基于机器学习的低浓度多巴胺快速电化学检测方法研究

A Rapid Electrochemical Detection Method of Low-Concentration Dopamine Based on Machine Learning

针对便携式恒电位仪精度较低、易受试验条件影响的问题,结合纳米材料修饰电极与机器学习算法,提出一种低浓度多巴胺(DA)电化学检测分析方法,以期在多个实验干扰因素存在的情况下实现DA的快速准确检测。利用计时电流法(CA)在玻碳电极(GCE)表面电沉积金纳米粒子制备AuNPs/GCE电极,采用循环伏安法(CV)验证其对DA的氧化还原具有良好的电催化活性。在不同底液pH和扫速下,基于AuNPs/GCE电极对不同浓度DA溶液进行重复性循环伏安检测,对检测数据进行峰高、峰电位、基线斜率、峰面积和起始氧化还原电位等重要特征参数的提取,并结合极端梯度提升树(XGBoost)和随机森林(RF)构建两阶段浓度预测模型。结果表明,对于不同pH和扫速干扰下的DA检测数据,相较于传统SVR模型,XGBoost-RF浓度预测模型的MAE、RMSE和MAPE%分别降低53.9%、39.7%和2.7%,RF预测模型的训练时间降低23%,预测准确度提升7%,预测值和真实值间的拟合度(R-Squared)为0.943。所提出方法有效降低了DA测定过程中不同实验干扰因素的影响,在提高检测精度的同时降低了实验的复杂度,对实现微量特征物的电化学现场快速检测具有重要意义。

Portable potentiostat is facing the problem that the accuracy is low and vulnerable under the test conditions. This paper proposed an electrochemical detection data analysis method by combining nanomaterial modified electrodes and machine learning. Under the presence of multiple experimental interference factors, this method was able to achieve the accurate detection of dopamine(DA). The AuNPs/GCE electrode was prepared by electrodepositing gold nanoparticles on the surface of glassy carbon electrode(GCE) by chronoamperometry, and the electrocatalytic activity for the redox of dopamine of AuNPs/GCE was verified by cyclic voltammetry(CV). Under the different solution pH values and scanning speeds, the AuNPs/GCE electrode was applied to perform repetitive accurate cyclic voltammetric detection of dopamine solutions of different concentrations. After the extraction of important characteristics including the peak height, peak potential, baseline slope, peak area and initial redox potential of the detection data, the extreme gradient boosting tree model(XGBoost) and the random forest model(RF) were applied to construct a two-stage concentration prediction analysis. The results showed that the MAE, RMSE and MAPE% of XGBoost-RF concentration prediction model were reduced to 53.9%, 39.7% and 2.7% respectively compared with the traditional SVR model. The training time of RF prediction model was reduced by 23%, the prediction accuracy was improved by 7%, and the fitting degree(R-squared) between predicted value and experimental value was 0.943. In conclusion, this method effectively reduced the influence of different experimental factors in the detection process. It Also improved the detection accuracy and reduced the complexity of the experiment. Therefore, it is of great significance to realize the on-site and rapid electrochemical detection of microscale element.