CN112630458B - Method and device for detecting quality of monoglyceride - Google Patents
Method and device for detecting quality of monoglyceride Download PDFInfo
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Abstract
The invention discloses a method and a device for detecting the quality of monoglyceride, wherein the method comprises the following steps: obtaining attribute information of a first product; obtaining taste demand information of a first product; inputting the attribute information of the first product and the taste requirement information into a neural network model to obtain a purity threshold value of the first monoglyceride; obtaining first detection information according to the first purity threshold value; obtaining purity information of a first monoglyceride to be detected; detecting the purity information of the first monoglyceride to be detected to obtain a first detection result; obtaining age threshold information for a first user of a first product; determining a first dynamic weight value according to age threshold information of a first user of the first product; and obtaining a second detection result according to the first dynamic weight value and the first detection result. Solves the technical problem that the quality of monoglyceride in the prior art can influence the quality safety and the taste of food when the monoglyceride is added into the food.
Description
Technical Field
The invention relates to the field of quality detection, in particular to a method and a device for detecting the quality of monoglyceride.
Background
Monoglyceride is an important nonionic surfactant, contains a lipophilic long-chain alkyl group and two hydrophilic hydroxyl groups, has good surface activity, and can be used as an emulsifier to be applied to the fields of foods, cosmetics, medicines and the like.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
the technical problem that when monoglyceride is added into food, the quality of monoglyceride influences the quality safety and the taste of the food is solved in the prior art.
Disclosure of Invention
The embodiment of the application provides a method and a device for detecting the quality of monoglyceride, so that the technical problem that when monoglyceride is added into food in the prior art, the quality of monoglyceride influences the quality safety and the taste of the food is solved, and the quality of monoglyceride is controlled according to product requirements, so that the technical effect of improving the flavor and quality of the product is achieved.
In view of the above problems, the embodiments of the present application provide a method and an apparatus for detecting the quality of monoglyceride.
In a first aspect, an embodiment of the present application provides a method for detecting quality of monoglyceride, where the method includes: obtaining attribute information of a first product; obtaining taste demand information of a first product; inputting the attribute information of the first product and the taste requirement information into a neural network model to obtain a purity threshold value of the first monoglyceride; obtaining first detection information according to the first purity threshold value; obtaining purity information of a first monoglyceride to be detected; detecting the purity information of the first monoglyceride to be detected according to the first detection information to obtain a first detection result; obtaining age threshold information for a first user of a first product; determining a first dynamic weight value according to age threshold information of a first user of the first product; and obtaining a second detection result according to the first dynamic weight value and the first detection result.
In another aspect, the present application further provides a device for detecting the quality of monoglyceride, wherein the system includes: a first obtaining unit for obtaining attribute information of a first product; the second obtaining unit is used for obtaining the taste demand information of the first product; a third obtaining unit, configured to input the attribute information of the first product and the taste requirement information into a neural network model, and obtain a purity threshold of the first monoglyceride; a fourth obtaining unit configured to obtain first detection information according to the first purity threshold; a fifth obtaining unit, configured to obtain purity information of the first monoglyceride to be detected; a sixth obtaining unit, configured to detect, according to the first detection information, the purity information of the first monoglyceride to be detected, and obtain a first detection result; a seventh obtaining unit for obtaining age threshold information of a first user of a first product; a first determining unit to determine a first dynamic weight value according to age threshold information of a first user of the first product; an eighth obtaining unit, configured to obtain a second detection result according to the first dynamic weight value and the first detection result.
In a third aspect, the present invention provides an apparatus for quality detection of monoglyceride, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method according to the first aspect when executing the program.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
because the attribute information of the first product is obtained, the taste demand information of the first product is obtained, the attribute information of the first product and the taste demand information are input into the neural network model to obtain the purity threshold value of the first monoglyceride, obtaining first detection information according to the first purity threshold value, obtaining purity information of first monoglyceride to be detected, detecting the purity information of the first monoglyceride to be detected to obtain a first detection result and age threshold information of a first user of a first product, determining a first dynamic weight value based on age threshold information for a first user of the first product, and obtaining a second detection result according to the first dynamic weight value and the first detection result, and further controlling the quality of monoglyceride according to product requirements, so that the technical effect of improving the flavor quality of the product is achieved.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
FIG. 1 is a schematic flow chart of a method for detecting the quality of monoglyceride according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an apparatus for quality detection of monoglyceride according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a fourth obtaining unit 14, a fifth obtaining unit 15, a sixth obtaining unit 16, a seventh obtaining unit 17, a first determining unit 18, an eighth obtaining unit 19, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, a bus interface 306.
