Method for forecasting phenol in flue-cured tobacco smoke based on robust regression modeling

A robust regression, flue gas technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as high cost, labor and time

Active Publication Date: 2014-11-12
CHINA TOBACCO YUNNAN IND
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In order to solve the problems of time-consuming, laborious, and extremely high cost in the process of detecting the phenol data in the flue gas of the roast

Method used

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  • Method for forecasting phenol in flue-cured tobacco smoke based on robust regression modeling
  • Method for forecasting phenol in flue-cured tobacco smoke based on robust regression modeling
  • Method for forecasting phenol in flue-cured tobacco smoke based on robust regression modeling

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0050] (1) Correspondingly list the physical and chemical data of the known baked slices with the smoke phenol data, and establish a data sample set, in which the physical and chemical data include total sugar, reducing sugar, nicotine, total volatile alkali, total nitrogen, nicotine nitrogen, protein , Shimuke value, nitrogen-alkaline ratio, chlorine, potassium, sugar-alkaline ratio and ammoniacal alkali, as shown in the following table:

[0051]

[0052]

[0053] (2) Calculate the column vector x of each physical and chemical data in the data sample set obtained in step (1) respectively 1 ~x n and the column vector y of smoke phenol data, the linear correlation coefficient r between each physical and chemical data and smoke phenol is calculated by the following formula:

[0054] r = Cov ( x , y ) ...

Embodiment 2

[0100] Same as steps (1) to (3) of Example 1, only replace other roasted slices to be tested, step (4) is as follows:

[0101] According to the characteristic index items selected in step (2), the corresponding physical and chemical data of the roasted slices to be tested, that is, nicotine = 2.26, total nitrogen = 1.9, nicotine nitrogen = 0.39, protein = 9.43, nitrogen-alkaline ratio = 0.84, sugar-alkaline Ratio = 11.48 is applied to the prediction model of step (3) as an input variable, and the model prediction value Y=-12.41160+5.63069*nicotine+87.22102*total nitrogen-60.11223* of the smoke phenol of the roasted sheet to be tested can be measured and calculated Nicotine nitrogen - 12.91135 * protein - 4.70685 * nitrogen-to-alkaline ratio + 0.50795 * sugar-to-alkaline ratio = 22.713. In order to verify the reliability of the prediction results of the model, the phenol value of the smoke of the roasted slice was determined to be 22.33 by using the traditional detection method...

Embodiment 3

[0103] Same as steps (1) to (3) of Example 1, only replace other roasted slices to be tested, step (4) is as follows:

[0104] According to the characteristic index items selected in step (2), the corresponding physical and chemical data of the roasted slices to be tested, that is, nicotine = 2.4, total nitrogen = 1.8, nicotine nitrogen = 0.42, protein = 8.66, nitrogen-alkaline ratio = 0.75, sugar-alkali Ratio = 11.67 is applied as an input variable to the prediction model of step (3), and the model prediction value Y=-12.41160+5.63069*nicotine+87.22102*total nitrogen-60.11223* of the smoke phenol of the roasted sheet to be tested can be measured and calculated Nicotine nitrogen - 12.91135 * protein - 4.70685 * nitrogen-to-alkaline ratio + 0.50795 * sugar-to-alkaline ratio = 23.438. In order to verify the reliability of the prediction results of the model, the phenol value of the smoke of the roasted slice was determined to be 23.75 by using the traditional detection method.

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Abstract

The invention provides a method for forecasting phenol in flue-cured tobacco smoke based on robust regression modeling. A model of physical and chemical index items and the smoke phenol is constructed according to existing flue-cured tobacco physical and chemical data and smoke phenol data, and a smoke phenol value of the flue-cured tobacco can be directly forecasted according to physical and chemical component data of an unknown flue-cured tobacco smoke phenol sample. According to the method, the steps of reeling, combustion, smoke catching, detection and the like of a conventional chemical method are eliminated; meanwhile, by the adoption of a robust regression model, the shortcomings caused by singular-value samples in the physical and chemical data or the smoke data can be effectively avoided; the robustness of the model can be guaranteed to an extremely large extent, and the robustness of the robust regression modeling is higher than that of common linear regression modeling. Practice shows that the smoke phenol value of the flue-cured tobacco can be effectively forecasted, the detection efficiency is greatly improved, and the detection cost is lowered.

Description

technical field [0001] The invention relates to a method for predicting phenol in flue gas of roasted slices based on robust regression modeling, and belongs to the technical field of specific calculation models. Background technique [0002] Tobacco smoke is an extremely complex mixture produced by the burning, cracking and distillation of tobacco during cigarette smoking. The harmfulness of cigarette products to the human body is produced through the process of burning and smoking. The harmful components in the smoke are mainly formed during the combustion process, and the chemical characteristics of the smoke change with the internal chemical components of the tobacco leaf raw materials. Therefore, the chemical properties of raw materials of cigarette tobacco leaves determine the chemical properties and safety of cigarette smoke. Phenol is an important harmful component in the mainstream smoke of cigarettes. When tobacco is burned, it directly enters the smoke, producin...

Claims

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Application Information

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IPC IPC(8): G06F19/00
Inventor 白晓莉段如敏周桂圆朱勇谢志强余贺龙王保兴刘挺卢伟
Owner CHINA TOBACCO YUNNAN IND
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