CN114166794B - Method, medium and equipment for predicting tobacco flake quality - Google Patents
Method, medium and equipment for predicting tobacco flake quality Download PDFInfo
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- 241000208125 Nicotiana Species 0.000 title claims abstract description 109
- 235000002637 Nicotiana tabacum Nutrition 0.000 title claims abstract description 109
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000001228 spectrum Methods 0.000 claims abstract description 38
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 27
- 235000019504 cigarettes Nutrition 0.000 claims abstract description 25
- 239000000779 smoke Substances 0.000 claims abstract description 10
- 238000006243 chemical reaction Methods 0.000 claims abstract description 8
- 238000013441 quality evaluation Methods 0.000 claims abstract description 8
- 238000011156 evaluation Methods 0.000 claims description 12
- 238000005070 sampling Methods 0.000 claims description 12
- 238000012937 correction Methods 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 8
- 238000002790 cross-validation Methods 0.000 claims description 7
- 238000012546 transfer Methods 0.000 claims description 5
- 238000004519 manufacturing process Methods 0.000 claims description 4
- 238000010606 normalization Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 2
- 238000009499 grossing Methods 0.000 claims description 2
- 238000011425 standardization method Methods 0.000 claims 1
- 238000013461 design Methods 0.000 description 4
- 230000001953 sensory effect Effects 0.000 description 4
- 241000196324 Embryophyta Species 0.000 description 3
- 239000000843 powder Substances 0.000 description 3
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 229910052739 hydrogen Inorganic materials 0.000 description 2
- 239000001257 hydrogen Substances 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 239000003513 alkali Substances 0.000 description 1
- 239000002585 base Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 229930014626 natural product Natural products 0.000 description 1
- 229910052757 nitrogen Inorganic materials 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 235000019505 tobacco product Nutrition 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
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- Physics & Mathematics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Manufacture Of Tobacco Products (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention provides a method for predicting the quality of tobacco flakes, which comprises the following steps: 1) Acquiring the near infrared spectrum data of the instrument A in the past year and the quality evaluation data corresponding to the near infrared spectrum data; 2) The instrument A and the instrument B of the redrying center respectively acquire near infrared spectrums of the same batch of standard samples, and based on the near infrared spectrum data, a spectrum conversion model of the instrument A and the instrument B of the redrying center is recommended; 3) According to the spectrum conversion model obtained in the step 2), suggesting a tobacco flake quality prediction model according to the converted near infrared spectrum data and the quality evaluation data of the step 1); 4) Collecting corresponding near infrared spectrum in an instrument B of raw tobacco to be processed, and predicting the quality of the batch according to the sheet tobacco quality prediction model obtained in the step 3); 5) According to the proportion of each grade in the original cigarette formula, weighting and calculating the predicted value of each module piece cigarette produced in a plan; 6) And correcting each piece of smoke predicted value to form a final quality predicted value.
Description
Technical Field
The invention belongs to the field of agricultural product (tobacco leaf) processing, and particularly relates to a method, a system, a medium and equipment for predicting the quality of a finished product (tobacco flake) after processing before processing.
Background
The quality of the cigarette product is the basis of the healthy development of cigarette enterprises, and the stability of the quality of the product mainly depends on the stability of the quality of tobacco raw materials. The tobacco shred in the cigarette is formed by mixing (formula) a plurality of tobacco shred modules according to a proportion. Each tobacco sheet module is formed by matching tobacco leaves of different producing areas (generally different cities, counties and villages), different varieties and different grades according to a certain proportion and then threshing and redrying the tobacco leaves.
At present, the design and evaluation of the sheet tobacco module are finished by combining the working experience of a formulator with sensory evaluation, and 2 problems exist: firstly, each grade of raw cigarettes used for sheet cigarette design and evaluation is obtained by sampling a large batch of samples, the sampling representativeness cannot be guaranteed, and the problem of workload cannot be solved by multiple batches of samples; and after the formula design of the tobacco flakes is finished, the tobacco flakes are produced and processed in a redrying workshop, and the quality of the tobacco flakes is evaluated through sensory evaluation of formulators at present. If the quality of the tobacco flakes does not meet the requirements or is greatly different from the expected quality, the quality of the tobacco flakes is degraded and the corresponding economic loss is caused.