Detailed Description
The embodiment of the application provides a method and a device for detecting the quality of monoglyceride, so that the technical problem that when monoglyceride is added into food in the prior art, the quality of monoglyceride influences the quality safety and the taste of the food is solved, and the quality of monoglyceride is controlled according to product requirements, so that the technical effect of improving the flavor and quality of the product is achieved. Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.
Summary of the application
Monoglyceride is an important nonionic surfactant, contains a lipophilic long-chain alkyl group and two hydrophilic hydroxyl groups, has good surface activity, and can be used as an emulsifier to be applied to the fields of foods, cosmetics, medicines and the like. However, the prior art has the technical problem that when the monoglyceride is added into food, the quality of the monoglyceride can affect the quality safety and the mouthfeel of the food.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the embodiment of the application provides a method for detecting the quality of monoglyceride, which comprises the following steps: obtaining attribute information of a first product; obtaining taste demand information of a first product; inputting the attribute information of the first product and the taste requirement information into a neural network model to obtain a purity threshold value of the first monoglyceride; obtaining first detection information according to the first purity threshold value; obtaining purity information of a first monoglyceride to be detected; detecting the purity information of the first monoglyceride to be detected according to the first detection information to obtain a first detection result; obtaining age threshold information for a first user of a first product; determining a first dynamic weight value according to age threshold information of a first user of the first product; and obtaining a second detection result according to the first dynamic weight value and the first detection result.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, an embodiment of the present application provides a method for detecting the quality of monoglyceride, wherein the method includes:
step S100: obtaining attribute information of a first product;
step S200: obtaining taste demand information of a first product;
specifically, the attribute information of the first product is characteristic information of the product, such as different product types of ice cream, cake, biscuit, candy and beverage, the mouthfeel of the first product is a direct feeling of food in the oral cavity of a person, generated by touch and chewing, and is another experience independent of taste, the mouthfeel requirement information of the first product is mouthfeel requirement information of the product, and mouthfeel generally comprises two basic aspects of a cold degree and a soft and hard degree of food: words describing the degree of cold or hot food, such as warm or cool blanching, etc.; words describing the degree of softness of foods such as soft glutinous rice, crisp and tender, etc.
Step S300: inputting the attribute information of the first product and the taste requirement information into a neural network model to obtain a purity threshold value of the first monoglyceride;
further, in the step S300 of the embodiment of the present application, the attribute information of the first product and the mouth feel requirement information are input into a neural network model, so as to obtain the purity threshold of the first monoglyceride, where:
step S310: inputting the attribute information and the taste requirement information of the first product into a neural network model, wherein the neural network model is obtained by training a plurality of groups of training data, and each group of the plurality of groups of training data comprises: attribute information of the first product, the mouthfeel requirement information, and identification information identifying a purity threshold of first monoglyceride; (ii) a
Step S320: obtaining a first output of the neural network model, the first output comprising a purity threshold of the first monoglyceride.
Specifically, the Neural network model is a Neural network model in machine learning, and Neural Networks (NN) are complex Neural network systems formed by widely interconnecting a large number of simple processing units (called neurons), reflect many basic features of human brain functions, and are highly complex nonlinear dynamical learning systems. Neural network models are described based on mathematical models of neurons. Artificial Neural Networks (ANN), is a description of the first-order properties of the human brain system. Briefly, it is a mathematical model. And inputting the attribute information of the first product and the taste requirement information into a neural network model through training of a large amount of training data, and outputting the purity threshold value of the first monoglyceride.
