CN114660247B - Physical-based fried beef maturity grading characterization method - Google Patents
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- 235000015278 beef Nutrition 0.000 title claims abstract description 96
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- 235000011389 fruit/vegetable juice Nutrition 0.000 description 2
- 108010050846 oxymyoglobin Proteins 0.000 description 2
- 235000020995 raw meat Nutrition 0.000 description 2
- 210000003491 skin Anatomy 0.000 description 2
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- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 1
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Abstract
The invention provides a physical-based fried beef doneness grading characterization method, which comprises the following steps: decocting beef samples to obtain different doneness grades, and measuring the center temperature; cooling to room temperature, and cutting the beef sample into small pieces; measuring the measured values of the physical properties of the small samples by using a texture analyzer; based on Fisher discrimination, utilizing the obtained measured value of the physical characteristic to establish a discrimination function; establishing a beef maturity degree model based on the maturity and the discriminant function, and establishing a maturity discriminant rule according to the beef maturity degree model; and measuring the measured value of the physical characteristic of the new fried beef by using a texture analyzer, and judging the doneness of the new fried beef based on the doneness judging rule. The method can represent the physical change rule of heat treatment on the beef doneness, has the characteristics of objectivity, high efficiency, accuracy and the like, and provides theoretical reference for the doneness classification of the meat products.
Description
Technical Field
The invention relates to the technical field of beef physical property measurement, in particular to a method for classifying and characterizing the cooked degree of fried beef based on physics.
Background
For the identification of meat cooking level, characterization is generally performed using methods such as time, temperature, color, appearance texture, and the like. Beef is subjected to a series of changes of muscle proteins in the cooking process, such as thermal gel formation of myofiber proteins, degradation of connective tissues and denaturation of pigment proteins, and changes of color, tenderness, juice loss and the like are reflected to the quality of the beef, and the physical characteristics can be used for predicting the organoleptic textures of the beef with different degrees of cooking better, so that the beef is widely applied to evaluation of meat products.
For meat products, tenderness is one of the important factors affecting meat product quality, consumer and evaluator acceptability. Tenderness values can be determined by physical methods, with shear force being the most common method. The maximum loading force when shearing a meat product is typically taken as the tenderness value of the meat. Hardness, chewiness, of meat products are also commonly used to analyze the quality characteristics of meat products. Therefore, the physical characteristic results obtained by utilizing the texture characteristics and the shearing characteristics can be used for predicting regression models of beef with different doneness, and the method has the characteristics of simplicity, convenience and effectiveness.
The patent of 201910157208.7 discloses a model for predicting the degree of ripeness of a roast meat product and a method for establishing the model, wherein characteristic peaks generated by vibration or rotation of chromaticity values L, a, b and hydrogen-containing groups XH of the roast meat product are collected on line, the deoxymyoglobin, oxymyoglobin and high-iron myoglobin content and moisture content of the roast meat product are calculated by on-line fusion data processing, the central temperature of the roast meat product in the roasting process is measured in real time, and a relational expression among the chromaticity values, the deoxymyoglobin, the oxymyoglobin and the moisture content and the central temperature is constructed. The invention solves the problems of strong subjectivity, poor consistency and unstable product quality of sensory evaluation, has the disadvantages of unstable result and large human error, and avoids error caused by subjective reasons by adopting objective score. But the core temperature established by color, protein and moisture content cannot directly characterize the doneness of the fried beef.
Disclosure of Invention
Aiming at the technical problems that the prior meat product doneness prediction is high in subjectivity and the physical characteristics of beef are not comprehensively considered, the invention provides a physical-based fried beef doneness classification characterization method, a prediction model is determined by quantitatively analyzing the hardness, the mastication degree and the maximum shearing force of beef, and theoretical reference is provided for classifying the fried beef doneness.
In order to achieve the above purpose, the technical scheme of the invention is realized as follows: a physical-based fried beef doneness grading characterization method comprises the following steps:
firstly, decocting beef samples into different doneness grades, and measuring the center temperature of the beef samples with different doneness grades by using an electronic thermometer;
step two, cooling the decocted beef sample to room temperature, and cutting the beef sample into small pieces of samples;
step three, testing all the small samples by using a texture analyzer, and measuring the measured value of the physical characteristic of each small sample;
step four, based on Fisher discrimination, utilizing the obtained measured value of the physical characteristic to establish a discrimination function;
establishing a beef maturity degree model based on the maturity and the discriminant function, and establishing a maturity discriminant rule according to the beef maturity degree model;
and measuring the measured value of the physical characteristic of the new fried beef by using a texture analyzer, and judging the doneness of the new fried beef based on the doneness judging rule.
