ArticleAnalytical chemistry2021
Deep Learning for Reconstructing Low-Quality FTIR and Raman Spectra─A Case Study in Microplastic Analyses.
Article in Analytical chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Autoencoder-Based Detection of Nanoplastics in Biological Matrices via Infrared Hyperspectral Imaging.ACS measurement science au · 2026Article
- PlasticAnalytics: A Deep Learning-Powered Spectral Library and Analytical Suite.Environmental science & technology · 2026Article
- Machine Learning-Driven Prediction of Microplastic Aging Processes and Environmental Risk Assessment Across Multi-Media Systems.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Denoising and Baseline Correction of Low-Scan FTIR Spectra: A Benchmark of Deep Learning Models Against Traditional Signal Processing.Bioengineering (Basel, Switzerland) · 2026Article
- Review
- Advancing microplastic pollution management in aquatic environments through artificial intelligence.Journal of environmental health science & engineering · 2025Review
- Rapid Detection of Perfluorooctanesulfonic Acid Using Surface-Enhanced Raman Spectroscopy and Deep Learning.ACS omega · 2025Article
- Separation and Detection of Microplastics in Human Exposure Pathways: Challenges, Analytical Techniques, and Emerging Solutions.Journal of xenobiotics · 2025Review
- Safe-by-Design Strategies for Intranasal Drug Delivery Systems: Machine and Deep Learning Solutions to Differentiate Epithelial Tissues via Attenuated Total Reflection Fourier Transform Infrared Spectroscopy.ACS pharmacology & translational science · 2025Article
- Inverse reconstruction of model cells: Extracting structural and molecular insights through infrared spectroscopic cytology.PloS one · 2025Article
- Artifacts and Anomalies in Raman Spectroscopy: A Review on Origins and Correction Procedures.Molecules (Basel, Switzerland) · 2024Review
- Membrane filter removal in FTIR spectra through dictionary learning for exploring explainable environmental microplastic analysis.Scientific reports · 2024Article
- Deep autoencoder as an interpretable tool for Raman spectroscopy investigation of chemical and extracellular vesicle mixtures.Biomedical optics express · 2024Article
- Article
- High-throughput microplastic assessment using polarization holographic imaging.Scientific reports · 2024Article
- A review of recent progress in the application of Raman spectroscopy and SERS detection of microplastics and derivatives.Mikrochimica acta · 2023Review
- Evaluation of the Deterioration of Untreated Commercial Polystyrene by Psychrotrophic Antarctic Bacterium.Polymers · 2023Article
- A Critical Review on Artificial Intelligence-Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges.International journal of environmental research and public health · 2023Review
- Raman Spectroscopy on Brain Disorders: Transition from Fundamental Research to Clinical Applications.Biosensors · 2022Review
- Accurate Tumor Subtype Detection with Raman Spectroscopy via Variational Autoencoder and Machine Learning.ACS omega · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Herein we report on a deep-learning method for the removal of instrumental noise and unwanted spectral artifacts in Fourier transform infrared (FTIR) or Raman spectra, especially in automated applications in which a large number of spectra have to be acquired within limited time. Automated batch workflows allowing only a few seconds per measurement, without the possibility of manually optimizing measurement parameters, often result in challenging and heterogeneous datasets. A prominent example of this problem is the automated spectroscopic measurement of particles in environmental samples regarding their content of microplastic (MP) particles. Effective spectral identification is hampered by low signal-to-noise ratios and baseline artifacts as, again, spectral post-processing and analysis must be performed in automated measurements, without adjusting specific parameters for each spectrum. We demonstrate the application of a simple autoencoding neural net for reconstruction of complex spectral distortions, such as high levels of noise, baseline bending, interferences, or distorted bands. Once trained on appropriate data, the network is able to remove all unwanted artifacts in a single pass without the need for tuning spectra-specific parameters and with high computational efficiency. Thus, it offers great potential for monitoring applications with a large number of spectra and limited analysis time with availability of representative data from already completed experiments.
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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.