Model and method for evaluating sensory whiteness of milk or dairy products

Abstract

A model and method for evaluating the sensory whiteness of milk or dairy products. The evaluation model is: y=b+k×W, where y represents sensory whiteness, W represents a whiteness value obtained by means of measurement performed by a spectrophotometer, b is a constant with a value ranging from -2.2 to 2.2, and k is a constant with a value ranging from 0.04 to 0.18. A sensory whiteness calculation model based on whiteness characteristics of milk or dairy products is introduced to convert sensory descriptions of the whiteness of dairy products into instrumental parameters, thereby solving the problems of individual subjective differences, excessive time consumption and high development costs that are caused by relying solely on human-panel-based sensory descriptive tests, thus overcoming the defect of low accuracy of a single test method, and achieving the aim of online, real-time, rapid and accurate prediction of the sensory whiteness of dairy products. Moreover, the method can be applied to sensory testing of both solid and liquid milk or dairy products.

Description

An evaluation model and method for the sensory whiteness of milk or dairy products.

[0001] priority

[0002] This application claims priority to Chinese patent application No. 202510104617.6, filed on January 23, 2025, entitled "An Evaluation Model and Evaluation Method for Sensory Whiteness of Milk or Dairy Products". Technical Field

[0003] This invention relates to the field of milk or dairy product analysis and testing technology, and in particular to an evaluation model and evaluation method for the sensory whiteness of milk or dairy products. Background Technology

[0004] Milk is a natural food that is nutritionally complete, easily digestible and absorbed, and has high nutritional value. It contains a variety of nutrients, including protein, fat, carbohydrates, vitamins, and minerals. Currently, the consumption of milk and its products has become an indispensable aspect of human nutrition. Milk and dairy products provide organisms with a variety of nutrients to promote their sustainable health and growth.

[0005] The white color of milk is caused by casein micelles and fat globules, which scatter light in the visible spectrum. Since most of the fat globules are removed from skim milk, the number, distribution, and size of these suspended particles are reduced, thus decreasing light scattering. This reduction in light scattering gives skim milk a bluish-gray or cyan appearance. Homogenized milk, on the other hand, has smaller fat globules, increasing its light scattering ability and making it whiter. Whey has a pale green color due to the presence of riboflavin, a characteristic color of whey. The color of dairy products such as butter and cheese is related to fat-soluble vitamins, especially carotenoids. These pigments are not synthesized by animals but are obtained from the plant-based feed in their diet. Therefore, feed has a significant impact on the color of milk, especially dairy products with a high milk fat content. Cows fed pasture produce yellower milk than those fed hay or concentrates. Different breeds and individuals of cows also vary in their ability to metabolize carotenoids into vitamin A.

[0006] Appearance is one of the important indicators for sensory evaluation of dairy products. Appearance assessment includes observations of color, transparency, texture, etc. For example, consumers generally prefer dairy products to be milky white or milky yellow. Studies have shown that whiteness is related to sensory drivers of dairy products and can maximize people's liking for the product and positive emotional responses. National food safety standards have clear sensory requirements for the color of different types of dairy products, but because these are qualitative descriptions, they lack quantitative indicators. Currently, there is also a lack of universally applicable product whiteness testing standards for the dairy industry in China. Therefore, many companies have established professionally trained sensory panel teams to quantify and score qualitative descriptive indicators to monitor product quality. While this method of evaluating product quality based on manual observation and judgment has a certain degree of intuitiveness, the evaluation process is cumbersome, requiring a large amount of manual observation and judgment, consuming considerable time and effort. Furthermore, it is highly subjective, and sensory evaluation results are easily influenced by individual subjective factors, potentially leading to subjective biases and a lack of objectivity.

