Visualization of the distribution of carbendazim on the arabidopsis leaf surface by VUV-postionization mass spectrometry

Abstract

Carbendazim is a broad-spectrum fungicide that is effective in controlling diseases caused by fungi (e.g., adenomycetes and polybacteriums) in crops. It is known that carbendazim is persistent and toxic to mammals, and has always been a carcinogen for humans. However, various carbendazim pesticide formulations are still allowed to be produced and used in crop farming. In this work, we used state-of-the-art vacuum ultraviolet (VUV, 118 nm) single-photon postionization mass spectrometry imaging to detect traces of pesticide carbendazim on the paraxial surface of growing plant leaves, and to explore the absorption and in situ distribution of pesticide carbendazim on the leaf surface. Compared to other mass spectrometry imaging methods, this method can achieve rapid detection with simple operation and requires no sample preparation. The results indicate that the limit of detection (LOD) of the proposed method is estimated to be ca. 50 pg per sampling spot. Additionally, we investigated the residues and distribution of pesticide carbendazim on plant leaves by visualizing the distribution of carbendazim on leaf surfaces to locate pesticide residues. The results of this study showed that the vacuum ultraviolet (VUV) single-photon postionization MSI method could be used to determine the quality of pesticide residues on the surface of plant leaves, which was of great significance for the hygienic detection of herbs, fruits, and vegetables.

Graphical abstract: Visualization of the distribution of carbendazim on the arabidopsis leaf surface by VUV-postionization mass spectrometry

Supplementary files

Article information

Article type
Paper
Submitted
28 Jul 2025
Accepted
15 Nov 2025
First published
16 Dec 2025

Anal. Methods, 2026, Advance Article

Visualization of the distribution of carbendazim on the arabidopsis leaf surface by VUV-postionization mass spectrometry

W. Nie, A. Li, X. Shang, Q. Lu and Y. Hu, Anal. Methods, 2026, Advance Article , DOI: 10.1039/D5AY01242B

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