CN113702259A - Method for detecting integral pore uniformity of cigarette - Google Patents
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- 239000011148 porous material Substances 0.000 title claims abstract description 105
- 235000019504 cigarettes Nutrition 0.000 title claims abstract description 72
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000003325 tomography Methods 0.000 claims abstract description 18
- 241000208125 Nicotiana Species 0.000 claims abstract description 17
- 235000002637 Nicotiana tabacum Nutrition 0.000 claims abstract description 17
- 230000011218 segmentation Effects 0.000 claims abstract description 17
- 238000012545 processing Methods 0.000 claims abstract description 6
- 238000005259 measurement Methods 0.000 claims description 10
- 239000000945 filler Substances 0.000 claims description 7
- 230000005251 gamma ray Effects 0.000 claims description 2
- 238000001514 detection method Methods 0.000 abstract description 5
- 238000004519 manufacturing process Methods 0.000 abstract description 3
- 235000019505 tobacco product Nutrition 0.000 abstract description 2
- 239000000463 material Substances 0.000 description 5
- 239000000126 substance Substances 0.000 description 5
- 238000002485 combustion reaction Methods 0.000 description 4
- 239000003814 drug Substances 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 238000011158 quantitative evaluation Methods 0.000 description 2
- 230000001953 sensory effect Effects 0.000 description 2
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- 238000009659 non-destructive testing Methods 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
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- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
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Abstract
The invention belongs to the field of detection of tobacco and tobacco products, and particularly relates to a method for detecting the integral pore uniformity of a cigarette. The method comprises the following steps: carrying out tomography on the sample to obtain a tomography gray image; b: solving a segmentation threshold value K according to the fault gray level image; c: carrying out binarization processing on the tomogram according to a threshold value K, and carrying out three-dimensional model reconstruction by using the processed tomogram; d: calculating the initial porosity delta0(ii) a E: expanding the pore volume and solving the porosity delta after pore expansion1(ii) a F: and calculating the overall pore uniformity h of the sample. The method can quantitatively evaluate the quality of the cigarette filling uniformity, and provides data support for improving the filling uniformity in the cigarette production process.
Description
Technical Field
The invention belongs to the field of detection of tobacco and tobacco products, and particularly relates to a method for detecting the integral pore uniformity of a cigarette.
Background
The filling uniformity of the cut tobacco of the cigarette is a key parameter influencing the quality stability and the sensory quality of the cut tobacco of the cigarette. The porosity is the gap between fillers such as cut tobacco stems and the like of the cigarettes and is filled with air, the porosity is the percentage of the gap in the volume of the cigarettes and can be used for representing the filling performance and the cigarette structure of the cigarettes, and the more uniform the porosity distribution is, the better the filling uniformity is, the higher the density consistency is, and the more stable the mouth-by-mouth sensory quality is. The porosity of the cigarette is different from the porosity in the fields of medicine, materials science and the like, the porosity of the cigarette does not contain the porosity of tobacco shreds, but only contains the gaps among the tobacco shreds, and the porosity in medicine and materials science generally refers to the internal porosity of materials. The calculation formula of the cigarette porosity can be obtained by the concept as follows:
in the formula V0Representing the total amount of the volume of the gaps among the tobacco shreds, and V represents the volume of the cigarette.
In the prior art, CN106568641A and CN106770382A propose that the non-destructive testing is performed on the combustion process and the combustion section after combustion of the cigarette by using the CT technology, so as to obtain the internal structural changes of the cigarette sample before, during and after combustion.
At present, an effective method for realizing quantitative evaluation of the integral pore uniformity of the cigarettes is still lacked.
