Near infrared spectrum information extraction method based on canonical correlation coefficients

A near-infrared spectroscopy and typical correlation technology, applied in the field of tobacco composition analysis, can solve the problems of reduced model accuracy, reduced model accuracy, near-infrared spectral absorbance interference, etc., to improve stability, improve interpretability, and eliminate multicollinearity. Effect

Inactive Publication Date: 2016-08-24
CHINA TOBACCO ZHEJIANG IND
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] First, the material information contained in the near-infrared spectral signal is very weak, and the effective spectrum difference of the sample in some spectral regions is very small, which cannot clearly provide the measured component information of the sample;
[0006] Second, in some bands, there is no linear correlation between the near-infrared spectral information of the sample and the measured composition or properties of the sample. Once the linear partial least squares method is selected for the modeling method, the accuracy of the model will be reduced;
[0008] Fourth, due to the noise of near-infrared spectroscopy instruments, in some bands, the sample spectral signal-to-noise ratio (SNP) is low, and the spectral quality is poor. If these noises are not filtered out, the model is not robust;
[0009] Fifth, if full-spectrum modeling is used, once the external environmental factors change, it will interfere with the absorbance of the near-infrared spectrum, making the detected sample an outlier;
[0010] Sixth, when

Method used

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  • Near infrared spectrum information extraction method based on canonical correlation coefficients
  • Near infrared spectrum information extraction method based on canonical correlation coefficients
  • Near infrared spectrum information extraction method based on canonical correlation coefficients

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0063] 358 tobacco leaf samples were selected online in Yunnan Rebaking Factory A. The selection method was as follows: on the production line, a sheet tobacco sample was picked manually every 5 seconds, and the sheet tobacco samples were mixed within 1 minute to form a mixed sample. Each mixed sample was for a tobacco leaf sample.

[0064] The nicotine content of the basic data of all tobacco leaf samples was tested according to "YC / T160 Determination of Total Phytoalkaloids in Tobacco and Tobacco Products".

[0065] The first 326 samples of the 358 tobacco leaf samples were used as the training set, and the last 32 tobacco leaf samples obtained online were used as the independent verification set of the model.

[0066] Utilize German Carl Zeiss online near-infrared instrument to scan to obtain the near-infrared spectra of all tobacco leaf samples, set the working parameters as: scanning range: 910-2200nm; wavelength accuracy is less than 0.5nm; wavelength repeatability is le...

Embodiment 2

[0074] The difference from Embodiment 1 is that the spectral segments are divided in a different way, and 35 wavelength points are randomly selected for each spectral segment, and there are no identical wavelength points in each spectral segment.

[0075] The wavelength point with a typical correlation coefficient greater than 0.9 is used as the wavelength point for modeling, the model is established by PLS, and the independent verification set is used for verification.

[0076] performance comparison

[0077] Using the independent verification set in Example 1 and using different wavelength point selection methods to compare the results of chemical value measurement results are shown in Table 1.

[0078] Table 1

[0079] serial number

[0080] As shown in Table 1, the method provided by the present invention can more effectively extract infrared spectral information, which confirms the existence of interactive information in near-infrared spectral information, and ...

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Abstract

The invention discloses a near infrared spectrum information extraction method based on canonical correlation coefficients. The method comprises the steps that 1, original near infrared spectra and chemical values of tobacco leaves are acquired; 2, the abnormal spectra in the original near infrared spectra and the abnormal values in the chemical values are eliminated; 3, the near infrared spectrum is partitioned into a plurality of spectrum sections, and canonical correlation analysis is conducted on the near infrared spectrum and the chemical value in each spectrum section to calculate the canonical correlation coefficients; 4, a threshold value is selected, and PLS modeling is conducted on the wavelength points where the canonical correlation coefficients are larger than the threshold value. According to the near infrared spectrum information extraction method based on the canonical correlation coefficients, the explanation capacity of the near infrared spectrum can be improved, the multicollinearity of near infrared spectrum information can be eliminated, and the stability of predicting a chemical component model through the near infrared spectrum can be improved.

Description

technical field [0001] The invention relates to the technical field of tobacco leaf component analysis, in particular to a method for extracting near-infrared spectral information based on typical correlation coefficients. Background technique [0002] Near-infrared spectroscopy is widely used in the field of quantitative rapid detection, and provides a good method for obtaining basic material data for on-site quality control. The near-infrared spectrum belongs to the electromagnetic wave in the 850-2500nm band, and different types of spectra reflect molecular information at different sample levels. Near-infrared spectral information approximately conforms to the Lambert-Beer formula, which provides a theoretical basis for extracting composition and structure information of detected substances from near-infrared spectral information. [0003] However, in the actual near-infrared testing process, it is faced with complex changes in the use environment, annual differences in ...

Claims

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Application Information

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IPC IPC(8): G01N21/359
CPCG01N21/359
Inventor 吴继忠徐清泉夏琛吴键廖付李石头张军夏骏苏燕毕一鸣慕继瑞张立立李永生何文苗郝贤伟
Owner CHINA TOBACCO ZHEJIANG IND
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