CN112903679A - Method for detecting blending proportion and blending uniformity of cut stems of cigarettes based on RGB image processing - Google Patents
Method for detecting blending proportion and blending uniformity of cut stems of cigarettes based on RGB image processing Download PDFInfo
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- 238000002156 mixing Methods 0.000 title claims abstract description 109
- 235000019504 cigarettes Nutrition 0.000 title claims abstract description 78
- 238000000034 method Methods 0.000 title claims abstract description 30
- 241000208125 Nicotiana Species 0.000 claims description 73
- 235000002637 Nicotiana tabacum Nutrition 0.000 claims description 73
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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Abstract
The invention discloses a method for detecting blending proportion and blending uniformity of cut stems of cigarettes based on RGB image processing. The method is simple and feasible, has higher scientificity and accuracy compared with the traditional manual selection method, and provides technical support for the optimization of the cigarette blending uniformity.
Description
Technical Field
The invention belongs to the technical field of tobacco processing, and particularly relates to a method for detecting blending proportion and blending uniformity of cut stems of cigarettes based on RGB image processing.
Background
The low-grade cigarette formula cut tobacco generally comprises cut tobacco leaves, cut stems and reconstituted cut tobacco leaves, and the stability of the content of each component has great influence on the smoking quality of cigarette products. In recent years, cut stems play more and more important roles in reducing cigarette harmfulness, regulating and controlling cigarette smoke level, improving application efficiency of tobacco leaf raw materials and the like, and the blending proportion of the cut stems in a formula and the blending uniformity of the cut stems in cigarette cigarettes are more and more emphasized. In order to grasp the actual proportion of cut stems in cut tobacco, the cut stems are usually obtained by manual selection and weighing according to the difference of morphological characteristics and colors of the cut stems. Although the method is simple to operate, the method has the defects of long time consumption, large artificial influence, unsuitability for mass detection and the like.
Chinese patent CN2018108195711A discloses a method for measuring blending uniformity of cut tobacco of cigarette, which is to perform a thermal weight loss test on the blended cut tobacco after being flavored, calculate the similarity between every two of the obtained thermal weight loss TG curves of each sample and the variation coefficient between the similarities, and measure the blending uniformity of the cut tobacco of cigarette according to the similarity. The method only represents the mixing uniformity of the flavored cut tobacco, does not represent the mixing uniformity of the cut tobacco of the finished cigarette, and cannot detect the mixing proportion of the cut stems.
In the prior art, no relevant report for representing the blending ratio of cut stems in cigarettes by RGB values of the cut stems and the cut leaves is found.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method for detecting the blending proportion and blending uniformity of cut stems of cigarettes based on RGB image processing, which is mainly characterized in that the average RGB value is taken as a marker according to the characteristic that the cut stems and the cut leaves have larger difference in color (the average RGB value of the cut stems is obviously lower than the characteristic of the cut leaves), cut stems with different proportions are added into a formula of a cigarette leaf group, the correlation between the average RGB value and the blending proportion of the cut stems is found by detecting the average RGB value, and the blending uniformity of the cut stems in the cigarettes is represented by the variation coefficient of the blending proportion for guiding the maintenance of the integrity of the formula of the cigarettes.
