Method and device for evaluating quality of black tea soup, electronic equipment and storage medium
By acquiring the color characteristics and chemical composition content of black tea liquor, and using a regression prediction model to calculate the taste score, the problems of subjectivity and consistency in black tea quality evaluation are solved, achieving efficient and accurate quality evaluation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SOUTH CHINA AGRICULTURAL UNIVERSITY
- Filing Date
- 2026-03-19
- Publication Date
- 2026-07-31
AI Technical Summary
Current methods for evaluating the quality of black tea mainly rely on manual sensory evaluation, which suffers from subjectivity, poor consistency, and low efficiency, making it difficult to meet the needs of rapid testing.
By acquiring the color characteristics and chemical composition of black tea liquor, a taste score is calculated using a predetermined regression prediction model, and the final quality evaluation result is determined. By combining the Lab color system and convolutional neural network model to extract color features, an objective and quantitative quality evaluation is achieved.
It achieves objectivity and consistency in the evaluation of black tea liquor quality, improves evaluation efficiency, avoids the limitations of single-indicator evaluation, and provides a comprehensive and accurate quality assessment.
Smart Images
Figure CN122487252A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of tea quality testing, and more specifically, to a method, apparatus, electronic device, and storage medium for evaluating the quality of black tea infusion. Background Technology
[0002] Black tea is a common traditional beverage, and its quality evaluation is of great significance to tea production, processing, and market sales.
[0003] Currently, the evaluation of black tea quality mainly relies on sensory evaluation by tea tasters through sight and taste. Although this evaluation method is widely used, it has a certain degree of subjectivity and it is difficult to ensure the consistency of evaluation, especially when different tea tasters evaluate different black teas. Furthermore, this evaluation method is relatively inefficient and cannot meet the needs of rapid testing and evaluation. Summary of the Invention
[0004] The purpose of this application is to provide a method, apparatus, electronic device, and storage medium for evaluating the quality of black tea infusion. By combining the color characteristics of the black tea infusion with the content of its chemical components, the quality of black tea infusion can be evaluated objectively, ensuring the consistency of the evaluation and effectively improving the efficiency of the evaluation.
[0005] To achieve the above objectives, firstly, this application provides a method for evaluating the quality of black tea infusion, including: Obtain the color characteristics corresponding to the target black tea liquor; Based on the color characteristics of the target black tea liquor and the predetermined first regression prediction model, the chemical composition content of the target black tea liquor is calculated. The taste score of the target black tea soup is calculated based on the chemical composition content of the target black tea soup and the predetermined second regression prediction model. The quality evaluation result of the target black tea soup is determined based on the taste score of the target black tea soup.
[0006] In a preferred embodiment of this application, the color characteristics of the target black tea infusion include the a-value and b-value corresponding to the target black tea infusion based on the Lab colorimetric system; The chemical composition content of the target black tea infusion includes the TF / TB ratio of the target black tea infusion, where TF represents the content of theaflavins and TB represents the content of thearubigins.
[0007] In a preferred embodiment of this application, the relationship of the first regression prediction model is as follows: Y = -11.996 + 5.72a - 1.482b Where a represents the a value corresponding to the target black tea soup, b represents the b value corresponding to the target black tea soup, and Y represents the TF / TB ratio of the target black tea soup.
[0008] In a preferred embodiment of this application, the relationship of the second regression prediction model is as follows: When x < 25, y = 0.483x + 83.478; When x ≥ 25, y = -0.248x + 95.935 Where x represents the TF / TB ratio of the target black tea soup, and y represents the taste score of the target black tea soup.
[0009] In a preferred embodiment of this application, obtaining the color features corresponding to the target black tea infusion includes: Acquire images of the target black tea liquor; The captured image of the target black tea soup is input into a trained convolutional neural network model to extract the color features corresponding to the target black tea soup.
[0010] In a preferred embodiment of this application, the convolutional neural network model is the AlexNet convolutional neural network model, and the convolutional neural network model uses the ReLU function as the activation function.