The texture analyzer in the third step is used for measuring the texture characteristics and shearing characteristics of the small sample to generate the hardness, the chewing degree and the shearing force of the beef sample; the physical properties include hardness, elasticity, cohesiveness, chewiness, recovery, and tackiness.
The different doneness grades of the beef sample include: 0 level-raw meat, 1 level-five division, 2 level-six division, 3 level-seven division, 4 level-eight division, and 5 level-full division.
The method for establishing the discriminant function in the fourth step comprises the following steps:
recording the k-level degree of ripeness of the fried beef as G k Hardness (kg), elasticity (%), cohesiveness, chewiness (kg), recovery (%) and tackiness are x, respectively 1 、x 2 、x 3 、x 4 、x 5 And x 6 And x= (x) 1 ,x 2 ,x 3 ,x 4 ,x 5 ,x 6 ) T Record degree G as 6-dimensional column vector k Mean value of mu k Covariance matrix v k The estimates of the mean and covariance matrices are recorded as
Wherein n is k 、Respectively represent the degree of ripeness G k Is the observed quantity and the sample volume of (1) Representing the variable x j In the degree of maturity G k The next i-th observation value is that,is a sample dispersion array; j=1, 2,.. 6,k =0, 1, once again, 5,/i>Degree of maturity G k A sample mean of the observations;
based on Fisher discrimination method, obtaining linear discrimination function u 1 (x),u 2 (x),…u r (x) Wherein u is j (x)＝(β (j) ) T x, j=1, 2, … r, r is the number of discriminant functions, β (j) A coefficient vector representing the j-th discriminant function.
The beef maturity digital model in the fourth step is as follows: e, e jk ＝E(u j (x)|G k )，
And e jk Is expressed in degree of maturity G k The expected value of the j-th discriminant function under the condition of (1) is based on the sample observation and the discriminant function u j (x) Obtained.
The number of the discriminant rules is rDetermining the function to the maturity G k Distance minimum set of (2):
if the new sample x satisfies the condition, the sample x is determined as having a doneness level k.
For the epidermis measurement index, r=4 discriminant functions are obtained, respectively
u 1 (x)＝1.099x 1 +22.132x 2 -33.922x 3 -2.589x 4 +6.944x 5 +4.127x 6
u 2 (x)＝65.247x 1 -54.099x 2 +958.947x 3 +2.862x 4 -17.808x 5 -94.702x 6
u 3 (x)＝-18.009x 1 -60.939x 2 -245.392x 3 +8.590x 4 +1.949x 5 +18.276x 6
u 4 (x)＝24.310x 1 +11.119x 2 +280.576x 3 -3.129x 4 -10.163x 5 -31.359x 6
And the estimated value of the vector x-means vector measured by the corresponding doneness:
obtaining expected value e jk Where j=1, 2,3,4, k=1, 2,3,4,5;
or providing the ratio based on the information of the discriminant function, taking the discriminant function as
u 1 (x)＝1.099x 1 +22.132x 2 -33.922x 3 -2.589x 4 +6.944x 5 +4.127x 6 。
For the central position measurement index, r=5 discriminant functions are obtained as follows:
u 1 (x)＝4.348x 1 +6.129x 2 +14.059x 3 -4.974x 4 +1.984x 5 +0.907x 6
u 2 (x)＝4.237x 1 +30.189x 2 +49.926x 3 -3.443x 4 +8.344x 5 -4.472x 6
u 3 (x)＝-7.193x 1 +1.047x 2 -13.893x 3 -0.602x 4 +3.022x 5 +9.950x 6
u 4 (x)＝4.421x 1 +2.014x 2 -12.347x 3 +0.608x 4 +20.305x 5 -6.064x 6
u 5 (x)＝-5.082x 1 +18.894x 2 -24.322x 3 -4.093x 4 -17.822x 5 +10.390x 6 ；
and is derived from the estimated value
Obtaining expected value e jk Where j=1, 2,3,4,5, k=0, 1,2,3,4,5.
