CN109165875A - Tealeaves sensory evaluation method and machine readable storage medium - Google Patents
Tealeaves sensory evaluation method and machine readable storage medium Download PDFInfo
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Abstract
The embodiment of the present invention provides a kind of tealeaves sensory evaluation method and machine readable storage medium, belongs to tealeaves sensory review's technical field, overcomes in the prior art for the defect of tea products sense organ quantization.The described method includes: obtaining the content of the characteristic component of Tea Samples to be evaluated；According to the content of the characteristic component of the Tea Samples to be evaluated, and the evaluation model of every kind of default sensory evaluation dimension and corresponding characteristic component of the tealeaves to be evaluated, obtain the score that the Tea Samples to be evaluated correspond to every kind of default sensory evaluation dimension.The embodiment of the present invention is suitable for tealeaves sensory evaluation process.
Description
Technical field
The present invention relates to tealeaves sensory review's technical fields, can more particularly to a kind of tealeaves sensory evaluation method and machine
Read storage medium.
Background technique
The one very big problem faced when selecting tealeaves for most of amateur, non-senior consumers just exists
It is not known about in tealeaves, it is not known that how to select.Consumer retouches in a series of tea leaf quality of professions of " mellow ", " pure and mild " etc.
Stating seems helpless in face of term, does not know how to choose.Therefore, one is needed between tea products organoleptic quality and consumer
The simple and effective quantization method of kind, facilitates consumption guidance person to be bought.
Summary of the invention
The purpose of the embodiment of the present invention is that being provided to overcome in the prior art for the defect of tea products sense organ quantization
A kind of tealeaves sensory evaluation method and machine readable storage medium, to realize to the scientific, quick of tea leaf quality and accurate examine
It comments.
To achieve the goals above, the embodiment of the present invention provides a kind of tealeaves sensory evaluation method, which comprises obtains
Take the content of the characteristic component of Tea Samples to be evaluated；According to the content of the characteristic component of the Tea Samples to be evaluated, and
The evaluation model of every kind of the tealeaves to be evaluated default sensory evaluation dimension and corresponding characteristic component, obtains described to be evaluated
Tea Samples correspond to the score of every kind of default sensory evaluation dimension.
Further, in the score for obtaining the Tea Samples to be evaluated and corresponding to every kind of default sensory evaluation dimension
Later, the method also includes: according to the corresponding relationship of score range and predetermined level, determine the Tea Samples to be evaluated
The corresponding grade of every kind of default sensory evaluation dimension, and the corresponding grade is shown using default sense organ mark.
Further, the evaluation mould of every kind of the tealeaves to be evaluated default sensory evaluation dimension and corresponding characteristic component
Type passes through following manner and establishes: obtaining the tealeaves to be evaluated in the scoring of every kind of default sensory evaluation dimension and described to be evaluated
The content of the physical and chemical composition of tealeaves；By correlation analysis, shadow is determined in the content of the physical and chemical composition of the tealeaves to be evaluated
Ring the characteristic component of every kind of default sensory evaluation dimension；According to the scoring and corresponding feature of every kind of default sensory evaluation dimension
The content of ingredient determines the characteristic component pair for influencing the scoring of every kind of default sensory evaluation dimension by data fitting algorithms
The weighted value answered；According to the corresponding weighted value of the characteristic component, every kind of default sensory evaluation of the tealeaves to be evaluated is established
The evaluation model of dimension and corresponding characteristic component.
Further, described by correlation analysis, shadow is determined in the content of the physical and chemical composition of the tealeaves to be evaluated
The characteristic component for ringing every kind of default sensory evaluation dimension includes: to obtain every kind of default sensory evaluation dimension according to correlation analysis
With the related coefficient between each physical and chemical composition；According to specification error probable range, default sensory evaluation dimension and physical and chemical composition
Default list related and the related coefficient absolute value, determine influence every kind of default sensory evaluation dimension feature at
Point.
It is further, described according to the scoring of every kind of default sensory evaluation dimension and the content of corresponding characteristic component,
The corresponding weighted value of the characteristic component for influencing the scoring of every kind of default sensory evaluation dimension is determined by data fitting algorithms
It include: using data fitting algorithms, respectively using the scoring of every kind of default sensory evaluation dimension as dependent variable, corresponding spy
The content of ingredient is levied as independent variable, the weighted value of the characteristic component is fitted as unknown parameter, obtains influencing every
The corresponding weighted value of the characteristic component of the scoring of the default sensory evaluation dimension of kind.
Further, the data fitting algorithms include Partial Least Squares and general global optimization approach.
Further, it is described establish every kind of default sensory evaluation dimension and the evaluation model of corresponding characteristic component it
Afterwards, the method also includes: obtain the scoring of the composite index of the tealeaves to be evaluated；According to every kind of default sensory evaluation dimension
Scoring and composite index scoring, determine that every kind of the scoring for influencing the composite index is default by data fitting algorithms
The corresponding weighted value of sensory evaluation dimension；According to the weighted value of every kind of default sensory evaluation dimension, the tealeaves to be evaluated is established
Composite index and every kind of default sensory evaluation dimension evaluation model.
Further, it must be divided in every kind of default sensory evaluation dimension for obtaining the Tea Samples to be evaluated
Afterwards, the method also includes: according to the score of every kind of default sensory evaluation dimension of the Tea Samples to be evaluated and described
The evaluation model of the composite index of tealeaves to be evaluated and every kind of default sensory evaluation dimension obtains the Tea Samples to be evaluated
The score of composite index.
