Method for designing a strip tobacco compounding module
By using a tobacco leaf classification and combination method based on the chemical composition and aroma similarity of tobacco leaves, the problem of lack of modular formulas for secondary blending of tobacco leaves was solved, enabling rapid classification and combination of stagnant tobacco leaves and improving the scientificity and efficiency of blending design.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA TOBACCO HENAN IND CO LTD
- Filing Date
- 2024-04-18
- Publication Date
- 2026-05-19
AI Technical Summary
In the existing technology, the secondary blending of tobacco leaves lacks a clear modular formulation technology, and mainly relies on experience judgment and sensory evaluation, which is labor-intensive and highly subjective.
By screening unsold tobacco leaves in the inventory, the tobacco leaves are classified and combined based on their chemical composition and aroma similarity. The similarity of tobacco leaves is calculated using chemical composition vectors and aroma vectors to form high-quality raw material combinations and other raw material combinations, ensuring that the combination scale reaches the preset value.
It enables rapid classification and combination of stagnant tobacco flakes, provides a scientific basis for tobacco flake combination design, and improves work efficiency and the reliability of combination quality.
Smart Images
Figure CN118235875B_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of tobacco quality evaluation technology, and in particular to a design method for a tobacco leaf blending module. Background Technology
[0002] Current raw material blending is determined by a combination of factors, including the number, scale, quality, and aroma style of leftover or stagnant raw materials in inventory. The core principle is to recombine and process tobacco leaves with similar quality and style to create new modular raw materials on a larger scale that meet production batch requirements. Currently, there is limited research in the industry regarding the secondary blending and reuse of tobacco leaves, and no clear modular formulation technology for secondary blending of tobacco leaves exists. It mainly relies on experience and sensory evaluation, which is labor-intensive and highly subjective. Summary of the Invention
[0003] One object of this disclosure is to provide a design method for a tobacco compounding module to solve the above-mentioned technical problems.
[0004] A method for designing a tobacco blending module includes: screening out stagnant tobacco leaves in the inventory based on the actual inventory situation; classifying the tobacco leaves to be blended based on the quality positioning of the raw materials to obtain high-quality raw materials and other raw materials; classifying and combining the high-quality raw materials according to the similarity of tobacco leaf quality; combining other raw materials with tobacco leaves from the same production area, and if the scale requirement does not reach the first preset value, combining them with tobacco leaves from neighboring production areas.
[0005] In some embodiments, classifying and combining high-quality raw materials based on tobacco leaf quality similarity includes: detecting the content of chemical component indicators in tobacco leaf samples; obtaining the location of the ecological zone where the tobacco leaves are located; and calculating the chemical vector similarity c between any two tobacco leaves in the tobacco leaf list. xy Similarity to fragrance vector o xy ; Calculate the similarity between any two tobacco flakes in the tobacco flake list, using the formula A. xy =0.6*c xy +0.4*o xy Select the largest quantity of tobacco slices from the sample list, denoted as tobacco slice Pi. Filter out all tobacco slices with a similarity to tobacco slice Pi that is greater than or equal to a second preset value, and ensure that the similarity between the filtered tobacco slices is also greater than or equal to the second preset value. Remove the substandard tobacco slices and denot them as tobacco slice combination G1, completing the first tobacco slice grouping. If the size of tobacco slice combination G1 does not reach the first preset value, the similarity requirement can be lowered as appropriate until the size of G1 reaches the first preset value. Perform a second grouping on the remaining tobacco slices in the sample list until all groups are completed.
[0006] In some embodiments, the detection of chemical component index content of tobacco leaf samples includes: characterizing the chemical component parameters of tobacco leaves by a chemical component vector c = {c1, c2, c3, c4, c5, c6, c7, c8, c9}, wherein c1, c2, c3, c4, c5, c6, c7, c8, and c9 represent total sugar, reducing sugar, total alkaloids, total nitrogen, potassium, chlorine, amino acid compounds, neophytadiene, and Maillard reactants, respectively.
[0007] In some embodiments, obtaining the location of the ecological zone where the tobacco leaves are located includes: using an aroma vector o = {o1, o2, o3} to characterize the aroma parameters of the tobacco leaves, where o1, o2, and o3 represent longitude, latitude, and altitude, respectively.
[0008] In some embodiments, the chemical vector similarity c between any two tobacco products in the tobacco product list is calculated according to the principle that the similarity between vectors is equal to cosine similarity / (Manhattan distance + 1). xy Similarity to fragrance vector o xy .