Using the discriminant function u 1 (x) And u 2 (x) Is determined by (1) or based on the information ratio provided by the discriminant function, the discriminant function u is taken 1 (x) And judging.
The invention has the beneficial effects that: the center temperature and the doneness of the fried beef in the cooking process are characterized based on physical texture characteristics, and the method has the characteristics of objectivity, high efficiency, accuracy and the like. According to the method, the physical characteristics such as hardness (kg), elasticity (%), cohesiveness, chewing degree (kg), resilience (%) and position are selected to represent the beef with different doneness, and the influence rule of the texture characteristic and the central temperature is explored, so that a physical method for representing the grading of the doneness of the beef is obtained. Meanwhile, by quantitatively analyzing the texture characteristics of the beef, the physical change rule of the heat treatment on the beef doneness can be characterized, and theoretical reference is provided for the doneness classification of the meat products.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, a physical-based method for classifying and characterizing the doneness of fried beef comprises the following steps:
step one, decocting beef samples to obtain different doneness grades, and measuring the center temperature of the beef samples with different doneness grades by using an electronic thermometer.
The center temperature of the heating end point is used as a reference premise of the degree of cooking classification, the beef is fried into 0 grade (raw meat), one grade of cooking degree, two grade of cooking degree, three grade of cooking degree, four grade of cooking degree and five grade of cooking degree, the corresponding relation between each degree of cooking degree grade and the center temperature is shown in table 1, and the center temperature of each of the six types of cooking degree beef is 50 ℃, 58 ℃, 66 ℃, 73 ℃ and 80 ℃. Niu Liji meat is selected as a sample, and after fascia removal, the trimmed beef is cut into 6cm multiplied by 2cm thick blocks along the myofiber direction for decoction. The six sides of the meat pieces are fried with oil and kept to be changed to the other side every 10 s. When the beef is fried, no flavoring and spice are added in order to avoid the influence of other flavoring on the test result. In order to avoid serious juice loss of meat in the frying process, the temperature of the electric braising oven should be increased to 180 ℃ in advance.
When the heating time is close to the target maturity, measuring the center temperature of the meat sample by an electronic thermometer, stopping decocting after the center temperature reaches the target temperature, cooling to room temperature, and performing vacuum freeze drying for 36h. The beef sample is cooled to room temperature and then subjected to vacuum cooling and drying, and the vacuum freeze drying is used for ensuring the data accuracy in the test process. The curing process of the product is a "time-temperature" process. Temperature is only one of the reference indicators, and the degree of maturity score still needs to be combined with the physical characteristics such as viscoelasticity and the like.
TABLE 1 degree of ripeness grading and temperature correspondence
| Degree of maturity classification | Temperature/. Degree.C |
| 0 level (raw meat) | 25.00 |
| Level 1 | 50.00 |
| Level 2 | 58.00 |
| 3 grade | 66.00 |
| Grade 4 | 73.00 |
| Grade 5 | 80.00 |
And step two, cooling the decocted beef sample to room temperature, and cutting the beef sample into small pieces of samples.
The test uses a sample which is cooled to room temperature after standard decoction, a sample which is cut into 2cm multiplied by 2cm along the myofiber direction by using a surgical knife, and samples with the thickness of 0.5cm are respectively taken at the epidermis position (calculated by the edge) and the center position (the center position which is equally divided), wherein one piece of meat is taken as one piece of the same meat. The fried beef is repeated for at least 10 times and is finished within 1h after the frying, so that the influence of the environment on the sample is eliminated.
