CN103592230B - The detection method of dry matter content in a kind of tealeaves - Google Patents
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Abstract
The invention discloses the detection method of dry matter content in a kind of tealeaves, comprise the following steps: (1) gathers the single band spectrum picture of tealeaves blade to be measured 11 characteristic wave strong points; Described 11 characteristic wavelengths are respectively 405nm, 410nm, 422nm, 462nm, 677nm, 696nm, 711nm, 734nm, 756nm, 973nm, 1023nm; (2) according to the gray-scale value of single band spectrum picture and the linear relationship of reflectivity, the single band spectrum picture in step (1) is converted into albedo image; (3) dry matter content of each pixel in the albedo image of tealeaves blade is obtained according to formulae discovery.The present invention can detect dry matter content in the tealeaves of spatially Arbitrary distribution simultaneously fast, obtains the space distribution information of dry matter content in tealeaves.
Description
Technical field
The present invention relates to Tea Processing detection field, be specifically related to the detection method of dry matter content in a kind of tealeaves.
Background technology
In Tea Processing process, the dry matter content of tealeaves blade is the key factor affecting Tea Processing quality, and the dry matter content of acquisition tealeaves blade is real-time the key improving Tea Processing quality.The universal test method of dry matter weight of leaf content dries constant weight method, and need destructive oven dry blade to constant weight, this process at least needs 4 hours, and institute's test sample originally has dewatered completely and loses edibility.
Have based on spectrum picture detection method and destruction is not had to material itself, Non-Destructive Testing can be reached, and the advantage such as testing process is quick, the existing method utilizing spectrum picture to detect dry matter content in tealeaves in prior art, application publication number a kind of method based on 11 characteristic wavelength Fast nondestructive evaluation dry matter of tea content that has been the disclosure of the invention of CN102435568A, comprise the following steps: obtain the diffuse reflection spectrum reflectivity of tealeaves sample 11 characteristic wave strong points, described 11 characteristic wavelengths comprise 404nm, 409nm, 421nm, 461nm, 676nm, 695nm, 710nm, 733nm, 755nm, 972nm and 1036nm, diffuse reflection spectrum reflectivity conversion is become absorbance, obtains the absorbance of tealeaves sample 11 characteristic wave strong points, calculate the dry matter content of described tealeaves sample thus.
Although this inventive method fast and effeciently can monitor the dynamic change of dry matter content in Tea Processing process, realize quick, harmless, the low cost of dry in Tea Processing process and detect, the distribution of dry in tealeaves cannot be detected.
Summary of the invention
The invention provides the detection method of dry matter content in a kind of tealeaves, can detect fast the dry matter content of the spatially tealeaves blade of Arbitrary distribution simultaneously, obtain the space distribution information of dry matter content in tealeaves.
A detection method for dry matter content in tealeaves, comprises the following steps:
(1) the single band spectrum picture of tealeaves blade to be measured 11 characteristic wave strong points is gathered; Described 11 characteristic wavelengths are respectively 405nm, 410nm, 422nm, 462nm, 677nm, 696nm, 711nm, 734nm, 756nm, 973nm, 1023nm;
(2) according to the gray-scale value of single band spectrum picture and the linear relationship of reflectivity, the single band spectrum picture in step (1) is converted into albedo image;
(3) dry matter content of each pixel in the albedo image of tealeaves blade is calculated according to following formula;
Y
dry=0.932215-1.667 λ
405+ 0.854 λ
410+ 1.746 λ
422-1.982 λ
462+ 1.563 λ
677-2.577 λ
696+ 4.977 λ
711-10.679 λ
734+ 11.538 λ
756-10.715 λ
973+ 6.772 λ
1023
In formula: λ
arepresent in the albedo image of anm characteristic wave strong point, the reflectivity of a certain pixel;
Y
dryrepresent the dry matter content at respective pixel point place.