Disclosure of Invention
The invention aims to provide a method for detecting the uniformity of the whole pores of cigarettes, which realizes quantitative description of the uniformity of the whole pores of the cigarettes.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a method for detecting the integral pore uniformity of cigarettes comprises the following steps:
(1) carrying out tomography on the cigarettes to obtain a tomography gray image;
(2) determining a segmentation threshold value according to the fault gray level image, and performing binarization processing on the fault gray level image according to the segmentation threshold value to obtain a binary image which distinguishes pores from cigarette fillers;
(3) utilizing the binary image to reconstruct a three-dimensional model of the cigarette to obtain a three-dimensional reconstruction model;
(4) according to the three-dimensional reconstruction model, the initial porosity delta is obtained0：
Where N is the total number of pixels in the measurement area, N0The number of pixels that are pores of the measurement region;
(5) assuming that pores in the three-dimensional reconstruction model are uniformly distributed, expanding the pore volume to occupy the space of the three-dimensional reconstruction model, and determining the multiple of the expanded pore volume relative to the original pore volume; enlarging the volume of the pore pixel points in the three-dimensional reconstruction model according to the multiple, neglecting the volume exceeding the model, not repeatedly measuring the volume of the pore overlapping area, and calculating the porosity delta after the pore is enlarged according to the following formula1：
Wherein V is the total volume of the measurement region, V1The volume occupied by the pores in the measurement area;
(6) calculating the overall pore uniformity h:
h＝(δ1-δ0)/(1-δ0)
evaluating the uniformity of pores according to h, wherein the larger the h value is, the better the uniformity of the whole pores of the cigarette is, and otherwise, the worse the uniformity is.
The method for detecting the integral pore uniformity of the cigarette comprises the steps of carrying out tomography on a cigarette sample, reconstructing a three-dimensional model to obtain the initial porosity of the sample, giving a volume parameter to the three-dimensional model, expanding the volume of pore pixels to obtain the porosity after pore expansion, defining an integral porosity uniformity parameter h, and obtaining the integral pore uniformity of the sample. The integral pore uniformity h obtained by the method can quantitatively evaluate the quality of the cigarette filling uniformity, and provides data support for improving the filling uniformity in the cigarette production process.
Preferably, in step (1), the emission source used for the tomography is one of an X-ray beam, a gamma ray and an ultrasonic wave.
In order to eliminate the influence of the pores of the cut tobacco, the resolution of the emission source is determined between the pore size of the cut tobacco and the size of the filling pore. Generally, the resolution of the emission source is 20-30 μm.
Preferably, in the step (1), the tomographic gray scale image has 8 bits and a gray scale range of 0 to 255.
Preferably, in the step (2), the segmentation threshold is determined according to a maximum inter-class variance method.
Preferably, in the step (2), a gradation histogram is drawn using the tomographic gradation image, and the division threshold value is determined from the gradation histogram.
Preferably, in step (5), it is assumed that one pixel in the three-dimensional reconstruction model is a cube, and the expanded pore volume is a side length of the expanded cube.
More preferably, the pore volume expansion rule is determined in the following manner: and expanding the pore volume under the condition of completely and uniformly distributing pores, wherein the expanded pore volume is larger than the volume of the cigarette sample.
More preferably, the pore volume enlargement factor is determined as follows: the expansion times the initial porosity is greater than 1 with reference to the initial porosity of the cigarette. Preferably, the value is as close to 1 as possible, and may be, for example, 1.05 to 1.2.
Drawings
FIG. 1 is a flowchart of a detection method according to embodiment 1 of the present invention;
FIG. 2 is a schematic view of a tomographic scanning method of a cigarette sample in example 1 of the present invention;
FIG. 3 is a scanned gray scale image of a cigarette according to embodiment 1 of the present invention;
fig. 4 is a binary image after binarization of a grayscale image in embodiment 1 of the present invention;
FIG. 5 is a schematic diagram of the enlarged volume of the aperture pixel in embodiment 1 of the present invention.
Detailed Description
The following further describes embodiments of the present invention with reference to the drawings.