[0009] In some embodiments, the first preset value is 5000 dan.
[0010] In some embodiments, the second preset value is 0.75.
[0011] Advantages of this disclosure: Based on the similarity study of tobacco leaves, this disclosure uses the chemical composition and aroma of stagnant tobacco leaves as the main variables, which can realize the rapid classification and combination of stagnant tobacco leaves, providing a basis for tobacco leaf blending design. Attached Figure Description
[0012] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments of the present disclosure and, together with their description, serve to explain the principles of the present disclosure.
[0013] Figure 1 This is a flowchart illustrating a design method for a tobacco blending module according to some embodiments of the present disclosure. Detailed Implementation
[0014] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values of the components and steps set forth in these embodiments do not limit the scope of the present disclosure.
[0015] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use.
[0016] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.
[0017] In all the examples shown and discussed herein, any specific values should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.
[0018] Figure 1 This is a flowchart illustrating a design method for a tobacco compounding module according to some embodiments of the present disclosure. For example... Figure 1 As shown, the design method for the tobacco compounding module includes steps 110 to 140.
[0019] In step 110, based on the actual inventory situation, the stagnant tobacco flakes in the inventory are screened out.
[0020] In some embodiments, based on the actual inventory situation, stagnant tobacco flakes in the inventory are screened to form a list of stagnant tobacco flakes to be blended. The tobacco flakes to be blended are divided according to the quality positioning of the raw materials into high-quality raw materials (quality positioning for use in Class I and II products) and other raw materials (quality positioning for use in Class III and below products).
[0021] In step 120, the tobacco to be blended is divided based on the quality of the raw materials to obtain high-quality raw materials and other raw materials.
[0022] In step 130, high-quality raw materials are classified and combined according to the similarity of tobacco leaf quality.
[0023] For high-quality raw materials to be mixed, classify and combine them according to the following steps.
[0024] In some embodiments, (a) the content of chemical components in the tobacco leaf sample is detected, specifically including total sugar, reducing sugar, total nitrogen, total alkaloids, potassium, chlorine, and Maillard reaction products. The chemical component parameters of the tobacco leaf are characterized by a chemical component vector c = {c1, c2, c3, c4, c5, c6, c7, c8, c9}, where c1 to c9 represent total sugar, reducing sugar, total alkaloids, total nitrogen, potassium, chlorine, amino acid compounds, neophytadiene, and Maillard reaction products, respectively.
[0025] (b) Obtain the location of the ecological zone where the tobacco leaves are located, including longitude, latitude, and altitude. It is known that the aroma of tobacco leaves mainly depends on the ecological zone. The aroma parameters of tobacco leaves are characterized by the aroma vector o = {o1, o2, o3}, where o1 to o3 represent longitude, latitude, and altitude, respectively.
[0026] (c) According to the vectors Calculate the chemical vector similarity and aroma vector similarity between any two tobacco leaves in the tobacco leaf list. Before the calculation, ensure that the component vectors of chemical composition and aroma are standardized, and then perform range standardization on the similarity results after the calculation.
[0027] (d) Any two tobacco products in the tobacco product list x and t y Similarity A of tobacco flakes xy =0.6*c xy +0.4*o xy .
[0028] (e) Select the largest quantity of tobacco flakes from the sample list to be mixed, denoted as tobacco flake Pi. Screen all tobacco flakes with a similarity of 0.75 or higher to tobacco flake Pi. Ensure that the similarity between all tobacco flakes in tobacco flake combination G1 is 0.75 or higher. Remove tobacco flakes that do not meet the requirements to form a new tobacco flake combination G1. The first tobacco flake grouping is complete. If the quantity of tobacco flake combination G1 is less than 5000 dan (a unit of weight), the similarity requirement can be appropriately lowered until the quantity of G1 reaches 5000 dan.
[0029] (f) Repeat step (e) for the remaining tobacco leaves in the blended tobacco leaf list until all groups are completed.
[0030] In step 140, other raw materials are combined with tobacco leaves from the same production area, and if the scale requirement does not reach the first preset value, cross-production area combination with neighboring production areas is carried out.
[0031] For other raw materials to be blended, tobacco from the same production area should be combined. If the scale requirement is less than 5,000 dan, it can also be combined from nearby production areas or across production areas.
[0032] This disclosure uses chemical composition and aroma as the main basis to conduct a quality similarity study on tobacco leaves, which solves the shortcomings of conventional methods in evaluating the similarity of tobacco leaves across production areas. It can further realize the rapid classification and combination of tobacco leaf quality, thereby providing technical support for tobacco leaf substitution in module design.
