Cigarette tobacco blend composition analysis method

Abstract

A cigarette tobacco blend composition analysis method, comprising the following steps: (1) preparing a cigarette sample to be analyzed and a single-grade tobacco sample; (2) collecting thermal analysis curves of said cigarette sample and the single-grade tobacco sample; and (3) analyzing the thermal analysis curves to obtain the tobacco composition and proportion of a cigarette to be analyzed. The method can complete the analysis of the composition of finished cigarettes on the market within a few minutes, can obtain a clear formulation composition and proportion value, is objective and efficient, has high universality, good repeatability and high sensitivity, and has unique advantages in analysis of finished cigarettes in the tobacco industry.

Description

A method for analyzing tobacco leaf composition Technical Field

[0001] The invention belongs to the technical field of tobacco, and in particular relates to a method for analyzing the composition of tobacco leaves. Background Art

[0002] The quality and style of cigarettes are primarily determined by product designers through the blending of tobacco leaves from different origins, varieties, and grades. This typically requires relying on formula experience and sensory evaluation. From a stockpile of hundreds of raw material grades, 10-20 types of tobacco are manually selected and formulated in varying proportions. This results in extremely complex leaf composition, making it difficult to manually analyze the composition of unknown tobacco leaves. Being able to analyze leaf composition through instrumental testing, objective data, and scientific techniques would be crucial for analyzing competitive cigarettes and designing leaf compositions.

[0003] Thermogravimetric analysis (TG / DTA) provides stable reaction conditions under programmed temperature, making it an ideal experimental tool for tobacco pyrolysis research. Derivative thermogravimetry, also known as derivative thermogravimetry, is a technique derived from thermogravimetry. This technique records the first-order derivative of the TG curve with respect to temperature or time; the resulting result is a derivative thermogravimetric curve, or DTG curve. The DTG curve's characteristics include: it accurately reflects the starting temperature, maximum reaction rate, and end temperature of each weight loss stage; the area of ​​each peak on the DTG curve is proportional to the corresponding sample weight loss on the TG curve; and the DTG curve can clearly distinguish certain thermal processes where steps are not clearly visible on the TG curve. The key advantage of thermogravimetry is its high quantitativeness, accurately measuring both the mass change and the rate of change. This characteristic allows thermogravimetry to be used to study any substance that undergoes a mass change when heated.

[0004] The analysis of tobacco leaf composition currently mostly adopts a combination of methods such as tobacco chemical composition analysis, smoke chemical composition analysis, and sensory evaluation. This method is labor-intensive and highly subjective, and the conclusions drawn are vague and of little reference value.

[0005] In order to solve the above problems, the present invention is proposed.

[0006] Summary of the Invention

[0007] Since the DTG curve can effectively characterize the quality information of tobacco shreds / tobacco leaves, the conformity of the formula analysis composition with the actual leaf group formula can be simulated and evaluated by measuring the differences in the DTG curve. In order to improve the versatility of tobacco leaf group analysis and the workload of analysts, the present invention uses thermal analysis patterns to analyze and characterize the quality information of tobacco shreds. The DTG difference association model and formula analysis combination optimization algorithm are designed to automatically search for the tobacco leaf ratio of the tobacco leaf group formula. The objective data is used to analyze the composition of the tobacco leaf group, and the leaf group formula composition and proportion can be clearly obtained. This is of great significance for the analysis of competing cigarettes and the design of leaf group formulas.

[0008] The present invention provides a method for analyzing the composition of tobacco leaves. The specific steps are to analyze competing cigarettes (cigarettes to be analyzed) based on existing finished cigarettes to analyze their specific tobacco leaf composition and formula ratio.

[0009] The technical solutions of the present invention are as follows:

[0010] A method for analyzing the composition of tobacco leaves comprises the following steps: (1) preparing a cigarette sample to be analyzed and a single-grade tobacco leaf sample; (2) collecting thermal analysis spectra of the cigarette sample to be analyzed and the single-grade tobacco leaf sample; and (3) analyzing the thermal analysis spectra to obtain the composition and proportion of the tobacco leaves of the cigarette to be analyzed.

