Calculation method, device and equipment for flavor amino acids in chilies and storage medium

Abstract

The invention relates to the technical field of agricultural measurement, and provides a method, device and equipment for calculating umami amino acids in chilies and a storage medium, and the method comprises the following steps: obtaining umami amino acid factors corresponding to the chilies; obtaining a umami amino acid calculation model corresponding to the pepper according to the umami amino acid factor; determining the specific amino acid content corresponding to the to-be-detected pepper, and inputting the specific amino acid content into the delicate flavor amino acid calculation model to obtain the delicate flavor amino acid content corresponding to the to-be-detected pepper; after the calculation model is determined through the umami amino acid factor, the content of specific amino acid corresponding to the umami amino acid factor in the to-be-detected hot pepper is obtained and input into the calculation model, and the umami amino acid content corresponding to the to-be-detected hot pepper can be calculated through the calculation model. Therefore, the content of the umami amino acid can be obtained through calculation by only measuring one umami amino acid factor, so that a tedious measurement process is avoided, and the measurement cost can be effectively reduced.

Description

Technical Field

[0001] The present invention relates to the field of agricultural measurement technology, and in particular to a method, device, equipment and storage medium for calculating umami amino acids in peppers. Background Art

[0002] As an important vegetable and condiment, pepper is famous for its spiciness and freshness. Pepper has a rich taste and different flavors when eaten, and these flavors are related to the rich free amino acids it contains. The amino acids that make pepper taste fresh are collectively called umami amino acids, mainly aspartic acid, glutamic acid, and lysine.

[0003] The existing method for calculating umami amino acids requires the use of a high performance liquid chromatograph to determine the contents of aspartic acid, glutamic acid, and lysine, and then to obtain the umami amino acids by summing up the contents obtained. This method requires high equipment conditions, high and cumbersome operation requirements, and high measurement costs. Summary of the invention

[0004] It has been found in long-term practice that the existing method for calculating umami amino acids requires the use of high performance liquid chromatography to determine the contents of aspartic acid, glutamic acid, and lysine, and then to obtain the umami amino acids by summing up the contents obtained. This method requires high equipment conditions, high and cumbersome operation, and high measurement costs.

[0005] The present invention provides a method, device, equipment and storage medium for calculating umami amino acids in peppers, so as to at least solve the above technical problems existing in the prior art.

[0006] According to a first aspect of the present invention, a method for calculating umami amino acids in chili peppers is provided, the method comprising: obtaining an umami amino acid factor corresponding to the chili pepper; the umami amino acid factor refers to the amino acids constituting the umami amino acids; obtaining an umami amino acid calculation model corresponding to the chili pepper based on the umami amino acid factor; determining the specific amino acid content corresponding to the chili pepper to be tested, inputting the specific amino acid content into the umami amino acid calculation model, and obtaining the umami amino acid content corresponding to the chili pepper to be tested; wherein the specific amino acid is one of the umami amino acid factors.

[0007] In one embodiment, according to the umami amino acid factor, an umami amino acid calculation model corresponding to the chili is obtained, including: determining the total amount of amino acid information corresponding to the umami amino acid factor; calculating the Pearson correlation coefficient between each of the umami amino acid factors and the total amount of amino acids; determining the umami amino acid factor with the largest Pearson correlation coefficient as the correlation factor; based on the correlation factor and the total amount of amino acids, determining an amino acid calculation model related to the correlation factor and the total amount of amino acids.

[0008] In one embodiment, the umami amino acid factor includes aspartic acid, glutamic acid, lysine and other amino acids.

[0009] In one embodiment, the calculation model includes at least a linear regression model.

[0010] In one embodiment, the correlation factor is aspartic acid; correspondingly, the calculation model for determining the correlation factor and the total amount of amino acids includes: Y=0.061+1.067X; Y refers to the total amount of amino acids in pepper, and X refers to the aspartic acid content in pepper.

[0011] In one embodiment, the related factor is aspartic acid, and correspondingly, the specific amino acid content refers to the aspartic acid content in the pepper to be tested; correspondingly, determining the specific amino acid content corresponding to the pepper to be tested includes: obtaining a test kit corresponding to the aspartic acid; based on the test kit, determining the aspartic acid content corresponding to the pepper to be tested.

