Scholarlink

<p>Innovative cloud algorithms-assisted portable Zn-based fluorescent sensor arrays (ZnMOF-74 and Mn:ZnS QDs)&#xa0;are proposed for rapid, sensitive and on-site detection of four antibiotics, enrofloxacin (ENX), chlortetracycline (CTC), oxytetracycline (OTC) and tetracycline (TC). This sensor arrays for the first time utilize the Lewis acid-base regulation of ZnMOF-74 and the crystal field effect of Mn:ZnS QDs to generate characteristic fluorescent “fingerprints” for the four antibiotics. Through the optimization of various chemometric algorithms (PCA-LDA, PLS-DA, KNN and RF), the recognition accuracy of four antibiotics in the complex matrix reached 98%. Moreover, a homemade smartphone APP based on cloud machine learning algorithms was developed, enabling rapid and accurate multiplexed detection with LODs of 8.9 nmol/L, 4.1 nmol/L, 5.7 nmol/L, and 6.7 nmol/L for ENX, CTC, OTC, and TC, respectively. The corresponding limits of quantification (LOQs) were 26.7 nmol/L, 12.3 nmol/L, 17.1 nmol/L, and 20.1 nmol/L, respectively. The proposed method was successfully applied to the detection of antibiotics in milk, honey, and beef samples, with satisfactory recoveries ranging from 97.33% to 107.40% and relative standard deviations (RSDs) below 5.63%, indicating good accuracy and reliability for real sample analysis. This work demonstrates the immense potential for universal, high-throughput, and accurate multi-target detection, providing transformative solutions in food safety.</p> Graphical abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Cloud algorithms-assisted Zn-based fluorescent sensor array for visual detection of multiple antibiotics in complex animal-derived foods

  • 期刊论文

  • 发表日期：

    2026年3月28日

查看全文
  • Gaoqiong Deng,
  • Huanhuan Lu,
  • Yulong Han,
  • Shuo Wang,
  • Hengye Chen,
  • Wanjun Long,
  • Yuanbin She

摘要

Innovative cloud algorithms-assisted portable Zn-based fluorescent sensor arrays (ZnMOF-74 and Mn:ZnS QDs) are proposed for rapid, sensitive and on-site detection of four antibiotics, enrofloxacin (ENX), chlortetracycline (CTC), oxytetracycline (OTC) and tetracycline (TC). This sensor arrays for the first time utilize the Lewis acid-base regulation of ZnMOF-74 and the crystal field effect of Mn:ZnS QDs to generate characteristic fluorescent “fingerprints” for the four antibiotics. Through the optimization of various chemometric algorithms (PCA-LDA, PLS-DA, KNN and RF), the recognition accuracy of four antibiotics in the complex matrix reached 98%. Moreover, a homemade smartphone APP based on cloud machine learning algorithms was developed, enabling rapid and accurate multiplexed detection with LODs of 8.9 nmol/L, 4.1 nmol/L, 5.7 nmol/L, and 6.7 nmol/L for ENX, CTC, OTC, and TC, respectively. The corresponding limits of quantification (LOQs) were 26.7 nmol/L, 12.3 nmol/L, 17.1 nmol/L, and 20.1 nmol/L, respectively. The proposed method was successfully applied to the detection of antibiotics in milk, honey, and beef samples, with satisfactory recoveries ranging from 97.33% to 107.40% and relative standard deviations (RSDs) below 5.63%, indicating good accuracy and reliability for real sample analysis. This work demonstrates the immense potential for universal, high-throughput, and accurate multi-target detection, providing transformative solutions in food safety.

Graphical abstract