For the quality evaluation of the tobacco flakes, a plurality of working bases for judging by chemical indexes, near infrared spectrum and other technologies exist. Near infrared spectra are relatively sensitive to such hydrogen-containing groups as C-H, O-H, N-H, and are suitable for analysis of components of natural products that are directly or indirectly related to such hydrogen-containing groups. The main components of the tobacco, such as total sugar, reducing sugar, total plant alkali, total nitrogen and the like, can be predicted through near infrared spectrum, and the difference of the main chemical components is closely related to the quality of the tobacco, so that the tobacco quality judgment by utilizing the near infrared spectrum has technical rationality.
Near infrared technology has several key problems in sheet smoke quality prediction applications: firstly, the redrying factory is limited by resources, talents, funds and the like, has only data acquisition capability, and does not have modeling and analysis capabilities. And even if a model is built, the prediction of the model is based on a tobacco flake real object, and when the situation that the quality difference from the expected quality is large occurs, the economic loss problem caused by degradation cannot be recovered.
Disclosure of Invention
The invention provides a method, a system, a medium and equipment for predicting the quality of tobacco flakes, and aims to predict the quality of tobacco flakes planned to be produced through a model transfer algorithm based on near infrared data of original tobacco sampling and a tobacco flake formula before processing the tobacco flakes:
in order to achieve the above object, according to a first aspect, the present invention provides a method for predicting quality of tobacco flakes, comprising the steps of:
1) Acquiring the near infrared spectrum data of the instrument A in the past year and the quality evaluation data corresponding to the near infrared spectrum data;
2) The instrument A and the instrument B of the redrying center respectively acquire near infrared spectrums of the same batch of standard samples, and based on the near infrared spectrum data, a spectrum conversion model of the instrument A and the instrument B of the redrying center is recommended;
3) According to the spectrum conversion model obtained in the step 2), the near infrared spectrum data of the instrument A in the step 1) are converted into the near infrared spectrum data of the instrument B, and according to the converted near infrared spectrum data and the quality evaluation data in the step 1), a tobacco quality prediction model is suggested;
4) Sampling raw cigarettes to be processed according to the formula design, collecting corresponding near infrared spectrums in an instrument B, and predicting the quality of the batch according to the sheet smoke quality prediction model obtained in the step 3);
5) According to the proportion of each grade in the original cigarette formula, weighting and calculating the predicted value of each module piece cigarette produced in a plan;
6) And correcting each piece of smoke predicted value to form a final quality predicted value.
In some embodiments, instrument a is an analytical instrument with data accumulation for years in a research and development center, a technical center. If the site instrument of the redrying plant meets the data accumulation requirement, a model can be directly built through the instrument B.
In some embodiments, the instrument a spectrum is converted to the instrument B spectrum using a piecewise direct normalization (Piecewise direct standardization, PDS) method.
In some embodiments, the near infrared spectrum obtained in step 2) is pre-processed to reduce scattering interference in the sample; the processing mode comprises one or more of first derivative, second derivative, vector normalization, multi-element signal correction, standard normal correction and spectrum smoothing.
In some embodiments, the modeling method in step 3) is partial least squares, 5-fold cross validation is used, and the number of latent variables of the model is selected according to the cross validation error.
In some embodiments, the range of raw smoke samples in step 4) is intended to cover all of the production sites, grades, varieties, and other factors involved in the recipe. If multiple sampling data are available for tobacco leaves of the same producing area, grade and variety, the subsequent calculation is carried out according to the average spectrum of the multiple data.
In some embodiments, the method of sheet smoke quality prediction in step 5) isWherein y is a tobacco flake quality predicted value. b h ,y h The ratio of the raw tobacco contained in the sheet tobacco and the predicted value of the quality of the raw tobacco are respectively obtained.
