CN110873786A - Pear sensory quality evaluation system and evaluation method - Google Patents
Pear sensory quality evaluation system and evaluation method Download PDFInfo
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Abstract
The invention discloses a pear sensory quality evaluation method and system. The evaluation method comprises the following steps: establishing a quality evaluation vocabulary database of the sensory quality of pears, wherein each quality evaluation vocabulary comprises a first-class attribute word, the first-class attribute word comprises a second-class attribute word, and a quality evaluation method and a reference sample corresponding to the second-class attribute word; obtaining scoring data of the appraiser on the appraised words in the pear sample, determining the evaluation ability of the appraiser, and determining the sensory quality evaluation result of the pear according to the evaluation ability of the appraiser and the scoring data of the corresponding appraiser on the pear sample. The evaluation method and the evaluation system can accurately evaluate the evaluation ability of a candidate for the sensory quality of the pears.
Description
Technical Field
The invention belongs to the technical field of agricultural product quality evaluation, and particularly relates to a pear sensory quality evaluation system and method.
Background
The pears are various in types, can be eaten raw or eaten as vegetables, are different in color and shape and large in pulp and mouthfeel difference, and the evaluation description of the pears is helpful for consumers to select the pears.
The descriptive analysis is a test method for qualitatively describing and quantitatively scoring the sensory characteristics of the products, and the result can accurately describe the differences of the sensory characteristics of different products, provide detailed information of the products and improve the basis of the product quality. However, the difference in the scores of the raters may cause inaccurate rating results to be pushed to the customers.
Disclosure of Invention
Technical problem to be solved
In view of the above, the present invention is to provide a system and a method for evaluating sensory quality of pear to solve at least some of the above technical problems.
(II) technical scheme
According to one aspect of the invention, the method for evaluating the sensory quality of the pears comprises the following steps:
establishing a quality evaluation vocabulary database of the sensory quality of pears, wherein each quality evaluation vocabulary comprises a first-class attribute word, the first-class attribute word comprises a second-class attribute word, and a quality evaluation method and a reference sample corresponding to the second-class attribute word;
obtaining scoring data of a appraiser on appraisal words in a pear sample, and determining the evaluation capability of the appraiser;
and determining the sensory quality evaluation result of the pears according to the evaluation ability of the appraisers and the scoring data of the corresponding appraisers on the pear samples.
In further embodiments, the evaluative capabilities of the panelist include at least one of: the distinguishing capability among the pear samples, the distinguishing capability of the first kind of attribute words and/or the second kind of attribute words in the pear samples, and the overall consistency of the groups in which the appraisers are located.
In a further embodiment, the ability to distinguish between pear samples is determined in a manner comprising: and (3) acquiring scoring data of the appraiser on each type of attribute words and/or second type of attribute words in the pear sample, and determining the MSE value of the variance in the group, wherein the smaller the MSE value is, the better the repeatability of the appraiser is.
In a further embodiment, the discriminative power of the class one attribute words and/or the class two attribute words in the pear sample is determined in a manner comprising: obtaining the scoring data of the appraiser on each pear sample, and determining the ratio F of the variance between the groups to the variance in the groups, wherein the larger the F value is, the better the distinguishing capability of the appraiser on each attribute of the pear sample is.
In a further embodiment, the manner of determining the identity of the panelist as a whole comprises: obtaining scoring data of a group where the appraisers are located, determining Profile Plots attribute graphs, wherein each graph represents one attribute, each line represents one appraiser, the closer the lines are, the higher the consistency of the appraisers on the attribute evaluation is, and otherwise, the worse the consistency is.
In a further embodiment, each class of attribute words further includes a scale corresponding to each class of two attribute words.
In a further embodiment, a class of attribute words includes at least one of: flesh characteristics, peel characteristics, and stones.
In further embodiments, the flesh feature includes a category two attribute term comprising at least one of: pulp color, hardness, brittleness, juice amount, pulp sweetness, pulp acidity, pulp astringency, pulp slagging degree, fiber feeling, stone cell amount, pear direct fragrance and pear chewing fragrance.