In order to solve the technical problems, the invention adopts the following technical scheme:
the invention firstly discloses a method for detecting the blending proportion of cut stems of cigarettes based on RGB image processing, which takes the RGB mean value of cut stems blended with cut stems as a marker, and establishes a linear regression equation between the blending proportion of the cut stems and the RGB mean value, thereby realizing the detection of the blending proportion of the cut stems of cigarettes. The method specifically comprises the following steps:
(1) taking representative dried and cooled cut tobacco leaves and cut stems in the cigarette brand, drying the cut tobacco leaves and the cut stems, then winnowing the cut stems, respectively crushing the cut tobacco leaves and the cut stems, and then balancing the cut tobacco leaves and the cut stems;
(2) respectively adding 0-50% of balanced cut stems into the balanced cut leaves, and respectively calculating the RGB mean value;
(3) analyzing the data, and finding out the correlation between the blending ratio of the cut stems in the cut tobacco formula and the RGB mean value through a scatter diagram to obtain a linear regression equation between the blending ratio and the RGB mean value;
(4) in the production process, at least 10 flavored finished cut tobacco samples to be detected are taken at equal time intervals on line in each batch, and after crushing and balancing, RGB mean value detection is respectively carried out; substituting the RGB mean value into the linear regression equation in the step (3) to obtain the blending proportion of the cut stems of each sample in the finished cut tobacco, wherein the mean value is the cut stem blending proportion of the finished cut tobacco in the batch;
or taking finished product cigarette samples to be detected at equal time intervals on line for each batch for not less than 10 times, taking 20 finished product cigarettes for each sample, cutting the cigarettes by using a cigarette cutting machine, taking out all cut tobaccos, crushing and balancing, and then respectively carrying out RGB mean value detection on each sample; and (4) substituting the RGB mean value into the linear regression equation in the step (3) to obtain the blending proportion of the cut stems of each sample in the finished cut tobacco, wherein the mean value is the blending proportion of the cut stems of the finished cigarettes in the batch.
Preferably, the balance between the step (1) and the step (4) is balance for 48 hours in an environment with the temperature of 22 +/-1 ℃ and the humidity of 60 +/-2%.
Preferably, the RGB mean value is obtained by detecting with PS image analysis software, and the absolute difference value of two parallel detection results of the RGB mean value should not exceed 0.5% of the arithmetic mean value.
The invention also discloses a method for detecting the blending uniformity of the cut stems of the cigarettes based on RGB image processing, which takes the RGB mean value of the cut stems blended with the cut stems as a marker, and establishes a linear regression equation between the blending proportion of the cut stems and the RGB mean value, thereby realizing the detection of the uniformity of the cut stems of the cigarettes. The method specifically comprises the following steps:
(1) taking representative dried and cooled cut tobacco leaves and cut stems in the cigarette brand, drying the cut tobacco leaves and the cut stems, then winnowing the cut stems, respectively crushing the cut tobacco leaves and the cut stems, and then balancing the cut tobacco leaves and the cut stems;
(2) respectively adding 0-50% of balanced cut stems into the balanced cut leaves, and respectively calculating the RGB mean value;
(3) analyzing the data, and finding out the correlation between the blending ratio of the cut stems in the cut tobacco formula and the RGB mean value through a scatter diagram to obtain a linear regression equation between the blending ratio and the RGB mean value;
(4) in the production process, at least 10 flavored finished cut tobacco samples to be detected are taken at equal time intervals on line in each batch, and after crushing and balancing, RGB mean value detection is respectively carried out; substituting the RGB mean value into the linear regression equation in the step (3) to obtain the blending proportion of the cut stems of each sample in the finished cut tobacco, calculating to obtain the standard deviation and the mean value of the blending proportion of the cut stems in the finished cut tobacco of the batch, and finally calculating to obtain the uniformity variation coefficient of the blending of the cut stems in the finished cut tobacco of the batch, wherein the uniformity variation coefficient is used for representing the uniformity and the effectiveness of the blending of the cut stems; the uniformity coefficient of variation is the ratio of the standard deviation to the mean;
or taking finished product cigarette samples to be detected at equal time intervals on line for each batch for not less than 10 times, taking 20 finished product cigarettes for each sample, cutting the cigarettes by using a cigarette cutting machine, taking out all cut tobaccos, crushing and balancing, and then respectively carrying out RGB mean value detection on each sample; substituting the RGB mean value into the linear regression equation in the step (3) to obtain the blending proportion of the cut stems of each sample in the finished cut tobacco, calculating to obtain the standard deviation and the mean value of the blending proportion of the cut stems in the finished cigarettes of the batch, and finally calculating to obtain the variation coefficient of the blending uniformity of the cut stems in the finished cigarettes of the batch, wherein the variation coefficient is used for representing the blending uniformity and the blending effectiveness of the cut stems; the coefficient of variation of uniformity is the ratio of the standard deviation to the mean.
Preferably, the balance between the step (1) and the step (4) is balance for 48 hours in an environment with the temperature of 22 +/-1 ℃ and the humidity of 60 +/-2%.
