Abstract
The pH of silage microenvironment is protean which affects the concentration of mycophenolic acid (MPA) that is an animal health-threatening mycotoxin produced by Penicillium roqueforti. This inspired us to develop a fast portable intelligent method for electrochemical detection of MPA in silage with the variable-pH microenvironment using Zn-Co MOF/Ti3C2 MXene/Fe3O4-MGO coupling with machine learning (ML). Zn-Co MOF (metal organic framework), Ti3C2 MXene (graphene-like titanium carbide MXene), Fe3O4-MGO (magnetic Fe3O4-graphene oxide) and their nanocompsite with excellent electrocatalytic capacity were prepared and characterized. The electrocatalytic mechanism of MPA was investigated by density functional theory (DFT) and electrochemical experiments, which clarified the most easily redox position of MPA. ML model via artificial neural network (ANN) algorithm for smart output of MPA through input of pH was discussed that adapt to the variable-pH microenvironment and realize intelligent analysis of MPA in silage with the variable-pH microenvironment. R2 near 1, lower both RMSE and MAE, and higher RPD value demonstrate the good predictive performance and high predictive accuracy of the proposed ANN model. This will provide a fast portable wireless intelligent sensing analytical technology for detecting hazardous substances in diverse complicated and changeable outdoor microenvironments
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