechanical characteristics) of the food. In addition, indicators such as “adhesiveness” and “cohesiveness” obtained by the technique described in Non-Patent Document 1 are often evaluated differently from “adhesive feeling” and “cohesive feeling” felt in the oral cavity. . Moreover, the food texture determined by the technique described in Patent Document 3 is mainly caused by “hardness”.

  The overall impression of food is relatively easy to correlate with the stress response characteristics in the conventional pressure test, but it is “sticky”, “moist”, “grainy”, “smooth”, “sticky” The surface impression such as “feel” depends on the tactile distribution on the tongue surface, oral mucosa, pharynx, etc., and it is difficult to combine this with sensory evaluation simply by observing the stress response. In particular, the texture of gel-like foods is not only about how much pressure or strain is applied, but also about what shape it takes after breaking and how the fragments flow. Furthermore, it varies greatly depending on the force response of each piece. In addition, the “feel of mouthfeel” of food is easily affected by changes in the viscoelasticity of the food due to changes in the temperature of the food in the oral cavity, and the observation result of stress response at a constant temperature often does not correlate with sensory evaluation. Therefore, in the prior art, it was difficult to evaluate the surface impression of the gel food.

  The present invention has been made in order to solve the above problems, and it is an object of the present invention to provide a texture evaluation system and method capable of objective and numerical evaluation of a surface impression strongly dependent on geometric characteristics. And

  In order to solve the above problems, a texture evaluation system according to the present invention includes a pressing device that presses a sample, a measuring device that measures a change over time in a pressure distribution received from the sample when the sample is pressed, and the pressure distribution. The food texture evaluation means for evaluating the food texture of the sample based on the feature value obtained from the above. In addition, the texture evaluation method according to the present invention includes a pressing step for pressing a sample, a measuring step for measuring a temporal change in pressure distribution received from the sample when the sample is pressed, and the sample based on the pressure distribution. And a texture evaluation step for evaluating the texture.

  In the above-described configuration, the texture of the sample is evaluated using the pressure distributions of both the phases before and after the fracture (compression phase) and after the fracture (rupture phase). Therefore, it is possible to objectively and numerically evaluate the surface impression of gel food or paste food such as “moist feeling”, “moist feeling”, “rough feeling”, “smooth feeling”, “tackiness”.

  In addition, by adding the pressure distribution data of the phase (holding phase) for 5 to 10 seconds after the end of the breaking phase, it becomes possible to objectively and numerically evaluate the “mouth melting feeling” of the gel-like or pasty food. . At this time, if the temperature of a part or all of the pressing device that comes into contact with the sample, which is an object to be evaluated for texture, is adjusted to around the human body temperature, the evaluation accuracy is further improved.

  In the food texture evaluation system, the measurement device outputs image information of a plurality of frames indicating the pressure distribution to the food texture evaluation unit, and the food texture evaluation unit calculates a space from the image information of each frame. From the feature quantity extraction means for extracting a texture feature quantity using a density level dependency method, a feature quantity vector calculation means for computing a feature quantity vector as the feature quantity based on the texture feature quantity, and the feature quantity vector The apparatus includes: a principal component vector calculating unit that calculates a principal component vector; and an estimated impression level calculating unit that calculates an estimated impression level indicating a texture evaluation result from the principal component vector.

  Further, the texture evaluation system has a mechanism for adjusting a part or the whole of the pressing device in contact with the sample from 25 ° C. to 40 ° C.

  Moreover, the said food texture evaluation system WHEREIN: The said estimated impression level calculation means calculates the said estimated impression level based on the estimation formula (1).

  Further, in the texture evaluation system, the estimation formula (1) is a measurement of a pressing by the pressing device and a time-dependent change in the pressure distribution by the measuring device with respect to a reference food whose sensory evaluation is performed and an impression level is determined in advance. Extraction of the texture feature quantity by the feature quantity extraction means, calculation of the feature quantity vector by the feature quantity vector calculation means, and calculation of the principal component vector by the principal component vector calculation means; and It is obtained by creating a multiple regression model representing the relationship between the component vector and the impression level.

  Moreover, the texture evaluation program for operating a computer as each means of the said texture evaluation system, and the computer-readable recording medium which recorded the said texture evaluation program are also contained in the technical scope of this invention.

  According to the present invention, it is possible to objectively and numerically evaluate a superficial impression that could not be evaluated by a texture evaluation technique based on conventional mechanical measurements. Therefore, the present invention is useful for the development of gelled foods that are not only easy to eat but also have a favorable texture.

It is the schematic of the food texture evaluation system which concerns on one Embodiment of this invention. It is a photograph which shows an example of a sample. (A)-(c) is a photograph which shows the state at the time of the press of a sample. (A) is a graph which shows the downward displacement of a plate, (b) is a graph which shows the time change of the force response which a load cell detects. (A)-(c) has shown an example of the image information of the pressure distribution data which the pressure distribution sensor measured at the time of the press of a sample. It is a block diagram for demonstrating the function of control PC. It is a flowchart which shows the food texture evaluation procedure. (A)-(e) is a graph which shows the change for every flame | frame about five types of feature-value calculated from the density | concentration co-occurrence matrix. The force response data obtained from the load cell when three types of jelly foods (C to E) having different impression levels are compression-ruptured are shown.

  Hereinafter, an exemplary embodiment of the present invention will be described with reference to the accompanying drawings.

(Device configuration)
FIG. 1 is a schematic diagram of a texture evaluation system 1 according to an embodiment of the present invention. The texture evaluation system 1 includes a pressing device 2, a pressure distribution sensor 3, and a control PC 4.

  The pressing device 2 is a device for pressing the sample 5 that is an object to be evaluated for texture, and includes two plates 21 and 22, a linear slider 23, and a load cell 24. The lower plate 21 is fixed to a base on which the pressing device 2 is installed. The pressure distribution sensor 3 is placed on the upper surface of the plate 21, and the sample 5 is placed on the pressure distribution sensor 3.

  The upper plate 22 is provided at a position facing the pressure distribution sensor 3 in the vertical direction, and is connected to the linear slider 23. The linear slider 23 is driven in the vertical direction with respect to the pressure sensor surface in accordance with a pressing operation control signal from the control PC 4. Thereby, the control PC 4 can control the pressing operation of the sample 5 by the pressing device 2.

  Moreover, it is also possible to keep the temperature within the human oral cavity (25 ° C. to 40 ° C.) with an electric heater or the like that can control the temperature of any one, two or all of the upper and lower plates 21 and 22 and the pressure distribution sensor 3. it can.

