Comparison and Analysis of Several Quantitative Identification Models of Pesticide Residues Based on Quick Detection Paperboard

Pesticide residues have long been a significant aspect of food safety, which has always major social concern. This study presents research and analysis on the identification pesticide residue fast detection cards based enzyme inhibition approach. In this study, image recognition technology is used to extract color information RGB eigenvalues from results quick card, four regression models are established quantitatively predict concentration indicated by card using eigenvalues. The linear model, quadratic polynomial exponential model RBF neural network model. Through comparison, it shown that superior at predicting rapid card. correlation value 0.900, root mean square error 0.106. There will be no negative prediction when expected near 0. gives novel concept data support for development equipment