KR101156055B1 - Method for predicting palatability for Hanwoo steer beef - Google Patents

Abstract

The present invention substitutes the carcass weight, backfat thickness, and intramuscular fat of castrated beef into the following formula to calculate a taste score of 5 and compares the calculated value with a taste grade boundary score standard to determine the taste grade of meat. It relates to a method for determining the taste grade of castrated beef including the step.
Taste score 5 = intercept value + (carcass weight regression coefficient × carcass weight of input subject axis) + (backfat thickness regression coefficient × backfat thickness of input subject axis) + (intramuscular fat regression coefficient × intramuscular fat figure of input subject axis)
The invention also relates to the intercept value and the regression coefficient for calculating the taste score 5 described above.

Description

Method for judging taste rating of castrated beef {Method for predicting palatability for Hanwoo steer beef}

The present invention relates to a taste grade determination method of castrated beef, and more particularly, calculating the taste score 5 by substituting the carcass weight, back fat thickness and intramuscular fat degree of castrated beef into the following equation and the calculated value: It relates to a taste grade determination method of castrated beef comprising the step of determining the taste grade of the meat by comparing with the taste grade boundary score criteria.

Taste score 5 = intercept value + (carcass weight regression coefficient × carcass weight of input subject axis) + (backfat thickness regression coefficient × backfat thickness of input subject axis) + (intramuscular fat regression coefficient × intramuscular fat figure of input subject axis)

The invention also relates to the intercept value and the regression coefficient for calculating the taste score 5 described above.

The evaluation of beef taste or research on the year improvement technology has been performed at home and abroad and published in specialized journals, but it was about the development of a fragmentary device or the maturation effect. If entered, there is no taste level management technology that can be easily utilized in slaughterhouses, processing plants and sales places in the form of computerized operation programs. Existing techniques have often been studied mainly in the sirloin or stupid area, and there was no technique for calculating the taste score estimation formula for meat grade, muscle fatness, maturation period and cooking method for the 11 representative sites as in the present invention.

The beef is not uniform in quality because it is not carried out after the slaughter, so it is important to reflect the effects of ripening in consideration of muscle characteristics of each part to ensure uniformity of meat. Therefore, an object of the present invention is to calculate the taste score estimation formula of Hanwoo beef by reflecting the weight score weight by factors influencing the taste of Hanwoo meat (quality grade, maturation period, site, cooking method and maturity) and computerized operation Develop a program.

Previously, castrated beef meat was distributed by only one meat grade per individual without considering the meat quality of each portion according to the carcass grade. It is a method that gives taste level by cooking method in consideration of changes in meat quality, so it is more advanced than the existing method, and it is the first technique that can predict the taste level in advance without having to eat Hanwoo meat directly.

Looking at the market size of the beef industry, Korean cattle accounted for 76.6% (588,003) of the total 767,671 cattle slaughtered in 2008. Among them, castrate accounted for 38%, and the prevalence of high-grade meats above grade 1 was 76%. There are about 198 Korean beef brand companies that distribute beef grade 1 or higher, and the market size is very large, including 300 slaughterhouses, processing plants, and sales sites. Therefore, if the domestic Korean beef brand companies utilize the technology of the present invention can be very marketable.

The present invention provides the refrigeration aging for the year, juiciness and flavor which is a factor for determining the taste of Korean beef in order to prepare a taste certification standard for each portion of the consumer-oriented Hanwoo meat and to provide consumers with uniform meat quality beef It is a technique to secure the uniformity of meat quality of Hanwoo meat in consideration of the effect of each part and cooking method.

The present invention is derived from the above requirements, the present inventors can predict the taste level of castrated beef in consideration of the effects of cooking method, site, meat grade, maturation period and maturity through multiple linear regression analysis. By devising the present invention was completed.

In order to solve the above problems, the present invention substitutes the carcass weight, back fat thickness and intramuscular fat degree of castrated beef into the following formula to calculate a taste score of 5 and compares the calculated value with a taste grade boundary score standard. Provided is a taste grade determination method of castrated beef comprising the step of determining the taste grade of meat.

Taste score 5 = intercept value + (carcass weight regression coefficient × carcass weight of input subject axis) + (backfat thickness regression coefficient × backfat thickness of input subject axis) + (intramuscular fat regression coefficient × intramuscular fat figure of input subject axis)

The present invention also provides the intercept values and regression coefficients described in Table 1 for calculating the taste score 5 described above.

Through the present invention, it is possible to secure uniformity of meat quality and year for 11 parts of Hanwoo meat in consideration of the maturation period, the characteristics of each part and the cooking method, and provide taste grade information when selling Hanwoo meat to consumers' selection. In addition to expanding purchasing opportunities, they can also drive quality and distribution differentiation with imported beef. In addition, by reflecting the maturation effect on the low-fat areas of tough meat quality, it is possible to improve the added value and increase the balance consumption by parts by improving the year and meat quality.

1 shows a density plot (a) and a post probability plot (b) of taste grade by discriminant analysis using year, juiciness and flavor score results evaluated by Korean consumers (n = 4,600).
Solid red line: Class 1, Green dotted line: Grade 2, Blue dotted line: Grade 3, Black dotted line: Grade 4
2 shows a taste management program that can be easily used in slaughterhouses, processing plants or sales floors.

In order to achieve the object of the present invention, the present invention substitutes the carcass weight, back fat thickness and intramuscular fat degree of castrated beef into the following formula to calculate a taste score of 5 and the calculated value is based on the taste grade boundary score criteria. Comparing the present invention provides a taste rating method of castrated beef comprising the step of determining the taste rating of the meat.