[0033] Example: Taking 39 stagnant tobacco leaves in inventory as an example, the technical content of this invention will be explained. The scale and location of the stagnant tobacco leaves are shown in Table 1.
[0034] Table 1. Quantity and Quality of Stored Tobacco Sheets
[0035] sample quantity Quality positioning sample quantity Quality positioning 1 785 A type 21 369 A type 2 764 Three categories 22 923 Three categories 3 867 Category II 23 435 Category I and II 4 95 Three categories 24 864 Category I and II 5 924 Category II 25 396 A type 6 561 Three categories 26 389 Category II 7 836 A type 27 561 Three categories 8 716 A type 28 890 Three categories 9 854 Three categories 29 821 A type 10 950 Category I and II 30 756 Category II 11 172 A type 31 307 Three categories 12 1005 Category II 32 848 Three categories 13 785 Three categories 33 815 Three categories 14 781 Three categories 34 819 Category II 15 227 A type 35 186 A type 16 400 Three categories 36 862 Three categories 17 820 Three categories 37 800 A type 18 870 A type 38 626 Category II 19 455 A type 39 716 Three categories 20 815 Category II
[0036] (1) Based on the quality positioning of raw materials, the 39 stagnant tobacco products were divided into high-quality raw materials (quality positioning for use in Class I and II products) and other raw materials (quality positioning for use in Class III and below products). The classification results are shown in Table 2.
[0037] Table 2 Classification results of stored tobacco leaves
[0038]
[0039] (2) For high-quality raw materials
[0040] (a) The content of chemical components in the sample is tested, specifically including total sugar, reducing sugar, total nitrogen, total alkaloids, potassium, chlorine, and Maillard reaction products, represented by the chemical component vector c.
[0041] (b) Obtain the location of the ecological zone where the tobacco leaves are located, including longitude, latitude, and altitude. It is known that the aroma of tobacco leaves mainly depends on the ecological zone, and is represented by the aroma vector o.
[0042] (c) According to the vectors Calculate the chemical composition similarity and aroma similarity between any two tobacco leaves in the tobacco leaf list. Before the calculation, standardize the component vectors of chemical composition and aroma. After the calculation, perform range standardization on the similarity of tobacco leaves and aroma.
[0043] (d) Then any two tobacco products in the tobacco product list t x and t y Similarity A of tobacco flakes xy = 0.6 * Chemical vector similarity + 0.4 * Fragrance vector similarity. Based on the above steps, the similarity calculation results between each pair of high-quality raw material samples to be mixed are shown in Table 3.
[0044] Table 3. Pairwise similarity between high-quality raw tobacco samples