In a further embodiment, the peel characteristics include the category two attribute words including at least one of: pericarp color, pericarp smoothness, fruit size, fruit point size, rust spot, pericarp thickness, ease of pericarp peeling, pericarp toughness, and wax size.
In a further embodiment, the evaluation vocabulary database is an initial evaluation vocabulary database or an adjusted evaluation vocabulary database after feedback by an evaluator.
According to another aspect of the invention, a pear sensory quality evaluation system is provided, which comprises:
the system comprises a pear sensory evaluation vocabulary determining module, a pear sensory evaluation vocabulary database and a control module, wherein the pear sensory evaluation vocabulary database is used for establishing pear sensory quality, each evaluation vocabulary comprises a first-class attribute word, the first-class attribute word comprises a second-class attribute word, and an evaluation method and a reference sample corresponding to the second-class attribute word;
the evaluation module is used for acquiring scoring data of the appraiser on the appraised words in the pear sample and determining the evaluation capability of the appraiser;
and the pear sensory quality decision module determines the sensory quality evaluation result of the pears according to the evaluation capability of the appraisers and the scoring data of the corresponding appraisers on the pear samples.
In further embodiments, in the evaluation module, the evaluation ability of the panelist includes at least one of: the distinguishing capability among the pear samples, the distinguishing capability of the first kind of attribute words and/or the second kind of attribute words in the pear samples, and the overall consistency of the groups in which the appraisers are located.
In a further embodiment, in the evaluation module, the ability to distinguish between pear samples is determined in a manner comprising: and (3) acquiring scoring data of the appraiser on each type of attribute words and/or second type of attribute words in the pear sample, and determining the MSE value of the variance in the group, wherein the smaller the MSE value is, the better the repeatability of the appraiser is.
In a further embodiment, the discriminative power of the class one attribute words and/or the class two attribute words in the pear sample is determined in a manner comprising: obtaining the scoring data of the appraiser on each pear sample, and determining the ratio F of the variance between the groups to the variance in the groups, wherein the larger the F value is, the better the distinguishing capability of the appraiser on each attribute in the pear sample is.
In a further embodiment, the manner of determining the identity of the panelist as a whole comprises: obtaining scoring data of a group where the appraisers are located, determining Profile Plots attribute graphs, wherein each graph represents one attribute, each line represents one appraiser, the closer the lines are, the higher the consistency of the appraisers on the attribute evaluation is, and otherwise, the worse the consistency is.
In a further embodiment, each class of attribute words further includes a scale corresponding to each class of two attribute words.
In a further embodiment, a class of attribute words includes at least one of: flesh characteristics, peel characteristics, and stones.
In further embodiments, the flesh feature includes a category two attribute term comprising at least one of: pulp color, hardness, brittleness, juice amount, pulp sweetness, pulp acidity, pulp astringency, pulp slagging degree, fiber feeling, stone cell amount, pear direct fragrance and pear chewing fragrance.
In a further embodiment, the peel characteristics include the category two attribute words including at least one of: pericarp color, pericarp smoothness, fruit size, fruit point size, rust spot, pericarp thickness, ease of pericarp peeling, pericarp toughness, and wax size.
In a further embodiment, the evaluation vocabulary database is an initial evaluation vocabulary database or an adjusted evaluation vocabulary data after feedback by an evaluator.
(III) advantageous effects
In the invention, the evaluation capability of the appraiser is evaluated by adopting the F value and the MSE value of the variance analysis and the Profile Plots diagram, so that the efficiency of judging the evaluation capability of the appraiser is improved;
the sensory quality of the pears is displayed in various ways, and the legibility of the evaluation data is improved.
Drawings
FIG. 1 is a flow chart of a pear sensory quality evaluation method according to an embodiment of the invention.
FIG. 2 is a roulette diagram of the evaluation vocabulary according to the embodiment of the present invention.
FIG. 3 is a spider web display of scoring data of the appraiser of the appraisal vocabulary in the pear sample according to an embodiment of the present invention.
FIG. 4 is a diagram illustrating F values according to an embodiment.
Fig. 5 is a diagram illustrating MSE values according to an embodiment.
FIG. 6 is a diagram of two classes of attribute words, according to an embodiment.
Fig. 7 is a block diagram of a pear sensory quality evaluation system according to an embodiment of the present invention.
