JP2020134526A - System and method for evaluating texture of food - Google Patents

Abstract

To provide a system and a method for evaluating the texture of food more accurately.SOLUTION: A texture evaluation system 10 includes: a pressure distribution sensor 13; first artificial teeth 17, 18, 19; second artificial teeth 27, 28, 29; an artificial tongue 33; and texture evaluation means for evaluating a texture based on occlusion force data obtained in a manner that the pressure distribution sensor 13 measures changes with time of a pressing force on the first artificial teeth when a measurement target sample is pressed between the first and second artificial teeth several times and based on and tongue pressure data obtained in a manner that the pressure distribution sensor 13 measures changes with time of a pressing force on the artificial tongue 33 when the measurement target sample is pressed between the artificial tongue 33 and the pressure distribution sensor 13 several times.SELECTED DRAWING: Figure 1

Description

The present invention relates to, for example, a texture evaluation system for evaluating a texture such as a crispy texture, a fluffy texture, a sticky texture, and a melting in the mouth.

It is said that the deliciousness of food is mainly composed of flavor, aroma, and texture. Among these, the texture is composed of extremely delicate and complex information. It is difficult to quantify such textures, for example, those determined by multiple factors such as stickiness and melting in the mouth. Therefore, such texture is generally sensory evaluated by a plurality of evaluators (panelists).

However, the sensory evaluation by the above panelists varies widely, and it is difficult to objectively evaluate the texture. In addition, it is necessary to train a large number of trained panelists, which is troublesome and costly.

Therefore, a system that can evaluate the texture as objectively as possible is desired. As such, Patent Document 1 below describes a pressing device that presses a sample, a measuring device that measures a change over time in the pressure distribution received from the sample when the sample is pressed, and a pre-break and break of the sample. A texture evaluation system including a texture evaluation means for evaluating the texture of the sample based on the feature amount obtained from the subsequent pressure distribution is disclosed. The pressing device has a pair of upper and lower plates, and a pressure distribution sensor forming a measuring device is arranged on the lower plate.

Japanese Unexamined Patent Publication No. 2014-167470

By the way, in the texture evaluation system of Patent Document 1, the texture is evaluated only based on the feature amount obtained from the change over time of the pressure distribution by one kind of pressing device. When evaluating texture, the evaluation result is determined by multiple factors such as texture and mouthfeel, there was a problem with its accuracy.

Therefore, an object of the present invention is to provide a texture evaluation system and a texture evaluation method capable of performing an evaluation closer to an actual texture.

In order to achieve the above object, the texture evaluation system of the present invention is arranged on the pressure distribution sensor for measuring the change of the pressure distribution with time and the pressure distribution sensor, and transmits the pressing force to the pressure distribution sensor. A possible first artificial tooth and a second artificial tooth which is arranged to face the tip of the first artificial tooth and is arranged so as to be close to and detached from the first artificial tooth, and are arranged to face the pressure distribution sensor. When the sample to be measured is pressed a plurality of times between the first artificial tooth and the second artificial tooth via the driving means, the artificial tongue arranged so as to be close to the pressure distribution sensor. The occlusal force data obtained by measuring the change over time of the pressing force applied to the first artificial tooth by the pressure distribution sensor, and the sample to be measured are pressed a plurality of times between the artificial tongue and the pressure distribution sensor. A texture evaluation means for evaluating the texture based on the tongue pressure data obtained by measuring the change with time of the pressing force applied to the artificial tongue by the pressure distribution sensor is provided. It is characterized by.

Since the texture evaluation system of the present invention has the texture evaluation means having the above configuration, the texture can be evaluated based on both the bite force data and the tongue pressure data, and more actual texture can be evaluated. It is possible to make an evaluation close to the texture.

In the texture evaluation system of the present invention, the texture evaluation means is derived from the texture sensory evaluation value of the model sample whose texture has been evaluated, the occlusal force data of the model sample, and the tongue pressure data. It is preferable to evaluate the texture of the sample to be measured by using the occlusal force data and the tongue pressure data of the sample to be measured based on the texture estimation formula. According to this aspect, the texture evaluation that more reflects the actual texture can be performed.

In the texture evaluation system of the present invention, the texture evaluation means applies the maximum value of the pressing force applied at the time of the first pressing by the first artificial tooth and the second artificial tooth at the time of f _B1 and u times of pressing. The characteristic amount of the occlusal force is calculated with the maximum value of the pressing force as f _Bu , the maximum value of the pressing force applied during the first pressing of the artificial tongue is f _T1 , and the maximum value of the pressing force applied during the uth pressing is f. The characteristic amount of tongue pressure is calculated as _Tu , and the cell area of the pressure distribution sensor at the time when the maximum value of the pressing force is detected at the first pressing of the artificial tongue is A ₁ , and the pressing pressure at the uth pressing is at the time of detection of the maximum value, the cell area of the pressure distribution sensor as a _u, calculates the first feature quantity of tongue pressure distribution, maximum the pressure distribution sensor of the pressing force during the first pressing of the artificial tongue The second feature of the tongue pressure distribution is that the standard deviation of the pressure at the time when the value is detected is S ₁ and the standard deviation of the pressure at the time when the pressure distribution sensor detects the maximum value of the pressing pressure at the uth pressing is S _2. The amount is calculated, and the texture is evaluated based on the characteristic amount of the occlusal force, the characteristic amount of the tongue pressure, the first characteristic amount of the tongue pressure distribution, and the second characteristic amount of the tongue pressure distribution. It is preferable to do so. According to this aspect, the bite force data and the tongue pressure data obtained by pressing the sample to be measured a plurality of times are converted into a plurality of feature quantities that are easy to evaluate the texture, and the food is eaten based on these feature quantities. It is possible to evaluate the texture, and it is possible to evaluate the texture even closer to the actual texture.

In the texture evaluation system of the present invention, it is preferable to have a driving means for simultaneously performing the pressing by the first artificial tooth and the second artificial tooth and the pressing by the artificial tongue. According to this aspect, by simultaneously performing the pressing by the first artificial tooth and the second artificial tooth and the pressing by the artificial tongue, it is possible to obtain measurement data close to the actual masticatory operation in the oral cavity.

In the texture evaluation system of the present invention, the base of the first artificial tooth is held by an elastic member, and the pressure is transmitted to the pressure distribution sensor via the elastic member. Is preferable. According to this aspect, the pressing force can be measured as a value close to the pressing force applied to the actual gums.

In the texture evaluation system of the present invention, the base of the first artificial tooth is supported by a support portion mounted on the pressure sensor, and the pressure applied to the first artificial tooth presses the base. It is preferable that the pressure distribution sensor is configured to transmit the pressure distribution sensor. According to this aspect, since the pressure applied to the first artificial tooth is transmitted to the pressure distribution sensor via the base portion, the loss of pressure transmitted to the pressure distribution sensor can be reduced.

In the texture evaluation system of the present invention, a plurality of the first artificial tooth and the second artificial tooth are arranged in an arc shape and opposed to each other, and the artificial tongue is the second artificial tooth. It is preferably arranged inside the arcuate array of teeth. According to this aspect, pressing data close to the actual texture can be obtained by bringing the tooth arrangement and the tongue arrangement closer to each other in the oral cavity.

On the other hand, in the texture evaluation method of the present invention, an uncrushed sample to be measured and a crushed sample to be measured are prepared by using any of the above texture evaluation systems, and each sample is measured by the texture evaluation system. Then, the occlusal force data and the tongue pressure data of each sample are obtained, and the texture is evaluated based on the occlusal force data and the tongue pressure data of each sample.