[0045] 1 3 5 7 8 10 11 12 15 18 19 20 21 23 24 25 26 29 30 34 35 37 38 1 1.00 0.86 0.79 0.79 0.80 0.34 0.33 0.45 0.08 0.09 0.12 0.13 0.07 0.10 0.07 0.05 0.09 0.08 0.08 0.07 0.07 0.08 0.06 3 0.86 1.00 0.80 0.89 0.89 0.35 0.30 0.41 0.09 0.09 0.11 0.18 0.08 0.13 0.08 0.05 0.11 0.10 0.09 0.08 0.08 0.10 0.08 5 0.79 0.80 1.00 0.76 0.75 0.34 0.31 0.44 0.10 0.11 0.11 0.20 0.09 0.15 0.09 0.09 0.11 0.13 0.10 0.09 0.11 0.11 0.09 7 0.79 0.89 0.76 1.00 0.88 0.36 0.32 0.40 0.11 0.10 0.12 0.19 0.10 0.15 0.11 0.06 0.13 0.12 0.11 0.10 0.10 0.12 0.10 8 0.80 0.89 0.75 0.88 1.00 0.36 0.30 0.39 0.09 0.09 0.11 0.16 0.09 0.12 0.09 0.05 0.11 0.10 0.10 0.09 0.09 0.08 0.07 10 0.34 0.35 0.34 0.36 0.36 1.00 0.47 0.31 0.48 0.47 0.46 0.30 0.49 0.36 0.47 0.40 0.48 0.40 0.47 0.50 0.43 0.28 0.32 11 0.33 0.30 0.31 0.32 0.30 0.47 1.00 0.72 0.36 0.37 0.44 0.27 0.39 0.31 0.36 0.39 0.36 0.30 0.33 0.35 0.31 0.43 0.40 12 0.45 0.41 0.44 0.40 0.39 0.31 0.72 1.00 0.17 0.19 0.23 0.21 0.21 0.21 0.18 0.24 0.20 0.19 0.19 0.19 0.18 0.32 0.27 15 0.08 0.09 0.10 0.11 0.09 0.48 0.36 0.17 1.00 0.93 0.78 0.70 0.79 0.67 0.79 0.78 0.74 0.70 0.74 0.74 0.73 0.72 0.66 18 0.09 0.09 0.11 0.10 0.09 0.47 0.37 0.19 0.93 1.00 0.78 0.70 0.82 0.68 0.78 0.77 0.75 0.71 0.75 0.76 0.73 0.74 0.68 19 0.12 0.11 0.11 0.12 0.11 0.46 0.44 0.23 0.78 0.78 1.00 0.72 0.67 0.53 0.64 0.64 0.64 0.56 0.62 0.64 0.58 0.70 0.61 20 0.13 0.18 0.20 0.19 0.16 0.30 0.27 0.21 0.70 0.70 0.72 1.00 0.72 0.87 0.71 0.68 0.72 0.81 0.73 0.69 0.76 0.72 0.68 21 0.07 0.08 0.09 0.10 0.09 0.49 0.39 0.21 0.79 0.82 0.67 0.72 1.00 0.80 0.88 0.80 0.89 0.82 0.88 0.88 0.83 0.65 0.70 23 0.10 0.13 0.15 0.15 0.12 0.36 0.31 0.21 0.67 0.68 0.53 0.87 0.80 1.00 0.79 0.75 0.79 0.86 0.81 0.77 0.83 0.66 0.70 24 0.07 0.08 0.09 0.11 0.09 0.47 0.36 0.18 0.79 0.78 0.64 0.71 0.88 0.79 1.00 0.82 0.86 0.81 0.86 0.87 0.87 0.63 0.67 25 0.05 0.05 0.09 0.06 0.05 0.40 0.39 0.24 0.78 0.77 0.64 0.68 0.80 0.75 0.82 1.00 0.76 0.79 0.77 0.78 0.81 0.63 0.66 26 0.09 0.11 0.11 0.13 0.11 0.48 0.36 0.20 0.74 0.75 0.64 0.72 0.89 0.79 0.86 0.76 1.00 0.82 0.91 0.89 0.83 0.65 0.70 29 0.08 0.10 0.13 0.12 0.10 0.40 0.30 0.19 0.70 0.71 0.56 0.81 0.82 0.86 0.81 0.79 0.82 1.00 0.87 0.81 0.92 0.62 0.68 30 0.08 0.09 0.10 0.11 0.10 0.47 0.33 0.19 0.74 0.75 0.62 0.73 0.88 0.81 0.86 0.77 0.91 0.87 1.00 0.91 0.88 0.62 0.67 34 0.07 0.08 0.09 0.10 0.09 0.50 0.35 0.19 0.74 0.76 0.64 0.69 0.88 0.77 0.87 0.78 0.89 0.81 0.91 1.00 0.85 0.62 0.67 35 0.07 0.08 0.11 0.10 0.09 0.43 0.31 0.18 0.73 0.73 0.58 0.76 0.83 0.83 0.87 0.81 0.83 0.92 0.88 0.85 1.00 0.61 0.66 37 0.08 0.10 0.11 0.12 0.08 0.28 0.43 0.32 0.72 0.74 0.70 0.72 0.65 0.66 0.63 0.63 0.65 0.62 0.62 0.62 0.61 1.00 0.92 38 0.06 0.08 0.09 0.10 0.07 0.32 0.40 0.27 0.66 0.68 0.61 0.68 0.70 0.70 0.67 0.66 0.70 0.68 0.67 0.67 0.66 0.92 1.00
[0046] (e) Select the largest quantity of tobacco flakes in the sample list to be mixed, namely tobacco flake #24. Screen all tobacco flakes with a similarity of 0.75 or higher to tobacco flake #24, namely #15, #18, #21, #23, #25, #26, #29, #30, #34, and #35. The pairwise similarity between these tobacco flakes must be 0.75 or higher. Remove tobacco flakes that do not meet the requirements, forming tobacco flake combination G1 [21, #23, #25, #26, #29, #30, #34, #35]. The size of tobacco flake combination G1 is 5046 dan (a unit of weight), and the first grouping of tobacco flakes is completed.
