Does Nonlinear Modeling Play a Role in Plasmid Bioprocess Monitoring Using Fourier Transform Infrared Spectra?. Issue 6 (June 2017)

• Record Type: Journal Article
• Authors: Lopes, Marta B.; Calado, Cecília R.C.; Figueiredo, Mário A.T.; Bioucas-Dias, José M.
• Is Part Of: Applied spectroscopy. Volume 71:Issue 6(2017)
• Page Start: 1148
• Page End: 1156
• Publication Date: 2017-06
• Subjects: Nonlinear modeling -- bioprocess monitoring -- plasmid bioprocess -- Fourier transform infrared spectroscopy FT-IR -- mid-infrared -- near-infrared; Spectrum analysis -- Periodicals; 543.505
• DOI: 10.1177/0003702816670913
• Languages: English
• ISSNs: 0003-7028

Abstract

The monitoring of biopharmaceutical products using Fourier transform infrared (FT-IR) spectroscopy relies on calibration techniques involving the acquisition of spectra of bioprocess samples along the process. The most commonly used method for that purpose is partial least squares (PLS) regression, under the assumption that a linear model is valid. Despite being successful in the presence of small nonlinearities, linear methods may fail in the presence of strong nonlinearities. This paper studies the potential usefulness of nonlinear regression methods for predicting, from in situ near-infrared (NIR) and mid-infrared (MIR) spectra acquired in high-throughput mode, biomass and plasmid concentrations in Escherichia coli DH5-α cultures producing the plasmid model pVAX-LacZ. The linear methods PLS and ridge regression (RR) are compared with their kernel (nonlinear) versions, kPLS and kRR, as well as with the (also nonlinear) relevance vector machine (RVM) and Gaussian process regression (GPR). For the systems studied, RR provided better predictive performances compared to the remaining methods. Moreover, the results point to further investigation based on larger data sets whenever differences in predictive accuracy between a linear method and its kernelized version could not be found. The use of nonlinear methods, however, shall be judged regarding the additional computational cost required to tune their additional parameters, especially when the less computationally demanding linear methods herein studied are able to successfully monitor the variables under study.