Neural Network Model for Surface Raman Spectroscopy and Pesticide Concentration of Spinach

Abstract

At present, there are over 1000 chemical pesticides. The widespread use of these pesticides not only causes serious environmental pollution, but also poses a threat to human health. Currently, there are over 1000 types of chemical pesticides. The widespread use of these pesticides has caused serious environmental pollution. Meanwhile, due to some workers not effectively removing pesticides from food during the picking and processing stages of the farm plants, so the long-term consumption of such food will also pose a threat to human health. An analytical technique called Raman spectroscopy is based on the Raman scattering effect, which was discovered by Indian scientist C.V. Raman. It is used in molecular structure research to get data on molecule vibration and rotation by analyzing scattering spectra at various frequencies from incident light. As a newborn way, the Raman spectroscopy analysis method has higher sensitivity. In this thesis, I put it and the BP neural network together and set up the model, to predict pesticide residues in spinach.