Construction method of different goat milk quality comparison model

Abstract

The invention belongs to the technical field of food safety, and particularly relates to a construction method of different goat milk quality comparison models. The method comprises the following steps: collecting different milk samples, and measuring various physicochemical indexes of the milk samples; performing principal component analysis on the obtained physicochemical index data to obtain a characteristic value, a variance contribution rate and a characteristic vector of a principal component; and constructing an expression of principal components by using the feature values and the feature vectors, and obtaining a goat milk quality comparison model in combination with the variance contribution rate. The comparison model constructed by using the method can provide an effective basis for quality evaluation of products.

Description

Technical Field

[0001] The present invention belongs to the technical field of food safety, and particularly relates to a method for constructing a comparison model of different goat milk qualities. Background Art

[0002] Goat milk contains rich nutrients, with high contents of protein, vitamins, minerals, and total fat, and a large variety and high content of beneficial fatty acids (such as short- and medium-chain fatty acids, unsaturated fatty acids, and conjugated acids, etc.). The protein digestibility reaches about 98%, making it the best drink for nutrition and health care, and having a good preventive effect on human gastrointestinal diseases, etc. Moreover, goat milk has a unique flavor, contains no allergens, and has complete nutritional components. Goat milk is becoming the new favorite in the dairy market. With the expansion of the market and the increasing abundance of people's material needs, the selection of dairy products is becoming more and more refined.

[0003] The existing methods for evaluating the quality of raw milk are: sensory evaluation and physical and chemical detection. Sensory evaluation mainly relies on people's senses to directly evaluate the external characteristics of raw milk, while physical and chemical detection uses laboratory instruments to detect the physical and chemical indicators of raw milk, and directly estimates the quality of raw milk through the detection results of physical and chemical indicators. However, sensory evaluation depends on people's subjective consciousness, and the evaluation results are not accurate enough; while the evaluation method through physical and chemical detection has relatively high requirements for operation techniques, and when comparing the qualities of different goat milk samples, it is necessary to detect various different physical and chemical indicators in each sample, with a large and cumbersome detection volume. Summary of the Invention

[0004] Based on the technical problems existing in the prior art, the present invention provides a method for constructing a comparison model of different goat milk qualities. The comparison model constructed by using this method can provide an effective basis for the quality evaluation of different dairy goat milk.

[0005] The specific technical solution provided by the present invention is as follows: In the first aspect of the present invention, a method for constructing a comparison model of different goat milk qualities is provided, including the following steps: Collect different milk samples and measure various physical and chemical indicators of the milk samples; Perform principal component analysis on the obtained physical and chemical index data to obtain the eigenvalue, variance contribution rate, and eigenvector of the principal component; Use the eigenvalue and the eigenvector to construct an expression of the principal component, and combine the variance contribution rate to obtain a comparison model of goat milk quality.

[0006] As a preferred embodiment of the present invention, the expression of the principal component is obtained by multiplying the arithmetic square root of the eigenvalue corresponding to the principal component by the eigenvector of the variable.

[0007] As a preferred embodiment of the present invention, the physical and chemical indexes include milk fat percentage, milk protein percentage, total solids content, non-fat milk solids content, lactose content, freezing point and acidity.

[0008] More preferably, the constructed comparison model expression is: F = 0.82 × milk fat percentage + 1.18 × milk protein percentage + 1.22 × total solids content + 1.34 × non-fat milk solids content + 0.55 × lactose content + 1.36 × freezing point + 1.14 × acidity.

[0009] In the second aspect of the present invention, a method for comparing the qualities of different goat milk using the comparison model is provided, including the following steps: Measure the physical and chemical indexes of different milk samples, substitute them into the comparison model, and the quality of the milk sample with a larger calculated value is higher than that of the milk sample with a smaller calculated value.

[0010] As a preferred embodiment of the present invention, measure the milk fat percentage, milk protein percentage, total solids content, non-fat milk solids content, lactose content, freezing point and acidity in the milk sample, and then substitute them into the comparison model for calculation.

[0011] More preferably, substitute the measured milk fat percentage, milk protein percentage, total solids content, non-fat milk solids content, lactose content, freezing point and acidity into the following expression of the comparison model to calculate the F value. The quality of the milk sample with a larger F value is higher than that of the milk sample with a smaller F value: The expression of the comparison model is: F = 0.82 × milk fat percentage + 1.18 × milk protein percentage + 1.22 × total solids + 1.34 × non-fat milk solids + 0.55 × lactose content + 1.36 × freezing point + 1.14 × acidity.

[0012] In the third aspect of the present invention, a computer-readable storage medium is provided. The storage medium is used to store a program. When the program runs, it controls the device where the storage medium is located to execute the above method for comparing the qualities of different goat milk.

[0013] In the fourth aspect of the present invention, a processor is provided. The processor is used to run a program. When the program runs, it executes the above method for comparing the qualities of different goat milk.

[0014] Compared with the prior art, the beneficial effects of the present invention are: The calculation process of the comparison model constructed by the present invention is relatively simple and there is not much calculation amount. Substitute the measured indexes into the evaluation model and compare the magnitudes of the obtained F values, which is easy to understand.