[0007] Therefore, there is a need in the field to develop an evaluation model for the sensory whiteness of milk or dairy products, so as to achieve accurate prediction of the sensory whiteness of milk or dairy products, which is of positive significance for the development and quality control of dairy products. Summary of the Invention

[0008] To address the aforementioned technical problems, this invention provides an evaluation model and method for the sensory whiteness of milk or dairy products. This invention develops a quantitative index for evaluating the appearance and color of dairy products. By introducing a calculation model for sensory whiteness based on the whiteness characteristics of milk or dairy products, the sensory language of dairy product whiteness is converted into instrument parameters. This solves the problems of individual subjective differences, time-consuming processes, and high development costs associated with relying solely on descriptive sensory tests by a population. It also overcomes the low accuracy of single testing methods, achieving the goal of online, real-time, rapid, and accurate prediction of the sensory whiteness of dairy products. Furthermore, this method is applicable to sensory testing of both solid and liquid milk or dairy products.

[0009] To achieve this objective, the present invention adopts the following technical solution:

[0010] In a first aspect, a method for evaluating the sensory whiteness of milk or dairy products is provided, comprising the following steps:

[0011] (1) Obtain the milk or dairy product sample to be tested;

[0012] (2) The instrument whiteness value W is obtained by measuring with a spectrophotometer and according to the preset instrument whiteness value calculation formula;

[0013] (3) Input the instrument whiteness value W into the processor, and calculate the sensory whiteness evaluation value y using a preset sensory whiteness evaluation model. The expression of the evaluation model is: y = b + k × W.

[0014] Where y represents sensory whiteness, W represents the whiteness value obtained by spectrophotometer, b is a constant ranging from -2.2 to 2.2, and k is a constant ranging from 0.04 to 0.18.

[0015] In some implementations, the evaluation method further includes the step of outputting the evaluation result of the sample based on the sensory whiteness evaluation value y.

[0016] In some implementations, the process of detecting the instrument whiteness value includes: placing milk or dairy products in a cuvette, detecting them using a spectrophotometer in reflectance mode, and / or performing the detection under a D65 light source and a 10° observer angle.

[0017] In some implementation schemes, the instrument whiteness value W is detected based on the Hunter whiteness formula.

[0018] In some implementation schemes, the method for detecting the instrument whiteness value W is based on the GB / T 5950-96 standard.

[0019] In some implementations, the instrument whiteness value W is obtained by measuring a spectrophotometer. The spectrophotometer is preset according to the formula for calculating the instrument whiteness value.

[0020] In some implementations, the Hunter whiteness formula is: W = 100 - [(100 - L)] 2 +a 2 +b 2 ] 1 / 2 ;

[0021] Where L represents the lightness value, and a and b represent the chromaticity values.

[0022] In some implementation schemes, the formula for calculating whiteness in the GB / T 5950-96 standard is: W = Y 10 +400x 10 -1000y 10 +205.5;

[0023] Among them, Y 10 x represents the tristimulus value. 10 y 10 Represents chromaticity coordinates.

[0024] In some implementation schemes, when the detection method for the instrument whiteness value W is based on the Hunter whiteness formula, b takes a value of 1.5 to 2.2 and k takes a value of 0.05 to 0.07 in the model.

[0025] In some implementation schemes, when the detection method for the instrument whiteness value W is based on the GB / T 5950-96 standard, the b value in the model is -2.2 to -1.0, and the k value is 0.12 to 0.18.

[0026] In some implementation schemes, the evaluation criteria for sensory whiteness y are as follows:

[0027] When 1≤y<4, the milk or dairy products appear bluish-white; the lower the score, the more bluish they appear.

[0028] When 4≤y<7, the milk or dairy products are milky white; the higher the score, the whiter they are.

[0029] When 7≤y≤9, the milk or dairy products are milky yellow; the higher the score, the more yellow they are.

[0030] Secondly, an evaluation model system for the sensory whiteness of milk or dairy products is provided, the evaluation model system comprising:

[0031] Processor; and

[0032] A memory storing a program, the program including instructions that, when executed by the processor, cause the processor to perform the method described in the first aspect.

[0033] Thirdly, a computer-readable storage medium is provided for storing a program, the program including instructions that, when executed by a processor of an electronic device, cause the electronic device to perform the method described in the first aspect.

