Development of surface molecular-imprinted electrochemical sensor for palmitic acid with machine learning assistance
Abstract
Palmitic acid (PA) is a kind of saturated high fatty acid, which is involved in physiological safety and food quality. A surface molecularly imprinted polymer (MIP) electrochemical sensor was prepared on MXene surface using dopamine (DA) as functional monomer. The electrode was modified with gold nanoparticles (AuNPs), ferrocene-graphene oxide-multiwalled carbon nanotubes (Fc-GO-MWCNT) composite to enhance the electroactive area and conductivity. The sensor was characterized by scanning electron microscope (SEM), energy-dispersive X-ray spectroscopy (EDS), electrochemical impedance spectroscopy (EIS) and Differential pulse voltammetry (DPV), respectively. The parameters concerning this assay and various regeneration conditions have been carefully studied. The sensor can detect PA in the range of 1 nM-1 mM (R2 = 0.995), the limit of detection (LOD) is 0.48 nM (S/N = 3), and the limit of quantification (LOQ) is 1.61 nM. The artificial neural network (ANN) model in machine learning is further used to analyze the data collected by the sensor. The results show that the back propagation (BP) neural network in ANN is more suitable for the intelligent analysis of PA. The practicality of the sensor was confirmed by detecting PA in pork samples. This is the first MIP-based electrochemical sensor for PA, and it has great potential in practical applications.
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