Taste score 5 = intercept value + (carcass weight regression coefficient × carcass weight of input subject axis) + (backfat thickness regression coefficient × backfat thickness of input subject axis) + (intramuscular fat regression coefficient × intramuscular fat figure of input subject axis)

The method may provide a taste grade and a recommended dish for each portion in association with a beef individual carcass grading factor. The application part of the taste score 5 calculation formula is 11 parts (loin fillet, chopped tip, neck core, burri, upjin, brisket, sesame, boseom, red sesame seeds, crucible or dungeon) When meat is cooked by three cooking methods (grill, roast, or hot water), the eleven parts can be divided into 0, 7 or 14 days after slaughter to predict the taste grade. The taste score 5 formula is calculated based on the results of 4,600 consumers directly eating Hanwoo meat in the same condition and evaluating the year, juiciness and flavor items.

In addition, the method can be used to produce a taste management program that can be easily used in slaughterhouses, processing plants or sales floors. The taste management program may be configured to give different taste grades for each cooking method according to the maturation period based on the date of slaughtered cattle. The taste grades are finally divided into four grades (dissatisfaction, excellent taste, very good taste, or very good taste) according to the result calculated based on the taste score 5 calculation according to the individual factor condition. The program can be designed to output the taste label of the time point. Since the calculation of the taste score 5 calculation formula is applied from 0 to 14 days after slaughter, after the 14 days have passed since the slaughter processing date, it can be limited to apply the taste grade judgment result to sell. As a follow-up plan to prevent fraudulent distribution of taste grade management program, it operates as 'weight-based labeling output limiting system' to limit the output of taste grade judgment result labels at the time of sale and to limit the number of labeling outputs in connection with the part meat production. It can monitor the labeling output frequency for the part weight and control the output when the quantity is exceeded. However, operation errors such as label damage or ink shortage can be operated with the 'Reissue' button so that only the label weight allowed is operated.

The taste grade boundary score criterion refers to dissatisfaction (less than 49), excellent taste (50 to 68), very good taste (69 to 82), or very good taste (83 or more).

The intercept value and all the regression coefficients are calculated by substituting the taste score 5 in Table 1 according to the cooking method, site, meat grade, ripening period and maturity.

The cooking method may be grilling, grilling or boiling water, but is not limited thereto.

The part may be ups, bottoms, tail end, sirloin, beak, crucible, neck, stupid, red thickness, sesame or bristle, but is not limited thereto.

The meat grade may be 1 ⁺⁺ , 1 ⁺ , 1 or 2 grade, but is not limited thereto.

The maturation period may be 0, 7 or 14 days, but is not limited thereto.

The degree of maturity refers to the item indicating the degree of ossification, and is from 1 to 9 (Agricultural Product Notification No. 2007-40 Livestock Grading Standard Article 5, Paragraph 2, No. 5). In general, maturity 2 can be estimated to be 24 to 32 months old, maturity 3 to 32 to 42 months old.

The back fat thickness refers to the measurement of the back fat at the point where two-thirds of the point enters the abdomen along the right side of the longitudinal root section of the abdominal muscle in mm.

The intramuscular fat level refers to a determination made by comparing the intramuscular fat standard of the small carcasses according to the Article 5 Paragraph (1) of the Livestock Grading Standard.

All the regression coefficients can be calculated based on the year, juiciness and flavor values of the castrated beef.

The present invention also provides the intercept values and regression coefficients described in Table 1 for calculating the taste score 5 described above. The section value and the regression coefficient are provided in Table 1 according to the cooking method, site, meat grade, ripening period and maturity.

Hereinafter, the present invention will be described in detail by way of examples. However, the following examples are illustrative of the present invention, and the present invention is not limited to the following examples.

Materials and methods

Sample preparation for sensory evaluation

The sample used in this study was 1⁺⁺, One⁺, 72 cows (24-30 months) of castrated Hanwoo (1 to 2 meat grades) were purchased and used. After slaughter, the carcases were stored in a 1 ° C cooling room and transported to the National Institute of Animal Science and Technology at the next day in a semiconductor state. Cuy, Upjin, Brisket, Sesame, Boseob, Red Sesame Seed, Crucible and Clue) were separated. Eleven parts separated from the left carcasses were pretreated by cooking methods, and the eleven parts separated from the right carcasses were pretreated for 7 or 14 days in a 2 ° C maturation room. After maturation, each of the eleven sites separated from the left carcass was pretreated according to the cooking method. After mixing the unaging sample and the ripening sample, each sample was sliced and vacuum-packed so that one sample could be provided to one consumer, and then stored at −20 ° C. for about three weeks until the consumer sensory evaluation was performed.

How to cook

Steak samples for grills are cut into 50 × 70 × 25 mm (horizontal × vertical × height) and cooked in double surface Panini Griller (Sirman, PD-R with timer, Italy) for 10 minutes at 220 to 230 ° C for 7 minutes. It was. After cooking, the steak blocks removed from the Griller were left at room temperature for 2 minutes and sliced in half to serve to the evaluators. At this time, the degree of ripening was set to medium-well done level, which reflects the tendency of Korean consumers to avoid steeping steak when cooked and provided at medium level in the preliminary test. Roasting samples were prepared in the form of strips of uniform size (75 × 20 × 4 mm), and each strip was baked under the same conditions on a tin plate plate attached with a water jacket (ca. 245 to 255 ° C). Served. The roasting method was to turn the grilled beef strips over to the point where the surface began to shrink as the water began to drain and then roasted until the red color disappeared and served to each customer. The hot dishes are prepared by slicing them to a constant size (25 × 70 × 4 mm) and boiled for 2 minutes in 100 ℃ water by using a cooker (Dasol Scientific Co. Ltd., Korea) for each customer. It became.