[0015] The indicators selected for the comparison model of the present invention are relatively easy to measure during construction. Except for acidity which needs to be obtained by acid-base titration, the remaining indicators can be measured using a milk composition analyzer. Both the principal component analysis and the cluster analysis are implemented using SPSS software, and the operation is simple. Description of the Drawings

[0016] Figure 1 It is the cluster analysis of milk samples; the vertical axis is the ranking of F values of each milk sample. Detailed Description of the Invention

[0017] The present invention will be described in detail below in conjunction with the drawings and specific embodiments.

[0018] The terms used in the present invention have the meanings commonly understood by those of ordinary skill in the relevant art.

[0019] Goat milk contains rich nutrients, with high contents of protein, vitamins, minerals, and total fat, and a large variety and high content of beneficial fatty acids (such as short- and medium-chain fatty acids, unsaturated fatty acids, and conjugated acids, etc.). As goat milk is increasingly recognized, people have put forward higher requirements for the quality of goat milk. The existing methods for evaluating the quality of raw milk are: sensory evaluation and physical and chemical detection. Sensory evaluation mainly relies on people's senses to directly evaluate the external characteristics of raw milk, while physical and chemical detection uses laboratory instruments to detect the physical and chemical indicators of raw milk, and directly estimates the quality of raw milk through the detection results of physical and chemical indicators. However, sensory evaluation depends on people's subjective consciousness, and the evaluation results are not precise enough; while the evaluation method through physical and chemical detection has relatively high requirements for operation techniques, and when comparing the quality of different goat milk samples, it is necessary to detect various different physical and chemical indicators in each sample, with a large and cumbersome detection volume.

[0020] Based on this, the present invention provides a method for constructing a comparison model of different goat milk qualities, including the following steps: Collect different milk samples and measure various physical and chemical indicators of the milk samples; Perform principal component analysis on the obtained physical and chemical index data to obtain the eigenvalue, variance contribution rate, and eigenvector of the principal component; Use the eigenvalue and eigenvector to construct an expression of the principal component, and combine the variance contribution rate to obtain a comparison model of goat milk quality.

[0021] The calculation process of the comparison model constructed by the present invention is relatively simple and does not have too much calculation amount. Substitute the measured indicators into the evaluation model and compare the magnitudes of the obtained F values, which is easy to understand.

[0022] The following is a specific description: To establish a comparison model for the milk quality of dairy goats, 20 lactating Saanen dairy goats with similar lactation stages and good health were randomly selected, and milk samples were collected during the peak lactation period. As many relevant milk components as possible were measured, mainly including milk fat percentage, fat content, milk protein percentage, protein content, lactose, pH value, total solid content, whole milk solid, non-fat milk solid content, dry matter content, freezing point, and acidity, etc.

[0023] First, the present invention constructed an evaluation model using all the indicators and found that the reliability was relatively low and the model evaluation was not very accurate. Secondly, one indicator was excluded and the evaluation model was constructed using the other indicators. Each indicator was excluded once, and it was found that the reliability of each combination was not ideal. Finally, the combinations with relatively better reliability were selected and further screened. And so on. Finally, it was found that the evaluation model established using the combination of milk fat percentage, milk protein percentage, total solid content, non-fat milk solid content, lactose content, freezing point, and acidity had the highest and most ideal reliability, and the evaluation results of the evaluation model constructed using these indicators were the most accurate.

[0024] The milk fat percentage, milk protein percentage, total solid, non-fat milk solid, lactose and other milk components were measured using a milk component analyzer, and the freezing point and acidity of the milk samples were measured. The data were sorted out using Excel software, and principal component analysis was carried out using SPSS software to obtain the eigenvalue and variance contribution rate of the principal components (Table 1) and the eigenvector of the principal components (Table 2).

[0025] Table 1 Eigenvalue and variance contribution rate of the principal components Table 2 Eigenvector of the principal components According to the calculation method of the variable coefficient: the arithmetic square root of the eigenvalue corresponding to this component is multiplied by the eigenvector of this variable, and the expressions of the two principal components are obtained as follows: F1 = 1.46X1 + 1.84X2 + 1.98X3 + 1.98X4 + 0.56X5 + 2.05X6 + 1.67X7; F2 = -0.67X1 - 0.09X2 - 0.35X3 + 0.25X4 + 0.98X5 + 0.15X6 + 0.29X7.

[0026] According to the variance contribution rates of F1 and F2 in Table 2, F = 0.65F1 + 0.19F2 is obtained, and thus the evaluation model expression for the milk quality of goats is obtained as: F = 0.82X1 + 1.18X2 + 1.22X3 + 1.34X4 + 0.55X5 + 1.36X6 + 1.14X7, that is, F = 0.82 × milk fat percentage + 1.18 × milk protein percentage + 1.22 × total solid content + 1.34 × non-fat milk solid content + 0.55 × lactose content + 1.36 × freezing point + 1.14 × acidity.