In the texture evaluation method of the present invention, bite force data and tongue pressure data can be obtained for each of the uncrushed sample to be measured and the crushed sample to be measured, so that the food changes depending on mastication. The state can be reflected, and the texture can be evaluated more realistically.

In the texture evaluation method of the present invention, it is preferable that the sample to be crushed is one type or two or more types corresponding to a predetermined number of times of chewing. According to this aspect, the texture can be evaluated by more appropriately reflecting the state of the food that changes due to chewing.

According to the present invention, the texture can be evaluated based on both the bite force data and the tongue pressure data, and the evaluation closer to the actual texture can be performed.

An embodiment of the texture evaluation system according to the present invention is shown, and is a perspective view of a schematic configuration thereof. It is an enlarged explanatory view of the main part near the 1st artificial tooth which constitutes the texture evaluation system. It is an enlarged explanatory view of the main part of the texture evaluation system. It is a block diagram which shows the schematic structure of the texture evaluation system. It is a flowchart of the texture evaluation method which concerns on this invention. It is a photograph which shows the state of the sample to be measured measured by the texture evaluation system. (A) is a graph showing the relationship between the interdental distance and time when the first artificial tooth and the artificial tongue are separated from each other, and (b) shows the bite force and the relationship between tongue force and time in the mastication process. It is a graph and the figure which shows the change of the tongue pressure distribution. It is a graph which shows the relationship between the bite force and time, and the relationship between tongue force and time in different kinds of samples to be measured. The relationship between the texture estimation value and the texture sensory evaluation value by the DA method is shown. (A) is a graph of the result of crispy feeling, (b) is a graph of the result of fluffy feeling, and (c) is a sticky feeling. The graph of the result, (d) is the graph of the result of melting in the mouth. The relationship between the texture estimated value by the TDS method and the texture sensory evaluation value is shown. (C) is a graph of the result of the sticky feeling in the bolus state II, and (d) is a graph of the result of the sticky feeling in the bolus state III. The relationship between the texture estimated value and the texture sensory evaluation value by the TI method is shown. (A) is a graph of the result of crispy feeling in the bolus state I, and (b) is the result of the crispy feeling in the bolus state II. (C) is a graph of the result of the sticky feeling in the bolus state II, and (d) is a graph of the result of the sticky feeling in the bolus state III. (A) is a bar graph comparing the coefficient of determination in the DA method of Examples and Comparative Examples 1 and 2, and (b) is a bar graph comparing the coefficient of determination of Examples and Comparative Examples 1 and 2 in the TDS method. (C) is a bar graph comparing the coefficients of determination in the TI method of Examples and Comparative Examples 1 and 2. The result of verifying the texture estimation value by the DA method by LOOCV is shown, (a) is a graph showing the result of crispy feeling, (b) is the graph of the result of fluffy feeling, and (c) is the result of sticky feeling. The graph of (d) is a graph of the result of melting in the mouth. It is a bar graph which shows the result of having verified the coefficient of determination in the DA method by LOOCV of Examples and Comparative Examples 1 and 2. Another embodiment of the texture evaluation system according to the present invention is shown, and is a perspective view of a schematic configuration thereof. It is an enlarged explanatory view of the main part near the 1st artificial tooth which constitutes the texture evaluation system. It is an enlarged explanatory view of the main part of the texture evaluation system. Of the two types of donuts produced by the texture evaluation system, the measurement results of one type of bite force and tongue force are shown, (a) is a graph showing the relationship between bite force and time, and (b) is tongue force. It is a graph which shows the relationship between time and time. Of the two types of donuts produced by the texture evaluation system, the measurement results of the bite force and tongue force of the other type are shown. (A) is a graph showing the relationship between bite force and time, and (b) is the tongue. It is a graph which shows the relationship between force and time.

Hereinafter, an embodiment of the texture evaluation system and the texture evaluation method according to the present invention will be described with reference to the drawings.

(Structure of texture evaluation system)
FIG. 1 is a perspective view showing a schematic configuration of a texture evaluation system according to the present invention. Further, FIG. 2 is an enlarged explanatory view of a main part in the vicinity of the first artificial tooth constituting the texture evaluation system, FIG. 3 is an enlarged explanatory view of a main part of the texture evaluation system, and FIG. 4 is the same. A block diagram showing a schematic configuration of a texture evaluation system, FIG. 5, shows a flowchart of a texture evaluation method according to the present invention.

The texture evaluation system 10 (hereinafter, also referred to as “system 10”) in this embodiment has a sensor support portion 11, and a pressure distribution on the sensor support portion 11 for measuring a change in pressure distribution over time. A sensor 13 (hereinafter, also simply referred to as “pressure sensor 13”) is arranged. Further, as shown in FIG. 1, the pressure sensor 13 is configured by arranging a plurality of cells 13a having a predetermined area Ac (mm ² ). The area Ac of the cell 13a in this embodiment is 1 mm ² . Further, as the pressure sensor 13, for example, I-SCAN 40 (measurement range 44 × 44 mm, spatial resolution 1 mm, time resolution 10 ms, pressure resolution 50 Pa) manufactured by Nitta Corporation can be used.

An annular support portion 15 having a substantially annular shape is arranged on the pressure sensor 13. A plurality of first artificial teeth 17, 18, and 19 made of, for example, ABS resin are arranged in the annular support portion 15 so as to form an arc shape. Referring to FIG. 2 together, a plurality of through holes 21 are formed in the annular support portion 15 along the circumferential direction, and each of the first through holes 21 is formed inside the through holes 21 via the elastic member 23. The bases 17a, 18a, 19a of the artificial teeth 17, 18, 19 are inserted and held, respectively.

The human tooth is composed of hard tissues such as enamel and alveolar bone and a thin and soft periodontal ligament between them, and has a holding structure of the first artificial teeth 17, 18 and 19 in the present embodiment. (The first artificial teeth 17, 18 and 19 correspond to enamel, the annular support portion 15 corresponds to the alveolar bone, and the elastic member 23 corresponds to the periodontal ligament).

Further, as shown in FIG. 2, the elastic member 23 arranged in the through hole 21 is in contact with the pressure sensor 13. Therefore, when a pressing force acts on the first artificial teeth 17, 18 and 19, the pressing force is detected by the pressure sensor 13 via the elastic member 23. The elastic member 23 in this embodiment is a rubber sheet having a thickness of 1 mm.

Further, a substantially disk-shaped pressing portion 25 is arranged at a position facing the annular support portion 15. A processing device 40 is connected between the pressing portion 25 and the pressure sensor 13 (see FIG. 1). Further, at the outer peripheral edge portion of the pressing portion 25, at positions corresponding to the plurality of first artificial teeth 17, 18, 19, for example, a plurality of second artificial teeth 27, 28, 29 made of ABS resin or the like are placed. , Arranged in an arc shape. Further, on the side of the pressing portion 25 facing the pressure distribution sensor 13, the inside of the arrangement of the plurality of second artificial teeth 27, 28, 29 is formed of, for example, an elastic material made of silicone resin or the like. In addition, an artificial tongue 33 is provided.

Further, a rod 25a is connected to the center of the upper surface side of the pressing portion 25 via a driving means 31 (composed of a motor, an actuator, an air cylinder, etc.) that operates based on a driving signal from the processing device 40. , The pressing portion 25 is driven so as to be close to and separated from the annular support portion 15 (see FIG. 4). As a result, the plurality of second artificial teeth 27, 28, 29 are separated from each other with respect to the tips 17b, 18b, 19b of the plurality of first artificial teeth 17, 18, 19 and the artificial tongue 33 is the pressure sensor 13. Close to each other.

