A method for constructing a rapid quantitative detection model of heavy metal element copper in mulberry leaves

Abstract

The invention provides a method for constructing a rapid quantitative detection model of copper, a heavy metal element in mulberry leaves, and belongs to the technical field of quality and safety detection. The invention first constructs a quantitative detection model of heavy metal copper, and then performs quantitative detection of heavy metal copper in blind mulberry leaf samples according to the quantitative detection model. When constructing the quantitative detection model, the present invention performs non-supervised clustering of the self-organizing neural network on the laser-induced breakdown spectrum data, and then uses the non-information variable elimination method to select the clustered variables, thereby avoiding information redundancy , to obtain the most relevant model variable information, and then establish a partial least squares regression model, select a partial least squares regression sub-model with high accuracy and stability, and fuse the partial least squares regression sub-model through a consensus fusion algorithm, A quantitative detection model for heavy metal copper was obtained.

Description

technical field [0001] The invention relates to the technical field of quality and safety detection, in particular to a method for constructing a rapid quantitative detection model of heavy metal element copper in mulberry leaves. Background technique [0002] Mulberry leaf tea is rich in a variety of biologically active substances and nutrients (such as multivitamins, minerals, polyphenols, flavonoids and 1-deoxynojirimycin), regular drinking can achieve intestinal detoxification, lower blood pressure and blood lipids, and resist oxidation and aging and treatment of diabetes. However, with the rapid development of industry and the improvement of people's living standards in recent years, a large amount of waste from industrial production, automobile traffic, and daily production has been discharged into rivers, soil, and the atmosphere, causing heavy metal pollution. As a woody plant with strong stress resistance, mulberry will not show obvious symptoms when it is stressed...

AI Technical Summary

Problems solved by technology

However, when LIBS technology is used to detect the heavy metal copper in mulberry leaves, the following problems are encountered. First, the LIBS spectrum has a high data dimension (usually containing tens of thousands of variables), which seriously affects the multivariate detection model. Calculation accuracy and calculation time; second, many important information variables in LIBS data were abandoned during the modeling process and were not fully utilized, making the established mulberry leaf heavy metal copper detection model less stable

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