As preferably, in described step (2), the obtaining step of the gray-scale value of single band spectrum picture and the linear relationship of reflectivity is as follows:
2-1, collection at least three pieces of diffuse reflection on-gauge plates are at the benchmark single band spectrum picture of 11 characteristic wave strong points, ask for the gray-scale value of every width benchmark single band image, within the scope of visible and near infrared spectrum, the diffuse reflection on-gauge plate adopted has reflectivity constant separately; Different diffuse reflection on-gauge plates has different reflectivity;
2-2, for each characteristic wavelength, the gray-scale value of corresponding benchmark single band image and reflectivity are carried out linear fit, obtains the linear relationship of gray-scale value and reflectivity.
As preferably, described diffuse reflection on-gauge plate is three ~ six pieces.
In each characteristic wave strong point, every block diffuse reflection on-gauge plate is a corresponding width single band image separately, and the corresponding gray-scale value of every width single band image, with the gray-scale value of diffuse reflection on-gauge plate for independent variable, take reflectivity as dependent variable, linear fit obtains the relation of gray-scale value and reflectivity.
The number of diffuse reflection on-gauge plate is more, the relation of the gray-scale value that linear fit obtains and reflectivity is more accurate, corresponding consuming time also longer, preferably, described diffuse reflection on-gauge plate is three pieces, is respectively 99% diffuse reflection on-gauge plate, 75% diffuse reflection on-gauge plate and 2% diffuse reflection on-gauge plate.
99% diffuse reflection on-gauge plate refers to: within the scope of whole visible and near infrared spectrum, and the reflectivity of diffuse reflection on-gauge plate is 99%.
75% diffuse reflection on-gauge plate refers to: within the scope of whole visible and near infrared spectrum, and the reflectivity of diffuse reflection on-gauge plate is 75%.
2% diffuse reflection on-gauge plate refers to: within the scope of whole visible and near infrared spectrum, and the reflectivity of diffuse reflection on-gauge plate is 2%.
Adopt 99% diffuse reflection on-gauge plate, 75% diffuse reflection on-gauge plate and 2% diffuse reflection on-gauge plate, at utmost cover the scope of reflectivity, make the linear relationship of gray-scale value and the reflectivity obtained more accurate.
The different diffuse reflection on-gauge plate acquisition gray-scale values at different characteristic wavelength place and the linear relationship of reflectivity is adopted to be respectively:
Y
405＝-679.2027+3.4269x
405；
Y
410＝-481.6752+2.4296x
410；
Y
422＝-284.6634+1.4352x
422；
Y
462＝-88.3859+0.4445x
462；
Y
677＝-8.4891+0.6692x
677；
Y
696＝-8.0818+0.0396x
696；
Y
711＝-7.9371+0.0390x
711；
Y
734＝-7.6967+0.6110x
734；
Y
756＝-8.1299+0.0400x
756；
Y
973＝-40.0854+0.2008x
973；
Y
1023＝-123.1594+0.6203x
1023；
In formula: x
bfor the gray-scale value of bnm characteristic wave strong point;
Y
bfor the reflectivity of bnm characteristic wave strong point.
Compared with prior art, the present invention has following useful technique effect:
(1) simple, the inventive method realizes the quantitative of dry matter of tea and positioning analysis by obtaining tealeaves at the spectrum picture at 11 wave band places, this detection, without the need to directly contacting with sample, be nondestructive measurement completely, and the computing method of operating process and dry is simple.
(2) quick, the spectrum picture acquisition time of a sample is less than 10 seconds, and need at least to dry the little operating process up to constant weight of sample 4 compared to conventional dry substance detecting method, detection speed is accelerated greatly.
(3) efficient, the method that the present invention proposes can realize the quantitative of dry matter of tea and detection and localization simultaneously, namely can the detection of each position dry matter of tea in implementation space, detect while being particularly useful for the dry matter of tea content that in tealeaves on-line machining process, space distribution is different.
