AU2013264002B2

AU2013264002B2 - Meat color grade determination method

Info

Publication number: AU2013264002B2
Application number: AU2013264002A
Authority: AU
Inventors: Keigo KUCHIDA
Original assignee: Obihiro University of Agriculture and Veterinary Medicine NUC
Current assignee: Obihiro University of Agriculture and Veterinary Medicine NUC
Priority date: 2012-05-21
Filing date: 2013-05-21
Publication date: 2016-11-17
Anticipated expiration: 2033-05-21
Also published as: AU2013264002A1; BR112014028922B1; BR112014028922A2; JP6200690B2; JP2014002136A; WO2013176114A1

Abstract

Without relying on a subjective assessment, the invention provides a meat color grade determination standard creation method and a meat color grade determination method that enable detailed meat color grade determination and automation of meat color grade determination. The invention is related to the meat color grade determination standard creation method, in which: (1) a digital image is extracted from the surfaces of multiple meat specimens; (2) a CIELab value for each meat specimen is found from an RGB value of the extracted digital image; (3) a grade of meat color is determined for each meat specimen; and (4) a meat color grade determination standard is found from the relationship between any two factors and the grade, said factors being of the CIELab values found for the multiple meat specimens. Moreover, the invention is related to a meat color grade determination method containing a step in which: (5) for the meat specimens, which are items for inspection, steps (1) and (2) are performed to find the CIELab values, and in reference to the meat color grade determination standard obtained by step (4), the grades of the meat colors of the meat specimens that are the items for inspection are found.

Description

DESCRIPTION TITLE OF THE INVENTION: Meat Color Grade Determination Method

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001]

This application claims benefit of priority to Japanese Patent Application No. 2012-115719 filed on May 21,2012, which is expressly incorporated herein by reference in its entirety.

FIELD OF THE INVENTION

[0002]

The present invention relates to a method of preparation of a standard for determination of meat color grade and a method of determination of meat color grade. More in detail, the present invention relates to a method of preparation of a standard for determination of color grade of meat, especially beef, and a method of determination of color grade by using CIE Lab values obtained from digital images. DISCUSSION OF THE BACKGROUND .

[0003]

Meat color is an important factor along with marbling in the quality of beef. The color of meat is graded by graders of the Japan Meat Grading Association based on the beef color standard ("BCS" hereinafter). Grading based on the BCS is conducted based on experience by a skilled grader employing a standard model. Grading by BCS number consists of grades of 1 to 7 and is discontinuous. Thus, the BCS number is a grading that is assigned subjectively by the grader. Even meats of Identical grade (the same BCS number) will sometimes present different colors.

[0004]

At meat processing plants, instead of the method in which the grader directly assigns a grading to the meat, the method in which images read by a digital camera or image scanner are displayed on a computer to determine the grading (assign a BCS number) is also applied. By using a color calibrator, the color of the meat is displayed 1 2013264002 20 Oct 2016 with good reproducibility. Based on this display, the grader is able to assign a BCS number with great precision (Kuchida et al., Journal of the Japan Society of Animal Science, 73(4): 521-528, Nonpatent Reference 1, the entire contents of which are hereby incorporated by reference).

SUMMARY OF THE INVENTION

[0005]

However, the method of grading meat color by reading an image is still carried out by a grader. Thus, the fine grading of meat color is impossible, and the problem of differences in meat color appearing in an identical grade of meat remains unsolved. To the extent of the knowledge of the present inventors, no objective method of grading meat color exists. Nor, to the knowledge of the present inventors, does any method of objectively and automatically grading meat color exist.

[0006]

Accordingly, the present invention seeks to provide a new method of grading meat color permitting the extremely fine grading of meat color independently of subjective determination. The present invention further seeks to provide a new method of grading meat color permitting the automated grading of meat color.

[0007]

The present inventors conducted extensive research into achieving the objects set forth above. As a result, they calculated the L*a*b from the RGB values of the cross-section of meat that had been imaged and successfully determined the correlation between the L*a*b and the conventional grading of meat color. They discovered that by calculating the L*a*b of meat samples being examined based on this correlation, they were able to provide an objective grading of meat color, and by further subdividing the grades, they were able to assign a fine grading. The present invention was devised on that basis.

[0008]

The present invention seeks to provide a method of objectively grading the color of meat. As a result, the grading of meat color, which requires skill, can be mechanized 2 2013264002 20 Oct 2016 and the grading of the meat color of edible meat that is being examined can be automated.

