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Spectrum baseline correction method, system and detection method in near infrared spectrum analysis of tea leaves

A near-infrared spectrum and baseline correction technology, which is applied in the field of spectral baseline correction in tea near-infrared spectrum analysis, can solve problems such as inaccurate correction results, unaccounted for error change trends, underfitting of weight updates, etc., to reduce optimization process, avoiding untimely weight updates, and the effect of accurate baseline correction analysis

Active Publication Date: 2020-06-19
ANHUI UNIVERSITY
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AI Technical Summary

Problems solved by technology

The most widely used method is the baseline correction method based on the minimum penalty method. Although this method can iteratively update the weights point by point, it does not consider the error trend in the iterative process, and when the corrected baseline contains multiple connected characteristic peaks , it is easy to produce underfitting phenomenon caused by untimely weight update, which leads to inaccurate correction results

Method used

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  • Spectrum baseline correction method, system and detection method in near infrared spectrum analysis of tea leaves
  • Spectrum baseline correction method, system and detection method in near infrared spectrum analysis of tea leaves
  • Spectrum baseline correction method, system and detection method in near infrared spectrum analysis of tea leaves

Examples

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Embodiment 1

[0070] Such as figure 1 As shown, this embodiment provides a method for correcting the baseline of the spectrum in the near-infrared spectrum analysis of tea, specifically:

[0071] Step 1: Data acquisition and parameter initialization; the collected spectral data of green tea is ( figure 2 ), the sugar content data is The initialization parameters are set as: smoothing coefficient λ=10 4 , iteration times T=30, balance coefficient α=60, weight coefficient W 0 =[1,1,...,1] and relative fitting error coefficient δ=10 -3 , fit the baseline difference matrix:

[0072]

[0073] Step 2: Calculate the fitting baseline; establish a penalty least squares optimization function based on the analysis data and initialization parameters:

[0074] J=(y-z) T W(y-z)+λz T D. T Dz (12)

[0075] Further calculate the partial derivative of formula (12), and obtain the calculation formula of the fitted baseline based on the weight coefficient:

[0076] z=(W+λD T D) -1 Wy (13)

...

Embodiment 2

[0097] Corresponding to Example 1, this example provides a spectral baseline correction system in the process of tea near-infrared spectral analysis, including:

[0098] Tea sample collection module: collect tea samples, obtain tea near-infrared spectral data, and form original data; data acquisition and parameter initialization module: collect green tea spectral data as ( figure 2 ), the sugar content data is The initialization parameters are set as: smoothing coefficient λ=10 4 , iteration times T=30, balance coefficient α=60, weight coefficient W 0 =[1,1,...,1], and the relative fitting error coefficient δ=10 -3 , fitting the baseline difference matrix

[0099]

[0100] Calculation and fitting baseline module: according to the penalty least squares method, the fitting baseline is calculated based on the weight coefficient;

[0101] Correction error module: Calculate the difference between the original data and the fitted baseline;

[0102] Interval confirmation ...

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Abstract

The invention discloses a spectrum baseline correction method, system and detection method in near infrared spectrum analysis of tea leaves. The method comprises the following steps: acquiring original data, initializing, and fitting a baseline; calculating a difference value between the original data and the fitting baseline to obtain a correction error; determining a baseline interval and a feature interval according to the correction error; calculating an index weight according to the weight function, calculating a change trend based on the error derivative, and determining an updated weight coefficient in combination with the balance coefficient; updating a baseline according to the calculated weight coefficient; and calculating a relative fitting error and a current iteration frequency of the original data and the fitting baseline, if the relative fitting error is less than a set error coefficient delta or the current iteration frequency is greater than a set iteration frequency T, turning to output, and otherwise, circulating. The self-adaptive derivative weighting function can analyze and correct the size and change trend of the error at the same time, so that weight updating can be performed according to the characteristics of the error, and the problems of untimely and inaccurate weight updating and the like can be avoided.

Description

technical field [0001] The invention belongs to the field of spectral analysis baseline correction, and specifically relates to a spectrogram baseline correction method, system and detection method in tea near-infrared spectral analysis. Background technique [0002] Tea is currently one of the most important beverages in our country. The classification of tea quality is related to various indicators such as tea polyphenols, caffeine, and amino acids. For a long time, people's grasp of the quality of each process in tea processing has mainly relied on sensory evaluation, lack of quantitative processing evaluation standards, and the identification of processed product quality is mainly based on sensory evaluation methods, lacking a method for the main chemical components and A digital rapid evaluation method that takes into account the external shape and characteristics. With the continuous development of my country's import and export trade and the continuous improvement o...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N21/3563G01N21/359
CPCG01N21/3563G01N21/359
Inventor 潘天红李鱼强陈琦宁井铭
Owner ANHUI UNIVERSITY
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