The specific embodiment of the method for detecting the integral pore uniformity of the cigarette is as follows:
example 1
The method for detecting the uniformity of the whole pores of the cigarette in the embodiment is shown in fig. 1, and comprises the following steps:
(1) carrying out tomography scanning on the cigarette section of the cigarette sample to obtain a tomography gray level image
The tomography is performed by using a tomography system as shown in fig. 2, and the tomography system consists of a micro-focus X-ray source, a digital flat panel detector and a high-precision rotary table.
When the device works, the emission source and the detector with extremely high sensitivity are utilized to perform section scanning at different positions on a sample to be detected, so that a gray level image of the sample can be obtained in a nondestructive mode, the gray level value in the image corresponds to the density of substances in the sample, different components and densities attenuate rays differently, and different gray levels on the reconstructed image correspond to the gray levels.
In order to eliminate the influence of the pores of the tobacco shreds, the resolution of a ray source during tomography needs to be adjusted, so that the resolution is in the range of the pores of the tobacco shreds and the gaps between the tobacco shreds. The diameter of the pores of tobacco leaves and other fillers is 1-10 microns. The cigarette paper volume accounts for a small proportion in the cigarette volume, the influence on the detection result is not obvious, and the influence of the cigarette paper on the porosity can be ignored in the detection.
The resolution of the ray source is set to be 20 mu m, which is larger than the pore diameter of the tobacco shred, so that the interference of the pore of the tobacco shred to the experimental result can be avoided. Adopting the tomography to scan the cigarette sample, putting the sample into the CT machine experiment chamber, vertically placing the filter tip downwards on the high-precision rotary table, rotating the rotary table for 360 degrees during projection to obtain a radial cigarette tomography gray image, uniformly moving the ray source from top to bottom to perform tomography on different axial positions, wherein the scanning area is a cigarette section, and scanning is performed for 260 times. 260 tomographic gray-scale images are obtained by reconstructing the projection images. The gray image is 8 bits, the gray range is 0-255, and the image size is 1921 pixels × 1921 pixels.
Taking an actual cigarette as an example, a part of the tomographic grayscale image is shown in fig. 3.
(2) And (4) solving a segmentation threshold value K by utilizing a maximum inter-class variance method according to the fault gray level image, and carrying out binarization processing on the image according to the segmentation threshold value K.
The maximum between-class variance method, also called Otsu algorithm, is an algorithm for determining an image binarization segmentation threshold, and after image binarization segmentation is carried out according to a threshold K, the between-class variance of a foreground image and a background image is maximum, so that pores and cigarette fillers can be distinguished. Assuming that a threshold K exists to divide all pixels of the gray-scale image into two categories, C1 (smaller than K) and C2 (larger than K), let the mean value of C1 be m1, the mean value of C2 be m2, the global mean value of the image be mG, the probability that a pixel is divided into C1 be p1, and the probability that a pixel is divided into C2 be p 2. Therefore, there are:
p1*m1+p2*m2＝mG (1)
p1+p2＝1 (2)
according to the concept of variance, the inter-class variance expression is:
σ2＝p1(m1-mG)2+p2(m2-mG)2 (3)
substituting equation (1) into equation (3) can yield:
σ2＝p1p2(n1-m2)2 (4)
in formula (4):
wherein i is a gray scale, PiK is the probability that a pixel is classified as either C1 or C2, and k is the threshold.
Selecting a fault gray image, traversing each gray level of 0-255, and solving sigma in the formula (4)2And the K with the maximum value is the threshold value of binary segmentation. One implementation of the open source Otsu algorithm in the GitHub is selected, and the 8-bit gray image is transmitted into a function, i.e., the segmentation threshold K can be returned. Processing the gray scale image into a binary image according to the threshold value K, wherein the processing logic is as follows:
the processed aperture pixel value is 0 and the filler pixel value is maximum.
Fig. 4 shows a binary image obtained from the tomographic grayscale image of step (1).
