CN111695825A - Food flavor detection method and device and electronic equipment - Google Patents
Food flavor detection method and device and electronic equipment Download PDFInfo
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Abstract
The invention discloses a food flavor detection method, a device and electronic equipment, relating to the technical field of food detection, wherein the food flavor detection method comprises the following steps: acquiring information of target food to be evaluated; determining evaluation data for evaluating the target food according to the information of the target food, wherein the evaluation data comprises attribute data to be evaluated of the target food and an evaluation method and an evaluation standard corresponding to the attribute data to be evaluated; obtaining the evaluation result of the target food by the target evaluation subject based on the evaluation data; and displaying the evaluation result and the analysis data corresponding to the evaluation result. Compared with the method for evaluating the products by the consumer degree in the prior art, the scheme disclosed by the embodiment of the application evaluates the target food by the set target evaluation subject in combination with the determined evaluation data, improves the accuracy of the evaluation result of the target food, and simultaneously improves the evaluation efficiency of the flavor of the food because the evaluation result is obtained by directly evaluating the target evaluation subject.
Description
Technical Field
The invention relates to the technical field of food detection, in particular to a method and a device for detecting food flavor and electronic equipment.
Background
The live-bacteria lactic acid bacteria milk beverage is a lactic acid bacteria beverage product, contains the nutrition of milk, has the characteristics of the beverage and the function of lactic acid, is popular with consumers in recent years, and also becomes one of the fastest-developing products in the dairy industry. The viable bacteria type lactobacillus milk beverage is different from flavored fermented milk in texture, the flavor is very special, and the similar products in the market have different attribute characteristics such as color, texture, flavor and the like. In order to enhance the market competitiveness of the product, the product strength of the lactic acid bacteria milk beverage needs to be evaluated to guide the subsequent product production test according to the evaluation result.
The existing evaluation method reflects the product quality through the preference degree of a consumer, the evaluation result of the evaluation method is influenced by the sample type of the consumer and the subjective preference degree of the consumer, and the evaluation efficiency is also influenced through the evaluation mode fed back by the consumer. Therefore, it is urgently needed to provide an objective and reasonable food flavor detection method to improve the accuracy and the evaluation efficiency of the evaluation result of the viable bacteria type lactobacillus milk beverage.
Disclosure of Invention
Therefore, the technical problem to be solved by the present invention is to overcome the defects of low accuracy and low detection efficiency of food flavor detection in the related art, so as to provide a method, an apparatus and an electronic device for detecting food flavor.
According to a first aspect, the embodiment of the invention discloses a food flavor detection method, which comprises the following steps: acquiring information of target food to be evaluated; determining evaluation data for evaluating the target food according to the information of the target food, wherein the evaluation data comprises attribute data to be evaluated of the target food and an evaluation method and an evaluation standard corresponding to the attribute data to be evaluated; obtaining the evaluation result of the target food by the target evaluation subject based on the evaluation data; and displaying the evaluation result and the analysis data corresponding to the evaluation result.
Optionally, the obtaining of the evaluation result of the target food by the target evaluation subject based on the evaluation data includes: and receiving the evaluation result of the target food uploaded by the target evaluation subject based on the evaluation data.
Optionally, the determining, according to the information of the target food, evaluation data for evaluating the target food includes: pre-establishing association relations of different foods and corresponding evaluation data of the different foods; and obtaining evaluation data for evaluating the target food according to the obtained information of the target food and the incidence relation.
Optionally, the target evaluation subject is an evaluation subject satisfying evaluation test requirements.
Optionally, the target food is a live-bacterial lactic acid bacteria milk beverage; the attribute data to be evaluated of the live lactobacillus milk beverage comprises attribute characteristics of the live lactobacillus milk beverage and attribute descriptors corresponding to the attribute characteristics, wherein the attribute characteristics comprise any one or more of color characteristics, texture characteristics, taste characteristics, milk flavor and mouthfeel characteristics and fruit flavor and mouthfeel characteristics.
