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Does Nonlinear Modeling Play a Role in Plasmid Bioprocess Monitoring Using Fourier Transform Infrared Spectra?

Marta B Lopes^1,2, Cecília R C Calado², Mário A T Figueiredo¹

¹1 Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.

Applied Spectroscopy

|November 18, 2016

Summary

This summary is machine-generated.

This study compared linear and nonlinear regression methods for biopharmaceutical monitoring using spectroscopy. Ridge regression (RR) showed superior predictive performance for biomass and plasmid concentrations in E. coli cultures.

Keywords:

Fourier transform infrared spectroscopy FT-IR Nonlinear modeling bioprocess monitoring mid-infrared near-infrared plasmid bioprocess

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Area of Science:

Bioprocess Engineering
Spectroscopic Analysis
Chemometrics

Background:

Biopharmaceutical monitoring often uses Fourier transform infrared (FT-IR) spectroscopy with calibration models.
Partial least squares (PLS) regression, a linear method, is commonly used but may fail with strong nonlinearities.
Accurate in situ monitoring of biomass and plasmid concentrations is crucial for biopharmaceutical production.

Purpose of the Study:

To evaluate the effectiveness of nonlinear regression methods for predicting biomass and plasmid concentrations.
To compare nonlinear methods against linear methods using near-infrared (NIR) and mid-infrared (MIR) spectra.
To assess the predictive performance of various regression techniques in Escherichia coli cultures.

Main Methods:

Acquisition of in situ NIR and MIR spectra from Escherichia coli DH5-α cultures producing pVAX-LacZ.
Comparison of linear methods (PLS, Ridge Regression - RR) with nonlinear methods (kernel PLS - kPLS, kernel RR - kRR, Relevance Vector Machine - RVM, Gaussian Process Regression - GPR).
Evaluation of predictive performances for biomass and plasmid concentrations.

Main Results:

Ridge Regression (RR) demonstrated better predictive performance than other linear and nonlinear methods evaluated.
No significant accuracy differences were found between linear methods and their kernelized (nonlinear) versions in this study.
Nonlinear methods require careful consideration of computational cost versus performance gains.

Conclusions:

Linear methods, particularly RR, can be effective for monitoring bioprocesses, even with potential nonlinearities.
Further investigation with larger datasets is warranted when nonlinear methods show marginal improvements over linear ones.
The choice of regression method should balance predictive accuracy with computational efficiency for practical bioprocess monitoring.