Method for rapidly detecting potassium content of tobacco leaves based on electronic nose-artificial neural network

An artificial neural network and electronic nose technology, applied in neural learning methods, biological neural network models, measuring devices, etc., can solve the problem of not seeing potassium, and achieve the effects of simple model, comprehensive and accurate data, and simple potassium content.

Active Publication Date: 2016-09-28
启东赢维数据信息科技有限公司
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  • Application Information

AI Technical Summary

Problems solved by technology

And there is no report on the application of electronic nose technology to determine potassium in tobacco

Method used

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  • Method for rapidly detecting potassium content of tobacco leaves based on electronic nose-artificial neural network
  • Method for rapidly detecting potassium content of tobacco leaves based on electronic nose-artificial neural network
  • Method for rapidly detecting potassium content of tobacco leaves based on electronic nose-artificial neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0118] Embodiment 1: the modeling result of the artificial neural network prediction model of potassium content in 132 tobacco leaves, as figure 2 shown.

[0119] The artificial neural network algorithm was used to perform regression modeling on 132 preprocessed tobacco electronic nose data, and the artificial neural network prediction model of potassium content in tobacco was established. The correlation coefficient between the calculated value and the experimental detection value of potassium content in tobacco leaves was 0.93, the average Error 8.41%.

Embodiment 2

[0120] Embodiment 2: the leave-one-out result of the artificial neural network prediction model of potassium content in 132 tobacco leaves, such as image 3 shown.

[0121] The internal cross-validation of the artificial neural network prediction model of potassium content established by 132 tobacco leaf samples was carried out by leave-one-out method. The correlation coefficient between the calculated potassium content in tobacco leaves and the experimentally detected value by leave-one-out method was 0.91, and the average relative error was 9.64%.

Embodiment 3

[0122] Embodiment 3: to the prediction result of potassium content of 41 new tobacco samples, as Figure 4 shown.

[0123] The artificial neural network prediction model of potassium content in tobacco leaves was used to predict 41 new tobacco samples, and good prediction results were obtained. The correlation coefficient between calculated potassium content and experimentally detected value was 0.90, and the average relative error was 10.91%.

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Abstract

The invention relates to a method for rapidly detecting the potassium content of tobacco leaves based on electronic nose-artificial neural network. The method comprises the following steps: 1, collecting several tobacco leaf samples in different producing areas, preprocessing the tobacco leaf samples, and carrying out electronic nose scanning to obtain electronic nose data of every tobacco leaf sample; 2, detecting the potassium content of every tobacco leaf sample by adopting flame photometry; 3, carrying out dimension reduction on the electronic nose data by adopting partial least squares to obtain the dimension reduction data of every tobacco leaf sample; 4, establishing a rapid forecasting model of the potassium content of tobacco leaves by adopting an artificial neural network algorithm with the dimension reduction data of every tobacco leaf sample as an independent variable and the potassium content of every tobacco leaf sample as a dependent variable; and 5, rapidly forecasting the potassium content of a tobacco leaf kind to be detected according to the established rapid forecasting model of the potassium content of tobacco leaves and the electronic nose data of the tobacco leaf kind to be detected. The method has the advantages of simplicity, rapidness, low cost, comprehensive and accurate data, no pollution and simple test.

Description

technical field [0001] The invention relates to the technical field of component testing in tobacco samples, in particular to a method for rapidly detecting potassium content in tobacco leaves based on an electronic nose-artificial neural network. Background technique [0002] The main chemical components in tobacco leaves are total sugar, reducing sugar, nicotine, starch, chlorogenic acid, scopoletin, rutin, total nitrogen, potassium, calcium, magnesium, water-soluble chlorine and petroleum ether extract, etc. The content of various chemical components in tobacco leaves is complex, and various chemical components in tobacco such as sugars, phenols, nitrogen heterocycles, nicotine, etc. will vary with the growing geographical environment and climatic conditions. The chemical components in these tobacco leaves have a decisive impact on the quality of the tobacco leaves, and the content of these chemical components is not only related to the quality of the tobacco, but also af...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N33/00G01N21/71G06N3/08
CPCG01N21/71G01N33/00G06N3/08
Inventor 刘太昂陆文聪纪晓波张庆卢凯亮胡彪
Owner 启东赢维数据信息科技有限公司
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