Optionally, the method further comprises: obtaining evaluation results of evaluating a plurality of target foods of the same category, determining differences among different target foods of the same category according to the evaluation results, and generating analysis data for guiding production.
According to a second aspect, an embodiment of the present invention discloses a food flavor detection device, including: the first acquisition module is used for acquiring information of target food to be evaluated; the determining module is used for determining evaluation data for evaluating the target food according to the information of the target food, wherein the evaluation data comprises attribute data to be evaluated of the target food and an evaluation method and an evaluation standard corresponding to the attribute data to be evaluated; the second acquisition module is used for acquiring the evaluation result of the target food by the target evaluation main body based on the evaluation data; and the display module is used for displaying the evaluation result and the analysis data corresponding to the evaluation result.
Optionally, the determining module is configured to pre-establish an association relationship between different foods and evaluation data corresponding to the different foods; and obtaining evaluation data for evaluating the target food according to the obtained information of the target food and the incidence relation.
According to a third aspect, an embodiment of the present invention discloses an electronic device, including: a processor, a memory and a computer program stored on the memory and executable on the processor, the processor when executing the program implementing the steps of the food flavor detection method according to the first aspect and any one of the alternatives of the first aspect.
According to a fourth aspect, the embodiments of the present invention disclose a readable computer storage medium, on which computer instructions are stored, which when executed by a processor, implement the steps of the food flavor detection method described in the first aspect and any one of the alternatives of the first aspect.
The technical scheme provided by the embodiment of the invention has the following advantages:
according to the food flavor detection method provided by the embodiment of the invention, the evaluation data for evaluating the target food is determined according to the acquired information of the target food to be evaluated, then the evaluation result of the target evaluation subject on the target food based on the evaluation data is acquired, and the evaluation result and the analysis data corresponding to the evaluation result are displayed. Compared with the method for evaluating the products by the consumer degree in the prior art, the scheme disclosed by the embodiment of the application evaluates the target food by the set target evaluation subject in combination with the determined evaluation data, improves the accuracy of the evaluation result of the target food, and simultaneously improves the evaluation efficiency of the flavor of the food because the evaluation result is obtained by directly evaluating the target evaluation subject.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a method for detecting the flavor of a food according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a food flavor detection device according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The embodiment of the application provides a food flavor detection method, which can be applied to electronic equipment such as a terminal or a server. The target food to be evaluated may be a new product developed by an enterprise or a product purchased by a consumer, and the present application does not limit the type of the target food. As shown in fig. 1, the method includes:
Illustratively, an enterprise user or a consumer can upload information of target food needing to be detected to a terminal through a user terminal where the enterprise user or the consumer is located, so that the terminal acquires the information of the target food needing to be evaluated. The information of the target food includes, but is not limited to, food name, food category, food composition, etc.
And step 102, determining evaluation data for evaluating the target food according to the information of the target food, wherein the evaluation data comprises attribute data to be evaluated of the target food and an evaluation method and an evaluation standard corresponding to the attribute data to be evaluated.
For example, the attribute data to be evaluated of the target food may include attribute characteristics of the target food and attribute descriptors corresponding to the attribute characteristics, for example, the attribute characteristics of the live lactobacillus milk beverage may include, but are not limited to, any one or more of color characteristics, texture characteristics, taste characteristics, milk flavor mouthfeel characteristics, fruit flavor mouthfeel characteristics; when the attribute feature is a texture, the attribute descriptor corresponding to the texture may include, but is not limited to, a color of the texture, smoothness of the texture, and the like. The embodiment of the present application does not limit the category of the attribute descriptor corresponding to each attribute feature.
