Texture evaluation method, texture standard model

Abstract

Provided is a food texture evaluation method that makes accurate food texture evaluation of a variety of foods for evaluation possible.　This food texture evaluation method comprises a step for creating a template T comprising pattern data to serve as an index for food texture evaluation, a step for carrying out a sensory evaluation of a model food FM for food texture evaluation and acquiring a sensory evaluation value λ, a step for acquiring measurement data A corresponding to pattern data for the model food FM and calculating the similarity SA of the measurement data A and the pattern data of the template T by comparing the same, a step for creating a standard food texture model M corresponding to a food texture type from the sensory evaluation value λ and the similarity SA, and a step for evaluating the food texture of a food FE for evaluation using the standard food texture model M.

Description

[Technical field]

[0001] The present invention relates to a food texture evaluation method capable of evaluating the food texture of various foods with high accuracy, and a food texture standard model for performing food texture evaluation. [Background technology]

[0002] It is said that the deliciousness of food is mainly composed of three elements: flavor, aroma, and texture. Among these, texture is composed of extremely delicate and complex information. For example, texture, which is determined by complex factors such as stickiness and melting in the mouth, is particularly difficult to quantify. For this reason, sensory evaluation of such texture is performed by multiple evaluators.

[0003] In recent years, a method for evaluating food texture has been used that uses a device with the same structure as the inside of the human mouth. For example, the tactile sensor in the following Patent Document 1 has a flexible layer and a substrate layer laminated together, with a magnet embedded inside the flexible layer. In addition, on both sides of the substrate of the substrate layer, a magnetoresistance element (GMR) and an inductor are arranged on the element arrangement surface opposite the flexible layer arrangement surface that contacts the flexible layer, and a circuit is formed on the element arrangement surface of the substrate.

[0004] Furthermore, a tooth-shaped hard member can be provided via a soft layer, and when a tooth-shaped member is provided, the tactile sensation applied to the teeth can be detected. In this way, the texture can be evaluated using a device that uses a tactile sensor (paragraphs 0010, 0012, 0019, 0025, Figure 1). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent No. 5187856 Summary of the Invention [Problem to be solved by the invention]

[0006] The tactile sensor in Patent Document 1 is a device that compares food texture based on the load and vibration of an element, and is capable of evaluating food texture such as softness and hardness. However, there is a problem that such a device alone does not provide an evaluation close to human senses and does not have sufficient accuracy in judgment.

[0007] The present invention has been made in consideration of the above circumstances, and has an object to provide a food texture evaluation method that can accurately evaluate the food texture of various food products for evaluation. [Means for solving the problem]

[0008] In order to achieve the above object, the food texture evaluation method of the present invention is a food texture evaluation method for performing a food texture evaluation on an evaluation food, the method comprising the steps of: creating a template consisting of pattern data, which is a plurality of virtual data serving as an index for the food texture evaluation; performing a sensory evaluation on a model food for the food texture evaluation and obtaining a sensory evaluation value; obtaining measurement data (A) corresponding to the pattern data of the model food, and comparing the measurement data (A) with each of the pattern data of the template to obtain a similarity (S A ) and calculating the sensory evaluation value and the similarity (S A ) to create a standard texture model according to the type of texture, and a process of evaluating the texture of the food under evaluation using the standard texture model.

[0009] In the food texture evaluation method of the present invention, first, a template is created consisting of a plurality of pattern data serving as an index for food texture evaluation. Then, the measurement data (A) of a model food is compared with the pattern data to determine the similarity (S A ) is calculated. This makes it possible to obtain information about which pattern data the model food is similar to.

[0010] In addition, a sensory evaluation was also conducted on the model foods, and the sensory evaluation score and the similarity (S A The standard texture model is created based on the sensory evaluation value and the similarity (S A) is combined, and the standard food texture model is used to evaluate the food texture of the food under evaluation. This method allows the food texture of various food under evaluation to be evaluated with high accuracy.

[0011] In the food texture evaluation method of the present invention, measurement data (B) corresponding to the pattern data of the food to be evaluated is obtained, and the measurement data (B) is compared with each of the pattern data of the template to determine a similarity (S B ) and calculating the similarity (S B ) to evaluate the texture of the food products to be evaluated.

[0012] According to this configuration, measurement data (B) is obtained for the evaluation food, and the measurement data (B) is compared with the pattern data to determine the similarity (S B ) is calculated. This makes it possible to obtain information on which pattern data the food under evaluation is similar to. The similarity (S B In this way, the method can evaluate the texture of the food under evaluation.

