Estimation method and estimation device for resilience of edible meat

Abstract

To provide an estimation method and an estimation device, capable of accurately estimating resilience felt by a user when masticating edible meat.SOLUTION: An estimation device 1 that estimates resilience of edible meat uses a measurement result during a load measurement test by a pressurizing tester 20 to acquire a load-time curve L1 using a load F and a time t as two axes (STEP 2), acquires an area ratio Ra as a ratio of a first area S1 to a second area S2 (STEP 3), and uses a regression equation (1) indicating a correlation between the area ratio Ra and the resilience of the edible meat and the area ratio Ra to calculate an estimation value El_est of the resilience of the edible meat (STEP 4).SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present invention relates to a method and device for estimating the elasticity of meat. [Background technology]

[0002] A conventional method for evaluating the texture of meat is described in Patent Document 1. In this evaluation method, heat-treated beef round meat is compressed while the plunger of a compression tester is moved at a constant speed, and the elastic modulus and elastic limit strain are measured. A two-axis graph is then created, with the elastic modulus and elastic limit strain on the respective axes. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 6917598 Summary of the Invention [Problem to be solved by the invention]

[0004] According to the above-mentioned conventional method for evaluating the texture of edible meat, although it is possible to obtain the relationship between the elastic modulus and elastic limit strain of edible meat, there is a problem in that it is not possible to estimate the elasticity that a user feels when chewing the edible meat.

[0005] The present invention has been made to solve the above-mentioned problems, and aims to provide a method and device for estimating the elasticity of edible meat that can accurately estimate the elasticity that a user feels when chewing edible meat. [Means for solving the problem]

[0006] In order to achieve the above object, the invention of claim 1 is a method for estimating the elasticity of edible meat, in which the elasticity of the meat is estimated by a calculation processing device using the results of measurement made over time by a measuring device of the load acting on the edible meat when the meat that has been processed to a edible state is pressed by a pressing tester, the method comprising the steps of: a curve acquisition step of acquiring a load-time curve with load and time as the two axes using the results of measurement of the load when a pressing operation in which the meat is pressed at a constant speed to a predetermined strain rate by a pressing member of the pressing tester and then the pressure is released is repeated twice; an area ratio acquisition step of acquiring an area ratio which is the ratio between a first area, which is the area between the load-time curve and the time axis during the first pressing operation, and a second area, which is the area between the load-time curve and the time axis during the second pressing operation; and an estimation step of estimating the elasticity of the meat using a model and area ratio which represent the correlation between the area ratio and the elasticity of the edible meat.

[0007] As will be described later, the applicant conducted a test in which meat that had been processed to a state where it was ready for consumption was pressed at a constant speed to a predetermined strain rate using a pressing member of a pressing tester, and then the pressure was released, repeating this pressing operation twice, and using the load measurements obtained during this test to obtain a load-time curve with load and time as the two axes. Furthermore, the applicant obtained an area ratio, which is the ratio between the first area, which is the area between the load-time curve and the time axis during the first pressing operation, and the second area, which is the area between the load-time curve and the time axis during the second pressing operation.

[0008] The applicant has confirmed that there is a high correlation between this area ratio and the elasticity felt by a user when chewing meat. Therefore, according to this method for estimating the elasticity of meat, the elasticity of meat can be accurately estimated by using a model that expresses the correlation between the area ratio and the elasticity of meat and the area ratio. Note that "meat" in this specification is not limited to animal meat, but also includes cultured meat, meat substitutes, processed meat, etc.

[0009] In the present invention, the model is preferably a linear function with the elasticity of the meat as the dependent variable and the area ratio as the independent variable.

[0010] As will be described later, the applicant used a regression analysis technique to derive a linear function with meat elasticity as the dependent variable and area ratio as the independent variable as a model that represents the high correlation between area ratio and meat elasticity.The applicant then confirmed through testing that this linear function has high estimation accuracy.Therefore, according to this method for estimating meat elasticity, by using such a linear function, it is possible to accurately estimate meat elasticity.

