Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

1.1K
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...
1.1K
Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

838
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...
838
MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

6.2K
Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.
Matrix-assisted laser desorption ionization (MALDI) is a commonly...
6.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Anomalous Diffusion of Nanoparticles in Semidilute Hyaluronic Acid Solutions.

Biomacromolecules·2026
Same author

Specific Ion Effects of Chaotropic and Superchaotropic Anions Probed by Raman Hydration-Shell Spectroscopy.

Angewandte Chemie (International ed. in English)·2026
Same author

ThermiQuantâ„¢ AquaStream: A portable instrument for quantitative colorimetric isothermal nucleic acid amplification reactions in paper and tube formats.

PloS one·2026
Same author

Effects of different mixing techniques on mRNA lipid nanoparticle physicochemistry and biological performance.

Nature communications·2026
Same author

In-Line Blend Potency Measurements of Low-Dose Formulations Using a Novel Deep-UV LIF Probe in the Feed Frame of a Rotary Tablet Press.

Analytical chemistry·2026
Same author

Detection of five viruses commonly implicated with bovine respiratory disease using loop-mediated isothermal amplification.

The veterinary quarterly·2026

Related Experiment Video

Updated: Dec 5, 2025

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy
15:04

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy

Published on: May 18, 2011

13.4K

Raman spectra-based deep learning: A tool to identify microbial contamination.

Murali K Maruthamuthu1,2, Amir Hossein Raffiee3, Denilson Mendes De Oliveira4

  • 1Birck Nanotechnology Center, Purdue University, West Lafayette, IN, USA.

Microbiologyopen
|October 16, 2020
PubMed
Summary
This summary is machine-generated.

Deep learning and Raman spectroscopy effectively detect microbial contamination in pharmaceutical manufacturing. A new tool accurately identifies common bacteria and fungi contaminating Chinese Hamster Ovary cells, crucial for biologic production.

Keywords:
CHO cellsbiologicsconvolution neural networkdeep learningmicrobial contaminationprocess analytical technology

More Related Videos

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects
07:37

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects

Published on: January 9, 2020

9.8K
Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy
13:48

Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy

Published on: May 29, 2012

17.4K

Related Experiment Videos

Last Updated: Dec 5, 2025

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy
15:04

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy

Published on: May 18, 2011

13.4K
An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects
07:37

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects

Published on: January 9, 2020

9.8K
Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy
13:48

Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy

Published on: May 29, 2012

17.4K

Area of Science:

  • Pharmaceutical Science
  • Analytical Chemistry
  • Biotechnology

Background:

  • Process Analytical Technology (PAT) in pharmaceuticals relies on instrumentation for real-time monitoring.
  • Microbial contamination poses a significant risk to biologic drug production, necessitating robust detection methods.
  • Chinese Hamster Ovary (CHO) cells are widely used in biopharmaceutical manufacturing.

Purpose of the Study:

  • To develop a deep learning-based tool for detecting microbial contamination using Raman spectroscopy.
  • To create a comprehensive Raman spectral dataset of common pharmaceutical contaminants and CHO cells.
  • To evaluate the performance of a convolutional neural network (CNN) for microbial identification.

Main Methods:

  • Construction of a Raman spectral dataset including 12 common microbial contaminants (bacteria and fungi) and Chinese Hamster Ovary (CHO) cells.
  • Application of deep learning, specifically a convolutional neural network (CNN), for classification of microbial contamination.
  • Generation of attention maps to identify key spectral regions for discrimination between microbes and CHO cells.

Main Results:

  • The CNN achieved high classification accuracy (95%-100%) for distinguishing between individual microbes and microbes mixed with CHO cells.
  • The developed model successfully identified a diverse set of 12 microbial species, including Gram-positive and Gram-negative bacteria and fungi.
  • Attention maps provided insights into spectral features critical for differentiating microbial species and host cells.

Conclusions:

  • Raman spectroscopy combined with deep learning offers a powerful approach for rapid and accurate microbial contamination detection in pharmaceutical processes.
  • The developed dataset and algorithm provide a viable strategy for implementing advanced analytical tools in biopharmaceutical quality control.
  • This technology has the potential to enhance the reliability and efficiency of PAT in pharmaceutical manufacturing.