[0034] Fourthly, this invention provides an evaluation model for the sensory whiteness of milk or dairy products, the evaluation model being: y = b + k × W,

[0035] Where y represents the sensory whiteness, W represents the whiteness value obtained by a spectrophotometer, b is a constant with a value of -2.2 to 2.2 (e.g., -2.2, -2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2, 2.2, etc.), and k is a constant with a value of 0.04 to 0.18 (e.g., 0.04, 0.06, 0.08, 0.1, 0.12, 0.14, 0.15, 0.16, 0.18, etc.).

[0036] In existing technologies, spectrophotometers are primarily used to detect sample whiteness in fields such as papermaking, textiles, and building materials. Milk or dairy products are generally evaluated using human senses to assess their appearance and color. However, this approach has drawbacks, including a cumbersome evaluation process, requiring extensive manual observation and judgment, consuming considerable time and effort, and being susceptible to individual subjective differences. Furthermore, it is time-consuming, incurs high personnel training and development costs. This invention develops a quantitative index for evaluating the appearance and color of dairy products. Specifically, it creatively correlates the sensory whiteness of milk or dairy products with instrumental whiteness measurements. By introducing a model, the sensory language of milk or dairy products is converted into instrument parameters, forming a stable measurement method. This achieves the goal of online, real-time, rapid, and accurate prediction of the sensory whiteness of dairy products. It solves the problems of individual subjective differences, time-consuming processes, and high development costs associated with relying solely on descriptive human sensory testing, and overcomes the low accuracy of single testing methods. This achieves the technical effect of efficiently and accurately obtaining sensory whiteness data for milk or dairy products.

[0037] In some implementations, the method for detecting the whiteness value is based on the Hunter whiteness formula or the GB / T5950-96 standard.

[0038] In this invention, the Hunter whiteness formula is: W = 100 - [(100 - L)] 2 +a 2 +b 2 ] 1 / 2 ;

[0039] Where L represents the lightness value, and a and b represent the chromaticity values.

[0040] In this invention, the formula for calculating whiteness in the GB / T5950-96 standard is: W = Y 10 +400x 10 -1000y 10 +205.5;

[0041] Among them, Y 10 x represents the tristimulus value. 10 y 10 Represents chromaticity coordinates.

[0042] The whiteness standards and their defined formulas disclosed in the existing technology vary, and are mostly used in the fields of papermaking, textiles, and building materials. Moreover, each whiteness formula has certain limitations and cannot meet the actual measurement needs of various industries and types of samples.

[0043] This invention creatively correlates the whiteness characteristics of dairy products with sensory whiteness, and addresses the gap in whiteness standards in the dairy industry by introducing specific whiteness standards through correlation analysis, thereby achieving quantitative analysis of sensory whiteness. Furthermore, the model constructed using the aforementioned standards has higher accuracy and can be used for precise prediction of sensory whiteness.

[0044] In some implementations, when the whiteness value detection method is based on the Hunter whiteness formula, the value of b in the model is 1.5 to 2.2 (e.g., 1.5, 1.6, 1.7, 1.8, 1.9, 2, 2.2, etc.), and the value of k is 0.05 to 0.07 (e.g., 0.05, 0.052, 0.055, 0.058, 0.06, 0.062, 0.065, 0.068, 0.07, etc.).

[0045] In some implementations, when the whiteness value detection method is based on the GB / T 5950-96 standard, the value of b in the model is -2.2 to -1.0 (e.g., -2.2, -2, -1.8, -1.6, -1.4, -1.2, -1, etc.), and the value of k is 0.12 to 0.18 (e.g., 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, etc.).

[0046] In this invention, the evaluation criterion for sensory whiteness y is:

[0047] When 1≤y<4, the milk or dairy products appear bluish-white; the lower the score, the more bluish they appear.

[0048] When 4≤y<7, the milk or dairy products are milky white; the higher the score, the whiter they are.

[0049] When 7≤y≤9, the milk or dairy products are milky yellow; the higher the score, the more yellow they are.