The corresponding attribute characteristics of different target food products can be determined according to the components of the target food products, for example, if a component of a certain beverage comprises milk and fruit, the attribute characteristics of the certain beverage can comprise milk flavor and mouthfeel characteristics and fruit flavor and mouthfeel characteristics. Specifically, the food description of the beverage may be input to the evaluation terminal, the food description may be recognized by an intelligent character recognition system integrated with the evaluation terminal, and the corresponding attribute feature may be determined according to the recognized component keyword, for example, when the recognized component keyword is "milk", the obtained attribute feature is "milk flavor and taste feature".
The attribute characteristics corresponding to different target foods can also be obtained by a database matching mode, namely, the attribute characteristics corresponding to different target foods are preset, different target foods and the attribute characteristics corresponding to the different target foods are matched and stored in the database one by one, and then when the information of the target foods is received, the corresponding attribute characteristics are matched through the database. The determination method of the attribute features in the embodiments of the present application is not limited, and those skilled in the art can determine the attribute features according to actual needs. Similarly, the attribute descriptors corresponding to different attribute features may also be obtained by matching in a database manner, which is not described herein again.
The evaluation method corresponding to the attribute data to be evaluated represents the mode adopted for evaluating the attribute data of the target food, for example, the mode adopted for evaluating the texture color of the live lactobacillus milk beverage is a binocular head-up product for visually observing the color of the product; the smoothness of the live lactobacillus milk beverage is evaluated by tasting a sample to evaluate the smoothness and easy swallowing degree of the product; the evaluation of the milk flavor in the milk flavor was carried out by evaluating the characteristics "milk flavor" and "raw milk" using the standard "deluxe milk" as a standard. Assuming that the "milk flavor" of the standard "deluxe milk" is characterized by 9 points, the "milk flavor" of the live lactobacillus milk beverage can be scored according to the difference in the "milk flavor" of the live lactobacillus milk beverage and the "deluxe milk".
The evaluation criterion for evaluating the target food may be "9-point scale", i.e. different evaluation results are scored according to 0-9 points to give quantitative evaluation results. For example, the color of the texture of the live lactic acid bacteria milk beverage is evaluated according to the evaluation criteria that the score is from low to high and the color is from light to dark. The evaluation data of the target food is not limited in the examples of the present application, and can be determined by those skilled in the art according to actual needs. The corresponding evaluation data for the live-type lactic acid bacteria milk beverage are shown in table 1.
The evaluation data may be determined by matching corresponding evaluation data based on the food component information of the target food based on the information of the target food, for example, when the food component information contains sugar, the corresponding evaluation data may be the sweetness of the taste, and the target food is evaluated from 0 to 9 points in terms of tasting the product.
And 103, acquiring the evaluation result of the target food by the target evaluation subject based on the evaluation data.
For example, the target evaluation subject may be a pre-designated evaluator for evaluating, and the evaluation data for evaluating the target food is transmitted to a user terminal where the evaluator is located, so that the evaluator evaluates the target food according to the evaluation data received by the user terminal. For example, when the live-type lactic acid bacteria milk beverage needs to be evaluated, the data included in table 1 is transmitted to the user terminal where the evaluator is located.
As an optional embodiment of the present application, the target evaluation subject is an evaluation subject that satisfies evaluation test requirements.
Illustratively, in order to improve the expertise of the evaluators and the credibility of the evaluation results, training tests including, but not limited to, a color vision and logic test, a main taste recognition test, a main flavor recognition test, a sweetness grading test, a sourness grading test, a product description test, etc. may be performed on the evaluators for evaluation in advance. And according to the test result, selecting a target number of evaluators from the evaluators meeting the test requirement to form a professional evaluation team as a target evaluation subject. The target number in the embodiment of the present application may be 14, and the embodiment of the present application does not limit the target number, and a person skilled in the art may determine the target number according to actual needs.