[0008A]

According to a first aspect the present invention provides a method of preparation of a standard for determination of meat color grade comprising steps of: (1) extracting digital images from surfaces of plural of meat samples, (2) obtaining a CIE Lab value of each meat sample from RGB values of the extracted digital image, (3) determining a meat color grade of each meat sample, and (4) obtaining a standard for determination of meat color grade from the relations between the grade and any of two factors of the CIE Lab value obtained on plural of the meat samples, provided that any order between (1) to (2) and (3) can be taken.

[0008B]

According to a second aspect the present invention provides a method of determination of meat color grade comprising steps of: (11) extracting digital images from a surface of a meat sample to be examined, (12) obtaining a CIE Lab value of the meat sample to be examined from RGB values of the extracted digital image, (13) obtaining a meat color grade of the meat sample to be examined by reference to a standard for determination of meat color grade; wherein the standard for determination of meat color grade is obtained in (a1) to (a4) mentioned below, provided that any order between (a1) to (a2) and (a3) can be taken, (a1) extracting digital images from surfaces of plural of meat samples, (a2) obtaining a CIE Lab value of each meat sample from RGB values of the extracted digital image, (a3) determining a meat color grade of each meat sample, and (a4) obtaining a standard for determination of meat color grade from the relations between the grade and any of two factors of the CIE Lab value obtained in (a2) on plural of the meat samples mentioned in the (a1) and the grade determined in the (a3).

2A 2013264002 20 Oct 2016

BRIEF DESCRIPTION OF THE DRAWINGS

[0009] [Figure 1] Figure 1 shows an image of an extracted transvers section (a) and a rib eye (b).

[Figure 2] Figure 2 shows a color chart (the drawing is in black and white) picked up by the rib camera of a stationary imaging device.

[Figure 3] Figure 3 shows the relation between measured L*a*b values obtained with a spectrophotometer and estimated L*a*b values calculated from RGB values obtained from an image picked up by a stationary imaging device.

[Figure 4] Figure 4 shows a scatter plot of rib muscle L* and muscle a*.

[Figure 5] Figure 5 shows a scatter plot of muscle L* and b*.

[Figure 6] Figure 6 shows a scatter plot of muscle a* and b*.

[Figure 7] Figure 7 shows a drawing of individual images between the numbers in a single increment in L* and a* that have been cut out and pasted from rib eye based on a scatter plot (Figure 4) of rib muscle L* and muscle a*.

[Figure 8] Figure 8 shows a straight line (y= 0.5117x-12.639) passing from the origin through the average value (63.8, 20.1), with the minimum value (an integer value) of all the data being adopted as the origin (54.0, 15.0); a line iBCS4 passing through the average value that is perpendicular to this straight line; and parallel lines iBCS 0 to 7 at intervals of about 2.5 based on Figure 7.

[Figure 9-1] Figure 9-1 shows samples with iBCS values of from 2.12 to 6.55 determined by a fine grading of meat color based on iBCS.

[Figure 9-2] Figure 9-2 shows samples with iBCS values of 2.12 to 6.55, indicating fine grading of meat color based on iBCS. 3

EMBODIMENTS FOR IMPLEMENTING THE INVENTION

[0010]

The method of preparation of a standard for determination of meat color grade of the present invention comprises steps (1) to (4) below: (1) extracting digital images from surfaces of plural of meat samples, (2) obtaining a CIE Lab value of each meat sample from RGB values of the extracted digital image, (3) determining a meat color grade of each meat sample, and (4) obtaining a standard for determination of meat color grade from the relations between the grade and any of two factors of the CIE Lab value obtained on plural of the meat samples, provided that any order between (1) to (2) and (3) can be taken.

[0011]

The method of determination of meat color grade of the present invention comprises steps (11), (12) and (13) below: (11) extracting digital images from a surface of a meat sample to be examined, (12) obtaining a CIE Lab value of the meat sample to be examined from RGB values of the extracted digital image, (13) obtaining a meat color grade of the meat sample to be examined by reference to a standard for determination of meat color grade; wherein the standard for determination of meat color grade is obtained in (a1) to (a4) mentioned below, provided that any order between (a1) to (a2) and (a3) can be taken, (a1) extracting digital images from surfaces of plural of meat samples, (a2) obtaining a CIE Lab value of each meat sample from RGB values of the extracted digital image, (a3) determining a meat color grade of each meat sample, and (a4) obtaining a standard for determination of meat color grade from the relations between the grade and any of two factors of the CIE Lab value obtained in (a2) on plural of the meat samples mentioned in the (a1) and the grade determined in the (a3).

