RESUMEN
A method for intelligent data analysis was designed by combining electrochemical sensing with machine learning (ML). Specifically, a voltammetric sensor is described for determination of the phytoinhibitor maleic hydrazide in crop samples. Carboxyl-functionalized poly(3,4-ethylenedioxythiophene) (PEDOT-C4-COOH) was electro-synthesized in aqueous micellar solution by direct anodic oxidation of its monomer. A nanosensor was then prepared by placing copper nanoparticles (CuNPs) on the PEDOT-C4-COOH film via electro-deposition of Cu (II) from aqueous micellar solutions. An artificial neural network (ANN) served as a powerful ML model to realize intelligent data analysis and smart transformation for digital output. Different established regression methods were selected for evaluating the ANN-based method that was found to be superior to known methods. The sensor has a wide working range (from 0.06-1000 µM), a low limit of detection (10 nM), good stability, selectivity and practicality. The method was applied to the determination of maleic hydrazide in (spiked) samples of onion, rice, potato and cotton leaf. Satisfactory results demonstrate that the feature of simultaneous data acquisition and analysis is highly attractive. Graphical abstract Schematic representation of an electrochemical sensor based on carboxyl-functionalized poly(3,4-ethylenedioxythiophene) (PEDOT-C4-COOH) and copper nanoparticles (CuNPs) by differential pulse voltammetry (DPV) to detect maleic hydrazide (MH). PEDOT-C4-COOH was electro-synthesized in 0.1 M LiClO4 aqueous micellar solution with 0.1 M sodium dodecyl benzene sulfonate (SDBS) by amperometry (CA). CuNPs was prepared by cyclic voltammetry (CV).