Then, as shown in FIG. 3, the sample to be measured or the model sample whose texture has been evaluated, which is arranged between the two artificial teeth, is pushed by the second artificial teeth 27, 28, 29, and the sample to be measured or the sample to be measured or A pressing force acts on the first artificial teeth 17, 18 and 19 via the model sample, and the pressing force is further transmitted to the pressure sensor 13 via the elastic member 23 and measured by the pressure sensor 13. At the same time, the sample to be measured or the model sample arranged between the artificial tongue 33 and the pressure sensor 13 is pushed by the artificial tongue 33, and the pressing pressure is measured by the pressure sensor 13. That is, the driving means 31 in this embodiment is configured to simultaneously perform pressing by the first artificial tooth and the second artificial tooth and pressing by the artificial tongue. The pressing force from the first artificial tooth measured by the pressure sensor 13 is also referred to as "occlusal force" in the following description, and the pressing force from the artificial tongue measured by the pressure sensor 13 is referred to as "pressing force from the artificial tongue" in the following description. It is also called "tongue pressure", and the pressure distribution is also called "tongue pressure distribution".

Further, the pressure sensor 13 of this embodiment is composed of one one arranged on the sensor support portion 11, and can measure both the occlusal force by the artificial tooth and the tongue pressure data by the artificial tongue 33. However, a plurality of pressure sensors may be provided to measure the occlusal force of the artificial tooth and the tongue pressure data of the artificial tongue. Further, although the first artificial tooth and the second artificial tooth are each composed of three teeth, the number may be one or two, or four or more.

Further, as described above, the processing device 40 sends a drive signal to the drive means 31 at a predetermined timing, and also has a texture evaluation means 50. When the texture evaluation means 50 presses the sample to be measured or the model sample a plurality of times between the first artificial teeth 17, 18, 19 and the second artificial teeth 27, 28, 29 via the driving means 31. In addition, the occlusal force data obtained by measuring the change over time of the pressing force applied to the first artificial teeth 17, 18 and 19 by the pressure sensor 13, and the test to be measured between the artificial tongue 33 and the pressure sensor 13. Alternatively, the texture is evaluated based on the tongue pressure data obtained by measuring the change with time of the pressing force applied to the artificial tongue 33 when the model sample is pressed a plurality of times by the pressure sensor 13.

As shown in FIG. 4, the texture evaluation means 50 in this embodiment is stored in the data storage unit 51 and the data storage unit 51, which stores the occlusal force data and the tongue pressure data measured by the pressure sensor 13. The texture of the sample to be measured is based on the feature amount calculation unit 53 that calculates the feature amount of the sample to be measured based on the occlusal force data and the tongue pressure data, and the feature amount calculated by the feature amount calculation unit 53. It has a texture estimation formula derivation unit 55 for deriving the estimation formula. The texture evaluation means 50 may be realized by hardware or software.

Further, the texture evaluation means 50 in this embodiment was derived based on the texture sensory evaluation value performed using the model sample, the occlusal force data and the tongue pressure data measured by the pressure sensor 13. The texture estimation formula is set in advance, and further, the texture evaluation method of the present invention introduces the bite force data and the tongue pressure data measured using the sample to be measured into the texture estimation formula. , It is possible to estimate the texture of the sample to be measured.

Next, the method for deriving the texture estimation formula in this embodiment will be described in more detail with reference to the flowchart of FIG.

This time, we prepared 6 types of donuts (A, B, C, D, E, F) as shown in Fig. 6, and made them "crispy", "hollow", "sticky", and "melting". For the four textures, texture estimation formulas were derived, and the four textures were estimated using the obtained texture estimation formulas.

For each of the six types of donuts, model samples of bolus state I, bolus state II, and bolus state III are prepared by the procedure of "(2) Bite force data and tongue pressure data measurement step" described later. .. Note that FIG. 6 shows each model sample produced.

In the texture evaluation method of FIG. 5, first, a human sensory evaluation is performed on the model sample to obtain a texture sensory evaluation value (step S1). After that, the standard deviation is calculated for each texture sensory item (crispy feeling, fluffy feeling, sticky feeling, melting in the mouth, etc.) by each evaluation method (DA method, TDS method, TI method, etc. described later), and this standard. It is determined whether or not the deviation is equal to or greater than a predetermined value (step S2). The item having a predetermined value or more is input to the texture estimation formula derivation unit 55 of the system 10.

In parallel with the above steps, a model sample of predetermined bolus states I to III was prepared as a preparation, and between the corresponding first artificial tooth and the second artificial tooth of the system 10 and with the artificial tongue 33. A model sample is set between the pressure sensor 13 and the pressure sensor 13. Then, the pressing portion 25 is driven via the driving means 31, the first artificial teeth 17, 18, 19 and the artificial tongue 33 are moved away from each other with respect to the pressure sensor 13, and the model sample is pressed a predetermined number of times (step S3). ), The bite force data and the tongue pressure data are measured (step S4). After that, based on the sensory evaluation value obtained in step S2, measurement data for which a texture estimation formula should be created is selected, a feature amount is extracted based on the measurement data, and the texture is based on this feature amount. An estimation formula is derived (step S5).

Further, in the texture evaluation means 50 (see FIG. 4) of this embodiment, (1) a step of performing sensory evaluation by a human on a model sample and acquiring a texture sensory evaluation value (hereinafter, “texture sensory evaluation”). "Value acquisition process"), (2) occlusal force data and tongue pressure data measurement process that measures the occlusal force data and tongue force data of the model sample by pressing the model sample, and (3) occlusal force data and The texture estimation formula derivation process, which derives the texture estimation formula based on the tongue strength data, has been performed. Each step will be described in detail below.

(1) Texture sensory evaluation value acquisition process Acquire texture sensory evaluation values for foods (here, donuts) with different materials and manufacturing methods. Specifically, for each food (hereinafter, also referred to as "sensory value measurement sample"), the sensory evaluation value is acquired by the sensory evaluation test actually eaten by humans. The sensory evaluation value is set for each texture evaluation item i (for example, crispy texture (i = 1), fluffy texture (i = 2), sticky texture (i = 3), mouthfeel (i = 4), etc.). ..

The above-mentioned "crispy feeling" means the feeling that the food hits the teeth, the "hollow feeling" means the feeling that the food crumbles in the mouth, and the "sticky feeling" means the feeling that the food clings to the mouth. Or, it means the feeling that the food is gathered in the mouth, and "melting in the mouth" means the feeling that the food becomes familiar with saliva and quickly disappears from the mouth. The texture to be evaluated may be other than the above.

Then, each texture evaluation item is evaluated by a predetermined texture evaluation means. In this embodiment, the texture sensory evaluation value is acquired by a well-known evaluation means, such as a DA (Descriptive Analysis) method, a TDS (Temporal Dominance Sensation) method, and a TI (Time Intensity) method. Further, in this embodiment, it is assumed that five examiners (panelists) are trained so that the definitions and strengths of the texture evaluation items are recognized and all can be evaluated on the same axis.

In the above DA method, when tasting one mouthful of the sensory value measurement sample, the degree of the target texture is focused on only one texture evaluation item (i = 1 to 4). To evaluate. Of the texture evaluation items i, the mouthfeel (i = 4) is obtained only by this DA method.

In the above TDS method, in the process of tasting the sensory value measurement sample, the most impressive texture evaluation item (i = 1 to 3) is evaluated over time. Specifically, 5 seconds after the start of the test, chewing of one mouthful of the sensory value measurement sample was started in accordance with the 1 Hz metronome, and the texture evaluation item having the strongest impression at each time was selected for that time. Record changes as time series data. Each examiner swallows freely to complete the test.

