Multi-task detection of pesticide residues in Allium tuberosum using a colorimetric sensor array based on anthocyanin dyes combined with a pattern recognition algorithm

Rapid and reliable detection of pesticide residues in Allium tuberosum is essential to ensure food safety and sustainable agriculture. In this study, a novel colorimetric sensor array (CSA) was developed using nine environmentally friendly anthocyanins as dyes and silica gel plates as carriers. And...

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Vydané v:Food chemistry Ročník 491; s. 145296
Hlavní autori: Zhu, Jingwen, Meng, Fanzhen, Deng, Jihong, Zhang, Chunyu, Chen, Hengke, Wang, Huazhi, Jiang, Hui
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Elsevier Ltd 01.11.2025
Predmet:
Algorithms
Allium tuberosum
anthocyanins
Anthocyanins - chemistry
color
Colorimetric sensor array
colorimetry
Colorimetry - instrumentation
Colorimetry - methods
Coloring Agents - chemistry
food chemistry
Food Contamination - analysis
Food safety
Gas Chromatography-Mass Spectrometry
headspace analysis
neural networks
Neural Networks, Computer
Pattern recognition model
Pesticide pollution
Pesticide Residues - analysis
Pesticide Residues - chemistry
Pesticide Residues - isolation & purification
pesticides
Qualitative and quantitative detection
regression analysis
silica gel
Solanum tuberosum - chemistry
solid phase microextraction
sustainable agriculture
volatile organic compounds
Qualitative and quantitative detection
Food safety
Pesticide pollution
Pattern recognition model
Colorimetric sensor array
ISSN:0308-8146, 1873-7072, 1873-7072
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Shrnutí:Rapid and reliable detection of pesticide residues in Allium tuberosum is essential to ensure food safety and sustainable agriculture. In this study, a novel colorimetric sensor array (CSA) was developed using nine environmentally friendly anthocyanins as dyes and silica gel plates as carriers. And the color change of the CSA in this study was verified to relate to the potential-of-Hydrogen of volatile organic compounds in the sample by headspace solid-phase microextraction combined with gas chromatography–mass spectrometry. This CSA was combined with pattern recognition models to achieve detection of pesticide category, concentration and content in Allium tuberosum: the convolutional neural network classifier achieved 100 % accuracy for pesticide types classification and 75 %–100 % accuracy for concentration classification. And the root mean square error of the support vector regression model reached 1.02–2.68 mg/kg, and the correlation coefficient reached 0.93–0.95. This method has great potential for routine, rapid food safety detection. •Volatile organic compounds vary by pesticide type in contaminated samples.•Anthocyanin sensor arrays could detect pesticide contamination in Allium tuberosum.•Proper qualitative/quantitative models helped improve sensor array performance.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0308-8146
1873-7072
1873-7072
DOI:10.1016/j.foodchem.2025.145296