[0013] Moreover, in the food texture evaluation method of the present invention, it is preferable that the pattern data is obtained by arbitrarily varying the intensity of a predetermined parameter over time.

[0014] The template pattern data is, for example, virtual data obtained by varying the parameter strength arbitrarily and over time using a program. By preparing multiple pieces of such pattern data, pattern data with a high degree of similarity can be extracted when comparing the measurement data with each pattern data. This makes it possible to use this method for evaluating the texture of food products under evaluation.

[0015] Moreover, in the food texture evaluation method of the present invention, it is preferable that the step of calculating the similarity is carried out by calculating a cumulative value of distances between corresponding elements of the pattern data obtained by changing over time and the measurement data (A) or the measurement data (B).

[0016] The waveforms of the pattern data are graphed because they are the result of changing the parameters over time. For example, the measurement data (B) of the evaluation food can be compared with the pattern data by calculating the cumulative distance between the corresponding elements of the two. The smaller the distance and the more the two waveforms overlap, the higher the similarity can be evaluated.

[0017] In the food texture evaluation method of the present invention, it is preferable that the measurement data (A) includes load change data which is the load change obtained when the model food is pressed. In this case, it is even more preferable that the measurement data (B) includes load change data which is the load change obtained when the evaluation food is pressed.

[0018] The measurement data (A) and (B) include data on the change in load when the model food and the evaluation food are pressed, respectively. For example, when pressing the evaluation food twice, the load changes between the first and second pressings depending on the type of food. Since the load change is data specific to that food, this method can be used to improve the accuracy of food texture evaluation.

[0019] In the food texture evaluation method of the present invention, it is preferable that the measurement data (A) includes vibration change data which is a vibration change obtained when the model food is pressed. In this case, it is even more preferable that the measurement data (B) includes vibration change data which is a vibration change obtained when the evaluation food is pressed.

[0020] The measurement data (A) and (B) may include vibration change data when the model food and the evaluation food are pressed, respectively. Since the vibration change data is also specific to the evaluation food to be evaluated, this method can be used to improve the accuracy of food texture evaluation.

[0021] In the food texture evaluation method of the present invention, the standard food texture model is configured to calculate the explanatory variables based on the similarity (S A ) and the evaluation prediction formula is preferably obtained by regression analysis using the sensory evaluation value as a response variable.

[0022] The standard texture model is similarity (S A The evaluation prediction formula is obtained by regression analysis using the similarity (S A ) and the sensory evaluation values ​​into the evaluation prediction formula, a food model can be easily created and updated.

[0023] Moreover, in the food texture evaluation method of the present invention, any linear model can be applied to the regression analysis, but it is particularly preferable to use a generalized linear mixed model (GLMM).

[0024] The regression analysis is a linear model including a generalized linear model (GLM) and a generalized linear mixed model (GLMM), and this technique enables the present method to easily and accurately evaluate the texture of the food under evaluation.

[0025] The standard food texture model of the present invention is a standard food texture model for evaluating the food texture of an evaluation food, and is a template consisting of a plurality of pattern data serving as an index for the food texture evaluation, a sensory evaluation value obtained by a sensory evaluation performed on a model food for the food texture evaluation, and a similarity (S) obtained by comparing measurement data (A) corresponding to the pattern data of the model food with the pattern data of the template. A ), and the sensory evaluation value and the similarity (S A and a food texture evaluation prediction means for predicting an evaluation based on the type of food texture obtained from the food texture evaluation and the food texture prediction means.

[0026] The standard food texture model of the present invention is provided with a template consisting of a plurality of pattern data serving as an index for food texture evaluation, and compares the measurement data (A) of a model food with the pattern data to calculate the similarity (S A ) is calculated. This makes it possible to obtain information about which pattern data the model food is similar to.

[0027] In addition, the evaluation prediction means performs a sensory evaluation on the model food, and calculates the sensory evaluation value and the similarity (S A ) and the evaluation prediction is performed based on the sensory evaluation value and the similarity (S A ) can be combined to evaluate the texture of the food under evaluation. Effect of the Invention

[0028] According to the present invention, the texture of various foods can be evaluated with high accuracy. [Brief description of the drawings]