[0012] According to a second aspect of the present invention, a device for calculating umami amino acids in chili peppers is provided, the device comprising: an acquisition module, used to obtain an umami amino acid factor corresponding to the chili pepper; the umami amino acid factor refers to the amino acids constituting the umami amino acids; a calculation module, used to obtain an umami amino acid calculation model corresponding to the chili pepper according to the umami amino acid factor; a determination module, used to determine the specific amino acid content corresponding to the chili pepper to be tested, input the specific amino acid content into the umami amino acid calculation model, and obtain the umami amino acid content corresponding to the chili pepper to be tested; wherein the specific amino acid is one of the umami amino acid factors.

[0013] In one embodiment, the determination module is further used to determine the total amount of amino acids corresponding to the umami amino acid factor; calculate the Pearson correlation coefficient between each of the umami amino acid factors and the total amount of amino acids; determine the umami amino acid factor with the largest Pearson correlation coefficient as the correlation factor; and determine the amino acid calculation model related to the correlation factor and the total amount of amino acids based on the correlation factor and the total amount of amino acids. In one embodiment, the umami amino acid factor includes aspartic acid, glutamic acid, lysine and other amino acids.

[0014] In one embodiment, the calculation model includes at least a linear regression model.

[0015] In one embodiment, the correlation factor is aspartic acid; correspondingly, the calculation model for determining the correlation factor and the total amount of amino acids includes: Y=0.061+1.067X; Y refers to the total amount of amino acids in pepper, and X refers to the aspartic acid content in pepper.

[0016] In one embodiment, the determination module is further used to obtain a test kit corresponding to the aspartic acid; based on the test kit, determine the aspartic acid content corresponding to the pepper to be tested.

[0017] According to a third aspect of the present invention, there is provided an electronic device, comprising:

[0018] at least one processor; and

[0019] a memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor so that the at least one processor can perform the method described in the present invention.

[0021] According to a fourth aspect of the present invention, there is provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the method of the present invention.

[0022] The umami amino acid calculation method, device, equipment and storage medium of the present invention include: obtaining an umami amino acid factor corresponding to a pepper; the umami amino acid factor refers to the amino acids constituting the umami amino acids; obtaining an umami amino acid calculation model corresponding to the pepper according to the umami amino acid factor; determining the specific amino acid content corresponding to the pepper to be tested, inputting the specific amino acid content into the umami amino acid calculation model, and obtaining the umami amino acid content corresponding to the pepper to be tested; wherein the specific amino acid is one of the umami amino acid factors; after determining the calculation model through the umami amino acid factor, the present invention obtains the specific amino acid content corresponding to the umami amino acid factor in the pepper to be tested, and inputs the specific amino acid content into the calculation model, so that the umami amino acid content corresponding to the pepper to be tested can be calculated through the calculation model; therefore, only one of the umami amino acid factors needs to be measured, that is, the umami amino acid content can be obtained through calculation, thus avoiding a tedious measurement process, thereby effectively reducing the measurement cost.

[0023] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0025] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts.

[0026] Figure 1 A schematic diagram of the implementation process of calculating the umami amino acids in peppers according to an embodiment of the present invention is shown;

[0027] Figure 2 The calculation data diagram corresponding to 56 varieties of peppers is shown in the calculation of the umami amino acids in peppers according to the embodiment of the present invention;

[0028] Figure 3 A graph showing the variation trend of aspartic acid and umami amino acids in the method for calculating umami amino acids in peppers according to an embodiment of the present invention is shown;

[0029] Figure 4 The figure shows the correlation coefficient between the umami amino acid factor and the umami amino acid in the method for calculating the umami amino acid in pepper according to the embodiment of the present invention;

[0030] Figure 5 It shows a schematic diagram of the structure of a device for calculating umami amino acids in peppers according to an embodiment of the present invention;

[0031] Figure 6 A schematic diagram of the structure of an electronic device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0032] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0033] It has been found in long-term practice that the existing method for calculating umami amino acids requires the use of high performance liquid chromatography to determine the contents of aspartic acid, glutamic acid, and lysine, and then to obtain the umami amino acids by summing up the contents obtained. This method requires high equipment conditions, high and cumbersome operation, and high measurement costs.

