Agricultural product element quantitative detection model building method based on X-ray fluorescence analysis

Abstract

The invention discloses an agricultural product element quantitative detection model building method based on X-ray fluorescence analysis. The method comprises the following steps: first, conducting pre-processing on an obtained sample spectrogram to obtain a standard spectrogram, wherein the pre-processing particularly comprises differential processing, normalization, multiplicative scatter correction (MSC), centralization, standard normal variable exchange (SNV), light spectrum smoothing and the like; then, carrying out sample set partition and abnormal sample rejecting through a principal component analysis (PCA) and partial least squares (PLS ) data processing method, so as to obtain a calibration set and a forecast set; finally, conducting calibration of indexes to be detected on agricultural products through principal component analysis and artificial neural network (PCA+ANN), as well as partial least squares and artificial neural network (PLS+ANN). The method adopts a peak value method in model building, so as to solve the problem of overlapping in application of the conventional peak area model building method, and improve the accuracy of a sample calibration model.

Description

technical field [0001] The invention relates to an X-ray fluorescence spectrum analysis technology, in particular to a peak method modeling of the X-ray fluorescence spectrum. Background technique [0002] Most traditional energy dispersive X-ray fluorescence spectrometers use the peak area method when establishing statistical models. The peak area method has the following advantages: it is not necessary to accurately know the injection volume of the sample, and the influence of a slight change in the operating conditions on the test results is relatively small, the calculation is convenient, and it is suitable for simultaneous analysis of multiple elements. [0003] Based on the principle of simulated annealing algorithm, Zeng Guoqiang et al. established a new peak-finding model algorithm. This algorithm uses simulated annealing to find the convergence characteristics of the global optimum, uses the Metropolis criterion as the basis for peak-valley judgment, and introduces ...

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Problems solved by technology

[0004] However, when using peak area modeling, different elements often overlap at the peak position, and the software used by most instruments needs to be manually input according to observation when determining the peak area and peak boundary, so it is easy to cause large errors

Comparing the data of the X-ray fluorescence spectrometer and the electrically coupled plasma emission spectrometer, it is found that there are large deviations in the measurement of some elements with low content. Comparing the measurement data of the X-ray fluorescence spectrometer and the electrically coupled plasma emission spectrometer, it can be found that: The accuracy of the model established by the peak area is relatively low, and the error is large for elements with small content values

In addition, most of the existing X-ray fluorescence spectrometers use the peak area method to model, the modeling method is relatively simple, and the selectivity is less

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