In a specific example, the center temperature of the first-stage doneness beef is 50 ℃, and the preparation of the sample is as follows: taking 4 beef (6 cm×6cm×2cm) pieces, wherein one piece is used as fresh meat, the rest is decocted by an electric braising oven, and the center temperature of 3 pieces is 50deg.C; the center temperature of the second-stage doneness beef is 58 ℃, and the preparation of the sample is as follows: 4 pieces of beef (6 cm multiplied by 2 cm) are taken, one piece is used as fresh meat, the rest is decocted by an electric braising oven, and the center temperature of 3 pieces is 58 ℃. The center temperature of the third-stage cooked beef is 66 ℃, and the preparation of the sample is as follows: 4 beef (6 cm. Times.6 cm. Times.2 cm) pieces are taken, one of the beef pieces is used as fresh meat, the rest beef pieces are decocted by an electric braising oven, and the center temperature of the 3 beef pieces is 66 ℃. The center temperature of beef with different cooking degrees is 73 ℃, and the preparation of samples is as follows: 4 pieces of beef (6 cm multiplied by 2 cm) are taken, one piece is used as fresh meat, the rest is decocted by an electric braising oven, and the center temperature of 3 pieces is 73 ℃. The center temperature of beef with different cooking degrees is 80 ℃, and the preparation of samples is as follows: 4 pieces of beef (6 cm multiplied by 2 cm) are taken, one piece is used as fresh meat, the rest is decocted by an electric braising oven, and the center temperature of 3 pieces is 80 ℃.
And thirdly, respectively testing all the small samples by using a texture analyzer to obtain a measured value of the physical property of each small sample.
A TPA program in a texture analyzer is adopted, a probe (P/50) with the diameter of 5cm is selected, the deformation of a compressed sample is 50%, the speeds of the front, middle and rear of the test are respectively 2.0mm/s, 1.0mm/s and 2.0mm/s, and the trigger force is set to be 5g. The hardness, elasticity, cohesiveness, tackiness, chewiness and resilience of the product can be measured by setting the measured parameters.
The tenderness measuring procedure in the texture analyzer is adopted, the shearing direction is selected from the direction perpendicular to the direction of the knife edge, the test result is that the sample is cut off, so that the procedure is set in a displacement mode, the probe HDP/BSK is adopted, and the test speed is 1.0mm/s. The device can measure the maximum shearing force, the height of the sample and the hardness data of the unit height.
And (3) measuring the texture characteristics of the sample, adopting a secondary compression mode by a texture analyzer, and generating a hardness and chewiness test result of the sample. And (3) measuring the shear characteristics of the sample, adopting a compression shear mode, and generating a maximum shear force test result of the sample. During shear property measurement, the beef sample needs to be run once without load, preheated and the stability of the instrument is checked. The data of elasticity, cohesion and recovery are derived from a texture analyzer, and are directly obtained through the texture characteristic and shearing characteristic test of the texture analyzer. And (3) finishing the test by respectively measuring the data through the two settings, wherein the result data of each parameter is directly given by an instrument.
The data include the data of hardness (kg), mastication (kg), elasticity (%), cohesiveness, recovery (%) and tackiness corresponding to the doneness of the beef samples with different doneness.
Step four, based on Fisher discrimination, utilizing the obtained measured value of the physical characteristic to establish a discrimination function; establishing a beef maturity degree model based on the maturity and the discriminant function, and establishing a maturity discriminant rule according to the beef maturity degree model; and measuring the measured value of the physical characteristic of the new fried beef by using a texture analyzer, and judging the doneness based on the doneness judging rule.
Based on the above process, hardness (kg), mastication degree (kg), elasticity (%), cohesiveness, recovery (%), tackiness are displayed by the texture analyzer results, and corresponding indexes are automatically given.
Recording the k-level degree of ripeness of the fried beef as G k Hardness (kg), elasticity (%), cohesiveness (kg), chewiness (%) and tackiness are x, respectively 1 、x 2 、x 3 、x 4 、x 5 And x 6 And x= (x) 1 ,x 2 ,x 3 ,x 4 ,x 5 ,x 6 ) T Record degree G as 6-dimensional column vector k Mean value of mu k Covariance matrix v k The estimates of the mean and covariance matrices are recorded as
Wherein n is k 、Respectively represent the degree of ripeness G k Sample size and observance of (1), wherein +.> Representing the variable x j (j=1, 2, …, 6) at the i-th observation of the doneness k (k=0, 1,2, …, 5),is a sample dispersion array.
Based on Fisher discrimination method, obtaining the linear discrimination function as u 1 (x),u 2 (x),…u r (x) Wherein u is j (x)＝(β (j) ) T x, j=1, 2, … r, r is the number of discriminant functions, β (j) The coefficient vector representing the j-th discriminant function is 6 in length, because there are 6 variables of hardness, elasticity, etc. measured. The coefficients of the linear discriminant function of the Fisher discriminant method are given based on the vector corresponding to the largest eigenvalue of the covariance matrix.
