A method and system for quantitative analysis of infrared spectroscopy based on self-paced learning

An infrared spectroscopy and quantitative analysis technology, applied in the field of infrared spectroscopy quantitative analysis and chemometrics, can solve the problems of unstable partial least squares weight vector solution process, unstable infrared spectroscopy quantitative analysis results, etc., to achieve good stability and The effect of generalization ability, good processing, strong anti-noise ability

Active Publication Date: 2019-10-22
百川实验科学技术研究院(广州)有限公司
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, the solution process of the partial least squares weight vector based on the least squares method is often unstable, which leads to unstable quantitative analysis results of infrared spectroscopy.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A method and system for quantitative analysis of infrared spectroscopy based on self-paced learning
  • A method and system for quantitative analysis of infrared spectroscopy based on self-paced learning
  • A method and system for quantitative analysis of infrared spectroscopy based on self-paced learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0101] The principles and features of the present invention are described below in conjunction with the accompanying drawings, and the examples given are only used to explain the present invention, and are not intended to limit the scope of the present invention.

[0102] Such as figure 1 As shown, a method for quantitative analysis of infrared spectroscopy based on self-paced learning, including:

[0103] S1. Extract the infrared spectrum matrix of the standard sample and the infrared spectrum matrix of the sample to be tested from the infrared spectrum data of the standard sample and the infrared spectrum data of the sample to be tested respectively, and obtain the concentration data of the standard sample;

[0104] Specifically, dimensionality reduction or band selection is performed on the infrared spectrum data of the standard sample and the sample to be tested, respectively, the infrared spectrum matrix of the standard sample and the infrared spectrum matrix of the sampl...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a self-paced learning based infrared spectroscopy quantitative analysis method and system. The method comprises the following steps: step one, extracting an infrared spectroscopy matrix of a standard sample and an infrared spectroscopy matrix of a sample to be detected from the infrared spectroscopy data of the standard sample and the infrared spectroscopy data of the sample to be detected, and obtaining the concentration data of the standard sample; step two, according to the infrared spectroscopy matrix of the standard sample and the concentration data of the standard sample, obtaining the partial least squares weight vector representing the relationship between the e infrared spectroscopy matrix and the concentration data; and step three, according to the partial least squares weight vector, establishing a partial least squares prediction model, and inputting the infrared spectroscopy matrix of the sample to be detected into the partial least squares prediction model to calculate the concentration data of the sample to be detected. Compared with the conventional partial least squares algorithm, the provided method has better stability and generalization ability and a strong anti-noise performance.

Description

technical field [0001] The invention relates to the fields of infrared spectrum quantitative analysis and chemometrics, in particular to an infrared spectrum quantitative analysis method and system based on self-paced learning. Background technique [0002] Infrared spectroscopy is an efficient and fast modern analysis technology, which comprehensively uses the research results of computer technology, spectroscopic technology and chemometrics, etc. an increasingly widespread role. Infrared spectroscopy has rich structure and composition information, can accurately reflect the degree of absorption of infrared light by different molecular structures, and is very suitable for the measurement of the composition properties of hydrocarbon organic substances (such as the component concentration of substances). With the rapid development of chemometrics and chemical analysis technology, the partial least squares regression algorithm is the most commonly used method in the quantitat...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Patents(China)
IPC IPC(8): G01N21/35
Inventor 彭江涛陈娜付辉敬
Owner 百川实验科学技术研究院(广州)有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap