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Related Concept Videos

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

529
A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
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Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

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The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...
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IR Spectroscopy: Molecular Vibration Overview01:24

IR Spectroscopy: Molecular Vibration Overview

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When Infrared (IR) radiation passes through a covalently bonded molecule, the bonds transition from lower to higher vibrational levels. The fundamental vibrational motions that result in infrared absorption can be classified as stretching or bending vibrations.
Stretching vibrations are vibrational motions that occur along the bond line, changing the bond length or distance between two bonded atoms. They are further distinguished as symmetric or asymmetric. In symmetric stretching, the...
2.7K
Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview01:02

Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview

3.0K
Ultraviolet–visible (UV–visible or UV–Vis) spectroscopy is an analytical technique that investigates the interaction between matter and UV–Vis light within the electromagnetic spectrum. This method is widely used for its versatility, simplicity, and relatively quick data acquisition, making it valuable for both qualitative and quantitative analysis. When UV–Vis radiation passes through a material,  molecules absorb light depending on the energy required for...
3.0K
IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration01:16

IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration

1.6K
A covalently bonded heteronuclear diatomic molecule can be modeled as two vibrating masses connected by a spring. The vibrational frequency of the bond can be expressed using an equation derived from Hooke's law, which describes how the force applied to stretch or compress a spring is proportional to the displacement of the spring. In this case, the atoms behave like masses, and the bond acts like a spring.
According to Hooke's law, the vibrational frequency is directly proportional to...
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Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy
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Visible Particle Identification Using Raman Spectroscopy and Machine Learning.

Han Sheng1, Yinping Zhao1, Xiangan Long1

  • 1Institute of Biomedical Engineering and Technology, Academy for Engineer and Technology, Fudan University, 220 Handan Road, Shanghai, 200433, China.

AAPS Pharmscitech
|July 5, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning algorithms combined with Raman spectroscopy accurately identify visible particles in biotherapeutics. This approach reduces expert reliance and enhances data analysis for manufacturing control.

Keywords:
InjectableMachine learningParticle identificationProcessingRaman spectroscopy

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Area of Science:

  • Pharmaceutical Manufacturing
  • Analytical Chemistry
  • Biotechnology

Background:

  • Visible particle identification is critical for injectable biotherapeutic manufacturing.
  • Raman spectroscopy offers chemical sensitivity for particle analysis but requires expertise.
  • Current methods for Raman spectral data interpretation are labor-intensive.

Purpose of the Study:

  • To apply machine learning algorithms for automated visible particle identification.
  • To improve the accuracy and reduce expert dependency in Raman spectral data analysis.
  • To develop a streamlined approach for biopharmaceutical process control.

Main Methods:

  • Preparation of ten standard particle solutions simulating manufacturing contaminants.
  • Establishment of a Raman spectral library with precise peak assignments.
  • Training five classification algorithms on visible particle Raman spectral data.

Main Results:

  • All trained machine learning models achieved prediction accuracy exceeding 98%.
  • The system demonstrated high accuracy across all ten tested particle types.
  • Successful simulation of typical manufacturing-observed particles.

Conclusions:

  • Raman spectroscopy coupled with machine learning provides an accurate and simple method for visible particle identification.
  • This integrated approach can significantly aid in process improvement and control for biotherapeutic drug products.
  • Automation of spectral data analysis minimizes the need for specialized expertise.