[0012] 4

The method of preparation of a standard for determination of meat color grade and the method of determination of meat color grade of the present invention can be applied to all meat that requires meat color grading, such as beef, pork, and chicken. The meat color can be the color of muscle and the color of fat, It is desirably the color of beef muscle, for which there is particular demand for meat color grading. However, no limitation thereto is intended.

[0013] < The method of preparation of a standard for determination of meat color grade of the present invention>

Step (1)

In this step, digital images are extracted from the surfaces of multiple meat samples. Steps (1) to (3) are steps for determining standards for grading meat in step (4). From the perspective of providing a method of grading meat color that is of high precision, it is desirable for the number of the plurality of meat samples to be large. By way of example, a number between 10 and 200 can be adopted. However, this is just an example, and fewer than 10 or more than 200 will also serve the purpose.

The surface of the meat sample is not specifically limited. In the case of beef muscle, for example, the area between ribs no. 6 and no. 7 on the left side of the carcass, which is used for meat color grading by the Japan Meat Grading Association, can be cut out and the surface employed. Alternatively, the surface of a sample of beef muscle can be the cut cross-section of the beef rib eye. However, there is no intention at limitation to the surface of these parts; the surface can be suitably determined based on the objective of the meat color grading.

[0014]

The taking of digital images can be done with a color image pickup device such as a digital camera or an image scanner. Because the prescribed portion is extracted from the digital image and RGB values are obtained, any digital image that is taken under conditions that provide RGB values will suffice. In the evaluation of muscle color, just the muscle portion is extracted, and in the case of fat color, just the fat portion is extracted, after which the average RGB values are determined.

[0015] 5

Step (2)

In this step, the ClE Lab values of each meat sample are determined from the RGB values of the digital images that have been extracted.

The red (R) average, green (G) average, and blue (B) average values of the muscle portions of the meat samples are determined from the digital images that have been extracted. This method is known, and can be implemented for example according to the methods described in the literature cited below. (1) Kenichiro Takahashi, Takeshi Hori, Michitaka Nami, Toshinori Honma, Hitoshige Kotaka, Keigo Kuchida, BCS number estimation using images from a high-precision carcass transverse section image pickup device, Journal of the Japan Meat Science Society, 77(2): 237-244, 2006. (2) Keigo Kuchida, Mio Hasegawa, Mitsuyoshi Suzuki,Shunzo Miyoshi, BCS number estimation using digital images picked up by a carcass transverse section image pickup device, Japan Meat Science Society, 72, 9, J321-J328, 2001.

[0016]

The R average, G average, and B average that have been determined above are then converted to an XYZ coordinate system. The conversion from RGB to XYZ can be done mathematically using the following conversion equations.

[Math. 1]

RGB XYZ

X= 0.412453x7? + 0.35758x G + 0.180423χ B Y = 0.212671 x 7? + 0.71516 x G + 0.072169 x B Z = 0.019334 x 7? + 0.119193 x G + 0.950227 x B

[0017]

The XYZ coordinate system values are then converted to the L*a*b* coordinate system. The conversion from the XYZ coordinate system to the L*a*b* coordinate system can be done mathematically using the following conversion equations.

[0018] [Math. 2] 6 XYZ -> Lab xr > 0.008856 x, <0.008856 yr > 0.008856 yr <> 0.008856 ^ > 0.008856 ^ <0.008856 / / / j v* [(903.3χ xr +16)/116 ί ^ (903.3-χ yr +16)/116 J & [(903.3 χ :,+16)/116 ,vr =Χ/Χ, y, = nr, :, =Z/Z, ί = 116x7;-16 Λ = 300 *{fx-fy) b = 200x (/^ -f,) [0019]

Step (3)

In this step, the meat color of various meat samples is graded.

In the case of beef, the meat color is graded as the beef color standard (BCS) number employed in meat grading by the Japan Meat Grading Association, for example. Other grading can also be suitably employed. The beef color standard (BCS) number is comprised of seven grades from 1 to 7, and is expressed as a seven-grade BCS standard model. A BCS number is normally assigned (the meat is graded) based on the BCS standard model. In meat color grading based on the BCS standard model, the meat being graded is visually compared to the BCS standard model and the number of the beef color standard (BCS) in the BCS standard model that most closely approximates the meat being graded is selected.