Accompanying drawing explanation
Fig. 1 is the single band spectrum picture of the tealeaves at the different characteristic wavelength place measured in embodiment 1 in the present invention;
Fig. 2 is the reflectivity of three pieces of diffuse reflection on-gauge plates and the graph of a relation of wavelength;
Fig. 3 is the single band spectrum pictures of three pieces of diffuse reflection on-gauge plates at different characteristic wavelength place;
Fig. 4 is the gray-scale values of three pieces of diffuse reflection on-gauge plates at different characteristic wavelength place;
Fig. 5 is the reflectivity at wavelength 677nm place and the linear relationship of gray-scale value;
Fig. 6 is the reflectivity at wavelength 734nm place and the linear relationship of gray-scale value;
Fig. 7 is the calculation process of the dry matter content of a pixel in the single band spectrum picture of tealeaves blade;
Fig. 8 is the distribution plan of dry matter content in tealeaves;
Fig. 9 is the prediction dry matter content of 576 tealeaves blades and the graph of a relation of actual dry matter content measured in embodiment 1;
Figure 10 is the prediction dry matter content of 576 tealeaves blades and the graph of a relation of actual dry matter content measured in comparative example 1;
Figure 11 is the prediction dry matter content of 576 tealeaves blades and the graph of a relation of actual dry matter content measured in comparative example 2.
Embodiment
Embodiment 1
First 576 tealeaves blades are collected, tealeaves blade comprises semi-manufacture and finished tea in fresh leaf, process, first adopt high spectrum image imaging system (ImSpectorV10E, SpectralImagingLtd., Oulu, Finland) scan the single band spectrum picture of each tealeaves blade 11 characteristic wave strong points respectively; 11 characteristic wavelengths are respectively 405nm, 410nm, 422nm, 462nm, 677nm, 696nm, 711nm, 734nm, 756nm, 973nm, 1023nm; The corresponding width single band spectrum picture in each wavelength place, (only illustrates 2 tealeaves blades) as shown in Figure 1 in figure, then adopt National Standard Method to measure the dry matter content of these 576 samples, the statistics of the dry matter content of tealeaves blade is as shown in table 1.
Table 1
According to the gray-scale value of single band spectrum picture and the linear relationship of reflectivity, each tealeaves blade is converted into albedo image at the single band spectrum picture of 11 characteristic wave strong points.
Wherein, the obtaining step of the gray-scale value of single band spectrum picture and the linear relationship of reflectivity is as follows:
2-1, collection three pieces of diffuse reflection on-gauge plates are at the benchmark single band spectrum picture of 11 characteristic wave strong points, ask for the gray-scale value of every width benchmark single band image, within the scope of whole visible and near infrared spectrum, the reflectivity that the diffuse reflection on-gauge plate adopted is corresponding is respectively 99%, 75% and 2%.
As shown in Figure 2, the reflectance curve of three pieces of diffuse reflection on-gauge plates within the scope of whole visible and near infrared spectrum, as can be seen from Fig. 2, three pieces of diffuse reflection on-gauge plate diffuse reflections within the scope of whole visible and near infrared spectrum are constant separately, for each block diffuse reflection on-gauge plate, the reflectivity at all wavelengths place is all identical.
As shown in Figure 3, in each characteristic wave strong point, gather the benchmark single band spectrum picture with the diffuse reflection on-gauge plate of different reflectivity, in Fig. 3, the corresponding reflectivity of R99 is the diffuse reflection on-gauge plate of 99%; The corresponding reflectivity of R75 is the diffuse reflection on-gauge plate of 75%; The corresponding reflectivity of R02 is the diffuse reflection on-gauge plate of 2%.
The corresponding gray-scale value of each width benchmark single band spectrum picture, as shown in Figure 4, three gray-scale values of each characteristic wave strong point are corresponding in turn to the benchmark single band spectrum picture that reflectivity is the diffuse reflection on-gauge plate of 99%, 75% and 2% from left to right.