Detailed Description
In order that the objects, technical solutions and advantages of the present invention will become more apparent, the present invention will be further described in detail with reference to the accompanying drawings in conjunction with the following specific embodiments.
According to the basic concept of the invention, the system and the method for evaluating the sensory quality of the pears are provided, and the evaluation result of a appraiser can be efficiently evaluated.
FIG. 1 is a flow chart of a pear sensory quality evaluation method according to an embodiment of the invention. As shown in fig. 1, an embodiment of the present invention provides a method for evaluating sensory quality of pears, including:
s101: establishing a quality evaluation vocabulary database of the sensory quality of pears, wherein each quality evaluation vocabulary comprises a first-class attribute word, the first-class attribute word comprises a second-class attribute word, and a quality evaluation method and a reference sample corresponding to the second-class attribute word;
s102: obtaining scoring data of a appraiser on appraisal words in a pear sample, and determining the evaluation capability of the appraiser;
s103: and determining the sensory quality evaluation result of the pears according to the evaluation ability of the appraisers and the scoring data of the corresponding appraisers on the pear samples.
Referring to fig. 2, the evaluation vocabulary of the sensory quality of the pears may include a first type of attribute words and a second type of attribute words, and for the second type of attribute words, evaluation methods and references corresponding to the second type of attribute words may also be provided. Preferably, each type of attribute word further includes a scale corresponding to each type of two attribute words.
In some embodiments, a class of attribute words includes at least one of: flesh characteristics, peel characteristics, and stones.
Further, the flesh feature may include two types of attribute words including at least one of: pulp color, hardness, brittleness, juice amount, pulp sweetness, pulp acidity, pulp astringency, pulp slagging degree, fiber feeling, stone cell amount, pear direct fragrance and pear chewing fragrance.
Further, the peel characteristics may include two categories of attribute words including at least one of: pericarp color, pericarp smoothness, fruit size, fruit point size, rust spot, pericarp thickness, ease of pericarp peeling, pericarp toughness, and wax size.
Furthermore, for reference and calibration, specific reference may be made to the contents of the reference and the available descriptors (i.e. calibration) described in the following table.
TABLE 1
In some embodiments, the evaluation vocabulary database is an initial evaluation vocabulary database or an adjusted evaluation vocabulary database after being fed back by an evaluator. For example, Spectrum is one of the most classical methods in traditional descriptive methods, and a screened and trained evaluation group can use standard terms, reference samples, scales and the like to describe differences of products in appearance, flavor and texture, and due to the fact that training time is long and the requirement on consistency of a rater is high, obtained data are accurate, and the method is widely applied to the food industry.
Further, the panelist provides a list of reference thesaurus to the panelist by reviewing the relevant literature on the pear and collecting the terms in the Spectrum thesaurus. After smelling and tasting the sample, the evaluator selects or self-generates descriptors capable of distinguishing sample differences from the standard thesaurus. After the descriptors of appearance, smell, taste and texture are preliminarily determined, the definition and evaluation method of each descriptor are determined by the panelists through consistency discussion, a preference class, a repetitive vocabulary and descriptors which are difficult to be agreed by the panelists are additionally listed as remark options, a final descriptive vocabulary library is comprehensively determined, and then the discussion is carried out to reduce the sensory fatigue of the panelists, wherein the training time is not more than 2h each time, and the table 1 is specifically shown.
After the training, the assessors are basically familiar with the sample attributes and the definition of each attribute, and next, the evaluation group needs to determine the standard reference sample of each attribute to accurately quantify all the attributes through consistency discussion. The standard reference sample is a stable reference sample which can well reflect the property of the sample. Before scoring, the panelist remembers the intensity of each standard reference sample corresponding to that attribute, compares the sample to the reference sample based on the reference sample, and scores the sample on a 15cm linear scale by a quantitative estimation method based on the difference in the fold of the two on the attribute.
In some embodiments, for step S102, the appraisal ability of the panelist includes at least one of: the distinguishing capability among the pear samples, the distinguishing capability of the first kind of attribute words and/or the second kind of attribute words in the pear samples, and the overall consistency of the groups in which the appraisers are located.
