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Quan Yuan¹, Jia-Wei Tang², Jie Chen³

¹School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province, China; Department of Laboratory Medicine, Shengli Oilfield Central Hospital, Dongying, Shandong Province, China.

Environmental pollution (Barking, Essex : 1987)

|March 20, 2025

概括

为了打击抗生素污染,创建了一个新的数据库,包含200种抗生素的12800种表面增强拉曼光谱 (SERS) 光谱. 一个卷积神经网络 (CNN) 模型实现了98.94%的识别准确度,有助于环境监测.

科学领域:

环境科学环境科学
分析化学分析化学
频谱学是一种光谱学.

背景情况:

水源中的抗生素污染是一个日益严重的环境和健康问题.
抗生素耐药性的传播因环境污染而加剧.
表面增强拉曼光谱 (SERS) 提供敏感的抗生素识别,但缺乏全面的光谱数据库.

研究的目的:

使用SERS开发一个大规模的,开放的抗生素光谱数据库.
从SERS数据建立可靠的机器学习模型,用于从SERS数据中识别抗生素.
促进对抗生素污染的环境监测和管理.

主要方法:

对200种环境相关的抗生素进行了12800个SERS光谱的系统收集.
开发一个基于Web的,开放式访问的SERS光谱数据库.
机器学习算法的比较和验证,包括卷积神经网络 (CNN),用于光谱识别.

主要成果:

为200种抗生素建立了一个开放的SERS光谱数据库 (http://sers.test.bniu.net/).
一个CNN模型在数据库中识别抗生素时达到98.94%的准确性.
对CNN模型的外部验证显示了82.8%的准确性,证明了它的实际实用性.

结论:

SERS光谱数据库和CNN模型为环境抗生素检测提供了一个新的,可扩展的资源.
该资源增强了SERS在环境监测计划中的整合.
这项研究支持改善抗生素污染管理和缓解抗生素耐药性的研究.

关键词:

抗生素抗生素是一种抗生素.卷积神经网络是一种卷积神经网络.机器学习算法机器学习算法拉曼光谱是拉曼光谱中的一个.表面增强的拉曼光谱学

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