[0027] This model is applicable to the quality evaluation of small-scale goat milk, mainly for the quality comparison among multiple milk samples. If the F value of one milk sample is larger than that of another, then the quality of this milk sample is better than that of the other. During the specific operation, the milk fat rate, milk protein rate, total solids content, non-fat milk solids content, lactose content, freezing point, and acidity of different milk samples are measured and substituted into this evaluation model. The larger the obtained F value, the higher the quality of the milk sample.

[0028] To verify the reliability of the evaluation model, the present invention collects 20 different milk samples with known quality. First, the F values of different milk samples are obtained using the above evaluation model, and then the milk sample data is subjected to cluster analysis using SPSS software to obtain Figure 1 . The clustering results show that the milk samples are roughly divided into three categories. The first category contains 3 milk samples, indicating that the qualities of these 3 milk samples are relatively close and they are ranked 1st, 2nd, and 3rd respectively in the model evaluation. The second category contains 4 milk samples, which are ranked 4th, 5th, 6th, and 7th respectively in the model evaluation. The third category is the other 13 samples grouped into one category. More specifically, it can be divided into 7 grades: The first grade: The milk sample ranked 1st is in a separate grade; The second grade: The milk samples ranked 2nd and 3rd are in one grade; The third grade: The three milk samples ranked 4th, 5th, and 6th are in one grade; The fourth grade: The milk sample ranked 7th is in a separate grade; The fifth grade: The milk samples ranked 9th and 10th are in one grade; The sixth grade: The milk samples ranked 8th and 11th - 18th are in one grade; The seventh grade: The milk samples ranked 19th and 20th are in one grade.

[0029] It can be seen from this that the rankings of milk samples with close quality are adjacent or similar when evaluated using the model, which indicates that the ranking of this evaluation model for each milk sample is relatively accurate, proving that this evaluation model is reliable.

[0030] It should be noted that during this process, only the milk sample ranked 8th has an error, which may be due to errors in the measurement of indicators. However, it is also sufficient to show that the evaluation model provided by the present invention is relatively accurate in ranking the qualities of these milk samples and can be fully used to screen the milk sample with the best quality among multiple milk samples.

[0031] Those skilled in the art should understand that those skilled in the art can achieve variation examples by combining the prior art and the above embodiments. Such variation examples do not affect the essence of this solution and will not be elaborated herein.

[0032] It should be understood that the present solution is not limited to the above specific embodiments, and the devices and structures not described in detail should be understood to be implemented in a common manner in the art; any person skilled in the art can make many possible changes and modifications to the technical solution of the present solution, or modify it into an equivalent embodiment with equivalent changes, without departing from the scope of the technical solution of the present solution, which does not affect the essence of the present solution. Therefore, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present solution without departing from the content of the technical solution of the present solution still fall within the scope of protection of the technical solution of the present solution.

Claims

1. A method for constructing a comparison model of different goat milk qualities, characterized in that: The following steps are involved: Collect different milk samples and measure various physical and chemical indicators of the milk samples; The principal component analysis was performed on the obtained physical and chemical index data to obtain the eigenvalue, variance contribution rate and eigenvector of the principal component; The expression of the principal component is constructed using the eigenvalue and the eigenvector, and a goat milk quality comparison model is obtained in combination with the variance contribution rate.

2. The construction method according to claim 1, characterized in that: The expression of the principal component is obtained by multiplying the arithmetic square root of the eigenvalue of the principal component by the eigenvector of the variable.

3. The construction method according to claim 1, characterized in that: The physical and chemical indicators include milk fat rate, milk protein rate, total solid content, non-fat milk solid content, lactose content, freezing point and acidity.

4. The construction method according to claim 3, characterized in that: The constructed comparative model expression is: F=0.82×milk fat rate+1.18×milk protein rate+1.22×total solid content+1.34×non-fat milk solid content+0.55×lactose content+1.36×freezing point+1.14×acidity.

5. A method for comparing the quality of goat milk of different types using the comparison model of claim 1, characterized in that: The following steps are involved: The physical and chemical indices of different milk samples are measured and substituted into the comparison model. The quality of the milk sample with a larger calculated value is higher than that of the milk sample with a smaller calculated value.

6. The method according to claim 1, characterized in that Determine the milk fat rate, milk protein rate, total solid content, non-fat milk solid content, lactose content, freezing point and acidity in the milk sample, and then substitute them into the expression of the following comparison model to calculate the F value. The quality of the milk sample with a large F value is higher than that of the milk sample with a small F value: The expression of the comparison model is: F=0.82×milk fat content+1.18×milk protein content+1.22×total solids+1.34×non-fat milk solids+0.55×lactose content+1.36×freezing point+1.14×acidity.

7. A computer-readable storage medium, characterized in that: The storage medium is used to store a program, wherein when the program is running, the device where the storage medium is located is controlled to execute the method for comparing the quality of different goat milks in claim 5.

8. A processor, characterized in that: The processor is used to run a program, wherein when the program is run, the method for comparing the quality of different goat milks in claim 5 is executed.

Images