[0011] Preferably, in step (1), there is one cigarette sample to be analyzed, and no fewer than fifty single-grade tobacco leaf samples are selected; each sample is placed in a constant temperature and humidity environment at (22±1)°C and a relative humidity of (60±2)% for equilibrium for no fewer than 48 hours. Generally, the analyzed cigarette sample is unflavored or unadded, the sample is no less than 5g, and the sample is crushed to a mesh size of no less than 100 mesh; generally, no fewer than fifty typical single-grade tobacco leaf samples are selected, and the tobacco leaf sample information must cover different grades, different origins, and different parts, and the smoking taste of the typical single-grade tobacco leaf samples varies greatly; the tobacco leaf sample is no less than 5g, and the sample is crushed to a mesh size of no less than 100 mesh.

[0012] Preferably, the thermal analysis spectrum acquisition step in step (2) is as follows: weigh (5.00±0.05) mg of sample and place it in a thermogravimetric alumina crucible and heat it up. The program is: initial temperature 50°C, heating rate 10°C / min; end temperature 900°C, constant temperature at 900°C for 5 minutes; the protective gas and reaction gas are both nitrogen, and the flow rate is 20 mL / min. The test is performed under the condition of temperature (°C) as the X-axis and mass (%) as the Y-axis, and the data is exported as TG result data. Before sample analysis, the thermogravimetric analyzer is set to maintain at 900°C for 10 minutes to allow the impurities in the selected thermogravimetric alumina crucible furnace to be completely discharged, and an empty crucible is used as a reference. The instrument balance sensitivity of the thermogravimetric analyzer is not less than 0.1 μg, and the curve resolution is not less than 50 million resolution points.

[0013] Preferably, the specific steps of analyzing the thermal analysis spectrum in step (3) and obtaining the tobacco composition and proportion of the cigarette to be analyzed are as follows:

[0014] (A) The first-order derivative of the obtained TG result data with respect to time (normalized value 1 / s) is obtained to obtain the differential weight loss DTG curve; the DTG matrix Y of the cigarette sample to be analyzed, the DTG matrix X of the single-grade tobacco leaf is [X1X2…X n ], n is the number of single-grade tobacco leaf samples:

[0015] (B) Formula ratio coding: The formula ratio of each single grade of tobacco leaves is coded as a real number R = [r1r2…r n ], n is the number of single-grade tobacco leaf samples;

[0016] (C) Randomly initialize the encoding matrix R; the r value is initialized to a real value between 0 and 1, and the sum of the values ​​of each encoding matrix should be 1: establish a search space in a range of more than 10 times the number of tobacco leaves in the recipe, and randomly initialize the encoding matrix, i.e., R1, R2, ...; since the number of tobacco leaves in the recipe is generally 10 to 20, establish a search space in a range of 10 times, and randomly initialize 200 encoding matrices, i.e., R1, R2, ..., R 200 ;

[0017] (D) Calculate the DTG matrix Z of the cigarettes after combining the single-grade tobacco leaves according to the blend ratio R; thus, a total of 200 possible blend ratio candidate solutions are obtained;

[0018] (E) Call the DTG difference association model to calculate the difference e between Z and Y;

[0019] (F) Convert the difference e value into a probability value P(e);

[0020] (G) Select several formulas according to the probability value, for example, 100 to participate in the next iteration, and randomly select two solutions for linear recombination: r (1) =r1+a*(r1-r2), and the reorganized real number coding matrix R1 is obtained (1) 、R2 (1) ,…; where a is a proportional factor, generated by a random number uniformly distributed in [-d, 1+d]; d is the value that limits the range of recombination;

[0021] (H) Repeat steps (C)-(F) to perform iterative search and iteratively calculate e (2) 、e (3) 、e (4) 、e (5) ,…, until e is less than a certain value;

[0022] (I) Sort by probability value P(e) from large to small and select several formulation ratios; thus, obtain the tobacco composition and ratio of the cigarette to be analyzed.

[0023] Preferably, step (C) should ensure that the sum of the values ​​of each encoding matrix should be 1, so the initialization formula is as follows:

[0024] Preferably, step (D) calculates the DTG matrix Z of the cigarette after the single-grade tobacco leaves are combined according to the recipe ratio R, and the calculation formula is as follows: Z i =X′×R i ; Among them, R i is the i-th random coding matrix, X is the tobacco leaf DTG matrix, Z i According to the formula ratio R i The formulation consists of DTG matrix.