Consumer sensory evaluation

Consumers divided the whole country into four regions (Seoul, Central, Yeongnam, and Honam) to perform sensory evaluation of the same treatment and conditions for each region. In order to ensure that the sensory evaluation is not influenced by any factors other than those related to consumer and beef samples, all relevant processes such as sample preparation, presentation order, and evaluation method can be subdivided and carried out under the same conditions as possible. Sensory evaluation was performed by session according to each cooking method, and one consumer evaluated a total of seven samples. In order to give the same evaluation criteria to all consumers who were evaluating, the first of the seven samples was presented as a standard sample, and the other six samples were provided in order by the 'Latin Square' arrangement method. Consumers filled out socio-demographic details on an individual basis before evaluation. Sensory evaluation was performed using line scale estimation prepared at four 100 mm for year, juiciness and flavor. The item map was constructed as follows: year = very tough (0), very light (100); Succulent = very dry (0), very succulent (100); Flavor = I don't like it very much (0), I like it very much (100).

Example  1: sensory evaluation results Taste score  1 and Taste score  5 calculation

The multiple linear regression models used to predict taste scores are as follows;

Ｙ = β ₀ + β ₁ Ｘ ₁ + β ₂ Ｘ ₂ + β ₃ Ｘ ₃ + ε

Ｙ: taste score, β ₀ : intercept, ε: error term

Ｘ ₁ , Ｘ ₂ , Ｘ ₃ : Explanation variable

β ₁ , β ₂ , β ₃ : estimated regression coefficients

The statistical model for taste score prediction was set as above, and the regression coefficient was estimated to minimize the error square sum using the least square method and calculated using the procedure proc reg built in SAS software. After arranging the necessary data in the data procedure, the regression coefficient was estimated by using proc reg for each case.

First, the taste score 1 was expressed as a linear function reflecting the measured year, juiciness, and flavor score results by performing a total sensory evaluation. The taste score 1 has different values by categorical variables (cooking method, site, meat grade, days of maturity and maturity), and (year score × 0.55) + (juicy score × 0.17) + (flavor score × 0.28) Is fixed.

Thereafter, the intercept and regression coefficient for carcass weight, backfat thickness and intramuscular fat degree were calculated by reflecting the derived taste score 1, and the results are shown in Table 1. That is, the carcass weight regression coefficient reflects the change in taste score according to the carcass weight value and the categorical variables (total 594) of the individual axis (72 heads) of the slaughter and consumer sensory evaluation. It was calculated by the first-order relation, reflecting the value of taste score 1 derived. The back fat thickness regression coefficient reflects the change in taste score according to the back fat thickness value and the categorical variables (total 594) of the individual axis (72 heads) evaluated by the consumer slaughter evaluation. It was calculated by the first-order relation, reflecting the value of taste score 1 derived. Intramuscular fat regression coefficients were derived from actual conditions of cathodic variables (total 594) and values reflecting the 'flavor score change according to the musculoskeletal fat value' of the individual axis (72 heads) evaluated by the slaughter and consumer sensory evaluation. It was calculated by the first-order relation reflecting one taste score 1 value. A total of 338 intercepts and regression coefficients are shown in Table 1, considering only the conditions that can be realistically obtained from the combination of all categorical variables (594 total).

Therefore, the taste score 5 was computed according to the following formula;

Taste score 5 = Intercept value + Actual taste score by carcass condition ( Taste score 2 ) + Actual fat score by backfat thickness condition ( Taste score 3 ) + Actual taste score by intramuscular fat condition ( Taste score 4 )

= Intercept value + (carcass weight regression coefficient × carcass weight of input target axis) + (back fat thickness regression coefficient × backfat thickness of input target axis) + (intramuscular fat regression coefficient × intramuscular fat map of input subject axis)

Taste score 2: Taste score calculated by measuring sensory evaluation results for each carcass condition

Taste score 3: Taste score calculated by measuring sensory evaluation results for each condition of back fat thickness

Taste score 4: Taste score calculated by measuring sensory evaluation results by intramuscular fat condition

The taste score 5 is the predicted taste score, the value of the explanatory variable and the value of the taste score 1 obtained from the measurement of each categorical variable (total 594: site 11, cooking method 3, the number of maturity 3, meat grade 3 and maturity 2) conditions Depends on.

Example  2: Taste score  5 using formula Taste grade  Judgment

The taste score 5 and taste grade can be predicted using the taste score 5 calculation formula described in Example 1. For example, in case of carcasses with carcass weight of 330.7 kg, back fat thickness of 6 mm, intramuscular fat degree of 6, meat grade 1 ⁺⁺ , the intercept value and regression coefficient for each specified condition are found in Table 1, and then the taste score 5 is calculated. Calculate by substituting for. That is, in the above example, the cooking method is roasting (b), the site is subsidiary (b), the sex is castration (s), the meat quality is 1 ⁺⁺ grade (1), the maturation is 0 days (00), the maturity is twice ( Assuming 2), if the labeling information is found in Table 1, bbs1002 is obtained. Therefore, by substituting each value into the taste score 5 equation,

The taste score is 5 = 106.14 + (-0.0925 × 330.7 kg) + (0.474 × 6) + (1.1886 × 6) = 85.52 points.