  The load cell 24 detects a force response from the plate 22. The force response data detected by the load cell 24 is output to the control PC 4.

  The pressure distribution sensor 3 is a measuring device that measures a change over time in the pressure distribution received from the sample 5 when the sample 5 is pressed. In the present embodiment, as the pressure distribution sensor 3, a multipoint pressure sensor having a spatial resolution of 1 [mm], a temporal resolution of 10 [ms], and a measurement range of 44 × 44 [mm] is used. The change over time of the measured pressure distribution is output to the control PC 4 as pressure distribution data of image information.

  The control PC 4 outputs a pressing operation control signal to control the pressing operation by the pressing device 2 and has a function of evaluating the texture of the sample 5 based on the pressure distribution data from the pressure distribution sensor 3. Specific functions of the control PC 4 will be described later.

(Pressing the sample)
FIG. 2 is a photograph showing an example of the sample 5. Sample 5 is a cylindrical jelly food having a diameter of 20 [mm] and a height of 10 [mm].

  FIGS. 3A to 3C are photographs showing the state when the sample 5 is pressed. Specifically, FIG. 3A shows a state immediately after the start of pressing the sample 5, FIG. 3B shows a state immediately before the sample 5 is broken, and FIG. The state after the fracture | rupture of the sample 5 is shown.

  FIG. 4A is a graph showing the downward displacement of the plate 22, and FIG. 4B is a graph showing the time change of the force response detected by the load cell 24. As shown in FIG. 4 (a), the plate 22 descends at a speed of 2 [mm / s], and then comes into contact with the pressure distribution sensor 3 (the distance between the plate 22 and the pressure distribution sensor 3 is 1 to 3 mm). For 5 to 10 seconds. As shown in FIG. 4 (b), the force response increases as the plate 22 compresses the sample 5. This is the compression phase. Thereafter, the sample 5 starts to break at t = 2.3 [s] at which the force response is maximized. At this time, the force response once falls, but the sample 5 continues to be pressed, and therefore continues to rise again until the plate 22 stops. Also, after the plate 22 stops, the stress drops. The period from the start of fracture of the sample 5 to the stop of the plate 22 is defined as a fracture phase. Further, a state after the plate 22 stops is set as a holding phase. Moreover, the maximum of the force response may not be seen depending on the sample such as pasty food. In this case, the first frame at the start of pressing is set as the compression phase, and the period until the plate 22 is stopped is set as the breaking phase.

  5A to 5C show an example of image information of pressure distribution data measured by the pressure distribution sensor 3 when the sample 5 is pressed. Specifically, FIG. 5A shows the pressure distribution immediately after the start of pressing of the sample 5, and FIG. 5B shows the pressure distribution immediately before the fracture of the sample 5, and FIG. ) Shows the pressure distribution after fracture of the sample 5. These pressure distribution images are obtained by dividing the pressure value obtained from the pressure distribution sensor 3 during a series of compression fracture operations into 16 parts every 4.8 [kPa] in the range of 3 to 80 [kPa], for example. The pressure distribution data is converted into pressure level values of 0 to 15, and the pressure distribution data thus converted is obtained as a frame group of 44 × 44 [pixel] image information having N = 16 density levels. In each image, the darker the block, the higher the pressure. 5 (a) and 5 (b), it can be seen that the pressure distribution rises in the shape of a circle that is a jelly bottom shape. Thereafter, the jelly is broken, and in FIG. 5C, the geometric and mechanical state of the jelly being broken can be confirmed.

(Control PC function)
FIG. 6 is a block diagram for explaining the functions of the control PC 4. The control PC 4 includes a pressing operation control unit 41 and a texture evaluation unit 42.

  The pressing operation control unit 41 outputs a pressing operation control signal for controlling the position and speed of the linear slider 23 to the pressing device 2 in accordance with a user instruction or the like. When a pressing operation control signal is input to the pressing device 2, the linear slider 23 is driven and a pressing operation on the sample 5 is started.

  The texture evaluation unit 42 includes a feature amount extraction unit 42a, a feature amount vector calculation unit 42b, a principal component vector calculation unit 42c, an estimated impression level calculation unit 42d, and an estimation formula storage unit 42e.

  The texture evaluation unit 42 may be realized by hardware, or may be realized by software using a CPU. When the texture evaluation unit 42 is realized by software, the control PC 4 which is a computer executes the texture evaluation program, thereby realizing each part of the texture evaluation unit 42. The texture evaluation program may be recorded on a computer-readable recording medium such as a CD-ROM, or the control PC 4 is connected to a communication network, and the program code of the texture evaluation program is downloaded via the communication network. Also good.

  The feature quantity extraction unit 42a, the feature quantity vector calculation unit 42b, the principal component vector calculation unit 42c, and the estimated impression level calculation unit 42d are stored in the pressure distribution data input from the pressure distribution sensor 3 and the estimation formula storage unit 42e. The texture of the sample 5 is evaluated based on the following estimation formula (1).

The variables in the estimation formula (1) are derived in advance by multiple regression analysis using principal component vectors obtained from a large number of samples whose impression levels have been confirmed by sensory evaluation as explanatory variables and impression levels as objective variables. is there. The estimated impression level derived from the estimation formula (1) is set for each evaluated food texture. For example, for the feeling of glutinousness, the stronger the impression (motivated), the larger the numerical value and the lower the impression (motivated). The value is set to be smaller as the

(Food texture evaluation procedure)
FIG. 7 is a flowchart showing a texture evaluation procedure. Hereinafter, the texture evaluation method will be described with reference to FIGS. 6 and 7.

  First, while the sample 5 is pressed (step S1), the feature amount extraction unit 42a acquires pressure distribution data from the pressure distribution sensor 3 and also acquires force response data from the load cell 24 (step S2). Subsequently, the feature quantity extraction unit 42a extracts a texture feature quantity for each frame of the pressure distribution data using the spatial density level dependent method (step S3). Subsequently, based on the extracted feature quantity, the feature quantity vector calculation unit 42b calculates a feature quantity vector (step S4). Subsequently, the principal component vector calculation unit 42c calculates a principal component vector from the feature quantity vector (step S5). Finally, the estimated impression level calculation unit 42d calculates an estimated impression level indicating a texture evaluation result from the principal component vector using the estimation formula (1) (step S6), and the evaluation result is output to the display unit 43. (Step S7). Hereinafter, steps S3 to S6 will be specifically described.

(Extraction of features)
Step S3 will be specifically described. First, the feature quantity extraction unit 42a calculates a density co-occurrence matrix S _{(d, θ)} from the image information of each frame. The density co-occurrence matrix is a matrix representing the density relationship between pixels in an image. Let g (x, y) be the density level of pixel (x, y), and d and θ be the scanning distance and scanning direction, respectively. In the image, for a pixel pair (x ₁ , y ₁ ) and (x ₂ , y ₂ ) whose relative positional relationship is given by (d, θ), the density pair is g (x ₁ , y ₁ ) = p And g (x ₂ , y ₂ ) = q. Here, p = {0,1,..., N-1}, q = {0,1,..., N-1}, and N are the number of density levels. At this time, the (p, q) element of the density co-occurrence matrix S _{(d, θ)} is counted up. A density co-occurrence matrix showing the frequency of density pairs (p, q) by scanning (d, θ) for all pixels in the image

Will be determined. Here, for each frame, a total of 20 density co-occurrence matrices S _{(d, θ} ) combining d = 1, 2, 4, 8, 16 [pixel] and θ = 0, 45, 90, 135 [deg]. ₎ Is calculated. Here, converts S _{(d, θ)} each element of S _{(d, θ)} and (p, q), the probability value P represented by the following formula _{(d, θ) (p,} q) on.

The density co-occurrence matrix thus obtained is substituted into the following equations (2) to (6), and energy, entropy, inertia, correlation, and local uniformity are calculated as five types of feature quantities.
·energy

・ Entropy

·inertia

·correlation

・ Local uniformity

_{However, E x, E y, D} x of the formula (5), _{D y} are as follows.

With the above calculation, 20 density co-occurrence matrices × 5 feature quantities = total 100 feature quantities are calculated for each frame.

  Thus, in step S3, the texture feature amount is extracted for each frame by the spatial density level dependent method. Thereby, it is possible to comprehensively grasp characteristics such as what kind of cracks are formed in the sample 5 and how the fragmented fragments are spread at a certain moment.

FIGS. 8A to 8E show the five types of feature amounts calculated from the density co-occurrence matrix P _{(d, θ)} with d = 1 [pixel] and θ = 0 [deg] within the fracture phase. It is a graph which shows the change for every frame. Note that the tendency of each feature amount is the same in the case of other d and θ.

(Calculation of feature vector)
Next, step S4 will be specifically described. Since the feature amounts shown in FIGS. 8A to 8E change in each frame according to time, the feature amount of the frame in which the difference between the impression levels appears remarkably is adopted as the representative feature amount. From FIG. 8, representative feature amounts in each phase are calculated for five types of feature amounts of energy, inertia, local uniformity, entropy, and correlation. The number of representative feature amounts in each phase may be any number as long as it is one or more, but usually 1 to 5 is employed. For example, “average value”, “maximum value”, “minimum value”, “standard deviation”, “range”, etc. in each phase are adopted as representative feature amounts. When E representative feature amounts are employed, the number of phases to be used (D) × representative feature amount of each phase (E pieces) × type of feature amount (100) for the pressure distribution data of one sample 5 F = D × 100 × E + D dimension feature vector x _{*, which is} a combination of the number of frames and the number of frames D (compression phase, fracture phase (2) or compression phase, fracture phase, hold phase (3)) = [X ₁ , x ₂ ,. . . , X _F ] ^T.

(Calculation of principal component vector)
Next, step S5 will be specifically described. The feature vector x _* obtained in step S4 is standardized as follows.

Using this standardized vector (bar x _* ), the principal component vector y _* of the unknown food is calculated as in the following equation.

(Calculation of estimated impression level)
In step S6, the estimated impression level calculation unit 42d evaluates the texture of the sample 5 based on the following estimation formula (1).

  Thus, in this embodiment, the food texture of the sample 5 is evaluated based on the frame image showing the temporal change in the pressure distribution in the compression phase, the fracture phase, and the holding phase. Therefore, the surface impression of gel foods such as “moist feeling”, “moist feeling”, “rough feeling”, “smooth feeling”, and sensuous impression in the oral cavity of the person such as “feeling of mouth” and “stickiness” Objective and numerical evaluation is possible.

(Derivation of impression level estimation formula)
Next, a method for deriving the variable of the above estimation formula (1) will be described. As preparation, a plurality of test foods that have been subjected to sensory evaluation by humans are prepared, and these are defined as reference foods for modeling the relationship between the pressure distribution and the impression level value. In addition, an impression level n is set for each texture (OO feeling, 00 feeling, etc.) to be evaluated. For these reference foods, an impression level estimation formula is derived by the following procedure.

  First, pressure distribution sensing using a masticatory experimental model is performed. In order to artificially reproduce tongue-type feeding, a feeding experiment model is constructed using the pressing device 2 shown in FIG. Humans simultaneously sense morphological changes and force responses during food breaking during tongue feeding. Based on this basic principle, the pressure device 2 is used to measure the change over time in the pressure distribution in the process of compressing and breaking the reference foods with different impression levels.

  Subsequently, modeling of the impression level and the temporal change of the pressure distribution by texture analysis is performed. That is, the relationship between the change in pressure distribution with time and the impression level is modeled. First, an image texture analysis method is applied to the pressure distribution data to calculate a feature vector x obtained by a spatial density level dependent method. Next, a principal component vector y for these feature amounts is calculated. Finally, multiple regression analysis is performed to create a multiple regression model representing the relationship between the principal component vector y and the impression level n, thereby deriving an impression level estimation formula. The texture of the unknown food can be estimated by using the obtained impression level estimation formula.

(Specific examples of pressure distribution sensing)
As a reference food, using the M-number of jelly, for each texture you want to evaluate, the impression level by the sensory evaluation of each jelly and _{n 1, n 2, n 3} ... n M. And the pressure distribution data in the said experimental model are acquired with respect to each jelly.

(Specific example of modeling of impression level and pressure distribution over time)
Similarly to steps S3 and S4 shown in FIG. 7 described above, texture features (energy, entropy, inertia, correlation, locality) for each frame of the pressure distribution data using the spatial density level dependent method for M jelly. Uniformity) is extracted, a feature vector is calculated based on the feature, and an F-dimensional feature vector x _* = [x ₁ , x ₂ ,. . . , X _F ] ^T.

  Subsequently, in order to remove a high correlation between feature amounts, principal component analysis is performed, and the dimension of the feature amount vector x is compressed. Let M be the total number of pressure distribution data obtained from the feeding experiment, and the feature vector obtained from the pressure distribution data k (k = 1, 2,..., M)

And Subsequently, a matrix X in which all feature vectors are arranged is created as in the following equation.

Further, a column vector is determined as in the following equation.

Next, the correlation coefficient matrix R of the matrix X is calculated, and a matrix U composed of the eigenvalues λ _i (i = 1, 2,..., F) of the matrix R and the eigenvector u _i of size 1 is expressed by the following equation: Calculate as follows.

Here, r _{i, j} represents a correlation coefficient between the vector x _i (i = 1, 2,..., F) and x _j (j = 1, 2,..., F). An eigenvector u _i corresponding to an eigenvalue of λ _i ≧ 1 is extracted from the matrix U, and a matrix of the following equation is created.

Where L is the total number of eigenvalues satisfying λ _i ≧ 1, and u _(l) (l = 1, 2,..., L) is the eigenvector u _i corresponding to the eigenvalue satisfying λ _i ≧ 1. . Then, the x _i constituting the matrix X are standardized, are prepared following matrix.

However,

It is.
Finally, the matrix Y constituting the principal component vector is calculated as follows:

Y _k in the above formula is a principal component vector of the pressure distribution data k,

It becomes. In this way, the principal component vector is calculated.

  Next, the derivation of the impression level estimation formula by multiple regression analysis will be described.

  A multiple regression model is created with the principal component vector y as an explanatory variable and the impression level n as an objective variable, and an impression level estimation formula is obtained. The relationship between the principal component vector y and the impression level n is a linear regression equation

Suppose that However,

It is. Further, a ₀ is a constant term, and a _l (l = 1, 2,..., L) is a partial regression coefficient for each principal component. Applying equation (7) to all test food data k (k = 1, 2,..., M), the following equation is obtained.
n = Za (8)
However,

It is. From equation (8), the estimated values of the constant term and the partial regression coefficient are calculated by the following equations.

^{However, Z # = (Z T Z} ) -1 Z T is a pseudo inverse matrix of Z. By the above procedure, the impression level estimation formula for the principal component vector y _k obtained from the pressure distribution data k

Get. The null hypothesis of the constant term and the partial regression coefficient a _l = 0 is tested, the constant term or the partial regression coefficient term having a risk rate of 5% or more is removed, and the multiple regression analysis is performed again. This operation is repeated until the risk factors of all constant terms and partial regression coefficients are 5% or less, and the final impression level estimation formula is determined.

[Estimation of “moist feeling”, “feeling sticky”, “feeling rough”, and “feeling smooth” of jelly-like food]
In this example, for five types (A to E) of jelly foods having different textures, an estimated impression level is obtained by the texture evaluation method of the present invention, and the estimated values are actually sensory evaluated by eight evaluators. This was compared with the impression level obtained.

[Model jelly food (AE) preparation method]
As a gelling agent, deacylated gellan gum (trade name: Kelcogel, Saneigen FFI Co., Ltd.), native gellan gum product name: Kelcogel LT-100, Saneigen FFI Co., Ltd.), agar (trade name) : Gel-up J-3531, Saneigen F.F.I.), Kappa Carrageenan (trade name: Carrageenin CS-606, Saneigen F.F.I.), and Iota Carrageenan (trade name: Carrageenin CS-599, Saneigen FFI Co., Ltd.) was used. 10 g of sucrose and the amount of gelling agent shown in Table 1 were mixed with powder. Ion-exchanged water is heated to 90 ° C for A and B, and 80 ° C for C- to E, and then added to ion-exchanged water while stirring at 1200 rpm using a propeller stirrer and stirred for 10 minutes. Continued. To this, 0.1 g of calcium lactate is added in the case of A and B, and 0.1 g of potassium chloride is added in the case of D with stirring. The total amount is adjusted to 100 g with ion-exchanged water, and then cooled in a constant temperature water bath at 8 ° C. Gelled.

The unit of numerical values in the table is the weight (g) of each component contained in 100 g of jelly food.

[Sensory evaluation method (impression level acquisition method)]
1. A sample of 20 ° C. was put in the mouth of each of eight subjects, and then allowed to eat freely. The shape of each jelly was a cylindrical shape (about 7 g) having a diameter of 20 mm and a height of 10 mm.
2. After the sample is eaten, the impression level can be determined by allowing the subject to draw a line at an arbitrary position on a straight line with a length of 100 mm for the four items “feeling of stickiness”, “feeling of stickiness”, “feeling of roughness” and “feeling of smoothness”. n was evaluated. The left end of the straight line was 0 point and the right end was 100 point.
3. The score was scored according to the following criteria.
0 points: 50 points that are not “moist” (e.g.) at all. 100 points that have a standard “mochi mochi (e.g.) feeling” as a small bite size. 100 points: When eating a gel food with a small bite size. The most “motivated” state that can be imagined

[How to get estimated impression level]
1. A cylindrical jelly having a diameter of 20 mm and a height of 10 mm is obtained by using a film type pressure distribution sensor I-Scan40 (manufactured by Nitta Corporation, with a spatial resolution of 1 [mm], a temporal resolution of 10 [ms], and a measurement range of 44 × 44 [mm]. The pressure on the multipoint sensor in contact with the lower surface of the cylindrical gel was measured over time by compressing the upper surface of the cylindrical gel vertically with a rigid planar plunger. A total of 90 jellys were measured, 18 for each jelly). All measurements were performed at room temperature (20 ° C.). At this time, the stress responses of the five types of jelly used in this example all had a maximum value, and the compression phase and the fracture phase could be easily separated.
2. A texture feature amount (energy, entropy, inertia, correlation, local uniformity) is extracted for each frame of the pressure distribution data, and a feature vector is calculated based on the feature amount. At this time, the “average value” of the feature values in each frame of the compression phase and the fracture phase was used as the representative feature value (the total number of representative feature values is “density co-occurrence matrix” (20) × “feature value” for each phase. (5 types) × “representative feature amount” (1 type) = 100). As a result, for one type of jelly pressure distribution data, F = 202 dimensions, combining 100 representative features in the compression phase and 1 frame, and 100 representative features in the fracture phase and 1 frame. The feature vector of was obtained.
3. The calculated 202-dimensional feature vector was compressed to 6 dimensions by principal component analysis.
4). The estimated impression level was obtained by multiple regression analysis with the 6-dimensional principal component vector as the explanatory variable and the impression level as the objective variable.
The results are shown in Tables 2-5.

  Tables 2 to 5 show the relationship between the impression level based on the sensory evaluation value and the estimated impression level for the “moist feeling”, “moist feeling”, “rough feeling”, and “smooth feeling” of the jelly foods A to E, respectively. .