[0011] In order to achieve the above-mentioned object, the invention of claim 3 provides a method for estimating the elasticity of edible meat by a calculation processing device, using the results of measurements made over time by a measuring device of the load acting on edible meat when the meat that has been processed to a edible state is pressed by a press tester, the method comprising the steps of: a first curve acquisition step of acquiring a first load-time curve with load and time as two axes, using the results of load measurements made when a pressing operation in which the meat is pressed at a constant speed to a predetermined strain rate by a pressing member of a first press tester and then the pressure is released is repeated twice; the area ratio acquisition step of acquiring an area ratio, which is the ratio of the first load-time curve to a second area, which is the area between the first load-time curve and the time axis during the pressing operation; a second curve acquisition step of acquiring a second load-time curve, which has load and time as its two axes, using the load measurement results when the edible meat is broken at a constant speed by the breaking member of the second pressing tester; a peak score acquisition step of acquiring the peak score, which is the number of peak points on the second load-time curve that are convex on the side of increasing load after the load reaches its maximum value; and an estimation step of estimating the elasticity of the edible meat using the area ratio and peak score, a model that represents the correlation between the area ratio, peak score, and elasticity of the edible meat.

[0012] As will be described later, the applicant conducted a test in which meat was pressed twice with a pressing member of a first pressing tester at a constant speed to a predetermined strain rate and then the pressure was released, and using the load measurements obtained when this was done, a first load-time curve with load and time as its two axes was obtained, and an area ratio was obtained between a first area, which was the area between the first load-time curve and the time axis during the first pressing operation, and a second area, which was the area between the first load-time curve and the time axis during the second pressing operation.Furthermore, the applicant used the load measurements obtained when meat was broken at a constant speed with the breaking member of a second pressing tester to obtain a second load-time curve with load and time as its two axes, and obtained the number of peaks, which was the number of peaks in the second load-time curve that appeared on the increasing load side after the load reached its maximum value.

[0013] The applicant has confirmed that there is a high correlation between these area ratios and peak scores and the elasticity that a user feels when chewing meat. Therefore, according to this method for estimating the elasticity of meat, the elasticity of meat can be accurately estimated by using a model that expresses the correlation between such area ratios, peak scores and meat elasticity, as well as the area ratios and peak scores.

[0014] In the present invention, the model is preferably a two-variable linear function with the elasticity of the meat as the dependent variable and the area ratio and the number of peak points as independent variables.

[0015] As will be described later, the applicant used a linear regression technique to derive a two-variable linear function in which meat elasticity is the dependent variable and area ratio and peak score are the independent variables as a model representing the correlation between area ratio, peak score, and meat elasticity. The applicant then confirmed through testing that this two-variable linear function has high estimation accuracy. Therefore, according to this method for estimating meat elasticity, by using such a two-variable linear function, it is possible to accurately estimate meat elasticity.

[0016] In order to achieve the above-mentioned object, the invention of claim 5 is a meat elasticity estimation device that estimates the elasticity of meat using the results of measurement made over time by a measuring device of the load acting on edible meat when meat that has been processed to a edible state is pressed by a pressing tester, the device being characterized by comprising: a curve acquisition unit that acquires a load-time curve with load and time as the two axes using the results of load measurement made when a pressing operation in which the meat is pressed at a constant speed to a predetermined strain rate by a pressing member of the pressing tester and then the pressure is released is repeated twice; an area ratio acquisition unit that acquires an area ratio that is the ratio between a first area, which is the area between the load-time curve and the time axis during the first pressing operation, and a second area, which is the area between the load-time curve and the time axis during the second pressing operation; and an estimation unit that estimates the elasticity of the meat using a model that represents the correlation between the area ratio and the elasticity of the meat and the area ratio.

