Method for detecting moisture in tobacco leaf redrying process by adopting infrared moisture meter and application

Abstract

The invention relates to a method for detecting moisture in the tobacco leaf redrying process through an infrared moisture meter and application. The method comprises the steps that tobacco leaves of different finished product grades are classified, the classified tobacco leaves are put into a drying machine to be redried, and the moisture at an outlet is detected through the infrared moisture meter. According to the method, the raw tobaccos are classified and detected in a specific detection time period, so that the moisture uniformity of the finished product is remarkably improved, the moisture content of the finished product grade is more uniform, the homogenization processing work is deepened, and the redrying quality of the tobacco leaves is further improved.

Description

Technical Field

[0001] The invention relates to the technical field of tobacco moisture detection, and in particular to a method and application of detecting moisture in a tobacco leaf redrying process by using an infrared moisture meter. Background Art

[0002] The application of infrared moisture meters in the tobacco redrying process is mainly concentrated at the entrance and exit of the roasting machine. The infrared moisture meter is used to measure the moisture content of tobacco leaves entering the redrying machine at the entrance of the roasting machine, and is used to detect the moisture content of tobacco leaves after redrying at the exit. However, it should be noted that the accuracy and stability of the infrared moisture meter in the tobacco redrying process may be affected by many factors, including the color, temperature and humidity of the tobacco leaves.

[0003] The temperature of tobacco leaves is a key factor affecting the accuracy and stability of infrared moisture meters. The moisture in tobacco is easily affected by temperature and is difficult to control within the quality index. If the temperature control is uneven during the redrying process, the moisture and temperature of the leaves will be uneven and unstable, thus affecting the measurement results of the infrared moisture meter.

[0004] Therefore, how to improve the accuracy of the detection value of the infrared moisture meter at the outlet has become a problem that needs to be solved urgently. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method and application of using an infrared moisture meter to detect the moisture in the redrying process of tobacco leaves. The present invention significantly improves the uniformity of the moisture of the finished product by classifying the raw tobacco and conducting detection in a specific detection time period. The moisture content of the finished product grade is more uniform, the homogenization processing work is further deepened, and the redrying quality of tobacco leaves is further improved.

[0006] To achieve this object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for detecting moisture in tobacco leaves during redrying by using an infrared moisture meter, the method comprising classifying tobacco leaves of different finished product grades, placing the classified tobacco leaves into a drying machine for redrying, and detecting the moisture at the outlet by using an infrared moisture meter;

[0008] The finished product grades include B, H, K, I, R, F, J, E, Q, M, A, G, P, C, N, O, L, D and S;

[0009] The classification method is as follows:

[0010] The first category includes B, H, and K;

[0011] The second category includes I and R;

[0012] The third category includes F and J;

[0013] The fourth category includes E, Q, M, A and G;

[0014] The fifth category includes P, C, N, O, L, D and S.

[0015] In the present invention, the finished product grades are classified, and the tobacco leaves are tested after classification. The difference between the displayed value and the test value at the discharge port is low. This method significantly improves the uniformity of the moisture content of the finished product, thereby promoting the homogeneity of the finished product. The finished product grade classification judgment standard is that the difference between classes (sum of squares) after clustering is as large as possible, and the difference within classes (sum of squares) is as small as possible. After K-means clustering, the sum of squares between classes accounts for 86.9% of the total sum of squares, and the clustering effect is good.

[0016] In the present invention, the above-mentioned classification standard is adopted to classify the tobacco leaves and then re-roast them separately. The absolute value average value, standard deviation and CV value of the difference are significantly reduced, indicating that this method can significantly improve the uniformity of the moisture content of the finished product and can further improve the homogeneity of the finished tobacco leaves.

[0017] The classification standards of various grades in the present invention are classified and coded based on GB2635-92 flue-cured tobacco grading standards.

[0018] Preferably, the detection time period is selected from 10:00-13:30 or 22:00-9:00.

[0019] In the present invention, it is found through experimental detection that there is no significant difference between the moisture detection value and the displayed value of tobacco leaves of various finished product grades in the time periods of 10:00-13:30 and 22:00-9:00, so this time is mostly used as the detection time.

[0020] Preferably, the ambient temperature of the redrying is 17-26°C, and the ambient humidity is 36%-60%. The 17-26°C may be, for example, 17°C, 18°C, 19°C, 20°C, 21°C, 22°C, 23°C, 24°C, 25°C or 26°C, etc. The 36%-60% may be, for example, 36%, 40%, 45%, 50%, 55% or 60%, etc.

[0021] Preferably, the re-baking includes drying, rehumidification, cooling and ventilation.

[0022] Preferably, the drying comprises a first drying, a second drying, a third drying, a fourth drying, a fifth drying and a sixth drying;

[0023] The temperature of the first drying is 58-62°C, the temperature of the second drying is 60-64°C, the temperature of the third drying is 62-66°C, the temperature of the fourth drying is 63-67°C, the temperature of the fifth drying is 57-66°C, and the temperature of the sixth drying is 56-64°C. The 58-62°C may be, for example, 58°C, 59°C, 60°C, 61°C or 62°C. The 60-64°C may be, for example, 60°C, 61°C, 62°C, 63°C or 64°C. The 62-66°C may be, for example, 62°C, 63°C, 64°C, 65°C or 66°C. The 63-67°C may be, for example, 63°C, 64°C, 65°C, 66°C or 67°C. The 57-66°C may be, for example, 57°C, 58°C, 59°C, 60°C, 61°C, 62°C, 63°C, 64°C, 65°C or 66°C, etc. The 56-64°C may be, for example, 56°C, 57°C, 58°C, 59°C, 60°C, 61°C, 62°C, 63°C or 64°C, etc.

[0024] Preferably, the moisture regain includes a first moisture regain, a second moisture regain, a third moisture regain and a fourth moisture regain;

[0025] The temperature of the first moisture regain is 54-58°C, the temperature of the second moisture regain is 63-67°C, the temperature of the third moisture regain is 75-79°C, and the temperature of the first moisture regain is 75-79°C. The 54-58°C may be, for example, 54°C, 55°C, 56°C, 57°C or 58°C. The 63-67°C may be, for example, 63°C, 64°C, 65°C, 66°C or 67°C. The 75-79°C may be, for example, 75°C, 76°C, 77°C, 78°C or 79°C.

[0026] Preferably, the cooling temperature is 36-42°C, for example, it can be 36°C, 37°C, 38°C, 39°C, 40°C, 41°C or 42°C.

[0027] Preferably, when the detection time is 10:00-13:30, the frequency of the ventilation and dehumidification is 28-34 Hz, and when the detection time is 22:00-9:00, the frequency of the ventilation and dehumidification is 27-33 Hz. The 28-34 Hz ​​may be, for example, 28 Hz, 29 Hz, 30 Hz, 31 Hz, 32 Hz, 33 Hz or 34 Hz. The 27-33 Hz may be, for example, 27 Hz, 28 Hz, 29 Hz, 30 Hz, 31 Hz, 32 Hz or 33 Hz.

[0028] In a second aspect, the present invention provides an application of the method for detecting moisture in tobacco leaves during redrying using an infrared moisture meter according to the first aspect in redrying tobacco leaves.

[0029] Compared with the prior art, the present invention has at least the following beneficial effects:

[0030] In the present invention, tobacco leaves are divided according to finished product grades and tested at specific times, which significantly improves the detection accuracy of the infrared moisture meter. This shows that this method further deepens the development of homogenization processing, the uniformity of finished product moisture is more stable, the moisture content of finished product grades is more uniform, and the redrying quality of tobacco leaves is further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 The box plot shows the moisture value at the outlet of the roaster.

[0032] Figure 2 This is a box plot of the moisture detection values ​​at the oven outlet.

[0033] Figure 3 This is a box plot of the moisture difference at the oven outlet (detected value - displayed value).

[0034] Figure 4 These are box plots of the displayed values, detected values, and difference values ​​of moisture at the outlet of the baking machine for 19 finished product grades in each time period. Figure A is the box plot of the displayed values ​​corresponding to the three time periods, Figure B is the box plot of the detected values ​​corresponding to the three time periods, and Figure C is the box plot of the difference values ​​corresponding to the three time periods.

[0035] Figure 5 This is a pairwise comparison chart of the moisture difference at the outlet of each grade of roasting machine.

[0036] Figure 6 This is the K-means clustering diagram of moisture at the outlet of each level of roasting machine.

[0037] Figure 7 This is the hierarchical clustering diagram of moisture at the outlet of each level of roasting machine. DETAILED DESCRIPTION

[0038] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and through specific implementation methods. However, the following examples are only simplified examples of the present invention and do not represent or limit the scope of protection of the present invention. The scope of protection of the present invention shall be subject to the claims.

[0039] Example 1

[0040] This embodiment detects the display value and detection value of the oven outlet

[0041] In this embodiment, an infrared moisture meter is used to detect the moisture at the outlet of the roasting machine after the formula module of 19 finished product grades is re-roasted in the whole last roasting season, and the detection value is obtained, and the moisture display value at the roasting machine outlet is counted, and a total of 1181 pairs of detection data are obtained. The ambient temperature and humidity conditions during the processing of the 19 finished product grades are shown in Table 1.

