Method for maintaining predication capability of spectrum correction model of complex system

A technology for calibrating models and spectra, applied in the measurement of color/spectral characteristics, special data processing applications, instruments, etc., can solve problems such as demanding data requirements, inappropriate maintenance methods of spectral calibration models for complex systems, changes, etc.

Inactive Publication Date: 2010-10-20
HUNAN UNIV
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Problems solved by technology

[0022] The technical problem to be solved by the present invention is, in order to overcome the disadvantages that the existing spectral correction model maintenance method is not suitable for complex systems, the requirements for data are relatively strict, and cannot be applied to online monitoring of complex multiphase chemical and biological systems, etc., a new method is proposed. A method for maintaining the prediction ability of the spectral correction model of a complex system, in order to solve the general problem that the accuracy of the prediction result of the spectral correction model changes with the change of the spectroscopic instrument or experimental conditions in practical applications, and realize that the spectral correction model is better than other instruments of the same kind sharing between, and effectiveness for long-term use even under changing experimental conditions

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  • Method for maintaining predication capability of spectrum correction model of complex system
  • Method for maintaining predication capability of spectrum correction model of complex system
  • Method for maintaining predication capability of spectrum correction model of complex system

Examples

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Effect test

Embodiment 1

[0058] Example 1: Maintenance of the near-infrared spectrum correction model of the active ingredient in the tablet

[0059] This example uses published near-infrared spectral data (http: / / www.idrc-chambersburg.org / shootout2002.html) to test the performance of the present invention in maintaining the predictive ability of the spectral correction model when the spectral instrument changes. The spectral data is composed of a total of 1308 absorption spectra of 655 tablets measured on two Foss NIRsystems near-infrared spectrometers (one called "master spectrometer" and the other called "slave spectrometer") (wavelength range: 600nm~1638nm, wavelength interval: 2nm). The content of the active ingredient in each tablet was determined by high performance liquid chromatography. The 655 spectra measured on each instrument were divided into calibration set (155 spectra), test set (460 spectra) and validation set (40 spectra). The content of the active ingredient in the calibration ta...

Embodiment 2

[0067] Example 2: Maintenance of the near-infrared spectrum calibration model of antibiotic products in the process of biological fermentation

[0068] In this embodiment, near-infrared spectral data recorded during a biological reaction is used to test the performance of the present invention in maintaining the prediction ability of the spectral correction model when the experimental conditions change. The bioreaction is a fermentation of Streptomyces in a 12-liter reactor to produce antibiotics. The process involves two phases, namely: the growth phase and the production phase. The production phase is a batch process with a duration of around 140 hours. In this example, a total of two sets of experiments were carried out. The first group consists of 7 batch processes called "correction batch processes". The second group consists of 3 batch processes called "Test Batch Process". The seven calibration batch processes were all carried out under the same experimental conditi...

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Abstract

The invention provides a method for maintaining the predication capability of a spectrum correction model of a complex system under the conditions that an optical spectrum instrument or experiment conditions are changed. The method comprises the following steps of: 1) detecting the spectrum data of a plurality of standard samples; 2) predicting the concentration (or other chemical and physical properties) of an ingredient to be of detected of the standard sample from the spectrum data of the standard sample by using an original spectrum connection model, and predicating system offset; 3) building a prediction result correction model between the spectrum data of the standard sample and the system offset of the prediction result of the correction module; and 4) combining the prediction result correction model with the original correction model in use so as to solve the common problem that the accuracy of the prediction result varies with the optical spectrum instrument or the experiment conditions. The method is convenient to use and wide in application scope, realizes the sharing of the spectrum correction model among the instruments of the same kind and has validity for long-term use even if the experiment conditions are changed.

Description

technical field [0001] The invention relates to a method for maintaining the prediction ability of a spectrum correction model, which belongs to the field of instrument analysis and detection of spectra or chromatography in chemical industry, food, pharmacy, environment and other industries; the method can realize the comparison of spectrum correction models among similar instruments sharing, and validity for long-term use even as experimental conditions change. Background technique [0002] In recent years, due to the rapid development of fiber optic probe technology, spectroscopic analysis technology has fast analysis speed, requires little or no sample pretreatment, and is very suitable for in-situ, real-time, and rapid analysis of a large number of complex chemical and biological samples. In-situ real-time spectral process analysis techniques such as Fourier transform infrared spectroscopy, near-infrared spectroscopy, and laser Raman spectroscopy have received more and m...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N21/25G06F19/00
Inventor 陈增萍俞汝勤
Owner HUNAN UNIV
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