[0029] [Figure 1] FIG. 1 is a diagram illustrating a food texture evaluation device used in the food texture evaluation method of the present invention. [Diagram 2] FIG. 2 is a bottom view of the circuit board of the food texture evaluation device. [Figure 3A] Load and vibration measurement results for sample S1. [Figure 3B] Load and vibration measurement results for sample S2. [Figure 3C] Load and vibration measurement results for sample S3. [Figure 3D] Load and vibration measurement results for sample S4. [Figure 3E] Load and vibration measurement results for sample S5. [Figure 4] FIG. 1 is a diagram for explaining an outline of the food texture evaluation method of the present invention. [Figure 5A] FIG. 4 is a diagram showing a template (load pattern data). [Figure 5B] FIG. 13 is a diagram showing a template (vibration pattern data). [Figure 6A] A graph showing the average scores of the sensory evaluation of seven types of samples. [Figure 6B] Histogram of the average sensory scores of the seven samples. [Figure 7A] Measurement results (area) using a texture analyzer. [Figure 7B] Measurement results (number of peaks) using a texture analyzer. [Figure 8A] Texture analyzer measurements (average drop-off). [Figure 8B] Measurement results (peak value) using a texture analyzer. [Figure 9A] Loading of seven types of samples by the texture evaluation device. [Figure 9B] Vibration of seven types of samples using a texture evaluation device. [Figure 9C] The cumulative vibration sum of seven types of samples measured by a texture evaluation device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0030] Hereinafter, one embodiment of the food texture evaluation method according to the present invention will be described with reference to the drawings.

[0031] Figures 1 and 2 show a food texture evaluation device 10 used in the food texture evaluation method according to the present invention. The food texture evaluation device 10 shown in Figure 1 has a configuration equivalent to human teeth, gums, etc. The food texture evaluation device 10 measures the occlusion of the evaluation food using various sensors in order to evaluate the texture of the evaluation food, which is the subject of evaluation.

[0032] The food texture evaluation device 10 is mainly composed of a contact part 11 corresponding to the teeth, an intermediate layer part 15 corresponding to the gums (periodontal ligament), and a base part 17 corresponding to the alveolar bone. The base part 17 is placed on a circuit board 19.

[0033] A hard material is used for the contact portion 11, and the contact portion 11 is, for example, wedge-shaped. Making the contact portion 11 wedge-shaped is preferable because it makes it easier to detect vibration. A permanent magnet 13 is embedded inside the contact portion 11. Then, a magnetic resistance element (GMR: Giant Magneto Resistive effect) 21 and an inductor element 23, which are food texture sensors provided on a circuit board 19, detect a change in magnetism when the food under evaluation presses the contact portion 11. The upper surface of the contact portion 11 may be processed to be flat so that it is easier to press the food under evaluation.

[0034] A neodymium magnet (e.g., 15 mm in diameter, 1.5 mm in thickness, and 115 mT in magnetic flux density on the surface) can be used as the permanent magnet 13. The magnetic resistance element 21 can detect the change in position (amount of displacement) of the permanent magnet 13, and the change when the food under evaluation presses against the contact portion 11 is recorded as a "load."

[0035] When the position of the permanent magnet 13 is displaced, an induced electromotive force is generated in response to the change in magnetic flux density. The inductor element 23 detects the induced electromotive force. The magnitude of the induced electromotive force depends more on the speed of change in magnetic flux density than on the amount of change. When the food product under evaluation presses against the contact portion 11, the change detected by the inductor element 23 is recorded as "vibration."

[0036] The intermediate layer 15 is made of elastic elastomer (natural rubber, synthetic rubber, etc.) or spring, and the base 17 is made of hard material to replicate human teeth. For this reason, the values ​​of "load" and "vibration" change depending on the type of food being evaluated.

[0037] FIG. 2 shows the circuit board 19 as viewed from the bottom side.

[0038] As shown in the figure, eight magnetoresistance elements 21 are arranged on the underside of base portion 17, indicated by a circular dashed line, with the center of the circle left empty. Since permanent magnet 13 (not shown) of contact portion 11 is located in the center of the circle, when the food under evaluation presses contact portion 11, the position change (load) of permanent magnet 13 can be detected by any of magnetoresistance elements 21.

[0039] Also, one inductor element 23 is disposed in the center of the circle. This makes it possible to detect changes (vibrations) in the magnetic flux density of the permanent magnet 13 located almost directly above the inductor element 23. Note that the numbers of the magnetoresistance elements 21 and the inductor elements 23 can be changed as appropriate. Also, signals detected by the magnetoresistance elements 21 and the inductor elements 23 are relayed by a connector 25 on the circuit board 19 and transmitted to a control unit (not shown).

[0040] Next, an example of measurement of load and vibration by the food texture sensor will be described with reference to Figs. 3A to 3E.

[0041] The texture evaluator (hereafter referred to as "evaluator") used shrimp tempura (five types, samples S1 to S5), which are considered to have a high contribution of mechanical properties to texture, as the evaluation foods, and performed eight measurements for each sample. Samples S1 to S5 are shrimp tempura with different batters, each cooked under different conditions.