[0020]

In addition to the beef color standard (BCS), for example, grading using a meat color standard from the United States of America or the meat color standard of Australia is also possible. An example of a meat color standard from the United States is the Beef 7

Quality Grades (http://meat.tamu.edu/beefgrading.html) provided by the Department of Animal Science at Texas A&M University, in the Lean Maturity item therein, a description of the grading of Lean Color is given. An example of a meat color standard from Australia is the Beef Chiller Assessment (http://www.australian-meat.com/Foodservice/Proteins/Beef/Beef_Chiller_Assessment/) provided by Meat and Livestock Australia, Ltd. (MLA). Grading by Meat Color is described therein.

[0021]

In the grading of meat color by the meat color standard of the United States or the meat color standard of Australia, the color or the like of a standard is visually compared to meat that is being graded and the number of the standard that most closely approximates the meat being graded is selected from the standard.

[0022]

The extraction of digital images in step (1) and the grading of meat color in step (3) are implemented on the same surface of a single meat sample.

For a single meat sample, either step (1) or step (3) can be conducted first, and following step (1), either step (2) or step (3) can be conducted next.

[0023]

Step (4)

In this step, a meat color grading standard is determined based on the relation between either of the two factors of the CIE Lab values determined in step (2) and the grading determined in step (3). The three factors of L*a*b are present in the CIE Lab values; any two of these factors can be employed. Which two are employed does not matter. In the case of beef, the grading of meat color can be done as a BCS number. As indicated in the embodiment, in the meat color grading of the Holstein species, the use of L* and a* facilitates grading and is comparatively precise when grading meat color using a BCS number. L* and a* can also be employed in the grading of meat color In species other than Holstein.

[0024]

The meat color grading standard In step (4) will be described for the example when L* and a* are employed. From the L*, a*, and BCS number of each meat sample, the BCS number of each meat sample is positioned within the L* and a* coordinates. In 8 positioning the BCS number within the L* and a* coordinates, the coordinates of the minimum value of L* and the minimum value of a* in the plurality of meat samples is adopted as the origin (one point). A straight line is drawn linking the origin and another point with coordinates in the form of the average value of L* and the average value of a* in the plurality of meat samples. Next, a plurality of perpendicular lines each denoting a prescribed grade are positioned at equal intervals from this straight line. In this manner, it is possible to determine a meat color grading standard denoting regions corresponding to the individual BCS numbers (grades) in L* and a* coordinates.

[0025]

Thus, by determining L* and a* of the meat samples being examined and applying this meat color grading standard, it is possible to determine a position in a region corresponding to a BCS number (grade) and automatically determine a BCS number (grade). Further, because the distance from the above perpendicular line is rendered as a number in the coordinates, the BCS number (grade) can be expressed more finely. For example, meat having an L* and an a* defining a point between the BCS numbers 2 and 3 can be expressed as a BCS number of 2.5. Based on the meat color grading standard thus obtained, the grade expressing a finer BCS number is referred to as an iBCS number in the Description of the present invention. The iBCS number is, for example, a beef color grading determined by utilizing the relation between L* and a* calculated by image analysis. The use of any two factors among the CIE Lab values other than L* and a* also permits the creation of a similar meat color grading standard and permits the determination of an iBCS number. Grading based on iBCS numbers will be described in greater detail in the embodiment.

[0026] < The method of determination of meat color grade of the present invention>

The method of determination of meat color grade of the present invention is a method by which CIE Lab values are determined in steps (11) and (12) for meat samples being examined, and these values are applied to a meat color grading standard to grade the meat color of a meat sample that is being examined.

First, steps (11) and (12) are conducted for the meat sample being examined to obtain CIE Lab values. With the exception that the meat sample is a meat sample that is 9 being examined, steps (11) and (12) are identical to steps (1) and (2) in the method of creating a meat color grading standard of the present invention set forth above. The CIE Lab values obtained are applied to the meat color grading standard to grade the meat color. In the same manner as in the method of preparation of a meat color grading standard of the present invention set forth above, the meat color grading standard is a standard that is determined by the method including steps (a1) to (a4). This meat color grading standard provides a calibration curve of the type set forth above (actually, not a calibration curve, but a calibration plane). This calibration curve (plane) is applied to grade the meat color of a meat sample that is being examined. When employing L* and a*, the L* and a* of the meat sample being examined and the distance from a perpendicular line obtained based on the meat color grading standard are determined to grade the meat color of a meat sample that is being examined.