[0050] Fifthly, the present invention provides a method for constructing an evaluation model for the sensory whiteness of milk or dairy products according to the fourth aspect, the method comprising the following steps:

[0051] (1) Collection of dairy product samples

[0052] Different types of dairy products were purchased from the market, and information was compiled. Three batches of each type of dairy product were purchased for parallel testing.

[0053] (2) Sample shelf life

[0054] All samples were stored according to the storage conditions specified in the packaging for their shelf life, and sensory evaluation and whiteness value instrument testing were performed on the samples every three months.

[0055] (3) Sensory Panel Quantitative Description Test

[0056] Twelve evaluators with the ability to identify differences in basic sensory properties were selected and given professional training and regular assessments in sensory evaluation of milk and dairy products. Test samples were presented in odorless transparent PET cups. All samples were randomly numbered by staff with three digits. A fully block-balanced design was used, and samples were randomly grouped and loaded. Quantitative descriptive analysis (QDA) was used to characterize the surface whiteness characteristics and differences between products, and records were kept. The sensory evaluation scoring criteria for surface whiteness of milk and dairy products are shown in Table 1.

[0057] Table 1

[0058] (4) Spectrophotometer whiteness index test

[0059] a. Record the whiteness of the sample using a KONICA MINOLTA CM-3600d spectrophotometer; install the target cover, which can be selected as CM-A107 / 106 / 105, respectively for measuring the aperture. Lighting aperture

[0060] b. Install the zeroing box and perform zeroing;

[0061] c. Place the white calibration plate for calibration;

[0062] d. Stir the sample thoroughly, use a 15mL syringe to take 10-14mL of the sample and transfer it into a 40×40×10mm quartz cuvette, and cover the cuvette with the lid; use a sample holder to fix the cuvette containing the sample.

[0063] e. Use a D65 light source and a 10° observer angle to measure the reflectance color mode, and record the whiteness of the sample according to the whiteness formula in Table 2.

[0064] Table 2

[0065] (5) Data statistical analysis

[0066] Statistical analysis, including correlation analysis and regression analysis, was performed on all data using the Excel add-in XLSTAT 2019.

[0067] (6) Model Validation

[0068] Unmodeled commercially available dairy product samples were collected, and sensory whiteness and spectrophotometer whiteness data were obtained. At the same time, the sensory whiteness of the products was predicted using a model fitted with the experimental data. Correlation analysis was performed between the measured sensory whiteness and the predicted sensory whiteness. If the correlation coefficient is ≥0.90 within the 95% confidence interval, it indicates that the model is accurate and reliable.

[0069] Sixthly, the present invention provides a method for evaluating the sensory whiteness of milk or dairy products, the evaluation method comprising:

[0070] The whiteness value of the sample is detected by a spectrophotometer and substituted into the evaluation model described in the first aspect to obtain the sensory whiteness of the sample.

[0071] In some implementations, the whiteness value detection process includes placing milk or dairy products in a cuvette and detecting them using a spectrophotometer in reflectance mode.

[0072] In some implementations, the detection is performed using a D65 light source and at a 10° observer angle.

[0073] In this application, when the whiteness value detection method is based on the Hunter whiteness formula, the value of b in the model is 1.5 to 2.2 (for example, it can be 1.5, 1.6, 1.7, 1.8, 1.9, 2, 2.2, etc.), and the value of k is 0.05 to 0.07 (for example, it can be 0.05, 0.052, 0.055, 0.058, 0.06, 0.062, 0.065, 0.068, 0.07, etc.).

[0074] In this application, when the whiteness value detection method is based on the GB / T 5950-96 standard, the value of b in the model is -2.2 to -1.0 (for example, it can be -2.2, -2, -1.8, -1.6, -1.4, -1.2, -1, etc.), and the value of k is 0.12 to 0.18 (for example, it can be 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, etc.).