[0011] In a preferred embodiment of this application, determining the quality evaluation result of the target black tea infusion based on the taste score of the target black tea infusion includes: Based on the taste score of the target black tea infusion and the predetermined quality evaluation rules for black tea infusion, the score level of the target black tea infusion and the corresponding evaluation comments are determined, and the score level of the target black tea infusion and the corresponding evaluation comments are taken as the quality evaluation result of the target black tea infusion.
[0012] Secondly, this application provides a device for evaluating the quality of black tea infusion, comprising: The acquisition module is used to acquire the color features corresponding to the target black tea liquor; The first calculation module is used to calculate the chemical composition content of the target black tea soup based on the color characteristics of the target black tea soup and a predetermined first regression prediction model. The second calculation module is used to calculate the taste score of the target black tea soup based on the chemical composition content of the target black tea soup and the predetermined second regression prediction model. The quality evaluation module is used to determine the quality evaluation result of the target black tea soup based on the taste score of the target black tea soup.
[0013] Thirdly, this application provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the above-described method for evaluating the quality of black tea infusion.
[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for evaluating the quality of black tea infusion.
[0015] This application discloses a method, apparatus, electronic device, and storage medium for evaluating the quality of black tea infusion, which, compared with the prior art, has at least the following beneficial effects: This application calculates the chemical composition content of the target black tea liquor by obtaining its color characteristics and using a predetermined first regression prediction model. Then, based on the chemical composition content and a predetermined second regression prediction model, it calculates the taste score of the target black tea liquor, thereby determining the quality evaluation result. This quality evaluation result combines the color characteristics and chemical composition content of the black tea liquor. Both the color characteristics and chemical composition content of the black tea liquor are objective and quantifiable. Moreover, these are two different quantitative indicators, avoiding the use of only a single indicator for quality evaluation. This allows for an objective and comprehensive evaluation of the black tea liquor quality, ensuring consistency in quality evaluation and effectively improving its efficiency. Attached Figure Description
[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 This is a flowchart illustrating the method for evaluating the quality of black tea liquor provided in this application embodiment; Figure 2 This is a scatter plot showing the correlation between the L value, a value, b value and TF / TB ratio of black tea soup provided in the embodiments of this application. Figure 3 This is a graph showing the relationship between the TF / TB ratio and the taste score of black tea soup, as provided in the embodiments of this application. Figure 4This is a schematic diagram of the main interface of the mini-program based on the method for evaluating the quality of black tea soup provided in this application embodiment; Figure 5 This is a schematic diagram of the quality evaluation results of the applet based on the black tea soup quality evaluation method provided in this application embodiment; Figure 6 This is a structural block diagram of the black tea infusion quality evaluation device provided in the embodiments of this application; Figure 7 This is a schematic diagram of the internal structure of the computer device provided in the embodiments of this application. Detailed Implementation
[0018] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.
[0019] Currently, the evaluation of black tea quality mainly relies on sensory evaluation by tea tasters through sight and taste. Although this evaluation method is widely used, it has a certain degree of subjectivity and it is difficult to ensure the consistency of evaluation, especially when different tea tasters evaluate different black teas. Furthermore, this evaluation method is relatively inefficient and cannot meet the needs of rapid testing and evaluation.
[0020] To address the problems in the prior art, this application provides a method, apparatus, electronic device, and storage medium for evaluating the quality of black tea infusion. By combining the color characteristics of the black tea infusion with the chemical composition content of the black tea infusion, the quality of the black tea infusion can be evaluated objectively, ensuring the consistency of the quality evaluation and effectively improving the efficiency of the quality evaluation.
[0021] See Figure 1 , Figure 1 This is a flowchart illustrating the method for evaluating the quality of black tea liquor provided in this application embodiment.
[0022] The method for evaluating the quality of black tea liquor described in this application embodiment can be applied to electronic devices such as computer devices, mobile devices, or tablet devices. In this embodiment, the method for evaluating the quality of black tea liquor described in this application is mainly applied to computer devices to elaborate, explain, and interpret the method for evaluating the quality of black tea liquor.
