Glyphosate detection with ammonium nitrate and humic acids as potential interfering substances by pulsed voltammetry technique

Abstract

ElsevierMartínez Gil, P.; Laguarda Miró, N.; Soto Camino, J.; Masot Peris, R. (2013) AbstractPulsed voltammetry has been used to detect and quantify glyphosate on buffered water in presence of ammonium nitrate and humic substances. Glyphosate is the most widely used herbicide active ingredient in the world. It is a non-selective broad spectrum herbicide but some of its health and environmental effects are still being discussed. Nowadays, glyphosate pollution in water is being monitored but quantification techniques are slow and expensive. Glyphosate wastes are often detected in countryside water bodies where organic substances and fertilizers (commonly based on ammonium nitrate) may also be present. glyphosate also forms complexes with humic acids so these compounds have also been taken into consideration. The objective of this research is to study the interference of these common pollutants in glyphosate measurements by pulsed voltammetry. The statistical treatment of the voltammetric data obtained lets us discriminate glyphosate from the other studied compounds and a mathematical model has been built to quantify glyphosate concentrations in a buffer despite the presence of humic substances and ammonium nitrate. In this model, the coefficient of determination (R 2 ) is 0.977 and the RMSEP value is 2.96x10 -5 so the model is considered statistically valid.

An Electrochemical Impedance Spectroscopy-Based Technique to Identify and Quantify Fermentable Sugars in Pineapple Waste Valorization for Bioethanol Production

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“…Previous studies suggested using non-oxidizable materials instead of oxidizable ones (e.g., Cu, Co, Ni, Ag) due to their rust resistance and easy handling, avoiding complex cleaning treatments of the electrodes [ 41 , 42 ]. Moreover stainless steel was selected among other non-oxidizable materials (e.g., Pt, Au, Ir, Rh) because of a clear economic motivation and its successful and extended use in food industry applications [ 21 , 28 , 30 ].…”

“…According to the literature and previous studies in this research line, PLS prediction models were created using two series of the experimental data (66% of the data for the calibration set). The model was then validated with the remaining series of experimental data (34% for the validation set) [ 41 , 44 ]. The accuracy was given by the root mean square error of prediction (RMSEP) and the coefficient of determination ( R 2 ).…”

“…In addition, ANNs are able to learn in their training process in order to improve themselves and find the optimal conditions to work showing high flexibility and adaptive capacity. In addition, ANNs are being used in a wide range of applications such as electronic noses [ 34 , 35 ] and tongues [ 36 , 37 ] and they are showing very interesting prediction models in several fields such as water [ 38 ], food [ 39 ] and the environment [ 40 , 41 ].…”

Aproximacion Al Biomimetismo Mediante Lenguas Y Narices Electrónicas en Medios Complejos: Detección De Explosivos, Agentes Nerviosos Y Control De Alimentos

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