Qualitative and quantitative combined near infrared quantitative model construction method

Abstract

The invention provides a qualitative and quantitative combined near infrared quantitative model construction method which comprises the following steps: obtaining an actual sample of a modeling correction set, and detecting a basic chemical component of the actual sample; scanning a spectrum corresponding to the correction sample, and culling an abnormal sample; qualitatively projecting an available spectrum; classifying projection data; forecasting a verification set consisting of a near infrared spectrum of each type and a chemical value through a modeling set, and calculating a forecasting error; randomly selecting near infrared wavelength points; calculating a general correction set error corresponding to each generation of the wavelength points; determining near infrared wavelength selection points and characteristic information of the infrared spectrums according to the minimum general correction set error; rebuilding a regression model through the spectrums and the chemical values of the correction set; detecting the chemical values of verification samples, obtaining corresponding spectrums, and quantitatively evaluating the regression model. According to the method, the spectrums of the correction set are qualitatively projected and analyzed, so that the method is adaptive to changes of the spectrums, and the forecasting stability of the model can be kept.

Description

technical field [0001] The invention relates to a method for constructing a near-infrared quantitative model combining qualitative and quantitative methods, and belongs to the field of near-infrared detection and analysis. Background technique [0002] Near-infrared spectroscopy is an electromagnetic wave belonging to the 850-2500nm band. Different types of spectra reflect molecular information at different sample levels. Because near-infrared spectroscopy contains a large amount of material information and has advantages such as rapid detection, near-infrared spectroscopy has achieved great success in online production quality monitoring. Wide range of applications; the traditional method of building a near-infrared quantitative model is: obtain the near-infrared spectral information of the sample, use the flow analyzer to detect the basic chemical value information, randomly divide the spectrum into a calibration level and a verification set, and eliminate abnormal samples....

AI Technical Summary

Problems solved by technology

[0002] Near-infrared spectroscopy is an electromagnetic wave belonging to the 850-2500nm band. Different types of spectra reflect molecular information at different sample levels. Because near-infrared spectroscopy contains a large amount of material information and has advantages such as rapid detection, near-infrared spectroscopy has achieved great success in online production quality monitoring. Wide range of applications; the traditional method of building a near-infrared quantitative model is: obtain the near-infrared spectral information of the sample, use the flow analyzer to detect the basic chemical value information, randomly divide the spectrum into a calibration level and a verification set, and eliminate abnormal samples. Band selection or wavelength point selection selects the wavelength of the spectrum to form a modeling interval, and then uses pls (partial least squares) to establish a relationship between the spectrum and the chemical value after screening the wavelength interval and verify the verification sample; the above methods exist as follows (1) Due to the many factors affecting the near-infrared spectrum, it is easily affected by the external environment, temperature, and sample type; in actual use, when the external spectrum changes and the calibration sample is inconsistent, it is easy to cause the model The external prediction error is too large; (2) For the construction of near-infrared models of complex sample types, in the traditional modeling method, there will be heavy calibration and light verification, so that in the actual quality monitoring process, when the actual sampling sample The chemical value and the corresponding spectrum change slightly, and the traditional modeling method will not have the consistency of the actual sample change trend

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