AIMC Topic: Spectrum Analysis, Raman

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Visible Particle Identification Using Raman Spectroscopy and Machine Learning.

AAPS PharmSciTech
Visible particle identification is a crucial prerequisite step for process improvement and control during the manufacturing of injectable biotherapeutic drug products. Raman spectroscopy is a technology with several advantages for particle identifica...

A deep learning model designed for Raman spectroscopy with a novel hyperparameter optimization method.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Raman spectroscopy is a spectroscopic technique typically used to determine vibrational modes of molecules and is commonly used in chemistry to provide a structural fingerprint by which molecules can be identified. With the help of deep learning, Ram...

Assessment of skin inflammation using near-infrared Raman spectroscopy combined with artificial intelligence analysis in an animal model.

The Analyst
Raman spectroscopy is a powerful method for estimating the molecular structure of a target that can be adapted for biomedical analysis given its non-destructive nature. Inflammatory skin diseases impair the skin's barrier function and interfere with ...

Raman spectroscopy for on-line monitoring of botanical extraction process using convolutional neural network with background subtraction.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Aqueous extraction is the most common and cost-effective means of obtaining active ingredients from medicinal plants. However, botanical extracts generally contain high pigment content and complex chemical composition posing a challenge for the proce...

Fast label-free recognition of NRBCs by deep-learning visual object detection and single-cell Raman spectroscopy.

The Analyst
Nucleated red blood cells (NRBCs) as a type of rare cell present in an adult's peripheral blood is a concern in hematology, intensive care medicine and prenatal diagnostics. However, it is labor-intensive to screen such rare cells from real complex c...

Identifying the charge density and dielectric environment of graphene using Raman spectroscopy and deep learning.

The Analyst
The impact of the environment on the properties of graphene such as strain, charge density, and dielectric environment can be evaluated by Raman spectroscopy. These environmental interactions are not trivial to determine since they affect the spectra...

Label-Free Differentiation of Cancer and Non-Cancer Cells Based on Machine-Learning-Algorithm-Assisted Fast Raman Imaging.

Biosensors
This paper proposes a rapid, label-free, and non-invasive approach for identifying murine cancer cells (B16F10 melanoma cancer cells) from non-cancer cells (C2C12 muscle cells) using machine-learning-assisted Raman spectroscopic imaging. Through quic...

Deep Learning-Based Spectral Extraction for Improving the Performance of Surface-Enhanced Raman Spectroscopy Analysis on Multiplexed Identification and Quantitation.

The journal of physical chemistry. A
Surface-enhanced Raman spectroscopy (SERS) has been recognized as a promising analytical technique for its capability of providing molecular fingerprint information and avoiding interference of water. Nevertheless, direct SERS detection of complicate...

Development of deep learning algorithms to discriminate giant cell tumors of bone from adjacent normal tissues by confocal Raman spectroscopy.

The Analyst
Raman spectroscopy is a non-destructive analysis technique that provides detailed information about the chemical structure of tumors. Raman spectra of 52 giant cell tumors of bone (GCTB) and 21 adjacent normal tissues of formalin-fixed paraffin embed...

Quantitative analysis of Raman spectra for glucose concentration in human blood using Gramian angular field and convolutional neural network.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
In this study, convolutional neural network based on Gramian angular field (GAF-CNN) was firstly proposed. The 1-D Raman spectral data was converted into images and used for predicting the biochemical value of blood glucose. 106 sets of blood spectru...