[0042] In Figures 3A to 3E, the left vertical axis is Force (N), the right vertical axis is Voltage (V), and the horizontal axis is Time (s). Samples S1 to S5 corresponding to Figures 3A to 3E have different load waveforms (thick lines: load waveform data) and vibration waveforms (solid lines: vibration change data), and for example, the measurement data of sample S1 in Figure 3A, sample S3 in Figure 3C, and sample S5 in Figure 3E show prominent load peaks at predetermined times.

[0043] As will be described in detail later, the evaluator calculates the degree of similarity by comparing such measurement data with a template consisting of multiple time-series data prepared in advance. Furthermore, the evaluator evaluates the texture of each sample (crispy, crunchy, crunch, etc.) by comparing it with a standard texture model created from the measurement data and sensory evaluation values ​​of the model foods.

[0044] Next, the food texture evaluation method of the present invention will be described in detail with reference to Fig. 4. Furthermore, the content of the templates and their creation will be further explained with reference to Figs. 5A and 5B.

[0045] First, the evaluator selected model food F M (Usually 6 to 7 types) are used to create a standard food texture model M. Model food F M It is advisable to select multiple foods that have a texture similar to that of the food being evaluated.

[0046] First, the model food F was evaluated by a sensory evaluator. M For example, when performing a sensory evaluation of chicken nuggets, the model food F M The same chicken nuggets are used as model food F. M The sensory evaluator chews the food and evaluates whether it is closest to one of the following textures: "crispy," "crunchy," "crunchy," "crunchy," or "crunchy." The evaluator assigns a "1" to the closest texture, a "2" to the second closest texture, and a "3" to the third closest texture, and may then quantify the texture.

[0047] Next, the evaluator will M The food texture is measured by a food texture sensor. The food texture evaluation device 10 described above is used for this measurement. Specifically, an evaluator places the model food F on the contact portion 11 of the food texture evaluation device 10. M The load and vibration are pressed twice to obtain measurement data A. The measurement data A is waveform data of the load and vibration (load change data and vibration change data) as shown in Figs. 3A to 3E.

[0048] Next, the evaluator compares the measurement data A with a previously prepared template T. Template T is composed of pattern data with different waveforms that serve as indices for food texture evaluation. The pattern data is virtual data obtained by generating random numbers to vary the strength of a given parameter arbitrarily (randomly) and over time.

[0049] Template T is for load (see FIG. 5A) and vibration (see FIG. 5B), and each waveform is one pattern data. M The “load” of the measurement data A is compared with each of the pattern data in FIG. 5A, and the similarity is calculated based on the shape of the waveform. M The "vibration" of the measurement data A is compared with each of the pattern data in FIG. 5B, and the similarity is calculated based on the shape of the waveform. A is used to create the standard texture model M.

[0050] Here, the model food F M In the measurement using the texture sensor, the model food F M is pressed against the contact portion 11 twice, the load pattern data shown in Fig. 5A has a waveform that changes with time accordingly. Specifically, peaks P1 and P2 of the pattern data correspond to the first pressing, and the pressure (Force [N]) is changed accordingly. Peaks P3 and P4 of the pattern data correspond to the second pressing, and the pressure is changed similarly to peaks P1 and P2. In this way, the evaluator creates 50 types of pattern data, each with a different peak p1 to p4.

[0051] FIG. 5B shows pattern data for vibration. The vibration detected by the food texture sensor (inductor element 23) is M For this reason, the evaluator creates 50 types of time-varying pattern data with the cumulative sum of the number of times positive induced electromotive force of vibration (cumulative sum of vibration) on the vertical axis.

[0052] Model Food F M The similarity between the measured data A and the template T can be quantitatively evaluated using dynamic time warping (DTW). In this evaluation, the evaluator measures the distance between the two waveforms (DTW distance), and the more similar the two data are, the smaller the DTW distance (the higher the similarity) and the closer the waveform shapes are. Here, each waveform is composed of a time series of measured values ​​(load, etc.) sampled at regular intervals, and each point (element of the present invention) is continuously shown. To measure the DTW distance, the distance between the elements of each waveform is compared in a brute-force manner, and the path with the shortest distance between the points is found. Then, the accumulated value of the distances found is the DTW distance.

[0053] The similarity between the measurement data A and the template T can also be evaluated by measuring, for example, the Euclidean distance, the Fourier transform distance, the autoregressive coefficient distance, or the EDR (Edit Distance on Real sequences) distance instead of the DTW distance described above.

[0054] Then, the sensory evaluation value λ and the similarity S A By linking these, for example, the pattern data of the texture evaluated by the sensory evaluator as "crispy" is determined. By accumulating such information, a standard texture model M that can be used to evaluate various textures is completed.

