Angelica dahurica decoction piece quality prediction method based on hyperspectral imaging depth analysis

A technology of hyperspectral imaging and quality prediction, applied in the direction of color/spectral characteristic measurement, analysis materials, measurement devices, etc., can solve problems affecting analysis results, etc., and achieve the effect of assisting product classification and production decision-making

Pending Publication Date: 2021-06-22
ZHEJIANG UNIV OF TECH
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Problems solved by technology

A large amount of redundant information may affect the analysis results

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  • Angelica dahurica decoction piece quality prediction method based on hyperspectral imaging depth analysis
  • Angelica dahurica decoction piece quality prediction method based on hyperspectral imaging depth analysis
  • Angelica dahurica decoction piece quality prediction method based on hyperspectral imaging depth analysis

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[0060] The present invention will be further described below in conjunction with the accompanying drawings.

[0061] refer to Figure 1 ~ Figure 2 , a method for predicting the quality of Angelica dahurica decoction pieces based on hyperspectral imaging depth analysis, the method comprises the following steps:

[0062] Step 1. Obtain a sample of Angelica dahurica powder

[0063] First obtain the sample of Angelica dahurica decoction pieces, then crush the sample of Angelica dahurica decoction pieces with a high-speed multifunctional grinder for 1 minute, and pass the powder through a No. 3 sieve. The fine powder of Angelica dahurica was collected for the collection of hyperspectral information. In order to obtain the powder samples of Angelica dahurica with a large concentration range of imperatorin content and establish a more universal model, some powder samples of Angelica dahurica were subjected to deliquescent treatment.

[0064] Step 2. Obtain the data information of ...

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Abstract

The invention discloses an angelica dahurica decoction piece quality prediction method based on hyperspectral imaging depth analysis. The method comprises the following steps of: step 1, obtaining anangelica dahurica powder sample; step 2, acquiring data information of the angelica dahurica powder sample; step 3, preprocessing hyperspectral data; 4, selecting a characteristic wave band, removing an abnormal sample, and preparing a modeling data set; 5, dividing the data set and establishing a DBN prediction model; and step 6, performing model performance evaluation. According to the method, modeling is carried out on the basis of a deep belief network with a wavelength feature selection method for diversified hyperspectral images, and the most efficient prediction model is found, so that the imperatorin content in various anangelica dahurica decoction pieces can be rapidly and accurately quantified, efficient quality prediction of traditional Chinese medicine preparations is realized, and product grading and production decision making are assisted.

Description

technical field [0001] The invention relates to the technical field of quality detection of traditional Chinese medicines, in particular to a method for predicting the quality of decoction pieces of Angelica dahurica based on hyperspectral imaging depth analysis. Background technique [0002] With its complete system and good therapeutic effect, traditional Chinese medicine is unique and widely used in traditional medicine around the world. At the same time, the safety incidents in the process of traditional Chinese medicine treatment have also attracted close attention from all over the world, and issues such as product quality compliance, norms and standards have yet to be resolved. Angelica dahurica is the dried root of Angelica dahurica or Hang Baizhi, which belongs to the genus Angelica in the family Umbelliferae. Because of its various pharmacological effects such as antipyretic, antibacterial, analgesic, antitumor and anti-inflammatory, it is often used to treat cold...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N21/25G01N30/02
CPCG01N21/25G01N30/02
Inventor 刘毅刘凯新徐宁娄维尧
Owner ZHEJIANG UNIV OF TECH
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