More specifically, the training process is essentially a supervised learning process, each group of supervised data includes attribute information of the first product, taste requirement information and identification information identifying a purity threshold of the first monoglyceride, the attribute information of the first product and the taste requirement information are input into a neural network model, the neural network model performs continuous self-correction and adjustment according to the identification information used for identifying the purity threshold meeting the first monoglyceride, and the neural network model finishes the group of data supervised learning and performs the next group of data supervised learning until an obtained first output result is consistent with the identification information; and when the output information of the neural network model reaches the preset accuracy rate/reaches the convergence state, finishing the supervised learning process. Through supervised learning of the neural network model, the neural network model can process the input information more accurately, the output purity threshold information of the first monoglyceride is more reasonable and accurate, and the monoglyceride quality is controlled according to product requirements, so that the technical effect of improving the product flavor quality is achieved.
Step S400: obtaining first detection information according to the first purity threshold value;
step S500: obtaining purity information of a first monoglyceride to be detected;
step S600: detecting the purity information of the first monoglyceride to be detected according to the first detection information to obtain a first detection result;
specifically, the first purity threshold is a purity critical value of the first product, which is a lowest value or a highest value that the product purity can generate, the first detection information is information for measuring the monoglyceride purity of the product to be detected according to the first purity threshold, the first purity information of the monoglyceride to be detected is the monoglyceride purity information of the product, and the first detection result is a purity detection result of the monoglyceride to be detected, which is obtained by detecting the purity information of the first monoglyceride to be detected according to the first detection information.
Step S700: obtaining age threshold information for a first user of a first product;
step S800: determining a first dynamic weight value according to age threshold information of a first user of the first product;
specifically, the age threshold information of the first user is the age threshold information of the first user eating the first product, and refers to the lowest value or the highest value that the age of the user can generate, and the first dynamic weight value is the weight value of the age threshold of the first user, which is different from a general specific gravity, and the weight value is not only the percentage of the age factor or indicator, but also emphasizes the relative importance degree of the age factor or indicator, which tends to contribute to or is important.
Step S900: and obtaining a second detection result according to the first dynamic weight value and the first detection result.
Specifically, the second detection result is detection result information obtained by weighting according to the first dynamic weight value and the first detection result in combination with different ages of the first users eating the first product, and is more accurate and humanized.
Further, in an embodiment of the present application, step S800 further includes:
step S810: obtaining a predetermined age threshold;
step S820: determining whether the age threshold of the first user is within the predetermined age threshold;
step S830: and if the age threshold of the first user is higher than the preset age threshold, obtaining a first weight value.
Specifically, the predetermined age threshold is a predetermined age threshold of the first user, and it is determined whether the age threshold of the first user is within the predetermined age threshold, where the age threshold of the first user is young and old, and if the age threshold of the first user is higher than the predetermined age threshold, such as old age, the first weight value is obtained according to the age threshold information of the first user of the first product.
Further, after determining whether the age threshold of the first user is within the predetermined age threshold, step S820 in this embodiment of the present application further includes:
step S821: obtaining a second weight value if the age threshold of the first user is within the predetermined age threshold;
step S822: and if the age threshold of the first user is lower than the preset age threshold, obtaining a third weight value, wherein the second weight value is higher than the first weight value, and the first weight value is higher than the third weight value.
Specifically, if the age threshold of the first user is within the predetermined age threshold, wherein the age threshold of the first user is young and young, a second weight value is obtained, and if the age threshold of the first user is below the predetermined age threshold, such as a child, a third weight value is obtained, because the mouth feel of the product is better and relatively unhealthy, the mouth feel of the product is relatively less weighted for health considerations for children and elderly people, and wherein the second weight value is higher than the first weight value, and the first weight value is higher than the third weight value.
Further, the embodiment of the present application further includes:
step S1010: obtaining category information of the first product;
step S1020: obtaining a predetermined ratio threshold of monoglyceride and diglyceride based on the species information;
step S1030: obtaining second detection information according to the preset proportion threshold;
step S1040: obtaining a first ratio threshold of monoglyceride and diglyceride of a first monoglyceride to be detected;
step S1050: and detecting the first proportional threshold according to the second detection information to obtain a third detection result.
