JP2001133374A - Method and system for evaluating feeling of crunch - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a method and a system for evaluating the of a food to teeth by using an objective indicator. SOLUTION: A fracture curve of the food to be tested is acquired to get a power spectrum from the fracture curve. A specific peak of the power spectrum in a desired frequency band is calculated as the evaluation indicator of feeling of crunch of the food to be tested.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method and a system for evaluating a texture, such as crispness, in a food, such as a porous food, and more particularly, to an objective index for measuring a texture by using both instrumental analysis and data processing. The present invention relates to a method and a system for evaluating the texture evaluated by the method.

[0002]

2. Description of the Related Art For porous foods such as cookies and snacks, crispy texture when put in the mouth and chewed is very important in terms of quality, and a decrease in the quality causes a significant loss of commercial value. It has become. Therefore, it is important to correctly evaluate the crisp texture of a porous food, and it has been conventionally evaluated by a sensory test. The sensory test is a method mainly used for evaluating flavor, texture, and the like, which are difficult to measure with an instrument, and it is known that accurate and accurate data can be obtained.

[0003] However, on the other hand, the sensory test has an inefficient aspect such that it is difficult to select and train panelists, and it takes time to adjust the schedule because a plurality of panelists are used.

[0004] In recent years, the texture of foods such as hardness (hardness), springiness (elasticity), blottleness (brittleness), chewyness (chewing), stickiness (stickiness), and crispness (crispness) has been improved. Attempts have been made to evaluate by means of instrument analysis such as a rheometer, and some results have been obtained.

For porous foods, the quantification of pore size and the relationship between pore structure and physical properties have been studied, but the direction of pores has not been evaluated (AH Barrett, M. Pele
g: J. Food Sci., 57, 1253-1257, 1992).

[0006] Regarding mechanical properties, evaluation of texture by bending test (EVHeck, K. Allaf and JMBouvie-r:
J. Texture Studies, 26, 11-25, 1995), frequency analysis by fast Fourier transform (FFT) of stress-strain curves (F. Rohde, MD Normand and M. Peleg: J. Textu
re Studies, 25, 77-95, 1993), analysis of stress-strain curves to determine the number of dimensions based on fractal theory (A.Borg
es, M. Peleg: J. Texture Studies, 27, 243-255, 199
6) and so on.

[0007] However, none of the studies mentions the correlation with the perception of texture and can only evaluate one aspect of the rheology of foodstuffs. Regarding mastication sound, frequency analysis of the sound obtained from the microphone by FFT (C. Dacre
mont: J. Texture Studies, 26, 27-43, 1995), but does not take into account time, which is an important factor for physical phenomena during food chewing.

Thus, in the texture of a porous food,
The most important crispness has been difficult to measure accurately in instrumental analysis.

[0009]

SUMMARY OF THE INVENTION It is an object of the present invention to provide a texture evaluation method and system for evaluating the texture of food with an objective index.

[0010]

Means for Solving the Problems The present inventors have conducted intensive studies in order to achieve the above-mentioned object, and as a result, a discrete Fourier transform (DFT) was applied to the value of a uniaxial compression rupture test obtained by a rheometer or the like. By applying the frequency analysis of the breaking curve according to the above, it was found that the value of crispness showed a correlation with the sensory test, and completed the present invention.

That is, the present invention obtains energy in an arbitrary frequency region by performing frequency analysis of a breaking curve by discrete Fourier transform on a value of a uniaxial compression breaking test of a food by a rheometer. It evaluates the texture, such as crispness, of a food, such as a porous food, using an objective index.

Specifically, the invention according to claim 1 includes a first step of obtaining a breaking curve of a test food, a second step of obtaining a power spectrum of the breaking curve obtained by the first step, And a third step of calculating a specific peak in a desired frequency region of the power spectrum obtained in the second step as a texture evaluation value of the test food. .

The invention according to claim 2 is characterized in that the breaking curve in the first step in claim 1 is obtained by a uniaxial compression breaking tester.

