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<p>Pesticide contamination is a global challenge, threatening agricultural production and environmental safety. To address the urgent need for efficient and reliable pesticide detection methods, we developed a novel multi-channel colorimetric sensor array based on HAuCl<sub>4</sub>-mediated oxidation reactions. This innovative system incorporates six distinct colorimetric channels coupled with advanced multivariate statistical analysis, enabling simultaneous detection and discrimination of multiple pesticide residues. Through systematic optimization of key reaction parameters, the developed sensor improved selectivity and classification accuracy, particularly for structurally similar pesticides. The platform successfully identifies 11 different classes of pesticides and distinguishes five specific pesticides across various concentration levels. This approach overcomes the limitations of traditional single-target detection methods by employing multi-parameter analysis for accurate pesticide identification. Notably, the proposed method offers several practical advantages, including simple operation, rapid detection (within 30&#xa0;min), high sensitivity (detection limits of 6&#xa0;μg/mL), and cost-effectiveness by eliminating the need for expensive instrumentation. These features make the developed sensor array particularly suitable for on-site pesticide screening and food safety monitoring applications.</p> Graphical Abstract <p></p>

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Multi-channel high-sensitivity oxidation colorimetric array based on HAuCl4 for pesticide detection

  • 期刊论文

  • 发表日期：

    2025年7月17日

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  • Yalong Pan,
  • Lijun Han,
  • Junxue Shen,
  • Jing Li,
  • Taolei Sun,
  • Yao Yu

摘要

Pesticide contamination is a global challenge, threatening agricultural production and environmental safety. To address the urgent need for efficient and reliable pesticide detection methods, we developed a novel multi-channel colorimetric sensor array based on HAuCl4-mediated oxidation reactions. This innovative system incorporates six distinct colorimetric channels coupled with advanced multivariate statistical analysis, enabling simultaneous detection and discrimination of multiple pesticide residues. Through systematic optimization of key reaction parameters, the developed sensor improved selectivity and classification accuracy, particularly for structurally similar pesticides. The platform successfully identifies 11 different classes of pesticides and distinguishes five specific pesticides across various concentration levels. This approach overcomes the limitations of traditional single-target detection methods by employing multi-parameter analysis for accurate pesticide identification. Notably, the proposed method offers several practical advantages, including simple operation, rapid detection (within 30 min), high sensitivity (detection limits of 6 μg/mL), and cost-effectiveness by eliminating the need for expensive instrumentation. These features make the developed sensor array particularly suitable for on-site pesticide screening and food safety monitoring applications.

Graphical Abstract