Preferably, the RGB mean value in step (2) is obtained by using PS image analysis software, and the absolute difference between two parallel detection results of the RGB mean value should not exceed 0.5% of the arithmetic mean value.
As a preferable scheme, the sample size of the blending uniformity detection of the cut rolled stems of the cigarettes in the step (4) is not less than 30 per batch.
The invention has the beneficial effects that: the method for detecting the blending proportion and blending uniformity of the cut stems of the cigarettes is established by taking the RGB mean value of the cut leaves and the cut stems as the markers, is simple and feasible, has higher scientificity and accuracy compared with the traditional manual selection method, and provides technical support for optimizing the blending uniformity of the cigarettes.
Detailed Description
The technical solution of the present invention will be described in detail and fully with reference to the following specific examples.
Example 1
A method for detecting blending proportion and blending uniformity of cut stems of cigarettes based on RGB image processing comprises the following steps:
(1) taking 1000g of dried and cooled leaf shreds and 500g of dried and air-separated stem shreds in the cigarette brand A, respectively crushing, and balancing for 48h in an environment with the temperature of 22 +/-1 ℃ and the humidity of 60 +/-2%.
(2) Adding 0%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50% of the balanced cut stem tobacco powder into the balanced cut leaf tobacco powder, and performing RGB mean detection respectively.
(3) Analyzing the data, finding out the correlation between the blending ratio of the cut stems in the cut tobacco formula and the RGB mean value through a scatter diagram, and obtaining a linear regression equation between the blending ratio (Y) of the cut stems and the RGB mean value (U):
Y＝-24.549U+107.64(R2＝0.9849)，R2the correlation coefficient of the RGB mean value and the blending ratio is shown.
(4) Taking finished product cigarette samples to be detected 30 times at equal time intervals on line in each batch, taking 20 finished product cigarettes for each sample, cutting the cigarettes by using a cigarette cutting machine, taking out all tobacco shreds, crushing and balancing, and respectively carrying out RGB mean value detection on each sample; substituting the RGB mean value into the linear regression equation in the step (3) to obtain the blending proportion of the cut stems of each sample in the finished cut tobacco, obtaining the standard deviation and the mean value of the blending proportion of the cut stems in the finished cigarettes of the batch through calculation, and finally calculating to obtain the variation coefficient of the blending uniformity of the cut stems in the finished cigarettes of the batch for representing the blending uniformity and the blending effectiveness of the cut stems; the coefficient of variation of uniformity is the ratio of the standard deviation to the mean.
Example 2
A method for detecting blending proportion and blending uniformity of cut stems of cigarettes based on RGB image processing comprises the following steps:
(1) taking 1000g of dried and cooled leaf shreds and 500g of dried and air-separated stem shreds in cigarette brand B, respectively pulverizing, and balancing for 48h at 22 + -1 deg.C and 60 + -2% humidity.
(2) Adding 0%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50% of the balanced cut stem tobacco powder into the balanced cut leaf tobacco powder, and performing RGB mean detection respectively.
(3) Analyzing the data, finding out the correlation between the blending ratio of the cut stems in the cut tobacco formula and the RGB mean value through a scatter diagram, and obtaining a linear regression equation between the blending ratio (Y) of the cut stems and the RGB mean value (U):
Y＝-20.263U+114.97(R2＝0.9959)，R2the correlation coefficient of the RGB mean value and the blending ratio is shown.
(4) In the production process, 10 flavored finished cut tobacco samples to be detected are taken at equal time intervals on line in each batch, and after crushing and balancing, RGB mean value detection is respectively carried out; substituting the RGB mean value into the linear regression equation in the step (3) to obtain the blending proportion of the cut stems of each sample in the finished cut tobacco, calculating to obtain the standard deviation and the mean value of the blending proportion of the cut stems in the finished cut tobacco of the batch, and finally calculating to obtain the uniformity variation coefficient of the blending of the cut stems in the finished cut tobacco of the batch, wherein the uniformity variation coefficient is used for representing the uniformity and the effectiveness of the blending of the cut stems; the coefficient of variation of uniformity is the ratio of the standard deviation to the mean.