In the above TI method, when tasting the sensory value measurement sample, attention is paid to only one texture evaluation item, and the temporal change of the texture is evaluated. Specifically, the sensory value measurement sample is chewed by the same method as the TDS method, the intensity of the target texture is evaluated, and it is recorded as time series data.

In human mastication, the state of food changes from moment to moment as the mastication progresses (the number of times of chewing and the number of times of crushing increase). In consideration of this, the mastication process is set in the preparatory stage (0 ≦ t ≦ 5 [s]), the mastication stage I (5 ≦ t ≦ 20 [s]), and the mastication stage II (20 ≦ t ≦ 35 [s]). , The mastication period III (35 ≦ t ≦ 50 [s]) is divided into four sections (the number of times of mastication is mastication period I <mastication period II <mastication period III). As will be described later, in this embodiment, model samples of the bolus states I to III that reproduce the bolus states of the chewing periods I to III as described above are prepared, and these are measured and textured by the present system 10. Be subject to evaluation. Correspondingly, regarding the sensory evaluation values of the above TDS method and TI method, the average value for 10 seconds after the start of each of the chewing periods I to III is used as the teacher data, and the texture sensory evaluation value n _i ^j is used. It shall be.

In addition, the standard deviation is calculated for each texture sensory item i by each evaluation method, and for the items whose standard deviation is equal to or greater than the predetermined value, the texture estimation formula is used in the (3) texture estimation formula calculation process. It shall be derived.

(2) Bite force data and tongue pressure data measurement process First, for foods with different materials and manufacturing methods (here, donuts), an uncrushed model sample (of the bolus state I) corresponding to the masticatory period I in the above sensory evaluation. A model sample) and a crushed model sample (model sample of bolus states II and III) corresponding to the mastication stages II and III are prepared. The uncrushed model sample is prepared by cutting a predetermined food into a predetermined size. Further, the crushed model sample is obtained by cutting a predetermined food into a predetermined size, adding a predetermined amount of water, crushing the model sample with a mixer (for example, a crush miller manufactured by Iwatani Corporation) for a predetermined time, and then crushing the sample. Make all together.

Specifically, as the model sample of the bolus state I corresponding to the chewing period I, 8 g of a bite-sized food is cut out to prepare an uncrushed model sample (see the bolus state I of FIG. 6). A model sample of the bolus state II corresponding to the chewing stage II is prepared by cutting out 8 g of food, adding 1.2 ml of water with a syringe, and crushing the bolus for 0.5 seconds with a mixer (the bolus of FIG. 6). See state II). The model sample of the bolus state III corresponding to the mastication stage III is prepared by adding 1.2 ml of water to the model sample of the mastication stage II and crushing the sample for 0.5 seconds with a mixer (FIG. 6). See bolus state III). The above-mentioned water content and crushing time were experimentally determined based on the amount of saliva secreted and the tactile sensation of the bolus when a human actually chewed the food.

Then, as shown in FIG. 3, the prepared model sample is bisected so that the center of each model sample coincides with the centers of the first artificial teeth 17, 18 and 19 and the center of the artificial tongue 33. Place in. In this state, as described above, by driving the pressing portion 25 via the driving means 31, the first artificial teeth 17, 18, 19 and the artificial tongue 33 are brought close to and separated from the pressure sensor 13. By pressing the model sample a predetermined number of times, bite force data and tongue pressure data are measured. The model samples in this embodiment are six kinds of foods (a, B, C, D, E, and F donuts shown in FIG. 6), and for various foods, bolus state I, bolus state II, and food. A measurement test is carried out 15 times for each of the three states of the mass state III, that is, 6 types × 3 states = 18 types of model samples (total number of data N is 270).

Further, the operation of the pressing portion 25 is set to have a sinusoidal trajectory as shown in the following mathematical formula 1 based on the human chewing operation.

In the above formula 1, h is the interdental distance between the first artificial tooth and the second artificial tooth, f is the frequency, and h ₀ is the offset. In this embodiment, the frequency f is 1 Hz, the amplitude α is 15 mm at 0 ≦ t ≦ 1 / (2f), 5 mm at 1 / (2f) ≦ t ≦ Ts, and the offset h ₀ is 0.3 mm. The mastication time Ts is 10 s (1 round trip 1 s, 10 round trips).

The bite force data is obtained by a pressure sensor 13 indicating a change over time in the pressing force (bite force) applied to the first artificial tooth when the model sample is pressed multiple times between the first artificial tooth and the second artificial tooth. Obtained by measurement. In the case of this embodiment, the first artificial tooth 17, 18, 19 (hereinafter, the first artificial tooth 17 will be described as k = 1, the first artificial tooth 18 will be described as k = 2, and the first artificial tooth 19 will be described as k = 3. ) Is integrated and calculated by the following mathematical formula 2.

Ac in the above formula 2 is an area of 1 mm ² of one cell 13a of the pressure sensor 13, and P _km (m = 1, ..., M _k ) is the base of each of the first artificial teeth 17, 18, and 19. It is the pressure value of the cell 13a in contact with the bottom surfaces of 17a, 18a, 19a (M _k is the number of cells in contact with each tooth). Let f _{k0 of the} above formula 2 be a temporary bite force. That is, as shown in FIG. 2, since the outer peripheral surfaces of the first artificial teeth 17, 18, and 19 are supported by the inner circumference of the through hole 21 via the elastic member 23, one of the bite forces. The part is dispersed and becomes smaller than the actual bite force. Therefore, the bite force _fx is derived by the calibration formula of the following formula 3.

In addition, b _k2 and b _k1 in the above formula 3 are coefficient parameters for each of the first artificial teeth 17, 18 and 19, and were obtained experimentally. These are shown in Table 1 below.

On the other hand, the tongue pressure data is obtained by measuring the time course of the pressing pressure (tongue pressure) applied to the artificial tongue 33 when the model sample is pressed a plurality of times between the artificial tongue 33 and the pressure sensor 13. In the case of this embodiment, the pressure sensor 13 is obtained by measuring in the inner portion of the annular support portion 15 having a substantially annular shape, and the distribution thereof is defined as the tongue pressure distribution P (x, y). ..

Then, the bite force f _k (t) (k = 1, 2, 3) and the tongue pressure distribution P (x, y, t) obtained by the above processing are used as data constituting the texture evaluation means 50. It is stored in the storage unit 51 (see FIG. 4).

With respect to the measured bite force f _k (t), the total bite force f _B (t) of the three first artificial teeth 17, 18 and 19 is calculated by the following mathematical formula 4. In the following description, the total bite force is simply referred to as the bite force.

For the tongue pressure distribution data, in addition to the tongue pressure distribution P (x, y, t), the tongue force f _T (t) shown in the following mathematical formula 5 is calculated.

Further, FIG. 7A shows the relationship between the interdental distance and the time when the first artificial tooth and the artificial tongue are separated from each other. As shown in FIG. 7 (a), the actual trajectory cannot follow the target trajectory in the vicinity of the interdental distance, but as shown in FIG. 7 (b), the bite force, tongue force, and tongue force in the mastication process, and Changes in tongue pressure distribution can be measured. Further, FIGS. 8A and 8B show the relationship between the bite force and time and the relationship between the tongue force and time for different types of model samples.

(3) Texture estimation formula derivation process (calculation of feature amount)
From the bite force, tongue force, and tongue pressure distribution calculated by the above-mentioned bite force data and tongue pressure data measurement steps, a feature amount for deriving a texture estimation formula is extracted. As shown in FIGS. 8 (a) and 8 (b), the temporal change of the maximum value in one stroke in bite force and tongue force differs depending on the type of model sample, and the pattern of tongue pressure distribution also differs. Can be seen. From this point of view, the features that represent these differences are set.

