In clinical diagnosis, the detection of dopamine (DA) is often interfered by epinephrine (EP), a neurotransmitter drug that shares a similar structure with DA, which affects the accurate prediction of DA concentration. In this study, an electrochemiluminescence (ECL) and differential pulse voltammetry (DPV) dual model biosensor combined with machine learning algorithm was proposed for improving the selective detection of DA under EP interference. First, reduced graphene and gold nanoparticles were applied to enhance the sensitivity and stability of the sensor in ECL and DPV measurement. Compared with single model sensing, the dual model sensor could combine the superiority of ECL and DPV while improving the accuracy and reliability of experimental data. Then, nine graphic features related to concentration prediction were extracted from the ECL and DPV response curves, and Pearson’s correlation coefficient (PCC), as well as random forest (RF) algorithm, was adopted for feature importance analysis. To achieve accurate signal decoupling, an intelligent biologically inspired algorithm, particle swarm optimization (PSO) algorithm, was introduced to optimize initial weights and thresholds of the traditional artificial neural network (ANN) for further model training based on multi-feature fusion, which greatly increased the prediction accuracy of DA in the presence of EP. The results demonstrated that DA could be accurately predicted in the concentration range of 9–

$200 ~\mu \text{M}$

, and the

${R}^{\,{2}}$

between the true and predicted values of DA concentration reached 0.99. The root-mean-square error (RMSE) and mean absolute error (MAE) of the test dataset were 5.77 and

$4.32 ~\mu \text{M}$

, respectively. In addition, the PSO-ANN-assisted ECL-DPV dual model detection scheme has been successfully applied for the determination of DA in real samples, such as goat serum and artificial urine.

Jixiang Zu

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