Example 3
A method for detecting blending proportion and blending uniformity of cut stems of cigarettes based on RGB image processing comprises the following steps:
(1) taking 1000g of dried and cooled leaf shreds and 500g of dried and air-separated stem shreds in cigarette brand C, respectively pulverizing, and balancing for 48h at 22 + -1 deg.C and humidity 60 + -2%.
(2) Adding 0%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50% of the balanced cut stem tobacco powder into the balanced cut leaf tobacco powder, and performing RGB mean detection respectively.
(3) Analyzing the data, finding out the correlation between the blending ratio of the cut stems in the cut tobacco formula and the RGB mean value through a scatter diagram, and obtaining a linear regression equation between the blending ratio (Y) of the cut stems and the RGB mean value (U):
Y＝-20.863U+105.54(R2＝0.9952)，R2the correlation coefficient of the RGB mean value and the blending ratio is shown.
(4) In the production process, 20 flavored finished cut tobacco samples to be detected are taken at equal time intervals on line in each batch, and after crushing and balancing, RGB mean value detection is respectively carried out; substituting the RGB mean value into the linear regression equation in the step (3) to obtain the blending proportion of the cut stems of each sample in the finished cut tobacco, calculating to obtain the standard deviation and the mean value of the blending proportion of the cut stems in the finished cut tobacco of the batch, and finally calculating to obtain the uniformity variation coefficient of the blending of the cut stems in the finished cut tobacco of the batch, wherein the uniformity variation coefficient is used for representing the uniformity and the effectiveness of the blending of the cut stems; the coefficient of variation of uniformity is the ratio of the standard deviation to the mean.
The detection results of the blending ratio and uniformity of the cut stems of the cigarettes in each embodiment are shown in table 1.
TABLE 1
As shown in the above Table 1, the standard deviation of the cut stem blending ratio data of the cigarette in example 2 is the smallest, which indicates that the dispersion degree among the data in the group is the smallest.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. A method for detecting the blending proportion of cut stems of cigarettes based on RGB image processing is characterized by comprising the following steps: and establishing a linear regression equation between the blending proportion of the cut stems and the RGB mean value by taking the RGB mean value of the cut stems blended with the cut stems as a marker, thereby realizing the detection of the blending proportion of the cut stems of the cigarettes.
2. The method for detecting the cut rolled stem blending ratio of the cigarettes based on the RGB image processing as claimed in claim 1, comprising the following steps:
(1) taking representative dried and cooled cut tobacco leaves and cut stems in the cigarette brand, drying the cut tobacco leaves and the cut stems, then winnowing the cut stems, respectively crushing the cut tobacco leaves and the cut stems, and then balancing the cut tobacco leaves and the cut stems;
(2) respectively adding 0-50% of balanced cut stems into the balanced cut leaves, and respectively calculating the RGB mean value;
(3) analyzing the data, and finding out the correlation between the blending ratio of the cut stems in the cut tobacco formula and the RGB mean value through a scatter diagram to obtain a linear regression equation between the blending ratio and the RGB mean value;
(4) in the production process, at least 10 flavored finished cut tobacco samples to be detected are taken at equal time intervals on line in each batch, and after crushing and balancing, RGB mean value detection is respectively carried out; substituting the RGB mean value into the linear regression equation in the step (3) to obtain the blending proportion of the cut stems of each sample in the finished cut tobacco, wherein the mean value is the cut stem blending proportion of the finished cut tobacco in the batch;
or taking finished product cigarette samples to be detected at equal time intervals on line for each batch for not less than 10 times, taking 20 finished product cigarettes for each sample, cutting the cigarettes by using a cigarette cutting machine, taking out all cut tobaccos, crushing and balancing, and then respectively carrying out RGB mean value detection on each sample; and (4) substituting the RGB mean value into the linear regression equation in the step (3) to obtain the blending proportion of the cut stems of each sample in the finished cut tobacco, wherein the mean value is the blending proportion of the cut stems of the finished cigarettes in the batch.