[0023] This application provides a method for evaluating the quality of black tea infusion, including the following steps: Step S110: Obtain the color features corresponding to the target black tea soup.
[0024] In one embodiment, the target black tea infusion is the black tea infusion to be evaluated, which is obtained by brewing the black tea to be evaluated. Understandably, the color characteristics corresponding to the target black tea soup are based on the color characteristics of the target black tea soup. For example, the color characteristics corresponding to the target black tea soup may be the chromaticity of the red color corresponding to the target black tea soup. When obtaining the color characteristics of the target black tea soup, the testing personnel can use the corresponding testing tools to detect the color characteristics of the target black tea soup, and then input or transmit them to the computer equipment.
[0025] When evaluating the quality of different black teas, the same brewing time, brewing temperature, brewing water quality, and brewing utensils can be used for different black teas. This can better ensure the objectivity and consistency of the quality evaluation of different black teas.
[0026] Step S120: Based on the color characteristics of the target black tea liquor and the predetermined first regression prediction model, calculate the chemical composition content of the target black tea liquor.
[0027] In one embodiment, the predetermined first regression prediction model may employ a linear regression equation, which is used to calculate the chemical composition content of the target black tea liquor based on the color characteristics of the target black tea liquor. The chemical composition content of the target black tea infusion may include the content of theaflavins, thearubigins, and other chemical components of the black tea infusion.
[0028] Step S130: Calculate the taste score of the target black tea soup based on the chemical composition content of the target black tea soup and the predetermined second regression prediction model.
[0029] In one embodiment, the predetermined second regression prediction model may employ a linear regression equation, which is used to calculate the taste score of the target black tea soup based on the chemical composition content of the target black tea soup. The taste score of the target black tea soup can be scored on a 100-point scale, meaning the score can range from 0 to 100. Preferably, the taste score can be accurate to one or two decimal points, for example, the taste score can be 86.6 or 85.65.
[0030] Step S140: Determine the quality evaluation result of the target black tea soup based on the taste score of the target black tea soup.
[0031] In one embodiment, when determining the quality evaluation result of a target black tea infusion based on its taste score, the result can be determined based on predetermined quality evaluation rules for black tea infusions. For example, the quality evaluation grade corresponding to the target black tea infusion can be determined based on its taste score as the corresponding quality evaluation result. For instance, the quality evaluation grades of black tea infusions can be divided into Grade 1, Grade 2, Grade 3, Grade 4, etc. First-grade black tea is of the highest quality, with second-grade, third-grade, and fourth-grade black tea decreasing in quality in that order. Alternatively, the quality of black tea infusion can be graded as follows: premium black tea, superior black tea, and ordinary black tea, with premium black tea being of the highest quality and superior black tea decreasing in quality in that order. Or, the quality of black tea infusion can be graded as follows: A-grade black tea, B-grade black tea, C-grade black tea, and D-grade black tea, with A-grade black tea being of the highest quality and B-grade, C-grade, and D-grade black tea decreasing in quality in that order.
[0032] Optionally, after determining the quality evaluation result of the target black tea infusion, the quality evaluation result of the target black tea infusion can be output to a computer device. The method of outputting the quality evaluation result of the target black tea infusion can be to display the quality evaluation result of the target black tea infusion on the computer device and / or broadcast the quality evaluation result of the target black tea infusion through the computer device, thereby informing and synchronizing the quality evaluation result of the target black tea infusion to the testing personnel.
[0033] The method for evaluating the quality of black tea infusion in this application calculates the chemical composition content of the target black tea infusion by acquiring its color characteristics and using a predetermined first regression prediction model. Then, based on the chemical composition content and a predetermined second regression prediction model, it calculates the taste score of the target black tea infusion, thereby determining the quality evaluation result. This quality evaluation result combines the color characteristics and chemical composition content of the black tea infusion. Both the color characteristics and chemical composition content are objective and quantifiable. Furthermore, these are different quantitative indicators, avoiding the use of only a single indicator for evaluating the quality of the black tea infusion. This allows for an objective and comprehensive evaluation of the quality of the black tea infusion, ensuring consistency in the quality evaluation and effectively improving its efficiency.
