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

Abstract

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

Effect of Aspect Ratio of a Gold-Nanorod-Modified Screen-Printed Carbon Electrode for Carbaryl Detection in Three Different Samples of Vegetables

“…One of the major challenges to ensuring food security for global consumers in the world is the development of rapid detection techniques for detecting pesticide residues and their metabolites in foodstuffs. , Their bioaccumulation in agricultural land that is highly dangerous for human health may be derived from unregulated and excessive usage and thus adversely impact the quality and safety of foodstuffs. , Therefore, developing a robust analytical method for detecting residual pesticides in foodstuffs and monitoring their concentration in the environment is necessary. Among those residual pesticides commonly found in foodstuffs, carbaryl as a carbamate family has been widely used to increase production in agricultural lands due to its high efficiency and powerful activity in controlling the population of harmful insects to a variety of plant crops. , When the carbaryl residue occurs in food and subsequently enters the human body, it can cause a great threat and lead to several serious failures and hazards in the metabolic system. − Moreover, the European Union (EU) has regulated the maximum residue limits (MRLs) of carbaryl concentration in several foodstuffs from 0.01 to 0.05 mg kg –1 .…”

“…One of the major challenges to ensuring food security for global consumers in the world is the development of rapid detection techniques for detecting pesticide residues and their metabolites in foodstuffs. 1 , 2 Their bioaccumulation in agricultural land that is highly dangerous for human health may be derived from unregulated and excessive usage and thus adversely impact the quality and safety of foodstuffs. 3 , 4 Therefore, developing a robust analytical method for detecting residual pesticides in foodstuffs and monitoring their concentration in the environment is necessary.…”