[0042] Table 1

[0043] grade Average temperature(℃) Average humidity (%RH) A 17.38 44.27 B 19.8 46.62 C 22.36 47.60 D 22.82 44.46 E 21.80 50.20 F 21.87 53.83 G 18.46 49.79 H 17.24 47.88 I 18.03 46.15 J 24.91 58.89 K 17.43 48.92 L 22.95 41.29 M 17.89 47.9 N 21.35 42.57 O 20.36 50.7 P 20.92 49.83 Q 19.94 44.82 R 17.79 43.67 S 25.53 58.81

[0044] Specific results such as Figure 1 , Figure 2 and Figure 3 As shown. It can be seen that in each level, there are abnormal values ​​in the display value, test value, and difference. By analyzing the data, the display value and test value of the moisture at the outlet of the roasting machine are beyond the processing index range (11.5%-13%). The data with the display value or test value of the roasting machine outlet less than 11.5% are deleted; other abnormal values ​​are within the processing index range, so they are retained. After eliminating the abnormal values, the data volume is 1154 pairs, as shown in Table 2.

[0045] Table 2

[0046] grade Number average value median Standard Deviation Minimum Maximum A 59 0.17 0.16 0.24 -0.34 0.76 B 19 0.29 0.29 0.28 -0.16 0.79 C 126 0.14 0.11 0.32 -0.67 0.98 D 19 0.11 0.15 0.23 -0.37 0.50 E 159 0.12 0.09 0.32 -0.74 1.16 F 90 0.01 -0.01 0.29 -0.61 1.00 G 35 0.18 0.2 0.25 -0.66 0.73 H 17 0.33 0.32 0.20 -0.08 0.70 I 27 0.25 0.18 0.32 -0.17 1.12 J 27 -0.10 -0.11 0.22 -0.61 0.26 K 47 0.30 0.30 0.26 -0.26 0.77 L 25 0.15 0.24 0.28 -0.30 0.55 M 68 0.14 0.12 0.26 -0.42 1.02 N 67 0.10 0.06 0.32 -0.48 0.94 O 65 0.13 0.08 0.27 -0.42 0.82 P 149 0.11 0.08 0.22 -0.52 0.67 Q 141 0.20 0.17 0.30 -0.43 1.26 R 7 0.31 0.24 0.32 -0.05 0.78 S 7 0.15 0.01 0.35 -0.3 0.69

[0047] From Table 2, we can see that the average value of the moisture difference at the outlet of each grade of roasting machine is less than 0.5%, and the standard deviation is less than 0.5, which means that the moisture difference at the outlet of each grade of roasting machine is relatively concentrated and has a low degree of variation (referring to the degree of dispersion between the observed value and the mean in a set of data). Among them, the maximum value of the difference of grade Q is 1.26%.

[0048] Example 2

[0049] This example analyzes the difference in moisture at the outlet of the oven at different times.

[0050] Verify the difference in the displayed values ​​of the infrared moisture meter at the oven outlet in different time periods. Combined with the actual situation of the processing workshop, divide the daily time period into three time periods, namely 10:00-13:30, 14:00-21:00 and 22:00-9:00. According to the time period, divide the data of the 19 finished product grades into the corresponding time periods, and process and analyze the data. Figure 4 The box-line diagram of the moisture display value, detection value, and difference value at the outlet of the baking machine for 19 finished product grades in each time period. The specific setting values ​​of the main process parameters in each time period are shown in Table 3.

[0051] Table 3

[0052]

[0053] The non-parametric test method Kruskal-Wallis was used to test the differences in the moisture values ​​of 19 finished product grades at the roasting machine outlet in the 2023 roasting season. As shown in Table 4, the test result P value was 0.0001103 (<0.05), which shows that the differences in the moisture values ​​of 19 grades of roasting machines at the three time periods were significant.

[0054] Table 4

[0055]

[0056] Multiple comparisons were performed between the groups. The P value of the time periods 10:00-13:30 and 14:00-21:00 was 0.001 (<0.05), the P value of the time periods 10:00-13:30 and 22:00-9:00 was 0.4598 (>0.05), and the P value of the time periods 14:00-21:00 and 22:00-9:00 was 0.0066 (<0.05). It can be seen that there are significant differences in the outlet moisture content of 19 finished product grades in the time periods 10:00-13:30 and 14:00-21:00, and in the time periods 14:00-21:00 and 22:00-9:00, but there is no significant difference in the outlet moisture content of 19 finished product grades in the time periods 10:00-13:30 and 22:00-9:00.