Specifically, the first product type information indicates the application type of the product, the monoglyceride contains 90% or more of a monoester and other di-and triesters, i.e., oils and fats, the monoester has an emulsifying and swelling action, and the di-triester has an action opposite to the emulsifying and swelling action and is a main component of the defoaming agent, so that the higher the monoester content is, the smaller the di-triester is, the smaller the amount of the added diester is, and the emulsifying and swelling efficiency is higher, the second detection information indicates a predetermined ratio threshold value of the monoglyceride and the di-triester of the first product is detected, and the third detection result indicates predetermined ratio threshold value information of the monoglyceride and the di-triester of the first product is detected.
Further, step S1050 in the embodiment of the present application further includes:
step S1051: obtaining color information of the first monoglyceride to be detected;
step S1052: obtaining first standard color information;
step S1053: obtaining first color difference information according to the color information of the first monoglyceride to be detected and the first standard color information;
step S1054: obtaining a first correction parameter according to the first color difference information;
step S1055: and correcting the third detection result according to the first correction parameter to obtain a fourth detection result.
Specifically, the color information of the first monoglyceride to be detected is a visual effect on light generated by eyes, brains and our life experiences, is generated by electromagnetic waves with narrow frequency ranges, the electromagnetic waves with different frequencies represent different colors, and the identification of colors is a visual nerve sensation caused by the stimulation of the naked eyes by electromagnetic wave radiant energy, and the color information has three characteristics, namely hue, lightness and saturation. The first standard color information is standard color information of a specific color or a group of color systems of the first monoglyceride to be detected, which is specified under a standard condition, the first color difference information is a difference between two colors of the color information of the first monoglyceride to be detected and the first standard color information, the first correction parameter is a correction of a color of the first color difference information, and the fourth detection result is a result of a color correction of the third detection result.
Further, step S1055 in the embodiment of the present application further includes:
step S10551: obtaining a second dynamic weight value according to the first dynamic weight value, wherein the sum of the first dynamic weight value and the second dynamic weight value is 1;
step S10552: obtaining a fifth detection result according to the second dynamic weight value and the fourth detection result;
step S10553: and obtaining a sixth detection result according to the fifth detection result and the second detection result.
Specifically, the second dynamic weight value is a color weight value of the product, which is different from a general specific gravity, the weight value represents not only a percentage of the color factor or the index, but also emphasizes a relative importance degree of the color factor or the index, which tends to contribute to a degree of contribution or importance, and a sum of the first dynamic weight value and the second dynamic weight value is 1. The fifth detection result is a detection result obtained by comprehensively evaluating the color weight value of the first monoglyceride to be detected and the result obtained by color correction of the first monoglyceride to be detected, and the sixth detection result is a comprehensive quality detection result obtained by weighting the first monoglyceride to be detected, the first monoglyceride to be detected and the first monoglyceride to be detected in combination with different ages of the first user eating the first product.
Further, the step S300 of inputting the attribute information of the first product and the mouth feel requirement information into a neural network model in the embodiment of the present application further includes:
step S310: obtaining first training data, second training data and Nth training data which are input into the neural network model, wherein N is a natural number larger than 1;
step S320: generating first identification codes according to the first training data, wherein the first identification codes correspond to the first training data one by one;
step S330: generating a second identification code according to the second training data and the first identification code, and generating an Nth identification code according to the Nth training data and the (N-1) th identification code by analogy;
step S340: all training data and identification codes are copied and stored on M electronic devices, wherein M is a natural number greater than 1.
In particular, the blockchain technique, also referred to as a distributed ledger technique, is an emerging technique in which several computing devices participate in "accounting" together, and maintain a complete distributed database together. The blockchain technology has been widely used in many fields due to its characteristics of decentralization, transparency, participation of each computing device in database records, and rapid data synchronization between computing devices. Generating first identification codes according to the first training data, wherein the first identification codes correspond to the first training data one to one; generating a second identification code according to the second training data and the first identification code, wherein the second identification code corresponds to the second training data one to one; and by analogy, generating an Nth identification code according to the Nth training data and the (N-1) th identification code, wherein N is a natural number greater than 1, and each group in the training data comprises attribute information of the first product, the taste requirement information and identification information for identifying a purity threshold of the first monoglyceride. Respectively copying and storing all training data and identification codes on M devices, wherein the first training data and the first identification code are stored on one device as a first block, the second training data and the second identification code are stored on one device as a second block, the Nth training data and the Nth identification code are stored on one device as an Nth block, when the training data are required to be called, after each subsequent node receives data stored by a previous node, the data are checked through a common identification mechanism and then stored, each storage unit is connected in series through a Hash function, so that the training data are not easy to lose and damage, the training data are encrypted through logic of a block chain, the safety of the training data is ensured, and the accuracy of a neural network model obtained through training the training data is further ensured, and the output purity threshold information of the first monoglyceride is more reasonable and accurate, so that the monoglyceride quality is controlled according to the product requirement, and the technical effect of improving the product flavor quality is achieved.