[0034] Figure 1 A schematic diagram of the implementation process of calculating the umami amino acids in peppers according to an embodiment of the present invention is shown; Figure 2 The calculation data diagram corresponding to 56 varieties of peppers is shown in the calculation of the umami amino acids in peppers according to the embodiment of the present invention; please refer to Figure 1 and Figure 2 ;

[0035] According to a first aspect of the present invention, a method for calculating umami amino acids in chili peppers is provided, the method comprising: step 101, obtaining an umami amino acid factor corresponding to the chili pepper; the umami amino acid factor refers to the amino acids constituting the umami amino acids; step 102, obtaining an umami amino acid calculation model corresponding to the chili pepper according to the umami amino acid factor; step 103, determining the specific amino acid content corresponding to the chili pepper to be tested, inputting the specific amino acid content into the umami amino acid calculation model, and obtaining the umami amino acid content corresponding to the chili pepper to be tested; wherein the specific amino acid is one of the umami amino acid factors.

[0036] Before obtaining the umami amino acid factor corresponding to pepper, a total of 19 free amino acids of 56 pepper varieties (including screw pepper, sheep horn pepper, pickled pepper, and string pepper) were determined using high performance liquid chromatography technology. At this stage, the umami amino acid content is obtained mainly by first using high performance liquid chromatography to determine the content of each umami amino acid and then summing them up to obtain the calculated data corresponding to the 56 varieties of pepper.

[0037] In step 101 of the specific embodiment of the present invention, an umami amino acid factor corresponding to the chili pepper is obtained; the umami amino acid factor refers to the amino acids constituting the umami amino acids; specifically, the umami amino acid factor includes at least aspartic acid, glutamic acid, lysine and other amino acids; among which aspartic acid, glutamic acid and lysine are mainly present.

[0038] Figure 3 A graph showing the variation trend of aspartic acid and umami amino acids in the method for calculating umami amino acids in peppers according to an embodiment of the present invention is shown; Figure 4 The umami amino acid factor and the correlation coefficient of the umami amino acid in the method for calculating the umami amino acid in pepper according to the embodiment of the present invention are shown; please refer to Figure 3 and Figure 4 ;

[0039] In step 102 of the specific embodiment of the present invention, according to the umami amino acid factor, the umami amino acid calculation model corresponding to the pepper is obtained; according to the above-mentioned calculation data, the total amount of amino acid information corresponding to the umami amino acid factor is determined; the umami amino acid factor refers to the amino acid content corresponding to each umami amino acid factor in a certain pepper, and the total amount of amino acid information refers to the total content of umami amino acids contained in a certain pepper. The calculation data obtained by measuring 56 pepper varieties are calculated to calculate the Pearson correlation coefficient between each umami amino acid factor and the total amount of amino acids; wherein the Pearson correlation coefficient is used to measure the linear correlation between two variables, that is, to determine the umami amino acid factor with the most linear correlation with the total amount of amino acids in the umami amino acid factor, so as to determine it as the correlation factor; that is, the umami amino acid factor with the largest Pearson correlation coefficient is determined as the correlation factor; based on the correlation factor and the total amount of amino acids, the amino acid calculation model of the correlation factor and the total amount of amino acids is determined. After determining that the correlation factor and the total amount of amino acids have a linear correlation, the linear regression model between the two can be calculated.

[0040] Taking aspartic acid as an example, if the correlation factor is aspartic acid; using R for correlation analysis, it is found that "umami amino acids" and "aspartic acid" have a very significant correlation, and they are positively correlated. The Pearson correlation coefficient between aspartic acid and umami amino acids is as high as 0.964, which is highly positively correlated. There is a linear correlation between the two, and a simple linear regression model is established as follows: Y = 0.061 + 1.067X; Y refers to the total amount of amino acids in peppers, and X refers to the content of aspartic acid in peppers.

[0041] Among them, the linear regression model can be obtained through data statistics software. SPSS19.0 software is used to model the data. Because P = 0.00325 < 0.01, it is considered that the model is significant at 0.01. The regression model is used to predict the umami amino acid content: For example, the aspartic acid content of material No. 45 is 0.1019895. When other factors are stable, the umami amino acid content of this numbered pepper variety is: Substitute into the formula, Y = 0.061 + 1.067X - Y = 0.061 + 1.067 * 0.1019895 = 0.169822796, and the actual umami amino acid content of this numbered pepper material measured by HPLC is 0.16968 mg g-1, and this deviation is acceptable. The determination coefficient R 2 , also known as goodness of fit or coefficient of determination, which is the square of the correlation coefficient R, is used to indicate the percentage of changes in the dependent variable that the fitted model can explain. 2The closer it is to 1, the better the regression model fits. R2 = 0.929, indicating that the model is good. The linear regression model obtained by SPSS19.0 analysis is: Y = 0.061 + 1.067X, where the standardized coefficient is 0.964, which is consistent with the correlation coefficient result.