The invention according to claim 3 is characterized in that the breaking curve in the first step in claim 1 or 2 includes a step of converting a force-deformation curve of the test food into a force-time curve. I do.

The invention according to a fourth aspect is characterized in that the power spectrum in the second step in any one of the first to third aspects is obtained by a discrete Fourier transform process.

According to a fifth aspect of the present invention, the third step in any one of the first to fourth aspects is such that the third step is performed by comparing a specific peak in a desired frequency region of the power spectrum with sensory test data. The method includes a correlation analysis step of evaluating the texture of the sample food.

According to a sixth aspect of the present invention, the texture of the test food is obtained by applying a specific peak in a desired frequency region of the power spectrum according to any one of the first to fourth aspects to a predetermined regression equation. Is represented by a regression analysis step.

The invention according to claim 7 is a tester for obtaining a breaking curve of a test food, a means for obtaining a power spectrum of the breaking curve obtained by the tester, and a desired frequency range of the power spectrum obtained by the means. A data processing device comprising: means for calculating the specific peak above as a texture evaluation value of the test food.

According to an eighth aspect of the present invention, the data processing apparatus according to the seventh aspect applies a specific peak in a desired frequency region of a power spectrum to a predetermined regression equation to improve the texture of the test food. It is characterized by including regression analysis means represented by a function expression.

According to a ninth aspect of the present invention, in the data processing device according to the seventh aspect, the given break curve is subjected to a discrete Fourier transform process, and a peak in a desired frequency region is calculated from the discrete Fourier transform result. An application program for evaluating the texture of the sample by comparing the value with reference data stored in advance is provided.

[0021] In the invention according to claim 102, the reference data in claim 9 expresses the texture evaluation of the test food by applying a specific peak in a desired frequency region of a power spectrum to a predetermined regression equation. It is characterized by including a function expression.

[0022]

DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described below in detail with preferred embodiments.

(First Embodiment) A food sample targeted by the present invention is typically a porous food 10A as shown in FIG. 1 (first sample). Porous foods include biscuits, crackers, press shells, cookies, corn puffs, corn flakes, popcorn, potato chips, and the like having a porous structure generated by diffusion of evaporative components in the material or expansion of microbubbles of gas injected into the material. Examples include rice snacks such as snacks, rice crackers, and hail, fried foods such as tempura batter, and kakiage. In the porous food 10A shown in FIG. 1, a large number of air holes 1 are provided in the material 10A1.
0A1 is formed.

Also, among porous foods, puff machines,
A puffed food 10B as shown in FIG. 2 which is puffed and formed by a pressure extruder or the like is preferable as an evaluation target (second sample). In the multi-puffed food 10B shown in FIG. 2, many air holes 10B1 are formed in the material 10B1.

On the other hand, the texture of foods includes hardness (hardness), springiness (elasticity), blottleness (brittleness), chewyness (chewing), stickiness (stickiness), and crispness (crispness). Be evaluated. In the case of a porous food, crispness that represents a crispy texture is particularly important.

In the test described below, "Kappa Ebisen" manufactured by Calbee, Inc. was used as the first sample, and "Curl" manufactured by Meiji Seika, Ltd. was used as the second sample. , And evaluation by an objective index.

In the present invention, the texture of food is evaluated by an objective index by using both instrumental analysis and data processing.

In the present embodiment, as shown in FIG. 3, a rheometer 100 is used for instrumental analysis, and a data processing device 200 composed of a personal computer is used for data processing. The rheometer 100 and the data processing device 200
They are connected by a cable 100A.

Here, the rheometer 100 is a general term for an apparatus for measuring the mechanical properties of a substance, and includes compression rupture strength, tensile strength, cutting strength, elasticity, viscoelasticity, brittleness, tackiness, stress relaxation, creep, etc. In this embodiment, a compression / tensile rheometer (RE-33005, load cell 2) is used as a uniaxial compression / rupture tester.
For Kgf, Yamaden Corporation) was used.