[0017] In order to achieve the above-mentioned object, the invention of claim 6 provides a meat elasticity estimation device that estimates the elasticity of meat using the results of measurements made over time by a measuring device of the load acting on edible meat when the meat that has been processed to a edible state is pressed by a press tester, the device comprising: a first curve acquisition unit that acquires a first load-time curve with load and time as two axes using the results of load measurements made when a pressing operation in which the meat is pressed at a constant speed to a predetermined strain rate by a pressing member of a first press tester and then the pressure is released is repeated twice; an area ratio acquisition unit that acquires an area ratio, which is the ratio of the first load-time curve to a second area, which is the area between the first load-time curve and the time axis during the pressing operation; a second curve acquisition unit that acquires a second load-time curve, with load and time as the two axes, using the load measurement results when the edible meat is broken at a constant speed by the breaking member of the second pressing tester; a peak score acquisition unit that acquires peak scores, which are the number of peak points on the second load-time curve that convex on the side of increasing load after the load reaches its maximum value; and an estimation unit that estimates the elasticity of the edible meat using a model that represents the correlation between the area ratio, peak scores, and the elasticity of the edible meat, as well as the area ratio and peak scores. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a diagram showing the specific configuration and functional configuration of a meat elasticity estimation device according to one embodiment of the present invention. FIG. [Figure 2] FIG. 1 is a perspective view showing meat that has been processed to an edible state. [Figure 3] FIG. 1 is a perspective view showing a pressing test machine. [Figure 4] FIG. 10 is a perspective view showing a state in which the plunger starts to press the meat during a load measurement test. [Figure 5A] FIG. 10 is a diagram showing the state when pressure on meat begins during a load measurement test. [Figure 5B] FIG. 10 is a diagram showing the state in which meat is compressed to a predetermined strain rate during a load measurement test. [Figure 5C] FIG. 10 is a diagram showing the state in which the pressure on the meat is released during the load measurement test. [Figure 6] FIG. 1 is a diagram showing a load-time curve. [Figure 7] FIG. 10 is a diagram showing the relationship between area ratio and elasticity. [Figure 8] 10 is a flowchart showing an elasticity estimation process. [Figure 9] FIG. 10 is a block diagram showing the functional configuration of an estimation device according to a second embodiment. [Figure 10] FIG. 10 is a perspective view showing the state in which the plunger starts to break meat during a break measurement test. [Figure 11] FIG. 10 is a diagram showing a second load-time curve. [Figure 12] 10 is a flowchart showing an elasticity estimation process according to the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0019] A method and device for estimating the elasticity of meat according to a first embodiment of the present invention will now be described with reference to the drawings. The device of this embodiment estimates the elasticity of meat using the estimation method described below.

[0020] As shown in Fig. 1, the estimation device 1 of this embodiment is a personal computer type, and includes a display 1a, a device main body 1b, and an input interface 1c. The device main body 1b includes a storage device such as an HDD, a processor, and memory (RAM, ROM, etc.) (none of which are shown). In this embodiment, the estimation device 1 corresponds to an arithmetic processing device.

[0021] The storage of the device main body 1b has installed therein application software for executing the elasticity estimation process described below, and also stores a model (regression equation) for estimating the elasticity of meat. The method for deriving this model will be described later.

[0022] The input interface 1c is configured with a keyboard, a mouse, and the like for operating the estimation device 1, and elasticity estimation processing, which will be described later, is executed by operating the input interface 1c by a user (not shown).

[0023] 1, the estimation device 1 of this embodiment has functions as a curve acquisition unit 10, an area ratio acquisition unit 11, and an estimation unit 12. The curve acquisition unit 10 acquires a load-time curve L1 by a method described later, and the area ratio acquisition unit 11 acquires an area ratio Ra by a method described later. Furthermore, the estimation unit 12 calculates an estimated value El_est of the elasticity of the meat by a method described later.

[0024] Next, the method and principles of deriving a model for estimating the elasticity of meat in this embodiment will be explained. In this case, eight types of meat are used as samples: beef shoulder, beef thigh, pork belly, pork tenderloin, pork loin, chicken thigh, chicken breast, and chicken fillet.

[0025] Each of these eight types of meat is cut into a predetermined size and shape (for example, 2 cm x 2 cm x 2 cm cubes) and multiple pieces (for example, three pieces) are prepared. All of the meat is then heated over medium heat using an induction heater and cooked in a frying pan for about eight minutes until cooked through, thereby creating eight types of edible meat for sampling m that have been processed to an edible state, as shown in Figure 2. In the following explanation, the eight types of edible meat for sampling m that have been processed to an edible state will be collectively referred to as "sample meat m" where appropriate.