[0055] In fact, the standard texture model M uses explanatory variables as similarity S A (Comparison results with 50 types of pattern data) a1 ,s a2 ,…s a50 ) and the objective variable is the sensory evaluation value λ, and the evaluation prediction formula is given below. log{λ / (1-λ)}=β 0 +β 1 s a1 +β 2 s a2 +…+β 50 s a50 +r i ...(Formula 1) Here, β0 ,β 1 ,β 2 ,…,β 50 is the coefficient, r i is a parameter that represents individual differences.

[0056] Specifically, the evaluator calculates the similarity S A and the sensory evaluation value λ are substituted into the evaluation prediction formula (Formula 1), and the coefficient β 0 ,β 1 ,β 2 ,…,β 50 (And r i The standard deviation of the standard texture model M is obtained by regression analysis. Note that this regression analysis can be performed by any method as long as it can obtain the values ​​of each parameter. Any linear model such as a generalized linear mixed model (GLMM) or a generalized linear model (GLM), a nonlinear model, a hierarchical Bayesian model, etc. can be applied.

[0057] In addition, in order to evaluate six types of texture, namely, "crispy," "crunchy," "crunchy," "crunchy," and "crunchy," there are six sensory evaluation values ​​λ. In other words, there are six types of evaluation prediction formulas (Formula 1), and the evaluators evaluate the similarity S A Substituting the above, we create a standard texture model M according to the texture.

[0058] Next, the evaluator will E In order to evaluate the food texture, the food for evaluation F was E The load and vibration measurement data B is obtained. The load and vibration measurement is performed on the model food F. M The measurement is carried out under the same conditions as those used for the measurement.

[0059] Next, the evaluator compares the measurement data B with the template T. Specifically, the evaluator compares the food product F E The "load" and "vibration" of the measurement data B are compared with the pattern data (50 types) of Fig. 5A and Fig. 5B, respectively, and the similarity is calculated based on the shape of the waveform. B (Comparison results with 50 types of pattern data) b1 ,sb2 ,…s b50 ) is obtained. In addition, the evaluation food F E For the similarity between the measurement data B and the template T, for example, the dynamic time warping method (DTW) can be used.

[0060] Finally, the evaluator selected the evaluation food F E Similarity S B is compared with a previously prepared standard texture model M. Specifically, the evaluator uses the following evaluation prediction formula to find the model evaluation value λ'. log{λ' / (1-λ')}=β 0 +β 1 s b1 +β 2 s b2 +…+β 50 s b50 +r i ...(Formula 2)

[0061] This time, the evaluator entered the similarity S of the evaluation food into the evaluation prediction formula (Formula 2). B (s b1 ,s b2 ,…s b50 ) to obtain the model evaluation value λ' (coefficient β 0 ,β 1 ,β 2 ,…,β 50 ,r i (The value obtained when creating the standard food texture model M is used.) A specific example of the model evaluation value λ' will be described later, but the above series of operations will result in the evaluation food F E The texture of the product can be evaluated.

[0062] Next, the results of a sensory evaluation performed using seven types of chicken nugget samples will be described with reference to Figs. 6A and 6B.

[0063] First, Fig. 6A shows the average scores (sensory evaluation values) of the sensory evaluations performed by sensory evaluators for Samples 1 to 7. There are six textures: "crispy," "quick," "crunchy," "crunchy," and "crunchy," and Samples 1 to 7 were each evaluated three times by five sensory evaluators (on a scale of 10 to 10). For example, Sample 2 had the highest score for "crunchy," and therefore was the sample that the sensory evaluators felt had that texture.

[0064] Here, we will explain the tentative definitions of each texture: "sakusaku" is the texture "where the coating crumbles with light force, and this sensation persists even as chewing continues." In addition, "sakuto" is the texture "where the coating crumbles with light force at the first chew," and "zakzak" is the texture "where the coating crumbles repeatedly into small pieces when biting into with the back teeth." Furthermore, "karikari" is the texture "where something hard and firm crumbles, and this sensation persists even as chewing continues," "crispy" is the texture "where something hard crumbles with light force between the front teeth," and "garigari" is the texture "where you bite into an ice cream cone."

[0065] In the figure, "Proba" is the test value used in one-way analysis of variance (ANOVA) to determine whether there is a possibility of a difference between each item, and "Significant" is the risk ratio of the sample with the most significant difference using multiple comparison testing (Tukey Test).