Wherein, the determination mode of the distinguishing capability of the first kind attribute words and/or the second kind attribute words in the pear sample comprises the following steps: obtaining the scoring data of the appraiser on each pear sample, and determining the ratio F of the variance between the groups to the variance in the groups, wherein the larger the F value is, the better the distinguishing capability of the appraiser on each attribute in the pear sample is.
Wherein, the determination mode of the distinguishing capacity between the pear samples can comprise the following steps: and (3) acquiring scoring data of the appraiser on each type of attribute words and/or second type of attribute words in the pear sample, and determining the MSE value of the variance in the group, wherein the smaller the MSE value is, the better the repeatability of the appraiser is.
The method for determining the overall consistency of the group of the appraisers comprises the following steps: obtaining scoring data of a group where the appraisers are located, determining Profile Plots attribute graphs, wherein each graph represents one attribute, each line represents one appraiser, the closer the lines are, the higher the consistency of the appraisers on the attribute evaluation is, and otherwise, the worse the consistency is.
For example, the F-value, MSE-value and Profile Plots of ANOVA can be used to evaluate the ability of a panelist to process with Panel Check v1.4.0 software.
FIG. 4 is a diagram illustrating F values according to an embodiment. The discrimination ability of the panelists was examined using the F-value in the Panel Check software with reference to fig. 4. The larger the F value, namely the ratio of the variance between the groups to the variance within the groups, the better the distinguishing capability of the appraiser on each attribute between the samples. The F values (ordinate expression) of 13 evaluators (abscissa representation) are reflected in fig. 4. A cluster corresponding to each evaluator represents 32 attributes including appearance, smell, taste and texture in turn from left to right. It can be seen that the F values for each attribute of each evaluator reached a 5% significance level, and most reached a 1% level, indicating that the evaluators were able to distinguish each attribute well between samples.
Fig. 5 is a diagram illustrating MSE values according to an embodiment. And examining the repeatability of the two grading results of the appraiser by using the MSE value. MSE values represent intra-group variance, with smaller values indicating better reproducibility for individual panelists. The MSE values are small, however, and may also be due to the fact that the panelists do not distinguish between samples, so that the reproducibility of the panelists should be discussed in conjunction with the F value on the basis that the panelists can distinguish between samples. When the panelist had a higher F value and a lower MSE value, it was indicated that the panelist had a good ability to evaluate the sample. The MSE values (ordinate representation) of the 13 evaluators (abscissa representation) are reflected in fig. 5. A cluster corresponding to each evaluator represents 32 attributes including appearance, smell, taste and texture in turn from left to right. It can be seen that the MSE value of each attribute of each evaluator is less than 1.0, which indicates that the repeatability of the evaluator is better. Considering that all evaluators have higher F values and lower MSE values, the evaluators can well distinguish differences of various attributes among samples and ensure consistency of each scoring result, so that the data of all evaluators are effective from the evaluation capability of a single evaluator.
FIG. 6 is a diagram of two classes of attribute words, according to an embodiment. The Profile Plots method was used to investigate the overall consistency of the panel. The method can reflect whether the performance of an evaluation group is consistent when evaluating a certain attribute, and can also reflect the difference between a single appraiser and the overall level. In FIG. 6 (i.e., the Profile Plots attributes map), each subgraph represents an attribute and each line represents a rater. The closer all the lines are, the higher the consistency of the evaluation of the attribute by the evaluation subgroup, and conversely, the worse the consistency. FIG. 6 reflects the overall consistency results for 13 evaluators. It can be seen that lines corresponding to most attributes are relatively gathered, and the consistency of the evaluation group is better. Therefore, the 13 evaluators have relatively consistent understanding of the attributes and use of the scale, and the obtained scoring data is relatively reliable.
In step S103, determining the sensory quality evaluation result of the pear according to the evaluation ability of the appraiser and the scoring data of the corresponding appraiser on the pear sample. Here, the scoring data of the appraiser whose evaluation ability is better than the set value may be considered, and the scoring data may be the scoring data of step S102 or the data of the appraiser who scores the pear sample again; then, statistical analysis is carried out on the scoring data to determine a final sensory quality evaluation result, and finally, all properties of the sample can be visually presented by adopting a spider-web diagram. FIG. 3 is a spider web display of scoring data of the appraiser of the appraisal vocabulary in the pear sample according to an embodiment of the present invention. Therefore, the quality of the pear variety can be visually displayed to the user in the display mode.
