ArticleACS omega2021
On Selecting a Suitable Spectral Matching Method for Automated Analytical Applications of Raman Spectroscopy.
Article in ACS omega, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Raman spectroscopic signatures of prostate cancer progression: correlation with gleason score.Molecular and cellular biochemistry · 2026Article
- Label-free biochemical imaging and time point analysis of neural organoids via deep learning-enhanced Raman microspectroscopy.Science advances · 2026Article
- Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples.ACS omega · 2026Article
- Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples.bioRxiv : the preprint server for biology · 2026Article
- Analyzing Spectral Similarities for Structural Identification Using a New Benchmark Database.The journal of physical chemistry. A · 2026Article
- Asymmetric Dimethylarginine Vibrational Spectroscopy Spectra and Density Functional Theory Model.Sensors (Basel, Switzerland) · 2025Article
- A novel signal processing approach enabled by machine learning for the detection and identification of chemical warfare agent simulants using a GC-QEPAS system.Forensic sciences research · 2025Article
- Spectroscopic Characterization of the Photolysis of Riboflavin (Vitamin B2) via Time-Resolved Mass Spectrometry and IRMPD Spectroscopy.The journal of physical chemistry. A · 2025Article
- Scalable miniature on-chip Fourier transform spectrometer for Raman spectroscopy.Light, science & applications · 2025Article
- In silico machine learning-enabled detection of polycyclic aromatic hydrocarbons from contaminated soil.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Spatial recognition and semi-quantification of epigenetic events in pancreatic cancer subtypes with multiplexed molecular imaging and machine learning.Scientific reports · 2025Article
- Comparison of Different Vibrational Spectroscopic Probes (ATR-FTIR, O-PTIR, Micro-Raman, and AFM-IR) of Lipids and Other Compounds Found in Environmental Samples: Case Study of Substrate-Deposited Sea Spray Aerosols.ACS measurement science au · 2025Article
- Rapid single-particle chemical imaging of nanoplastics by SRS microscopy.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- A microfluidic approach for label-free identification of small-sized microplastics in seawater.Scientific reports · 2023Article
- Editorial: Exploring complex biosphere molecular signaling networks: plant-microbes symbiosis at microscopic to macroscopic levels.Frontiers in plant science · 2023Article
- A Raman spectral reference library of potential anthropogenic and biological ocean polymers.Scientific data · 2022Article
- Raman Molecular Fingerprints of SARS-CoV-2 British Variant and the Concept of Raman Barcode.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Raman spectra are molecular structure-specific and hence are employed in applications requiring chemical identification. The advent of efficient handheld and smartphone-based Raman instruments is promoting widespread applications of the technique, which often involve less trained end users. Software modules that enable spectral library searches based on spectral pattern matching is an essential part of such applications. The Raman spectrum recorded by end users will naturally have varying levels of signal to noise (SN), baseline fluctuations, etc., depending on the sample environment. Further, in biological, forensic, food, pharmaceuticals, etc., fields where a vast amount of Raman spectral data is generated, careful removal of background is often impossible. In other words, a 100% match between the library spectrum and user input cannot be often guaranteed or expected. Often, such influences are discounted upon developing mathematical methods for general applications. In this manuscript, we carefully examine how such effects would determine the results of spectral similarity-based library search. We show that several popular mathematical spectral matching approaches give incorrect results under the influence of small changes in the baseline and/or the noise. We also discuss the points to be carefully considered while generating a spectral library. We believe our results will be a guiding note for developing applications of Raman spectroscopy that uses a standard spectral library and mathematical spectral matching.
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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.