  From Tables 2 to 5, it can be seen that the estimated values by the texture evaluation method approximate the impression level obtained from the actual sensory evaluation for the four textures of the jelly foods A to E. From the results of the present example, it was shown that the texture impression system and method of the present invention can accurately estimate the surface impressions such as “rough feeling”, “smooth feeling”, “moist feeling”, and “moist feeling”.

[Estimation of “feeling of stickiness”, “feeling of stickiness”, “feeling of roughness”, and “feeling of smoothness” of commercially available jelly foods]
In this example, in addition to the five types (A to E) of jelly food used in Example 1, ten types of jelly foods having different textures (F to O) prepared by the following method were used. A texture evaluation model was constructed by multiple regression analysis of the representative feature vectors and impression levels obtained from these 15 types of jelly. In addition, using this texture evaluation model, “moist feeling”, “feeling wet”, “feeling rough”, and “feeling smooth” of commercial jelly foods (a to h) whose sensory evaluation values are unknown were estimated.

[Model jelly food (FO) preparation method]
As a gelling agent, deacylated gellan gum (trade name: Kelcogel, Saneigen FFI Corporation), native type gellan gum product name: Kelcogel LT-100, Saneigen FFI Corporation), two types of agar (Brand name: Gel-up J-4503, Gel-up J-4504, both Saneigen F.F.I.), Kappa Carrageenan (Brand name: Carrageenin CS-606, San-Eigen F.F.I.), Mannan (Trade name: Sun Support P-180, Saneigen FFI Co., Ltd.), hydroxypropylated starch (trade name: Sun Support P-181) and 5 types of thickening polysaccharide preparations (trade name: Sun Support K) -S (F), Sun Support G-1016, Sun Support G-1028, Sun Support G-1014, Sun Support G-1054 F. F. Aisha) was used. 10 g of sucrose or 0.1 g of a sweetener preparation (trade name: Sun Sweet SU-100, Saneigen FFI Co., Ltd.) and the gelling agents in the amounts shown in Tables 6 and 7 were mixed with powder. In the case of F, G, and H, warm to 90 ° C, and in the case of I, J, L, and N, heat to 80 ° C, then add this to ion-exchanged water while stirring at 1200 rpm using a propeller stirrer, and stir for 10 minutes Continued. In the case of F, J, and N, calcium lactate having the amount shown in Tables 6 and 7 was added, and in the case of I, 0.1 g of potassium chloride was added with stirring, and the total amount was adjusted to 100 g with ion-exchanged water. Was cooled in a constant temperature water bath and gelled. In the case of K, M, and O, this was added to ion-exchanged water while stirring at 1200 rpm using a propeller stirrer, heated to 80 ° C., and then stirred for 10 minutes. In the case of K and M, calcium lactate of the amount shown in Table 7 was added with stirring, and the total amount was adjusted to 100 g with ion-exchanged water, and then cooled in a constant temperature water bath at 8 ° C. for gelation.

The unit of numerical values in the table is the weight (g) of each component contained in 100 g of jelly food.

[Sensory evaluation method (impression level acquisition method)]
Model jelly food (A to O, 15 types) and commercially available jelly food (a to h, 8 types) were subjected to sensory evaluation in the same manner as the sensory evaluation method [sensory evaluation method (impression level acquisition method)] of Example 1 It was.

[Food texture evaluation model construction method]
1. A cylindrical model jelly food (A to O, 15 types) having a diameter of 20 mm and a height of 10 mm is placed on a film type pressure distribution sensor I-Scan 40, and the cylindrical gel upper surface is compressed vertically with a rigid planar plunger. Then, the time-dependent change of the pressure applied to the multipoint sensor in contact with the lower surface of the cylindrical gel was measured (total of 90 jellys were measured, 6 for each jelly). All measurements were performed at room temperature (20 ° C.). At this time, there was a maximum value in the stress responses of 14 types of jelly other than G used in this example, and the compression phase and the fracture phase could be easily separated. Since there is no maximum value in the stress response of the jelly of G, the first frame at the start of pressing is set as the compression phase, and the period until the rigid plunger is stopped is set as the fracture phase.
2. A texture feature amount (energy, entropy, inertia, correlation, local uniformity) is extracted for each frame of the pressure distribution data, and a feature vector is calculated based on the feature amount. At this time, five types of values of “average value”, “maximum value”, “minimum value”, “standard deviation”, and “range” of feature values in each frame of the compression phase and the fracture phase are used as representative feature values (representative features). The total number of feature quantities is “density co-occurrence matrix” (20) × “feature quantity” (5 types) × “representative feature quantity” (5 types) = 500 for each phase). As a result, for the pressure distribution data of one type of jelly, F = 1002 dimensional sum of 500 representative feature quantities and one frame number in the compression phase, 500 representative feature quantities in the fracture phase and one frame number. The feature vector was obtained.
3. The calculated 1002-dimensional feature vector was compressed to L dimension by principal component analysis. L is the total number of eigenvalues having a value of 1 or more.
4). A regression line (texture evaluation model) of the estimated impression level was obtained by multiple regression analysis with the L-dimensional principal component vector as the explanatory variable and the impression level as the objective variable.

[How to get estimated impression level of commercial gel food]
1. 8 types of commercial jelly food samples a to h are shaped into a cylindrical shape having a diameter of 20 mm and a height of 10 mm, placed on a film type pressure distribution sensor I-Scan 40, and the cylindrical gel upper surface is vertically fixed with a rigid planar plunger. The time-dependent change in pressure applied to the multipoint sensor in contact with the lower surface of the cylindrical gel was measured (48 jelly were measured, 6 for each jelly). All measurements were performed at room temperature (20 ° C.). At this time, there was a maximum value in the stress responses of the eight types of jelly used in this example, and the compression phase and the fracture phase could be easily separated.
2. A texture feature amount (energy, entropy, inertia, correlation, local uniformity) is extracted for each frame of the pressure distribution data, and a feature vector is calculated based on the feature amount. At this time, five types of values of “average value”, “maximum value”, “minimum value”, “standard deviation”, and “range” of feature values in each frame of the compression phase and the fracture phase are used as representative feature values (representative features). The total number of feature quantities is “density co-occurrence matrix” (20) × “feature quantity” (5 types) × “representative feature quantity” (5 types) = 500 for each phase). As a result, for one type of jelly pressure distribution data, F = 1002 dimensional sum of 500 representative feature quantities and one frame number in the compression phase, and 500 representative feature quantities and one frame number in the fracture phase. The feature vector was obtained.
3. The calculated 1002-dimensional feature vector was compressed to L dimension by principal component analysis. L is the total number of eigenvalues having a value of 1 or more.
4). The estimated impression level was obtained by substituting the L-dimensional principal component vector into the multiple regression equation (texture evaluation model) constructed by the above-mentioned [texture evaluation model construction method].
The results are shown in Tables 8-11.