[0026] Furthermore, as described below, a load measurement test is carried out using a press tester 20 shown in Figure 3, whereby changes in load when pressing the sample meat m are measured over time. A texture analyzer can be used as this press tester 20, such as a Texture Analyzer (product name: EZ-SX) manufactured by Shimadzu Corporation. This press tester 20 includes a plunger 21, a sample stage 22, and a load cell (not shown).

[0027] The plunger 21 can be a cylindrical metal member having a predetermined diameter (for example, 5 mm), which is driven by the pressing tester 20 to move up and down at a constant speed, and during a load measurement test, presses the sample meat m on the sample stage 22 from above, as shown in Fig. 4. In this embodiment, the plunger 21 corresponds to the pressing member.

[0028] Furthermore, the pressing tester 20 is configured to be able to switch between a number of different load cells as a measuring device, and in the load measurement test of this embodiment, a load cell with a rated capacity of 50 N is used.

[0029] In this load measurement test, plunger 21 is driven to move at a predetermined downward speed (e.g., 1 mm / sec), and as a result, as shown in Fig. 5A, plunger 21 begins to press against sample meat m on sample stage 22. Then, due to the pressure of plunger 21, sample meat m is compressed to a predetermined strain rate (e.g., a strain rate of 50%) (see Fig. 5B).

[0030] In this case, the distortion rate (%) is determined by the initial thickness of the meat sample m and the distance the plunger 21 presses down on the meat sample m after contacting the top surface of the meat sample m. That is, the distortion rate (%) is calculated as follows: (distance the plunger 21 presses down on the meat sample m / initial thickness of the meat sample) × 100. Then, the plunger 21 moves upward, thereby releasing the pressure load acting on the meat sample m (see FIG. 5C).

[0031] The above operation is counted as one pressing operation, and this pressing operation is performed twice in succession. The lower limit position to which the meat is lowered the second time is set to the same as the first time. The load F during this time is measured over time with a load cell, and a load-time curve L1 is obtained, which shows the relationship between the load F acting on the meat sample m and time t, as shown in Figure 6.

[0032] 6, during the load measurement test, the load F increases from time t1 when the first pressing operation starts, and the sample meat m is compressed to a predetermined strain rate at time t2. As a result, after time t2, as the plunger 21 moves upward, the load F decreases and reaches 0 at time t3, ending the first pressing operation.

[0033] The load F increases from time t4 when the second pressing operation starts, and the sample meat m is compressed to the predetermined strain rate at time t5. As a result, as the plunger 21 moves upward after time t5, the load F decreases and becomes 0 at time t6, and the second pressing operation ends.

[0034] When the load measurement test is carried out as described above, if the area between the load-time curve L1 and the time axis from time t1 to time t3 (the area of ​​the hatched region in Figure 6) is defined as the first area S1, this first area S1 represents the energy applied to the sample meat m during the first pressing operation.

[0035] Furthermore, if the area between the load-time curve L1 and the time axis from time t4 to time t6 (the area of ​​the region shown by dotted lines in FIG. 6) is taken as the second area S2, this second area S2 represents the energy applied to the sample meat m during the second pressing operation. Therefore, if the ratio S1 / S2 of the first area S1 to the second area S2 is taken as the area ratio Ra, the area ratio Ra represents the cohesiveness of the sample meat m.

[0036] After the above load measurement test is performed a predetermined number of times (e.g., five times) for each type of meat sample m, the area ratio Ra for the eight types of meat sample m is obtained as the average value of the results of the predetermined number of load measurement tests. Separately from the load measurement tests, a sensory evaluation value El for the elasticity of the meat sample m is obtained using the VAS (visual analog scale) method. Specifically, the sensory evaluation value El for elasticity is obtained as the average value when a predetermined number of subjects (e.g., several tens of subjects) actually chew the meat sample m and evaluate the elasticity within a range of 1 to 10.

[0037] The relationship between the area ratio Ra obtained under the conditions described above and the sensory evaluation value El of the elasticity of the meat sample m was plotted as shown in Figure 7. As is clear from Figure 7, there is a high correlation between the area ratio Ra and the sensory evaluation value El of the elasticity of the meat sample m.

[0038] For the above reasons, the relationship between the area ratio Ra and the sensory evaluation value El of the elasticity of the meat sample m is subjected to regression analysis using the partial least squares method, and the following regression equation (1) is derived as a model. This regression equation (1) represents the regression line L2 shown in Figure 7.