In summary, the method and the device for detecting the quality of monoglyceride provided by the embodiments of the present application have the following technical effects:
1. because the attribute information of the first product is obtained, the taste demand information of the first product is obtained, the attribute information of the first product and the taste demand information are input into the neural network model to obtain the purity threshold value of the first monoglyceride, obtaining first detection information according to the first purity threshold value, obtaining purity information of first monoglyceride to be detected, detecting the purity information of the first monoglyceride to be detected to obtain a first detection result and age threshold information of a first user of a first product, determining a first dynamic weight value based on age threshold information for a first user of the first product, and obtaining a second detection result according to the first dynamic weight value and the first detection result, and further controlling the quality of monoglyceride according to product requirements, so that the technical effect of improving the flavor quality of the product is achieved.
2. Due to the fact that the mode that the attribute information of the first product and the taste requirement information are input into the neural network model is adopted, the output purity threshold value information of the first monoglyceride is more reasonable and accurate, the monoglyceride quality is controlled according to the product requirements, and the technical effect of improving the flavor quality of the product is achieved.
Example two
Based on the same inventive concept as the method for detecting the quality of monoglyceride in the previous embodiment, the invention also provides a device for detecting the quality of monoglyceride, as shown in fig. 2, wherein the system comprises:
a first obtaining unit 11, wherein the first obtaining unit 11 is used for obtaining attribute information of a first product;
a second obtaining unit 12, where the second obtaining unit 12 is configured to obtain the mouth feel requirement information of the first product;
a third obtaining unit 13, where the third obtaining unit 13 is configured to input the attribute information of the first product and the mouth feel requirement information into a neural network model, and obtain a purity threshold of the first monoglyceride;
a fourth obtaining unit 14, where the fourth obtaining unit 14 is configured to obtain first detection information according to the first purity threshold;
a fifth obtaining unit 15, where the fifth obtaining unit 15 is configured to obtain purity information of the first monoglyceride to be detected;
a sixth obtaining unit 16, where the sixth obtaining unit 16 is configured to detect, according to the first detection information, the purity information of the first monoglyceride to be detected, and obtain a first detection result;
a seventh obtaining unit 17, said seventh obtaining unit 17 being configured to obtain age threshold information of the first user of the first product;
a first determining unit 18, the first determining unit 18 being configured to determine a first dynamic weight value according to age threshold information of a first user of the first product;
an eighth obtaining unit 19, where the eighth obtaining unit 19 is configured to obtain a second detection result according to the first dynamic weight value and the first detection result.
Further, the system further comprises:
a ninth obtaining unit for obtaining a predetermined age threshold;
a first judging unit configured to judge whether an age threshold of the first user is within the predetermined age threshold;
a tenth obtaining unit, configured to obtain a first weight value if the age threshold of the first user is higher than the predetermined age threshold.
Further, the system further comprises:
an eleventh obtaining unit, configured to obtain a second weight value if the age threshold of the first user is within the predetermined age threshold;
a twelfth obtaining unit, configured to obtain a third weight value if the age threshold of the first user is lower than the predetermined age threshold, where the second weight value is higher than the first weight value, and the first weight value is higher than the third weight value.
Further, the system further comprises:
a first input unit, configured to input attribute information and taste requirement information of the first product into a neural network model, where the neural network model is obtained by training multiple sets of training data, and each set of the multiple sets of training data includes: attribute information of the first product, the mouthfeel requirement information, and identification information identifying a purity threshold of first monoglyceride;
a thirteenth obtaining unit for obtaining a first output result of the neural network model, the first output result comprising a purity threshold of the first monoglyceride.