In the present embodiment, such a rheometer 100
As shown in FIG. 3, a sample stage 10
The sample food 10 is placed on 1 and the sample table 101 is moved up and down to crush the sample food 10 at a constant speed by the plunger 102, and a uniaxial compression rupture test for measuring the load is performed. Note that, as the plunger 102, a cylindrical plunger is used assuming biting by a human molar.

The data processing device 200 has a reference data storage unit 201. The reference data storage unit 201 stores reference data required for evaluating a test food in an embodiment described later.

The data processing device 200 includes a storage medium 400
The application program written in (401, 402, 403) can be executed. The application program includes a force-deformation curve described later as a force-
A program for converting to a time curve, discrete Fourier transform (D
FT) program, data processing program, program shown in FIG. 10, program shown in FIG. 15, and the like. Further, these application programs and reference data can be downloaded via the modem 202 via the network 300 such as the Internet or a public telephone line. Further, the above-described application program and reference data may be stored in an external storage device 204 to be described later, and may be called as needed.

The data processing device 200 is connected to a printer 203, an external storage device 204, and a monitor 205.

In the uniaxial compression rupture test using the above-mentioned rheometer 100, the pushing speed of the plunger 102 is
It can be measured at 0.1 to 10 mm / sec, and there is no particular limitation on the shape of the plunger 102. The data collection interval (distance resolution) is not particularly limited, but it is preferable that the data collection interval is small and the number of data collection is large. For example, the data collection interval is 0.01 to 0.00.
1 mm.

In this embodiment, in order to obtain a breaking curve of the test food, a cylindrical plunger (diameter 8 mm × height 25 mm) 102 is mounted on the rheometer 100, the compression speed is 1.0 mm / sec, and the data is collected. A uniaxial compression rupture test was performed at an interval of 0.01 mm (sampling frequency: 100 Hz) and a maximum strain of 0.8. Thereby, the force-deformation curve of the porous food is obtained by the uniaxial compression rupture test using the rheometer 100.

The data relating to the force-deformation curve is input to the data processing device 200. In the data processing device 200,
The force-deformation curve is converted into a force-time curve by an application program downloaded from the storage medium 400 or the like. The conversion is obtained from the moving speed of the plunger because the deformation of the test food and the plunger moving distance are the same.

The force-time curve is subjected to a discrete Fourier transform (DFT) by an application program downloaded from the storage medium 400 or the like to obtain a power spectrum. In the present invention, the scientific and technical calculation software “MATLAB Ver.5.3” (The MATHWORKS Inc.)
Perform a discrete Fourier transform using.

The statistical analysis of the thus obtained power spectrum and the crispness of the sensory test is performed by the application program loaded from the storage medium 400 or the like, and the low frequency region (0 to 15 Hz) of the power spectrum is obtained. , Medium frequency range (15-35 Hz),
The first peak (peak having the highest intensity), the second peak (peak having the second highest intensity), and the third peak (3
The second highest intensity peak) was correlated.

The sensory test was performed on a plurality of first and second samples having different moisture contents by a plurality of panelists using the sensory test paper shown in FIG.

According to the analysis results, regarding the power spectrum and the crispness of the sensory test, the first sample, that is, the puffed confectionery using flour as the main raw material, has the first peak, the second peak, and the third peak in the low, medium, and high frequency regions. The peak has a correlation between the power spectrum and the crispness of the sensory test. In particular, the power of the third peak in the high frequency region among these peaks is highly negatively correlated with the crispness of the sensory test (r = −).
0.73).

In the case of the second sample, that is, puffed confectionery containing corn grits as a main raw material, the first, second, and third peaks in the low, middle, and high frequency regions similarly show correlations. First in the low frequency region
Peak power showed a high negative correlation (r = -0.80) with sensory crispness.

Accordingly, it has been found that by determining the energy value in the frequency region of these power spectra, the kripness of the porous food can be evaluated.

By analyzing the measurement conditions of the rheometer and the output data by discrete Fourier transform, it is possible to objectively evaluate the texture, especially the crispness.