Further, the method also includes: according to the corresponding relationship of score range and predetermined level, determine described to be evaluated
The corresponding grade of the composite index of valence Tea Samples, and the corresponding grade is shown using default sense organ mark.
Correspondingly, the embodiment of the present invention also provides a kind of machine readable storage medium, deposited on the machine readable storage medium
Instruction is contained, which is used for so that machine executes tealeaves sensory evaluation method described above.
Through the above technical solutions, after obtaining the content of characteristic component of Tea Samples to be evaluated, it will be described to be evaluated
The content of the characteristic component of valence Tea Samples substitutes into every kind of default sensory evaluation dimension of the tealeaves to be evaluated and corresponding spy
In the evaluation model for levying ingredient, so that obtaining the Tea Samples to be evaluated corresponds to obtaining for every kind of default sensory evaluation dimension
Point.The evaluation mould of every kind of default sensory evaluation dimension and corresponding characteristic component of tealeaves to be evaluated is utilized in the embodiment of the present invention
The Tea Samples to be evaluated can be obtained corresponding to every kind in the content of the characteristic component of type and the Tea Samples to be evaluated of acquisition
The score of default sensory evaluation dimension intuitively embodies the organoleptic quality of tealeaves by way of score, is convenient for consumption guidance
Person's purchase.
The other feature and advantage of the embodiment of the present invention will the following detailed description will be given in the detailed implementation section.
Detailed description of the invention
Attached drawing is to further understand for providing to the embodiment of the present invention, and constitute part of specification, under
The specific embodiment in face is used to explain the present invention embodiment together, but does not constitute the limitation to the embodiment of the present invention.Attached
In figure:
Fig. 1 is a kind of flow diagram of tealeaves sensory evaluation method provided in an embodiment of the present invention；
Fig. 2 is a kind of example of default sense organ mark provided in an embodiment of the present invention；
Fig. 3 is the example of another default sense organ mark provided in an embodiment of the present invention；
Fig. 4 be it is provided in an embodiment of the present invention by taking black tea as an example when, determine the data instance of characteristic component；
Fig. 5 be it is provided in an embodiment of the present invention by taking black tea as an example when, preset sense organ mark example；
Fig. 6 be it is provided in an embodiment of the present invention by taking ripe Pu'er tea as an example when, determine the data instance of characteristic component；
Fig. 7 be it is provided in an embodiment of the present invention by taking ripe Pu'er tea as an example when, preset sense organ mark example；
Fig. 8 be it is provided in an embodiment of the present invention by taking Longjing tea as an example when, determine the data instance of characteristic component；
Fig. 9 be it is provided in an embodiment of the present invention by taking Longjing tea as an example when, preset sense organ mark example.
Specific embodiment
It is described in detail below in conjunction with specific embodiment of the attached drawing to the embodiment of the present invention.It should be understood that this
Locate described specific embodiment and be merely to illustrate and explain the present invention embodiment, is not intended to restrict the invention embodiment.
The bitter, puckery, fresh of tealeaves, sweet tea, alcohol, thickness, cunning etc. are characterized in due to Tea Polyphenols in Tea, caffeine, catechin, amino
Content of the taste compounds such as acid, sugar, Tea Pigment in millet paste, ratio, flavor characteristic is different and formed.At present in document about
The comprehensive flavor characteristics of tealeaves and the qualitative research of each taste composition are more, but for the differences such as bitter, puckery, fresh, sweet tea, alcohol, thickness, cunning
The qualitative and quantitative study of flavour and each taste composition is less, and all rests on scientific research level, in practical consumption guidance person
The application aspect for choosing tealeaves still belongs to blank.The embodiment of the present invention by by the content of the characteristic component of Tea Samples to be evaluated,
Substitute into the evaluation model of every kind of default sensory evaluation dimension and corresponding characteristic component of tealeaves to be evaluated, thus obtain it is described to
The score that evaluation Tea Samples correspond to every kind of default sensory evaluation dimension intuitively embodies tealeaves by way of score
Organoleptic quality, convenient for consumption guidance person buy.
Fig. 1 is a kind of flow diagram of tealeaves sensory evaluation method provided in an embodiment of the present invention.As shown in Figure 1, institute
The method of stating includes the following steps:
Step 101, the content of the characteristic component of Tea Samples to be evaluated is obtained；
Step 102, according to the every of the content of the characteristic component of the Tea Samples to be evaluated and the tealeaves to be evaluated
The evaluation model of kind default sensory evaluation dimension and corresponding characteristic component obtains the Tea Samples to be evaluated corresponding to every kind
The score of default sensory evaluation dimension.
Wherein, the score that Tea Samples to be evaluated correspond to every kind of default sensory evaluation dimension is obtained, it is necessary first to build
Found the evaluation model of every kind of default sensory evaluation dimension and corresponding characteristic component of tealeaves to be evaluated.The tool of the evaluation model
It is as described below that body establishes mode:
1) tealeaves to be evaluated is obtained in the scoring of every kind of default sensory evaluation dimension and the reason of the tealeaves to be evaluated
The content of chemical conversion point；
2) by correlation analysis, determining in the content of the physical and chemical composition of the tealeaves to be evaluated influences every kind of default sense
The characteristic component of official's evaluative dimension；
3) it according to the scoring of every kind of default sensory evaluation dimension and the content of corresponding characteristic component, is fitted by data
Algorithm determines the corresponding weighted value of the characteristic component for influencing the scoring of every kind of default sensory evaluation dimension；
4) according to the corresponding weighted value of the characteristic component, every kind of default sensory evaluation dimension of the tealeaves to be evaluated is established
It spends and the evaluation model of corresponding characteristic component.
