ArticleSensors (Basel, Switzerland)2024
Denoising and Baseline Correction Methods for Raman Spectroscopy Based on Convolutional Autoencoder: A Unified Solution.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Autoencoder-Based Detection of Nanoplastics in Biological Matrices via Infrared Hyperspectral Imaging.ACS measurement science au · 2026Article
- Detection of congeners and contaminants in commercial and homemade alcoholic beverages under varying temperatures using metal-phenolic film-coated quartz crystal microbalances.Scientific reports · 2026Article
- Denoising and Baseline Correction of Low-Scan FTIR Spectra: A Benchmark of Deep Learning Models Against Traditional Signal Processing.Bioengineering (Basel, Switzerland) · 2026Article
- Portable Raman spectroscopy combined with machine learning for rapid and label-free recognition of five pathogenic bacteria.Frontiers in microbiology · 2026Article
- Advances in surface-enhanced Raman scattering applications for precision agriculture: monitoring plant health and crop quality.RSC advances · 2025Review
- IR Spectroscopy: From Experimental Spectra to High-Resolution Structural Analysis by Integrating Simulations and Machine Learning.The journal of physical chemistry. B · 2025Article
- Article
- Artifacts and Anomalies in Raman Spectroscopy: A Review on Origins and Correction Procedures.Molecules (Basel, Switzerland) · 2024Review
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Authors and funding
3 authors.
Funding
Abstract
Preprocessing plays a key role in Raman spectral analysis. However, classical preprocessing algorithms often have issues with reducing Raman peak intensities and changing the peak shape when processing spectra. This paper introduces a unified solution for preprocessing based on a convolutional autoencoder to enhance Raman spectroscopy data. One is a denoising algorithm that uses a convolutional denoising autoencoder (CDAE model), and the other is a baseline correction algorithm based on a convolutional autoencoder (CAE+ model). The CDAE model incorporates two additional convolutional layers in its bottleneck layer for enhanced noise reduction. The CAE+ model not only adds convolutional layers at the bottleneck but also includes a comparison function after the decoding for effective baseline correction. The proposed models were validated using both simulated spectra and experimental spectra measured with a Raman spectrometer system. Comparing their performance with that of traditional signal processing techniques, the results of the CDAE-CAE+ model show improvements in noise reduction and Raman peak preservation.
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