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Spectral baseline correction method, system and detection method in tea near-infrared spectral analysis

A near-infrared spectroscopy and baseline correction technology, which is applied in the field of spectral baseline correction in the near-infrared spectroscopy analysis of tea, can solve the problems of under-fitting in weight update, inaccurate correction results, and failure to consider error trends, and achieve a wide range of parameter selection. , reduce the optimization process, and avoid the effect of untimely weight update

Active Publication Date: 2021-09-03
ANHUI UNIVERSITY
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  • Application Information

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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  • Spectral baseline correction method, system and detection method in tea near-infrared spectral analysis
  • Spectral baseline correction method, system and detection method in tea near-infrared spectral analysis
  • Spectral baseline correction method, system and detection method in tea near-infrared spectral analysis

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Embodiment 1

[0070] like figure 1 As shown, the present embodiment provides a method of calculating the spectrum of tea near infrared spectroscopy, in particular:

[0071] Step 1: Data acquisition and parameter initialization; collecting green tea spectrum data is ( figure 2 ), The sugar content data is The initialization parameter is set to: smooth coefficient λ = 10 4 , Number of iterations T = 30, balance coefficient α = 60, weight coefficient W 0 = [1, 1, ..., 1] and relative fitting error coefficient δ = 10 -3 , Fit baseline differential 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 Di T DZ (12)

[0075] Further, the equation (12) is further deflected, and the calculation formula based on the weight coefficient calculation of the calculation of the proposed baseline:

[0076] z = (w + λd T D) -1 WY (13)

[0077] Step ...

Embodiment 2

[0097] Compared with Embodiment 1, the present embodiment provides a spectral baseline correction system during a tea near infrared spectroscopy, including:

[0098] Tea sample collection module: collecting tea samples, gets tea near infrared spectroscopy, forming raw data; data acquisition and parameter initialization module: collecting green tea spectral data ( figure 2 ), The sugar content data is The initialization parameter is set to: smooth coefficient λ = 10 4 , Number of iterations T = 30, balance coefficient α = 60, weight coefficient W 0 = [1, 1, ..., 1], and relative fitting error coefficient Δ = 10 -3 , Fit baseline differential matrix

[0099]

[0100] Calculate the fitting baseline module: according to the penalty minimum multiplier, based on the equipped baseline calculated by the weight coefficient;

[0101] Correction error module: calculate the difference between the original data and the fitting baseline;

[0102] Interval Confirmation Module: Determine the ...

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Abstract

The invention discloses a spectrogram baseline correction method and system in tea near-infrared spectrum analysis and a tea detection method, including: acquiring original data and initializing, fitting the baseline; calculating the difference between the original data and the fitting baseline, and obtaining the correction Error; determine the baseline interval and characteristic interval according to the size of the correction error; calculate the index weight according to the weight function, calculate the change trend based on the error derivative, and determine the update weight coefficient in combination with the balance coefficient; update the baseline according to the calculated weight coefficient; calculate the original data and The relative fitting error of the fitted baseline and the current number of iterations. If the relative fitting error is less than the set error coefficient δ or the current iteration number is greater than the set iteration number T, turn to output, otherwise loop. The adaptive derivative weighting function can simultaneously analyze the correction error size and change trend, so it can update the weight according to the characteristics of the error itself, so as to avoid the problems of untimely and inaccurate weight update.

Description

Technical field [0001] The present invention belongs to the spectral baseline correction, and in particular relates to tea Near Infrared Spectroscopy spectra baseline correction method, system, detection methods. Background technique [0002] Tea is now one of the most important of our drinks. A variety of indicators and its level of quality tea division contains polyphenols, caffeine, amino acids and other related. For a long time, people tea processing quality control of each step depends sensory evaluation, the lack of quantitative evaluation criteria processing, after processing method for determining the quality of the product is mainly used sensory evaluation, the lack of a major chemical composition and external morphological characteristics into account, the digitization rapid assessment methods. With the import and export trade in my country and the continuous improvement of people's material needs, grading and quality for the industry to identify the requirements put fo...

Claims

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Application Information

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Patent Type & Authority Patents(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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