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Visualization method for detecting spatial distribution uniformity of ginkgo leaves by adopting artificial intelligence hyperspectral imaging

A technology of hyperspectral imaging and Ginkgo biloba leaves, which is applied in color/spectral characteristic measurement, material analysis through optical means, image enhancement, etc. It can solve the problems that the spatial distribution uniformity of ingredients affects the efficacy of drugs, and affects the safety and effectiveness of clinical medication. Achieve the effect of improving quality control level, ensuring safety and effectiveness

Pending Publication Date: 2022-07-26
BEIJING UNIV OF CHINESE MEDICINE
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AI Technical Summary

Problems solved by technology

[0003] Ginkgo biloba leaves are a large variety of traditional Chinese medicine, and the spatial distribution uniformity of the ingredients may affect the efficacy of the medicine, which will closely affect the safety and effectiveness of clinical medication.

Method used

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  • Visualization method for detecting spatial distribution uniformity of ginkgo leaves by adopting artificial intelligence hyperspectral imaging
  • Visualization method for detecting spatial distribution uniformity of ginkgo leaves by adopting artificial intelligence hyperspectral imaging
  • Visualization method for detecting spatial distribution uniformity of ginkgo leaves by adopting artificial intelligence hyperspectral imaging

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0033] Example 1: Prediction of Ginkgo biloba leaf content based on PLS model

[0034] Sample preparation: Take 18 batches of 4 tablets in each batch, a total of 72 ginkgo leaf element tablets (uncoated tablets) as samples, in which the content of ginkgo leaf extract is about 25.57%.

[0035] Hyperspectral image acquisition: The ginkgo leaf samples were placed horizontally on the workbench, and images were acquired in push-broom mode using SisuCHEMA hyperspectral chemical imaging workstation (SPECIM, Finland). Under the condition that the lens can focus normally, adjust the distance between the lens and the sample, and finally determine the parameters of image acquisition: the lens height is 15cm, the frame frequency is 42.02Hz, the integration time is 4.20ms, the spectral range is 970-2575nm, and the spectral resolution is The size of the acquired hyperspectral data cube is 50 pixels × 50 pixels × 288, that is, the length and width are both 50 pixels, and the number of spectr...

Embodiment 2

[0039] Example 2: Prediction of Ginkgo biloba leaf content based on CLS model

[0040] Sample preparation: Take 18 batches of 4 tablets each, with a total of 72 ginkgo leaf elements as samples. The components of ginkgo leaf include ginkgo leaf extract (about 25.57%) and 7 kinds of excipients (starch, low-substituted hydroxypropyl cellulose, microcrystalline cellulose, lactose, sodium starch glycolate, aluminum hydroxide and magnesium stearate).

[0041] Hyperspectral image acquisition: The ginkgo leaf sample was placed horizontally on the workbench, and the ginkgo leaf extract and 7 kinds of excipient powders were placed in a watch dish with a size of 30 mm × 30 mm, and the surface was compacted and kept as flat as possible. Using SisuCHEMA hyperspectral chemical imaging workstation (SPECIM, Finland), in push-broom mode, hyperspectral images of the above samples were collected with the following parameters: the lens height was 15 cm, the frame frequency was 42.02 Hz, the integ...

Embodiment 3

[0046] Example 3: Prediction of Ginkgo biloba leaf content based on MCR-ALS model

[0047] Sample preparation: Take 18 batches of 4 tablets each, with a total of 72 ginkgo leaf element tablets as samples. The components of ginkgo leaf leaf include Ginkgo biloba leaf extract (about 25.57%) and 7 kinds of auxiliary materials (starch, low-substituted hydroxypropyl cellulose, microcrystalline cellulose, lactose, sodium starch glycolate, aluminum hydroxide and magnesium stearate), a total of 8 ingredients.

[0048] Hyperspectral image acquisition: The ginkgo leaf sample was placed horizontally on the workbench, and the SisuCHEMA hyperspectral chemical imaging workstation (SPECIM, Finland) was used to collect hyperspectral images of the above samples in push-broom mode with the following parameters: the lens height was 15cm, The frame frequency is 42.02Hz, the integration time is 4.20ms, the spectral range is 970-2575nm, and the spectral resolution is 8nm. The size of the acquired h...

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Abstract

The invention provides a visualization method for detecting the spatial distribution uniformity of ginkgo leaves by adopting artificial intelligence hyperspectral imaging. The method comprises the following steps: step 1, constructing a prediction model for the content of a target component in the ginkgo leaves; 2, reconstructing a concentration spatial distribution diagram of target components in the ginkgo leaves; step 3, establishing a ginkgo leaf space distribution uniformity evaluation method; and step 4, obtaining the evaluation result of the spatial distribution uniformity of the ginkgo leaves, firstly applying an artificial intelligence visualization method to research on the spatial distribution uniformity of the traditional Chinese medicine large-variety ginkgo leaves, creatively constructing a target component content prediction model, further evaluating the spatial distribution uniformity, and providing a new technical support for quality control of the ginkgo leaves.

Description

technical field [0001] The invention belongs to the field of artificial intelligence, and in particular relates to a visualization method for detecting the spatial distribution uniformity of ginkgo leaves by using artificial intelligence hyperspectral imaging. [0002] technical background [0003] Ginkgo biloba leaves are a large variety of traditional Chinese medicine, and the spatial distribution uniformity of the components may affect the efficacy and the safety and effectiveness of clinical drug use. As the key quality attribute of Ginkgo biloba leaves, the spatial distribution uniformity, the visualization research of it is in line with the practical needs. [0004] Hyperspectral imaging can obtain three-dimensional data, including spectral information and spatial information of samples, and is a key technology to visualize the uniformity of material spatial distribution. Hyperspectral imaging as a Process Analytical Technology (PAT) is FDA-approved and actively recomm...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N21/27G01N21/84G06T5/40
CPCG01N21/27G01N21/84G06T5/40G01N2021/8466
Inventor 吴志生林玲
Owner BEIJING UNIV OF CHINESE MEDICINE
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