Wherein, for step 1), due to before establishing the evaluation model, being not ready-made calculation method or detection side
Method, thus by investigate profession comment tea teacher, senior Tea Consumption person, ordinary tea leaves consumer et al. to tealeaves to be evaluated (such as
It is green tea, black tea, oolong tea, dark green tea, ripe general, raw general etc.) sensory evaluation dimension, such as flavour (bitter degree, sugariness, fresh refreshing degree
Deng), fragrance, soup look, resistance to bubble degree etc..
By commenting the experience of tea teacher to select the default sensory evaluation dimension of tealeaves to be evaluated by profession, and it is default at every kind
Tea Samples to be evaluated are filtered out in sensory evaluation dimension, guarantee all representative sample in every kind of default sensory evaluation dimension
Product.Then tea teacher is commented to evaluate according to the reviewing method in national standard to tealeaves to be evaluated by profession, and in every kind of default sense organ
It scores on evaluative dimension the tealeaves to be evaluated, to get the tealeaves to be evaluated in every kind of default sensory evaluation
The scoring of dimension.
Then do not have to moisture, water extraction, tea polyphenols, catechin total amount, gallic acid, the table in the tealeaves to be evaluated
It is infanticide catechin, epi-nutgall base catechin and gallate, epicatechin, L-Epicatechin gallate, caffeine, solvable
The component contents such as property sugar, general flavone, theaflavin, thearubigin, theabrownin, total amino acid content and composition according to national standard detection method and
The self-built detection method of instrument or laboratory is detected.For example, catechin total amount and composition are according to GBT 8313-2008 method
And it is detected using high performance liquid chromatograph (HPLC)；The detection self-built according to laboratory of theaflavin, thearubigin and theabrownin
Method is carried out sample preparation and is detected using microplate reader, to obtain the content of the physical and chemical composition of the tealeaves to be evaluated.
For step 2), the content for each physical and chemical composition that every kind of default sensory evaluation dimension is obtained with detection carries out phase
The analysis of closing property.For example, using the Pearson came correlation analysis method in SPSS software, by every kind of default sensory evaluation dimension and detection
The content of obtained each physical and chemical composition as variable carry out correlation analysis, obtain every kind of default sensory evaluation dimension with it is each
Related coefficient between physical and chemical composition, then according to specification error probable range, default sensory evaluation dimension and physical and chemical composition
The absolute value of default list related and the related coefficient determines the characteristic component for influencing every kind of default sensory evaluation dimension.
Wherein, when carrying out correlation analysis, hypothesis testing is carried out.P value in hypothesis testing is decision of testing
A foundation.P value is probability, that is, reflects a possibility that a certain event occurs size.Statistics is according to significance test method
Obtained P value is generally to have statistical difference with P < 0.05, and P < 0.01 is to have significant statistical difference, and P < 0.001 is to have extremely
Significant statistical difference, meaning is probability of the difference caused by sampling error between sample respectively less than 0.05,0.01,
0.001.When carrying out correlation analysis, P value is looked first at, statistical judgment criteria is generally 0.05.If the explanation of P < 0.05
Related coefficient is statistically significant, then considers further that the bigger related coefficient, the absolute value of related coefficient the more related；If P≤0.05
Illustrate that related coefficient is not statistically significant, no matter the value of related coefficient is how much all not account for meaning.Implement in the present invention
In example, the specification error probable range is set as P < 0.05, then checks P < 0.05, and in every kind of default sensory evaluation dimension
Degree and the default list related of physical and chemical composition are (for example, the default sensory evaluation dimension that is obtained according to literature research result and physical and chemical
The default list related of ingredient) in, while the higher physical and chemical composition of absolute value of related coefficient, it is determined as influencing every kind of default sense
The characteristic component of official's evaluative dimension.For example, to certain tealeaves to be evaluated establish every kind of default sensory evaluation dimension with it is corresponding
When the evaluation model of characteristic component, presetting sensory evaluation dimension is thick slippery, and physical and chemical composition has 51 kinds.Carrying out correlation point
Analysis, when determining the characteristic component for influencing thick slippery in this 51 kinds of physics and chemistry, search at the same meet specification error probable range P <
0.05, and be present in the default list related of thick slippery and physical and chemical composition, while the higher physics and chemistry of absolute value of related coefficient
Ingredient is characterized ingredient, for example, determining that the characteristic component for influencing thick slippery is caffeine and tea polyphenols.
For step 3), using data fitting algorithms, respectively using the scoring of every kind of default sensory evaluation dimension as because becoming
Amount, the content of corresponding characteristic component are carried out as independent variable using the weighted value of the characteristic component as unknown parameter
Fitting, obtains the corresponding weighted value of the characteristic component for influencing the scoring of every kind of default sensory evaluation dimension.For example, default sense
Official's evaluative dimension is bitter taste, and corresponding characteristic component is tea polyphenols and caffeine, using the content of the two as independent variable,
Bitter taste=unknown parameter 1* tea polyphenols content+unknown parameter is obtained using the weighted value of the characteristic component as unknown parameter
The content of 2* caffeine, by data fitting algorithms, such as Partial Least Squares or general global optimization approach, fitting obtains two
The weighted value of kind characteristic component, i.e. unknown parameter 1 and unknown parameter 2.
