支持向量机
杀虫剂
农药残留
多路复用
残留物(化学)
生物系统
鉴定(生物学)
化学
干扰(通信)
传感器阵列
荧光
计算机科学
人工智能
食品安全
模式识别(心理学)
信号处理
信号(编程语言)
色谱法
生物传感器
数据挖掘
生化工程
食品
机器学习
定量分析(化学)
定性分析
作者
Si Li,Xu Liu,Linpin Luo,Kai Guo,Fengjiao He,Zhi Zheng,Yongning Wu,Yizhong Shen
标识
DOI:10.1021/acs.jafc.6c05019
摘要
Analytical array detection holds great promise for multipesticide residue analysis, yet faces key challenges such as cross-channel material interference and error accumulation. Herein, we report a multifunctional "all-in-one" Cu@Zr-MOF nanozyme with intrinsic fluorescence (FL), phosphatase-like (OPH), laccase-like (LAC), and peroxidase-like (POD) activities, which are integrated into a response array for the distinguishing six pesticides ranging from 1.0 to 225.0 ppm via machine learning (ML) technology. This array enables high-throughput discrimination of six pesticides via unsupervised methods. Furtherore, an intelligent stepwise machine-learning approach integrating support vector machine classification and support vector regression achieves both qualitative and quantitative analysis. The entire multichannel analysis is efficient, requiring just 40.0 min to operate (15.0 min for sample-nanozyme preincubation and 25.0 min for signal output). Crucially, the proposed response array enables pesticide detection in six foods with satisfactory recoveries and dynamic monitoring on tomatoes with good accuracy, advancing single-material arrays for food safety monitoring.
科研通智能强力驱动
Strongly Powered by AbleSci AI