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Fan B, Wang Y, Li Z, Xun D, Dong J, Zhao X, Fan X, Wang Y. Si@Ag@PEI substrate-based SERS sensor for rapid detection of illegally adulterated sulfur dioxide in traditional Chinese medicine. Talanta 2022; 238:122988. [PMID: 34857322 DOI: 10.1016/j.talanta.2021.122988] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 10/14/2021] [Accepted: 10/20/2021] [Indexed: 01/06/2023]
Abstract
The illegal adulteration of sulfur dioxide in natural healthcare products may lead to serious health problems, which raise an urgent demand of straightforward approach for detecting sulfur dioxide. In this paper, surface-enhanced Raman scattering (SERS) sensor with sample preparation apparatus for headspace adsorption of SO2 has been developed, which was successfully applied to detect illegal adulteration of sulfur dioxide in traditional Chinese medicine (TCM). Functional membrane substrate of Si@Ag@PEI composite was synthesized to enhance the adsorption and Raman signal of SO2. A 3D-printed headspace extraction device was designed to adsorbed SO2 by Si@Ag@PEI membrane after micro-extraction of TCM samples in 15 min. The content of sulfur dioxide was subsequently quantitatively measured by SERS sensor. The linear range of sensor is between 2.5 and 250 mg/kg with limit of detection of 0.25 mg/kg, which is lower than the strictest standard of Chinese Pharmacopoeia (10 mg/kg). The proposed approach was used to detect the SO2 residue in TCMs including ginseng, Salvia miltiorrhiza, and bitter almonds. The fabricated sensor exhibited satisfied sensitivity and stability, which provide a simple approach for on-site detection of illegal adulteration of sulfur dioxide.
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Affiliation(s)
- Bo Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Yingchao Wang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Hangzhou, 310018, China
| | - Zhenhao Li
- Zhejiang Shouxiangu Institute of Rare Medicine Plant, Wuyi, 321200, China
| | - Dejin Xun
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Jian Dong
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Xiangwei Zhao
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China; State Key Laboratory of Component-Based Chinese Medicine, Tianjin, 301617, China
| | - Yi Wang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Hangzhou, 310018, China; State Key Laboratory of Component-Based Chinese Medicine, Tianjin, 301617, China.
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