The tealeaves to be evaluated is established according to the weighted value for two characteristic components that step 3) obtains for step 4)
The evaluation model of every kind of default sensory evaluation dimension and corresponding characteristic component, for example, unknown parameter 1=0.8, unknown parameter 2
=0.2, to obtain the content+0.2* coffee of default sensory evaluation dimension bitter taste=0.8* tea polyphenols of the tealeaves to be evaluated
The content of alkali, i.e., the evaluation model of default sensory evaluation dimension bitter taste and corresponding characteristic component tea polyphenols, caffeine.
Every kind of default sensory evaluation dimension for obtaining the tealeaves to be evaluated through the above way and corresponding feature at
After the evaluation model divided, as long as getting the content of the characteristic component of Tea Samples to be evaluated, the evaluation model is substituted into
In, the score that the Tea Samples to be evaluated correspond to every kind of default sensory evaluation dimension can be obtained.
In one embodiment of the invention, felt according to consumer's finding, such as consumer by bitter degree point
It is 5 grades with regard to much of that, if the grade divided is too many, such as 10 grades, when judging that it, bitterness of product is spent when buying product
Directive significance is little, or selection gets up to bring selection syndrome etc. to ask from the product of 10 different bitterness degree grades
Topic and the review result of tealeaves evaluation expert, experience and suggest that (such as the tealeaves evaluation expert of profession is according to tradition evaluation warp
Test, it is believed that the bitter degree quality of such tealeaves to be evaluated can probably be divided into 4 ranks), or pass through clustering (such as basis
20 Tea Samples bitterness degree scores to be evaluated carry out clustering, from cluster analysis result it can be seen that 20 tealeaves to be evaluated
The methods of the bitter degree of sample can be divided into 3 major class, so as to which bitter degree is divided into 3 ranks), each default sensory evaluation is tieed up
Degree is divided into several grades, and determines the score range of the default sensory evaluation dimension in each grade.Such as bitterness degree full marks
It is 50 points, and finally determines and bitter degree is divided into 5 ranks, is equally divided into 5 ranks, the i.e. corresponding scoring model of level-one for 50 points
Enclosing is 1.0-10.0 points, and the corresponding scoring range of second level is 10.1-20.0 points, and the corresponding scoring range of three-level is 20.1-30.0
Point, the corresponding scoring range of level Four is 30.1-40.0 points, and the corresponding scoring range of Pyatyi is 40.1-50.0 points.Then using pre-
If sense organ mark display corresponding grade, for example, will be to be evaluated with the mode of the figures such as digital, text or radar map, column diagram
Tea Samples show in the corresponding grade of each default sensory evaluation dimension, help consumer's more intuitive understanding product
Organoleptic quality feature.As shown in Fig. 2, 2 to represent * * * black tea sugariness be 2 grades, 5 represent the black tea of most sweet tea as 5 grades.Or such as Fig. 3
Shown, it is 2 grades that two ★, which represent * * * black tea sugariness, and 5 stars represent the black tea of most sweet tea as 5 grades altogether.
In another embodiment of the invention, every kind of default sensory evaluation dimension and corresponding feature are established described
After the evaluation model of ingredient, the composite index of the tealeaves to be evaluated and every kind of default sensory evaluation dimension can also be established
Evaluation model, to obtain the score of the composite index of the Tea Samples to be evaluated.Specific embodiment is as described below:
1) scoring of the composite index of the tealeaves to be evaluated is obtained；
2) true by data fitting algorithms according to the scoring of every kind of default sensory evaluation dimension and the scoring of composite index
The corresponding weighted value of the default sensory evaluation dimension of every kind of the scoring of the fixing sound composite index；
3) according to the weighted value of every kind of default sensory evaluation dimension, the composite index of the tealeaves to be evaluated and every kind are established
The evaluation model of default sensory evaluation dimension.
4) according to the score of every kind of default sensory evaluation dimension of the Tea Samples to be evaluated and the tea to be evaluated
The evaluation model of the composite index of leaf and every kind of default sensory evaluation dimension obtains the composite index of the Tea Samples to be evaluated
Score.
Wherein, similar in the scoring of every kind of default sensory evaluation dimension with acquisition tealeaves to be evaluated, and tea is commented by profession
Teacher comprehensively considers the overall merit of product quality obtained from the sensory evaluations dimension such as soup look, flavour, fragrance, thus to it is described to
The composite index of evaluation tealeaves scores.
Then, using data fitting algorithms, using the scoring of composite index as dependent variable, every kind of default sensory evaluation dimension
Scoring as independent variable, be fitted, obtain using the corresponding weighted value of every kind of default sensory evaluation dimension as unknown parameter
Influence the corresponding weighted value of every kind of the scoring of the composite index default sensory evaluation dimension.For example, default sensory evaluation dimension
Degree is thick slippery and sugariness, regard the scoring of the two as independent variable, by the corresponding weighted value work of every kind of default sensory evaluation dimension
Scoring+unknown parameter 2* sugariness the scoring of composite index=unknown parameter 1* thickness slippery is obtained for unknown parameter, passes through data
Fitting algorithm, such as Partial Least Squares or general global optimization approach, it is corresponding that fitting obtains two kinds of default sensory evaluation dimensions
Weighted value, i.e. unknown parameter 1 and unknown parameter 2.