[0042] In step 103 of the specific embodiment of the present invention, the specific amino acid content corresponding to the pepper to be tested is determined, and the specific amino acid content is input into the umami amino acid calculation model to obtain the umami amino acid content corresponding to the pepper to be tested; wherein the specific amino acid is one of the umami amino acid factors.

[0043] Taking aspartic acid as an example, when the relevant factor is aspartic acid, the corresponding specific amino acid content refers to the aspartic acid content in the pepper to be tested. Correspondingly, determining the specific amino acid content corresponding to the pepper to be tested includes: obtaining a kit corresponding to aspartic acid; based on the kit, determining the aspartic acid content corresponding to the pepper to be tested. By using the aspartic acid determination kit, the aspartic acid in the pepper is measured without the need for high performance liquid chromatography, thereby reducing the measurement cost and operation difficulty. In addition, the measurement efficiency of umami amino acids is improved.

[0044] Further, model training can be performed by calculating data to obtain an umami amino acid prediction model corresponding to the umami amino acid. Specifically, the umami amino acid prediction model can enter the category and weight as input values. Perform BP neural network model training and add an attention mechanism. A mapping relationship between input and output is realized. After determining the umami amino acid content, the umami amino acid content is determined as the first umami amino acid content, the category and weight of the corresponding pepper are input into the umami amino acid prediction model to obtain the second umami amino acid content, and the first umami amino acid content and the second umami amino acid content are compared. When it is less than a specified threshold, the first umami amino acid is determined as the amino acid content. Thereby realizing the double calculation of the umami amino acid content, thereby ensuring the calculation accuracy. Wherein, the specified threshold can be 3%. When the comparison value is greater than the specified threshold, the umami amino acid is recalculated.

[0045] Figure 5 The schematic diagram of the structure of the device for calculating the umami amino acids in pepper according to the embodiment of the present invention is shown; please refer to Figure 5 ;

[0046] According to a second aspect of the present invention, a device for calculating umami amino acids in chili peppers is provided, the device comprising: an acquisition module 201, for obtaining an umami amino acid factor corresponding to the chili pepper; the umami amino acid factor refers to the amino acids constituting the umami amino acids; a calculation module 202, for obtaining an umami amino acid calculation model corresponding to the chili pepper according to the umami amino acid factor; a determination module 203, for determining the specific amino acid content corresponding to the chili pepper to be tested, inputting the specific amino acid content into the umami amino acid calculation model, and obtaining the umami amino acid content corresponding to the chili pepper to be tested; wherein the specific amino acid is one of the umami amino acid factors.

[0047] In one embodiment, the determination module is also used to determine the total amount of amino acids corresponding to the umami amino acid factor; calculate the Pearson correlation coefficient between each umami amino acid factor and the total amount of amino acids; determine the umami amino acid factor with the largest Pearson correlation coefficient as the correlation factor; based on the correlation factor and the total amount of amino acids, determine the amino acid calculation model related to the correlation factor and the total amount of amino acids.

[0048] In one embodiment, the umami amino acid factor includes aspartic acid, glutamic acid, lysine and other amino acids.

[0049] In one embodiment, the computational model includes at least a linear regression model.

[0050] In one embodiment, the correlation factor is aspartic acid; correspondingly, the calculation model for determining the correlation factor and the total amount of amino acids includes: Y=0.061+1.067X; Y refers to the total amount of amino acids in pepper, and X refers to the aspartic acid content in pepper.

[0051] In one embodiment, the determination module is further used to obtain a test kit corresponding to aspartic acid; based on the test kit, determine the aspartic acid content corresponding to the pepper to be tested.

[0052] According to a third aspect of the present invention, there is provided an electronic device, comprising:

[0053] at least one processor; and

[0054] a memory communicatively connected to at least one processor; wherein,

[0055] The memory stores instructions that can be executed by at least one processor. The instructions are executed by at least one processor so that the at least one processor can perform the method of the present invention.

[0056] According to a fourth aspect of the present invention, there is provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the method of the present invention.