[0025] Preferably, the calculation formula of the difference degree e in step (E) is as follows: Among them, Y is the DTG matrix of the cigarette to be analyzed, Z is the DTG matrix of the tobacco leaves in proportion, and Σ is the covariance matrix of Y and Z.

[0026] Preferably, step (F) converts the difference e value into a probability value P(e) between 0 and 1, and the calculation formula is as follows:

[0027] Preferably, the value of d in step (G) is 0.2-0.3; in order to limit the range of recombination and not to be too large, the value of d is generally 0.25.

[0028] Preferably, step (H) iteratively calculates until e<0.0001.

[0029] The present invention has the following beneficial effects:

[0030] 1. The method of the present invention designs a DTG difference association model and a formula analysis combination optimization algorithm to automatically search for the tobacco leaf ratio of the cigarette leaf group formula. The composition analysis of the finished cigarettes to be analyzed on the market can be completed within a few minutes, and a clear formula composition and proportion value can be obtained. It is objective, efficient, and versatile, and has good repeatability and high sensitivity. It has unique advantages in the analysis of finished cigarettes in the tobacco industry.

[0031] 2. The method of the present invention avoids the wet chemical operation methods such as the analysis of the chemical composition of a large amount of tobacco cut and the chemical composition analysis of smoke in conventional cigarette leaf group analysis, and switches to dry chemical operation. The operation is simple, the sample amount used is extremely small, within 10 mg, it is non-toxic and harmless, does not cause any harm to the operator, and does not cause any environmental pollution.

[0032] 3. The method of the present invention significantly reduces workload and the number of experiments while providing thermal analysis spectra of finished cigarettes and single-grade tobacco leaves. Through thermogravimetric temperature-programmed kinetic studies, thermochemical reaction models for different formulations can be established, providing access to data such as activation energy, pre-exponential factor, reaction order, and heat and mass transfer information. This provides concrete formulation design goals, rich data support, and digital technical means for cigarette product development. It enables automated search and objective evaluation of formulation design solutions, effectively avoiding the subjective influence and differential characterization associated with traditional reliance on expert experience and sensory evaluation. DETAILED DESCRIPTION

[0033] The present invention is further illustrated below by way of examples, but is not intended to be limiting. Experimental procedures not specifically specified in the examples generally followed conventional conditions, those described in manuals, or those recommended by the manufacturers. The general equipment, materials, and reagents used were all commercially available unless otherwise specified. The raw materials required in the following examples and comparative examples were all commercially available.

[0034] Example: A method for analyzing the composition and proportion of tobacco leaf components of a finished product sample of a well-known domestic brand of cigarettes (cigarettes to be analyzed) is as follows:

[0035] (1) One finished cigarette sample of a well-known domestic brand (cigarette to be analyzed) and 50 single-grade tobacco samples of different origins, different parts, and different grades (5 grams each) (single-grade tobacco leaves) were selected. Both the cigarette to be analyzed and the single-grade tobacco leaves were sieved through a 100-mesh sieve and equilibrated in a constant temperature and humidity environment of (22±1)℃ and (60±2)% for 48 hours.

[0036] (2) Before sample analysis, set the thermogravimetric analyzer at 900°C for 10 minutes to exhaust impurities in the furnace, and use an empty crucible as a reference. Weigh (5.00±0.05) mg of sample and place it in a thermogravimetric platinum crucible. The heating program is: initial temperature 50°C, heating rate 10°C / min, end temperature 900°C, constant temperature at 900°C for 5 minutes, and the protective gas and reaction gas are both nitrogen, with a flow rate of 20 mL / min. The test results are plotted with temperature (°C) as the X-axis and mass (%) as the Y-axis, and the exported data are TG result data;

[0037] (3) The differential weight loss curve data (DTG matrix) can be obtained by taking the derivative of the weight data with respect to time. The DTG matrix Y of cigarettes to be analyzed and the DTG matrix of single-grade tobacco leaves X = [X1X2…X 50 ];

[0038] Table 1: Cigarette DTG Matrix Y

[0039] Table 2: DTG matrix X for single-grade tobacco leaves

[0040] (4) Set the formula ratio real number coding matrix R = [r1r2…r 50 ]; where r1, r2…r 50 They represent the usage ratios of 50 single-grade tobacco leaves in the recipe, as shown in Table 3 below:

[0041] Table 3: Recipe ratio real number encoding matrix R

[0042] (5) Change r in the above table i Randomly initialize to a real value between 0 and 1, and i Normalization is performed to ensure that the sum of the proportion values ​​of each tobacco leaf is 1; the formula is as follows: n is 50;

[0043] Table 4: Random initialization results of the recipe ratio real number encoding matrix R

[0044] (6) According to the above method, 200 real number encoding matrices R1, R2, ..., R200 are initialized at the same time to establish the formula composition analytical search space, as shown in the following table:

[0045] Table 5: Recipe ratio real number encoding initialization

[0046] (7) The single-grade tobacco leaf DTG matrix X is calculated according to the formula ratio real number coding matrix R to form the combined cigarette DTG matrix Z; i =X′×R i ;

[0047] Table 6: Combined cigarette DTG matrix Z

[0048] (8) Call the DTG difference association model: The difference e between Z and Y was calculated to evaluate the conformity of the analysis of the tobacco leaf composition, as shown in Table 7 below:

[0049] Table 7: Conformity of analysis of tobacco leaf composition (difference between Z and Y)

[0050] (9) Convert the difference e into a probability value P(e): As shown in Table 8 below:

[0051] Table 8: Conversion of difference into probability values

[0052] (10) Randomly screen the first 100 candidate solutions for formula ratio according to the probability value, and perform linear reorganization on the formula ratios of the candidate solutions, r (1) =r1+a*(r1-r2); where a is a proportional factor, generated by a random number uniformly distributed in [-d, 1+d]. To limit the recombination range to a small value, d is set to 0.25.

[0053] Get the reorganized real number coding matrix R1 (1) 、R2 (1) ,…,R 200 (1) , as shown in Table 9 below:

[0054] Table 9: Real number coding of the formula ratio after the first reorganization

[0055] (11) According to the real number coding of the reorganized formula ratio, calculate the combined cigarette DTG matrix Z (1) , call the DTG difference association model to calculate the difference e between Z and Y (1) , iterative calculation e (2) 、e (3) 、e (4) 、e (5) ,…, until e=0.000098<0.0001.

[0056] (12) Sort by probability value P(e) from large to small and output the top 5 candidate formula ratio solutions, as shown in Table 10 below:

[0057] Table 10: Recipe analysis results and P(e) values ​​(select the top 5 candidates with the highest probability)

[0058] (13) According to the first five candidate formula ratios in the table above, the tobacco leaves with a formula ratio of 0 are filtered out to obtain the composition and ratio of the complete tobacco leaf formula, as shown in Tables 11-15 below:

[0059] Table 11: R 75 Corresponding leaf group formula

[0060] Table 12: R 24 Corresponding leaf group formula

[0061] Table 13: R 13 Corresponding leaf group formula

[0062] Table 14: R 15 Corresponding leaf group formula

[0063] Table 15: R 180 Corresponding leaf group formula

[0064] Sensory evaluation and smoking verification: According to the five recipes shown in Tables 11-15 above, the tobacco leaf samples involved were blended into cigarette cut tobacco. Nine sensory evaluation experts were organized to conduct sensory evaluation and score the sensory quality differences between the blended cut tobacco samples and the cut tobacco samples of the cigarettes to be analyzed. The average value was taken as the actual smoking value of the quality difference, and the quality difference was qualitatively characterized after rounding off. The scoring gradient setting is shown in Table 16 below:

[0065] Table 16: Sensory quality difference scoring gradient settings

[0066] The sensory evaluation results are shown in Table 17 below:

[0067] Table 17: Sensory evaluation and vaping verification results

[0068] As can be seen from Table 17, the formulation candidate R 75 The conformity with the cigarette to be analyzed was 70.30%, and there was no difference in the sensory evaluation results; the formulation candidate R 24 、R 13 、R 15 、R 180 The conformity with the cigarettes to be analyzed was 13.91%, 4.46%, 2.24% and 1.15% respectively, and the sensory evaluation results were slightly different.

[0069] The above-described embodiments merely represent several implementation methods of the present invention. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A method for analyzing the composition of tobacco leaves, characterized in that: The method comprises the following steps: (1) preparing cigarette samples and single-grade tobacco leaf samples to be analyzed; (2) collecting thermal analysis spectra of the cigarette samples and single-grade tobacco leaf samples to be analyzed; and (3) analyzing the thermal analysis spectra to obtain the tobacco leaf composition and proportion of the cigarettes to be analyzed.