[0027]

By inputting information relating to the meat color grading standard (such as the relation between L* and a* and the grades) and a grade calculating equation based on L* and a* into a computer, it is possible to automatically determine a grade based on the L* and a* of a meat sample that is being examined. Here, the term "grades" can refer to iBCS numbers that are finer than the above BCS number.

EMBODIMENTS

[0028]

The present invention is described below in greater detail through an embodiment. However, the present invention is not intended to be limited to the embodiment.

[0029] A stationary Imaging device was used to take images of the thigh and a transverse section between ribs 6 and 7 on the left side of carcass, yielding high-precision transverse section images. The transverse section between ribs 6 and 7, the rib eye, and the thigh were extracted from the images that were taken (Figure 1). Image analysis software (Beef Analyzer II, Hayasaka Rikoh, Sapporo) was used to calculate image analysis traits. The image analysis traits targeted for investigation in the present 10 embodiment were the muscle R average, the muscle G average, and the muscle B average obtained by binarizing the rib eye and performing calculations on just the muscle portion.

[0030]

The Beef Analyzer II was used on the image taken by the stationary imaging device to extract the rib eye. The muscle R average, muscle G average, and muscle B average of the image analysis traits were cohverted to an XYZ coordinate system.

Those values were then converted to an L*a*b* coordinate system. The conversion equations were as set forth above.

[0031] L*a*b* were values simply determined from the RGB image. They presented the possibility of some deviation from the actual L*a*b*. To examine the error with measured values, a spectrophotometer CM-1000 (Konica Minolta) was used to check the correlation between values of L*a*b* measured with a color chart (Color Checker Passport, X-rite Corp.) against L*a*b* determined from RGB values of a color chart image (Figure 2: rib camera) taken by a stationary imaging device.

[0032]

For detailed meat color grading, the relation between two dimensional distributions of L* vs a*, L* vs b*, and a* vs b*, and BCS numbers obtained by grading were examined. Next, closeness between the combinations of L*a*b* values and the meat color grades visually determined was examined, and a continuous variable in the form of i'BCS was created.

[0033]

Results

Figure 3 shows a scatter plot of measured values of L*a*b* obtained by measurement with a CM-1000 spectrophotometer (Konica Minolta) and L*a*b* determined from the RGB of an image (rib camera, thigh camera) taken by a stationary imaging device. All of the coefficients determined between the traits revealed high correlations of 0.88 or higher. The points of L* and b* were all plotted in a nearly straight line. However, there was a point of a* that was far removed from the approximation curve. The greatly deviating value was from yellow and orange. This was thought to be 11 a problem relating to the characteristics of the digital camera. By reducing this error, it is possible to improve precision.

[0034]

Figures 4 to 6 are scatter plots of rib muscle L* and muscle a*, muscle L* and muscle b*, and muscle a* and muscle b*, respectively. The correlation coefficients were 0.35, 0.53, and 0.37, respectively. When the correlation is strong, that trait alone can be employed. However, no strong association was seen. A look at variation in the BCS number revealed it to be great in the L* and a* scatter plot. Accordingly, it was determined that muscle L* and muscle a* had the lowest correlation, and that meat color would be graded based on the combination of muscle L* and muscle a*, which had the greatest variation in BCS.

[0035]

Based on the scatter plot (Figure 4) of rib muscle L* and muscle a*, individual images between the various numbers of single increments were cut out of the rib eye in L* and a* and are given in Figure 7. Based on visual grading, the meat color tended to become pale when both L* and a* were high. Based on the figure that was drafted (Figure 7), L* was assigned to the X-axis and a* was assigned to the Y-axis. The smallest value (an integer value) of all of the data was made the origin (54.0,15.0) and the function of a line running from the starting point to the average value (63.8, 20.1) was determined (y=0.5117x-12.639). A line crossing through the average value and perpendicular to this line was drawn. The average BCS number of the bovine material employed this time around was 3.9. Thus, the perpendicular line passing through the average value was established as iBCS4. With this straight line as a reference, parallel lines with a spacing of approximately 2.5 were drawn and iBCS 0 to 7 were established, as shown in Figure 8. A meat color grading standard was thus established.

