ArticleJournal of advanced research2025
Rapid discrimination and ratio quantification of mixed antibiotics in aqueous solution through integrative analysis of SERS spectra via CNN combined with NN-EN model.
Article in Journal of advanced research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 45 citations in OpenAlex.
- ConvCGP: A convolutional neural network to predict genetic values of agronomic traits from compressed genome-wide polymorphisms.The plant genome · 2026Article
- SERS Mixture Recognition from Pure-Substance Spectra via Component Evidence Learning and Two-Stage Inference.Molecules (Basel, Switzerland) · 2026Article
- A zero-shot prompt learning approach on fine-grained text classification.Scientific reports · 2026Article
- Rapid classification and quality assessment of citrus essential oils via machine-learning-assisted Raman spectroscopy.Current research in food science · 2026Article
- Ratio Quantification of Geranium and Rose Essential Oil Mixtures via Deep Learning Analysis of Complex Raman Spectra.ACS measurement science au · 2025Article
- An Interpretable SERS-AI Platform for Rapid and Quantitative Diagnosis of Polymicrobial UTIs: Powered by Positively Charged Plasmonic Nanoparticles and Attention-Based Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Enhancing glyphosate detection in rice by SERS: controlled aggregation of silver nanoparticles and carbon disulfide-mediated signal amplification.Food chemistry: X · 2025Article
- Accurate Classification of Multi-Cultivar Watermelons via GAF-Enhanced Feature Fusion Convolutional Neural Networks.Foods (Basel, Switzerland) · 2025Article
- Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids.mSystems · 2025Article
- Article
- Deep Learning-Based Analysis of Mammographic Images for Breast Cancer Detection Using Transfer Learning.Healthcare technology lettersArticle
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAbusing 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.
objectivesIn 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.
methodsWe 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.
resultsThe 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.
conclusionIntegrating 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.
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