[0039] El_est=a1·Ra+b1 (1)

[0040] In this regression equation (1), El_est on the left side is the estimated value of the elasticity of the sample meat m. Furthermore, the regression coefficients a1 and b1 on the right side are a1 = 22.44 and b1 = -7.29 in the example of the regression line L2 shown in Figure 7.

[0041] The coefficient of determination R is calculated using the estimated value El_est of the elasticity of the sample meat m calculated using the regression equation (1) above and the sensory evaluation value El of the elasticity. 2 When we calculated the value, R 2 = 0.71. Therefore, it can be seen that the elasticity of the sample meat m can be estimated with high accuracy by using the above regression equation (1).

[0042] Next, the elasticity estimation process performed by the estimation device 1 of this embodiment will be described with reference to Figure 8. This process estimates the elasticity of heat-treated meat as described above, and more specifically, calculates an estimated value El_est of the elasticity of the meat using the regression equation (1) described above.

[0043] As shown in Fig. 8, first, a data reading process is executed (Fig. 8 / STEP 1). In this data reading process, measurement data obtained when a load measurement test is carried out on the meat for which elasticity is to be estimated, as described above, is read.

[0044] In this case, the measurement data may be data input to the estimation device 1 by a user operating the input interface 1c, or data transmitted from the pressing tester 20.

[0045] Next, a curve acquisition process is executed (FIG. 8 / STEP 2). In this curve acquisition process, a load-time curve such as that shown in FIG. 7 is acquired from the above measurement data. In this embodiment, this curve acquisition process corresponds to the curve acquisition step.

[0046] Following this curve acquisition process, an area ratio calculation process is executed (FIG. 8 / STEP 3). In this area ratio acquisition process, the area ratio Ra is calculated by the above-mentioned method. In this embodiment, this area ratio calculation process corresponds to the area ratio acquisition step.

[0047] Next, an estimated value calculation process is executed (FIG. 8 / STEP 4). In this estimated value calculation process, an estimated value El_est of the elasticity of the meat is calculated using the regression equation (1) described above. In this embodiment, this estimated value calculation process corresponds to the estimation step.

[0048] As described above, the elasticity estimation method of the first embodiment derived the regression formula (1) based on the finding that there is a high correlation between the area ratio Ra and the elasticity felt when chewing meat. It was then demonstrated that the elasticity of meat can be estimated with high accuracy by using this regression formula (1).

[0049] Although the first embodiment is an example in which regression equation (1) is used as a model, a map showing the relationship between the elasticity of meat and the area ratio Ra may be used instead. Furthermore, although the first embodiment is an example in which partial least squares is used as the regression analysis method, other regression analysis methods such as general least squares, ridge regression, and lasso regression may be used instead.

[0050] Furthermore, although the first embodiment is an example in which eight types of sample meat m were used, it is also possible to increase the number of sample meat m and perform the load measurement test on them more repeatedly, and use the results of the regression analysis to perform the regression analysis. In this case, the accuracy of the estimation of elasticity using regression equation (1) can be further improved.

[0051] Furthermore, the size of the sample meat m is not limited to 2 cm x 2 cm x 2 cm as in the first embodiment, but may be other sizes. In addition, the method for preparing the sample meat m from edible meat is not limited to the grilling method using an IH heater and frying pan as in the first embodiment, but may be a method of grilling edible meat using other tools.

[0052] The plunger 21 used in the load measurement test is not limited to the cylindrical plunger with a diameter of 5 mm as in the first embodiment, but may be of other sizes and shapes. Furthermore, the descending speed and predetermined strain rate when driving the plunger 21 in the load measurement test are not limited to 1 mm / sec and 50% as in the first embodiment, but the plunger 21 may be configured to be driven at other values.

[0053] On the other hand, the first embodiment is an example in which a personal computer type device is used as the estimation device 1 and the arithmetic processing device, but instead, a server or the like may be used as the estimation device 1 and the arithmetic processing device.

[0054] Next, an estimation device 30 according to a second embodiment will be described with reference to Figures 9 to 12. The estimation device 30 of this embodiment is configured as a personal computer type hardware, similar to the estimation device 1 of the first embodiment, and some of its functions are different from those of the estimation device 1 of the first embodiment, so the following description will focus on the differences. In this embodiment, the estimation device 30 corresponds to a calculation processing device.