(3) Three-dimensional model reconstruction using binary images
Due to the instability of the initial slice images of the cigarette sample, 10 tomograms at the head are removed, and the rest tomograms are adopted to reconstruct a three-dimensional model. And (3) introducing the 250 binary images after threshold segmentation into VGstudio MAX2.2 software to carry out three-dimensional visualization of pores, so as to obtain a three-dimensional reconstruction model.
In the method, three-dimensional visualization is realized by using VGstudio MAX2.2 software to obtain a three-dimensional reconstruction model.
(4) According to the three-dimensional reconstruction model, the initial porosity delta is obtained0
In the three-dimensional reconstruction model, the pore pixels are white, the filler pixels are black, and the total pixel number N is counted0And the number of pore pixels N, based on the formula (5)Calculating the overall porosity delta of the cigarette sample0：
In the formula (5), N is the total number of pixels in the measurement region, and N0The number of pixels measuring the area aperture.
(5) And (3) assuming that the pores are uniformly distributed, expanding the pore volume to occupy the space of the three-dimensional reconstruction model, and determining the multiple of the expanded pore volume relative to the original pore volume. Enlarging the volume of a pore pixel point in the three-dimensional reconstruction model according to multiple times, neglecting the volume exceeding the model, not repeatedly measuring the volume of a pore overlapping area, and calculating the porosity delta after pore enlargement according to the formula (6)1。
Specifically, in order to expand the pore volume, it is first necessary to assign volume parameters to the three-dimensional reconstruction model, and it is assumed that one pixel in the three-dimensional reconstruction model is a cube with a side length of a, as shown in fig. 5. Experiments and data show that the porosity of the cigarette is between 35% and 40%, if the pores are uniformly distributed, the volume of the pores is expanded to three times of the original volume to occupy the space of the whole reconstruction model, so that the volume of each pore pixel with the pixel value of 0 is expanded to three times to be changed into a cube with the side length of 3^1/3a, the volume exceeding the model is ignored, the volume of the pore overlapping area is not repeatedly measured, and the porosity delta after the pore expansion is calculated according to the formula (6)1：
In formula (6): v is the total volume of the measurement area, V1To measure the volume occupied by the pores in the area.
(6) Calculating the integral pore uniformity h of the sample
In order to research the uniformity of the overall distribution of pores in a cigarette sample, the overall pore uniformity h is defined, the uniformity h is a key parameter for representing the uniformity of the pore distribution, and the uniformity is determined by the porosity delta of a three-dimensional reconstruction model of the cigarette sample0Porosity delta after pore enlargement1Obtaining:
h＝(δ1-δ0)/(1-δ0) (7)
according to formula (7), h-0 indicates that pores are connected into a piece to form a continuous fracture to separate the material, and h-1 indicates that the pores are uniformly distributed in the material, i.e., the uniformity of the pores is positively correlated with the size of h.
The quality of the cigarette filling uniformity can be evaluated by comparing the h value, the larger the h value is, the more uniform the pore distribution of the sample is, the better the filling uniformity is, and otherwise, the worse the filling uniformity is. According to the numerical value, data support can be provided for improving the filling uniformity in the cigarette production process.
Example 2
The method for detecting the uniformity of the whole pores of the cigarette is basically the same as that of the cigarette in the embodiment 1, and the difference is only that:
in the step (2), a gray level histogram is drawn by utilizing the fault gray level image, and a segmentation threshold value K is determined according to the gray level histogram.
Specifically, substances with different densities in the sample correspond to different gray values on the tomographic image and are influenced by noise, a scattering reconstruction algorithm and the like in the process of actually reconstructing the tomographic image, the substances with the same density correspond to a certain gray range on the tomographic image, and the substances with different densities on the image are in continuous transition on the adjacent boundaries of the gray levels. And drawing a gray level histogram of the tomographic image, wherein each density substance in the histogram corresponds to a peak, the minimum density of air corresponds to the minimum gray level, the peak is at the leftmost side of the histogram, and the gray level corresponding to the trough between the leftmost peak and the second-left peak is selected as a segmentation threshold K.