Hereinafter, the present invention will be described more specifically with reference to two specific examples of tests, but the present invention is not limited thereto. Unless otherwise specified,% means mass basis.

First, the first test will be described.

In the first test, the first sample was used as a test food (moisture content, 1.9%), measured with a uniaxial compression / tensile rheometer under various conditions, and by discrete Fourier transform (DFT). Frequency analysis was performed.

The test food for measurement was prepared as follows. A saturated aqueous solution of lithium chloride, potassium acetate, magnesium chloride, and potassium carbonate was placed in each desiccator, and a humidity control treatment was performed using phosphorus pentoxide and silica gel for drying.

In the atmosphere, the test food was allowed to stand for 10 hours (25 hours).
℃) by storage, the water content is 1.4, respectively
A test food consisting of 1.6, 2.8, 3.7, 4.8, 5.4% was obtained.

Next, for each test food,
Tensile rheometer (RE-33005, load cell 2K)
gf, Yamaden Corporation), cylindrical plunger (No.
6, diameter 8 mm x height 25 mm) and the compression speed is 1
A uniaxial compression rupture test was performed with a data collection interval of 0.01 mm (sampling frequency 100 Hz) and a maximum strain of 0.8 mm / sec.

Further, the force-deformation curve of the digital quantity obtained in the uniaxial compression rupture test is converted into a force-time curve, and the scientific and technical calculation software “MATLAB Ver.5.3” (The MA
Discrete Fourier Transform (DFT) was performed using THWORKS Inc.). The result of the obtained power spectrum is shown in FIG.

On the other hand, a sensory test using the same sample was performed using the sensory test paper shown in FIG. Using 12 expert panelists, test foods without moisture adjustment (moisture content 1.
9%) as a control, crispness was evaluated on a five-point category scale of -2 to +2. FIG. 6 shows the result.

The correlation between the power spectrum at each frequency (FIG. 5) obtained by performing a discrete Fourier transform (DFT) on the force-time curve of the uniaxial compression rupture test result and the sensory test result (FIG. 6) was examined. . The result is shown in FIG.

From the results shown in FIG. 7, there is a correlation between the first peak, the second peak and the third peak in the low, middle and high frequency regions. In particular, the power of the third peak in the high frequency region is different from the crispness of the sensory test. A negative correlation (r = -0.73) was shown. It was confirmed that the results obtained from the crispness measurement method using a rheometer showed a correlation with a sensory test by frequency analysis.

Next, the second test will be described.

In the second test, the second sample was used as a test food (water content: 3.7%), and the water content was adjusted to 1.6, 2.6, and 2.6 by the same adjustment method as in the first test.
Test foods consisting of 4.4, 5.6, 6.6, and 7.4% were obtained. The uniaxial compression rupture test and the frequency analysis were performed in the same manner as in Example 1. The result is shown in FIG. Sensory test is 1
The test was performed in the same manner as the first test by three specialized panelists. FIG. 9 shows the results.

The correlation between the power spectrum at each frequency (FIG. 8) obtained by discrete Fourier transform (DFT) of the force-time curve of the uniaxial compression fracture test result and the sensory test result (FIG. 9) was examined. . The result is shown in FIG.

From the results shown in FIG. 10, there is a correlation between the first peak, the second peak, and the third peak in the low, middle, and high frequency regions. In particular, the power of the first peak in the low frequency region is different from the crispness of the sensory test. A negative correlation (r = -0.80) was shown.

As described above, according to the first embodiment, the first
For the sample, the crispness of the first sample can be evaluated by the value of the third peak in the high frequency region, and for the second sample, the crispness of the second sample can be evaluated by the value of the first peak in the low frequency region. It turns out that you can.

(Second Embodiment) In the first embodiment, the correlation between the power spectrum at each frequency obtained by discrete Fourier transform of the force-time curve of the uniaxial compression fracture test result and the sensory test result is disclosed. However, in the second embodiment, a regression analysis method as well as the correlation analysis method used in the first embodiment is disclosed as a method for analyzing a power spectrum obtained by discrete Fourier transform.