[0034] In one embodiment, the color characteristics of the target black tea infusion include the a-value and b-value corresponding to the target black tea infusion based on the Lab colorimetric system; The chemical composition content of the target black tea infusion includes the TF / TB ratio of the target black tea infusion, where TF represents the content of theaflavins and TB represents the content of thearubigins.
[0035] Specifically, the Lab color system is the CIE Lab color system. Here, L represents lightness, ranging from 0 to 100; a higher value indicates a brighter color, while a lower value indicates a darker color. a represents the red-green axis, typically ranging from -128 to +127; +a indicates a reddish tint, and -a indicates a greenish tint; the larger the absolute value, the more intense the corresponding red / green tone. b represents the yellow-blue axis, typically ranging from -128 to +127; +b indicates a yellowish tint, and -b indicates a bluish tint; the larger the absolute value, the more intense the corresponding yellow / blue tone.
[0036] Optionally, when obtaining the a and b values corresponding to the target black tea infusion, a testing personnel can use a colorimeter to measure the Lab value of the target black tea infusion, record the corresponding a and b values, and then input the corresponding a and b values into a computer device.
[0037] For the correlation between the L, a, and b values of black tea infusion and the TF / TB ratio, please refer to [reference needed]. Figure 2 , Figure 2 This is a scatter plot showing the correlation between the L, a, and b values of some black tea infusions and the TF / TB ratio, obtained through testing. Figure 2 The ratio in the text is the TF / TB ratio, which is obtained through... Figure 2 It can be seen that the values of a and b are highly significantly correlated with the TF / TB ratio, while the L value is not significantly correlated. Therefore, when calculating the TF / TB ratio of the target black tea soup, the values of a and b are mainly used. This can reduce the variables in the predetermined first regression prediction model, reduce the complexity of the predetermined first regression prediction model, and thus calculate the TF / TB ratio of the target black tea soup more quickly and accurately. The content of theaflavins and thearubigins are good indicators for evaluating the quality of black tea infusion. This method for evaluating the quality of black tea infusion calculates the taste score of the target black tea infusion by using the TF / TB ratio, which can accurately calculate the taste score of the target black tea infusion and thus improve the accuracy of the quality evaluation of the target black tea infusion.
[0038] As an optional implementation method, the first regression prediction model has the following relationship: Y = -11.996 + 5.72a - 1.482b Where a represents the a value corresponding to the target black tea soup, b represents the b value corresponding to the target black tea soup, and Y represents the TF / TB ratio of the target black tea soup.
[0039] The aforementioned first regression prediction model's relationship was obtained by the applicant through testing, simulation analysis, and calculation. Specifically, the applicant used a colorimeter to measure the Lab values of various black tea infusions, recording the L, a, and b values. Furthermore, referring to Roberts' systematic analysis method, a visible spectrophotometer was used to measure the content of theaflavins (TF) and thearubigins (TR) in the corresponding black tea infusions, and their ratio TF / TB was calculated. Correlation analysis was then performed on the L, a, and b values corresponding to the collected black tea infusions and the TF / TB ratio. The results showed that the a and b values were highly significantly correlated with the TF / TB ratio, while the L value showed no significant correlation. For further details, please refer to [link to relevant documentation]. Figure 2 Therefore, using the values of a and b as independent variables and the TF / TB ratio as the dependent variable, a multiple linear regression model was constructed. Regression analysis was performed using SPSS (Statistical Package for the Social Sciences) software. The regression coefficients were calculated using the measured L, a, and b values and the TF / TB ratio, ultimately yielding the equation for the first regression prediction model: Y = -11.996 + 5.72a - 1.482b. The calculated R-squared value of this first regression prediction model is... 2 The value of 0.764 indicates that the model fits well and can explain 76.4% of the TF / TB variation.
[0040] Using the relationship from the first regression prediction model described above, the TF / TB ratio of the target black tea soup can be calculated relatively quickly and accurately.
