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Combinatorial Chemistry & High Throughput Screening

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ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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Research Article

Detection of Ningnanmycin Using Fluorescence Spectroscopy Combined with BP Neural Network

Author(s): Xiaoyan Wang, Zhezhen Jiang, Rendong Ji*, Yue Han, Haiyi Bian, Yudong Yang, Xiaotao Feng, Jiangyu Xu and Mengyuan Wang

Volume 26, Issue 7, 2023

Published on: 17 October, 2022

Page: [1414 - 1423] Pages: 10

DOI: 10.2174/1386207325666220823124530

Price: $65

Abstract

Background: Ningnanmycin is a new antibiotic pesticide with good bactericidal and antiviral efficacy, which is widely used in the control of fruit and vegetable diseases, and the excessive pesticide residues pose a serious threat to the environment and human health.

Methods: In this study, we used fluorescence spectrometer to scan the three-dimensional spectrum of ningnanmycin samples. We used a BP neural network to complete the regression analysis of content prediction based on the fluorescence spectra. After that, the prediction performance of the BP neural network was compared with the exponential fitting method.

Results: The results of the BP neural network modeling based on the obtained samples showed that the mean square error of the prediction results of the test set is less than 10-4, the R-square is greater than 0.99, the average recovery is 99.11%, and the model performance of the BP neural network is better than exponential fitting.

Conclusion: Studies have shown that fluorescence spectroscopy combined with BP neural network can effectively predict the concentration of ningnanmycin.

Keywords: Ningnanmycin, fluorescence spectrum, quantitative analysis, BP neural network, chromatography, pesticides.

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