  Tables 8 to 11 show the relationship between the impression level based on the sensory evaluation value and the estimated impression level for the “feeling of stickiness”, “feeling of stickiness”, “feeling of roughness” and “feeling of smoothness” of the jelly-like foods a to h, respectively. Yes.

  From Tables 8 to 11, it can be seen that the estimated value by the texture evaluation method approximates the impression level obtained from the actual sensory evaluation for the texture of the four items of the commercially available jelly foods a to h. From the results of the present example, it was shown that the texture impression system and method of the present invention can accurately estimate the surface impressions such as “rough feeling”, “smooth feeling”, “moist feeling”, and “moist feeling”.

[Estimation of “stickiness” when a commercial paste food passes through the oral cavity and pharynx]
In this example, while obtaining the estimated impression level of "stickiness" in the oral cavity and pharynx passing by the texture evaluation method of the present invention using four types (i to 1) of commercially available paste foods having different textures, The estimated value was compared with the impression level obtained by actual sensory evaluation by eight evaluators.

[Sensory evaluation method (impression level acquisition method)]
1. A commercially available paste food sample at 20 ° C. was included in the mouth of each of eight subjects, and allowed to eat freely. The weight of each paste food was 10 g.
2. Impression level n by letting the subject draw a line at an arbitrary position of a straight line having a length of 100 mm with respect to two items of “stickiness (in the oral cavity)” and “stickiness (when passing through the pharynx)” after feeding the sample. Was evaluated. The left end of the straight line was 0 point and the right end was 100 point.
3. The score was scored according to the following criteria.
0 points: No “stickiness” at all 50 points: Standard “stickiness” as a small bite size 100 points: The most conceivable of eating a gel food with a small bite size “ State with "stickiness"

[How to get estimated impression level]
Place 1.3 g of commercial paste food on the film type pressure distribution sensor I-Scan40 (the shape is arbitrary), and compress the upper surface of the paste food vertically with a rigid flat plunger to contact the lower surface of the paste food The change with time of the pressure applied to the multipoint sensor is measured (total of 24 paste foods, 6 for each paste food). At this time, there is no maximum value in the stress response of the four types of paste foods used in this example, the first frame at the start of pressing is the compression phase, and the period until the rigid plunger stops The break phase was assumed.
2. A texture feature amount (energy, entropy, inertia, correlation, local uniformity) is extracted for each frame of the pressure distribution data, and a feature vector is calculated based on the feature amount. At this time, five types of values of “average value”, “maximum value”, “minimum value”, “standard deviation”, and “range” of feature values in each frame of the compression phase and the fracture phase are used as representative feature values (representative features). The total number of feature quantities is “density co-occurrence matrix” (20) × “feature quantity” (5 types) × “representative feature quantity” (5 types) = 500 for each phase). As a result, for the pressure distribution data of one type of paste food, F = 1002 dimensions, which is obtained by combining the representative feature amount 500 in the compression phase and one frame number, the representative feature amount 500 in the fracture phase and the one frame number. The feature vector of was obtained.
3. The calculated 501 dimensional feature vector was compressed to L dimension by principal component analysis. L is the total number of eigenvalues having a value of 1 or more.
4). The estimated impression level was obtained by multiple regression analysis with the L-dimensional principal component vector as the explanatory variable and the impression level as the objective variable.
The results are shown in Tables 12-13.

  From Tables 12 to 13, it can be seen that the estimated values by the texture evaluation method approximate the impression level obtained from the actual sensory evaluation for the two textures of the commercially available paste foods i to l. From the results of this example, it is shown that the texture impression system and method of the present invention can accurately estimate the surface impression of paste food such as “stickiness (in the oral cavity)” and “stickiness (when passing through the pharynx)”. Thus, it was confirmed that the present invention can be applied not only to gel foods with good shape retention but also to sol foods such as paste foods.

[Estimation of “feeling of mouthfeel” of commercial jelly foods]
In this embodiment, the texture evaluation model is obtained by performing multiple regression analysis on the representative feature vector obtained using five types (P to T) of model jelly foods having different textures and the impression level obtained from the sensory evaluation. Built. In addition, using this texture evaluation model, the “feel of mouthfeel” of a commercially available jelly food (f, h, m) whose sensory evaluation value is unknown was estimated.

[Model jelly food (PT) preparation method]
Five types of thickening polysaccharide preparations as gelling agents (trade names: Sun Support G-1043, Sun Support G-1015, Sun Support G-12, Sun Support G-22, Sun Support G-1066, all of which are Saneigen FFI Corporation) was used. 2 g of coconut oil is added to ion-exchanged water, 10 g of sucrose, 6 g of skim milk powder, the amount of gelling agent and glycerin fatty acid ester shown in Table 14 (trade name: Homogen DM-S, Saneigen F. F.I.) powder mixture was added and stirred at 1200 rpm using a propeller stirrer, except for P, heated to 80 ° C., in the case of P to 90 ° C., and then stirred for 10 minutes. . The total amount is adjusted to 100 g with ion-exchanged water, homogenized at 150 kg / m ² , 75 ° C. using a high-pressure homogenizer, further heated at 85 ° C. for 30 minutes, and then cooled in a constant temperature water bath at 8 ° C. for gelation. I let you.

The unit of numerical values in the table is the weight (g) of each component contained in 100 g of jelly food.

[Sensory evaluation method (impression level acquisition method)]
Using model jelly food (P to T) and commercially available jelly food (f, h, m), sensory evaluation was performed in the same manner as the sensory evaluation method [sensory evaluation method (impression level acquisition method)] of Example 1. .

