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Spectrum Analysis, Raman

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Scale-Adaptive Deep Model for Bacterial Raman Spectra Identification.

IEEE journal of biomedical and health informatics
The combination of Raman spectroscopy and deep learning technology provides an automatic, rapid, and accurate scheme for the clinical diagnosis of pathogenic bacteria. However, the accuracy of existing deep learning methods is still limited because o...

High-Speed Diagnosis of Bacterial Pathogens at the Single Cell Level by Raman Microspectroscopy with Machine Learning Filters and Denoising Autoencoders.

ACS chemical biology
Accurate and rapid identification of infectious bacteria is important in medicine. Raman microspectroscopy holds great promise in performing label-free identification at the single-cell level. However, due to the naturally weak Raman signal, it is a ...

Automated Hyperspectral 2D/3D Raman Analysis Using the Learner-Predictor Strategy: Machine Learning-Based Inline Raman Data Analytics.

Analytical chemistry
Synchronously detecting multiple Raman spectral signatures in two-dimensional/three-dimensional (2D/3D) hyperspectral Raman analysis is a daunting challenge. The underlying reasons notwithstanding the enormous volume of the data and also the complexi...

Deep learning data augmentation for Raman spectroscopy cancer tissue classification.

Scientific reports
Recently, Raman Spectroscopy (RS) was demonstrated to be a non-destructive way of cancer diagnosis, due to the uniqueness of RS measurements in revealing molecular biochemical changes between cancerous vs. normal tissues and cells. In order to design...

SERSNet: Surface-Enhanced Raman Spectroscopy Based Biomolecule Detection Using Deep Neural Network.

Biosensors
Surface-Enhanced Raman Spectroscopy (SERS)-based biomolecule detection has been a challenge due to large variations in signal intensity, spectral profile, and nonlinearity. Recent advances in machine learning offer great opportunities to address thes...

Identification of antibiotic resistance and virulence-encoding factors in Klebsiella pneumoniae by Raman spectroscopy and deep learning.

Microbial biotechnology
Klebsiella pneumoniae has become the number one bacterial pathogen that causes high mortality in clinical settings worldwide. Clinical K. pneumoniae strains with carbapenem resistance and/or hypervirulent phenotypes cause higher mortality comparing w...

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

Analytical chemistry
Raman spectroscopy enables nondestructive, label-free imaging with unprecedented molecular contrast, but is limited by slow data acquisition, largely preventing high-throughput imaging applications. Here, we present a comprehensive framework for high...

Non-invasive diagnosis of Crohn's disease based on SERS combined with PCA-SVM.

Analytical methods : advancing methods and applications
Crohn's disease (CD) is an idiopathic chronic inflammatory bowel disease without a cure. Most of the CD patients are firstly diagnosed by invasive endoscopy, and clinical and pathological examinations are further required to confirm the diagnosis. He...

Rapid identification of ore minerals using multi-scale dilated convolutional attention network associated with portable Raman spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Electron portable Raman spectroscopy tools for ore mineral identification are widely used in raw ore analysis and mineral process engineering. This paper demonstrates an extremely fast and accurate method for identifying unknown ore mineral samples b...

Chemometric analysis in Raman spectroscopy from experimental design to machine learning-based modeling.

Nature protocols
Raman spectroscopy is increasingly being used in biology, forensics, diagnostics, pharmaceutics and food science applications. This growth is triggered not only by improvements in the computational and experimental setups but also by the development ...