2-2, for each characteristic wavelength, the gray-scale value of corresponding benchmark single band image and reflectivity are carried out linear fit, obtains the linear relationship of gray-scale value and reflectivity.
For each characteristic wavelength, there are three groups of corresponding gray-scale values and reflectance value, linear fit carried out to these three groups of gray-scale values and reflectance value, obtains the linear relationship of gray-scale value and reflectance value.
Such as, wavelength be the linear relationship of the gray-scale value at 677nm place and reflectivity as shown in Figure 5, wavelength be the linear relationship of the gray-scale value at 734nm place and reflectivity as shown in Figure 6, in Fig. 5, Fig. 6, the corresponding reflectivity of R99 is the diffuse reflection on-gauge plate of 99%; The corresponding reflectivity of R75 is the diffuse reflection on-gauge plate of 75%; The corresponding reflectivity of R2 is the diffuse reflection on-gauge plate of 2%.
The gray-scale value at different characteristic wavelength place set up and the linear relationship of reflectivity are respectively:
Y
405＝-679.2027+3.4269x
405；
Y
410＝-481.6752+2.4296x
410；
Y
422＝-284.6634+1.4352x
422；
Y
462＝-88.3859+0.4445x
462；
Y
677＝-8.4891+0.6692x
677；
Y
696＝-8.0818+0.0396x
696；
Y
711＝-7.9371+0.0390x
711；
Y
734＝-7.6967+0.6110x
734；
Y
756＝-8.1299+0.0400x
756；
Y
973＝-40.0854+0.2008x
973；
Y
1023＝-123.1594+0.6203x
1023；
In formula: x
bfor the gray-scale value of bnm characteristic wave strong point;
Y
bfor the reflectivity of bnm characteristic wave strong point.
Based on the gray-scale value of 11 characteristic wave strong points and the relation of reflectivity, can the single band spectrum picture of a tealeaves blade to be measured (site on the corresponding tealeaves blade of each pixel difference in single band spectrum picture, each pixel has different gray-scale values) be converted to albedo image, the reflectivity that each pixel in albedo image is corresponding different.
For these 576 tealeaves blades, based on the albedo image of each tealeaves blade, the average reflectance of each tealeaves blade is obtained after average, the dry matter content of each tealeaves blade and average Reflectance curve fitting is utilized to obtain the relation of dry matter content and average reflectance as shown in the formula shown in (I)
Y’＝0.932215-1.667λ’
405+0.854λ’
410+1.746λ’
422-1.982λ’
462+1.563λ’
677-2.577λ’
696+4.977λ’
711-10.679λ’
734+11.538λ’
756-10.715λ’
973+6.772λ’
1023(I)
In formula (I): λ '
arepresent the average reflectance of the albedo image of anm characteristic wave strong point;
Y' represents the dry matter content at corresponding average reflectance place.
The formula (I) utilizing average reflectance and dry matter content matching to obtain have expressed the relation of average reflectance and dry matter content, formula (I) has also reacted the relation of each pixel place reflectivity and dry matter content, obtains formula (II) as follows according to formula (I):
Y
dry=0.932215-1.667 λ
405+ 0.854 λ
410+ 1.746 λ
422-1.982 λ
462+ 1.563 λ
677-
(II)
2.577λ
696+4.977λ
711-10.679λ
734+11.538λ
756-10.715λ
973+6.772λ
1023
In formula (II): λ
arepresent in the albedo image of anm characteristic wave strong point, the reflectivity of a certain pixel;
Y
dryrepresent the dry matter content at respective pixel point place.
Reflectivity corresponding to each pixel in albedo image is substituted into the calculating carrying out dry matter content in formula (II), obtain the dry matter content at each pixel place in tealeaves leaf image to be measured, as shown in Figure 7, for the pixel of on albedo image, the reflectivity of this pixel is substituted into the dry matter content calculating this pixel in formula (II), and then draw the dry matter content distribution plan of tealeaves blade accordingly, obtain the dry matter content distributed intelligence at tealeaves blade each point place, as shown in Figure 8.