After obtaining the corresponding weighted value of every kind of default sensory evaluation dimension, the synthesis for establishing the tealeaves to be evaluated refers to
Several evaluation models with every kind of default sensory evaluation dimension, for example, unknown parameter 1=0.2, unknown parameter 2=0.8, thus
To the score of composite index=0.2* thickness slippery score+0.8* sugariness of the tealeaves to be evaluated.
Equally, the score of the composite index can also determine institute according to the corresponding relationship of score range and predetermined level
The corresponding grade of the composite index of Tea Samples to be evaluated is stated, and shows the corresponding grade using default sense organ mark.Wherein,
The score range and predetermined level of score range and the corresponding relationship of predetermined level and default sensory evaluation dimension described above
Corresponding relationship it is identical, the corresponding grade of score of the composite index of the Tea Samples to be evaluated can refer to above for default
The division of the corresponding grade of sensory evaluation dimension, and the corresponding grade is shown using default sense organ mark.
The implementation process of embodiment will be illustrated the present invention with the tealeaves to be evaluated of several classifications below.
The first, is illustrated by taking black tea as an example.
Firstly, commenting the senior consumer of tea teacher, black tea, black tea ordinary consumer by investigating profession, dense intensity and fresh is determined
Refreshing degree chooses dense intensity and the discrepant typical black tea 20, sample of fresh refreshing degree as default sensory evaluation dimension, by profession
It comments tea teacher to evaluate according to the method in GBT22111-2008 to tea sample, and is commented from 2 kinds of default sense organs of dense intensity and fresh refreshing degree
Valence dimension scores, while the score of default sensory evaluation dimension is divided into 5 grades, as shown in table 1.
Table 1
| Grade | Score range |
| 1 | 0.0-1.0 |
| 2 | 1.1-2.0 |
| 3 | 2.1-3.0 |
| 4 | 3.1-4.0 |
| 5 | 4.1-5.0 |
Secondly, in tea sample moisture, water extraction, tea polyphenols, catechin total amount and composition, caffeine, soluble sugar,
General flavone, total amino acid content and composition, theaflavin, thearubigin, dark brown cellulose content are detected, and the physical and chemical composition of tea sample is obtained
Content.
The scoring of dense intensity and fresh refreshing degree is subjected to correlation analysis with the content of the physical and chemical composition of detection respectively, in conjunction with pre-
If the absolute value of sensory evaluation dimension and the default list related of physical and chemical composition, specification error probable range and related coefficient,
As shown in figure 4, final determine that the characteristic component for influencing dense intensity is tea polyphenols and theaflavin, the characteristic component for influencing fresh refreshing degree is
Theaflavin and free amino acid.Wherein, for fresh refreshing degree, from the point of view of the data of related coefficient, tea polyphenols are than theaflavin and dissociate
The related coefficient of amino acid is also big, but combines default sensory evaluation dimension and the default list related of physical and chemical composition it is found that tea
Polyphenol is bitter taste ingredient, uncorrelated to fresh refreshing degree, so be not determined as tea polyphenols to influence the characteristic component of fresh refreshing degree；Together
Sample, for dense intensity, the free amine group according to default sensory evaluation dimension and the default list related of physical and chemical composition
Acid is mainly in fresh refreshing taste, little with dense strength relationship, so be not determined as free amino acid to influence the spy of dense intensity
Levy ingredient.
Then, respectively using the scoring of dense intensity and fresh refreshing degree as dependent variable, tea polyphenols, theaflavin, free amino acid
Content is independent variable, using general global optimization approach, using tea polyphenols, theaflavin, free amino acid weighted value as unknown
Parameter is fitted, and obtains the corresponding weighted value of the characteristic component for influencing the scoring of every kind of default sensory evaluation dimension, from
And every kind of default sensory evaluation dimension for establishing black tea and the evaluation model (1) of corresponding characteristic component:
The score of dense intensity: the content (%) of N=12.1 × tea polyphenols content (%)+51.6 × theaflavin
The score of fresh refreshing degree: the content (%) of X=53.4 × theaflavin content (%)+109.6 × free amino acid
When carrying out sensory evaluation to certain black tea sample, detect respectively the characteristic component tea polyphenols of the sample, theaflavin,
The content of free amino acid, as shown in table 2.
Table 2
| Physical and chemical composition | Content |
| Tea polyphenols | 25.25% |
| Theaflavin | 1.30% |
| Free amino acid | 3.21% |
Above-mentioned institute's detection level is substituted into evaluation model (1), the score of dense intensity and fresh refreshing degree is obtained:
The score of dense intensity: N=12.1 × 25.25%+51.6 × 1.30%=3.71
The score of fresh refreshing degree: X=53.4 × 1.30%+109.6 × 3.21%=4.22
And the corresponding relationship of the score range according to shown in table 1 and predetermined level, determine the black tea sample dense intensity and
The grade of fresh refreshing degree is respectively 4,5.In addition, can use default sense organ mark as shown in Figure 5 for the ease of consumer's understanding
Show corresponding grade.
It second, is illustrated by taking ripe Pu'er tea as an example.