In the above example 2, the experimental effect substantially equivalent to that of the example 1 was obtained. In other embodiments of the method for detecting the uniformity of the whole pores of the cigarettes, gamma rays, ultrasonic waves and the like can be used as an emission source, the resolution of a ray source can be simply adjusted according to actual conditions, and the amplification factor can be correspondingly adjusted according to different samples, so that quantitative evaluation results of the uniformity of the whole pores of the cigarettes can be obtained.
Claims (9)
1. A method for detecting the uniformity of the whole pores of cigarettes is characterized by comprising the following steps:
(1) carrying out tomography on the cigarettes to obtain a tomography gray image;
(2) determining a segmentation threshold value according to the fault gray level image, and performing binarization processing on the fault gray level image according to the segmentation threshold value to obtain a binary image which distinguishes pores from cigarette fillers;
(3) utilizing the binary image to reconstruct a three-dimensional model of the cigarette to obtain a three-dimensional reconstruction model;
(4) according to the three-dimensional reconstruction model, the initial porosity delta is obtained0：
Where N is the total number of pixels in the measurement area, N0The number of pixels that are pores of the measurement region;
(5) assuming that pores in the three-dimensional reconstruction model are uniformly distributed, expanding the pore volume to occupy the space of the three-dimensional reconstruction model, and determining the multiple of the expanded pore volume relative to the original pore volume; enlarging the volume of the pore pixel points in the three-dimensional reconstruction model according to the multiple, neglecting the volume exceeding the model, not repeatedly measuring the volume of the pore overlapping area, and calculating the porosity delta after the pore is enlarged according to the following formula1：
Wherein V is the total volume of the measurement region, V1The volume occupied by the pores in the measurement area;
(6) calculating the overall pore uniformity h:
h＝(δ1-δ0)/(1-δ0)
evaluating the uniformity of pores according to h, wherein the larger the h value is, the better the uniformity of the whole pores of the cigarette is, and otherwise, the worse the uniformity is.
2. The method for detecting the uniformity of the pores of the whole cigarette according to claim 1, wherein in the step (1), the emission source used for the tomography is one of an X-ray beam, a gamma ray and an ultrasonic wave.
3. The method for detecting the uniformity of the pores of the whole cigarette as claimed in claim 2, wherein the resolution of the emission source is determined between the pore size of the cut tobacco itself and the size of the filling pore.
4. The method for detecting the integral pore uniformity of the cigarette according to any one of claims 1 to 3, wherein in the step (1), the gray level image of the fault is 8 bits, and the gray level range is 0 to 255.
5. The method for detecting the uniformity of the pores of the whole cigarette according to claim 1, wherein in the step (2), the segmentation threshold is determined according to a maximum inter-class variance method.
6. The method for detecting the uniformity of the pores of the whole cigarette according to claim 1, wherein in the step (2), a gray histogram is drawn by using a tomographic gray image, and the segmentation threshold is determined according to the gray histogram.
7. The method for detecting the uniformity of the pores of the whole cigarette according to claim 1, wherein the rule of pore volume expansion is determined as follows: and expanding the pore volume under the condition of completely and uniformly distributing pores, wherein the expanded pore volume is larger than the volume of the cigarette sample.
8. The method for detecting the uniformity of the overall pores of the cigarette according to claim 1, wherein the pore volume expansion factor is determined as follows: the expansion times the initial porosity is greater than 1 with reference to the initial porosity of the cigarette.
9. The method for detecting the uniformity of the pores of the whole cigarette according to claim 1, 7 or 8, wherein in the step (5), a pixel in the three-dimensional reconstruction model is assumed to be a cube, and the expanded pore volume is the side length of the enlarged cube.
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