【目的/意义】 电化学传感器受限于基底电极的品控波动、修饰过程误差及环境干扰等因素，导致不同批次间存在性能差异，使得每个传感器在实际检测前均需进行独立、耗时的人工校准，严重制约其规模化应用。为避免繁琐的人工校准流程，提出了一种数据驱动的机器学习（Machine Learning, ML）校准策略，该策略能够在保证电化学传感器检测精度的同时，提升检测效率。 【方法】 以检测猪肉中棕榈酸（Palmitic Acid, PA）的分子印迹电化学传感器为模型体系，首先制备传感器并表征。进而构建多模态数据集：从45根电极的电化学阻抗谱、循环伏安曲线和差分脉冲伏安曲线中提取具有物理意义的多个特征作为电极指纹，以指纹特征和浓度为输入，相应浓度下的响应电流变化值为输出，构建一个根据电极指纹信息智能预测传感器标准曲线的ML校准模型。 【结果和讨论】 经ML校准后，传感器对猪肉样本PA含量的检测结果显示出良好的准确性，检测同一样本的相对标准偏差为5.01%，回收率在96.05%至105.30%之间，均符合检测要求。此外，将传感器检测结果与传统气相色谱-质谱联用技术的测量值进行对比，两者结果差异在可接受范围内。 【结论】 本研究证实了提出的ML校准策略在实际样品分析中的可靠性，为实现电化学传感器的免人工标定提供了一种切实可行的技术策略，为实现其规模化应用奠定了技术基础。

[Objective] Electrochemical sensors offer advantages such as high sensitivity, rapid response, and ease of miniaturization. However, performance variations across different sensor batches—caused by factors including substrate quality fluctuations, modification process errors, and environmental interference—necessitate independent and time-consuming manual calibration for each sensor prior to practical use. To address this issue, a data-driven calibration strategy integrating machine learning (ML) with electrochemical fingerprinting is proposed. This approach aims to uncover the intrinsic relationship between sensor characteristics and response behavior, enabling accurate prediction of calibration curves for newly fabricated electrodes, thereby enhancing testing efficiency while maintaining detection accuracy. [Methods] Using a molecularly imprinted polymer (MIP) electrochemical sensor for detecting palmitic acid (PA) in pork as a model system, the following investigations were systematically conducted. First, a MIP/Thi-COOH-GO-ZIF-8-CS/screen-printed electrode (SPE) sensor was fabricated and comprehensively characterized for its electrochemical behavior using electrochemical methods. Subsequently, a multimodal dataset for ML model training was constructed: electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) curves were collected from 45 bare SPEs in [Fe(CN)₆]³⁻ solution, from which five characteristic parameters—charge transfer resistance (R_ct), constant phase element (CPE), anodic peak current (I_pa), cathodic peak current (I_pc), and peak potential separation (ΔEp)—were extracted. After fabricating these electrodes into MIP sensors, the thionine oxidation peak current I₀ was measured in blank solution using differential pulse voltammetry (DPV) as the sixth feature. Each electrode was then tested with six concentrations of PA standard solutions, recording the corresponding DPV response currents Iᵢ. The standard solution concentration served as the seventh feature, forming 270 sets of "feature-concentration-response" data pairs. Using electrode fingerprint features and PA standard solution concentrations as inputs, and the response current change ΔI (i.e., I₀-Iᵢ) as the output, four models—artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector regression (SVR)—were constructed, with hyperparameters optimized via grid search. Finally, based on comprehensive evaluation of metrics including coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and relative prediction deviation (RPD) on the prediction set, the optimal model was selected for subsequent validation with real samples. [Results and Discussions] Electrochemical characterization confirmed successful fabrication of the MIP/Thi-COOH-GO-ZIF-8-CS/SPE sensor: DPV showed significant recovery of the oxidation peak current after template molecule elution, while EIS revealed a decrease in charge transfer resistance R_ct from 1.04 kΩ before elution to 240 Ω after elution, confirming effective formation of imprinted cavities. The sensor exhibited good selectivity for PA, with interference responses substantially lower than that for PA. Within the concentration range of 1–1 000 μM, ΔI showed excellent linear correlation with LogC_PA. Among the ML models compared, ANN demonstrated optimal performance, outperforming DT, RF, and SVR across all metrics. The ANN also showed the closest performance between training and prediction sets with no obvious overfitting, indicating its effectiveness in learning deep mappings between multimodal features and sensor responses. Using the trained ANN model, 14 newly fabricated MIP/Thi-COOH-GO-ZIF-8-CS/SPE sensors were employed to validate the practical applicability of the predicted calibration curves. First, in tests with 80 μM PA standard solution, concentrations measured by five electrodes closely matched the actual value, with a relative standard deviation (RSD) of 4.34%. Second, in actual pork sample analysis, results from five electrodes showed excellent agreement with reference values obtained by traditional gas chromatography-mass spectrometry (GC-MS), yielding an RSD of 5.01%. Finally, in spiking recovery experiments, four electrodes at spiking levels of 300, 400, 500, and 600 μM achieved recovery rates ranging from 96.05% to 105.30%. These results conclusively demonstrate that the ANN model trained on electrode fingerprint features can accurately predict electrode-specific calibration curves, enabling direct application of sensors to real sample analysis without manual calibration curve determination. [Conclusions] This study successfully developed and validated an intelligent sensor calibration strategy integrating electrochemical fingerprinting with machine learning.The strategy achieves accurate prediction of individual sensor calibration curves. In practical application to PA detection in pork samples, the ML-calibrated sensors demonstrated excellent accuracy and stability. Without reliance on complex equipment or massive labeled datasets, this approach balances detection precision and efficiency, providing a viable technical pathway for standardized fabrication and scalable deployment of electrochemical sensors, with potential to advance their transition from laboratory research to practical commercial applications.

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