4.7 Article

Silver Nanoparticle-Based Surface-Enhanced Raman Spectroscopy for the Rapid and Selective Detection of Trace Tropane Alkaloids in Food

Journal

ACS APPLIED NANO MATERIALS
Volume 2, Issue 10, Pages 6592-6601

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acsanm.9b01493

Keywords

SERS; hotspots; coadsorption; competitive adsorption; tropane alkaloids

Funding

  1. National Key Research & Development Program of China [2018YFC0807201, 2018YFC1602600]
  2. National Natural Science Foundation of China [41876099, 21473140, 21703187]
  3. Xiamen Science and Technology Project [3502Z20183002]

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Recently, surface-enhanced Raman Spectroscopy (SERS) has been widely applied for rapid detection of trace targets in various fields. However, two key issues are still being explored: (1) how to form effective SERS hotspots for the trace qualitation with high sensitivity; (2) how to quantitatively identify trace target in complicated matrix in a highly selective and fast manner. Here, aiming at these two key points, we developed an effective and simple protocol for the rapid detection of three tropane alkaloids (TAs), typical toxicants in public security. Taking the advantage of the coadsorption with the specific adsorbed I- during the formation of hotspots by the highly concentrated halide salts, the detection sensitivity of TAs was improved up to 4 orders over the case of Cl-. Under the optimized condition, the lowest detectable concentration of these three TAs was down to the 1 mu g/L level. Through the use of drink and food samples, the strong nonspecific competitive adsorption from the complicated matrix was significantly weakened by the simple dilution. The recovery was increased almost 2 orders over that of direct detection, irrespectively of the distinctly changed interferences in different samples. The proposed strategy, improving qualitative sensitivity by forming effective hotspots and quantitative analysis by dilution, not only provides a rapid detection for trace TAs in various foods in case of emergency first response but also could be easily extended to other SERS-based analysis.

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