Machine learning-assisted laccase-like activity nanozyme for intelligently onsite real-time and dynamic analysis of pyrethroid pesticides.

Journal: Journal of hazardous materials
PMID: 39366039

Abstract

The intelligently efficient, reliable, economical and portable onsite assay toward pyrethroid pesticides (PPs) residues is critical for food safety analysis and environmental pollution traceability. Here, a fluorescent nanozyme Cu-ATP@ [Ru(bpy)] with laccase-like activity was designed to develop a versatile machine learning-assisted colorimetric and fluorescence dual-modal assay for efficient onsite intelligent decision recognition and quantification of PPs residues. In the presence of alkaline phosphatase (ALP), the laccase-like activity of Cu-ATP@ [Ru(bpy)] was enhanced to oxidize colorless o-phenylenediamine (OPD) into dark-yellow 2,3-diaminophenazine (DAP) via electron transfer, appearing a new yellow fluorescence at 550 nm. Meanwhile, the red fluorescence of Cu-ATP@ [Ru(bpy)] at 600 nm was quenched due to the internal filter effect (IFE) of DAP towards Cu-ATP@ [Ru(bpy)]. However, the selective inhibition of PPs toward ALP activity enabled to observe a dual-modal response of PPs concentration-dependent decrease in colorimetric signal and enhancement in the fluorescence intensity ratio of F/F. On this basis, both the colorimetric and fluorescence images were captured and processed with a home-made WeChat applet-installed smartphone to extract the corresponding image color information, thus achieving machine learning-assisted onsite real-time and dynamic intelligent decision recognition and quantification of PPs residues in real samples, which shows a promising potential in safeguarding food safety and environmental health.

Authors

  • Guojian Wu
    School of Food & Biological Engineering, Anhui Province Key Laboratory of Agricultural Products Modern Processing, Hefei University of Technology, Hefei 230009, China.
  • Chenxing Du
    School of Food & Biological Engineering, Anhui Province Key Laboratory of Agricultural Products Modern Processing, Hefei University of Technology, Hefei 230009, China.
  • Chuanyi Peng
    State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China. Electronic address: pcy0917@ahau.edu.cn.
  • Zitong Qiu
    College of Information Engineering, Sichuan Agricultural University, Ya'an, 625014, China.
  • Si Li
    School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, 150081, China.
  • Wenjuan Chen
    School of Biological Science and Engineering, North Minzu University, Yinchuan, Ningxia 750021, China.
  • Huimin Qiu
    Hunan University of Technology Zhuzhou 412007, PR China.
  • Zhi Zheng
    Department of Chemical Engineering, School of Chemistry and Chemical Engineering, Nanjing University.
  • Zhiwei Lu
    College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an, 625014, China, PR China. Electronic address: zhiweilu@sicau.ecu.cn.
  • Yizhong Shen
    School of Food & Biological Engineering, Anhui Province Key Laboratory of Agricultural Products Modern Processing, Hefei University of Technology, Hefei 230009, China. Electronic address: yzshen@hfut.edu.cn.

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