After the target evaluation subject is determined, before the evaluation of the live lactobacillus milk beverage, in order to further improve the reliability and accuracy of the evaluation result, training of the evaluation data of the live lactobacillus milk beverage can be performed on the target evaluation subject. Specifically, the live-bacteria type lactobacillus milk beverage evaluation data can be trained on 14 evaluators according to GB/T16291.1-2010. The first stage is to introduce the sensory analysis basic knowledge and yogurt product and descriptive methods to the target evaluators. And in the second stage, descriptor and reference sample training is carried out on the target evaluation subject according to the scale of 0-9 points. The third stage enhances training for attributes that are difficult for some target evaluation subjects to master. And in the fourth stage of overall test, fragrance is smelled firstly, then taste is carried out, the 2 live bacteria type lactobacillus milk beverage samples are subjected to 0-9-point grading test, and the memory of an evaluator is continuously strengthened in the aspects of texture, taste, milk flavor and fruit flavor, and repeated training is carried out. After the training is continued for a period of time, the discrimination ability, the repeatability and the team consistency of 14 target evaluation subjects can be tested and evaluated by referring to GB/T16291.1-2012 general guide of sensory analysis and selection, training and management evaluators.
As an alternative embodiment of the present application, the manner of obtaining the evaluation result of the target food by the target evaluation subject includes: and receiving the evaluation result of the target food uploaded by the target evaluation subject based on the evaluation data.
Illustratively, after the target evaluation subject finishes evaluating the target food based on the evaluation data, the evaluation result uploaded by the target evaluation subject through the user terminal can be received through communication connection with the user terminal where the target evaluation subject is located; or setting an interactive interface and receiving evaluation data uploaded by the target evaluation main body through the interactive interface. The obtaining mode of the evaluation data is not limited in the embodiments of the present application, and those skilled in the art can determine the evaluation data according to actual needs.
And 104, displaying the evaluation result and the analysis data corresponding to the evaluation result.
For example, the analysis data corresponding to the evaluation result may be obtained by transmitting the obtained evaluation data to a user terminal where the analyst is located, so that the analyst analyzes the received evaluation data according to a target analysis method, and receives analysis data fed back by the analyst through the user terminal; or directly analyzing the obtained evaluation data according to a target analysis method to obtain analysis data. The target analysis method may be an analysis of variance method, that is, variance calculation is performed on the received evaluation data to obtain an analysis of variance result, and the analysis of variance result may be used to compare differences between different products, and the significance level of the analysis of variance may be set to 0.1 in the embodiment of the present application. The evaluation result and the display mode of the Analysis data corresponding to the evaluation result may be in a table form and a graphic form, and the graphic display mode in the embodiment of the present application includes, but is not limited to, a differential line graph, a spider web graph, a Principal Component Analysis (PCA) graph, a cluster Analysis graph and a correlation Analysis graph corresponding to a quantitative descriptive test (QDA). The evaluation result and the analysis data corresponding to the evaluation result are displayed, so that a user can conveniently and visually check the data.
According to the food flavor detection method provided by the embodiment of the application, the evaluation data for evaluating the target food is determined according to the acquired information of the target food to be evaluated, then the evaluation result of the target evaluation subject on the target food based on the evaluation data is acquired, and the evaluation result and the analysis data corresponding to the evaluation result are displayed. Compared with the method for evaluating the products by the consumer degree in the prior art, the scheme disclosed by the embodiment of the application evaluates the target food by the set target evaluation subject in combination with the determined evaluation data, improves the accuracy of the evaluation result of the target food, and simultaneously improves the evaluation efficiency of the flavor of the food because the evaluation result is obtained by directly evaluating the target evaluation subject.
TABLE 1 evaluation data of viable cell type lactic acid bacteria milk beverage
As an alternative embodiment of the present application, step 102 includes: pre-establishing association relations of different foods and corresponding evaluation data of the different foods; and obtaining evaluation data for evaluating the target food according to the obtained information of the target food and the incidence relation.