Firstly, commenting tea teacher, senior ripe general consumer, ripe general ordinary consumer by investigating profession, thickness slippery and sweet tea are determined
Degree chooses thick slippery and the discrepant typical black tea 20, sample of sugariness as default sensory evaluation dimension, comments tea by profession
Teacher evaluates tea sample according to the method in GBT22111-2008, and from thick slippery and 2 kinds of sugariness default sensory evaluation dimensions
It scores, in addition it can carry out the scoring of composite index, while by the score of default sensory evaluation dimension and composite index
5 grades are divided into, as shown in table 1.
Secondly, to moisture, water extraction, tea polyphenols, catechin total amount, gallic acid, epi-nutgall catechu in tea sample
It is element, epi-nutgall base catechin and gallate, epicatechin, L-Epicatechin gallate, caffeine, soluble sugar, total
The content of 51 kinds of physical and chemical compositions such as flavones, theaflavin, thearubigin, theabrownin, total amino acid content and composition is detected.
The scoring of thick slippery and sugariness is subjected to correlation analysis with the content of 51 kinds of physical and chemical compositions of detection respectively, in conjunction with
Default sensory evaluation dimension and the default list related of physical and chemical composition, specification error probable range and related coefficient it is absolute
Value, as shown in fig. 6, final determine that influencing the characteristic component of thick slippery is caffeine, tea polyphenols, the characteristic component for influencing sugariness is
Tea polyphenols and tea polysaccharide.
Then, respectively using the scoring of thick slippery and sugariness as dependent variable, the content of caffeine, tea polyphenols and tea polysaccharide is made
It is carried out using general global optimization approach using the weighted value of caffeine, tea polyphenols and tea polysaccharide as unknown parameter for independent variable
Fitting, obtains the corresponding weighted value of the characteristic component for influencing the scoring of every kind of default sensory evaluation dimension, to establish red
The evaluation model (2) of the default sensory evaluation dimension of every kind of tea and corresponding characteristic component:
The score of thick slippery: the content (%) of H=27.2 × tea polyphenols content (%)+30.6 × caffeine
The score of sugariness: the content (%) of T=-2.1 × tea polyphenols content (%)+39.1 × tea polysaccharide
Additionally, it is also contemplated that the scoring event of composite index.It is thick i.e. first using the scoring of composite index as dependent variable
The scoring of slippery and sugariness obtains the weighted value of thick slippery and sugariness using general global optimization approach as independent variable, thus
Establish the composite index of the ripe Pu'er tea and the evaluation model (3) of every kind of default sensory evaluation dimension:
The score of composite index: Z=0.78H+0.22T
When carrying out sensory evaluation to certain ripe Pu'er tea sample, characteristic component caffeine, the tea of the sample are detected respectively
The content of polyphenol, as shown in table 3.
Table 3
| Physical and chemical composition | Content |
| Caffeine | 3.8% |
| Tea polyphenols | 8.5% |
| Tea polysaccharide | 4.2% |
Above-mentioned institute's detection level is substituted into evaluation model (2), the score of thick slippery and sugariness is obtained:
The score of thick slippery: H=27.2 × 8.5%+30.6 × 3.8%=3.5
The score of sugariness: T=-2.1 × 8.5%+39.1 × 4.2%=1.5
Then the score of said two devices is substituted into evaluation model (3), obtains the score of composite index:
Composite index score: Z=0.78 × 3.5+0.22 × 1.5=3.1
According to the corresponding relationship of score range shown in table 1 and predetermined level, determine the ripe Pu'er tea sample thick slippery,
The distribution of grades of sugariness and composite index is 4,2,4.In addition, can use as shown in Figure 7 pre- for the ease of consumer's understanding
If sense organ mark display corresponding grade.
The third, is illustrated by taking Longjing tea as an example.
Firstly, comment the deeply raw general consumer of tea teacher, green tea, green tea ordinary consumer by investigating profession, bitterness degree, fresh is determined
Refreshing degree, fragrance height choose bitter degree, fresh refreshing degree, the discrepant Longjing tea of fragrance height as default sensory evaluation dimension
10, sample, tea teacher is commented to evaluate according to the method in GBT23776-2009 to tea sample by profession, and from bitter degree, fresh refreshing
Degree, 3 kinds of fragrance height default sensory evaluation dimensions score, while the score of default sensory evaluation dimension are divided into 5
Grade, as shown in table 1.
Secondly, in tea sample moisture, water extraction, tea polyphenols, catechin total amount and composition, caffeine, soluble sugar,
The content for the physical and chemical composition that general flavone, total amino acid content and composition, fragrance form is detected.
The scoring of bitter degree, fresh refreshing degree, fragrance height is subjected to correlation point with the content of the physical and chemical composition of detection respectively
Analysis, in conjunction with default sensory evaluation dimension and the default list related of physical and chemical composition, specification error probable range and related coefficient
Absolute value, as shown in figure 8, final determine that the characteristic component for influencing bitterness degree is tea polyphenols；Influence the characteristic component of fresh refreshing degree
For theanine, glutamic acid；The characteristic component for influencing fragrance height is 2- n-pentyl furans, 4- methyl=3 amylene -2- ketone, bigcatkin willow
Sour methyl esters.
