Synthesis, Characterization of Benzimidazole Derivatives carrying Pyridine Moiety

Prasad, K. Durga; Rani, M. Sandhya; Anusha, D.

doi:10.5958/0974-4150.2018.00045.7

Cited by 1 publication

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“…Benzimidazole (BZM) pesticides are widely used as agricultural insecticides and fungicides to control parasitic infections as well as crop diseases [ 1 , 2 ]. However, the overuse, misuse, or mixed-use of multiple BZM pesticides is potentially hazardous to human health, and even to the environment and ecosystems [ 3 ]. In particular, the mixed-use of BZM pesticides poses a challenge in the qualitative detection of multiple residues, because the BZM pesticide molecules have a similar functional group structure to that of benzimidazole and similar spectral vibration information [ 4 ].…”

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confidence: 99%

Trace Identification and Visualization of Multiple Benzimidazole Pesticide Residues on Toona sinensis Leaves Using Terahertz Imaging Combined with Deep Learning

Nie

Lin

et al. 2021

IJMS

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Molecular spectroscopy has been widely used to identify pesticides. The main limitation of this approach is the difficulty of identifying pesticides with similar molecular structures. When these pesticide residues are in trace and mixed states in plants, it poses great challenges for practical identification. This study proposed a state-of-the-art method for the rapid identification of trace (10 mg·L−1) and multiple similar benzimidazole pesticide residues on the surface of Toona sinensis leaves, mainly including benzoyl (BNL), carbendazim (BCM), thiabendazole (TBZ), and their mixtures. The new method combines high-throughput terahertz (THz) imaging technology with a deep learning framework. To further improve the model reliability beyond the THz fingerprint peaks (BNL: 0.70, 1.07, 2.20 THz; BCM: 1.16, 1.35, 2.32 THz; TBZ: 0.92, 1.24, 1.66, 1.95, 2.58 THz), we extracted the absorption spectra in frequencies of 0.2–2.2 THz from images as the input to the deep convolution neural network (DCNN). Compared with fuzzy Sammon clustering and four back-propagation neural network (BPNN) models (TrainCGB, TrainCGF, TrainCGP, and TrainRP), DCNN achieved the highest prediction accuracies of 100%, 94.51%, 96.26%, 94.64%, 98.81%, 94.90%, 96.17%, and 96.99% for the control check group, BNL, BCM, TBZ, BNL + BCM, BNL + TBZ, BCM + TBZ, and BNL + BCM + TBZ, respectively. Taking advantage of THz imaging and DCNN, the image visualization of pesticide distribution and residue types on leaves was realized simultaneously. The results demonstrated that THz imaging and deep learning can be potentially adopted for rapid-sensing detection of trace multi-residues on leaf surfaces, which is of great significance for agriculture and food safety.

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