State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, Institute of Zoonosis, and College of Veterinary Medicine, Jilin University, Changchun 130062, China.
School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen 518107, China.
Anal Chem. 2024 Jul 23;96(29):12197-12204. doi: 10.1021/acs.analchem.4c02500. Epub 2024 Jul 11.
Given the harmful effect of pesticide residues, it is essential to develop portable and accurate biosensors for the analysis of pesticides in agricultural products. In this paper, we demonstrated a dual-mode fluorescent/intelligent (DM-f/DM-i) lateral flow immunoassay (LFIA) for chloroacetamide herbicides, which utilized horseradish peroxidase-IgG conjugated time-resolved fluorescent nanoparticle probes as both a signal label and amplification tool. With the newly developed LFIA in the DM-f mode, the limits of detection (LODs) were 0.08 ng/mL of acetochlor, 0.29 ng/mL of metolachlor, 0.51 ng/mL of Propisochlor, and 0.13 ng/mL of their mixture. In the DM-i mode, machine learning (ML) algorithms were used for image segmentation, feature extraction, and correlation analysis to obtain multivariate fitted equations, which had high reliability in the regression model with of 0.95 in the range of 2 × 10-2 × 10 pg/mL. Importantly, the practical applicability was successfully validated by determining chloroacetamide herbicides in the corn sample with good recovery rates (85.4 to 109.3%) that correlate well with the regression model. The newly developed dual-mode LFIA with reduced detection time (12 min) holds great potential for pesticide monitoring in equipment-limited environments using a portable test strip reader and laboratory conditions using ML algorithms.
鉴于农药残留的有害影响，开发用于分析农产品中农药的便携式和准确的生物传感器至关重要。在本文中，我们展示了一种用于氯乙酰胺类除草剂的双模式荧光/智能（DM-f/DM-i）侧向流动免疫分析（LFIA），该方法利用辣根过氧化物酶-IgG 偶联的时间分辨荧光纳米颗粒探针作为信号标记和放大工具。在新开发的 DM-f 模式下 LFIA 中，检测限（LOD）分别为乙草胺 0.08ng/mL、异丙甲草胺 0.29ng/mL、异丙草胺 0.51ng/mL 及其混合物 0.13ng/mL。在 DM-i 模式下，机器学习（ML）算法用于图像分割、特征提取和相关分析，以获得多元拟合方程，在 2×10-2×10pg/mL 范围内，回归模型的相关系数为 0.95，具有很高的可靠性。重要的是，通过用便携式测试条读取器在设备有限的环境中以及使用 ML 算法在实验室条件下成功验证了玉米样品中氯乙酰胺类除草剂的实际适用性，回收率良好（85.4%至 109.3%），与回归模型相关性良好。新开发的双模式 LFIA 具有较短的检测时间（12 分钟），对于使用便携式测试条读取器在设备有限的环境中和使用 ML 算法在实验室条件下进行农药监测具有很大的潜力。
