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Raman Spectroscopy Instrumentation: Overview01:26

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A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
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Less Is More: Practical Insights Into Multivariate Regression Models for Raman Spectroscopy in Bioprocess Monitoring.

Antoine Borg1,2,3, Mourad Elhabiri2, Stéphane Le Calvé3

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Biotechnology and Bioengineering
|December 20, 2025
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Summary

This review simplifies chemometrics for Process Analytical Technology (PAT) in bioprocessing. It offers practical Raman spectroscopy methods for robust, real-time glucose and lactate monitoring, making PAT more accessible.

Keywords:
PATRaman spectroscopybioprocesschemometricsonline analysis

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Area of Science:

  • Biotechnology
  • Analytical Chemistry
  • Process Engineering

Background:

  • Process Analytical Technology (PAT) is crucial for real-time bioprocess monitoring but faces limited adoption.
  • Perceived complexity of chemometrics and supplier communication hinder PAT implementation, especially for quantitative online monitoring.

Purpose of the Study:

  • To demystify chemometrics for Raman spectroscopy-based multivariate regression models in bioprocessing.
  • To provide a simplified, practical workflow for developing robust and maintainable PAT models.
  • To challenge the notion that chemometrics is inaccessible for bioprocess applications.

Main Methods:

  • Critically evaluated workflow for chemometric model development: Pretreatment, pre-processing, modeling, and evaluation.
  • Focus on Raman spectroscopy for multivariate regression analysis.
  • Case studies on glucose monitoring in fermentation and lactate monitoring in cell culture.

Main Results:

  • Demonstrated a practical workflow for developing Raman spectroscopy-based chemometric models.
  • Highlighted common pitfalls and best practices in model development and application.
  • Showcased successful applications in real-time glucose and lactate monitoring.

Conclusions:

  • Chemometrics can be simplified for practical PAT implementation in bioprocessing.
  • Robust, transparent, and maintainable models are achievable through simplified methodologies.
  • Researchers can develop accurate and reliable models for real-world bioprocess monitoring using Raman spectroscopy.