[0055] 9, the estimation device 30 of this embodiment has functions as a first curve acquisition unit 31, an area ratio acquisition unit 32, a second curve acquisition unit 33, a peak point number acquisition unit 34, and an estimation unit 35. The first curve acquisition unit 31 acquires a first load-time curve by the same method as the curve acquisition unit 10 described above, and the area ratio acquisition unit 32 acquires the area ratio Ra by the same method as the area ratio acquisition unit 11 described above.

[0056] The second curve acquisition unit 33 acquires a second load-time curve L3 using a method described later, and the peak score acquisition unit 34 acquires a peak score Pn using a method described later. Furthermore, the estimation unit 35 calculates an estimated value El_est of the elasticity of the meat using a model described later.

[0057] Next, a method for deriving a model (regression equation) for estimating the elasticity of meat in this embodiment and its principles will be described. In the second embodiment, meat of the same type, number and size as in the first embodiment is used as the meat for sampling, and these meats are subjected to the same heat treatment as in the first embodiment to create eight types of sample meat m.

[0058] Furthermore, by carrying out a load measurement test under the same conditions as in the first embodiment using the above-described pressing tester 20, the change in load when pressing the sample meat m is measured over time. Then, a first load-time curve (not shown) that shows the relationship between the load F acting on the sample meat m and time t, similar to the above-described load-time curve L1, is obtained, and the area ratio Ra is obtained from this first load-time curve.

[0059] Furthermore, a breaking measurement test of the sample meat m is carried out using the pressing tester 20 as described below. In this breaking measurement test, a plunger 23 shown in FIG. 10 is used instead of the plunger 21 described above. This plunger 23 is formed in the shape of a blade with a pointed tip, 7 cm wide and 3 mm thick. A load cell with a rated capacity of 500 N is used. In this embodiment, the plunger 23 corresponds to the breaking member.

[0060] In this fracture measurement test, the plunger 23 presses the surface of the sample meat m to a strain rate of 99% at a descending speed of 1 mm / sec, thereby fractures the sample meat m. Then, by measuring the load F over time during this time, the second load-time curve L3 shown in FIG. 11 is obtained.

[0061] 11, during the fracture measurement test, the load F increases from the start of the pressing operation, and fracture of the sample meat m begins at the timing (time t11) when the load F reaches its maximum value, the fracture load F1. After that, as the fracture of the sample meat m progresses, multiple peak points P, at which the load F reaches its peak, occur.

[0062] In the example of Fig. 11, peak points P occur three times in total, between times t12 and t14. In this embodiment, these peak points P are detected with a detection sensitivity of 0.1% of the load cell capacity (0.5 N in this embodiment). Hereinafter, the number of detected peak points P will be referred to as "peak points Pn."

[0063] The above-mentioned breaking measurement test was carried out five times for each type of meat sample m, and then the peak score Pn for the eight types of meat sample m was obtained as the average value of the five breaking measurement test results. In addition, the sensory evaluation value El of the elasticity of the meat sample m was obtained using the method described above.

[0064] The applicant assumed that there was a high correlation between the area ratio Ra, the peak score Pn, and the sensory evaluation value El of the elasticity of the meat sample m obtained as described above. Therefore, the applicant performed a regression analysis using partial least squares on the relationship between the area ratio Ra, the peak score Pn, and the sensory evaluation value El of the elasticity of the meat sample m, and derived the following regression equation (2) as a model.

[0065] El_est=a2·Ra+b2·Pn+c2 ··· (2)

[0066] In this regression equation (2), the regression coefficients a2, b2, and c2 on the right side were a2=28.34, b2=-0.33, and c2=-9.34 in the example test results of the second embodiment.

[0067] The coefficient of determination R is calculated using the estimated value El_est of the elasticity of the sample meat m calculated using the regression equation (2) above and the sensory evaluation value El of the elasticity. 2 When we calculated the value, R 2 = 0.8. Therefore, it can be seen that by using the above regression formula (2), the elasticity of the sample meat m can be estimated with higher accuracy than when using the above regression formula (1).

