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 Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

297
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...
297
Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

324
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...
324

You might also read

Related Articles

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

Sort by
Same author

Host-dependent optimization of rabies virus-like particle making in insect cells using a low-level control system.

Journal of microbiological methods·2026
Same author

Exploring Key Regulators of Mitochondrial Dynamics and Immune Response in SARS-CoV-2 Infection.

Viruses·2026
Same author

Genotoxicity Integration into Bioprocess Optimization Reveals Progressive DNA Damage During Bioreactor Expansion of Adipose-Derived Stem Cells.

International journal of molecular sciences·2026
Same author

Controlled vs. non-controlled culture systems for SARS-CoV-2 VLP production using the baculovirus/SF9 platform.

Preparative biochemistry & biotechnology·2026
Same author

Selecting Raman spectra filtering based on an exhaustive statistical approach for inline bioprocesses monitoring using Sf9 insect cells.

Bioprocess and biosystems engineering·2026
Same author

Stability of recombinant baculoviruses for biopharmaceutical applications in chemically defined medium.

Biotechnology letters·2026

Related Experiment Video

Updated: Jun 11, 2025

Author Spotlight: Advancing Pathogen Diagnostics with Standardized LAMP
05:34

Author Spotlight: Advancing Pathogen Diagnostics with Standardized LAMP

Published on: September 8, 2023

657

Inline Raman spectroscopy as process analytical technology for SARS-CoV-2 VLP production.

Felipe Moura Dias1,2, Milena Miyu Teruya1, Samanta Omae Camalhonte2

  • 1Laboratório de Engenharia de Bioprocessos. Escola de Artes, Ciências E Humanidades (EACH), Universidade de São Paulo, Rua Arlindo Béttio, 1000, São Paulo, SP, CEP 03828-000, Brazil.

Bioprocess and Biosystems Engineering
|October 9, 2024
PubMed
Summary

Inline Raman spectroscopy effectively monitors SARS-CoV-2 virus-like particle (VLP) production. Chemometric models, particularly artificial neural networks (ANN) and partial least square (PLS), accurately predict key biochemical parameters in bioreactors.

Keywords:
Artificial neural networkBiochemical kinetic profilesBioprocessContinuous monitoringMultivariate analysisPartial least squaresSARS-CoV-2 VLP

More Related Videos

Real-time Monitoring of Reactions Performed Using Continuous-flow Processing: The Preparation of 3-Acetylcoumarin as an Example
09:56

Real-time Monitoring of Reactions Performed Using Continuous-flow Processing: The Preparation of 3-Acetylcoumarin as an Example

Published on: November 18, 2015

9.7K
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

17.0K

Related Experiment Videos

Last Updated: Jun 11, 2025

Author Spotlight: Advancing Pathogen Diagnostics with Standardized LAMP
05:34

Author Spotlight: Advancing Pathogen Diagnostics with Standardized LAMP

Published on: September 8, 2023

657
Real-time Monitoring of Reactions Performed Using Continuous-flow Processing: The Preparation of 3-Acetylcoumarin as an Example
09:56

Real-time Monitoring of Reactions Performed Using Continuous-flow Processing: The Preparation of 3-Acetylcoumarin as an Example

Published on: November 18, 2015

9.7K
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

17.0K

Area of Science:

  • Biotechnology
  • Process Analytical Technology (PAT)
  • Spectroscopy

Background:

  • Monitoring bioprocesses like SARS-CoV-2 VLP production is crucial for optimizing yield and quality.
  • Traditional offline methods for monitoring biochemical parameters are time-consuming and labor-intensive.
  • Inline process analytical technology offers real-time insights into bioprocess dynamics.

Purpose of the Study:

  • To develop and validate inline Raman spectroscopy methods for real-time monitoring of SARS-CoV-2 VLP production.
  • To compare the performance of chemometric models, including Partial Least Square (PLS) and Artificial Neural Network (ANN), for predicting biochemical parameters.
  • To assess the applicability of these models across different culture media.

Main Methods:

  • Inline Raman spectroscopy was employed for real-time data acquisition during SARS-CoV-2 VLP production.
  • Chemometric models, including linear, PLS, and ANN, were developed to correlate spectral data with biochemical parameters.
  • Key parameters monitored included viable cell density, cell viability, glucose, lactate, glutamine, glutamate, ammonium, and viral titer.

Main Results:

  • ANN models generally provided better fitting for most biochemical parameters, while PLS models were more suitable for viable cell density and glucose.
  • The developed models demonstrated good accuracy in predicting parameters within their quantified ranges, with low mean absolute errors.
  • The models showed robust performance across two different culture media, indicating broad applicability.

Conclusions:

  • Inline Raman spectroscopy, coupled with chemometric modeling, is a powerful tool for real-time monitoring of SARS-CoV-2 VLP production.
  • ANN and PLS models offer effective strategies for predicting critical biochemical parameters, enabling better process control and optimization.
  • This approach facilitates efficient bioprocess monitoring and can be adapted for various cell host systems and culture conditions.