ArticleScientific reports2020
A Statistical Approach of Background Removal and Spectrum Identification for SERS Data.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Machine Learning-Enabled Intelligent Analysis of Surface-Enhanced Raman Scattering: Methods, Applications, and Perspectives.Molecules (Basel, Switzerland) · 2026Review
- Automated Background Noise Removal from Substrate Materials in Raman Spectra Using Dynamic Time Warping.ACS omega · 2025Article
- Seeing Is Believing: How Does the Surface of Silver Nanocubes from a Polyol Synthesis Change during Sample Collection, Washing, and Redispersion.Langmuir : the ACS journal of surfaces and colloids · 2025Article
- Article
- Machine Learning-Assisted Surface-Enhanced Raman Spectroscopy Detection for Environmental Applications: A Review.Environmental science & technology · 2024Review
- Machine Learning-Based Heavy Metal Ion Detection Using Surface-Enhanced Raman Spectroscopy.Sensors (Basel, Switzerland) · 2022Article
- SERSNet: Surface-Enhanced Raman Spectroscopy Based Biomolecule Detection Using Deep Neural Network.Biosensors · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
SERS (surface-enhanced Raman scattering) enhances the Raman signals, but the plasmonic effects are sensitive to the chemical environment and the coupling between nanoparticles, resulting in large and variable backgrounds, which make signal matching and analyte identification highly challenging. Removing background is essential, but existing methods either cannot fit the strong fluctuation of the SERS spectrum or do not consider the spectra's shape change across time. Here we present a new statistical approach named SABARSI that overcomes these difficulties by combining information from multiple spectra. Further, after efficiently removing the background, we have developed the first automatic method, as a part of SABARSI, for detecting signals of molecules and matching signals corresponding to identical molecules. The superior efficiency and reproducibility of SABARSI are shown on two types of experimental datasets.
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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.