In some embodiments, the predictive value correction method in step 6) isWherein y is i For the predicted value of the ith tobacco flake, < +.>For the evaluation value of the ith tobacco flake, the correction aims to eliminate homogeneous deviation in model transfer among instruments and tobacco flake-original tobacco model prediction.
In some embodiments, the obtaining of the sheet tobacco quality label in step 1) comprises the following: sensory evaluation method, YC/T530-2015 flue-cured tobacco quality style characteristic sensory evaluation method or quality score obtained by performing evaluation specified in other national or local standards; the quality score is given in a manner of objectively accounting the application grade, the cost price and the like of the tobacco flakes in cigarettes.
In a second aspect, the present invention provides a medium having stored thereon a computer program, characterized in that the computer program is executed by a processor to perform the aforementioned sheet smoke quality prediction method.
In a third aspect, the present invention provides an apparatus comprising a processor and a memory; the memory is used for storing a computer program, and the processor is used for executing the computer program stored in the memory, so that the device executes the tobacco flake quality prediction method.
Compared with the prior art, the invention has the following beneficial effects:
the method realizes the prediction of the quality of the tobacco flakes before the tobacco flakes are processed; the method solves the problem that near infrared instruments in a production workshop have no prediction model in a model transfer mode; the quality label obtained by prediction is given by industry standard evaluation or by objective accounting modes such as application grade of the tobacco flakes in cigarettes, cost price of the tobacco flakes and the like.
Drawings
FIG. 1 is a flow chart of a method provided by the present invention;
FIG. 2 is a spectrum of an instrument A according to an embodiment of the invention;
FIG. 3 is a spectrum of instrument B in an embodiment of the invention;
FIG. 4 is an instrument difference spectrum of instrument A and instrument B in an embodiment of the invention;
FIG. 5 is a spectrum of an instrument A according to an embodiment of the invention;
FIG. 6 is a graph showing the conversion of the spectrum of instrument A to that of instrument B in accordance with an embodiment of the present invention;
FIG. 7 is a difference spectrum of the transformed spectrum of FIG. 6 and instrument B according to an embodiment of the present invention;
FIG. 8 is a cross-validation error curve of a sheet tobacco quality prediction model;
FIG. 9 quality model regression coefficient graph;
FIG. 10 compares the quality score predicted by raw tobacco with the actual score of sheet tobacco (before correction);
fig. 11 compares the quality score predicted by raw tobacco with the actual score of sheet tobacco (after correction).
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and the scope of the present invention is not limited by the embodiments, and is determined by the claims. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
A method for predicting the quality of tobacco flakes, as shown in fig. 1, the method comprising the steps of:
step 1) taking tobacco flake quality prediction of a redrying processing center in Yunnan as an example; 40 tobacco leaf samples of different counties and cities and grades are selected, the samples are prepared into powder samples according to the tobacco industry standard YC/T31-1996 preparation of tobacco and tobacco product samples and moisture determination oven method after sampling (the tobacco leaves are placed in an oven and dried for 4 hours at 40 ℃, and are ground by a cyclone mill (FOSS) and sieved by a 40-mesh sieve), and the powder samples are sealed and balanced.
Step 2) collecting spectra of sample powder in a near infrared instrument A of a technical center and a near infrared instrument B of a redrying plant respectively, and converting the spectrum of the instrument A into the spectrum of the instrument B by using a PDS method; wherein, the window width parameter of PDS is set to 1;
FIGS. 2-4 illustrate the spectra of instrument A, instrument B, and the difference spectrum between the two; after conversion, fig. 5-7 illustrate the spectrum of the instrument a, the spectrum of the instrument a is converted into the spectrum of the instrument B, and the difference spectrum between the converted spectrum and the spectrum of the instrument B, where it can be found that the spectrum converted from the instrument a into the spectrum of the instrument B overlaps with the spectrum actually measured by the instrument B, which proves that the conversion is achieved;
step 3) converting the spectrum of 132 samples in total of the historical sheet smoke accumulated in the instrument A into the spectrum of the instrument B; the quality label of the tobacco flakes is determined by scoring the tobacco flakes according to the cigarette brand grade of the tobacco flakes, which is used subsequently, and the distribution range of the quality score is 6-8.
