ReviewEnvironmental science & technology2024
Machine Learning-Assisted Surface-Enhanced Raman Spectroscopy Detection for Environmental Applications: A Review.
Review in Environmental science & technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed.
- Unveiling the Gastric Microbiome: Novel Insights into Early Detection and Pathogenesis of Gastric Cancer.Probiotics and antimicrobial proteins · 2026Review
- Microfluidic-Integrated CRISPR-Cas Biosensor for Marine Pollutant and Pathogen Monitoring: A Review.Biosensors · 2026Review
- Raman reporters for SERS/SERRS biosensing and biomedicine: design, applications, and challenges.Mikrochimica acta · 2026Review
- Research progress of AI assisted nano-SERS technology in rapid identification of multi species oral pathogenic biofilms.Mikrochimica acta · 2026Review
- Conditional superiorities and unaddressed bottlenecks: a critical review of artificial intelligence for waterborne microbial detection.Applied microbiology and biotechnology · 2026Review
- A Tutorial on Best Practices and Pitfalls in Applying Machine Learning to Environmental Research.ACS environmental Au · 2026Article
- Emerging Plasmonic Nanomaterials for SERS-Based Disease Diagnostics: Innovations, Clinical Challenges, and AI Integration.Molecules (Basel, Switzerland) · 2026Review
- AI-Assisted Surface-Enhanced Raman Spectroscopy for Cardiovascular Diagnostics: From Plasmonic Materials to Clinical Translation.Nanomaterials (Basel, Switzerland) · 2026Review
- AI/ML-Assisted SERS Biosensing for Biomolecular Detection: From Direct Spectral Response to Integrated Diagnostic Systems.Biosensors · 2026Review
- Roadmap for light interaction with biophotonic surfaces and their diverse applications.Journal of biomedical optics · 2026Article
- Next-Generation SERS Probes: Engineering Hotspots, Intelligent Molecular Targeting, and AI-Driven Spectral Analysis for Emerging Applications.Nanomaterials (Basel, Switzerland) · 2026Review
- Bioanalysis Using Surface-Enhanced Raman Spectroscopy: From the Perspective of Single Molecules.ACS nano · 2026Review
- Machine Learning-Enabled Intelligent Analysis of Surface-Enhanced Raman Scattering: Methods, Applications, and Perspectives.Molecules (Basel, Switzerland) · 2026Review
- Machine learning-enhanced identification of fluorophilic interactions for improved SERS detection of PFOA.Environmental science. Nano · 2026Article
- Rapid Machine Learning-Driven Detection of Pesticides and Dyes Using Raman Spectroscopy.Journal of chemical information and modeling · 2026Article
- BactoRamanBioNet: A Multimodal Neural Network for Bacterial Species Identification Using Raman Spectroscopy and Biological Knowledge.Sensors (Basel, Switzerland) · 2026Article
- Label-free saliva screening platform using M13 bacteriophage-based 3D plasmonic structures for MRONJ diagnosis.Scientific reports · 2026Article
- AI-driven routing and layered architectures for intelligent ICT in nanosensor networked systems.iScience · 2026Review
- Impact of Farm Management Practices onFoods (Basel, Switzerland) · 2026Review
- Immunochromatographic SERS sensor-deep learning combined strategy for intelligent diagnosis of chronic kidney disease.Mikrochimica acta · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Surface-enhanced Raman spectroscopy (SERS) has gained significant attention for its ability to detect environmental contaminants with high sensitivity and specificity. The cost-effectiveness and potential portability of the technique further enhance its appeal for widespread application. However, challenges such as the management of voluminous quantities of high-dimensional data, its capacity to detect low-concentration targets in the presence of environmental interferents, and the navigation of the complex relationships arising from overlapping spectral peaks have emerged. In response, there is a growing trend toward the use of machine learning (ML) approaches that encompass multivariate tools for effective SERS data analysis. This comprehensive review delves into the detailed steps needed to be considered when applying ML techniques for SERS analysis. Additionally, we explored a range of environmental applications where different ML tools were integrated with SERS for the detection of pathogens and (in)organic pollutants in environmental samples. We sought to comprehend the intricate considerations and benefits associated with ML in these contexts. Additionally, the review explores the future potential of synergizing SERS with ML for real-world applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.