The operation flow shown in FIG. 11 shows the processing of this embodiment. Steps S1 to S4 are substantially the same as those of the first embodiment, and the regression analysis processing in step S5 and the analysis result are stored. Step S6 is added to the first embodiment.

In FIG. 11, the force of the first sample and the second sample is measured using the rheometer 100 in step S1.
Obtain the deformation curve. Next, in step S2, the data is converted into a force-time curve by a conversion application program as in the first embodiment. Next, in step S3, the first
Similarly to the embodiment, the discrete Fourier transform processing is performed on the force-time curve by the discrete Fourier transform application program, as shown in FIGS. 12 and 13 for the first sample, and FIGS. 14 and 1 for the second sample.
As shown in FIG. 5, a force-deformation curve, a discrete Fourier transform diagram,
A peak value data diagram and the like are obtained.

Next, the data subjected to the discrete Fourier transform in step S3 is subjected to correlation analysis in step S4, and regression analysis is performed in step S5.

The regression analysis performed in step S5 is a result of the correlation analysis obtained in step S4, that is, the power of the sample
It is assumed that there is a correlation between a specific peak value in a specific frequency region obtained by frequency analysis of the deformation curve and a crispness sensory test actually obtained by a panelist.

That is, in the regression analysis of this step, the frequency analysis peak value of the sample that is correlated with the above-mentioned sensory test is determined by the Fechner (Fec
hner's law and Stevens
This is applied to the law of s) to make the evaluation of crispness a function.

Fechner's law, which is a logarithmic regression function, is expressed as follows.

R = k · logS + C where R is the sensory amount, k is a constant, S is the stimulus amount, C is the intercept (If the stimulus amount when R = 0 is So, C = −k · l
ogSo).

Stephen's law, which is a power regression function, is expressed as follows.

R = k · S ⁿ where R is the amount of sensation, k is a constant, S is the amount of stimulation, and n is an index.

In fact, a high negative correlation (r = −0.73)
, The value of the third peak (H3) in the high frequency region is defined as an independent variable (one independent variable),
When the logarithmic regression function equation and the power regression function equation are calculated using the crispness in the sensory test as a dependent variable, the result is as follows.

In the first sample, the logarithmic regression function equation crispness when the number of independent variables is one is 0.75 ·
InH3 -0.68 (r = 0.68) In the first sample, the power regression function equation crispness when there is one independent variable = -0.73 * H3 ^0.07 (r = 0.7
3) On the other hand, for the second sample showing a high negative correlation (r = -0.80), the value of the first peak (H
When 1) is an independent variable (one independent variable) and crispness in a sensory test is a dependent variable, a logarithmic regression function equation and a power regression function equation are calculated as follows.

In the second sample, the logarithmic regression function crispness when there is one independent variable = 0.48 ·
InH1 -0.68 (r = 0.68) In the second sample, the power regression function equation crispness when there is one independent variable = -0.77 * H1 ^1.10 (r = 0.7
7) Above, the independent variables for the first and second samples are 1
Although the logarithmic regression function equation and the power regression function equation in the two cases are used, a regression function equation using a plurality of independent variables can also be calculated.