That is, the maximum value (maximum bite force) of the pressing force applied during the first pressing by the first artificial tooth and the second artificial tooth is f _B1 , and the maximum value of the pressing force applied during the u-th pressing is f _Bu (u stroke). The feature amount of the bite force is calculated as the maximum bite force), and the three feature amounts x ₁ , x ₂ , and x ₃ shown in the following formulas 6, 7, and 8 are set.

Similarly, the maximum value of the pressing force (maximum tongue force) applied during the first pressing of the artificial tongue is f _T1 , and the maximum value of the pressing force applied during the u-th pressing is f _Tu (maximum tongue force of the u stroke). The characteristic amount of tongue pressure is calculated, and the three characteristic amounts x ₄ , x ₅ , and x ₆ shown in the following formulas 9, 10 and 11 are set.

Also, during the first pressing of the artificial tongue, at the time of detection of the maximum value of the pressing force, the cell area of the pressure sensor 13 at the time of pressing the A _1, u th, at the time of detection of the maximum value of the pressing force, the pressure the cell area of the sensor 13 as a _u, sets the first feature amount x _7, x ₈ tongue pressure distribution shown in the following equation 12.

Further, the standard deviation of the pressure at the time when the pressure sensor 13 detects the maximum value of the pressing force at the first pressing of the artificial tongue is S ₁ , and the pressure sensor 13 determines the maximum value of the pressing force at the time of the u-th pressing. Let S _{2 be} the standard deviation of the pressure at the time of detection, and set the second characteristic quantities x ₉ and x ₁₀ of the tongue pressure distribution shown in the following equations 14 and 15.

The above feature quantities x _{1 to} x ₁₀ are set for each model sample for each state (food mass states I to III). That is, 10 feature quantities x ₁ ^{I to} x ₁₀ ^I are calculated from the data of the bite force, tongue force, and tongue pressure distribution of each model sample in the bolus state ^I, and the bite of each model sample in the bolus state II. From the data of force, tongue force, and tongue pressure distribution, 10 feature quantities x ₁ ^{II to} x ₁₀ ^II are calculated, and from the data of bite force, tongue force, and tongue pressure distribution of each model sample in the bolus state III. Calculate ₁₀ feature quantities x ₁ ^{III to} x ₁₀ ^III .

(Derivation of texture estimation formula)
Based on the feature amount calculated by the predetermined texture evaluation means, a texture estimation formula is derived for the predetermined texture evaluation item i. Regarding the sensory evaluation value by the above DA method, the texture evaluation item i (i = "1" crispy feeling, i = "2" fluffy feeling, i = "3" sticky feeling, i = "4" Create a texture estimation formula for each mouthfeel). That is, the object variable texture sensory evaluation value n _i, 10 pieces of feature quantities respectively for each bolus state I to III, the feature quantity of 30 in total ^{_{x 1 I ~x 10 II, x}} 1 II ~x 10 II , X ₁ ^{III to} x ₁₀ ^III are set as explanatory variables, and multiple regression analysis is performed to create a texture estimation formula as shown in the following formula 16. In the following mathematical formula 16, a ₀ is a constant term, and a ₁ ^{I to} a ₁₀ ^III are partial regression coefficients.

On the other hand, regarding the sensory evaluation values by the TDS method and the TI method described above, for each bolus state j (j = I to III), and the texture evaluation item i (i = "1" crispy feeling, i = " A texture estimation formula is created for each (2) fluffy feeling and i = “3” sticky feeling). That is, the object variable texture sensory evaluation value n _i ^j, and sets the 10 feature quantities x ₁ ^j ~x _ten ^j in each bolus state I~III the explanatory variables by performing multiple regression analysis, the following A texture estimation formula as shown in Equation 17 is created. In the following mathematical formula 17, a ₀ is a constant term, and a ₁ ^{j to} a ₁₀ ^j are partial regression coefficients.

In addition, after creating the texture estimation formulas of the above equations 16 and 17, the null hypothesis of the partial regression coefficient a _l ^j = 0 (j = I to III, l = 1 to 10) is tested, and the risk rate is 5 If% or more of the features are present, the largest feature is removed and the multiple regression analysis is performed again. This process is repeated until the risk factor of all the features is less than 5%, and the final estimation formula of the texture sensory evaluation value is derived.

The model sample in this embodiment is a donut, but the model sample is not particularly limited as long as it is a solid food, and examples thereof include bakery foods, processed meat products, rice crackers, and fried foods.

Then, in the texture evaluation method of the present invention using the texture evaluation means 50 in this embodiment, the measurement sample is also the same as the model sample described above, (2) occlusal force data and tongue pressure data measurement step. , And (3) In the process of deriving the texture estimation formula, the occlusal force data and tongue pressure data of the sample to be measured are measured, and the feature amount is calculated to obtain the texture estimation formula of the preset model sample. Based on this, the texture of the sample to be measured is estimated using the characteristic amount of the sample to be measured.

Next, the action and effect of the texture evaluation system 10 and the texture evaluation method having the above configuration will be described.

That is, in this system 10, (1) the texture sensory evaluation value of the model sample whose texture has been evaluated is obtained, and (2) the first artificial teeth 17, 18, 19 and the second are obtained for this model sample. When the model sample is pressed multiple times between the artificial teeth 27, 28, and 29, the occlusal force data obtained by measuring the change with time of the pressing force applied to the first artificial tooth by the pressure sensor 13 and the artificial tooth and the artificial tooth. When the model sample is pressed multiple times between the tongue 33 and the pressure sensor 13, the texture of the artificial tongue 33 based on the tongue pressure data obtained by measuring the change over time in the pressing force applied to the artificial tongue 33 by the pressure sensor 13. Derivation of the estimation formula, and estimation of the texture using the bite force data and tongue pressure data of the sample to be measured based on the texture estimation formula derived from the bite force data and tongue pressure data of this model sample. Can be done. Therefore, for example, even if the texture is difficult to estimate in the past, such as a sticky texture or melting in the mouth, it is possible to evaluate the texture closer to the actual texture.

Then, in the above texture evaluation method, bite force data and tongue pressure data can be obtained for each of the uncrushed sample to be measured and the crushed sample to be measured, so that the food changes depending on chewing. The state can be reflected, and the texture can be evaluated more realistically.

Further, in this texture evaluation method, it is preferable that the sample to be crushed is one type or two or more types corresponding to a predetermined number of times of chewing. In this embodiment, a sample to be measured in the bolus state III is prepared corresponding to the sensory evaluation value measurement sample in the mastication period II having a predetermined number of mastications, and in the mastication period III in which the number of mastications is larger than that in the mastication period II. , A sample to be measured in the bolus state III has been prepared corresponding to the sample for measuring the sensory evaluation value. Therefore, the texture can be evaluated by more appropriately reflecting the state of the food that changes due to mastication.

