Glyphosate Pattern Recognition Using Microwave-Interdigitated Sensors and Principal Component Analysis

Abstract

Glyphosate is an herbicide used worldwide with harmful health effects, and efforts are currently being made to develop sensors capable of detecting its presence. In this work, an array of four interdigitated microwave sensors was used together with the multivariate statistical technique of principal component analysis, which allowed a well-defined pattern to be found that characterized waters for agricultural use extracted from the Bustillos lagoon. The variability due to differences between the samples was explained by the first principal component, amounting to 86.3% of the total variance, while the variability attributed to the measurements and sensors was explained through the second principal component, amounting to 13.2% of the total variance. The time evolution of measurements showed a clustering of data points as time passed, which was related to microwave–sample interaction, varied with the fluctuating dynamical structure of each sample, and tended to have a stable mean value.