For any food needing to be evaluated, initial evaluation data can be constructed according to food information, the initial evaluation data can be provided according to enterprises producing the food, and the obtained initial evaluation data is stored in a database. And classifying, deleting, merging and the like the obtained initial evaluation data to screen out the evaluation data meeting the evaluation requirement, associating the obtained evaluation data with the corresponding food, and establishing the association relationship between the food and the evaluation data. The association relationship may be an association relationship between the name of the food and the evaluation data, so that different foods correspond to different evaluation data; or the association relationship between the food category and the evaluation data may be such that different types of food correspond to different evaluation data, and the association relationship is not limited in the embodiment of the present application. When the information of the target food to be evaluated is acquired, evaluation data for evaluating the target food is acquired according to the information of the target food and the pre-established association relationship.
As an optional embodiment of the present application, the method further comprises: obtaining evaluation results of evaluating a plurality of target foods of the same category, determining differences among different target foods of the same category according to the evaluation results, and generating analysis data for guiding production.
For example, the obtained evaluation results of a plurality of target food products of the same category may be qualitative evaluation results or quantitative evaluation results, and the characterization mode of the evaluation results is not limited in the embodiment of the present application. The differences among different target foods of the same type can be obtained according to the evaluation result, and in order to facilitate visual checking of the differences among different target foods of the same type, the differences among different target foods of the same type can be displayed through image-text forms, so that visual differential transverse comparison is realized, and the product research and development efficiency is facilitated to be improved. The graphics and text display mode includes, but is not limited to, differential line graph, spider graph, Principal Component Analysis (PCA) graph, cluster Analysis graph and correlation Analysis graph corresponding to quantitative descriptive test (QDA). And generating analysis data for guiding production according to the difference data, so that enterprise users can adjust food processing technology or formula according to the analysis data to reduce the difference with ideal or popular products and further meet the use requirements of consumers.
The following table 2 shows evaluation data and corresponding analysis data obtained by performing taste detection on products 1 and 2 of the same category; table 3 shows the evaluation data and the corresponding analysis data obtained based on the consumer evaluation; table 4 below is a comparative test set of products for three different species.
Table 2. evaluation data obtained by performing taste detection on products 1 and 2 of the same category and corresponding analysis data
For the evaluation data and the corresponding analysis data described in table 2, specifically, the data in the second column and the third column in table 2 are the average values of the evaluation results of the plurality of target evaluation subjects, the analysis data corresponding to the evaluation data is the anova result, and when the obtained anova result corresponding to product 1 and product 2 is less than 0.1, it indicates that there is a significant difference in the corresponding attributes of product 1 and product 2 as a whole; in table 2, "a" indicates that the differences between the evaluation results of the multiple target evaluation subjects are large, and "B" indicates that the differences between the evaluation results of the multiple target evaluation subjects are small, so that different difference results can further indicate the differences between different products.
The specific difference determination manner may be determined by a difference between a maximum evaluation result and a minimum evaluation result of the plurality of evaluation results, and when the difference is greater than a target value, the difference characterizing the plurality of evaluation results is large, and when the difference is less than the target value, the difference characterizing the plurality of evaluation results is small. The size of the target value is not limited in the embodiments of the present application, and can be determined by those skilled in the art according to actual needs.
The differences between the textures, tastes, flavors of the two products that can be objectively obtained from table 2 are: within the 90% confidence interval, fermentation flavour: the score of product 2 is higher than that of product 1, and there is a significant difference between product 2 and product 1; and (3) lime: the score of product 2 is higher than that of product 1, and there is a significant difference between product 2 and product 1; flower fragrance: product 2 scores higher than product 1 and there was a significant difference between product 2 and product 1. Namely, the difference between the two products is mainly in flavor, and the product 2 has slightly strong fermentation flavor and green lemon flavor and weak flower fragrance flavor.