And 4) establishing a relation between the near infrared spectrum and the quality score by utilizing partial least square, adopting 5-fold cross validation, and selecting the latent variable number of the model according to the cross validation error. As shown in fig. 4, the model latent variable number established takes a value of 8. The regression coefficients of the model are shown in figure 5.
Step 5) before the start of the tobacco season processing, the spectra of the raw tobacco used in each tobacco sheet module are collected in the same manner as the tobacco sheet sampling. If there are multiple samples of raw cigarettes of the same production place, grade and variety, the average spectrum is taken as the spectrum of the sample.
Step 6) for a given sheet cigarette formula, carrying all the grades of raw cigarettes in the formula into the model in the step 4) to obtain a quality prediction score; the quality prediction score of the tobacco flake module is calculated by adding the prediction scores of all the raw tobacco grades according to the formula proportion.
The tobacco flake quality prediction method is thatWherein y is a tobacco flake quality predicted value. b h ,y h The ratio of the raw tobacco contained in the sheet tobacco and the predicted value of the quality of the raw tobacco are respectively obtained.
Table 1 shows the recipe of the YN-CKF module and the grade, quantity and model predictive value of raw tobacco contained in the recipe. The quality predicted value of the module can be obtained by adding the ratio and the original smoke quality predicted value, and the quality predicted value of YN-CKF is: 6.84.
calculating the prediction results of all pieces of cigarettes in sequence: the verification data includes 2019, 2020 two years. 15 tobacco containing modules in 2019, 1047 raw tobacco sampling numbers, 12 tobacco containing modules in 2020, 945 raw tobacco sampling numbers.
The predicted value of the tobacco flake quality calculated by the raw tobacco is shown in table 1. And (3) after the processing of the tobacco flake module is finished, performing quality evaluation on the tobacco flake in the same manner as in the step (3) to obtain an actual quality score value. The comparison of the two is shown in FIG. 10. As calculated from table 1, the correlation coefficient of the predicted result and the actual result was 0.82.
TABLE 1 formulation of YN-CKF Module and raw tobacco quality prediction score
| Sheet cigarette code | Raw tobacco producing area | County region | Variety of flue-cured tobacco | Post-selection grade | Quantity of | Proportion of | Quality score |
| YN-CKF | JQ | LN | Cloud 87 | C2FC3 | 2000 | 5.2％ | 7.16 |
| YN-CKF | JQ | LN | Cloud 87 | C2FA1 | 2761.6 | 7.2％ | 7.00 |
| YN-CKF | JQ | LM | Cloud 87 | C1FC3 | 2000 | 5.2％ | 7.10 |
| YN-CKF | JQ | LM | Cloud 87 | C2FC3 | 3242.64 | 8.4％ | 7.01 |
| YN-CKF | JQ | LM | Cloud 87 | C3FA1 | 2200 | 5.7％ | 7.02 |
| YN-CKF | JQ | LP | Cloud 87 | C1FC3 | 4197.28 | 10.9％ | 6.67 |
| YN-CKF | JQ | LP | Cloud 87 | C2FC3 | 5000 | 13.0％ | 6.61 |
| YN-CKF | JQ | LP | Cloud 87 | C1FA1 | 1700.04 | 4.4％ | 6.70 |
| YN-CKF | DO | DJ | Cloud 87 | C2FC3 | 4000 | 10.4％ | 6.50 |
| YN-CKF | DO | DH | Cloud 87 | C1FA1 | 1500 | 3.9％ | 7.04 |
| YN-CKF | DO | DH | Cloud 87 | C1FC3 | 1000 | 2.6％ | 7.16 |
| YN-CKF | DO | DN | Cloud 87 | C2FC3 | 715.42 | 1.9％ | 6.85 |
| YN-CKF | NI | XD | Cloud 87 | C2FC3 | 1688.76 | 4.4％ | 7.01 |
| YN-CKF | NI | XD | Cloud 87 | C2FA1 | 606.84 | 1.6％ | 6.89 |
| YN-CKF | NI | XD | Cloud 87 | C1FC3 | 2817.62 | 7.3％ | 6.90 |
| YN-CKF | NI | XD | Cloud 87 | C1FA1 | 933.78 | 2.4％ | 6.82 |
| YN-CKF | GH | SJ | Cloud 87 | C3FA1 | 2095.42 | 5.4％ | 6.70 |
Step 7) because the tobacco flakes are formed by threshing and redrying the raw tobacco, the characterization of the tobacco flakes may be different from that of the raw tobacco. Correcting the predicted data by using the above method, wherein the predicted value correction method is as followsWhere yi is the predicted value of the ith cigarette in sheet form,/->For the evaluation value of the ith tobacco flake, the correction aims to eliminate homogeneous deviation in model transfer among instruments and tobacco flake-original tobacco model prediction; the corrected value is calculated to be-0.21, the corrected result is shown in table 2 and fig. 11, and compared with fig. 10 before uncorrected, the corrected predicted value is matched with the actual result.