The meaning according to the labeling sequence shown in Table 1 is as follows;

How to cook: Grilled (b), Grilled (g) and Tang (s)

Site: Upjin (a), Boseob (b), Chassis (c), Sirloin (d), Curi (g), Crucible (k), Core (m), Udon (u), Red sesame seeds (h), Sesame (s) ) And bristle (y)

Gender: Castion (s)

Meat Grade: 1 ⁺⁺ and 1 ⁺ Grade (1), Grade 1 (2) and Grade 2 (3)

Aging days: 0 (00), 7 (07), and 14 (14)

Maturity: 2 (2) and 3 (3)

FIG. 1 shows a density function calculated using a kernel function, which is a nonparametric statistical method, and is a result calculated using a procedure proc discrim embedded in SAS software. This calculation calculates the probability of belonging to each taste class for values between 0 and 100, and estimates the point where the probability changes as the boundary point. Therefore, 85.52 points are judged as taste very excellent taste grades based on the taste grade boundary scores estimated in FIG. 1 (satisfaction less than 49, excellent taste of 50-68, very good 69-82, and very good taste of 83). Can be.

The taste score 5 calculation and taste grade determination process can be easily performed through a taste grade management program (FIG. 2). That is, when the carcass weight, backfat thickness, intramuscular fat degree, meat grade and maturity are inputted into the taste grade management program, the taste score is automatically calculated and the taste grade determination result is shown for each part and cooking method. And its programs are expected to be very useful in slaughterhouses, processing plants or sales floors.