3. The method for detecting the cut stem blending ratio of the cigarettes based on the RGB image processing as claimed in claim 2, wherein: the balance in the step (1) and the step (4) is carried out for 48 hours in an environment with the temperature of 22 +/-1 ℃ and the humidity of 60 +/-2%.
4. The method for detecting the cut stem blending ratio of the cigarettes based on the RGB image processing as claimed in claim 2, wherein: in the step (2), the RGB mean value is obtained by adopting PS image analysis software for detection, and the absolute difference value of two parallel detection results of the RGB mean value should not exceed 0.5 percent of the arithmetic mean value.
5. A method for detecting blending uniformity of cut stems of cigarettes based on RGB image processing is characterized by comprising the following steps: and (3) establishing a linear regression equation between the blending proportion of the cut stems and the RGB mean value by taking the RGB mean value of the cut stems blended with the cut stems as a marker, thereby realizing the detection of the uniformity of the cut stems of the cigarettes.
6. The method for detecting blending uniformity of cut rolled stems of cigarettes based on RGB image processing as claimed in claim 5, comprising the following steps:
(1) taking representative dried and cooled cut tobacco leaves and cut stems in the cigarette brand, drying the cut tobacco leaves and the cut stems, then winnowing the cut stems, respectively crushing the cut tobacco leaves and the cut stems, and then balancing the cut tobacco leaves and the cut stems;
(2) respectively adding 0-50% of balanced cut stems into the balanced cut leaves, and respectively calculating the RGB mean value;
(3) analyzing the data, and finding out the correlation between the blending ratio of the cut stems in the cut tobacco formula and the RGB mean value through a scatter diagram to obtain a linear regression equation between the blending ratio and the RGB mean value;
(4) in the production process, at least 10 flavored finished cut tobacco samples to be detected are taken at equal time intervals on line in each batch, and after crushing and balancing, RGB mean value detection is respectively carried out; substituting the RGB mean value into the linear regression equation in the step (3) to obtain the blending proportion of the cut stems of each sample in the finished cut tobacco, wherein the mean value is the blending proportion of the cut stems of the finished cut tobacco in the batch, calculating to obtain the standard deviation and the mean value of the blending proportion of the cut stems in the finished cut tobacco in the batch, and finally calculating to obtain the uniformity variation coefficient of the blending of the cut stems in the finished cut tobacco in the batch for representing the uniformity and the effectiveness of the blending of the cut stems; the uniformity coefficient of variation is the ratio of the standard deviation to the mean;
or taking finished product cigarette samples to be detected at equal time intervals on line for each batch for not less than 10 times, taking 20 finished product cigarettes for each sample, cutting the cigarettes by using a cigarette cutting machine, taking out all cut tobaccos, crushing and balancing, and then respectively carrying out RGB mean value detection on each sample; substituting the RGB mean value into the linear regression equation in the step (3) to obtain the blending proportion of the cut stems of each sample in the finished cut tobacco, calculating to obtain the standard deviation and the mean value of the blending proportion of the cut stems in the finished cigarettes of the batch, and finally calculating to obtain the variation coefficient of the blending uniformity of the cut stems in the finished cigarettes of the batch, wherein the variation coefficient is used for representing the blending uniformity and the blending effectiveness of the cut stems; the coefficient of variation of uniformity is the ratio of the standard deviation to the mean.
7. The method for detecting the blending uniformity of the cut rolled stems of cigarettes according to claim 6, wherein the method comprises the following steps: the balance in the step (1) and the step (4) is carried out for 48 hours in an environment with the temperature of 22 +/-1 ℃ and the humidity of 60 +/-2%.
8. The method for detecting the blending uniformity of the cut rolled stems of cigarettes according to claim 6, wherein the method comprises the following steps: in the step (2), the RGB mean value is obtained by adopting PS image analysis software for detection, and the absolute difference value of two parallel detection results of the RGB mean value should not exceed 0.5 percent of the arithmetic mean value.
9. The method for detecting the blending uniformity of the cut rolled stems of cigarettes according to claim 6, wherein the method comprises the following steps: and (4) detecting the blending uniformity of the cut stems of the cigarettes in the step (4), wherein the sample size of each batch is not less than 30.
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