Further, the texture evaluation means 50 in this embodiment sets the maximum value of the pressing force applied at the time of the first pressing by the first artificial teeth 17, 18, 19 and the second artificial teeth 27, 28, 29 to f _B1 , u. The characteristic amount of occlusal force is calculated with the maximum value of the pressing force applied during pressing as f _Bu , and the maximum value of the pressing force applied during the first pressing of the artificial tongue 33 is f _T1 , and the maximum value of the pressing force applied during the uth pressing. The characteristic amount of tongue pressure is calculated with the value as f _Tu , and the cell area of the pressure sensor 13 at the time when the maximum value of the pressing pressure is detected at the first pressing of the artificial tongue 33 is A ₁ , and the pressing pressure at the uth pressing. the cell area of the pressure sensor 13 at the time of detection of the maximum value of as a _u, calculates the first feature quantity of tongue pressure distribution, the maximum value the pressure sensor 13 of the pressing force during the first pressing of the artificial tongue 33 The second feature amount of the tongue pressure distribution is calculated with the standard deviation of the pressure at the time of detection as S ₁ and the standard deviation of the pressure at the time when the pressure sensor 13 detects the maximum value of the pressing pressure at the uth pressing as S _2. However, the texture is evaluated based on the occlusal force feature, the tongue pressure feature, the tongue pressure distribution first feature, and the tongue pressure distribution second feature. .. Therefore, the bite force data and the tongue pressure data obtained by pressing the sample to be measured a plurality of times can be converted into a plurality of feature quantities that are easy to evaluate the texture, and the texture can be estimated based on these feature quantities. It is possible to make an evaluation that is closer to the actual texture.

Further, the system 10 of this embodiment has a driving means 31 that simultaneously performs pressing by the first artificial teeth 17, 18, 19 and the second artificial teeth 27, 28, 29 and pressing by the artificial tongue 33. There is. Therefore, by simultaneously performing the pressing by the first artificial teeth 17, 18, 19 and the second artificial teeth 27, 28, 29 and the pressing by the artificial tongue 33, measurement data close to the actual masticatory movement in the oral cavity can be obtained. be able to.

Further, in the system 10 in this embodiment, as shown in FIG. 2, the first artificial teeth 17, 18, 19 have their bases 17a, 18a, 19a held via the elastic member 23, and the elasticity thereof. It is configured to transmit pressure to the pressure sensor 13 via the member 23. Therefore, the pressing force can be measured as a value close to the pressing force applied to the actual gums.

Further, in the system 10 of this embodiment, a plurality of first artificial teeth 17, 18, 19 and second artificial teeth 27, 28, 29 are arranged in an arc shape and arranged to face each other, and the artificial tongue 33 is , A plurality of second artificial teeth 27, 28, 29 are arranged inside an arcuate array. Therefore, by bringing the first artificial teeth 17, 18, 19 and the second artificial teeth 27, 28, 29, and the artificial tongue 33 closer to the arrangement of the teeth and the arrangement of the tongue in the oral cavity, the pressing close to the actual texture. You can get the data.

(Texture evaluation method)
Next, the texture evaluation method according to the present invention will be described. In the texture evaluation method in this embodiment, an uncrushed sample to be measured and a crushed sample to be measured are prepared, each sample is measured by the texture evaluation system 10, and the occlusal force data and tongue of each sample are measured. The pressure data is obtained, and the texture is evaluated based on the occlusal force data and the tongue pressure data of each sample. Further, in this embodiment, the sample to be crushed is one type or two or more types corresponding to a predetermined number of times of chewing.

(Sensory evaluation and selection of samples to be estimated)
For each of the six types of donuts as a model sample, the texture sensory evaluation value is acquired by the procedure described in "(1) Texture sensory evaluation value acquisition step" described above. The results are shown in Tables 2-4 below. Table 2 shows the results by the DA method, Table 3 shows the results by the TDS method, and Table 4 shows the results by the TI method.

Among the results in Tables 2 to 4 above, when the standard deviation of the texture sensory evaluation value is extremely small, it is meaningless to create a texture estimation formula based on the measurement data. That is, the texture estimation formula is derived only for the texture evaluation items in which the standard deviation of the texture sensory evaluation value is equal to or more than a predetermined value. Here, in the DA method, evaluation items having a standard deviation of the distribution of texture sensory evaluation values of 0.1 or more are targeted for estimation, and all evaluation items (crispy, fluffy feeling, sticky feeling, melting in the mouth) are estimated. Further, in the TDS method, evaluation items having a standard deviation of the distribution of the texture sensory evaluation value of 10 or more are estimated, and the “crispy feeling” and “hollow feeling” of the loaf state I and the “sticky feeling” of the loaf state II. Estimate the "feeling" and the "stickiness" of the loaf state III. Furthermore, in the TI method, evaluation items having a standard deviation of the distribution of texture sensory evaluation values of 0.1 or more are estimated, and the “crispy feeling” of the loaf state I and the “crispy feeling” and “crispy feeling” of the loaf state II are used. Estimate the "stickiness" and the "stickiness" of the loaf state III.

(Calculation of features and derivation of texture estimation formula)
First, for each model sample, the bite force data and the tongue pressure data are measured by the procedure described in "(2) Bite force data and tongue pressure data measurement step" described above. After that, the feature amount is calculated based on the above formulas 6 to 15 by the procedure described in the above-mentioned "(3) Texture estimation formula derivation step", and then the texture estimation formula shown in the above formulas 16 and 17 is derived. To do. The results are shown in Tables 5 to 7 below. Table 5 shows the results by the DA method, Table 6 shows the results by the TDS method, and Table 7 shows the results by the TI method. In addition, in each Table 5-7, the constant term and the partial regression coefficient in the texture estimation formula are described. Further, the blank indicates a _l ^j = 0. For the sample to be measured, the feature amount is calculated after measuring the bite force data and the tongue pressure data in the same procedure as the model sample.

Based on the results in Tables 5 to 7 above and the formulas 16 and 17 above, a texture estimation formula for each texture evaluation item in each evaluation method is derived. For example, the texture estimation formula for "crispy texture" by the DA method is as shown in the following formula 18.

(Estimation of texture using texture estimation formula)
The feature amount calculated for each model sample was input to the texture estimation formula of each texture evaluation item in each evaluation method derived as in the above formula 18, and the texture estimation value of each model sample was calculated. .. Then, each texture estimated value was compared with the texture sensory evaluation value. The results are shown in FIGS. 9-11. FIG. 9 shows the result by the DA method, FIG. 10 shows the result by the TDS method, and FIG. 11 shows the result by the TI method. Further, in each graph of FIGS. 9 to 11, the horizontal axis is the texture sensory evaluation value, and the vertical axis is the texture estimated value. The results of the coefficient of determination R ² for comparing the estimation accuracy are also shown in each graph. Generally, if the value of the coefficient of determination R ² is larger than 0.49, it is considered that the estimated texture value fits well with the texture sensory evaluation value. Then, for the sample to be measured for which the texture is to be estimated, the texture can be obtained by introducing the feature amount obtained from the bite force data and the tongue pressure data of the sample to be measured into the texture estimation formula derived for the model sample. Can be estimated.

(Estimation of texture by comparative example)
In order to compare the accuracy of texture estimation based on both bite force data and tongue pressure data according to the present invention, texture estimation is performed using only bite force data for each model sample (Comparative Example 1), and tongue pressure data. The texture was estimated only by using only (Comparative Example 2). That is, in Comparative Example 1, a texture estimation formula was created using only the feature amount related to bite force (subscripts 1 to 3), and in Comparative Example 2, the feature amount related to tongue pressure (subscript 4). A texture estimation formula is created using only 10), and the texture is estimated for each. The result is shown in FIG. FIG. 12 (a) is a bar graph of the coefficient of determination in the DA method, FIG. 12 (b) is a bar graph of the coefficient of determination in the TDS method, and FIG. 12 (c) is a determination in the DA method. A bar graph of the coefficients is shown, and each graph also shows the coefficient of determination based on both the bite force data and the tongue pressure data.

(Accuracy of texture estimate)
The accuracy of each texture estimate of each model sample was examined. This estimation accuracy is evaluated by the coefficient of determination R ² between the sensory evaluation value and the estimated value.