[0068] Next, the elasticity estimation process performed by the estimation device 30 of this embodiment will be described with reference to Figure 12. This process estimates the elasticity of heat-treated meat as described above, and more specifically, calculates an estimated value El_est of the elasticity of the meat using the regression equation (2) described above.

[0069] As shown in Fig. 12, first, a data reading process is executed (Fig. 12 / STEP 11). In this data reading process, measurement data obtained when the load measurement test and the breaking measurement test are carried out on the meat to be subjected to elasticity estimation, as described above, is read.

[0070] As this measurement data, data input to the estimation device 30 by a user operating the input interface 1c may be used, or data transmitted from the pressing tester 20 may be used.

[0071] Next, a first curve acquisition process is executed (FIG. 12 / STEP 12). In this first curve acquisition process, a first load-time curve such as that shown in FIG. 7 is acquired from the measurement data obtained when the load measurement test described above is executed. In this embodiment, this first curve acquisition process corresponds to the first curve acquisition step.

[0072] Following this first curve acquisition process, an area ratio calculation process is executed (FIG. 12 / STEP 13). In this area ratio acquisition process, the area ratio Ra is calculated by the above-mentioned method. In this embodiment, this area ratio calculation process corresponds to the area ratio acquisition step.

[0073] Next, a second curve acquisition process is executed (FIG. 12 / STEP 14). In this second curve acquisition process, a second load-time curve such as that shown in FIG. 11 is acquired from the measurement data obtained when the above-mentioned fracture measurement test is executed. In this embodiment, this second curve acquisition process corresponds to the second curve acquisition step.

[0074] Following this second curve acquisition process, a peak score acquisition process is executed (FIG. 12 / STEP 15). In this peak score acquisition process, the peak score Pn is acquired from the second load-time curve by program processing. In this embodiment, this peak score acquisition process corresponds to the peak score acquisition step.

[0075] Next, an estimated value calculation process is executed (FIG. 12 / STEP 16). In this estimated value calculation process, an estimated value El_est of the elasticity of the meat is calculated using the regression equation (2) described above. In this embodiment, this estimated value calculation process corresponds to the estimation step.

[0076] As described above, according to the method for estimating elasticity of the second embodiment, the regression equation (2) described above was derived based on the assumption that there is a high correlation between the area ratio Ra, the number of peak points Pn, and the elasticity felt when chewing meat. It was then demonstrated that the elasticity of meat can be estimated with high accuracy by using this regression equation (2).

[0077] Although the second embodiment is an example in which regression equation (2) is used as a model, a map showing the relationship between the elasticity of the meat, the number of peak points Pn, and the area ratio Ra may be used instead. Furthermore, the second embodiment is an example in which partial least squares is used as the regression analysis method, but other regression analysis methods such as general least squares, ridge regression, and lasso regression may be used instead.

[0078] Furthermore, the second embodiment is an example configured to obtain the peak score Pn from the data of the second load-time curve by program processing, but instead, the peak score Pn may be obtained by reading the data of the peak score Pn input by the user through the input interface operation.

[0079] Furthermore, the second embodiment is an example in which one pressing tester 20 is used as the first pressing tester and the second pressing tester, but two different pressing testers may be used as the first pressing tester and the second pressing tester, respectively. [Explanation of symbols]

[0080] 1. Estimation device (arithmetic processing unit) 10 Curve acquisition section 11 Area ratio acquisition part 12 Estimation part 20 Pressurization tester 21 Plunger (pressure member) 23 Plunger (breaking member) 30 Estimation device (arithmetic processing device) 31 First curve acquisition part 32 Area ratio acquisition part 33 Second curve acquisition part 34 Peak score acquisition section 35 Estimation part F load t time L1 load-time curve L3 2nd load-time curve S1 1st area S2 2nd area Ra area ratio P Peak point Pn Peak score El_est Estimated elasticity