[0041] As an optional implementation method, the relationship of the second regression prediction model is as follows: When x < 25, y = 0.483x + 83.478; When x ≥ 25, y = -0.248x + 95.935 Where x represents the TF / TB ratio of the target black tea soup, and y represents the taste score of the target black tea soup.
[0042] The aforementioned second regression prediction model's formula was obtained by the applicant through testing, simulation analysis, and calculation. Specifically, through data analysis, the applicant learned that when the TF / TB ratio is around 25, the flavor score of black tea soup shows a clear turning trend. Therefore, a piecewise linear regression model was established, segmented with a TF / TB ratio of 25. In addition, based on the tea tasters' scores of the various black tea soups mentioned above, combined with the TF / TB ratios corresponding to the various black tea soups, the aforementioned second regression prediction model's formula was calculated: when x < 25, y = 0.483x + 83.478; when x ≥ 25, y = -0.248x + 95.935.
[0043] For the flavor ratings of black tea infusions corresponding to different TF / TB ratios, please refer to [reference needed]. Figure 3 , Figure 3 This is a graph showing the relationship between the TF / TB ratio and the taste score of black tea soup. Figure 3 The ratio on the horizontal axis is the TF / TB ratio, and the score on the vertical axis is the taste score of the black tea soup.
[0044] Using the above-mentioned second regression prediction model, the taste score of the target black tea soup can be calculated relatively quickly and accurately.
[0045] In one embodiment, obtaining the color features corresponding to the target black tea infusion may include the following steps: Acquire images of the target black tea liquor; The captured image of the target black tea liquor is input into a trained convolutional neural network model to extract the color features corresponding to the target black tea liquor.
[0046] Specifically, the convolutional neural network model has been trained and can be used to extract the color features corresponding to the target black tea soup from the captured image of the target black tea soup. In this embodiment, the color features corresponding to the black tea soup extracted by the convolutional neural network model can be the a value and b value corresponding to the black tea soup. When training the convolutional neural network model, images of the tea soup corresponding to various different black teas can be captured, and the a value and b value corresponding to the tea soup corresponding to various different black teas can be measured by using a colorimeter. These are used as samples to train the convolutional neural network model, so that the convolutional neural network model can extract the a value and b value corresponding to the black tea soup based on the captured image of the black tea soup. When evaluating the quality of black tea infusions corresponding to different black teas, the images of the black tea infusions corresponding to different black teas can be taken under basically the same shooting conditions. Basically the same shooting conditions can include the same shooting equipment, the same shooting parameters, and the same brightness of the shooting background.
[0047] The above method allows for a convenient and rapid extraction of the color characteristics of the target black tea liquor, eliminating the need for inspectors to measure the a and b values of the tea liquor using a colorimeter. This further improves the efficiency of black tea liquor quality evaluation, resulting in a greater improvement in the overall quality evaluation efficiency.
[0048] Preferably, the convolutional neural network model is the AlexNet convolutional neural network model, which uses the ReLU function as the activation function.
[0049] AlexNet contains multiple convolutional layers, pooling layers, and fully connected layers, which can effectively extract high-level features such as color and texture of images, and thus more accurately extract the a and b values of the tea soup corresponding to black tea. Using the ReLU function as the activation function can solve the gradient vanishing problem in deep networks, which improves the training speed of convolutional neural network models by several times.
[0050] In one embodiment, determining the quality evaluation result of the target black tea infusion based on its taste score may include the following steps: Based on the taste score of the target black tea infusion and the predetermined quality evaluation rules for black tea infusion, the score level of the target black tea infusion and the corresponding evaluation comments are determined, and the score level of the target black tea infusion and the corresponding evaluation comments are taken as the quality evaluation result of the target black tea infusion.