Let e jk ＝E(u j (x)|G k )， (2)
For a new fried beef sample x, doneness G k The discrimination rules of (a) are: the following conditions are satisfied:
wherein e jk Is expressed in degree of maturity G k Expected value of jth discriminant function under k-level condition based on sample observation and linear discriminant function u j (x) Obtained. Meaning of model: representing r discrimination functions to k-level maturity G k If the new sample x satisfies the condition, the sample x is determined to be the doneness level k.
For the epidermis measurement index, r=4 discriminant functions are obtained, respectively
u 1 (x)＝1.099x 1 +22.132x 2 -33.922x 3 -2.589x 4 +6.944x 5 +4.127x 6
u 2 (x)＝65.247x 1 -54.099x 2 +958.947x 3 +2.862x 4 -17.808x 5 -94.702x 6
u 3 (x)＝-18.009x 1 -60.939x 2 -245.392x 3 +8.590x 4 +1.949x 5 +18.276x 6
u 4 (x)＝24.310x 1 +11.119x 2 +280.576x 3 -3.129x 4 -10.163x 5 -31.359x 6
The discriminant function is obtained based on the Fisher (Fisher) discriminant method, and the algorithm implementation is based on lda () function in the MASS package in the R software (version 4.1.2).
And is composed of
Can find e jk Where j=1, 2,3,4, k=1, 2,3,4,5.
Wherein,when the level of the maturity is k, the estimated value of the mean value vector of the measured variable (hardness, elasticity and the like) is estimated based on the measured value, and a specific algorithm is shown in a formula #1)；e jk The method is based on the condition that the doneness level is k, and the average value of the j-th discriminant function is obtained, and the formula (2) is shown.
The skin doneness level can be given for the new fried beef sample in combination with the discriminant rule. For the skin measurement sample, the discrimination results using 4 discrimination functions are shown in table 2.
Table 2 4 discrimination results of discrimination functions
Data x in Table 2 ij The number of actual doneness i and doneness j is shown, the diagonal is the positive number, the off-diagonal is the erroneous number, and in Table 2, only x is shown 43 =1, indicating that the erroneous judgment of the doneness level 4 is doneness level 3, and there is only one erroneous judgment. As shown in Table 2, the doneness of the fried beef sample was 4, one observed value was misjudged as doneness level 3, and the other was correctly judged, and the positive judgment rate was 93.33%.
Of course, in order to simplify the discrimination, r=1 (first discrimination function) discrimination functions may be taken based on the information providing ratio of the discrimination functions, and discrimination rules are similarly used, and the discrimination results obtained are shown in table 3.
Table 3 1 discrimination results of discrimination functions
As can be seen from table 3, one observation value of the doneness level 3 is erroneously determined as level 4, and one of the doneness levels 4 is erroneously determined as level 3, and the positive determination rate of one determination function is 86.67%.
For the central position measurement index, r=5 discriminant functions are obtained as follows:
u 1 (x)＝4.348x 1 +6.129x 2 +14.059x 3 -4.974x 4 +1.984x 5 +0.907x 6
u 2 (x)＝4.237x 1 +30.189x 2 +49.926x 3 -3.443x 4 +8.344x 5 -4.472x 6
u 3 (x)＝-7.193x 1 +1.047x 2 -13.893x 3 -0.602x 4 +3.022x 5 +9.950x 6
u 4 (x)＝4.421x 1 +2.014x 2 -12.347x 3 +0.608x 4 +20.305x 5 -6.064x 6
u 5 (x)＝-5.082x 1 +18.894x 2 -24.322x 3 -4.093x 4 -17.822x 5 +10.390x 6
the discriminant function is obtained based on the Fisher (Fisher) discriminant method, and the algorithm implementation is based on lda () function in the packet MASS in the R software (version 4.1.2).
And is composed of
Can find e jk Where j=1, 2,3,4,5, k=0, 1,2,3,4,5, and the criterion is combined with the new decocted beef sample to give the doneness level of the center position.