[0057] Example 3

[0058] This example analyzes the difference in moisture at the outlet of the roasting machine for different finished product grades.

[0059] (1) Normality test

[0060] The differences between the detection values ​​and the displayed values ​​in the 1154 pairs of data after the outliers were removed in Example 1 were tested for normality. Since the test value was less than 2000, the Shapiro-Wilk test was used. The specific test results are shown in Table 5: A (P = 0.952 > 0.05), B (P = 0.263 > 0.05), D (P = 0.644 > 0.05), F (P = 0.083 > 0.05), H (P = 0.945 > 0.05). ), J (P = 0.672> 0.05), K (P = 0.618> 0.05), N (P = 0.085> 0.05), P (P = 0.080> 0.05), R (P = 0.549> 0.05), S (P = 0.534> 0.05), 11 levels conformed to the normal distribution, while the other 8 levels did not. Therefore, variance analysis could not be performed, and the non-parametric test method Kruskal-Wallis test was used.

[0061] Table 5

[0062] grade statistics Degrees of Freedom Significance A 0.991 59 0.952 B 0.940 19 0.263 C 0.976 126 0.022 D 0.964 19 0.644 E 0.970 159 0.001 F 0.975 90 0.083 G 0.935 35 0.038 H 0.979 17 0.945 I 0.887 27 0.007 J 0.973 27 0.672 K 0.981 47 0.618 L 0.914 25 0.038 M 0.948 68 0.007 N 0.968 67 0.085 O 0.957 65 0.023 P 0.984 149 0.080 Q 0.976 141 0.012 R 0.930 7 0.549 S 0.928 7 0.534

[0063] (2) Kruskal-Wallis test

[0064] The Kruskal-Wallis test was performed on the difference between the detection value and the display value in the 1154 pairs of data after the outliers were removed in Example 1, wherein the Kruskal-Wallis H was 86.633, the degree of freedom was 18, and the asymptotic significance p<0.001. The specific test results are shown in Table 6.

[0065] Table 6

[0066]

[0067]

[0068]

[0069]

[0070]

[0071] A pairwise comparison chart is constructed for the moisture difference at the outlet of each grade of roasting machine, such as Figure 5 As shown, the above results show that there is no significant difference in the moisture difference at the roasting machine outlet between some grades. Therefore, the clustering method is used to conduct cluster analysis on the moisture at the roasting machine outlet of each grade.

[0072] Example 4

[0073] This example performs cluster analysis on the mean of the detection values ​​and the mean of the display values ​​of 19 finished product levels.

[0074] (1) K-means cluster analysis

[0075] K-means cluster analysis was performed using the mean of the display value and detection value of each level, and the 19 levels were clustered into 5 categories, such as Figure 6 The mean clustering results shown are as follows: Class I includes 3 levels, namely: B, H, K; Class II includes 2 levels, I, R; Class III includes 2 levels, F, J; Class IV includes 5 levels, E, Q, M, A, G; Class V includes 7 levels, P, C, N, O, L, D, S. The difference in moisture difference at the outlet of each type of roasting machine was tested by one-way ANOVA, and the test result P<0.001, indicating that the moisture difference at the outlet of each type of roasting machine is significantly different, and the clustering effect has certain statistical significance.

[0076] (2) Hierarchical cluster analysis

[0077] Hierarchical cluster analysis was performed using the mean of the display value and detection value of each level, and the 19 levels were clustered into 5 categories. The specific results are as follows Figure 7As shown in the figure, the hierarchical clustering results are: Class I includes 3 levels, namely: B, H, K; Class II includes 2 levels, namely: I, R; Class III includes 2 levels, namely: F, J; Class IV includes 5 levels, namely: E, Q, M, A, G; Class V includes 7 levels, namely: P, C, N, O, L, D, S.

[0078] According to the above analysis results, five channels were initially constructed, namely: three finished product grades such as B, H, and K are the first channel; finished product grades such as I and R are the second channel; finished product grades such as F and J are the third channel; finished product grades such as E, Q, M, A, and G are the fourth channel; finished product grades such as P, C, N, O, L, D, and S are the fifth channel. Tobacco leaves of different finished product grades were classified according to the constructed channels, and tested in the time periods of 10:00-13:30 and 22:00-9:00, and a complete method for detecting moisture in tobacco leaves during redrying using an infrared moisture meter was constructed.

[0079] Example 5

[0080] This example tests the effect of the constructed method.