[0036]

Based on the above meat color grading standard, the value of an iBCS for finer grading of meat color was obtained by determining the distance from the individual (L*, a*) point to the iBCS 0 line (y = 1.9543x+164.418) and dividing that value by 2.5. Figure 9 shows samples from iBCS 2.12 to 6.55 indicated by fine meat color grading based on iBCS, an actual fine color grading method. 12 2013264002 20 Oct 2016

INDUSTRIAL APPLICABILITY

[0037]

The present invention is useful in the field of meat and animal husbandry.

[0038]

Throughout this specification and the claims which follow, unless the context requires otherwise, the word "comprise", and variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps.

[0039]

The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as, an acknowledgement or admission or any form of suggestion that that prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates. 13

Claims

The claims defining the invention are as follows;

1. A method of preparation of a standard for determination of meat color grade comprising steps of: (1) extracting digital images from surfaces of plural of meat samples, (2) obtaining a CIS. Lab value of each meat sample from RGB values of the extracted digital image, (3} determining a meat color grade of each meat sample, and (4) obtaining a standard for determination of meat color grade from the relations between the grade and any of two factors of the CIE Lab value obtained on plural of the meat samples, provided that any order between (1) to (2} and (3} can be taken, and determination of a position in L* and a* coordinates of the grade in (4) is made by appointing a minimum value of L* and a minimum value of a* of the plural of meat samples as the starting point in the coordinates, obtaining straight lines connecting the starting point and a coordinate of an average value of l* and an average value of a* of the plural of the meat samples, and obtaining plural of perpendicular lines from the above straight lines at regular intervals and such that each domain shows a certain grade.
2. The method of claim i, wherein when the meat to be examined is a beef, the grade of the meat color in (3) is Beef Color Standard (BOS) number.
3. The method of claim 2, wherein the standard for determination of meat color grade in (4} is obtained by positioning 8CS number of each of the meat samples in coordinates from the L* and a* and the BG5 number of each of the meat samples.
4. The method of any one of claims 1 to 3, wherein when the meat to be examined is a beef, the grade of the meat color in (3) is either a Beef Quality Grades on a Lean Color in the beef quality standard provided by Department of Animal Science at Texas A&M University Beef Center or a meat color grade in the Beef Chiller Assessment on a frozen beef evaluation provided by Meat and Livestock Australia, Ltd.
5. A method of determination of meat color grade comprising steps of: (11) extracting digital images from a surface of a meat sample to be examined, (12) obtaining a CIE Lab value of the meat sample to be examined from RGB values of the extracted digital image, (1.3) obtaining a meat color grade of the meat sample to be examined by reference to a standard for determination of meat color grade; wherein the standard for determination of meat color grade is obtained in (ai) to (a4) mentioned below, provided that any order between (a 1) to (a2) and (a3) can be taken, (ai) extracting digital images from surfaces of plural of meat samples, (a2) obtaining a CIE Lab value of each meat sample from RGB values of the extracted digital image, (a3) determining a meat color grade of each meat sample, and (a4) obtaining a standard for determination of meat color grade from the reiations between the grade and L* and a* of the CIE Lab value obtained in (a2) on plural of the meat samples mentioned in.the (ai) and the grade determined in the (a3), wherein determination of a position in L* and a* coordinates of the grade in (4) is made by appointing a minimum value of L* and a minimum value of a* of the plural of meat samples as the starting point in the coordinates, obtaining straight lines connecting the starting point and a coordinate of an average value of L* and an average value of a* of the plural of the meat samples, and obtaining plural of perpendicular lines from the straight lines at regular intervals and such that each domain shows a certain grade.
6. The method of claim 5, wherein the meat color grade of the meat sample to be examined in (a3) is obtained from a distance between any of the perpendicular lines and the L* and a* of the meat sample to be examined.
7. The method of claim 5 or 8, wherein when the meat to be examined is a beef, the meat color grade in (a3) is Beef Color Standard (BCS) number.
8. The method of claim 7, wherein the standard for determination of meat color grade in (a4) is obtained by positioning BGS number of each of the meat samples in coordinates from the L* and a* and BGS number of each of the meat samples,
9. The method of any one of claims 5 to 8, wherein when the meat to be examined is a beef, the grade of the meat color in (a3} is either a Beef Quality Grades on a Lean Color in the beef quality standard provided by Department of Animal Science at Texas A&M University Beef Center or a meat color grade in the Beef Chiller Assessment on a frozen beef evaluation provided by Meat and Livestock Australia, Ltd,