[0047] (f) The largest tobacco leaf sample in the remaining list is 1#. Repeat the above steps to determine the second tobacco leaf combination G2 [1#, 3#, 5#, 7#, 8#].
[0048] (g) The largest remaining tobacco flake is 15#, and the third tobacco flake combination G3[15#, 18#, 19#] is determined. Since the size of G3 does not reach 5000 dan, the similarity requirement is reduced to 0.7, and the third tobacco flake combination G3[15#, 18#, 19#, 20#, 37#] is determined.
[0049] (h) The remaining tobacco flakes 10#, 11#, 12#, and 38# cannot be combined into a new module according to the grouping steps. A case-by-case analysis is needed. Tobacco flake 38# has a high similarity to the existing combination and is classified into combination G1. Tobacco flakes 10#, 11#, and 12# are relatively small and do not affect the overall quality of the module; therefore, they can be considered for combination with low-quality raw materials from the same production area and classified into combination G2.
[0050] (i) For other raw materials, combine them from the same or adjacent production areas to form G4 combination [2#, 4#, 6#, 9#, 13#, 14#, 16#, 17#] and G5 [22#, 27#, 28#, 32#, 33#, 36#, 39#].
[0051] Sensory evaluation was conducted on the five module combinations, and the results are shown in Table 4. It is evident that, based on the technical content, modules G1, G2, and G3 exhibit good sensory quality and are suitable for use with materials no less than the original components. Modules G4 and G5 can be used in three product categories. This demonstrates the high reliability and feasibility of this technology, providing a reference for tobacco blending.
[0052] Table 4. Sensory evaluation results of the five module combinations.
[0053]
[0054] While specific embodiments of this disclosure have been described in detail by way of example, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of this disclosure. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of this disclosure. The scope of this disclosure is defined by the appended claims.
Claims
1. A design method for a tobacco blending module, characterized in that, The method includes: Based on the actual inventory situation, identify the stagnant tobacco flakes in the inventory; Based on the quality of raw materials, the tobacco products to be blended are divided into high-quality raw materials and other raw materials; High-quality raw materials are classified and combined based on the similarity of tobacco leaf quality, including: To detect the content of chemical components in tobacco leaf samples; The location of the ecological zone where the tobacco leaves are located is obtained, including: the aroma parameters of the tobacco leaves are represented by the aroma vector o = { o1, o2, o3}, where o1, o2, and o3 represent longitude, latitude, and altitude, respectively; Calculate the chemical vector similarity c between any two tobacco leaves in the tobacco leaf list. xy Similarity to fragrance vector o xy ; The formula for calculating the similarity between any two tobacco products in the tobacco product list is as follows: A xy =0.6*c xy +0.4*o xy ; Select the largest quantity of tobacco slices in the sample list, denoted as tobacco slice Pi. Filter out all tobacco slices with a similarity to tobacco slice Pi that is greater than or equal to a second preset value. All similarity between the filtered tobacco slices is greater than or equal to the second preset value. Remove the substandard tobacco slices and denot them as tobacco slice combination G1. This completes the first tobacco slice grouping. If the size of tobacco slice combination G1 does not reach the first preset value, lower the similarity requirement as appropriate until the size of G1 reaches the first preset value. The remaining tobacco leaves in the tobacco leaf list are grouped a second time until all groups are completed; Other raw materials are combined with tobacco leaves from the same production area, and if the scale requirement does not reach the first preset value, cross-production area combination is carried out with neighboring production areas.
2. The design method for the tobacco compounding module according to claim 1, characterized in that, The content of chemical components in the tobacco leaf samples being tested includes: The chemical composition vector c = {c1, c2, c3, c4, c5, c6, c7, c8, c9} represents the chemical composition parameters of tobacco leaves, where c1, c2, c3, c4, c5, c6, c7, c8, and c9 represent total sugar, reducing sugar, total alkaloids, total nitrogen, potassium, chlorine, amino acid compounds, neophytadiene, and Maillard reactants, respectively.
3. The design method for the tobacco compounding module according to claim 1 or 2, characterized in that, Calculate the chemical vector similarity *c* between any two tobacco products in the tobacco product list, based on the formula: the similarity between vectors equals cosine similarity / (Manhattan distance + 1). xy Similarity to fragrance vector o xy .
4. The design method for the tobacco compounding module according to claim 1, characterized in that, The first preset value is 5000 dan.
5. The design method for the tobacco blending module according to claim 1, characterized in that, The second preset value is 0.75.