Analysis method for artificial intelligence learning materials on basis of infrared and Raman spectrum data

A technology of artificial intelligence and Raman spectroscopy, applied in the field of spectral analysis and detection, can solve the problems of a large number of professionals and time costs, and cannot be widely used on a large scale, achieving huge application prospects and potentials, and reducing time and labor costs.

Inactive Publication Date: 2017-05-24
中山市腾创贸易有限公司
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] It can be seen from the above that traditional spectral analysis methods rely on the calibration and modeling of each independently analyzed sample, which usually requires a large amount of chemical analysis and model

Method used

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  • Analysis method for artificial intelligence learning materials on basis of infrared and Raman spectrum data
  • Analysis method for artificial intelligence learning materials on basis of infrared and Raman spectrum data

Examples

Experimental program
Comparison scheme
Effect test

Embodiment approach 1

[0026] Quality inspection and monitoring of apple samples:

[0027] Step 1: Collect infrared absorption spectra and Raman emission spectra of 5000 apples respectively. For each apple sample, 50 parallel tests were performed, thus obtaining 50*5000*2=500000 sets of spectral data;

[0028] Step 2: Perform statistical analysis on infrared and Raman spectral data to remove statistically unexpected or invalid data, thereby obtaining effective spectral data sets (about 499,800 sets of data);

[0029] Step 3, use the artificial intelligence analysis program to automatically identify and classify the effective spectral data groups, process the infrared and Raman spectral data in parallel, and automatically classify the samples (about 500 groups);

[0030] Step 4: Perform sampling chemical method analysis on samples in each group to obtain accurate chemical composition data (moisture, texture, sweetness, acidity, pesticide residues, pollutant content, etc.) of each sampling sample;

...

Embodiment approach 2

[0036] Quality inspection and anti-counterfeiting of pharmaceutical tablets

[0037] In step 1, infrared absorption spectra and Raman emission spectra were collected for 5000 kinds of drug tablets respectively. For each drug sample, 50 parallel tests were performed, thus obtaining 50*5000*2=500000 sets of spectral data;

[0038] Step 2: Statistically analyze the infrared and Raman spectral data of each drug sample to remove statistically unexpected or invalid data, so as to obtain an effective spectral data set (about 499,800 sets of data);

[0039] Step 3, use the artificial intelligence analysis program to automatically identify and classify the effective spectral data sets marked with drug types, process the infrared and Raman spectral data in parallel, and automatically classify random samples, in which each drug can appear In multiple classifications to establish different latitude distinguishing algorithm models;

[0040] Step 4: Collect infrared absorption spectrum an...

Embodiment approach 3

[0046] Composition determination and health analysis of human skin

[0047] In step 1, the infrared absorption spectrum and Raman emission spectrum are collected respectively on the facial skin of 10,000 tested people. For each tested person, 50 parallel tests are performed at different positions on the face, thus obtaining 50*10000*2=1000000 sets of spectral data;

[0048] Step 2: Statistically analyze the infrared and Raman spectral data of each tested person to remove statistically unexpected or invalid data, so as to obtain an effective spectral data set (about 999,800 sets of data);

[0049] Step 3, using an artificial intelligence analysis program to automatically identify and classify effective spectral data groups with source object tags, parallel processing of infrared and Raman spectral data, and automatically classify random samples;

[0050] Step 4: Collect infrared absorption spectra and Raman spectra from more people under test, import the spectral data into the...

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Abstract

The invention provides a rapid analysis and processing method for infrared and Raman spectrum data on the basis of artificial intelligence. The rapid analysis and processing method comprises the following steps of: acquiring an infrared and Raman spectrum library of a single-sample learning group; carrying out statistical filtering on data of the spectrum library to remove suspect and invalid data; carrying out unsupervised learning classification on the spectrum library of the learning group by artificial intelligence; carrying out chemical analysis, verification and classification on every type of samples distinguished by classification results; if a sampling result can not meet the application needs, returning an artificial intelligence program and carrying out supervised learning classification by using chemical analysis results; by multiple repeated classification and iteration, using the artificial intelligence program to generate an analysis prediction model capable of meeting the application needs automatically; and using the model for rapid spectrum analysis and detection of unknown samples in the type of samples. The rapid analysis and processing method provided by the invention is applicable to rapid spectrum analysis and detection of a large amount of samples, can realize accuracy adjustment according to the needs, and can be rapidly applied in different sample testing scenes, so that the application prospect and potential are great.

Description

technical field [0001] The invention belongs to the field of spectral analysis and detection, and in particular relates to a method for performing spectral analysis modeling based on artificial intelligence self-learning and mapping the chemical components of a target analyte. Background technique [0002] Both infrared absorption spectroscopy and Raman emission spectroscopy are spectral analysis techniques based on molecular vibration information of substances. Through the specific absorption of different molecules in different light bands, infrared absorption spectroscopy can provide information on the molecular components of substances. Through the specific emission of different wavelength bands produced by different molecules under excitation light, Raman spectroscopy can provide information on the molecular composition of substances. These two spectral analysis methods are widely used in production quality monitoring and hazardous substance detection due to their advan...

Claims

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Application Information

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IPC IPC(8): G01N21/3563G01N21/65
CPCG01N21/3563G01N21/65
Inventor 刘玮赵耀黄晋卿
Owner 中山市腾创贸易有限公司
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