Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Method for predicting B[a]P in smoke of flue-cured tobacco strips based on Robust regression modeling

A robust regression and benzopyrene technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems of time-consuming, laborious, high cost, etc., to ensure robustness, reduce detection costs, and improve detection efficiency Effect

Active Publication Date: 2014-10-15
CHINA TOBACCO YUNNAN IND
View PDF3 Cites 9 Cited by
  • 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 B[a]P data in the flue gas of the baking sheet in the prior art, the present invention proposes a method based on robust regression modeling to predict benzopyrene in the flue gas of the baking sheet

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method for predicting B[a]P in smoke of flue-cured tobacco strips based on Robust regression modeling
  • Method for predicting B[a]P in smoke of flue-cured tobacco strips based on Robust regression modeling
  • Method for predicting B[a]P in smoke of flue-cured tobacco strips 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 and the flue gas B[a]P 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, smoke Alkali nitrogen, protein, Schmuck value, nitrogen-alkaline ratio, chlorine, potassium, sugar-alkali ratio and ammoniacal alkali, as shown in the following table:

[0051]

[0052] (2) Calculate the column vector x of each physical and chemical data in the data sample set obtained in step (1) 1 ~x nand the column vector y of the flue gas B[a]P data, the linear correlation coefficient r between each physical and chemical data and the flue gas B[a]P is calculated by the following formula:

[0053] (1)

[0054] In the formula: x is a column vector of some physical and chemical data, y is the column vector of the flue gas B[a]P data; the linear correlation coefficient r b...

Embodiment 2

[0098] Same as steps (1) to (3) of Example 1, only replace other roasted slices to be tested, step (4) is as follows:

[0099] According to the characteristic index items selected in step (2), the corresponding physical and chemical data of the baked slices to be tested, that is, total sugar = 25.94, reducing sugar = 22.43, total volatile State alkali=0.04 is applied to the prediction model of step (3) as an input variable, and the model prediction value Y=6.41152+0.81641*total sugar-0.27671*reduction of the flue gas B[a]P of the roasted slice to be measured can be calculated Sugar+2.26355*total volatile bases-3.43614*Shimuke value+0.09806*potassium-4.12070*ammonia base=12.589. In order to verify the reliability of the prediction results of the model, the B[a]P value of the flue gas of the baked sheet was determined to be 12.671 by using the traditional detection method.

Embodiment 3

[0101] Same as steps (1) to (3) of Example 1, only replace other roasted slices to be tested, step (4) is as follows:

[0102] According to the characteristic index items selected in step (2), the corresponding physical and chemical data of the baked slices to be tested, that is, total sugar = 28.01, reducing sugar = 24.86, total volatile alkali = 0.29, Shimuke value = 3.24, potassium = 2.03, State alkali=0.04 is applied to the prediction model of step (3) as an input variable, and the model prediction value Y=6.41152+0.81641*total sugar-0.27671*reduction of the flue gas B[a]P of the roasted slice to be measured can be calculated Sugar+2.26355*total volatile bases-3.43614*Shimuke value+0.09806*potassium-4.12070*ammonia base=11.958. In order to verify the reliability of the prediction results of the model, the B[a]P value of the flue gas of the baked sheet was determined to be 11.3 by using the traditional detection method.

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention provides a method for predicting B[a]P (benzopyrene) in smoke of flue-cured tobacco strips based on Robust regression modeling. According to the method, a model from a physicochemical index item to smoke B[a]P is built through the existing flue-cured tobacco strip physicochemical data and the smoke B[a]P data; and for unknown flue-cured tobacco strip smoke B[a]P samples, physicochemical ingredient data of the flue-cured tobacco strip smoke B[a]P samples can be used for directly predicting the flue-cured tobacco strip smoke B[a]P value. The method provided by the invention has the advantages that the steps of rolling, burning, smoke catching, detection and the like in a traditional chemical mode are omitted; meanwhile, a Robust regression model is adopted; the defects caused by singular value samples in the physicochemical data or smoke data can be effectively avoided; and the robustness of the model is ensured to a great degree, and the point is the advantage of the Robust regression modeling superior to the ordinary linear regression modeling. Practice proves that the model can be used for effectively predicting the smoke B[a]P value, the detection efficiency is greatly improved, and the detection cost is reduced.

Description

technical field [0001] The invention relates to a method for predicting benzopyrene in flue gas of baked sheets 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. Benzopyrene (hereinafter referred to as: B[a]P) is a class of organic compounds with obvious carcinogenic effects. The traditional way to o...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 白晓莉彭国岗段如敏吴丽君周桂圆王保兴卢伟刘挺
Owner CHINA TOBACCO YUNNAN IND
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com