[0066] Next, Fig. 6B is a histogram showing the average scores of the sensory evaluation for each texture for the above samples 1 to 7. For sample 1, the average scores for the textures "crispy" and "quick" were high, but the overall score was low, and it was judged to be "uncharacteristic." A low absolute score means that the texture is weak; for example, if the score for "crispy" is low, the examiner judged the texture to be weak.

[0067] Also, based on the average scores, it was determined that Sample 2 had a strong "crunchy" texture, Sample 3 had a strong "crunchy" texture, and Sample 4 had a strong "crunchy" texture. Similarly, it was determined that Sample 5 had a strong "crispy" texture, Sample 6 had a strong "crunchy" texture, and Sample 7 had a strong "crunchy" texture. In Sample 4, the "crunchy" and "crunchy" textures received the same score, but since the "crunchy" texture of Sample 7 was clearly evident, Sample 4 was determined to have a "crunchy" texture.

[0068] In addition, different symbols are assigned to samples that show significant differences (P<0.05) in multiple comparison tests. For example, there is a significant difference between "a," "b," and "c," but there is no significant difference between "a" and "ab," or between "ab" and "b."

[0069] Next, with reference to Figs. 7A to 8B, the results of an evaluator measuring the same samples 1 to 7 using a commercially available texture analyzer (TA.XTplus, manufactured by Stable Micro Systems) will be described. A texture analyzer is a device that measures numerical data related to the chewiness, texture, mouthfeel, etc. of food. The measurement of the chicken nuggets using the texture analyzer was repeated four times for each of samples 1 to 7.

[0070] First, Figure 7A shows the results of measuring the "area" of each sample. "Area" is the measurement of the surface area S, which is the product of time (Time [sec]) and stress (Force [g]), and the vertical axis of Figure 7A is S [g sec]. The "area" value is the total rebound force when the texture analyzer presses into the sample, and is therefore generally an index of the elasticity and hardness of the sample. The line at the end of the histogram indicates the standard deviation of the measured values ​​within the sample.

[0071] In the "area" measurement, the textures of "Sample 3 (crunchy)," "Sample 6 (crispy)," and "Sample 7 (crispy)" were high at about 1,000 [g·sec], but the other textures were also around 800 [g·sec], with no significant difference being observed. Therefore, this was not very useful in evaluating the texture of chicken nuggets.

[0072] Next, Figure 7B shows the results of measuring the "number of peaks" for each sample. When the texture analyzer presses into the sample and the sample breaks, a phenomenon occurs in which stress is released (a peak). The number of times this occurs is counted to give the "number of peaks." In measuring the "number of peaks," "Sample 6 (crispy)" and "Sample 7 (crunchy)" had high textures. Generally, a high "number of peaks" value indicates a stronger "crispy" texture, so this is an indicator of this.

[0073] Next, Figure 8A shows the results of measuring the "average drop-off" of each sample. "Drop-off" is the value of how much stress (Force [g]) was released at the peak. Furthermore, "average drop-off" is the average value of all the drop-offs in one measurement. In measuring the "average drop-off," "Sample 7 (crisp)" had a high texture. Generally, "average drop-off" is an index of brittleness, and the higher the value, the more force is required to crush it.

[0074] Next, Figure 8B shows the results of measuring the "peak value" of each sample. The "peak value" is the average value of the maximum stress of each peak in one measurement. Although no significant differences were observed between the textures, the "peak value" is generally an indicator of the hardness of a sample.

[0075] The sensory evaluation revealed clear differences between "Sample 4 (crunchy)" and "Sample 6 (crispy)" (see Figures 6A and 6B). However, in the evaluation using the texture analyzer, the evaluators performed a t-test (one-tailed, unpaired) on both samples at a significance level of 5%, and no significant differences were found in any of the following: "area" (significance probability p=0.058%), "number of peaks" (significance probability p=0.501%), "average drop-off" (significance probability p=0.444%), and "peak value" (significance probability p=0.231%). As mentioned above, there was no clear difference in the measured values ​​of both samples, and the difference in texture could not be fully evaluated.

[0076] Next, with reference to Figs. 9A to 9C, the results of measuring the same samples 1 to 7 using the food texture evaluation apparatus 10 according to the embodiment of the present invention will be described.

[0077] 9A to 9C show the results of measuring the "load," "vibration," and "cumulative vibration sum" of Samples 1 to 7, respectively, using the food texture evaluation device 10. Specifically, the evaluator pressed each sample twice against the contact part 11 of the food texture evaluation device 10, and obtained the measurement data of the load (load waveform data) and the measurement data of the vibration (vibration waveform data).