TABLE 2 predicted and actual evaluation values of tobacco flake quality based on raw tobacco
Claims (8)
1. A method for predicting the quality of a sheet of tobacco, the method comprising the steps of:
1) Acquiring the near infrared spectrum data of the instrument A in the past year and the quality evaluation data corresponding to the near infrared spectrum data;
2) The method comprises the steps that an instrument A and an instrument B of a redrying processing center respectively collect near infrared spectrums of the same batch of standard samples, and based on the near infrared spectrum data, the spectrum of the instrument A is converted into a spectrum of the instrument B by a segmented direct standardization method;
3) Establishing a tobacco flake quality prediction model according to the near infrared spectrum data of the instrument B after the conversion in the step 2) and the quality evaluation data in the step 1);
4) Sampling raw cigarettes to be processed according to a raw cigarette formula, collecting corresponding near infrared spectrums in an instrument B, and predicting the quality of the batch of raw cigarettes according to the sheet cigarette quality prediction model obtained in the step 3);
5) According to the proportion of each grade in the original cigarette formula, weighting and calculating the predicted value of each module piece cigarette produced in a plan;
6) And correcting the predicted value of the tobacco quality of each module to form a final predicted value of the quality.
2. The method according to claim 1, wherein the near infrared spectrum obtained in step 2) is pre-processed to reduce scattering disturbances in the sampling; the processing mode comprises one or more of first derivative, second derivative, vector normalization, multi-element signal correction, standard normal correction and spectrum smoothing.
3. The method of claim 1, wherein the modeling method in step 3) is partial least squares, 5-fold cross validation is used, and the number of latent variables of the model is selected based on the cross validation error.
4. The method of claim 1, wherein the range of raw smoke samples in step 4) is to cover all production sites, grades, variety factors involved in the recipe; if multiple sampling data are available for tobacco leaves of the same producing area, grade and variety, the subsequent calculation is carried out according to the average spectrum of the multiple data.
5. The method according to claim 1, wherein the tobacco flake quality prediction method in step 5) isWherein y is a tobacco flake quality predicted value; b h ,y h The ratio of the raw tobacco contained in the sheet tobacco and the predicted value of the quality of the raw tobacco are respectively obtained.
6. The method according to claim 1, wherein the predicted value correction method in step 6) isWherein y is i For the predicted value of the ith tobacco flake, < +.>And the evaluation value of the ith tobacco flakes is used for eliminating homogeneous deviation in model transfer among instruments and tobacco flakes quality prediction model prediction.
7. A medium having stored thereon a computer program, characterized in that the computer program is executed by a processor to perform the sheet tobacco quality prediction method according to any one of claims 1 to 6.
8. An apparatus, comprising: a processor and a memory; the memory is used for storing a computer program, and the processor is used for executing the computer program stored in the memory, so that the device executes the tobacco flake quality prediction method according to any one of claims 1 to 6.
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