Intercept and Regression Information Labeling Information Intercept Conductor weight
Regression coefficient Back fat thickness
Regression coefficient Intramuscular fat map
Regression coefficient One bbs1002 106.14 -0.0925 0.474 1.1886 2 bcs1002 33.86 -0.0202 -0.739 7.6732 3 bds1002 114.44 -0.0499 0.603 -2.665 4 bgs1002 127.85 -0.1696 2.635 -2.714 5 bhs1002 58.66 0.0251 1.14 -2.495 6 bks1002 -26.19 0.4696 -10.83 -0.171 7 bms1002 126.05 -0.0851 0.658 -5.33 8 bss1002 122.56 -0.1358 2.444 -3.983 9 bus1002 110.16 -0.1304 3.471 -4.455 10 bbs1072 129.22 -0.0793 0.418 -2.866 11 bcs1072 99.03 -0.0203 -0.991 0.0105 12 bds1072 110.28 -0.0113 0.3 -3.188 13 bgs1072 75.16 -0.0045 1.562 -3.616 14 bhs1072 178.74 -0.1608 0.534 -6.919 15 bms1072 57.07 0.1515 -1.179 -5.511 16 bss1072 52.38 0.0264 -4.97 7.1936 17 bus1072 150.98 -0.0488 0.702 -10.05 18 bbs1142 36.39 0.2173 -5.825 1.3151 19 bcs1142 63.07 0.273 -7.007 -4.957 20 bds1142 90.95 -0.0643 0.237 2.5489 21 bgs1142 239.39 -0.8379 20.581 -1.741 22 bhs1142 -21.96 0.5783 -15.22 -2.068 23 bks1142 307.53 -1.3497 32.406 3.2474 24 bms1142 -1384 10.6548 -248.9 -98.34 25 bss1142 165.36 -0.3773 3.357 4.6898 26 bus1142 185.12 -0.6865 17.04 0.9626 27 bbs1003 79.83 -0.0033 0.392 -1.51 28 bcs1003 0.83 0.1163 -0.717 4.4744 29 bds1003 47.38 0.0642 -0.043 1.4389 30 bgs1003 54.54 0.0244 0.807 -2.091 31 bhs1003 44.37 0.0098 -0.077 2.0741 32 bks1003 -7.68 -0.0529 0.554 15.431 33 bms1003 5.71 0.1027 0.187 0.6759 34 bss1003 43.29 0.0229 1.33 -0.962 35 bus1003 -5.02 0.1087 -0.139 2.1617 36 bbs1073 71.08 -0.0151 -0.394 2.7314 37 bcs1073 131.8 -0.0805 0.696 -3.391 38 bds1073 146.23 -0.1212 3.602 -6.939 39 bgs1073 219.26 -0.2494 8.233 -17.87 40 bhs1073 -34.72 0.2187 -1.146 1.2637 41 bms1073 90.32 -0.096 5.806 -6.432 42 bss1073 11.98 0.1703 -1.832 0.6871 43 bus1073 98.94 -0.0196 0.997 -5.538 44 bbs1143 61.96 0.2533 -0.468 -13.77 45 bcs1143 -21.37 0.128 1.278 5.6869 46 bds1143 60.53 0.068 -0.162 -0.573 47 bgs1143 110.63 0.077 -0.91 -9.105 48 bhs1143 118.97 0.2195 -1.289 -22.18 49 bks1143 67.3 0.3525 -1.639 -18.68 50 bms1143 -27.77 0.0867 0.109 8.5379 51 bss1143 33.96 -0.0015 -0.617 5.8254 52 bus1143 106.08 0.2209 -3.521 -15.54 53 bbs2002 94.38 0.046 -3.053 -2.634 54 bcs2002 84.44 0.0385 -4.153 0.0296 55 bds2002 73.92 -0.0919 1.753 5.627 56 bgs2002 52.43 -0.0263 -3.267 9.248 57 bhs2002 74.76 0.0402 -3.412 -2.194 58 bks2002 17.34 0.0284 -0.193 8.6127 59 bms2002 -41.24 -0.0095 0.588 21.534 60 bss2002 -21 0.0731 0.461 10.797 61 bus2002 49.35 0.0422 -6.388 8.4246 62 bbs2072 71.22 0 0 0 63 bcs2072 68.31 0 0 0 64 bds2072 79.79 0 0 0 65 bgs2072 60.36 0 0 0 66 bhs2072 52.59 0 0 0 67 bks2072 63.75 0 0 0 68 bms2072 55.5 0 0 0 69 bss2072 55.36 0 0 0 70 bus2072 57.43 0 0 0 71 bbs2142 44.15 0.0305 -4.481 11.748 72 bcs2142 76.94 0 0 0 73 bds2142 13.61 0.0161 -2.999 17.484 74 bgs2142 61.54 0 0 0 75 bhs2142 117.7 0.067 -6.979 -5.657 76 bks2142 19.16 0.0306 -9.092 22.76 77 bms2142 -14.23 -0.0124 -1.357 18.91 78 bss2142 -1.537 0.01364 -11.18 30.344 79 bus2142 56.535 0.03231 0.7524 -2.726 80 bbs2003 59.657 -0.0927 2.6737 5.5173 81 bcs2003 116.19 0.02471 0.2275 -12.62 82 bds2003 86.662 -0.16759 2.6488 7.3174 83 bgs2003 205.86 -0.23093 1.6549 -13.79 84 bhs2003 39.064 0.23605 -4.756 -7.301 85 bks2003 70.476 0 0 0 86 bms2003 86.035 -0.00268 0.2368 -4.045 87 bss2003 55.676 0.07927 -0.988 -3.278 88 bus2003 2.317 0.04932 3.7282 -0.221 89 bbs2073 148.44 -0.17329 2.8042 -5.896 90 bcs2073 127.75 -0.22297 3.7494 3.0189 91 bds2073 93.94 -0.06811 0.8315 1.7237 92 bgs2073 -64.69 0.26619 1.706 0.9548 93 bhs2073 146.7 0.03645 -2.032 -17.06 94 bms2073 303.19 -0.25259 -0.564 -25.96 95 bss2073 339.12 -0.32366 -1.225 -24.9 96 bus2073 114.39 -0.03981 0.2106 -7.93 97 bbs2143 76.795 0 0 0 98 bcs2143 72.187 0 0 0 99 bds2143 79.363 0 0 0 100 bgs2143 69.263 0 0 0 101 bhs2143 63.991 0 0 0 102 bks2143 75.978 0 0 0 103 bms2143 69.068 0 0 0 104 bss2143 75.818 0 0 0 105 bus2143 70.888 0 0 0 106 gbs1002 70.495 -0.18594 1.3751 7.4613 107 gcs1002 -9.131 0.00237 -1.162 13.106 108 gds1002 28.86 0.0848 -1.783 3.9671 109 ggs1002 71.039 -0.06994 3.463 -3.214 110 ghs1002 