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Surface‐Enhanced Raman Scattering Imaging Assisted by Machine Learning Analysis: Unveiling Pesticide Molecule Permeation in Crop Tissues

Surface‐Enhanced Raman Scattering Imaging Assisted by Machine Learning Analysis: Unveiling Pesticide Molecule Permeation in Crop Tissues

Authors :
Xiaotong Wang
Xiaomeng Sun
Zhehan Liu
Yue Zhao
Guangrun Wu
Yunpeng Wang
Qian Li
Chunjuan Yang
Tao Ban
Yu Liu
Jian‐an Huang
Yang Li
Source :
Advanced Science, Vol 11, Iss 32, Pp n/a-n/a (2024)
Publication Year :
2024
Publisher :
Wiley, 2024.

Abstract

Abstract Surface‐enhanced Raman scattering (SERS) imaging technology faces significant technical bottlenecks in ensuring balanced spatial resolution, preventing image bias induced by substrate heterogeneity, accurate quantitative analysis, and substrate preparation that enhances Raman signal strength on a global scale. To systematically solve these problems, artificial intelligence techniques are applied to analyze the signals of pesticides based on 3D and dynamic SERS imaging. Utilizing perovskite/silver nanoparticles composites (CaTiO3/Ag@BONPs) as enhanced substrates, enabling it not only to cleanse pesticide residues from the surface to pulp of fruits and vegetables, but also to investigate the penetration dynamics of an array of pesticides (chlorpyrifos, thiabendazole, thiram, and acetamiprid). The findings challenge existing paradigms, unveiling a previously unnoticed weakening process during pesticide invasion and revealing the surprising permeability of non‐systemic pesticides. Of particular note is easy to overlook that the combined application of pesticides can inadvertently intensify their invasive capacity due to pesticide interactions. The innovative study delves into the realm of pesticide penetration, propelling a paradigm shift in the understanding of food safety. Meanwhile, this strategy provides strong support for the cutting‐edge application of SERS imaging technology and also brings valuable reference and enlightenment for researchers in related fields.

Details

Language :
English
ISSN :
21983844
Volume :
11
Issue :
32
Database :
Directory of Open Access Journals
Journal :
Advanced Science
Publication Type :
Academic Journal
Accession number :
edsdoj.776f84438e99434d996350d69bbc3fc1
Document Type :
article
Full Text :
https://doi.org/10.1002/advs.202405416