[0078] According to the "Load" data in Figure 9A, almost all of the waveforms were similar except for samples 5 and 7. However, according to the "Cumulative Vibration Sum" data in Figure 9C, although the waveforms of samples 2, 3, and 5 were similar, the waveforms of samples 1, 4, 6, and 7 could be distinguished from the waveforms of samples 2, 3, and 5, and were characteristic waveforms. The "Cumulative Vibration Sum" was the largest for sample 7 and the smallest for sample 1.

[0079] After that, the evaluator compares the load waveform data and vibration waveform data (measurement data A in FIG. 4) with each pattern data (50 types) of template T, and calculates the similarity (similarity S in FIG. 4). ASpecifically, the evaluator measured the "load" and "cumulative vibration sum" ten times for each of the samples 1 to 7 using the food texture evaluation device 10, and compared them with the respective pattern data (50 types) of the template T to obtain the similarity S. A The similarity score S for 10 times is obtained. A The average value was calculated.

[0080] Next, the evaluator evaluates the obtained similarity S A From the average score of each of the above and the average score of the sensory evaluation (see Figure 6A), six types of evaluation prediction equations (Equation 1) were created using a generalized linear mixed model (GLMM), and the parameter β 0 ,β 1 ,β 2 ,…,β 50 ,r i The value of was calculated.

[0081] Next, the evaluator used another chicken nugget sample 3' that was produced and cooked through the same process as the above sample 3, and obtained measurement data of the load and vibration (measurement data B in FIG. 4) using the texture evaluation device 10 under the same conditions as when measuring sample 3. The measurement of sample 3' was repeated 10 times.

[0082] After that, the evaluator compares these measurement data B with each pattern data (50 types) of template T and evaluates the similarity (similarity S in Figure 4). B ) was obtained.

[0083] Furthermore, the evaluator evaluates the obtained similarity S B and the parameter β calculated from the evaluation prediction formula (Equation 1) for samples 1 to 7. 0 ,β 1 ,β 2 ,…,β 50 ,r i The model evaluation value λ' for Sample 3' was calculated using the values ​​of λ and λ. Since the measurement of Sample 3' was repeated 10 times, the evaluators calculated the average value of the model evaluation values ​​λ' and used this as the evaluation result based on the food texture evaluation model.

[0084] Next, with reference to Table 1, the correlation between the evaluation results using the food texture evaluation model according to the embodiment of the present invention and the sensory evaluation results will be described.

[0085] Table 1 shows the evaluation value (sensory evaluation value λ) when sample 3 was subjected to a sensory evaluation, and the evaluation value (model evaluation value λ') when sample 3' was evaluated using the food texture evaluation model M of this embodiment.

[0086] [Table 1]

[0087] The evaluation values ​​using the texture evaluation model M showed the same tendency as the sensory evaluation value λ, and the correlation coefficient between the sensory evaluation value λ and the model evaluation value λ' was 0.97. In general, a correlation coefficient with an absolute value exceeding 0.4 is considered to be correlated, and a correlation with an absolute value exceeding 0.7 is considered to be strong. Therefore, it can be said that there is a very strong correlation between sample 3 and sample 3'.

[0088] Similarly to sample 3', model evaluation values ​​λ' were also obtained for samples 4' and 6' corresponding to samples 4 and 6. The correlation coefficient between the sensory evaluation value λ and the model evaluation value λ' for sample 4 was 0.86. Similarly, the correlation coefficient between the sensory evaluation value λ and the model evaluation value λ' for sample 6 was 0.87. Therefore, the model evaluation value λ' for both sample 5 and sample 6 was consistent with the sensory evaluation value λ, providing good results.

[0089] As mentioned above, measurements using the texture analyzer did not allow a clear distinction between Sample 4 and Sample 6. However, evaluation using the standard texture model M showed a significant difference in the evaluation values ​​of both samples, indicating that the difference in texture could be evaluated as distinguishable.

[0090] In this way, in the food texture evaluation device 10 according to the present invention, the food to be evaluated F E By measuring the "load" and "vibration" and combining the results, it is possible to evaluate the texture with high accuracy.

[0091] It should be noted that food items with textures relatively similar to those of chicken nuggets (fried chicken, minced meat cutlets, spring rolls, croquettes, etc.) can also be evaluated using the template T created this time. In addition, by using a different template, it is also possible to evaluate meat buns, fruits, etc., which have textures completely different from those of chicken nuggets, making it possible to evaluate the textures of a wider range of various evaluation foods.