63.446 -0.02386 3.698 -4.715 111 gks1002 53.907 0 0 0 112 gms1002 101.98 -0.14882 0.8118 1.5197 113 gss1002 17.808 0.08993 -0.728 0.1372 114 gus1002 -20.54 0.2095 -2.637 2.0493 115 gbs1072 129.1 -0.11725 -0.357 -0.516 116 gcs1072 182.8 -0.20149 0.9195 -4.093 117 gds1072 114.54 -0.14201 2.3457 -0.921 118 ggs1072 -52.74 0.12866 -3.951 12.504 119 ghs1072 280.97 -0.39501 0.6737 -7.488 120 gms1072 146.36 -0.10538 2.0968 -9.371 121 gss1072 189.92 -0.27738 -5.544 6.3864 122 gus1072 197.88 -0.27141 1.3361 -5.651 123 gbs1142 67.596 0 0 0 124 gcs1142 79.278 0 0 0 125 gds1142 73.061 0 0 0 126 ggs1142 60.78 0 0 0 127 ghs1142 63.141 0 0 0 128 gks1142 60.18 0 0 0 129 gss1142 57.012 0 0 0 130 gus1142 55.101 0 0 0 131 gbs1003 12.121 0.06581 1.4265 -0.916 132 gcs1003 -46.38 0.14459 -2.937 12.203 133 gds1003 -24.07 0.16801 -0.025 4.1568 134 ggs1003 6.38 0.07538 1.0536 -0.631 135 ghs1003 -24.88 0.09878 2.2644 2.7894 136 gks1003 -105.2 -0.01779 2.3924 20.375 137 gms1003 18.844 -0.02601 3.6466 -0.362 138 gss1003 41.982 0.02992 -0.387 -0.271 139 gus1003 -28.33 0.08622 -0.922 6.8508 140 gbs1073 -26.9 0.07385 -3.066 11.322 141 gcs1073 214.15 -0.22918 3.2016 -9.414 142 gds1073 173.17 -0.19728 3.3997 -6.814 143 ggs1073 289.83 -0.29486 0.8541 -15.54 144 ghs1073 52.268 0.06086 -2.24 1.1107 145 gms1073 56.024 0 0 0 146 gss1073 149.13 -0.02884 -1.805 -8.479 147 gus1073 81.875 -0.00751 0.4096 -4.451 148 gbs1143 4.405 -0.00868 3.0516 6.2113 149 gcs1143 97.883 0.25344 -2.303 -16.25 150 gds1143 166.36 -0.14543 -1.876 -2.826 151 ggs1143 2.85 0.34001 -0.409 -12.98 152 ghs1143 -22.27 0.1728 0.3339 1.6135 153 gks1143 143.35 0.19281 -1.452 -23.9 154 gms1143 47.303 0 0 0 155 gss1143 74.849 0.20857 -0.737 -17.81 156 gus1143 33.65 0.04403 -1.675 3.2336 157 gbs2002 50.297 -0.01821 0.8874 3.1357 158 gcs2002 142.3 0.0099 -1.553 -17.28 159 gds2002 26.957 -0.00235 -1.086 12.774 160 ggs2002 -49.33 -0.1082 5.3166 20.482 161 ghs2002 56.726 -0.05952 2.7347 -1.212 162 gks2002 -41.68 -0.15787 1.7564 29.596 163 gms2002 37.292 0 0 0 164 gss2002 29.333 0.00542 -1.007 2.6013 165 gus2002 104.1 -0.09084 -2.814 -0.437 166 gbs2072 75.851 0 0 0 167 gcs2072 81.435 0 0 0 168 gds2072 66.405 0 0 0 169 ggs2072 67.328 0 0 0 170 ghs2072 52.171 0 0 0 171 gks2072 43.937 0 0 0 172 gms2072 53.83 0 0 0 173 gss2072 57.304 0 0 0 174 gus2072 47.174 0 0 0 175 gbs2142 141.22 0.05052 4.8429 -24.87 176 gcs2142 100.79 -0.03425 -3.903 4.856 177 gds2142 36.358 0.01848 1.8403 4.7149 178 ggs2142 19.485 -0.13385 -11.56 35.186 179 ghs2142 62.184 0.09104 2.3945 -12.08 180 gks2142 -48.63 -0.04294 -9.752 39.687 181 gms2142 49.921 0 0 0 182 gss2142 24.089 0.04017 9.9713 -10.87 183 gus2142 -28.2 -0.05469 2.3383 16.505 184 gbs2003 152.46 0.03412 -6.601 -13.18 185 gcs2003 107.2 -0.16019 2.5824 -1.291 186 gds2003 130.89 0.03315 -2.32 -11.25 187 ggs2003 -66.92 0.34044 -3.858 3.4752 188 ghs2003 165.32 -0.07075 -3.051 -13.38 189 gks2003 48.536 0 0 0 190 gms2003 41.479 0 0 0 191 gss2003 70.914 0.10856 -1.942 -10.56 192 gus2003 32.391 0.10071 -2.279 -1.211 193 gbs2073 276.06 -0.18289 -3.335 -24.01 194 gcs2073 138.42 -0.39548 8.85 4.6345 195 gds2073 51.116 -0.1844 4.6779 10.315 196 ggs2073 76.943 -0.0889 2.0538 0.1313 197 ghs2073 112.03 -0.12565 0.5677 -0.028 198 gms2073 55.922 0 0 0 199 gss2073 42.739 0.29192 -4.425 -12.81 200 gus2073 111.06 -0.10304 1.9264 -9.354 201 gbs2143 77.16 0 0 0 202 gcs2143 77.761 0 0 0 203 gds2143 79.875 0 0 0 204 ggs2143 62.387 0 0 0 205 ghs2143 69.177 0 0 0 206 gks2143 63.926 0 0 0 207 gms2143 51.524 0 0 0 208 gss2143 65.692 0 0 0 209 gus2143 52.868 0 0 0 210 sbs1002 92.312 -0.11089 2.1166 -0.361 211 scs1002 -11.83 0.12155 -2.788 7.4641 212 sds1002 162.17 -0.22378 5.41 -7.649 213 sgs1002 29.323 0.14308 -1.639 -2.211 214 shs1002 45.922 -0.0171 1.372 0.3563 215 sks1002 131.7 -0.75522 23.575 4.2375 216 sms1002 222.08 -0.3399 2.25 -5.97 217 sss1002 55.613 -0.06083 2.6669 -1.278 218 sus1002 60.911 -0.07367 2.0253 -0.656 219 sas1002 82.162 -0.02315 -0.311 0.6668 220 sys1002 8.417 0.0344 -1.482 7.1661 221 sbs1072 118.94 -0.08118 -0.302 -1.87 222 scs1072 192.83 -0.18019 0.6578 -7.816 223 sds1072 114.04 -0.06531 1.5397 -4.059 224 sgs1072 -112.2 0.18412 1.9495 6.9329 225 shs1072 67.683 -0.06232 0.6128 0.6792 226 sms1072 92.821 -0.09949 0.01 -0.331 227 sss1072 86.376 -0.05161 -0.691 0.1787 228 sus1072 100.88 0.00126 0.2387 -7.098 229 sas1072 45.937 0.07315 -0.665 0.778 230 sys1072 142.43 -0.06219 -0.635 -5.769 231 sbs1142 175.61 -0.67949 15.784 4.0492 232 scs1142 74.33 0 0 0 233 sds1142 111.87 -0.6033 18.34 5.619 234 sgs1142 102.08 -0.4394 15.789 -0.603 235 shs1142 -0.53 0.3468 -7.264 -2.557 236 sks1142 130.4 -0.4657 12.768 2.218 237 sms1142 -157.3 2.598 -63.72 -41.37 238 sss1142 29.03 0.4095 -13.51 -2.902 239 sus1142 162.52 -0.4859 10.95 -2.538 240 sas1142 -36.2 0.5868 -12.3 -1.969 241 sys1142 -11.23 0.3267 -8.277 1.866 242 sbs1003 74.1 -0.1131 -0.473 5.528 243 scs1003 -91.69 0.2201 0.145 7.564 244 sds1003 101.99 -0.0367 0.872 -3.162 245 sgs1003 192.65 -0.2127 3.662 -12.66 246 shs1003 -44.08 0.1271 2.976 2.07 247 sks1003 170.66 -0.1604 -3.199 -1.17 248 sms1003 102.16 -0.1679 3.257 -2.892 249 sss1003 -22.72 0.0727 1.027 4.03 250 sus1003 70.14 -0.0727 -0.922 2.054 251 sas1003 54.78 0.0677 0.062 -2.16 252 sys1003 66.06 -0.0697 0.895 2.656 253 sbs1073 -144.9 0.3189 -3.405 15.047 254 scs1073 19.42 0.0997 -0.184 1.288 255 sds1073 81.22 -0.0597 1.947 -0.372 256 sgs1073 60.09 0 0 0 257 shs1073 16.62 0.0688 -2.566 4.944 258 sms1073 2922.9 -5.4161 87.436 -205.4 259 sss1073 220.9 -0.2165 2.039 -12.4 260 sus1073 36.7 0.0763 -3.934 3.081 261 sas1073 54.42 0.0466 1.381 -2.005 262 sys1073 71.11 0.0073 -2.209 0.888 263 sbs1143 48.85 0.1503 0.887 -6.99 264 scs1143 246.87 0.3817 -1.586 -52.77 265 sds1143 107.03 -0.1518 -1.289 6.489 266 sgs1143 -1790 -1.2752 39.209 305.44 267 shs1143 258.35 0.2504 -1.949 -43.86 268 sks1143 178.54 0.1313 -0.464 -25.78 269 sms1143 94.08 -0.019 -0.889 -2.992 270 sss1143 74.32 0.3668 -1.575 -23.26 271 sus1143 99.86 0.4654 -0.014 -39.08 272 sas1143 41.13 0.2851 0.192 -12.46 273 sys1143 7.2 -0.0409 -2.869 15.853 274 sbs2002 42.24 0.0618 -4.855 8.484 275 scs2002 44.79 0.0047 1.002 1.863 276 sds2002 -20 0.0608 2.626 10.385 277 sgs2002 1695.5 -11.9638 403.77 -168.6 278 shs2002 -38.28 0.0509 -0.913 16.479 279 sks2002 159.92 -0.0641 -3.532 -13.6 280 sms2002 -30.75 0.0042 -0.248 16.157 281 sss2002 14.37 0.1445 -4.727 3.622 282 sus2002 59.81 -0.0402 -0.518 0.434 283 sas2002 76.63 -0.003 -1.654 0.385 284 sys2002 44.57 -0.0501 -0.101 5.021 285 sbs2072 66.53 0 0 0 286 scs2072 64.79 0 0 0 287 sds2072 67.91 0 0 0 288 sgs2072 45.73 0 0 0 289 shs2072 45.65 0 0 0 290 sks2072 52.31 0 0 0 291 sms2072 53.91 0 0 0 292 sss2072 46.21 0 0 0 293 sus2072 46.67 0 0 0 294 sas2072 76.14 0 0 0 295 sys2072 48.66 0 0 0 296 sbs2142 31.94 -0.0839 -2.113 17.608 297 scs2142 71.91 0 0 0 298 sds2142 118.89 -0.0325 2.906 -10.12 299 sgs2142 59.89 0 0 0 300 shs2142 32.66 -0.0074 2.89 1.488 301 sks2142 119.25 0.0352 12.602 -33.43 302 sms2142 122.42 -0.0022 5.979 -21.92 303 sss2142 1.57 0.0414 2.908 4.085 304 sus2142 -50.94 -0.0622 -5.161 34.419 305 sas2142 68.39 -0.0408 -1.292 6.318 306 sys2142 98.4 -0.0342 -7.885 7.409 307 sbs2003 153.46 -0.3161 1.915 2.386 308 scs2003 -30.4 0.0449 3.29 10.257 309 sds2003 120.23 -0.13719 0.3546 1.5386 310 sgs2003 51.122 0 0 0 311 shs2003 307.48 -0.46709 3.0823 -20.7 312 sks2003 67.899 0 0 0 313 sms2003 -12.52 0.15115 0.8928 -3.665 314 sss2003 71.955 -0.07331 4.3188 -5.026 315 sus2003 66.481 0.02472 -1.49 -5.076 316 sas2003 45.023 -0.08394 3.2583 8.1063 317 sys2003 29.298 0.02319 1.5171 -1.334 318 sbs2073 269.56 -0.59103 3.6596 -0.46 319 scs2073 61.991 0 0 0 320 sds2073 235.13 -0.23095 -0.601 -14.3 321 sgs2073 54.117 0 0 0 322 shs2073 321.03 -0.37242 3.0069 -29.75 323 sms2073 67.967 0 0 0 324 sss2073 -52.68 0.11435 3.1222 8.8959 325 sus2073 323.45 -0.56035 5.5213 -20.08 326 sas2073 217.39 -0.07592 -2.715 -19.94 327 sys2073 -93.96 0.19835 0.7195 14.19 328 sbs2143 71.16 0 0 0 329 scs2143 67.327 0 0 0 330 sds2143 78.881 0 0 0 331 sgs2143 61.329 0 0 0 332 shs2143 53.484 0 0 0 333 sks2143 66.469 0 0 0 334 sms2143 56.243 0 0 0 335 sss2143 54.664 0 0 0 336 sus2143 56.965 0 0 0 337 sas2143 69.359 0 0 0 338 sys2143 54.842 0 0 0