Based on the same concept, the embodiment of the invention also provides a pear sensory quality evaluation system 700, which comprises:
the pear sensory evaluation vocabulary determining module 710 establishes an evaluation vocabulary database of pear sensory quality, each evaluation vocabulary comprises a first-class attribute word, the first-class attribute word comprises a second-class attribute word, and an evaluation method and a reference corresponding to the second-class attribute word;
the evaluation module 720 is used for acquiring scoring data of the appraiser on the appraised words in the pear sample and determining the evaluation capability of the appraiser;
the pear sensory quality decision module 730 determines the sensory quality evaluation result of the pears according to the evaluation ability of the appraisers and the scoring data of the corresponding appraisers on the pear samples.
For the functions realized by the modules, the corresponding introduction in the pear sensory quality evaluation system is not described herein.
In the embodiments provided by the present invention, it should be understood that the disclosed related system and method can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the described parts or modules is only one logical division, and in actual implementation, there may be other divisions, for example, multiple parts or modules may be combined or integrated into one system, or some features may be omitted or not executed. Various operations and methods have been described. Some methods have been described in a relatively basic manner in a flow chart form, but operations may alternatively be added to and/or removed from the methods. Additionally, while the flow diagrams illustrate a particular order of operation according to example embodiments, it is understood that this particular order is exemplary. Alternative embodiments may optionally perform these operations in a different manner, combine certain operations, interleave certain operations, etc. The components, features, and specific optional details of the devices described herein may also optionally be applied to the methods described herein, which may be performed by and/or within such devices in various embodiments.
Each functional block in the present invention may be hardware, for example, the hardware may be a circuit, including a digital circuit, an analog circuit, and the like. Physical implementations of hardware structures include, but are not limited to, physical devices including, but not limited to, transistors, memristors, and the like. The memory module may be any suitable magnetic or magneto-optical storage medium, such as RRAM, DRAM, SRAM, EDRAM, HBM, HMC, and the like.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (20)
1. A pear sensory quality evaluation method comprises the following steps:
establishing a quality evaluation vocabulary database of the sensory quality of pears, wherein each quality evaluation vocabulary comprises a first-class attribute word, the first-class attribute word comprises a second-class attribute word, and a quality evaluation method and a reference sample corresponding to the second-class attribute word;
obtaining scoring data of a appraiser on appraisal words in a pear sample, and determining the evaluation capability of the appraiser;
and determining the sensory quality evaluation result of the pears according to the evaluation ability of the appraisers and the scoring data of the corresponding appraisers on the pear samples.
2. The method of claim 1, wherein the evaluative capabilities of the panelist include at least one of:
the distinguishing capability among the pear samples, the distinguishing capability of the first kind of attribute words and/or the second kind of attribute words in the pear samples, and the overall consistency of the groups in which the appraisers are located.
3. The method of claim 2, wherein the ability to distinguish between pear samples is determined in a manner comprising:
and (3) acquiring scoring data of the appraiser on each type of attribute words and/or second type of attribute words in the pear sample, and determining the MSE value of the variance in the group, wherein the smaller the MSE value is, the better the repeatability of the appraiser is.
4. The method of claim 2, wherein the determination of the discriminative power of the first class of attribute words and/or the second class of attribute words in the pear sample comprises:
obtaining the scoring data of the appraiser on each pear sample, and determining the ratio F of the variance between the groups to the variance in the groups, wherein the larger the F value is, the better the distinguishing capability of the appraiser on each attribute in the pear sample is.
5. The method of claim 2, wherein the overall consistency of the panel of assessors comprises:
obtaining scoring data of a group where the appraiser is located, determining attribute graphs which belong to various attribute words and/or two types of attribute words, wherein each graph represents one attribute, each line represents one appraiser, the closer the lines are, the higher the consistency of the appraisal of the group to the attribute is, and otherwise, the worse the consistency is.
