Ultrasensitive and specific electrochemical detection of Escherichia coli via functionalized magnetic nanoparticles by time-frequency analysis.

IF 3.8 2区 化学 Q1 BIOCHEMICAL RESEARCH METHODS
Siqi Dong, Xiaobin Zhang, Shijuan Cao, Chenpan Lei, Hanyang Bao, Ying Xu
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Abstract

The prompt and accurate identification of pathogenic bacteria is crucial for mitigating the transmission of infections. Conventional detection methods face limitations, including lengthy processing, complex sample pretreatment, high instrumentation costs, and insufficient sensitivity for rapid on-site screening. To address these challenges, an aptamer (Apt)-sensor based on functionalized magnetic nanoparticles (MNPs) was developed for detecting Escherichia coli. Fe3O4@Au nanoparticles were synthesized by stepwise modification, followed by Apt conjugation via Au-S bonds to form Fe3O4@Au@Apt. Subsequently, efficient capture and separation of target bacteria was achieved by combining the specific Apt-E. coli recognition sites with magnetic solid-phase extraction. A time-frequency domain feature-assisted XGBoost model was constructed for the sensor to achieve accurate prediction of bacterial concentration. Six equivalent-circuit frequency domain and six time domain characteristic parameters were extracted from the equivalent circuit model (ECM) and the distribution of relaxation times (DRT), respectively, and Bayesian optimization (BO) was subsequently adopted for automatic hyperparameter search to reduce prediction errors. Furthermore, the SHapley Additive exPlanations (SHAP) analysis demonstrated the necessity of time-frequency feature fusion for enhancing prediction accuracy. The experimental results indicated that the Fe3O4@Au@Apt-modified magnetic glassy carbon electrode (MGCE) can achieve quantitative detection of E. coli in a concentration range of 100-107 CFU/mL, with a detection Limit down to 1 CFU/mL. In addition, the intelligent detection framework based on BO-XGBoost exhibited excellent predictive performance, with an R2 value of 0.990, mean absolute error (MAE) of 0.087 CFU/mL, and root mean square error (RMSE) of 0.158 CFU/mL. This approach shows significant potential for future E. coli monitoring applications in food safety and environmental surveillance.

基于时频分析的功能化磁性纳米颗粒对大肠杆菌的超灵敏特异电化学检测。
及时和准确地鉴定病原菌对减轻感染的传播至关重要。传统的检测方法存在局限性,包括处理时间长、样品预处理复杂、仪器成本高、现场快速筛选灵敏度不足等。为了解决这些问题,研究人员开发了一种基于功能化磁性纳米颗粒(MNPs)的适体(Apt)传感器,用于检测大肠杆菌。通过逐步修饰合成Fe3O4@Au纳米粒子,然后通过Au-S键将Apt偶联形成Fe3O4@Au@Apt。随后,结合特异的Apt-E,实现了对目标细菌的高效捕获和分离。磁固相萃取法识别大肠杆菌位点。为实现细菌浓度的准确预测,构建了时频域特征辅助的XGBoost模型。从等效电路模型(ECM)和松弛时间(DRT)分布中分别提取6个等效电路频域和6个时域特征参数,并采用贝叶斯优化(BO)进行自动超参数搜索,降低预测误差。此外,SHapley加性解释(SHAP)分析证明了时频特征融合对于提高预测精度的必要性。实验结果表明,Fe3O4@Au@ apt修饰磁玻碳电极(MGCE)可实现100-107 CFU/mL浓度范围内大肠杆菌的定量检测,检出限低至1 CFU/mL。此外,基于BO-XGBoost的智能检测框架具有出色的预测性能,R2值为0.990,平均绝对误差(MAE)为0.087 CFU/mL,均方根误差(RMSE)为0.158 CFU/mL。该方法在未来的食品安全和环境监测中具有重要的应用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.00
自引率
4.70%
发文量
638
审稿时长
2.1 months
期刊介绍: Analytical and Bioanalytical Chemistry’s mission is the rapid publication of excellent and high-impact research articles on fundamental and applied topics of analytical and bioanalytical measurement science. Its scope is broad, and ranges from novel measurement platforms and their characterization to multidisciplinary approaches that effectively address important scientific problems. The Editors encourage submissions presenting innovative analytical research in concept, instrumentation, methods, and/or applications, including: mass spectrometry, spectroscopy, and electroanalysis; advanced separations; analytical strategies in “-omics” and imaging, bioanalysis, and sampling; miniaturized devices, medical diagnostics, sensors; analytical characterization of nano- and biomaterials; chemometrics and advanced data analysis.
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