Cascaded Deep Convolutional Neural Networks as Improved Methods of Preprocessing Raman Spectroscopy Data

Article Optics

Deep convolutional neural networks as a unified solution for Raman spectroscopy-based classification in biomedical applications

Mohammadrahim Kazemzadeh et al.

Summary: A new method for classifying biological Raman spectra without the need for preprocessing steps has been proposed, showing superiority over conventional methods.

OPTICS COMMUNICATIONS (2022)

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Article Chemistry, Multidisciplinary

Classification of Preeclamptic Placental Extracellular Vesicles Using Femtosecond Laser Fabricated Nanoplasmonic Sensors

Mohammadrahim Kazemzadeh et al.

Summary: Placental extracellular vesicles (EVs) play important roles in pregnancy, but they are also implicated in the progression of preeclampsia. A fabrication process called laser-induced nano-structuring of SERS-active thin films (LINST) was developed to produce substrates that allow the biochemical fingerprinting of EVs. The findings encourage the exploration of surface-enhanced Raman spectroscopy (SERS) of EVs as a promising method for preeclampsia liquid biopsies.

ACS SENSORS (2022)

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Article Chemistry, Analytical

Deep Learning for Reconstructing Low-Quality FTIR and Raman Spectra-A Case Study in Microplastic Analyses

Josef Brandt et al.

Summary: The reported deep-learning method addresses the removal of instrumental noise and unwanted spectral artifacts in automated spectral measurements, especially in scenarios with limited time for acquiring a large number of spectra. Through training a neural network, the method effectively reconstructs complex spectral distortions and removes unwanted artifacts without the need for tuning spectra-specific parameters, offering high computational efficiency in automated analyses.

ANALYTICAL CHEMISTRY (2021)

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Article Chemistry, Analytical

High-Throughput Molecular Imaging via Deep-Learning-Enabled Raman Spectroscopy

Conor C. Horgan et al.

Summary: By utilizing deep learning, denoising and reconstructing low signal-to-noise ratio Raman molecular signatures is achieved, with a 10x improvement in mean-squared error over common Raman filtering methods. A neural network for robust 2-4X spatial super-resolution of hyperspectral Raman images is developed, resulting in Raman imaging speed-ups of up to 40-90X, enabling good-quality cellular imaging in under 1 min. Transfer learning is then applied to extend DeepeR from cell to tissue-scale imaging, providing a foundation for higher-throughput Raman spectroscopy and molecular imaging applications across biomedicine.

ANALYTICAL CHEMISTRY (2021)

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Article Spectroscopy

Refinement of spectra using a deep neural network: Fully automated removal of noise and background

Medhanie Tesfay Gebrekidan et al.

Summary: The study demonstrates the potential of the U-Net deep neural network for efficient noise and background removal in Raman spectra. Test results show high structural similarity index for recovered Raman spectra features.

JOURNAL OF RAMAN SPECTROSCOPY (2021)

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Article Chemistry, Analytical

Deep Learning for Biospectroscopy and Biospectral Imaging: State- of-the-Art and Perspectives

Hao He et al.

Summary: With the rapid growth of data from molecular and cellular samples, deep learning has been increasingly applied in biospectroscopy. It is utilized for data preprocessing, feature detection, and modeling in spectral analysis and spectroscopic imaging. However, there are challenges and limitations that need to be addressed for future advancements.

ANALYTICAL CHEMISTRY (2021)

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Review Oncology

Surface-Enhanced Raman Scattering (SERS) Spectroscopy for Sensing and Characterization of Exosomes in Cancer Diagnosis

Luca Guerrini et al.

Summary: Exosomes serve as important cancer biomarkers, while nanoplasmonic methods incorporating surface-enhanced Raman spectroscopy technique have become effective tools for detecting and discriminating exosomes. The main challenge lies in achieving rapid, sensitive, and cost-effective analysis of exosomes.

CANCERS (2021)

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Article Chemistry, Analytical

Convolution Network with Custom Loss Function for the Denoising of Low SNR Raman Spectra

Sinead Barton et al.

Summary: In this work, Convolutional Neural Networks are enhanced with a non-standard loss function to improve the signal-to-noise ratio of Raman spectra while preserving spectral peaks. The proposed algorithm shows improvements in signal to noise ratios by up to 100% for low intensity spectra, making it suitable for low light or high throughput applications. Overall, this method offers advantages compared to traditional Savitsky-Golay filtering.

SENSORS (2021)

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Article Biochemical Research Methods

Space curvature-inspired nanoplasmonic sensor for breast cancer extracellular vesicle fingerprinting and machine learning classification

Mohammadrahim Kazemzadeh et al.