The prediction dry matter content (substituted into by average reflectance in formula (I) and try to achieve) utilizing the inventive method to obtain 576 tealeaves crop leaf measuring is illustrated in fig. 9 shown below with the distribution of the true dry matter content of sample utilizing National Standard Method to detect, the coefficient of determination is 0.922, as can be seen from Fig. 9, the detection method that the present invention proposes to predict the outcome with the measured value of National Standard Method be high correlation.
Comparative example 1
Choose 11 characteristic wavelengths, be respectively 404nm, 409nm, 421nm, 461nm, 676nm, 695nm, 710nm, 733nm, 755nm, 972nm, 1022nm, and the relation setting up dry matter content and reflectivity based on these 11 characteristic wavelengths is in the same manner such as formula shown in (III):
Y
dry=0.682130+0.351 λ
404+ 0.411 λ
409-0.573 λ
421-0.839 λ
461+ 1.066 λ
676-
(III)
2.315λ
695+5.543λ
710-12.311λ
733+13.194λ
755-11.787λ
972+7.246λ
1022
In formula (III): λ
arepresent in the albedo image of anm characteristic wave strong point, the reflectivity of a certain pixel;
Y
dryrepresent the dry matter content at respective pixel point place.
At 11 characteristic wavelengths (404nm, 409nm, 421nm, 461nm, 676nm, 695nm, 710nm, 733nm, 755nm, 972nm, 1022nm) place obtains the single band spectrum picture of tealeaves blade, and the dry matter content of tealeaves is calculated based on formula (III), be illustrated in fig. 10 shown below with the distribution of the true dry matter content of the sample utilizing National Standard Method to detect, the coefficient of determination is 0.9038.
Comparative example 2
Choose 11 characteristic wavelengths, be respectively 411nm, 416nm, 428nm, 468nm, 683nm, 702nm, 717nm, 740nm, 762nm, 979nm, 1029nm, and the relation setting up dry matter content and reflectivity based on these 11 characteristic wavelengths is in the same manner such as formula shown in (IV):
Y
dry=0.821901-0.713 λ
411+ 1.747 λ
416-0.591 λ
428-1.428 λ
468+ 1.097 λ
683-
(IV)
2.971λ
702+7.897λ
717-17.103λ
740+16.964λ
762-8.907λ
979+4.170λ
1029
In formula (IV): λ
arepresent in the albedo image of anm characteristic wave strong point, the reflectivity of a certain pixel;
Y
dryrepresent the dry matter content at respective pixel point place.
At 11 characteristic wavelengths (411nm, 416nm, 428nm, 468nm, 683nm, 702nm, 717nm, 740nm, 762nm, 979nm, 1029nm) place obtains the single band spectrum picture of tealeaves blade, and the dry matter content of tealeaves is calculated based on formula (IV), be illustrated in fig. 11 shown below with the distribution of the true dry matter content of the sample utilizing National Standard Method to detect, the coefficient of determination is 0.8876.
By the result of embodiment 1 and comparative example 1,2, whether selected characteristic wavelength accurately has material impact for detection dry matter content, the present invention is by choosing suitable characteristic wavelength, obtain the testing result that the coefficient of determination is very high, for carrying out the spatial distribution result of dry matter of tea content fast.