Wherein,mean vector, μ of measured variables (hardness, elasticity, etc.) representing the doneness level k k The symbols on the table represent estimated values which are estimated based on measured values, and the specific algorithm is shown in a formula (1); e, e jk The method is based on the condition that the doneness level is k, and the average value of the j-th discriminant function is obtained, and the formula (2) is shown.
For the measurement sample at the center position, discrimination results using the first 2 discrimination functions are shown in table 4.
Table 4 1 discrimination results of discrimination functions
As can be seen from table 4, there was no sample misjudgment, and the positive judgment rate was 100%.
Similarly, r=1 discriminant functions are taken based on the information ratio provided by the discriminant functions, and the discriminant results are shown in table 5 based on the discriminant rules.
TABLE 5 discrimination results of 1 discriminant function under the measurement index of center position
As can be seen from Table 5, there is one misjudgment to level 1, one misjudgment to level 2, and one misjudgment to level 0 for doneness level 0; the overall positive judgment rate reaches 83.33 percent.
The measured values of hardness (kg), elasticity (%), cohesiveness, chewiness (kg) and recovery (%) are taken into a beef doneness degree model, and doneness degree of beef can be judged based on formula (3).
In summary, the beef doneness grading characterization method provided by the invention utilizes the temperature and doneness of the fried beef in the doneness process to characterize based on physical texture characteristics, and has the characteristics of objectivity, high efficiency, accuracy and the like. According to the method, the physical characteristics of hardness (kg), elasticity (%), cohesiveness, chewing degree (kg), resilience (%) and tackiness and the like are selected to represent the beef with different doneness, and the influence rule of the texture characteristic and the central temperature is explored, so that a physical method for representing the grading of the doneness of the beef is obtained. Meanwhile, the physical change rule of the heat treatment on the beef maturity can be represented by the texture characteristics of the beef, so that theoretical reference is provided for the meat product maturity classification.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (4)
1. The physical-based fried beef doneness grading characterization method is characterized by comprising the following steps of:
firstly, decocting beef samples into different doneness grades, and measuring the center temperature of the beef samples with different doneness grades by using an electronic thermometer;
step two, cooling the decocted beef sample to room temperature, and cutting the beef sample into small pieces of samples;
step three, testing all the small samples by using a texture analyzer, and measuring the measured value of the physical characteristic of each small sample;
step four, based on Fisher discrimination, utilizing the obtained measured value of the physical characteristic to establish a discrimination function;
establishing a beef maturity degree model based on the maturity and the discriminant function, and establishing a maturity discriminant rule according to the beef maturity degree model;
measuring the measured value of the physical characteristic of the new fried beef by using a texture analyzer, and judging the degree of ripeness based on a degree of ripeness judging rule;
the different doneness grades of the beef sample include: 0 level-raw meat, 1 level-five-division ripening, 2 level-six-division ripening, 3 level-seven-division ripening, 4 level-eight-division ripening, 5 level-full ripening;
the method for establishing the discriminant function in the fourth step comprises the following steps:
recording the k-level degree of ripeness of the fried beef as G k Hardness (kg), elasticity (%), cohesiveness, chewiness (kg), recovery (%) and tackiness are x, respectively 1 、x 2 、x 3 、x 4 、x 5 And x 6 And x= (x) 1 ,x 2 ,x 3 ,x 4 ,x 5 ,x 6 ) T Record degree G as 6-dimensional column vector k Mean value of mu k Covariance matrix v k The estimates of the mean and covariance matrices are recorded as
Wherein n is k 、Respectively represent the degree of ripeness G k Sample size and observed quantity of (2), and observed quantity +.> Representing the variable x j In the degree of maturity G k The ith observation below, +.>Is a sample dispersion array; j=1, 2,.. 6,k =0, 1, once again, 5,/i>Degree of maturity G k A sample mean of the observations;
based on Fisher discrimination method, obtaining linear discrimination function u 1 (x),u 2 (x),…u r (x) Wherein u is j (x)＝(β (j) ) T x, j=1, 2, … r, r is the number of discriminant functions, β (j) Represents the j-th discriminationCoefficient vectors of the functions;
the beef maturity digital model in the fourth step is as follows: e, e jk ＝E(u j (x)|G k )，
And e jk Is expressed in degree of maturity G k The expected value of the j-th discriminant function under the condition of (1) is based on the sample observation and the discriminant function u j (x) Obtaining;
the discrimination rule is that r discrimination functions reach maturity G k Distance minimum set of (2):
if the new sample x satisfies the condition, the sample x is determined as having a doneness level k.