In the first sample, logarithmic regression function equation when there are a plurality of independent variables. Crispness = -0.219.InL2 + 0.291
-InL3-1.576-InM3-16.133 (r =
0.757) Crispness = -0.155 * InL1 / InM1 + 1.
097.InL1 / InM3 -3.581.InL2 / I
nL3-0.002 InL3 / InH1 + 0.181
InM1 / InH2-0.176.InM2 / InH2-
0.203 · InH1 / InH3 + 2.598 (r = 0.
792)-Preference = -0.590-InL1 +0.405
InL3-1.153 InH3 + 8.002 (r =
0.728) Palatability = 0.080 · M2 / H3-0.002 ·
L1 / L3-1.80 x ^10-5 L1 / M3 + 0.507
× 10 ^-3 · L3 / M3 + 0.601 · L2 / H1-3.5
61 (r = 0.715) Logarithmic regression function of a plurality of independent variables in the second sample Crispness = -0.880 InL1-0.529
InM1 +0.206 InL3 +16.133 (r =
0.785) Crispness = 1.906 * InL1 / InM3 + 3.4
43 InM3 / InH1-0.555 InL1 / In
H1-7.187 (r = 0.776)-Preference = -2.773-InH3 -2.773
InL1 +2.214 InH1 +14.686 (r =
0.744) Preference = 1.906 · InL1 / InM3 + 3.
443.InM3 / InH1-0.555.InL1 / I
nH1-7.187 (r = 0.776) Next, in step S6, a logarithmic regression function formula and a power of one or more independent variables, which are the correlation analysis data and the regression analysis data obtained in steps S4 and S5. The regression function formula is stored in the reference data storage unit 201 in FIG.

(Third Embodiment) In a second embodiment, a power spectrum obtained by performing discrete Fourier transform on a sample is analyzed by a correlation analysis method and a regression analysis method, and a specific peak value in a specific frequency region is compared with an actual peak value. Discloses that there is a correlation between the sensory test of crispness obtained by a panel and that the texture such as crispness is represented by a regression function equation.

In the third embodiment, data such as the function formula for each sample obtained in the second embodiment is compiled as an application program, and this is stored in the reference data storage unit 202 as reference data, and the data of the sample to be measured is stored. The texture evaluation value is measured.

As shown in FIG. 16, steps T1, T
2, T3, to obtain a force-deformation curve of the test food of the same kind as the first and second samples, convert this to a force-time curve,
A discrete Fourier transform process is performed to obtain a force-deformation curve, a discrete Fourier transform diagram, a peak value data diagram, and the like as shown in FIGS.

In step T4, an evaluation method for correlation analysis or regression analysis is selected. In the case of correlation analysis, reference data is called from the reference data storage unit 202 in step T5, and the reference data and measured data are compared and provided. Evaluate the test food.

In the case of the regression analysis, the reference data is called from the reference data storage unit 202 in step T6, and the logarithmic regression function formula and the power regression function formula of one or more independent variables based on the reference data are represented by H1 to H3. , M1-M3, L1-L
To calculate values such as crispness.

At step T7, the values calculated at steps T5 and T6 are displayed on the screen 205A as shown in FIG.
Display with.

As described above, according to the present embodiment, the force-deformation curve of the test food of the same kind as the sample is obtained by using the correlation analysis and / or regression analysis data stored in advance for the sample as the evaluation reference data. Is converted into a force-time curve, subjected to a discrete Fourier transform process, and subjected to comparison calculation, substitution calculation, and the like, whereby it is possible to quantitatively indicate the texture such as crispness of the test food.

In the above embodiment, the reference data for two samples is used. However, by preparing the reference data for more than two types of samples, the texture of more porous foods is quantitatively evaluated. be able to.

Further, the system shown in FIG. 3 is arranged in a food manufacturing factory, and new, changed or added reference data is transmitted from the head office or research institute via the network 300 to the system so that the latest reference data is always obtained. Can be held in the data processing device 200, and it is possible to quantitatively show the texture such as crispness of various foods.

[0082]

As described above, according to the present invention, a breaking curve of a test food is obtained, a power spectrum of the breaking curve is obtained, and a specific peak in a desired frequency region of the power spectrum is obtained. By calculating as a texture evaluation value, a texture evaluation method and system for evaluating the texture of a food with an objective index can be provided.

[Brief description of the drawings]

FIG. 1 is a schematic sectional view showing a first sample.

FIG. 2 is a schematic sectional view showing a second sample.

FIG. 3 is a block diagram showing a first embodiment of a texture evaluation system according to the present invention.

FIG. 4 is a diagram showing an example of a survey sheet for a sensory test.