6. The method of claim 1, wherein each type of attribute word further comprises a scale corresponding to each type of attribute word.
7. The method of claim 1, wherein the class of attribute words comprises at least one of:
flesh characteristics, peel characteristics, and stones.
8. The method of claim 1, wherein the flesh feature comprises two categories of attribute words including at least one of:
pulp color, hardness, brittleness, juice amount, pulp sweetness, pulp acidity, pulp astringency, pulp slagging degree, fiber feeling, stone cell amount, pear direct fragrance and pear chewing fragrance.
9. The method of claim 1, wherein said peel characteristics include two categories of attribute words including at least one of:
pericarp color, pericarp smoothness, fruit size, fruit point size, rust spot, pericarp thickness, ease of pericarp peeling, pericarp toughness, and wax size.
10. The method of claim 1, wherein the evaluation vocabulary database is an initial evaluation vocabulary database or an adjusted evaluation vocabulary database after feedback from an evaluator.
11. A pear sensory quality evaluation system comprises:
the system comprises a pear sensory evaluation vocabulary determining module, a pear sensory evaluation vocabulary database and a control module, wherein the pear sensory evaluation vocabulary database is used for establishing pear sensory quality, each evaluation vocabulary comprises a first-class attribute word, the first-class attribute word comprises a second-class attribute word, and an evaluation method and a reference sample corresponding to the second-class attribute word;
the evaluation module is used for acquiring scoring data of the appraiser on the appraised words in the pear sample and determining the evaluation capability of the appraiser;
and the pear sensory quality determining module is used for determining the sensory quality evaluation result of the pears according to the evaluation capability of the appraisers and the scoring data of the corresponding appraisers on the pear samples.
12. The system of claim 11, wherein the evaluation module wherein the evaluators' evaluation capabilities comprise at least one of:
the distinguishing capability among the pear samples, the distinguishing capability of the first kind of attribute words and/or the second kind of attribute words in the pear samples, and the overall consistency of the groups in which the appraisers are located.
13. The system of claim 12, wherein the evaluation module determines the ability to distinguish between the pear samples by:
and (3) acquiring scoring data of the appraiser on each type of attribute words and/or second type of attribute words in the pear sample, and determining the MSE value of the variance in the group, wherein the smaller the MSE value is, the better the repeatability of the appraiser is.
14. The system of claim 12, wherein the determination of the discriminative power of the first class of attribute words and/or the second class of attribute words in the pear sample comprises:
obtaining the scoring data of the appraiser on each pear sample, and determining the ratio F of the variance between the groups to the variance in the groups, wherein the larger the F value is, the better the distinguishing capability of the appraiser on each attribute of the pear sample is.
15. The system of claim 12, wherein the overall consistency of the panel of panelists is determined by:
obtaining scoring data of a group where the appraisers are located, determining Profile Plots attribute graphs, wherein each graph represents one attribute, each line represents one appraiser, the closer the lines are, the higher the consistency of the appraisers on the attribute evaluation is, and otherwise, the worse the consistency is.
16. The system of claim 11, wherein each class of attribute words further comprises a scale corresponding to each class of two attribute words.
17. The system of claim 11, wherein the class of attribute words comprises at least one of:
flesh characteristics, peel characteristics, and stones.
18. The system of claim 1, wherein the flesh feature comprises two categories of attribute words including at least one of:
pulp color, hardness, brittleness, juice amount, pulp sweetness, pulp acidity, pulp astringency, pulp slagging degree, fiber feeling, stone cell amount, pear direct fragrance and pear chewing fragrance.
19. The system of claim 11, wherein the peel characteristics include a category two attribute term comprising at least one of:
pericarp color, pericarp smoothness, fruit size, fruit point size, rust spot, pericarp thickness, ease of pericarp peeling, pericarp toughness, and wax size.
20. The system of claim 11, wherein the evaluation vocabulary database is an initial evaluation vocabulary database or an adjusted evaluation vocabulary data after being fed back by an evaluator.
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| CN111912941A (en) * | 2020-08-11 | 2020-11-10 | 浙江养生堂天然药物研究所有限公司 | Sensory test method, sensory test system, and computer-readable storage medium |
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