Class-Specific Immunochromatographic Assay Enabled by Mesoporous Nanozyme-Catalyzed Signal Amplification for On-Site Screening of Sulfonylureas


Li Y., He Z., He P., Tang Z., BAĞDA E., BAĞDA E., ...Daha Fazla

Foods, cilt.15, sa.5, 2026 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Sayı: 5
  • Basım Tarihi: 2026
  • Doi Numarası: 10.3390/foods15050944
  • Dergi Adı: Foods
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Directory of Open Access Journals
  • Anahtar Kelimeler: functional foods, immunochromatographic assay, nanozymes, PCN-224@Pt, sulfonylureas
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

Conventional immunochromatographic assays (ICAs) face limitations in sensitivity and dynamic range, hindering their application in on-site, class-specific screening of sulfonylurea (SU) adulteration in functional foods. To address this, a signal amplification strategy was developed by engineering high-density platinum nanozymes on a mesoporous metal–organic framework (PCN-224). The mesoporous architecture of PCN-224 facilitated high-density and stable loading of catalytically active Pt sites. The established PCN-224@Pt-based ICA achieved detection limits of 0.52–7.94 μg/kg in tea and 0.69–7.02 μg/kg in capsules, with linear ranges of 1.69–513.01 μg/kg and 2.05–716.47 μg/kg, respectively. Compared with traditional colloidal gold immunochromatographic assays (CG-ICAs), sensitivity was improved by up to 57-fold, while the linear detection range was expanded by over 5-fold relative to the previously reported PCN-224@PDA- ICA. The method demonstrated recovery rates of 81.8–119.8% and coefficients of variation between 2.5% and 11.4%. Validation against LC-MS/MS using 20 real samples showed excellent agreement (R2 > 0.99). This work not only provides a sensitive and rapid tool for the surveillance of SU adulteration in functional foods but also establishes a generalizable nanozyme design strategy applicable to enhancing the performance of a wide range of ICA-based detection platforms.