FIG. 5 is a diagram showing a frequency analysis result of a force-time curve of a first sample.

FIG. 6 is a diagram showing a result of a sensory test of a first sample.

FIG. 7 is a diagram showing a correlation between a frequency analysis of a first sample and a sensory test.

FIG. 8 is a diagram showing a frequency analysis result of a force-time curve of a second sample.

FIG. 9 is a diagram showing a result of a sensory test of a second sample.

FIG. 10 is a diagram showing a correlation between a frequency analysis of a second sample and a sensory test.

FIG. 11 is a flowchart showing the operation of the texture evaluation system according to the second embodiment of the present invention.

FIG. 12 is a diagram showing a breaking curve and a power spectrum of a first sample.

FIG. 13 is a diagram showing a correlation between a frequency and a peak value of a first sample.

FIG. 14 shows a breaking curve and a power spectrum of a second sample.

FIG. 15 is a diagram showing a correlation between a frequency and a peak value of a second sample.

FIG. 16 is a flowchart showing the operation of the texture evaluation system according to the third embodiment of the present invention.

FIG. 17 is an exemplary view showing a display example in the texture evaluation system of the embodiment.

[Explanation of symbols]

 Reference Signs List 100 rheometer 200 data processing device 201 reference data storage unit 202 modem 203 printer 204 external storage device 205 monitor 300 network 400 storage medium

 ────────────────────────────────────────────────── ─── Continuing from the front page (72) Inventor Yuji Tomita 705-46, Nagara, Hayama-cho, Miura-gun, Kanagawa Prefecture (72) Inventor Yasushi 1-14-4 Kitamatsuen, Morioka-shi, Iwate F-term (reference) 2G061 AA02 AB03 BA20 CA20 EC02

Claims (10)

[Claims] 1. A first step of obtaining a breaking curve of a test food, a second step of obtaining a power spectrum of the breaking curve obtained by the first step, and a desired frequency of the power spectrum obtained by the second step A third step of calculating a specific peak on the region as a texture evaluation value of the test food. 2. The breaking curve in the first step is as follows:
The texture evaluation method according to claim 1, wherein the texture is obtained by a uniaxial compression rupture tester. 3. The breaking curve in the first step is as follows:
3. The texture evaluation method according to claim 1, further comprising a step of converting a force-deformation curve of the test food into a force-time curve. 4. The texture evaluation method according to claim 1, wherein the power spectrum in the second step is obtained by a discrete Fourier transform process. 5. The method according to claim 1, wherein the third step includes a correlation analysis step of evaluating a texture of the test food by comparing a specific peak in a desired frequency region of the power spectrum with sensory test data. The method for evaluating texture according to any one of claims 1 to 4. 6. A regression analysis step of applying a specific peak in a desired frequency region of the power spectrum to a predetermined regression equation to express the texture of the test food as a functional equation. The method for evaluating texture according to any one of claims 1 to 4. 7. A tester for obtaining a breaking curve of a test food, means for obtaining a power spectrum of the breaking curve obtained by the tester, and a specific peak in a desired frequency region of the power spectrum obtained by the means. A data processing device including means for calculating a texture evaluation value of the test food. 8. The data processing apparatus according to claim 1, further comprising: a regression analysis unit that applies a specific peak in a desired frequency region of a power spectrum to a predetermined regression equation to express the texture of the test food by a functional equation. The texture evaluation system according to claim 7, characterized in that: 9. The data processing apparatus according to claim 1, wherein the data processing device performs a discrete Fourier transform process on the given break curve, calculates a peak in a desired frequency region from the discrete Fourier transform result, and calculates the measured peak value and reference data previously stored. 8. The texture evaluation system according to claim 7, further comprising means for loading an application program for evaluating the texture of the sample by comparing the sample program with the sample program via a storage medium. 10. The reference data includes a function formula representing a texture evaluation of the test food by applying a specific peak in a desired frequency region of a power spectrum to a predetermined regression formula. The texture evaluation system according to claim 9.