[Food texture evaluation model construction method 1 (room temperature (pressed at 20 ° C, no holding phase)]
1. A cylindrical model jelly food (P to T, 5 types) having a diameter of 20 mm and a height of 10 mm is placed on a film type pressure distribution sensor I-Scan 40, and the cylindrical gel upper surface is compressed vertically with a rigid planar plunger. Then, the time-dependent change of the pressure applied to the multipoint sensor in contact with the lower surface of the cylindrical gel was measured (total of 30 jellys were measured, 6 for each jelly). All measurements were performed at room temperature (20 ° C.). At this time, the stress responses of the five types of jelly used in this example all had a maximum value, and the compression phase and the fracture phase could be easily separated.
2. A texture feature amount (energy, entropy, inertia, correlation, local uniformity) is extracted for each frame of the pressure distribution data, and a feature vector is calculated based on the feature amount. At this time, five types of values of “average value”, “maximum value”, “minimum value”, “standard deviation”, and “range” of feature values in each frame of the compression phase and the fracture phase are used as representative feature values (representative features). The total number of feature quantities is “density co-occurrence matrix” (20) × “feature quantity” (5 types) × “representative feature quantity” (5 types) = 500 for each phase). As a result, for one type of jelly pressure distribution data, F = 1002 dimensional sum of 500 representative feature quantities and one frame number in the compression phase, and 500 representative feature quantities and one frame number in the fracture phase. The feature vector was obtained.
3. The calculated 1002-dimensional feature vector was compressed to L dimension by principal component analysis. L is the total number of eigenvalues having a value of 1 or more.
4). A regression line (texture evaluation model) of the estimated impression level was obtained by multiple regression analysis with the L-dimensional principal component vector as the explanatory variable and the impression level as the objective variable.

[Method for obtaining estimated impression level of commercial gel food 1]
1. Three types of commercially available jelly food samples (f, h, m) are shaped into a cylindrical shape having a diameter of 20 mm and a height of 10 mm, placed on a film-type pressure distribution sensor I-Scan 40, and the upper surface of the cylindrical gel is vertically rigid. The time-dependent change in pressure applied to the multipoint sensor in contact with the lower surface of the cylindrical gel was measured by compressing with a flat plunger (total of 18 jelly was measured, 6 for each jelly). All measurements were performed at room temperature (20 ° C.). At this time, the stress responses of the three types of jelly used in this example all had a maximum value, and the compression phase and the fracture phase could be easily separated.
2. A texture feature amount (energy, entropy, inertia, correlation, local uniformity) is extracted for each frame of the pressure distribution data, and a feature vector is calculated based on the feature amount. At this time, five types of values of “average value”, “maximum value”, “minimum value”, “standard deviation”, and “range” of feature values in each frame of the compression phase and the fracture phase are used as representative feature values (representative features). The total number of feature quantities is “density co-occurrence matrix” (20) × “feature quantity” (5 types) × “representative feature quantity” (5 types) = 500 for each phase). As a result, for one type of jelly pressure distribution data, F = 1002 dimensional sum of 500 representative feature quantities and one frame number in the compression phase, and 500 representative feature quantities and one frame number in the fracture phase. The feature vector was obtained.
3. The calculated 1002-dimensional feature vector was compressed to L dimension by principal component analysis. L is the total number of eigenvalues having a value of 1 or more.
4). The estimated impression level is calculated by substituting the L-dimensional principal component vector into the multiple regression equation (texture evaluation model) constructed by the above [texture evaluation model construction method 1 (room temperature (pressed at 20 ° C., no holding phase)]]. Obtained.

[Food texture evaluation model construction method 2 (pressing device temperature adjusted to 40 ° C, with holding phase)]
1. A cylindrical model jelly food (P to T, 5 types) having a diameter of 20 mm and a height of 10 mm is placed on a film type pressure distribution sensor I-Scan 40, and the cylindrical gel upper surface is compressed vertically with a rigid planar plunger. Then, the time-dependent change of the pressure applied to the multipoint sensor in contact with the lower surface of the cylindrical gel was measured (a total of 18 jelly was measured, 6 for each jelly). At this time, the temperature of the film type pressure distribution sensor was adjusted to 40 ° C. with a heater. At this time, the stress responses of the five types of jelly used in this example all had a maximum value, and the compression phase and the fracture phase could be easily separated. Further, the pressure distribution for 10 seconds after the rigid flat plate plunger stopped was also measured, and this was used as the holding phase.
2. A texture feature amount (energy, entropy, inertia, correlation, local uniformity) is extracted for each frame of the pressure distribution data, and a feature vector is calculated based on the feature amount. At this time, five types of values of “average value”, “maximum value”, “minimum value”, “standard deviation”, and “range” of the feature values in each frame of the compression phase, the fracture phase, and the holding phase are represented as representative feature values. (The total number of representative feature amounts is “density co-occurrence matrix” (20) × “feature amount” (5 types) × “representative feature amount” (5 types) = 500 for each phase). Thus, for one type of jelly pressure distribution data, 500 representative feature quantities and one frame number in the compression phase, 500 representative feature quantities and one frame number in the fracture phase, and 500 representative feature quantities in the holding phase. An F = 1503 dimensional feature vector combining the number of frames and one frame was obtained.
3. The calculated 1503 dimensional feature vector was compressed to L dimension by principal component analysis. L is the total number of eigenvalues having a value of 1 or more.
4). A regression line (texture evaluation model) of the estimated impression level was obtained by multiple regression analysis with the L-dimensional principal component vector as the explanatory variable and the impression level as the objective variable.

[Estimated impression level acquisition method 2 (temperature adjustment to 40 ° C, with holding phase)]
1. Three types of commercially available jelly food samples (f, h, m) are shaped into a cylindrical shape having a diameter of 20 mm and a height of 10 mm, placed on a film-type pressure distribution sensor I-Scan 40, and the upper surface of the cylindrical gel is vertically rigid. The time-dependent change in pressure applied to the multipoint sensor in contact with the lower surface of the cylindrical gel was measured by compressing with a flat plunger (total of 18 jelly was measured, 6 for each jelly). At this time, the temperature of the film type pressure distribution sensor was adjusted to 40 ° C. with a heater. At this time, the stress responses of the three types of jelly used in this example all had a maximum value, and the compression phase and the fracture phase could be easily separated. Further, the pressure distribution for 10 seconds after the rigid flat plate plunger stopped was also measured, and this was used as the holding phase.
2. A texture feature amount (energy, entropy, inertia, correlation, local uniformity) is extracted for each frame of the pressure distribution data, and a feature vector is calculated based on the feature amount. At this time, five types of values of “average value”, “maximum value”, “minimum value”, “standard deviation”, and “range” of the feature values in each frame of the compression phase, the fracture phase, and the holding phase are represented as representative feature values. (The total number of representative feature amounts is “density co-occurrence matrix” (20) × “feature amount” (5 types) × “representative feature amount” (5 types) = 500 for each phase). Thus, for one type of jelly pressure distribution data, 500 representative feature quantities and one frame number in the compression phase, 500 representative feature quantities and one frame number in the fracture phase, and 500 representative feature quantities in the holding phase. An F = 1503 dimensional feature vector combining the number of frames and one frame was obtained.
3. The calculated 1503 dimensional feature vector was compressed to L dimension by principal component analysis. L is the total number of eigenvalues having a value of 1 or more.
4). Estimated impression by substituting the L-dimensional principal component vector into the multiple regression equation (texture evaluation model) constructed by the above [Food texture evaluation model construction method 2 (pressurizing device is adjusted to 40 ° C, with holding phase)] Got a level.
The results are shown in Table 15.