The four texture evaluation items by the DA method shown in FIGS. 9 (a) to 9 (d) can be estimated with an accuracy of R ² ≥ 0.75, and the texture sensation of the texture estimated value by the texture estimation formula. It can be seen that the fit to the evaluation value is good. In addition, as shown in Table 5, for all texture estimation formulas of each texture evaluation item in the DA method, feature quantities based on bite force data and tongue pressure data (subscripts 1 to 3 and 4 to 10, respectively). )It is included. This result means that both tooth and tongue functions are utilized. Further, as shown in FIG. 12A, the coefficient of determination when both feature amounts are used is the highest value for all four types of texture evaluation items, and both artificial teeth and artificial tongues are included. The effectiveness of the present invention can be confirmed. Furthermore, as shown in Table 5, in the estimation formulas of all four types of texture evaluation items, the feature amounts in the loaf states I, II, and III (the superscripts are I, II, and III, respectively) are included. This means that the bolus states I, II, and III are utilized.

On the other hand, in the TDS method and the TI method, as shown in FIGS. 10 (a), 10 (b) and 11 (a), the texture estimation in the bolus state I can be estimated with an accuracy of R ² ≥ 0.70. ing. On the other hand, as shown in FIGS. 10 (c), 11 (b), and 11 (c), the texture estimation in the loaf states II and III has an accuracy of R ² <0.60. Therefore, it is probable that an appropriate feature amount for evaluating the characteristics of the semi-solid loaf states II and III containing water was not set. This can be seen from the fact that, as shown in Tables 6 and 7, the feature amounts included in the estimation formula, that is, the effective feature amounts are as small as 2 to 4. However, as shown in FIGS. 12 (b) and 12 (c), in the texture estimation results by the TDS method and the TI method, both the bite force data and the tongue pressure data feature amounts are used for all texture evaluation items. The coefficient of determination when used is the highest value. That is, the effectiveness of the present invention having both an artificial tooth and an artificial tongue can be confirmed.

(Verification of estimation accuracy by LOOCV)
In order to confirm the validity of the method for producing the texture estimation formula, texture estimation using well-known Leave-one-out cross-validation (LOOCV) was also performed. The procedure for calculating the estimated value is as follows. That is, assuming that all the data (set of measurement data and texture sensory evaluation value) are N, one data is removed as unknown loaf data. An estimation formula is created using the remaining N-1 data, and the estimated value of the texture sensory evaluation value is calculated for the removed one data. This work is repeated for each of all N data. The texture sensory evaluation value was obtained by the DA method.

Further, in FIG. 14, for each model sample, the texture was estimated using both the bite force data and the tongue pressure data (Example), and the texture was estimated using only the bite force data (Comparative Example 1). ), A bar graph comparing the determination coefficients of the texture estimation performed only by the tongue pressure data (Comparative Example 2) is shown. Note that FIG. 14 is a bar graph showing the coefficient of determination in the DA method. As shown in FIG. 14, the determination coefficient in the example in which the texture was estimated using both the bite force data and the tongue pressure data is only the comparative example 1 in which the texture was estimated using only the bite force data and the tongue pressure data. It was larger than Comparative Example 2 in which the texture was estimated in, and the validity of the texture estimation formula in the present invention was confirmed.

(Consideration of features that contribute to the texture estimation formula)
The features that contribute to the texture estimation formula of "mouth melting" in the DA method shown in Table 5 will be considered. In the bolus state I, a ₇ ^I <0 corresponds to the smaller the initial tongue pressure area, the larger the “melting in the mouth”. In addition, a ₁₀ ^I <0 corresponds to the fact that as the mastication progresses, the smaller the standard deviation of the tongue pressure distribution, the greater the “melting in the mouth”. Further, in the bolus state II, a ₁ ^II > 0 corresponds to the larger the value of the initial bite force, the larger the “melting in the mouth”. In addition, a ₆ ^II <0 corresponds to the fact that the “melting in the mouth” increases as the tongue strength decreases as the mastication progresses. In the bolus state III, a ₃ ^III <0 and a ₈ ^III <0 correspond to an increase in “melting in the mouth” as the bite force and tongue pressure area decrease as mastication progresses. The above properties can be summarized as "the more difficult it is to break in the early stage of mastication and the more chewy it is, and the less crunchy and texture it feels in the latter stage of mastication, the greater the estimated value of" melting in the mouth "." This is intuitively consistent with the factors that humans evaluate for "melting in the mouth." As described above, it may be a tool for physically elucidating what kind of phenomenon occurs in the oral cavity and what is perceived by humans in the texture estimation and the target texture evaluation item according to the present invention.

FIGS. 15 to 19 show other embodiments of the food evaluation system and texture evaluation method according to the present invention. The same parts as those in the above embodiment are designated by the same reference numerals, and the description thereof will be omitted.

As shown in FIG. 15, in the food evaluation system 10A of this embodiment (hereinafter, also simply referred to as “system 10A”), the annular support portion 15A (which forms the “support portion” in the present invention) forms a substantially square ring. There is. That is, the annular support portion 15A is composed of a pair of side walls 35, 35 arranged parallel to each other and a pair of connecting walls 36, 37 connecting the ends of the pair of side walls 35, 35. A substantially rectangular elongated hole 16 is formed by the wall of the above. It should be noted that one connecting wall 36 is formed wider than the other connecting wall 37.

A plurality of first artificial teeth 17, 18, and 19 are arranged in a straight line on the wide connecting wall 36. As shown in FIG. 16, a plurality of through holes 21 are formed in the connecting wall 36 at predetermined intervals along the longitudinal direction thereof, and a flat plate-shaped hard member 38 is formed at the bottom of each through hole 21. Are placed in contact with the upper surface of the pressure sensor 13. Further, the hard member 38 is fixed to and integrated with the bottom surfaces of the bases 17a, 18a, 19a of the first artificial teeth 17, 18, 19. Further, the hard member 38 is formed to be one size larger than the outer diameter of the bases 17a, 18a, 19a of the first artificial teeth 17, 18, 19 so as to be held at the bottom of each through hole 21. (See FIG. 16).

Further, the hard member 38 is made of a material that is as hard as or harder than the first artificial teeth 17, 18, 19. Further, the base portions 17a, 18a, 19a of the first artificial teeth 17, 18, 19 are inserted and held in the through holes 21 via the hard member 38, respectively. That is, in this embodiment, the hard member 38 is interposed between the first artificial teeth 17, 18, 19 and the pressure sensor 13. Therefore, when a pressing force acts on the first artificial teeth 17, 18 and 19, the pressing force is detected by the pressure sensor 13 via the hard member 38.

Further, the artificial tongue 33A arranged to face the pressure sensor 13 has a substantially rectangular shape that fits the elongated hole 16 formed in the annular support portion 15A, and can be inserted into and detached from the elongated hole 16. In the pressing portion 25 arranged to face the annular support portion 15A, a plurality of second artificial teeth 27, 28, 29 are located at positions consistent with the first artificial teeth 17, 18, 19 on the annular support portion 15A side. In addition to being provided, the artificial tongue 33A is arranged. Further, also in this system 10A, similarly to the system 10, a plurality of second artificial teeth 27, 28, 29 are provided with respect to the tip portions 17b, 18b, 19b of the plurality of first artificial teeth 17, 18, 19. The artificial tongue 33A is moved away from the pressure sensor 13 at the same time.

Further, in the texture evaluation system of this embodiment as well as the texture evaluation system of the above-described embodiment, even if the texture is conventionally considered to be difficult to estimate, such as a sticky texture or a melting in the mouth. , It is possible to evaluate the texture closer to the actual texture.