[0051] In this embodiment, the rating of black tea infusion can be divided into Grade A, Grade B, and Grade C. For example, Grade A corresponds to a taste score of ≥92 points, Grade B corresponds to a taste score of 88-91 points, and Grade C corresponds to a taste score of <88 points. Different rating grades of black tea infusion correspond to different evaluation terms. For example, see [link to relevant documentation]. Figure 4 and Figure 5 ,in, Figure 4 and Figure 5 These are schematic diagrams illustrating different interfaces of a mobile app running on a mobile device based on the black tea infusion quality evaluation method according to embodiments of this application. Figure 5 The description of "the tea liquor is red and deep, and the taste is generally strong but lacks freshness" is the evaluation language corresponding to a grade C rating for black tea liquor; this can be understood. Figure 5 The rating terms in the text represent only one evaluation method and are not the only evaluation terms for a grade C rating of black tea soup. If other evaluation methods are used, a grade C rating for black tea soup can also be based on other evaluation terms, such as "the soup color is red enough, the taste is strong, but the concentration is not enough".
[0052] In order to perform the methods corresponding to the above embodiments and achieve the corresponding functions and technical effects, a black tea soup quality evaluation device is provided below.
[0053] See Figure 6 , Figure 6 This is a structural block diagram of the black tea infusion quality evaluation device provided in the embodiments of this application.
[0054] The black tea infusion quality evaluation device provided in this application embodiment includes: The acquisition module 610 is used to acquire the color features corresponding to the target black tea soup; The first calculation module 620 is used to calculate the chemical composition content of the target black tea soup based on the color characteristics of the target black tea soup and the predetermined first regression prediction model. The second calculation module 630 is used to calculate the taste score of the target black tea soup based on the chemical composition content of the target black tea soup and the predetermined second regression prediction model. The quality evaluation module 640 is used to determine the quality evaluation result of the target black tea soup based on the taste score of the target black tea soup.
[0055] The black tea liquor quality evaluation device provided in this application calculates the chemical component content of the target black tea liquor by acquiring its color characteristics and a predetermined first regression prediction model; then, based on the chemical component content and a predetermined second regression prediction model, it calculates the taste score of the target black tea liquor, thereby determining the quality evaluation result of the target black tea liquor. This quality evaluation result combines the color characteristics and chemical component content of the black tea liquor. The color characteristics and chemical component content of the black tea liquor are objective and quantifiable. Moreover, the color characteristics and chemical component content of the black tea liquor are two different quantitative indicators, which can avoid using only a single indicator for the quality evaluation of black tea liquor. In this way, the quality of black tea liquor can be evaluated objectively and comprehensively, and the consistency of the quality evaluation of black tea liquor can be fully guaranteed, and the efficiency of the quality evaluation of black tea liquor can be effectively improved.
[0056] As an optional implementation, the color characteristics of the target black tea infusion include the a-value and b-value corresponding to the target black tea infusion based on the Lab colorimetric system; The chemical composition content of the target black tea infusion includes the TF / TB ratio of the target black tea infusion, where TF represents the content of theaflavins and TB represents the content of thearubigins.
[0057] As an optional implementation method, the first regression prediction model has the following relationship: Y = -11.996 + 5.72a - 1.482b Where a represents the a value corresponding to the target black tea soup, b represents the b value corresponding to the target black tea soup, and Y represents the TF / TB ratio of the target black tea soup.
[0058] As an optional implementation method, the relationship of the second regression prediction model is as follows: When x < 25, y = 0.483x + 83.478; When x ≥ 25, y = -0.248x + 95.935 Where x represents the TF / TB ratio of the target black tea soup, and y represents the taste score of the target black tea soup.
[0059] As an optional implementation, the acquisition module 610 can be specifically used for: Acquire images of the target black tea liquor; The captured image of the target black tea liquor is input into a trained convolutional neural network model to extract the color features corresponding to the target black tea liquor.
[0060] As an optional implementation, the convolutional neural network model is the AlexNet convolutional neural network model, which uses the ReLU function as the activation function.
[0061] As an optional implementation, the quality evaluation module 640 can be specifically used for: Based on the taste score of the target black tea infusion and the predetermined quality evaluation rules for black tea infusion, the score level of the target black tea infusion and the corresponding evaluation comments are determined, and the score level of the target black tea infusion and the corresponding evaluation comments are taken as the quality evaluation result of the target black tea infusion.
