Detection of glyphosate in deionised water using machine learning techniques with microwave spectroscopy

Glyphosate is a commonly used herbicide which carries some risks of ecotoxicity and has been shown to be harmful to human beings with high levels of exposure. Existing methods of glyphosate detection often struggle to achieve the level of sensitivity required to meet regulatory requirements without the use of complicated analytical methods with multiple intermediary steps. We propose the use of microwave spectroscopy to determine the concentration of glyphosate in aqueous solutions, using machine learning methodology to identify a minimum feature set for our model. The resulting model had a limit of detection of roughly 10
^-3
mg/L, fitted values were significantly (Pearson's R = 0.8833, P<;10
^-29
) correlated with the target glyphosate concentration.