Issue 15, 2025

On-site rapid detection of ancient leather using a dual recognition strategy

Abstract

Leather has been widely used since ancient times, and the discovery of ancient leather is of great value for studying the origin and development of costume culture. However, due to contamination and degradation of leather relics in the buried environment, traditional analytical methods face challenges in detecting microtraces of ancient leather. Therefore, an immunosensor based on a dual recognition strategy was proposed in this work for the detection of leather artifacts at archaeological sites. Anti-collagen antibodies type I (Anti-COL I) and type II (Anti-COL II) were prepared through animal immunization. Next, the antibodies on the surfaces of magnetic beads (MBs) and polystyrene microspheres (PMs) underwent a specific binding reaction with the antigens, which were magnetically separated and placed in sucrose solution, further catalyzed by sucrose invertase on functionalized polystyrene microspheres (FPMs). Finally, the collagen concentration was detected using a personal glucose meter (PGM). The prepared immunosensor exhibited excellent sensitivity, specificity, and stability, with a limit of detection (LOD) of 4.92 ng mL−1, a relative standard deviation (RSD) of 8.39% for sensitivity, and a linear detection range of 10 ng mL−1 to 100 μg mL−1. The coefficient of variation of specificity was less than 4.34%, and the sensor demonstrated a lifespan of up to three weeks. Moreover, the sensor outperforms enzyme-linked immunosorbent assay (ELISA) in terms of accuracy, specificity, and reproducibility. Therefore, this sensor provides a new strategy for the on-site detection of leather artifacts.

Graphical abstract: On-site rapid detection of ancient leather using a dual recognition strategy

Article information

Article type
Paper
Submitted
02 Jan 2025
Accepted
10 Mar 2025
First published
14 Mar 2025

Anal. Methods, 2025,17, 2978-2986

On-site rapid detection of ancient leather using a dual recognition strategy

H. Du, Y. Deng, L. Lv, J. Li, C. Zhang, Y. Li, Y. Zhou, Z. Peng, H. Yang and B. Wang, Anal. Methods, 2025, 17, 2978 DOI: 10.1039/D5AY00004A

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