  Table 15 shows the impression level based on the sensory evaluation value, “estimated impression level acquisition method 1 (pressed at normal temperature (20 ° C.), no holding phase)” and “for mouthfeel” of commercially available jelly-like foods f to h, respectively. The relationship between the estimated impression levels acquired by the two methods “estimated impression level acquisition method 2 (temperature adjustment of the pressing device to 40 ° C., with holding phase)” is shown. The estimated impression level obtained by either method showed a value close to the impression level obtained from the actual sensory evaluation, but after the temperature of the pressing device was adjusted to 40 ° C. close to the human oral cavity temperature and the pressing stopped The estimated impression level acquired by the “estimated impression level acquisition method 2” in which the change in the pressure distribution during the period (holding phase) was added to the evaluation system was more approximate. When evaluating a mouthfeel that is easily affected by changes in the temperature of the sample (food), the surface impression can be estimated more accurately by holding the pressing device close to the human body temperature and holding it for a while after pressing. It was shown that it can be done.

  In this example, “feeling of stickiness”, “feeling of stickiness”, “feeling of roughness”, “feeling of slippery”, “feeling of stickiness”, and “feeling of mouth-feeling” were evaluated. Can be similarly evaluated by the present invention.

(Comparison with conventional method)
Here, as a comparative example, the accuracy of texture evaluation by a conventional texture profile method based on force response was verified.

  FIG. 9 shows force response data obtained from the load cell 24 when the three types of jelly foods (C to E) having different impression levels are compression-ruptured. From FIG. 9, it is easy to distinguish the jelly force response of jelly food C from the remaining two jelly force responses. However, it can be seen that it is difficult to distinguish between the jelly food D and the jelly food E based on the force response. That is, it can be said that it is difficult to evaluate the difference in food materials and the difference in food texture based on their geometric characteristics by the conventional texture profile method.

  On the other hand, in this embodiment, it can be seen that the differences between the jelly foods D and E, which are difficult to discriminate by the conventional texture profile method, can be clearly discriminated as shown in Tables 2 to 5.

(Summary)
As described above, in the texture evaluation system according to the present embodiment, a change in pressure distribution when the sample is broken by pressing the sample is measured, and a plurality of feature quantities are obtained from the measurement data using a spatial concentration level dependent method. To extract. From the extracted feature quantity group, a parameter that better indicates a characteristic unique to the sample is calculated by principal component analysis, and a multiple regression model with texture is created using this parameter. By using this multiple regression model, the texture is evaluated from changes in pressure distribution over time. Spatial concentration level-dependent methods, such as what kind of cracks are in food at a certain moment, how the broken pieces spread, and what kind of force response each piece shows Can be seen comprehensively. Therefore, the texture evaluation system of the embodiment can comprehensively evaluate the breaking mode caused by pressing the food, and is suitable for various texture evaluations. In particular, it is suitable for evaluating the surface impression of a gel-like food, which was difficult to evaluate with the prior art.

  The present invention is not limited to the above-described embodiment, and various modifications can be made within the scope described in the claims, and a mode obtained by appropriately combining the technical means disclosed in the above-described embodiment is also possible. Are included in the technical scope of the present invention.

  By this invention, the food-drinks with the optimal food texture for various eaters can be provided. In particular, the present invention can be applied to the development of gel-like foods that have a high level of safety in eating and swallowing and “taste”, and are prepared for, for example, nursing foods (for example, “feeling of stickiness” is suppressed). E) Can be applied to improve the texture of edible jelly such as hydration jelly, dessert jelly, pudding and egg tofu.

DESCRIPTION OF SYMBOLS 1 Texture evaluation system 2 Press apparatus 21 Plate 22 Plate 23 Linear slider 24 Load cell 3 Pressure distribution sensor 4 Control PC (texture evaluation means)
41 Pressing operation control unit 42 Texture evaluation unit (texture evaluation means)
42a Feature quantity extraction unit (feature quantity extraction means)
42b Feature quantity vector calculation unit (feature quantity vector calculation means)
42c principal component vector calculation unit (principal component vector calculation means)
42d Estimated impression level calculation unit (estimated impression level calculation means)
42e Estimation Formula Storage Unit 43 Display Unit 5 Sample

Claims (8)

A pressing device for pressing the sample;
A measuring device that measures a change over time in the pressure distribution received from the sample when the sample is pressed;
A texture evaluation system comprising texture evaluation means for evaluating the texture of the sample based on a characteristic amount obtained from a pressure distribution before and after the fracture of the sample. The measuring device outputs image information of a plurality of frames indicating the pressure distribution to the texture evaluation unit,
The texture evaluation means includes a feature quantity extraction means for extracting a texture feature quantity as the feature quantity from the image information of each frame of the image information using a spatial density level dependent method;
Feature quantity vector calculating means for calculating a feature quantity vector based on the texture feature quantity;
A principal component vector calculating means for calculating a principal component vector from the feature quantity vector;
The texture evaluation system according to claim 1, further comprising estimated impression level calculation means for calculating an estimated impression level indicating a texture evaluation result from the principal component vector.   The texture evaluation system according to claim 1 or 2, further comprising a mechanism for adjusting a part or the whole of the pressing device in contact with the sample from 25 ° C to 40 ° C. The texture evaluation system according to claim 2, wherein the estimated impression level calculation means calculates the estimated impression level based on the following estimation formula (1).
The estimation formula (1) is
For the reference food that has been sensory-evaluated in advance, pressing by the pressing device, measuring the temporal change of the pressure distribution by the measuring device, extracting the texture feature by the feature extracting unit, by the feature vector calculating unit Calculating the feature vector, and calculating the principal component vector by the principal component vector calculating means;
The food texture evaluation system according to claim 4, further obtained by creating a multiple regression model representing a relationship between the principal component vector and an impression level.   A texture evaluation program for operating a computer as each means according to claim 1.   The computer-readable recording medium which recorded the food texture evaluation program of Claim 6. A pressing step for pressing the sample;
A measuring step for measuring a change over time in a pressure distribution received from the sample when the sample is pressed;
A texture evaluation method comprising: a texture evaluation step of evaluating the texture of the sample based on the change over time.