[0062] The aforementioned black tea infusion quality evaluation device can implement the black tea infusion quality evaluation method described above. For specific limitations and other details of the above-described black tea infusion quality evaluation device embodiment, please refer to the content of the black tea infusion quality evaluation method described above; further details will not be repeated in the embodiment.
[0063] This application also provides an electronic device, including a memory and a processor. The memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the above-described method for evaluating the quality of black tea infusion.
[0064] Alternatively, the aforementioned electronic device may be a computer device.
[0065] In one embodiment, the internal structure of the computer device of this application can be as follows: Figure 7 As shown.
[0066] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for evaluating the quality of black tea infusion.
[0067] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.
[0068] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.
[0069] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0070] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.
[0071] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
[0072] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Similarly, in the description of this application, the terms "first," "second," etc., are used only for descriptive distinction and should not be construed as indicating or implying relative importance. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
Claims
1. A method for evaluating the quality of black tea infusion, characterized in that, include: Obtain the color characteristics corresponding to the target black tea liquor; Based on the color characteristics of the target black tea liquor and the predetermined first regression prediction model, the chemical composition content of the target black tea liquor is calculated. The taste score of the target black tea soup is calculated based on the chemical composition content of the target black tea soup and the predetermined second regression prediction model. The quality evaluation result of the target black tea soup is determined based on the taste score of the target black tea soup.
2. The method for evaluating the quality of black tea liquor according to claim 1, characterized in that, The color characteristics of the target black tea liquor include the a-value and b-value corresponding to the target black tea liquor based on the Lab colorimetric system; The chemical composition content of the target black tea infusion includes the TF / TB ratio of the target black tea infusion, where TF represents the content of theaflavins and TB represents the content of thearubigins.
3. The method for evaluating the quality of black tea liquor according to claim 2, characterized in that, The first regression prediction model has the following relationship: Y = -11.996 + 5.72a - 1.482b Where a represents the a value corresponding to the target black tea soup, b represents the b value corresponding to the target black tea soup, and Y represents the TF / TB ratio of the target black tea soup.
4. The method for evaluating the quality of black tea liquor according to claim 2 or 3, characterized in that, The second regression prediction model has the following relationship: When x < 25, y = 0.483x + 83.478; When x ≥ 25, y = -0.248x + 95.935 Where x represents the TF / TB ratio of the target black tea soup, and y represents the taste score of the target black tea soup.
5. The method for evaluating the quality of black tea liquor according to claim 1, characterized in that, The acquisition of the color features corresponding to the target black tea soup includes: Acquire images of the target black tea liquor; The captured image of the target black tea soup is input into a trained convolutional neural network model to extract the color features corresponding to the target black tea soup.
6. The method for evaluating the quality of black tea liquor according to claim 5, characterized in that, The convolutional neural network model is the AlexNet convolutional neural network model, which uses the ReLU function as the activation function.
7. The method for evaluating the quality of black tea liquor according to claim 1, characterized in that, The step of determining the quality evaluation result of the target black tea soup based on the taste score of the target black tea soup includes: Based on the taste score of the target black tea infusion and the predetermined quality evaluation rules for black tea infusion, the score level of the target black tea infusion and the corresponding evaluation comments are determined, and the score level of the target black tea infusion and the corresponding evaluation comments are taken as the quality evaluation result of the target black tea infusion.
8. A device for evaluating the quality of black tea infusion, characterized in that, include: The acquisition module is used to acquire the color features corresponding to the target black tea liquor; The first calculation module is used to calculate the chemical composition content of the target black tea soup based on the color characteristics of the target black tea soup and a predetermined first regression prediction model. The second calculation module is used to calculate the taste score of the target black tea soup based on the chemical composition content of the target black tea soup and the predetermined second regression prediction model. The quality evaluation module is used to determine the quality evaluation result of the target black tea soup based on the taste score of the target black tea soup.
9. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the method for evaluating the quality of black tea infusion according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the method for evaluating the quality of black tea infusion as described in any one of claims 1 to 7.