Further, in the system 10A of this embodiment, the first artificial teeth 17, 18 and 19 are supported by the base portions 17a, 18a and 19a by the support portion (here, the annular support portion 15A) mounted on the pressure sensor 13. (See FIG. 16), the pressure applied to the first artificial teeth 17, 18, 19 is configured to be transmitted to the pressure sensor 13 via the bases 17a, 18a, 19a. Therefore, the pressure applied to the first artificial teeth 17a, 18a, 19a is transmitted to the pressure sensor 13 via the bases 17a, 18a, 19a, so that the loss of pressure transmitted to the pressure sensor 13 can be reduced. ..

Further, in this embodiment, the first artificial teeth 17, 18, 19 have their bases 17a, 18a, 19a held via the hard member 38, respectively, and the pressure sensor 13 is held via the hard member 38. It is configured to transmit pressure. Therefore, the pressing force applied to the first artificial teeth 17a, 18a, 19a is transmitted to the pressure sensor 13 via the hard hard member 38, so that the pressure from the first artificial teeth 17a, 18a, 19a is lost. With less, it can be efficiently transmitted to the pressure sensor 13.

FIG. 18 shows the measurement results of the bite force and the tongue force when the two types of donuts G and H are measured by the texture evaluation system 10A of the above embodiment. The donuts G and H were cut into 8 g each and used as donuts equivalent to the 0th chewing. In addition, in order to reproduce the bolus in a state where mastication has progressed, donut G was cut into 32 g, divided into four, and 4.8 mL of water was added to blend them, and a 7-speed blender manufactured by Trio Science was used. Using this, a crushing treatment was carried out at 1st speed for 2.4 seconds, and 8 g of each bolus was prepared in a state where mastication had progressed. It was confirmed by three evaluators that this bolus was of the same quality as the state of donut G chewed by humans after 15 chews.

On the other hand, the donut H was cut into 32 g, sliced into 5 mm widths, and further divided into halves from the vertical direction toward the rings. Furthermore, 4.8 mL of water is added and blended, and a 7-speed blender manufactured by Trio Science is used to crush the food at 1st speed for 1.8 seconds, and 8 g of the food is chewed in batches. Created a lump. It was confirmed by three evaluators that this bolus was of the same quality as the state of donut H chewed by humans after 15 chews.

18 (a) and 18 (b) are data when the bolus corresponding to the 0th time and the 15th time of chewing in the donut G was measured by using the texture measurement system 10A, and (a). ) Is a graph showing the relationship between the bite force and time, and (b) is a graph showing the relationship between the tongue force and time.

19 (a) and 19 (b) are data when the bolus corresponding to the 0th time and the 15th time of chewing in the donut H was measured by using the texture measurement system 10A, and (a). ) Is a graph showing the relationship between the bite force and time, and (b) is a graph showing the relationship between the tongue force and time.

Comparing these data, the bite force and tongue force detected by the type of donut (G, H) are different, and the difference depending on the state of the bolus can be objectively evaluated from the measurement data. did it.

The present invention is not limited to the above-described embodiments, and various modified embodiments are possible within the scope of the gist of the present invention, and such embodiments are also included in the scope of the present invention. ..

10,10A Texture evaluation system (system)
11 Sensor support 13 Pressure distribution sensor (pressure sensor)
13a Cell 15, 15A Circular support 16 Long holes 17, 18, 19 First artificial teeth 17a, 18a, 19a Base 17b, 18b, 19b Tip 21 Through hole 23 Elastic member 25 Pressing part 25a Rod 27, 28, 29 2 Artificial tooth 31 Driving means 33, 33A Artificial tongue 35 Side wall 36, 37 Connecting wall 38 Hard member 40 Processing device 50 Texture evaluation means 51 Data storage unit 53 Feature amount calculation unit 55 Texture estimation formula derivation unit

Claims (9)

A pressure distribution sensor that measures changes in pressure distribution over time,
A first artificial tooth arranged on the pressure distribution sensor and capable of transmitting a pressing force to the pressure distribution sensor,
A second artificial tooth that is arranged to face the tip of the first artificial tooth and is arranged so as to be close to and separated from the first artificial tooth.
An artificial tongue that is placed facing the pressure distribution sensor and is placed so that it can be separated from the pressure distribution sensor.
When the sample to be measured is pressed a plurality of times between the first artificial tooth and the second artificial tooth via a driving means, the pressure distribution changes over time of the pressing force applied to the first artificial tooth. The occlusal force data measured by the sensor and the change over time in the pressing force applied to the artificial tongue when the sample to be measured is pressed a plurality of times between the artificial tongue and the pressure distribution sensor. A texture evaluation system including a texture evaluation means for evaluating a texture based on tongue pressure data measured by the pressure distribution sensor. The texture evaluation means is based on the texture sensory evaluation value of the model sample whose texture has been evaluated, and the texture estimation formula derived from the occlusal force data and the tongue pressure data of the model sample. The texture evaluation system according to claim 1, wherein the texture of the sample to be measured is evaluated by using the bite force data and the tongue pressure data of the sample to be measured. In the texture evaluation means, the maximum value of the pressing force applied during the first pressing by the first artificial tooth and the second artificial tooth is f _B1 , and the maximum value of the pressing force applied during the u-th pressing is f _Bu. Calculate the force feature and
The feature amount of tongue pressure was calculated by setting the maximum value of the pressing force applied during the first pressing of the artificial tongue as f _T1 and the maximum value of the pressing force applied during the u-th pressing as f _Tu .
The cell area of the pressure distribution sensor at the time when the maximum value of the pressing force is detected at the time of the first pressing of the artificial tongue is A ₁ , and the pressure distribution at the time of detecting the maximum value of the pressing force at the time of the u-th pressing. the cell area of the sensor as a _u, calculates the first feature quantity of tongue pressure distribution,
The pressure distribution sensor standard deviation of the pressure of time when the pressure distribution sensor during the first pressing of the artificial tongue detects a maximum value of the pressing force during the pressing of the S _1, u th detects a maximum value of the pressing force The second feature of the tongue pressure distribution was calculated with the standard deviation of the pressure at the time point as S ₂ .
Claim 1 which evaluates the texture based on the characteristic amount of the bite force, the characteristic amount of the tongue pressure, the first characteristic amount of the tongue pressure distribution, and the second characteristic amount of the tongue pressure distribution. Or the texture evaluation system according to 2. The texture evaluation system according to any one of claims 1 to 3, further comprising a driving means for simultaneously performing pressing by the first artificial tooth and the second artificial tooth and pressing by the artificial tongue. .. Any of claims 1 to 4, wherein the base of the first artificial tooth is held via an elastic member, and the pressure is transmitted to the pressure distribution sensor via the elastic member. The texture evaluation system described in item 1. The base of the first artificial tooth is supported by a support portion mounted on the pressure sensor, and the pressure applied to the first artificial tooth is transmitted to the pressure distribution sensor via the base. The texture evaluation system according to any one of claims 1 to 4, which is configured as described above. A plurality of the first artificial tooth and the second artificial tooth are arranged in an arc shape and arranged to face each other, and the artificial tongue is inside the arc-shaped arrangement of the second artificial tooth. The texture evaluation system according to any one of claims 1 to 6, which is arranged. Using the texture evaluation system according to any one of claims 1 to 7,
Prepare an uncrushed sample to be measured and a crushed sample to be measured.
Each sample was measured by the texture evaluation system to obtain the bite force data and the tongue pressure data of each sample.
A texture evaluation method characterized in that texture evaluation is performed based on the bite force data and the tongue pressure data of each sample. The texture evaluation method according to claim 8, wherein the crushed sample to be measured is one type or two or more types corresponding to a predetermined number of times of chewing.