[0081] In this embodiment, the equipment is calibrated during the baking season of this year, an infrared moisture meter channel at the baking machine outlet is constructed, and tobacco leaves of different finished product grades are fed according to the channel divisions. Inspections are carried out in the time periods of 10:00-13:30 and 22:00-9:00, and experimental verification is carried out using the corresponding finished product grades. The re-baking processing work of this baking season is in an unfinished state. Among the finished product grades of the formula modules that have been completed, the raw material feeding properties of 4 finished product modules are the same as those of the previous baking season, but the data volume of the finished product grade S module is small, and the analysis results are not representative, so it is discarded, and the data of the following 3 finished product grades are selected for data comparison. The specific results are shown in Table 7.

[0082] Table 7

[0083]

[0084] From the results, we can see that by comparison, the experimental data of the three grades of finished product grade E of the original tobacco Lijiang Yulong C3F-A grade, the finished product grade J of the original tobacco Lijiang Yulong X2F-A grade, and the finished product grade E of the original tobacco Chuxiong Lufeng C3F-A grade are compared. The absolute value average, standard deviation and CV value of the difference in this year's baking season are all smaller than those in the previous baking season, indicating that the experimental results of the previous baking season and the channel construction of the infrared moisture meter are feasible, which further deepens the development of the homogenization processing work, the uniformity of the moisture content of the finished product is more stable, the moisture content of the finished product grade is more uniform, and the redrying quality of tobacco leaves is further improved.

[0085] In summary, the present invention significantly improves the uniformity of moisture in the finished product by classifying the raw tobacco and conducting testing at a specific testing time period. The moisture content of the finished product grades is more uniform, the homogenization processing work is further deepened, and the redrying quality of tobacco leaves is further improved.

[0086] The applicant declares that the above is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention shall fall within the protection scope and disclosure scope of the present invention.

Claims

1. A method for detecting the moisture content of tobacco leaves during redrying using an infrared moisture meter, characterized in that: The method comprises classifying tobacco leaves of different finished product grades, placing the classified tobacco leaves into a drying machine for redrying, and using an infrared moisture meter to detect outlet moisture; The finished product grades include B, H, K, I, R, F, J, E, Q, M, A, G, P, C, N, O, L, D and S; The classification method is as follows: The first category includes B, H, and K; The second category includes I and R; The third category includes F and J; The fourth category includes E, Q, M, A and G; The fifth category includes P, C, N, O, L, D and S.

2. The method for detecting moisture in tobacco leaves during redrying using an infrared moisture meter according to claim 1, characterized in that: The detection time period is selected from 10:00-13:30 or 22:00-9:

00.

3. The method for detecting moisture in tobacco leaves during redrying using an infrared moisture meter according to claim 1 or 2, characterized in that: The ambient temperature of the re-baking is 17-26° C., and the ambient humidity is 36%-60%.

4. The method for detecting moisture in tobacco leaves during redrying using an infrared moisture meter according to any one of claims 1 to 3, characterized in that: The re-baking includes drying, rehumidification, cooling and ventilation to remove moisture.

5. The method for detecting moisture in tobacco leaves during redrying using an infrared moisture meter according to claim 4, characterized in that: The drying comprises a first drying, a second drying, a third drying, a fourth drying, a fifth drying and a sixth drying; The temperature of the first drying is 58-62°C, the temperature of the second drying is 60-64°C, the temperature of the third drying is 62-66°C, the temperature of the fourth drying is 63-67°C, the temperature of the fifth drying is 57-66°C, and the temperature of the sixth drying is 56-64°C.

6. The method for detecting moisture in tobacco leaves during redrying using an infrared moisture meter according to claim 4 or 5, characterized in that: The moisture regain includes the first moisture regain, the second moisture regain, the third moisture regain and the fourth moisture regain; The temperature of the first moisture regain is 54-58°C, the temperature of the second moisture regain is 63-67°C, the temperature of the third moisture regain is 75-79°C, and the temperature of the fourth moisture regain is 75-79°C.

7. The method for detecting moisture content in tobacco leaves during redrying using an infrared moisture meter according to any one of claims 4 to 6, characterized in that: The cooling temperature is 36-42°C.

8. The method for detecting moisture content in tobacco leaves during redrying using an infrared moisture meter according to any one of claims 4 to 7, characterized in that: When the detection time is 10:00-13:30, the frequency of ventilation and dehumidification is 28-34Hz, and when the detection time is 22:00-9:00, the frequency of ventilation and dehumidification is 27-33Hz.

9. Use of the method for detecting moisture content in a tobacco leaf redrying process using an infrared moisture meter according to any one of claims 1 to 8 in redrying tobacco leaves.

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