2. The method for analyzing tobacco leaf composition according to claim 1, characterized in that: Step (1) There is one cigarette sample to be analyzed, and no less than fifty single-grade tobacco leaf samples are selected; each sample is placed in a constant temperature and humidity environment at (22±1)°C and a relative humidity of (60±2)% for equilibrium for no less than 48 hours.

3. The method for analyzing tobacco leaf composition according to claim 1, characterized in that: Step (2) Thermal analysis spectrum acquisition steps are as follows: the samples are placed in a thermogravimetric crucible and heated, the program is: initial temperature 50°C, heating rate 10°C / min; end temperature 900°C, constant temperature 900°C for 5min; the protective gas and reaction gas are both nitrogen, and the test is performed under the condition of a flow rate of 20mL / min; the test results are plotted with temperature (°C) as the X-axis and mass (%) as the Y-axis, and the derived data are TG result data.

4. The method for analyzing tobacco leaf composition according to claim 1, characterized in that: The specific steps of step (3) analyzing the thermal analysis spectrum and obtaining the tobacco composition and proportion of the cigarette to be analyzed are as follows: (A) The first-order derivative of the obtained TG result data with respect to time is obtained to obtain the differential weight loss DTG curve; the DTG matrix Y of the cigarette sample to be analyzed, the DTG matrix X of the single-grade tobacco leaf is [X1 X2…X n ], n is the number of single-grade tobacco leaf samples: (B) Formula ratio coding: The formula ratio of each single grade of tobacco leaves is coded as a real number R = [r1 r2…r n ], n is the number of single-grade tobacco leaf samples; (C) Randomly initialize the encoding matrix R; wherein the value of r is initialized to a real value between 0 and 1, and the sum of the values ​​of each encoding matrix should be 1: establish a search space in a range of more than 10 times the number of tobacco leaves in the recipe, and randomly initialize the encoding matrices, i.e., R1, R2, ...; (D) Calculate the DTG matrix Z of cigarettes after combining single-grade tobacco leaves according to the formulation ratio R; (E) Call the DTG difference association model to calculate the difference e between Z and Y; (F) Convert the difference e value into a probability value P(e); (G) According to the probability value, select several formula ratios to participate in the next iteration, and randomly select two solutions for linear recombination: r (1) =r1+a*(r1-r2), and the reorganized real number coding matrix R1 is obtained (1) 、R2 (1) ,…; where a is a proportional factor, generated by a random number uniformly distributed in [-d, 1+d]; d is the value that limits the range of recombination; (H) Repeat steps (C)-(F) to perform iterative search and iteratively calculate e (2) 、e (3) 、e (4) 、e (5) ,…, Until e is less than a certain value; (I) Sort by probability value P(e) from large to small and select several formulation ratios; thus, obtain the tobacco composition and ratio of the cigarette to be analyzed.

5. The method for analyzing tobacco leaf composition according to claim 4, characterized in that: The initialization formula in step (C) is as follows:

6. The method for analyzing tobacco leaf composition according to claim 4, characterized in that: Step (D) calculates the DTG matrix Z of the cigarette after the single-grade tobacco leaves are combined according to the recipe ratio R. The calculation formula is as follows: Z i =X′×R i ; Among them, R i is the i-th random coding matrix, X is the tobacco leaf DTG matrix, Z i According to the formula ratio R i The formulation consists of DTG matrix.

7. The method for analyzing tobacco leaf composition according to claim 4, characterized in that: The calculation formula of the difference degree e in step (E) is as follows: Among them, Y is the DTG matrix of the cigarette to be analyzed, Z is the DTG matrix of the tobacco leaves in proportion, and ∑ is the covariance matrix of Y and Z.

8. The method for analyzing tobacco leaf composition according to claim 4, characterized in that: Step (F) converts the difference e value into a probability value P(e) between 0 and 1. The calculation formula is as follows:

9. The method for analyzing tobacco leaf composition according to claim 4, characterized in that: The value of d in step (G) is 0.2-0.

3.

10. The method for analyzing tobacco leaf composition according to claim 4, characterized in that: Step (H) iterates the calculation until e<0.0001.

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