Claims

1. A method for estimating the elasticity of meat, in which the elasticity of meat processed to an edible state is estimated by a calculation processing device using the results of measurements made over time by a measuring device of the load acting on the meat when the meat is pressed by a pressing tester, a curve acquisition step of acquiring a load-time curve with the load and time as two axes using the measurement results of the load when a pressing operation in which the pressing member of the pressing tester presses the meat at a constant speed to a predetermined strain rate and then releases the pressure is repeated twice; an area ratio acquisition step of acquiring an area ratio, which is a ratio between a first area, which is an area between the load-time curve and a time axis during a first pressing operation, and a second area, which is an area between the load-time curve and the time axis during a second pressing operation; an estimation step of estimating the elasticity of the meat using a model representing the correlation between the area ratio and the elasticity of the meat and the area ratio; A method for estimating the elasticity of meat, comprising:

2. The method for estimating the elasticity of meat according to claim 1, A method for estimating the elasticity of meat, wherein the model is a linear function with the elasticity of the meat as a dependent variable and the area ratio as an independent variable.

3. A method for estimating the elasticity of meat, in which the elasticity of meat processed to an edible state is estimated by a calculation processing device using the results of measurements made over time by a measuring device of the load acting on the meat when the meat is pressed by a pressing tester, a first curve acquisition step of acquiring a first load-time curve using the load measurement results when a pressing operation is performed twice, in which the meat is pressed by a pressing member of the first pressing tester at a constant speed to a predetermined strain rate and then the pressure is released, with the load and time being used as two axes; an area ratio acquisition step of acquiring an area ratio, which is a ratio between a first area, which is an area between the first load-time curve and a time axis during a first pressing operation, and a second area, which is an area between the first load-time curve and the time axis during a second pressing operation; a second curve acquisition step of acquiring a second load-time curve using the load measurement results when the meat is broken at a constant speed by the breaking member of the second pressing tester, with the load and time as two axes; a peak score acquisition step of acquiring a peak score, which is the number of peak points on the second load-time curve that are convex on the increasing side of the load after the load reaches a maximum value; an estimation step of estimating the elasticity of the meat using a model representing the correlation between the area ratio, the peak score, and the elasticity of the meat, the area ratio, and the peak score; A method for estimating the elasticity of meat, comprising:

4. The method for estimating the elasticity of meat according to claim 3, A method for estimating the elasticity of meat, characterized in that the model is a two-variable linear function with the elasticity of the meat as a dependent variable and the area ratio and the peak score as independent variables.

5. A meat elasticity estimation device estimates the elasticity of meat by using the results of measurements made over time using a measuring device to measure the load acting on meat when the meat has been processed to an edible state and is pressed by a pressing tester. a curve acquisition unit that acquires a load-time curve with the load and time as two axes, using the measurement results of the load when a pressing operation is performed twice, in which the pressing member of the pressing tester presses the meat at a constant speed to a predetermined strain rate and then releases the pressure; an area ratio acquisition unit that acquires an area ratio that is a ratio between a first area that is an area between the load-time curve and a time axis during a first pressing operation and a second area that is an area between the load-time curve and the time axis during a second pressing operation; An estimation unit that estimates the elasticity of the meat using a model that represents the correlation between the area ratio and the elasticity of the meat and the area ratio; A meat elasticity estimation device comprising:

6. A meat elasticity estimation device estimates the elasticity of meat by using the results of measurements made over time using a measuring device to measure the load acting on meat when the meat has been processed to an edible state and is pressed by a pressing tester. a first curve acquisition unit that acquires a first load-time curve using the load measurement results when a pressing operation is performed twice, in which the meat is pressed by a pressing member of the first pressing tester at a constant speed to a predetermined strain rate and then the pressure is released, and that acquires a first load-time curve with the load and time as the two axes; an area ratio acquisition unit that acquires an area ratio that is a ratio between a first area that is an area between the first load-time curve and a time axis during a first pressing operation and a second area that is an area between the first load-time curve and the time axis during a second pressing operation; a second curve acquisition unit that acquires a second load-time curve using the load measurement results when the meat is broken at a constant speed by a breaking member of the second pressing tester, with the load and time as the two axes; a peak score acquiring unit that acquires a peak score, which is the number of peak points that are convex on the increasing side of the load after the load reaches a maximum value in the second load-time curve; an estimation unit that estimates the elasticity of the meat using a model that represents the correlation between the area ratio, the peak score, and the elasticity of the meat, and the area ratio and the peak score; A meat elasticity estimation device comprising:

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