Further, the system further comprises:
a fourteenth obtaining unit configured to obtain category information of the first product;
a fifteenth obtaining unit configured to obtain a predetermined ratio threshold of monoglyceride and triglyceride based on the species information;
a sixteenth obtaining unit, configured to obtain second detection information according to the predetermined ratio threshold;
a seventeenth obtaining unit for obtaining a first ratio threshold of monoglyceride and ditriester of the first monoglyceride to be detected;
an eighteenth obtaining unit, configured to detect the first proportional threshold according to the second detection information, and obtain a third detection result.
Further, the system further comprises:
a nineteenth obtaining unit, configured to obtain color information of the first monoglyceride to be detected;
a twentieth obtaining unit for obtaining first standard color information;
a twenty-first obtaining unit, configured to obtain first color difference information according to the color information of the first monoglyceride to be detected and the first standard color information;
a twenty-second obtaining unit, configured to obtain a first correction parameter according to the first color difference information;
and a twenty-third obtaining unit, configured to correct the third detection result according to the first correction parameter, and obtain a fourth detection result.
Further, the system further comprises:
a twenty-fourth obtaining unit, configured to obtain a second dynamic weight value according to the first dynamic weight value, where a sum of the first dynamic weight value and the second dynamic weight value is 1;
a twenty-fifth obtaining unit, configured to obtain a fifth detection result according to the second dynamic weight value and the fourth detection result;
a twenty-sixth obtaining unit, configured to obtain a sixth detection result according to the fifth detection result and the second detection result.
Various modifications and specific examples of the method for detecting the quality of monoglyceride in the first embodiment of fig. 1 are also applicable to the apparatus for detecting the quality of monoglyceride in this embodiment, and the method for implementing the apparatus for detecting the quality of monoglyceride in this embodiment will be apparent to those skilled in the art from the foregoing detailed description of the method for detecting the quality of monoglyceride, and therefore, for the sake of brevity of the description, detailed description thereof will not be provided here.
Exemplary electronic device
The electronic device of the embodiment of the present application is described below with reference to fig. 3.
Fig. 3 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.
Based on the inventive concept of a method for detecting the quality of monoglyceride as described in the previous embodiments, the present invention also provides an apparatus for detecting the quality of monoglyceride, on which a computer program is stored, which when executed by a processor implements the method for detecting the quality of monoglyceride and the steps of any one of the methods described above.
Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 306 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other systems over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
The embodiment of the invention provides a method for detecting the quality of monoglyceride, which comprises the following steps: obtaining attribute information of a first product; obtaining taste demand information of a first product; inputting the attribute information of the first product and the taste requirement information into a neural network model to obtain a purity threshold value of the first monoglyceride; obtaining first detection information according to the first purity threshold value; obtaining purity information of a first monoglyceride to be detected; detecting the purity information of the first monoglyceride to be detected according to the first detection information to obtain a first detection result; obtaining age threshold information for a first user of a first product; determining a first dynamic weight value according to age threshold information of a first user of the first product; and obtaining a second detection result according to the first dynamic weight value and the first detection result. The technical problem that when the monoglyceride is added into food in the prior art, the quality of the monoglyceride can affect the quality safety and the taste of the food is solved, and the technical effect of improving the flavor quality of the product by controlling the quality of the monoglyceride according to the product requirement is achieved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (8)
1. A method for quality detection of monoglyceride, wherein the method comprises:
obtaining attribute information of a first product;
obtaining taste demand information of a first product;
inputting the attribute information of the first product and the taste requirement information into a neural network model to obtain a purity threshold value of the first monoglyceride;
obtaining first detection information according to the purity threshold of the first monoglyceride;
obtaining purity information of a first monoglyceride to be detected;
detecting the purity information of the first monoglyceride to be detected according to the first detection information to obtain a first detection result;
obtaining age threshold information for a first user of a first product;
determining a first dynamic weight value according to age threshold information of a first user of the first product;
obtaining a second detection result according to the first dynamic weight value and the first detection result;
wherein the method further comprises:
obtaining category information of the first product;
obtaining a predetermined ratio threshold of monoglyceride and diglyceride based on the species information;
obtaining second detection information according to the preset proportion threshold;
obtaining a first ratio threshold of monoglyceride and diglyceride of a first monoglyceride to be detected;
and detecting the first proportional threshold according to the second detection information to obtain a third detection result.