2. The method for classifying and characterizing the doneness of the fried beef based on physics according to claim 1, wherein the texture analyzer in the third step is used for measuring the texture characteristics and the shearing characteristics of the small sample respectively to generate the hardness, the mastication degree and the shearing force of the beef sample; the physical properties include hardness, elasticity, cohesiveness, chewiness, recovery, and tackiness.
3. The method for classifying and characterizing the doneness of fried beef based on physics according to claim 1, wherein r=4 discriminants are obtained under the surface measurement index, respectively
u 1 (x)＝1.099x 1 +22.132x 2 -33.922x 3 -2.589x 4 +6.944x 5 +4.127x 6
u 2 (x)＝65.247x 1 -54.099x 2 +958.947x 3 +2.862x 4 -17.808x 5 -94.702x 6
u 3 (x)＝-18.009x 1 -60.939x 2 -245.392x 3 +8.590x 4 +1.949x 5 +18.276x 6
u 4 (x)＝24.310x 1 +11.119x 2 +280.576x 3 -3.129x 4 -10.163x 5 -31.359x 6
And the estimated value of the vector x-means vector measured by the corresponding doneness:
obtaining expected value e jk Where j=1, 2,3,4, k=1, 2,3,4,5;
or providing the ratio based on the information of the discriminant function, taking the discriminant function as
u 1 (x)＝1.099x 1 +22.132x 2 -33.922x 3 -2.589x 4 +6.944x 5 +4.127x 6 。
4. The physical-based decocted beef doneness classification characterization method according to claim 1, wherein r=5 discriminant functions are obtained under the central position measurement index as follows:
u 1 (x)＝4.348x 1 +6.129x 2 +14.059x 3 -4.974x 4 +1.984x 5 +0.907x 6
u 2 (x)＝4.237x 1 +30.189x 2 +49.926x 3 -3.443x 4 +8.344x 5 -4.472x 6
u 3 (x)＝-7.193x 1 +1.047x 2 -13.893x 3 -0.602x 4 +3.022x 5 +9.950x 6
u 4 (x)＝4.421x 1 +2.014x 2 -12.347x 3 +0.608x 4 +20.305x 5 -6.064x 6
u 5 (x)＝-5.082x 1 +18.894x 2 -24.322x 3 -4.093x 4 -17.822x 5 +10.390x 6 ；
and is derived from the estimated value
Obtaining expected value e jk Where j=1, 2,3,4,5, k=0, 1,2,3,4,5;
using the discriminant function u 1 (x) And u 2 (x) Is determined by (1) or based on the information ratio provided by the discriminant function, the discriminant function u is taken 1 (x) And judging.
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| CN102507882A (en) * | 2011-12-19 | 2012-06-20 | 中国农业大学 | Beef quality multi-parameter compressive evaluation method |
| CN102519906A (en) * | 2011-12-19 | 2012-06-27 | 中国农业大学 | Beef quality multi-parameter simultaneous detection method by multichannel near-infrared spectroscopy |
| CN109977095A (en) * | 2019-03-01 | 2019-07-05 | 中国农业科学院农产品加工研究所 | The prediction model and its method for building up of the ripe degree of grilled meat products |
| CN110084315A (en) * | 2019-05-06 | 2019-08-02 | 河南农业大学 | A kind of meat emulsion product texture stage division |
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| CN102507882A (en) * | 2011-12-19 | 2012-06-20 | 中国农业大学 | Beef quality multi-parameter compressive evaluation method |
| CN102519906A (en) * | 2011-12-19 | 2012-06-27 | 中国农业大学 | Beef quality multi-parameter simultaneous detection method by multichannel near-infrared spectroscopy |
| CN109977095A (en) * | 2019-03-01 | 2019-07-05 | 中国农业科学院农产品加工研究所 | The prediction model and its method for building up of the ripe degree of grilled meat products |
| CN110084315A (en) * | 2019-05-06 | 2019-08-02 | 河南农业大学 | A kind of meat emulsion product texture stage division |
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