[0075] Compared with the prior art, the present invention has at least the following beneficial effects:

[0076] This invention develops a quantitative index for evaluating the appearance and color of dairy products. Specifically, it creatively correlates the sensory whiteness of milk or dairy products with instrumental whiteness measurements. By introducing a model, the sensory characteristics of milk or dairy products are converted into instrumental parameters, forming a stable measurement method. This achieves the goal of online, real-time, rapid, and accurate prediction of the sensory whiteness of dairy products. It solves the problems of individual subjective differences, time-consuming processes, and high development costs associated with relying solely on descriptive sensory tests by the population. It also overcomes the low accuracy of single testing methods, achieving the technical effect of efficiently and accurately obtaining sensory whiteness data of milk or dairy products. The analytical method provided by this invention is applicable not only to liquid dairy products but also to solid (e.g., powdered) dairy products. Detailed Implementation

[0077] definition

[0078] As used in this article, the term "dairy products" refers to food made primarily from raw milk (such as cow's milk or goat's milk), processed through sterilization, concentration, drying, fermentation, separation, and may contain added vitamins, minerals, and additives, and that complies with regulations and standards.

[0079] As used in this article, the term "sensory whiteness" is a subjective evaluation based on human visual perception and a comprehensive judgment of the "whiteness" of an object. Sensory whiteness is an important quality indicator for dairy products. Sensory whiteness is affected by the light source, background, observer, and sample condition, and is often not entirely equivalent to instrument values.

[0080] As used herein, the term "GB / T 5950-96 standard" refers to the national standard of the People's Republic of China, namely GB / T5950-1996, entitled "Method for measurement of whiteness of building materials and non-metallic mineral products". GB / T 5950-96 adopts the International Commission on Illumination (CIE) 1964 Supplementary Standard Colorimetric System and the standard illuminant D65, calculating whiteness using tristimulus values ​​and chromaticity coordinates. The chromaticity coordinates are calculated as follows: x 10 =X 10 / (X 10 +Y 10 +Z 10 );y 10 =Y 10 / (X 10 +Y 10 +Z 10 ); and z 10 =1-x 10 -y 10 =Z 10 / (X 10 +Y 10 +Z 10 The spectrophotometer can be set to measure whiteness values ​​according to the GB / T 5950-96 standard.

[0081] As used in this article, the term "standard illuminator D65" is one of the standard illuminators specified by the CIE (International Commission on Illumination), representing average northern sky daylight. It is currently the most commonly used color evaluation light source in industries such as textiles, printing and dyeing, coatings, and plastics. Spectrophotometers can be set to standard illuminator D65.

[0082] As used in this article, the term "10° observer" is the CIE-defined standard colorimetric observer spectral tristimulus function. It simulates the human eye's color perception capability when observing a large field of view. Spectrophotometers can be set to a 10° observer.

[0083] To facilitate understanding of the present invention, the following embodiments are provided. Those skilled in the art should understand that these embodiments are merely illustrative and should not be construed as limiting the scope of the invention.

[0084] Example

[0085] This embodiment provides a sensory whiteness calculation model for milk or dairy products and its construction method, including:

[0086] (1) Collection of dairy product samples

[0087] Different types of dairy products were purchased from the market, and the information was compiled and summarized as shown in Table 3. Three batches of each type of dairy product were purchased for parallel testing.

[0088] Table 3

[0089] (2) Sample shelf life

[0090] All samples were stored according to the storage conditions specified in the packaging for their shelf life, and sensory evaluation and whiteness value instrument testing were performed on the samples every three months.

[0091] (3) Sensory Panel Quantitative Description Test

[0092] Twelve evaluators with the ability to identify differences in basic sensory properties were selected and given professional training and regular assessments in sensory evaluation of milk and dairy products. All samples were randomly numbered by staff with three digits and randomly sampled in groups using a fully block-balanced design. Quantitative descriptive analysis (QDA) was used to characterize the surface whiteness characteristics and differences between products and the results were recorded to obtain the sensory whiteness of dairy products.