[0092] This time, 50 types of pattern data for template T were prepared, but the number of types is not limited to 50, as it depends on the food to be evaluated. Conventionally, for example, waveform data for a typical "crunchy" texture was created, and the waveform data and the evaluation food F were compared. E The texture was evaluated by comparing the measured data with the texture evaluation device 10. However, the texture evaluation device 10 can obtain the degree of "crunchiness" by preparing multiple pattern data corresponding to textures. In other words, a more detailed evaluation can be performed, such as "crunchy" close to "crispy" or "crunchy" close to "crunchy."

[0093] In the above embodiment, the evaluator uses the model food F M and Evaluation Food F E While the data was measured by pressing the sample against the contact part 11 of the food texture evaluation device 10 twice, the number of times of pressing may be only once or may be three or more times.

[0094] Furthermore, in the above embodiment, the food texture evaluation device 10 was used to obtain measurement data from changes in magnetism, but for example, a device that obtains measurement data from changes in vibration or sound may be used, and a texture tester, rheometer, creep meter, etc. may also be applied. [Explanation of symbols]

[0095] S1 to S5, 1 to 7...samples, 10...food texture evaluation device, 11...contact part, 13...permanent magnet, 15...intermediate layer part, 17...base part, 19...circuit board, 21...magnetic resistance element, 23...inductor element, 25...connector.

Claims

1. A food texture evaluation method for evaluating the food texture of an evaluation food, comprising: creating a template consisting of a plurality of pattern data, the plurality of virtual data being indexes for the food texture evaluation; a step of allocating a model food for the food texture evaluation using each of a plurality of samples, conducting a sensory evaluation of each model food, and obtaining a sensory evaluation value (λ) for each sample and for each type of food texture; Measurement data (A) corresponding to the pattern data is obtained for each of the model foods by sample, and each of the measurement data (A) for each sample is compared with each of the pattern data of the template to calculate a similarity (S Ａ ) and The sensory evaluation value (λ) for each sample and for each type of texture and the similarity (S Ａ ) and creating a standard texture model for each type of texture; A step of evaluating the texture of the food product for evaluation using the standard texture model for each texture type; Equipped with The food texture evaluation method, wherein the standard food texture model is an evaluation prediction formula obtained by regression analysis in which the similarity (S A ) is included in explanatory variables and the sensory evaluation value (λ) is used as a response variable.

2. The measurement data (B) of the food to be evaluated is acquired, and the measurement data (B) is compared with each of the pattern data of the template to determine the similarity (S Ｂ ) and The similarity (S Ｂ ) and performing a texture evaluation of the evaluation food products according to texture type; The food texture evaluation method according to claim 1, further comprising:

3. 3. The food texture evaluation method according to claim 2, wherein the pattern data is obtained by arbitrarily varying the intensity of a predetermined parameter over time.

4. 4. The food texture evaluation method according to claim 3, wherein the step of calculating the similarity is carried out by calculating a cumulative value of distances between corresponding elements of the pattern data obtained by subjecting the pattern data to change over time and the measurement data (A) or the measurement data (B).

5. 5. The food texture evaluation method according to any one of claims 1 to 4, wherein the measurement data (A) includes load change data which is a change in load obtained when the model food is pressed.

6. 6. The food texture evaluation method according to claim 5, wherein the measurement data (B) includes load change data which is a load change obtained when the food product under evaluation is pressed.

7. 7. The food texture evaluation method according to any one of claims 1 to 6, wherein the measurement data (A) includes vibration change data which is a vibration change obtained when the model food is pressed.

8. 8. The food texture evaluation method according to claim 7, wherein the measurement data (B) includes vibration change data which is a vibration change obtained when the food product under evaluation is pressed.

9. 9. The food texture evaluation method according to any one of claims 1 to 8, wherein a linear model is applied in the regression analysis.

10. The food texture evaluation method according to claim 9, wherein a generalized linear mixed model (GLMM) is used for the regression analysis.

11. A method for creating a standard texture model for evaluating the texture of a food product to be evaluated, comprising the steps of: creating a template consisting of a plurality of pattern data, the plurality of virtual data being indexes for the food texture evaluation; a step of allocating a model food for the food texture evaluation using each of a plurality of samples, conducting a sensory evaluation of each model food, and obtaining a sensory evaluation value (λ) for each sample and for each type of food texture; Measurement data (A) corresponding to the pattern data is obtained for each of the model foods by sample, and each of the measurement data (A) for each sample is compared with each of the pattern data of the template to calculate a similarity (S Ａ ) and The sensory evaluation value (λ) for each sample and for each type of texture and the similarity (S Ａ a step of creating a model for each type of texture from the sensory evaluation value (λ) and the similarity (SA) as an explanatory variable by a regression analysis using the sensory evaluation value (λ) as a response variable, and A method for creating a standard food texture model, comprising:

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