TABLE 3 evaluation data based on consumer evaluation and corresponding analytical data
| Properties | Product 1 | Product 2 | Analysis of variance results |
| Overall preference | 4.77A | 3.7B | <0.0001 |
| Preference for flavor | 4.73A | 3.61B | <0.0001 |
| Degree of pleasure of cool | 4.75A | 3.61B | <0.0001 |
| Smooth mouthfeel | 4.16 | 4.14 | 0.8552 |
| Sweetness by acidity | 4.2A | 3.45B | <0.0001 |
For the evaluation data and the corresponding analysis data described in table 3, specifically, the data in the second column and the third column in table 3 are the average values of the evaluation results of the target evaluation subjects on the products 1 and 2, the analysis data corresponding to the evaluation data is the analysis of variance result, and when the obtained analysis of variance result corresponding to the products 1 and 2 is less than 0.1, it indicates that the products 1 and 2 have significant difference in the corresponding attributes as a whole; in table 3, "a" indicates that the differences between the evaluation results of the multiple target evaluation subjects are large, and "B" indicates that the differences between the evaluation results of the multiple target evaluation subjects are small, so that different difference results can further indicate the differences between different products.
The specific difference determination manner may be determined by a difference between a maximum evaluation result and a minimum evaluation result of the plurality of evaluation results, and when the difference is greater than a target value, the difference characterizing the plurality of evaluation results is large, and when the difference is less than the target value, the difference characterizing the plurality of evaluation results is small. The size of the target value is not limited in the embodiments of the present application, and can be determined by those skilled in the art according to actual needs.
The differences between products that can be objectively obtained by table 3 are: within the 90% confidence interval, overall preference: product 1 has a high score, and two products have significant difference; flavor preference: product 1 has a high score, and two products have significant difference; degree of coolness like: product 1 has a high score, and two products have significant difference; smooth mouthfeel: the scores of the two products are similar and smooth; acid sweetness: product 1 is sweet, product 2 is sour, and the two products have significant difference. By combining the difference between the specific attributes of the two products obtained in table 2 and the difference between the consumer likings shown in table 3, guidance information can be provided for the subsequent processing technology and the research and development direction comprehensively.
TABLE 4 comparison test set for different strains of products
As for the evaluation data and the corresponding analysis data described in table 4, specifically, the data in the second column, the third column and the fourth column in table 4 are the average values of the evaluation results of the target evaluation subjects on the products 1, 2 and 3, respectively, the analysis data corresponding to the evaluation data is the anova result, and when the obtained anova result is less than 0.1, it indicates that the different products have significant differences in the corresponding attributes as a whole; in table 4, "a" indicates that the differences between the evaluation results of the multiple target evaluation subjects obtained are large, "B" indicates that the differences between the evaluation results of the multiple target evaluation subjects obtained are small, "AB" indicates that the differences between the evaluation results of the multiple target evaluation subjects obtained are general, and the different difference results can further indicate the differences between different products.
The specific difference determining method may be determined by a difference between a maximum evaluation result and a minimum evaluation result in the plurality of evaluation results, and when the difference is greater than a first target value, the difference characterizing the plurality of evaluation results is large, and when the difference is smaller than the first target value and larger than a second target value, the difference characterizing the plurality of evaluation results is general, and when the difference is smaller than the second target value, the difference characterizing the plurality of evaluation results is small, wherein the first target value is larger than the second target value.
Within the 90% confidence interval according to table 4 above; for textural attributes: (1) thickness in mouth: the product 2 has the highest score, the product 1 is centered, the product 3 is lower, and the product 2 and the product 3 have significant differences; (2) fatty feeling: the product 1 is highest, the product 3 is centered, the product 2 is lower, and the product 1 and the product 2 have significant differences; analytical data for production guidance based on the evaluation data were: the thickness of the product 2 in the mouth is thicker, and the fat feeling of the product 1 is stronger.