[0093] (4) Spectrophotometer whiteness index test

[0094] a. Record the whiteness of the sample using a KONICA MINOLTA CM-3600d spectrophotometer; install the target cover, select CM-A106, and measure the aperture. Lighting aperture

[0095] b. Install the zeroing box and perform zeroing;

[0096] c. Place the white calibration plate for calibration;

[0097] d. Stir the sample thoroughly, use a 15mL syringe to take 13mL of the sample and transfer it into a 40×40×10mm quartz cuvette, and cover the cuvette with the lid; use a sample holder to fix the cuvette containing the sample.

[0098] e. Use a D65 light source and a 10° observer angle to measure the reflected color mode and record the whiteness of the sample.

[0099] (5) Data statistical analysis

[0100] Statistical analysis, including correlation and regression analysis, was performed on all the data using the Excel add-in XLSTAT 2019.

[0101] The sample test data is summarized in Table 4 (0M, 3M, and 6M represent storage for 0 months, 3 months, and 6 months, respectively):

[0102] Table 4

[0103] a. Correlation analysis

[0104] Based on all the data in Table 4, a correlation analysis was performed. The Pearson correlation matrix between the sensory whiteness and instrumental whiteness of dairy products is shown in Table 5, and the significance test results are shown in Table 6.

[0105] Table 5 Note: * indicates a significant difference in data, i.e., p-values ​​< 0.05. W(CIE86), W(Ganz), W(Berger), W(Hunter), W(Hunter), W(Stens), W(R457), W(GT5950-96), and W(QT1503-92) are instrument whiteness values ​​determined by selecting different whiteness value calculation formulas in a spectrophotometer.

[0106] Table 6

[0107] The results showed that, within the 95% confidence interval, sensory whiteness and instrument whiteness were both highly significantly positively correlated, with correlation coefficients with W(Hunter) and W(GB / T 5950-96) reaching over 0.85.

[0108] b. Regression Analysis

[0109] Based on all the data in Table 4, a sensory whiteness prediction model based on the instrument whiteness index was established, and the regression equation in Table 7 was obtained.

[0110] The determination coefficient R of the embodiment 2 A value greater than 0.8 indicates good predictive performance and a coefficient of determination R for the proportion. 2 A value less than 0.8 indicates poor predictive performance.

[0111] Table 7

[0112] Therefore, using W(Hunter) to predict the sensory whiteness of dairy products is more accurate and reliable, and the optimal regression equation is y = 2.08 + 0.057 × W(Hunter), R0. 2 =0.96, P<0.01.

[0113] (6) Model Validation

[0114] Twenty unmodeled commercially available dairy products (P1-P20) were collected for sensory whiteness Sw and instrumental whiteness W(Hunter). The optimal experimental model y = 2.08 + 0.057 × W(Hunter) was used to predict sensory whiteness Pw, and the predicted Pw was compared with the measured sensory whiteness Sw to validate the model. Linear regression analysis of Sw and Pw was performed using the Excel add-in XLSTAT 2019 to obtain the multiple correlation coefficient R. 2 The verification results are shown in Table 8.

[0115] Table 8

[0116] The above data results show that the goodness of fit of the prediction model of this invention can reach 0.922, indicating that the fitting accuracy of the evaluation model of this application is high and can replace sensory methods to evaluate the sensory whiteness of yogurt products.

[0117] The applicant declares that the above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A method for evaluating the sensory whiteness of milk or dairy products, comprising the following steps: (1) Obtain the milk or dairy product sample to be tested; (2) The instrument whiteness value W is obtained by measuring with a spectrophotometer and according to the preset instrument whiteness value calculation formula; (3) Input the instrument whiteness value W into the processor, and calculate the sensory whiteness evaluation value y using a preset sensory whiteness evaluation model. The expression of the evaluation model is: y = b + k × W. Where y represents sensory whiteness, W represents the whiteness value obtained by spectrophotometer, b is a constant ranging from -2.2 to 2.2, and k is a constant ranging from 0.04 to 0.

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2. The evaluation method according to claim 1, further comprising the following steps: The evaluation result of the sample is output based on the sensory whiteness evaluation value y.

