Abusing antibiotic residues in the natural environment has become a severe public health and ecological environmental problem. The side effects of its biochemical and physiological consequences are severe. To avoid antibiotic contamination in water, implementing universal and rapid antibiotic residue detection technology is critical to maintaining antibiotic safety in aquatic environments. Surface-enhanced Raman spectroscopy (SERS) provides a powerful tool for identifying small molecular components with high sensitivity and selectivity. However, it remains a challenge to identify pure antibiotics from SERS spectra due to coexisting components in the mixture. In this study, an intelligent analysis model for the SERS spectrum based on a deep learning algorithm was proposed for rapid identification of the antibiotic components in the mixture and quantitative determination of the ratios of these components. We established a water environment system containing three antibiotic residues of ciprofloxacin, doxycycline, and levofloxacin. To facilitate qualitative and quantitative analysis of the SERS spectra antibiotic mixture datasets, we developed a computational framework integrating a convolutional neural network (CNN) and a non-negative elastic network (NN-EN) method. The experimental results demonstrate that the CNN model has a recognition accuracy of 98.68%, and the interpretation analysis of Shapley Additive exPlanations (SHAP) shows that our model can specifically focus on the characteristic peak distribution. In contrast, the NN-EN model can accurately quantify each component's ratio in the mixture. Integrating the SERS technique assisted by the CNN combined with the NN-EN model exhibits great potential for rapid identification and high-precision quantification of antibiotic residues in aquatic environments.

中文翻译：

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CNN结合NN-EN模型对SERS光谱进行综合分析，快速判别水溶液中的混合抗生素并进行比例定量

自然环境中抗生素残留的滥用已成为严重的公共卫生和生态环境问题。其生化和生理后果的副作用是严重的。为了避免水体中的抗生素污染，实施通用、快速的抗生素残留检测技术对于维护水生环境中的抗生素安全至关重要。表面增强拉曼光谱 (SERS) 提供了一种强大的工具，可以高灵敏度和选择性地识别小分子成分。然而，由于混合物中存在共存成分，从 SERS 光谱中识别纯抗生素仍然是一个挑战。本研究提出了一种基于深度学习算法的SERS谱智能分析模型，用于快速识别混合物中的抗生素成分并定量测定这些成分的比例。我们建立了含有环丙沙星、多西环素、左氧氟沙星三种抗生素残留的水环境系统。为了促进 SERS 光谱抗生素混合物数据集的定性和定量分析，我们开发了一个集成卷积神经网络（CNN）和非负弹性网络（NN-EN）方法的计算框架。实验结果表明CNN模型的识别准确率达到98.68%，Shapley Additive exPlanations (SHAP)的解释分析表明我们的模型可以专门关注特征峰分布。相比之下，NN-EN 模型可以准确量化混合物中每种成分的比例。将CNN辅助的SERS技术与NN-EN模型相结合，在水生环境中抗生素残留的快速识别和高精度定量方面展现出巨大的潜力。