Non-destructive study on identifying and monitoring of Cu-Pb pollution in corn based on near-infrared spectroscopy.

Abstract

The problem of heavy metal pollution in the environment is of increasing concern, especially in the agricultural environment. In this study, a method of element identification and pollution degree monitoring of Cu and Pb pollution in corn was proposed by using variational mode decomposition (VMD) and bispectrum estimation. The results showed that the u3 component of VMD shows sensitivity to corn heavy metal monitoring. The bispectrum graphs of healthy corn leaves, Cu polluted, and Pb polluted were significantly different; these differences could quickly distinguish whether corn was polluted by heavy metals and the categories of pollution elements. The pollution prediction models constructed by the combination of the normalized frequency (fm) and the energy entropy (Wee) were proven to have a high fitting degree and application accuracy after verification, which can effectively monitor the pollution of Cu and Pb in corn. It provides a basis for the monitoring of pollution in the agricultural environment and the formulation of targeted control plans.