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Machine Learning-Assisted Synchronous Fluorescence Sensing Approach for Rapid and Simultaneous Quantification of Thiabendazole and Fuberidazole in Red Wine

Rapid analysis of components in complex matrices has always been a major challenge in constructing sensing methods, especially concerning time and cost. The detection of pesticide residues is an important task in food safety monitoring, which needs efficient methods. Here, we constructed a machine l...

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Autores principales: He, Jia-Rong, Wei, Jia-Wen, Chen, Shi-Yi, Li, Na, Zhong, Xiu-Di, Li, Yao-Qun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9785232/
https://www.ncbi.nlm.nih.gov/pubmed/36560348
http://dx.doi.org/10.3390/s22249979
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author He, Jia-Rong
Wei, Jia-Wen
Chen, Shi-Yi
Li, Na
Zhong, Xiu-Di
Li, Yao-Qun
author_facet He, Jia-Rong
Wei, Jia-Wen
Chen, Shi-Yi
Li, Na
Zhong, Xiu-Di
Li, Yao-Qun
author_sort He, Jia-Rong
collection PubMed
description Rapid analysis of components in complex matrices has always been a major challenge in constructing sensing methods, especially concerning time and cost. The detection of pesticide residues is an important task in food safety monitoring, which needs efficient methods. Here, we constructed a machine learning-assisted synchronous fluorescence sensing approach for the rapid and simultaneous quantitative detection of two important benzimidazole pesticides, thiabendazole (TBZ) and fuberidazole (FBZ), in red wine. First, fluorescence spectra data were collected using a second derivative constant-energy synchronous fluorescence sensor. Next, we established a prediction model through the machine learning approach. With this approach, the recovery rate of TBZ and FBZ detection of pesticide residues in red wine was 101% ± 5% and 101% ± 15%, respectively, without resorting complicated pretreatment procedures. This work provides a new way for the combination of machine learning and fluorescence techniques to solve the complexity in multi-component analysis in practical applications.
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spelling pubmed-97852322022-12-24 Machine Learning-Assisted Synchronous Fluorescence Sensing Approach for Rapid and Simultaneous Quantification of Thiabendazole and Fuberidazole in Red Wine He, Jia-Rong Wei, Jia-Wen Chen, Shi-Yi Li, Na Zhong, Xiu-Di Li, Yao-Qun Sensors (Basel) Article Rapid analysis of components in complex matrices has always been a major challenge in constructing sensing methods, especially concerning time and cost. The detection of pesticide residues is an important task in food safety monitoring, which needs efficient methods. Here, we constructed a machine learning-assisted synchronous fluorescence sensing approach for the rapid and simultaneous quantitative detection of two important benzimidazole pesticides, thiabendazole (TBZ) and fuberidazole (FBZ), in red wine. First, fluorescence spectra data were collected using a second derivative constant-energy synchronous fluorescence sensor. Next, we established a prediction model through the machine learning approach. With this approach, the recovery rate of TBZ and FBZ detection of pesticide residues in red wine was 101% ± 5% and 101% ± 15%, respectively, without resorting complicated pretreatment procedures. This work provides a new way for the combination of machine learning and fluorescence techniques to solve the complexity in multi-component analysis in practical applications. MDPI 2022-12-18 /pmc/articles/PMC9785232/ /pubmed/36560348 http://dx.doi.org/10.3390/s22249979 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
He, Jia-Rong
Wei, Jia-Wen
Chen, Shi-Yi
Li, Na
Zhong, Xiu-Di
Li, Yao-Qun
Machine Learning-Assisted Synchronous Fluorescence Sensing Approach for Rapid and Simultaneous Quantification of Thiabendazole and Fuberidazole in Red Wine
title Machine Learning-Assisted Synchronous Fluorescence Sensing Approach for Rapid and Simultaneous Quantification of Thiabendazole and Fuberidazole in Red Wine
title_full Machine Learning-Assisted Synchronous Fluorescence Sensing Approach for Rapid and Simultaneous Quantification of Thiabendazole and Fuberidazole in Red Wine
title_fullStr Machine Learning-Assisted Synchronous Fluorescence Sensing Approach for Rapid and Simultaneous Quantification of Thiabendazole and Fuberidazole in Red Wine
title_full_unstemmed Machine Learning-Assisted Synchronous Fluorescence Sensing Approach for Rapid and Simultaneous Quantification of Thiabendazole and Fuberidazole in Red Wine
title_short Machine Learning-Assisted Synchronous Fluorescence Sensing Approach for Rapid and Simultaneous Quantification of Thiabendazole and Fuberidazole in Red Wine
title_sort machine learning-assisted synchronous fluorescence sensing approach for rapid and simultaneous quantification of thiabendazole and fuberidazole in red wine
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9785232/
https://www.ncbi.nlm.nih.gov/pubmed/36560348
http://dx.doi.org/10.3390/s22249979
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