1 Example of Taste Score of Parts by Cooking Method by Taste Estimation Regression How to cook part year Succulent Flavor Taste Score 1 Grill





Bo Sub 74.26 (20.73) 76.55 (17.95) 72.88 (18.93) 74.30 (17.89) Tip 71.08 (22.55) 76.01 (17.86) 72.89 (18.17) 72.46 (18.31) sirloin 82.54 (16.30) 82.77 (14.15) 76.03 (16.89) 80.82 (13.74) Pack 61.94 (22.67) 68.98 (19.58) 68.02 (19.98) 64.85 (19.31) Red bean 55.32 (23.82) 65.07 (20.88) 64.66 (20.37) 59.60 (20.04) Crucible 70.28 (20.90) 72.26 (19.37) 70.81 (18.26) 70.78 (17.84) Thirsty 56.74 (25.25) 68.20 (20.68) 66.59 (20.48) 61.47 (20.80) Sesame 60.84 (25.64) 70.06 (19.96) 69.05 (19.85) 64.72 (20.65) stupidity 54.69 (23.31) 63.93 (21.03) 65.32 (20.48) 59.22 (19.70) Grill







Upjin 53.26 (41.01) 78.78 (33.63) 70.46 (19.30) 62.49 (33.77) Bo Sub 58.22 (26.89) 64.05 (22.69) 65.39 (21.95) 61.21 (22.36) Tip 68.58 (26.10) 72.33 (21.68) 71.63 (19.89) 70.07 (21.70) sirloin 68.97 (22.22) 72.20 (19.98) 72.97 (18.78) 70.63 (19.02) Pack 48.44 (26.04) 61.73 (21.96) 61.70 (22.03) 54.41 (21.67) Red bean 56.18 (24.05) 60.81 (22.83) 64.63 (20.92) 59.30 (20.93) Crucible 45.99 (26.37) 56.64 (24.11) 63.25 (22.32) 52.57 (22.25) Thirsty 42.28 (25.61) 61.00 (23.61) 60.99 (21.55) 50.70 (21.25) Sesame 47.12 (26.45) 61.68 (22.20) 64.00 (21.72) 54.30 (21.72) stupidity 42.82 (23.93) 55.88 (22.41) 60.78 (21.50) 50.02 (20.30) bang





Upjin 72.77 (20.95) 69.27 (20.60) 71.00 (18.98) 71.66 (18.52) Bo Sub 64.08 (24.72) 60.63 (23.86) 64.45 (21.84) 63.56 (22.02) Tip 61.34 (23.40) 62.72 (21.78) 65.22 (19.57) 62.64 (19.99) sirloin 73.25 (20.10) 70.09 (19.45) 67.56 (19.62) 71.14 (17.57) Pack 54.59 (22.64) 54.36 (20.12) 60.40 (20.76) 56.12 (19.36) Red bean 52.85 (23.11) 52.38 (22.29) 60.61 (19.61) 54.86 (20.10) Crucible 63.45 (24.82) 58.22 (24.31) 63.60 (20.68) 62.55 (21.51) Thirsty 48.98 (24.67) 52.32 (22.68) 57.00 (21.75) 51.75 (21.48) Sesame 50.79 (25.20) 53.19 (23.56) 59.69 (21.52) 53.63 (21.82) stupidity 46.10 (23.53) 45.64 (23.08) 54.87 (22.11) 48.39 (20.96) Sunny place 57.58 (24.78) 56.50 (23.41) 61.80 (21.49) 58.52 (21.72)

The year, juiciness, flavor and taste score 1 values are expressed as mean (standard deviation).

Claims (9)

Calculating a taste score of 5 by substituting the carcass weight, back fat thickness and intramuscular fat of the castrated beef into the following formula; And
A taste grade determination method of castrated beef comprising the step of determining the taste grade of meat by comparing the calculated value with a taste grade boundary score standard,
Taste score 5 = intercept value + (carcass weight regression coefficient × carcass weight of input subject axis) + (backfat thickness regression coefficient × backfat thickness of input subject axis) + (intramuscular fat regression coefficient × intramuscular fat figure of input subject axis)
The slice value and all regression coefficients are determined according to the cooking method, site, meat grade, maturation period and maturity of the graded beef taste. 2. The method of claim 1, wherein the taste grade boundary score criteria are unsatisfactory (49 or less), excellent taste (50 to 68), very good taste (69 to 82), or very good taste (83 or more). delete The method of claim 1, wherein the cooking method is grilling, grilling, or boiling water. The method according to claim 1, wherein the part is ups, bottoms, tails, fillets, packs, crucibles, necks, clues, redness, scoops or briskets. The method of claim 1 wherein the meat grade is 1 ⁺⁺ , 1 ⁺ , 1 or 2 grades. The method of claim 1 wherein the maturation period is 0, 7 or 14 days. The method of claim 1, wherein all of the regression coefficients are calculated based on the year, juiciness, and flavor values of the castrated beef. delete

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