Taste profile: (1) sweet taste: product 1 has the highest score, product 2 is centered, product 3 is lower, and there are significant differences between product 1 and product 2, respectively, and product 3. Namely, the product 1 and the product 2 are relatively sweet;
flavor attributes: (1) fermentation flavor: the product 1 has a high score, the product 2 is centered, the product 3 is low, and the product 1 and the product 3 have significant differences; (2) lemon flavor: the product 1 has a high score, the product 2 is centered, the product 3 is low, and the product 1 is respectively different from the product 2 and the product 3 in significance; (3) lemon juice: the product 1 has a high score, the product 2 is centered, the product 3 is low, and the product 1 is respectively different from the product 2 and the product 3 in significance; (4) citral: the product 1 has a high score, the product 2 is centered, the product 3 is low, and the product 1 and the product 2 have significant differences from the product 3; (5) fresh orange: the product 3 has the highest score, the product 2 is centered, the product 1 has a lower score, and the product 3 and the product 2 have significant differences from the product 1; (6) orange juice: product 2 has the highest score, product 3 is higher, product 1 is lower, and there is a significant difference between product 2 and product 3, respectively, and product 1. Namely, the product 1 has stronger fermentation flavor, green lemon, lemon juice flavor and citral flavor; the product 2 has stronger orange juice flavor; product 3 has a strong fresh orange flavor.
In conclusion, it can be obtained that: the differences between the three products are mainly in texture and flavor. In texture, the product 2 is thicker, and the product 1 has stronger fat feeling; in taste, the product 1 and the product 2 are sweet; in terms of flavor, the product 1 has stronger fermentation flavor, green lemon flavor, lemon juice flavor and citral flavor, the product 2 has stronger orange juice flavor, and the product 3 has stronger fresh orange flavor. And generating analysis data for guiding production according to the difference data, so that enterprise users can adjust food processing technology or formula according to the analysis data to reduce the difference with ideal or popular products and further meet the use requirements of consumers.
An embodiment of the present application further provides a food flavor detection device, as shown in fig. 2, including:
a first obtaining module 301, configured to obtain information of a target food to be evaluated;
a determining module 302, configured to determine, according to the information of the target food, evaluation data for evaluating the target food, where the evaluation data includes attribute data to be evaluated of the target food, and an evaluation method and an evaluation standard corresponding to the attribute data to be evaluated;
a second obtaining module 303, configured to obtain an evaluation result of the target food by the target evaluation subject based on the evaluation data;
a display module 304, configured to display the evaluation result and the analysis data corresponding to the evaluation result.
The food flavor detection device provided by the embodiment of the application determines evaluation data for evaluating target food according to the acquired information of the target food to be evaluated, then acquires the evaluation result of the target evaluation subject on the target food based on the evaluation data, and displays the evaluation result and the analysis data corresponding to the evaluation result. Compared with the method for evaluating the products by the consumer degree in the prior art, the scheme disclosed by the embodiment of the application evaluates the target food by the set target evaluation subject in combination with the determined evaluation data, improves the accuracy of the evaluation result of the target food, and simultaneously improves the evaluation efficiency of the flavor of the food because the evaluation result is obtained by directly evaluating the target evaluation subject.
As an optional embodiment of the present application, the second obtaining module 303 is configured to receive an evaluation result of the target food, which is uploaded by the target evaluation subject and based on the evaluation data.
As an optional embodiment of the present application, the determining module 302 is configured to pre-establish an association relationship between different food products and evaluation data corresponding to the different food products; and obtaining evaluation data for evaluating the target food according to the obtained information of the target food and the incidence relation.
As an optional embodiment of the present application, the target evaluation subject is an evaluation subject that satisfies evaluation test requirements.
As an alternative embodiment of the present application, the target food is a live-type lactic acid bacteria milk beverage; the attribute data to be evaluated of the live lactobacillus milk beverage comprises attribute characteristics of the live lactobacillus milk beverage and attribute descriptors corresponding to the attribute characteristics, wherein the attribute characteristics comprise any one or more of color characteristics, texture characteristics, taste characteristics, milk flavor and mouthfeel characteristics and fruit flavor and mouthfeel characteristics.