Claims (5)
1. the detection method of dry matter content in tealeaves, is characterized in that, comprise the following steps:
(1) the single band spectrum picture of tealeaves blade to be measured 11 characteristic wave strong points is gathered; Described 11 characteristic wavelengths are respectively 405nm, 410nm, 422nm, 462nm, 677nm, 696nm, 711nm, 734nm, 756nm, 973nm, 1023nm;
(2) according to the gray-scale value of single band spectrum picture and the linear relationship of reflectivity, the single band spectrum picture in step (1) is converted into albedo image;
(3) dry matter content of each pixel in the albedo image of tealeaves blade is calculated according to following formula;
Y
dry=0.932215-1.667 λ
405+ 0.854 λ
410+ 1.746 λ
422-1.982 λ
462+ 1.563 λ
677-2.577 λ
696+ 4.977 λ
711-10.679 λ
734+ 11.538 λ
756-10.715 λ
973+ 6.772 λ
1023
In formula: λ
arepresent in the albedo image of anm characteristic wave strong point, the reflectivity of a certain pixel;
Y
dryrepresent the dry matter content at respective pixel point place.
2. the detection method of dry matter content in tealeaves as claimed in claim 1, it is characterized in that, in described step (2), the obtaining step of the gray-scale value of single band spectrum picture and the linear relationship of reflectivity is as follows:
2-1, collection at least three pieces of diffuse reflection on-gauge plates are at the benchmark single band spectrum picture of 11 characteristic wave strong points, ask for the gray-scale value of every width benchmark single band image, within the scope of visible and near infrared spectrum, the diffuse reflection on-gauge plate adopted has reflectivity constant separately;
2-2, for each characteristic wavelength, the gray-scale value of corresponding benchmark single band image and reflectivity are carried out linear fit, obtains the linear relationship of gray-scale value and reflectivity.
3. the detection method of dry matter content in tealeaves as claimed in claim 2, it is characterized in that, described diffuse reflection on-gauge plate is three ~ six pieces.
4. the detection method of dry matter content in tealeaves as claimed in claim 3, it is characterized in that, described diffuse reflection on-gauge plate is three pieces, is respectively 99% diffuse reflection on-gauge plate, 75% diffuse reflection on-gauge plate and 2% diffuse reflection on-gauge plate.
5. the detection method of dry matter content in tealeaves as claimed in claim 4, it is characterized in that, the gray-scale value at different characteristic wavelength place and the linear relationship of reflectivity are respectively:
Y
405＝-679.2027+3.4269x
405；
Y
410＝-481.6752+2.4296x
410；
Y
422＝-284.6634+1.4352x
422；
Y
462＝-88.3859+0.4445x
462；
Y
677＝-8.4891+0.6692x
677；
Y
696＝-8.0818+0.0396x
696；
Y
711＝-7.9371+0.0390x
711；
Y
734＝-7.6967+0.6110x
734；
Y
756＝-8.1299+0.0400x
756；
Y
973＝-40.0854+0.2008x
973；
Y
1023＝-123.1594+0.6203x
1023；
In formula: x
bfor the gray-scale value of bnm characteristic wave strong point;
Y
bfor the reflectivity of bnm characteristic wave strong point.
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| CN105136704B (en) * | 2015-09-15 | 2018-01-26 | 唐山拓普生物科技有限公司 | A kind of method of quick measure brewer's yeast milk dry matter content |
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| JP2000304694A (en) * | 1999-04-22 | 2000-11-02 | Kawasaki Kiko Co Ltd | Method and apparatus for grading of tea leaf |
| CN101424636A (en) * | 2008-12-04 | 2009-05-06 | 中国计量学院 | A kind of device and method of rapidly and nondestructively detecting content of green tea composition |
| CN102435568A (en) * | 2011-11-23 | 2012-05-02 | 浙江大学 | Method for quick and nondestructive detection of dry matter content in tea based on 11 characteristic wavelengths |
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| JP2000304694A (en) * | 1999-04-22 | 2000-11-02 | Kawasaki Kiko Co Ltd | Method and apparatus for grading of tea leaf |
| CN101424636A (en) * | 2008-12-04 | 2009-05-06 | 中国计量学院 | A kind of device and method of rapidly and nondestructively detecting content of green tea composition |
| CN102435568A (en) * | 2011-11-23 | 2012-05-02 | 浙江大学 | Method for quick and nondestructive detection of dry matter content in tea based on 11 characteristic wavelengths |
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