3. The evaluation method according to claim 1 or 2, wherein the detection process of the instrument whiteness value W includes: The milk or dairy product is placed in a cuvette and detected using a spectrophotometer in reflectance mode, and / or the detection is performed under a D65 light source and a 10° observer angle.

4. The evaluation method according to any one of claims 1-3, wherein the detection method of the instrument whiteness value W is based on the Hunter whiteness formula or on the GB / T 5950-96 standard.

5. The evaluation model according to claim 4, wherein the Hunter whiteness formula is: W = 100 - [(100 - L)] 2 +a 2 +b 2 ] 1 / 2 ; in, L represents the lightness value, and a and b represent the chroma values; or The formula for calculating whiteness in the GB / T 5950-96 standard is as follows: W=Y 10 +400x 10 -1000y 10 +205.5; Among them, Y 10 x represents the tristimulus value. 10 y 10 Represents chromaticity coordinates.

6. The evaluation model according to any one of claims 1-5, wherein when the detection method of the instrument whiteness value W is based on the Hunter whiteness formula, b in the model takes a value of 1.5 to 2.2, and k takes a value of 0.05 to 0.07; or When the detection method for the whiteness value W of the instrument is based on the GB / T 5950-96 standard, b in the model takes a value of -2.2 to -1.0, and k takes a value of 0.12 to 0.

18.

7. The evaluation model according to claim 1, wherein the evaluation criterion for sensory whiteness y is: When 1≤y<4, the milk or dairy products appear bluish-white; the lower the score, the more bluish they appear. When 4≤y<7, the milk or dairy products are milky white; the higher the score, the whiter they are. When 7≤y≤9, the milk or dairy products are milky yellow; the higher the score, the more yellow they are.

8. An evaluation model system for the sensory whiteness of milk or dairy products, the evaluation model system comprising: Processor; and A memory storing a program, the program comprising instructions that, when executed by the processor, cause the processor to perform the method according to any one of claims 1-7.

9. A computer-readable storage medium storing a program, the program comprising instructions that, when executed by a processor of an electronic device, cause the electronic device to perform the method according to any one of claims 1-7.

10. An evaluation model for the sensory whiteness of milk or dairy products, characterized in that, The evaluation model is: y = b + k × W, Where y represents sensory whiteness, W represents the whiteness value obtained by spectrophotometer, b is a constant ranging from -2.2 to 2.2, and k is a constant ranging from 0.04 to 0.

18.

11. The evaluation model according to claim 10, characterized in that, The method for detecting the whiteness value is based on the Hunter whiteness formula or the GB / T 5950-96 standard.

12. The evaluation model according to claim 11, characterized in that, The Hunter whiteness formula is: W = 100 - [(100 - L)] 2 +a 2 +b 2 ] 1 / 2 ; Where L represents the lightness value, and a and b represent the chromaticity values.

13. The evaluation model according to claim 11, characterized in that, The formula for calculating whiteness in the GB / T 5950-96 standard is: W = Y 10 +400x 10 -1000y 10 +205.5; Among them, Y 10 x represents the tristimulus value. 10 y 10 Represents chromaticity coordinates.

14. The evaluation model according to claim 11, characterized in that, When the whiteness value detection method is based on the Hunter whiteness formula, b takes a value of 1.5 to 2.2 and k takes a value of 0.05 to 0.07 in the model.

15. The evaluation model according to claim 11, characterized in that, When the whiteness value detection method is based on the GB / T 5950-96 standard, the b value in the model is -2.2 to -1.0, and the k value is 0.12 to 0.

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16. The evaluation model according to claim 10, characterized in that, The evaluation criteria for the sensory whiteness y are as follows: When 1≤y<4, the milk or dairy products appear bluish-white; the lower the score, the more bluish they appear. When 4≤y<7, the milk or dairy products are milky white; the higher the score, the whiter they are. When 7≤y≤9, the milk or dairy products are milky yellow; the higher the score, the more yellow they are.

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