通过表面增强的拉曼光谱和深度学习,用于草药的等离子人造检查器
Hongdoo Kim1, Jemin Lee1, Sung Won Kim2
1Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang, 37673, Gyeongbuk, Republic of Korea.
一个新的等离子人造检查器使用表面增强的拉曼光谱 (SERS) 和深度学习 (DL) 来识别草药 (HM). 这项技术达到~95%的准确性,为传统的器官感知检查提供了一种节省劳动力的替代方案,以确保HM安全.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 人工智能的人工智能
背景情况:
- 体感检查对于草药 (HM) 的安全性至关重要,但是劳动密集型的.
- 需要有效和可靠的方法来补充传统的HM检查流程.
研究的目的:
- 开发一个集成表面增强拉曼光谱 (SERS) 和深度学习 (DL) 的等离子体人工检查器.
- 评估SERS-DL系统用于区分HM物种的准确性和可靠性.
主要方法:
- 利用SERS从HM标本中获得光谱指纹信息,反映生物活性化合物.
- 应用深度学习 (DL) 算法来分析HM识别的SERS光谱.
- 评估了系统在区分35个HM物种的性能,包括外观相似的物种.
主要成果:
- 该SERS-DL系统在区分HM物种方面取得了约95%的准确性.
- 快速获取数据 (秒) 使SERS-DL适合进行补充检查.
- 证明了复杂的HM样品的劳动节约差异化潜力.
结论:
- SERS和DL的协同集成为HM检查提供了准确和高效的方法.
- 这种SERS-DL方法可以帮助传统的感官检查,并增强HM数据库.
- 该技术为改善草药安全监测提供了一个有希望的解决方案.
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