Near infrared spectrum nondestructive testing method and device for material component content

Abstract

The invention discloses a near infrared spectrum nondestructive testing method and a near infrared spectrum nondestructive testing device for material component content. The method comprises the following steps of: collecting calibration set samples by using a spectrometer, pretreating a spectrum, selecting an optimal calibration sample set through sample optimization and establishing a nonlinear calibration model by using the optimal calibration sample set, collecting spectrum samples with unknown component content by using the spectrometer, pretreating the spectrum in a mode which is the same as that of pretreating the calibration set samples, and detecting the component content of the unknown sample through the established nonlinear calibration model. The method and the device can effectively solve the problems of complex calibration model, slow training speed and difficult hardware implementation in the conventional near infrared nondestructive testing method for the material component content and obviously improve the accuracy and stability of a nondestructive testing result of the material component content.

Description

technical field [0001] The invention relates to a near-infrared spectrum analysis technology, in particular to a near-infrared spectrum non-destructive detection method and device for the content of material components. Background technique [0002] Near-infrared spectroscopy detection technology has the characteristics of non-destructive, non-polluting, real-time and rapid determination of the concentration of substance components (or property parameters), so it is widely used in agriculture, biomedicine, chemical industry and other fields. Although near-infrared spectroscopy can carry a wealth of molecular structure information, and is easy to obtain and be processed, it can be used to analyze the component concentrations (or property parameters) of most types of compounds and their mixtures. The infrared spectrum area is low, the spectral peak is wide, and the effective information rate of the sample near infrared spectrum is low. The near-infrared spectroscopy analysis ...

AI Technical Summary

Problems solved by technology

The selection of samples by the random method is completely random, without any rules, or only follows simple rules. The samples that make up the calibration set through this method may vary greatly each time, and the representativeness of the selected samples and the extrapolation ability of the model cannot be guaranteed.

The modeling method of selecting the nearest samples of the same kind can only reduce the nonlinear correction error, and cannot find and remove the interference of samples with abnormal spectral characteristics, which is often not reliable and accurate enough

The advantage of the Kennard-Stone method is that it can ensure that the samples in the training library are evenly distributed according to the spatial distance, but it needs to perform data conversion and calculate the pairwise spatial distance of the samples, which requires a large amount of calculation.

[0007] To sum up, the near-infrared spectroscopy detection technology using the commonly used nonlinear multivariate calibration method and the calibration set sample selection method has the problems of complex models, large amount of calculation, and the correction of material composition in the near-infrared spectroscopy non-destructive testing. The training speed of the model is slow, and it is not easy to realize the hardware; and the existing nonlinear correction model is not suitable for small sample modeling, and it is prone to overfitting, which affects the accuracy of the non-destructive testing results of near-infrared spectroscopy for material composition content

In addition, due to the complexity of the correction model for off-line training, the correction model written in the hardware of the near-infrared spectroscopy non-destructive testing device for the usual material composition content is fixed, which is not suitable for the detection of the material composition content of the object with particularly complex components. good

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