As an optional embodiment of the present application, the apparatus further comprises: the generating module is used for obtaining the evaluation results of evaluating a plurality of target foods of the same category, determining the difference among different target foods of the same category according to the evaluation results and generating analysis data for guiding production.
An electronic device is further provided in the embodiments of the present application, as shown in fig. 3, and includes a processor 401 and a memory 402, where the processor 401 and the memory 402 may be connected by a bus or in another manner, and fig. 3 takes the connection by the bus as an example.
The memory 402, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the food flavor detection method in embodiments of the present invention. The processor 401 executes various functional applications and data processing of the processor by executing non-transitory software programs, instructions and modules stored in the memory 402, that is, implements the method in the above-described method embodiments.
The memory 402 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 401, and the like. Further, the memory 402 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 402 may optionally include memory located remotely from processor 401, which may be connected to processor 401 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 402 and, when executed by the processor 401, perform the method of the embodiment shown in fig. 1.
The details of the electronic device may be understood with reference to the corresponding related description and effects in the embodiment shown in fig. 1, and are not described herein again.
The embodiment of the application also provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions can execute the food flavor detection method in any method embodiment. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (10)
1. A method for detecting the flavor of food, which is characterized by comprising the following steps:
acquiring information of target food to be evaluated;
determining evaluation data for evaluating the target food according to the information of the target food, wherein the evaluation data comprises attribute data to be evaluated of the target food and an evaluation method and an evaluation standard corresponding to the attribute data to be evaluated;
obtaining the evaluation result of the target food by the target evaluation subject based on the evaluation data;
and displaying the evaluation result and the analysis data corresponding to the evaluation result.
2. The method according to claim 1, wherein the obtaining of the evaluation result of the target food by the target evaluating body based on the evaluation data comprises:
and receiving the evaluation result of the target food uploaded by the target evaluation subject based on the evaluation data.
3. The method of claim 1, wherein determining the evaluation data for evaluating the target food product based on the information of the target food product comprises:
pre-establishing association relations of different foods and corresponding evaluation data of the different foods;
and obtaining evaluation data for evaluating the target food according to the obtained information of the target food and the incidence relation.
4. The method according to claim 1, wherein the target evaluation subject is an evaluation subject satisfying evaluation test requirements.
5. The method according to claim 1, wherein the target food is a live-type lactic acid bacteria milk beverage; the attribute data to be evaluated of the live lactobacillus milk beverage comprises attribute characteristics of the live lactobacillus milk beverage and attribute descriptors corresponding to the attribute characteristics, wherein the attribute characteristics comprise any one or more of texture characteristics, taste characteristics, milk flavor and mouthfeel characteristics and fruit flavor and mouthfeel characteristics.
6. The method according to any one of claims 1-5, further comprising: obtaining evaluation results of evaluating a plurality of target foods of the same category, determining differences among different target foods of the same category according to the evaluation results, and generating analysis data for guiding production.
7. A food flavor detection device, comprising:
the first acquisition module is used for acquiring information of target food to be evaluated;
the determining module is used for determining evaluation data for evaluating the target food according to the information of the target food, wherein the evaluation data comprises attribute data to be evaluated of the target food and an evaluation method and an evaluation standard corresponding to the attribute data to be evaluated;
the second acquisition module is used for acquiring the evaluation result of the target food by the target evaluation main body based on the evaluation data;
and the display module is used for displaying the evaluation result and the analysis data corresponding to the evaluation result.
8. The device according to claim 7, wherein the determining module is configured to pre-establish an association relationship between different food products and evaluation data corresponding to the different food products; and obtaining evaluation data for evaluating the target food according to the obtained information of the target food and the incidence relation.
9. An electronic device, comprising:
a processor, a memory and a computer program stored on the memory and executable on the processor, the processor when executing the program implementing the steps of the food flavor detection method of any one of claims 1-6.
10. A readable computer storage medium having stored thereon computer instructions, characterized in that the instructions, when executed by a processor, carry out the steps of the food flavor detection method according to any one of claims 1-6.
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