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Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

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 the...
Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

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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Related Experiment Video

Updated: Jul 14, 2026

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

Cell culture product quality attribute prediction using convolutional neural networks and Raman spectroscopy.

Hamid Khodabandehlou1, Mohammad Rashedi1, Tony Wang2

  • 1Digital Integration & Predictive Technologies, Process Development Department, Amgen Inc., Thousand Oaks, California, USA.

Biotechnology and Bioengineering
|January 29, 2024
PubMed
Summary

A deep convolutional neural network (CNN) model uses Raman spectroscopy to accurately predict biopharmaceutical quality attributes in real-time. This generic model works across different cell lines and conditions without recalibration.

Keywords:
Raman spectroscopybiopharmaceutical manufacturingbioprocess monitoringconvolutional neural networksdeep learningpredictive modeling

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Area of Science:

  • Biopharmaceutical process monitoring
  • Spectroscopic analysis
  • Machine learning in bioprocessing

Background:

  • Advanced process control in biopharmaceuticals is limited by the lack of real-time measurements.
  • Raman spectroscopy with Partial Least Squares (PLS) models offers real-time monitoring but struggles with accuracy across different cell lines.
  • PLS model accuracy degrades when predicting quality attributes for cell lines not included in the training data.

Purpose of the Study:

  • To develop a robust and versatile deep convolutional neural network (CNN) model for accurate, real-time prediction of biopharmaceutical quality attributes.
  • To overcome the limitations of traditional Partial Least Squares (PLS) models in predicting quality attributes across diverse cell lines and operating conditions.
  • To create a generic offline model using Raman spectroscopy that requires no recalibration for deployment.

Main Methods:

  • Implemented asymmetric least squares smoothing to adjust Raman spectra baselines.
  • Created a two-dimensional model input by amalgamating Raman spectra from various cell lines and operating conditions, along with their derivatives.
  • Developed and validated a deep convolutional neural network (CNN) model for predicting quality variables using this enhanced dataset.

Main Results:

  • The deep CNN model demonstrated accurate prediction of real-time quality attributes.
  • The model proved effective even for experimental runs not present in the training data.
  • Validation confirmed the model's robustness and versatility across different cell lines and experimental conditions.

Conclusions:

  • The developed deep CNN model serves as an accurate generic tool for real-time quality attribute prediction in biopharmaceutical manufacturing.
  • The model eliminates the need for recalibration when deployed across different sites, monitoring various cell lines and experimental runs.
  • This approach enhances process control by providing reliable, real-time insights into bioprocess quality.