[0057] The umami amino acid calculation method, device, equipment and storage medium of the present invention include: obtaining an umami amino acid factor corresponding to a pepper; the umami amino acid factor refers to the amino acids constituting the umami amino acids; obtaining an umami amino acid calculation model corresponding to the pepper according to the umami amino acid factor; determining the specific amino acid content corresponding to the pepper to be tested, inputting the specific amino acid content into the umami amino acid calculation model, and obtaining the umami amino acid content corresponding to the pepper to be tested; wherein the specific amino acid is one of the umami amino acid factors; after determining the calculation model through the umami amino acid factor, the present invention obtains the specific amino acid content corresponding to the umami amino acid factor in the pepper to be tested, and inputs the specific amino acid content into the calculation model, so that the umami amino acid content corresponding to the pepper to be tested can be calculated through the calculation model; therefore, only one of the umami amino acid factors needs to be measured, that is, the umami amino acid content can be obtained through calculation, thus avoiding a tedious measurement process, thereby effectively reducing the measurement cost.

[0058] According to an embodiment of the present invention, the present invention also provides an electronic device and a readable storage medium.

[0059] Figure 6 A schematic block diagram of an example electronic device 300 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0060] like Figure 6 As shown, the device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 303 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the device 300 can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0061] A number of components in the device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 303, such as a disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0062] The computing unit 301 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 301 performs the various methods and processes described above, such as the umami amino acid measurement method in pepper. For example, in some embodiments, the umami amino acid measurement method in pepper may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 303. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the umami amino acid measurement method in pepper described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to execute the method for measuring umami amino acids in chili peppers in any other appropriate manner (eg, by means of firmware).

[0063] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0064] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0065] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0066] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0067] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0068] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0069] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in the present disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document is not limited here.

[0070] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0071] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for calculating umami amino acids in peppers, characterized in that: The method comprises: Obtaining an umami amino acid factor corresponding to the pepper; the umami amino acid factor refers to the amino acids constituting the umami amino acids; According to the umami amino acid factor, obtaining an umami amino acid calculation model corresponding to the pepper; Determine the specific amino acid content corresponding to the pepper to be tested, input the specific amino acid content into the umami amino acid calculation model, and obtain the umami amino acid content corresponding to the pepper to be tested; wherein the specific amino acid is one of the umami amino acid factors.

2. The method according to claim 1, characterized in that According to the umami amino acid factor, a calculation model of umami amino acid corresponding to the pepper is obtained, comprising: Determining the total amount of amino acids corresponding to the umami amino acid factor; Calculating the Pearson correlation coefficient between each of the umami amino acid factors and the total amount of amino acids; The umami amino acid factor with the largest Pearson correlation coefficient was determined as the correlation factor; Based on the correlation factor and the total amount of amino acids, an amino acid calculation model related to the correlation factor and the total amount of amino acids is determined.

3. The method according to claim 2, characterized in that The umami amino acid factors include aspartic acid, glutamic acid, lysine and other amino acids.

4. The method according to claim 3, characterized in that The calculation model at least includes a linear regression model.

5. The method according to claim 4, characterized in that The related factor is aspartic acid; Correspondingly, the calculation model for determining the correlation factor and the total amount of amino acids includes: Y = 0.061 + 1.067X; Y refers to the total amount of amino acids in peppers, and X refers to the aspartic acid content in peppers.

6. The method according to claim 3, characterized in that The related factor is aspartic acid, and correspondingly, the specific amino acid content refers to the aspartic acid content in the pepper to be tested; Correspondingly, the specific amino acid content corresponding to the pepper to be tested is determined, including: Obtaining a kit corresponding to the aspartic acid; Based on the kit, the aspartic acid content corresponding to the pepper to be tested is determined.

7. A device for calculating umami amino acids in peppers, characterized in that: The device comprises: An acquisition module, used to acquire an umami amino acid factor corresponding to the pepper; the umami amino acid factor refers to the amino acids constituting the umami amino acids; A calculation module, for obtaining an umami amino acid calculation model corresponding to the pepper according to the umami amino acid factor; A determination module is used to determine the specific amino acid content corresponding to the pepper to be tested, and input the specific amino acid content into the umami amino acid calculation model to obtain the umami amino acid content corresponding to the pepper to be tested; wherein the specific amino acid is one of the umami amino acid factors.

8. The device according to claim 7, characterized in that The determination module is further used to determine the total amount of amino acids corresponding to the umami amino acid factors; and calculate the Pearson correlation coefficient between each of the umami amino acid factors and the total amount of amino acids; The umami amino acid factor with the largest Pearson correlation coefficient was determined as the correlation factor; Based on the correlation factor and the total amount of amino acids, an amino acid calculation model related to the correlation factor and the total amount of amino acids is determined.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

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