Summary: Extracellular vesicles (EVs) are micro and nanoscale lipid-enclosed packages that have shown potential as liquid biopsy targets for cancer due to their reflection of the cell of origin, but clinical applications are hindered by the low abundance of disease-specific EVs. A novel space curvature-inspired surfaced-enhanced Raman spectroscopy (SERS) substrate was designed and tested, demonstrating the ability to effectively fingerprint and classify distinct subtypes of breast cancer EVs.

BIOMEDICAL OPTICS EXPRESS (2021)

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Article Instruments & Instrumentation

Single-Step Preprocessing of Raman Spectra Using Convolutional Neural Networks

Joel Wahl et al.

APPLIED SPECTROSCOPY (2020)

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Article Spectroscopy

Single-layer multiple-kernel-based convolutional neural network for biological Raman spectral analysis

Won Bum Sohn et al.

JOURNAL OF RAMAN SPECTROSCOPY (2020)

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Article Spectroscopy

Vector casting for noise reduction

Medhanie Tesfay Gebrekidan et al.

JOURNAL OF RAMAN SPECTROSCOPY (2020)

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Article Biochemical Research Methods

SciPy 1.0: fundamental algorithms for scientific computing in Python

Pauli Virtanen et al.

NATURE METHODS (2020)

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Review Chemistry, Multidisciplinary

Plasmonic Sensors for Extracellular Vesicle Analysis: From Scientific Development to Translational Research

Lip Ket Chin et al.

ACS NANO (2020)

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Review Chemistry, Multidisciplinary

Nanoplasmonic Approaches for Sensitive Detection and Molecular Characterization of Extracellular Vesicles

Tatu Rojalin et al.

FRONTIERS IN CHEMISTRY (2019)

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Article Instruments & Instrumentation

An Automated Baseline Correction Method Based on Iterative Morphological Operations

Yunliang Chen et al.

APPLIED SPECTROSCOPY (2018)

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Article Biochemical Research Methods

Identification and characterization of bladder cancer by low-resolution fiber-optic Raman spectroscopy

Hao Chen et al.

JOURNAL OF BIOPHOTONICS (2018)

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Article Chemistry, Analytical

Deep convolutional neural networks for Raman spectrum recognition: a unified solution

Jinchao Liu et al.

ANALYST (2017)

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Article Chemistry, Analytical

An algorithm for estimation of background signal of Raman spectra from biological cell samples using polynomial functions of different degrees

Crescenzio Gallo et al.

VIBRATIONAL SPECTROSCOPY (2016)

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Article Instruments & Instrumentation

Goldindec: A Novel Algorithm for Raman Spectrum Baseline Correction

Juntao Liu et al.

APPLIED SPECTROSCOPY (2015)

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Article Multidisciplinary Sciences

scikit-image: image processing in Python

Stefan van der Walt et al.

PEERJ (2014)

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Article Automation & Control Systems

Intelligent background correction using an adaptive lifting wavelet

Yan Liu et al.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2013)

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Article Spectroscopy

Improved methods for fluorescence background subtraction from Raman spectra

P. J. Cadusch et al.

JOURNAL OF RAMAN SPECTROSCOPY (2013)

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Review Biotechnology & Applied Microbiology

Surface-enhanced Raman scattering in cancer detection and imaging

Marc Vendrell et al.

TRENDS IN BIOTECHNOLOGY (2013)

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Article Chemistry, Analytical

Baseline correction using adaptive iteratively reweighted penalized least squares

Zhi-Min Zhang et al.

ANALYST (2010)

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Article Chemistry, Analytical

Asymmetric least squares for multiple spectra baseline correction

Jiangtao Peng et al.

ANALYTICA CHIMICA ACTA (2010)

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Article Instruments & Instrumentation

Morphology-Based Automated Baseline Removal for Raman Spectra of Artistic Pigments

Rosanna Perez-Pueyo et al.

APPLIED SPECTROSCOPY (2010)

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Article Spectroscopy

Noise reduction in Raman spectra:: Finite impulse response filtration versus Savitzky-Golay smoothing

Martin Clupek et al.

JOURNAL OF RAMAN SPECTROSCOPY (2007)

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Article Automation & Control Systems

A background elimination method based on wavelet transform for Raman spectra

Yaogai Hu et al.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2007)

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Article Spectroscopy

Noise and background removal in Raman spectra of ancient pigments using wavelet transform

PM Ramos et al.

JOURNAL OF RAMAN SPECTROSCOPY (2005)

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Article Multidisciplinary Sciences

Diagnosing breast cancer by using Raman spectroscopy

AS Haka et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2005)

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Article Chemistry, Analytical

A perfect smoother

PHC Eilers

ANALYTICAL CHEMISTRY (2003)

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Article Chemistry, Analytical

Spike removal and denoising of Raman spectra by wavelet transform methods

F Ehrentreich et al.

ANALYTICAL CHEMISTRY (2001)

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Cascaded Deep Convolutional Neural Networks as Improved Methods of Preprocessing Raman Spectroscopy Data

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