2. The method of claim 1, wherein said determining a first dynamic weight value based on age threshold information for a first user of the first product comprises:
obtaining a predetermined age threshold;
determining whether the age threshold of the first user is within the predetermined age threshold;
and if the age threshold of the first user is higher than the preset age threshold, obtaining a first weight value.
3. The method of claim 2, wherein said determining if the age threshold of the first user is within the predetermined age threshold comprises:
if the age threshold of the first user is within the preset age threshold, obtaining a second weight value;
and if the age threshold of the first user is lower than the preset age threshold, obtaining a third weight value, wherein the second weight value is higher than the first weight value, and the first weight value is higher than the third weight value.
4. The method of claim 1, wherein said inputting the attribute information of the first product and the mouthfeel requirement information into a neural network model to obtain a purity threshold of the first monoglyceride comprises:
inputting the attribute information and the taste requirement information of the first product into a neural network model, wherein the neural network model is obtained by training a plurality of groups of training data, and each group of the plurality of groups of training data comprises: attribute information of the first product, the mouthfeel requirement information, and identification information identifying a purity threshold of first monoglyceride;
obtaining a first output of the neural network model, the first output comprising a purity threshold of the first monoglyceride.
5. The method of claim 1, wherein the method comprises:
obtaining color information of the first monoglyceride to be detected;
obtaining first standard color information;
obtaining first color difference information according to the color information of the first monoglyceride to be detected and the first standard color information;
obtaining a first correction parameter according to the first color difference information;
and correcting the third detection result according to the first correction parameter to obtain a fourth detection result.
6. The method of claim 5, wherein the method comprises:
obtaining a second dynamic weight value according to the first dynamic weight value, wherein the sum of the first dynamic weight value and the second dynamic weight value is 1;
obtaining a fifth detection result according to the second dynamic weight value and the fourth detection result;
and obtaining a sixth detection result according to the fifth detection result and the second detection result.
7. An apparatus for quality testing of monoglyceride, wherein the apparatus comprises:
a first obtaining unit for obtaining attribute information of a first product;
the second obtaining unit is used for obtaining the taste demand information of the first product;
a third obtaining unit, configured to input the attribute information of the first product and the taste requirement information into a neural network model, and obtain a purity threshold of the first monoglyceride;
a fourth obtaining unit, configured to obtain first detection information according to a purity threshold of the first monoglyceride;
a fifth obtaining unit, configured to obtain purity information of the first monoglyceride to be detected;
a sixth obtaining unit, configured to detect, according to the first detection information, the purity information of the first monoglyceride to be detected, and obtain a first detection result;
a seventh obtaining unit for obtaining age threshold information of a first user of a first product;
a first determining unit to determine a first dynamic weight value according to age threshold information of a first user of the first product;
an eighth obtaining unit, configured to obtain a second detection result according to the first dynamic weight value and the first detection result;
a fourteenth obtaining unit configured to obtain category information of the first product;
a fifteenth obtaining unit configured to obtain a predetermined ratio threshold of monoglyceride and triglyceride based on the species information;
a sixteenth obtaining unit, configured to obtain second detection information according to the predetermined ratio threshold;
a seventeenth obtaining unit for obtaining a first ratio threshold of monoglyceride and ditriester of the first monoglyceride to be detected;
an eighteenth obtaining unit, configured to detect the first proportional threshold according to the second detection information, and obtain a third detection result.
8. An apparatus for quality testing of monoglyceride, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method according to any one of claims 1 to 6 are carried out when the program is executed by the processor.
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| CN110349647A (en) * | 2019-05-24 | 2019-10-18 | 平安科技（深圳）有限公司 | Dietary management method, system, electronic equipment and storage medium |
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| CN110097946A (en) * | 2019-03-01 | 2019-08-06 | 西安电子科技大学 | A kind of dietary recommendations continued method based on Analysis of Nutritive Composition |
| CN110349647A (en) * | 2019-05-24 | 2019-10-18 | 平安科技（深圳）有限公司 | Dietary management method, system, electronic equipment and storage medium |
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