Then, respectively using the scoring of bitter degree, fresh refreshing degree, fragrance height as dependent variable, the influence of above-mentioned determination is each
The content of the characteristic component of a default sensory evaluation dimension is as independent variable, using general global optimization approach, by each feature
The weighted value of ingredient is fitted as unknown parameter, obtains the feature for influencing the scoring of every kind of default sensory evaluation dimension
The corresponding weighted value of ingredient, thus the evaluation mould of every kind of default sensory evaluation dimension and corresponding characteristic component establishing Longjing tea
Type (4):
The score of bitter degree: K=23 × tea polyphenols content (%)
The score of fresh refreshing degree: the content (%) of X=107 × theanine content (%)+328 × glutamic acid
The score of fragrance height: content (%)+19 × 4- methyl of Q=151 × 2- n-pentyl furans=3 amylene -2- ketone
Content (%)+32 × gaultherolin content (%)
To certain Longjing tea sample carry out sensory evaluation when, detect respectively the sample characteristic component catechin total amount,
The content of theanine, glutamic acid, 2- n-pentyl furans, 4- methyl=3 amylene -2- ketone, gaultherolin, as shown in table 4.
Table 4
| Physical and chemical composition | Content |
| Catechin total amount | 15.4% |
| Theanine | 0.29% |
| Glutamic acid | 0.261% |
| 2- n-pentyl furans | 2.125% |
| 4- methyl=3 amylene -2- ketone | 1.41% |
| Gaultherolin | 0.88% |
Will above-mentioned institute's detection level substitute into evaluation model (4) in, obtain bitter degree, it is fresh it is refreshing degree, fragrance height score:
The score of bitter degree: K=23 × 15.4%=3.54
The score of fresh refreshing degree: X=107 × 0.29%+328 × 0.261%=1.17
The score of fragrance height: Q=151 × 2.125%+19 × 1.41%+32 × 0.88%=3.19
According to the corresponding relationship of score range shown in table 1 and predetermined level, the bitter degree, fresh of the Longjing tea sample is determined
Refreshing degree, fragrance height distribution of grades be 4,2,4.In addition, can use as shown in Figure 9 pre- for the ease of consumer's understanding
If sense organ mark display corresponding grade.
Through the embodiment of the present invention, by establishing every kind of default sensory evaluation dimension of tealeaves and corresponding characteristic component
Evaluation model, additionally by the mode of digital quantization, illustrates the sense organ of tealeaves to consumer for instructing new product development
Feature, guide product purchase.
Correspondingly, the embodiment of the present invention also provides a kind of machine readable storage medium, deposited on the machine readable storage medium
Instruction is contained, which is used for so that machine executes tealeaves sensory evaluation method described in above-described embodiment.
The optional embodiment of the embodiment of the present invention is described in detail in conjunction with attached drawing above, still, the embodiment of the present invention is simultaneously
The detail being not limited in above embodiment can be to of the invention real in the range of the technology design of the embodiment of the present invention
The technical solution for applying example carries out a variety of simple variants, these simple variants belong to the protection scope of the embodiment of the present invention.
It is further to note that specific technical features described in the above specific embodiments, in not lance
In the case where shield, it can be combined in any appropriate way.In order to avoid unnecessary repetition, the embodiment of the present invention pair
No further explanation will be given for various combinations of possible ways.
It will be appreciated by those skilled in the art that implementing the method for the above embodiments is that can pass through
Program is completed to instruct relevant hardware, which is stored in a storage medium, including some instructions are used so that single
Piece machine, chip or processor (processor) execute all or part of the steps of each embodiment the method for the application.And it is preceding
The storage medium stated includes: USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory
The various media that can store program code such as (RAM, Random Access Memory), magnetic or disk.
In addition, any combination can also be carried out between a variety of different embodiments of the embodiment of the present invention, as long as it is not
The thought of the embodiment of the present invention is violated, equally should be considered as disclosure of that of the embodiment of the present invention.
Claims (10)
1. a kind of tealeaves sensory evaluation method, which is characterized in that the described method includes:
Obtain the content of the characteristic component of Tea Samples to be evaluated；
It is commented according to every kind of default sense organ of the content of the characteristic component of the Tea Samples to be evaluated and the tealeaves to be evaluated
The evaluation model of valence dimension and corresponding characteristic component obtains the Tea Samples to be evaluated corresponding to every kind of default sensory evaluation
The score of dimension.
2. the method according to claim 1, wherein obtaining the Tea Samples to be evaluated corresponding to every described
After the score of the default sensory evaluation dimension of kind, the method also includes:
According to the corresponding relationship of score range and predetermined level, every kind of default sensory evaluation of the Tea Samples to be evaluated is determined
The corresponding grade of dimension, and the corresponding grade is shown using default sense organ mark.
3. the method according to claim 1, wherein every kind of default sensory evaluation dimension of the tealeaves to be evaluated
It is established with the evaluation model of corresponding characteristic component by following manner:
The tealeaves to be evaluated is obtained in the scoring of every kind of default sensory evaluation dimension and the physical and chemical composition of the tealeaves to be evaluated
Content；
By correlation analysis, determining in the content of the physical and chemical composition of the tealeaves to be evaluated influences every kind of default sensory evaluation
The characteristic component of dimension；
It is true by data fitting algorithms according to the scoring of every kind of default sensory evaluation dimension and the content of corresponding characteristic component
It is fixed the corresponding weighted value of the characteristic component for ringing the scoring of every kind of default sensory evaluation dimension；
According to the corresponding weighted value of the characteristic component, establish every kind of default sensory evaluation dimension of the tealeaves to be evaluated with it is right
The evaluation model for the characteristic component answered.
4. according to the method described in claim 3, it is characterized in that, described by correlation analysis, in the tealeaves to be evaluated
Physical and chemical composition content in determine influence every kind of default sensory evaluation dimension characteristic component include:
According to correlation analysis, the related coefficient between every kind of default sensory evaluation dimension and each physical and chemical composition is obtained；
According to specification error probable range, default sensory evaluation dimension and the default list related of physical and chemical composition and described related
The absolute value of coefficient determines the characteristic component for influencing every kind of default sensory evaluation dimension.
5. according to the method described in claim 4, it is characterized in that, the scoring according to every kind of default sensory evaluation dimension with
And the content of corresponding characteristic component, the institute for influencing the scoring of every kind of default sensory evaluation dimension is determined by data fitting algorithms
Stating the corresponding weighted value of characteristic component includes:
Using data fitting algorithms, respectively using the scoring of every kind of default sensory evaluation dimension as dependent variable, corresponding spy
The content of ingredient is levied as independent variable, the weighted value of the characteristic component is fitted as unknown parameter, obtains influencing every
The corresponding weighted value of the characteristic component of the scoring of the default sensory evaluation dimension of kind.
6. according to the method described in claim 5, it is characterized in that, the data fitting algorithms include Partial Least Squares and lead to
Use global optimization approach.
7. according to the method described in claim 3, it is characterized in that, it is described establish every kind of default sensory evaluation dimension with it is corresponding
Characteristic component evaluation model after, the method also includes:
Obtain the scoring of the composite index of the tealeaves to be evaluated；
According to the scoring of every kind of default sensory evaluation dimension and the scoring of composite index, passing through data fitting algorithms and determining influences
The corresponding weighted value of the default sensory evaluation dimension of every kind of the scoring of the composite index；
According to the weighted value of every kind of default sensory evaluation dimension, the composite index and every kind of default sense of the tealeaves to be evaluated are established
The evaluation model of official's evaluative dimension.
8. the method according to the description of claim 7 is characterized in that pre- in every kind for obtaining the Tea Samples to be evaluated
If after the score of sensory evaluation dimension, the method also includes:
According to the comprehensive of the score of every kind of default sensory evaluation dimension of the Tea Samples to be evaluated and the tealeaves to be evaluated
The evaluation model of hop index and every kind of default sensory evaluation dimension obtains obtaining for the composite index of the Tea Samples to be evaluated
Point.
9. according to the method described in claim 8, it is characterized in that, the method also includes:
According to the corresponding relationship of score range and predetermined level, the correspondence etc. of the composite index of the Tea Samples to be evaluated is determined
Grade, and the corresponding grade is shown using default sense organ mark.
10. a kind of machine readable storage medium, which is characterized in that be stored with instruction on the machine readable storage medium, the instruction
For making machine execute the described in any item tealeaves sensory evaluation methods of the claims 1-9.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109900867A (en) * | 2019-04-11 | 2019-06-18 | 广东省农业科学院茶叶研究所 | A kind of criticism method of tea leaf quality |
| CN110763806A (en) * | 2019-10-25 | 2020-02-07 | 三只松鼠股份有限公司 | Method for evaluating spicy grade of duck neck |
| CN112505267A (en) * | 2021-01-12 | 2021-03-16 | 金华市经济特产技术推广站 | Ground tea and matcha quality evaluation system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101620180A (en) * | 2009-05-08 | 2010-01-06 | 合肥美亚光电技术有限责任公司 | Method for rapidly detecting tea quality through near infrared technology |
| CN102222164A (en) * | 2011-05-30 | 2011-10-19 | 中国标准化研究院 | Food sensory quality evaluation method and system thereof |
| CN102539326A (en) * | 2012-01-13 | 2012-07-04 | 江苏大学 | Method for carrying out quantitative evaluation on soup hue quality of tea |
| CN105606777A (en) * | 2015-12-30 | 2016-05-25 | 李容若 | Detection method and detection system for nutritional quality based on nutritional ingredients in vegetable oil |
-
2018
- 2018-09-25 CN CN201811115597.9A patent/CN109165875A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101620180A (en) * | 2009-05-08 | 2010-01-06 | 合肥美亚光电技术有限责任公司 | Method for rapidly detecting tea quality through near infrared technology |
| CN102222164A (en) * | 2011-05-30 | 2011-10-19 | 中国标准化研究院 | Food sensory quality evaluation method and system thereof |
| CN102539326A (en) * | 2012-01-13 | 2012-07-04 | 江苏大学 | Method for carrying out quantitative evaluation on soup hue quality of tea |
| CN105606777A (en) * | 2015-12-30 | 2016-05-25 | 李容若 | Detection method and detection system for nutritional quality based on nutritional ingredients in vegetable oil |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109900867A (en) * | 2019-04-11 | 2019-06-18 | 广东省农业科学院茶叶研究所 | A kind of criticism method of tea leaf quality |
| CN110763806A (en) * | 2019-10-25 | 2020-02-07 | 三只松鼠股份有限公司 | Method for evaluating spicy grade of duck neck |
| CN110763806B (en) * | 2019-10-25 | 2022-08-02 | 三只松鼠股份有限公司 | Method for evaluating spicy grade of duck neck |
| CN112505267A (en) * | 2021-01-12 | 2021-03-16 | 金华市经济特产技术推广站 | Ground tea and matcha quality evaluation system |
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