Robust model development and enhancement techniques for improved on-line spectroscopic monitoring of bioprocesses

Abstract

The recent phenomenal growth of the field of biotechnology has contributed to a mounting pressure to improve process efficiency and productivity and to increase the quality and safety of end-products. This demand gave rise to the discipline of on-line bioprocess monitoring encompassing tools that provide a live analytical window into the process and create extensive opportunities for process development, control and optimization. Among these tools, on-line spectroscopy has surfaced as one of the prominent techniques of monitoring the concentration of process metabolites and biomass. Unfortunately, one of the major obstacles currently impeding the industrial spread of the technology is the chronic lack of robustness and long-term stability of spectrometers in on-line monitoring conditions. The work presented in this dissertation aims to explore various ways of improving the reliability of spectroscopic bioprocess monitoring instruments without interfering with their real-time functionality. A Fourier-transform mid-infrared (FTIR) and a dielectric (capacitance) spectrometer are used as the model instruments in a series of experiments involving the cultivation of yeasts. A general review of methods that help maintain the on-line reliability of bioprocess spectrometers is presented first. A clear distinction is made between techniques that involve retrospective reprocessing of the obtained predictions using off-line measurements, and methods that perform the signal or calibration model correction in real-time. A case study, included in the review, demonstrates the effectiveness of some of the latter techniques in correcting mid-IR spectral drift comparable in magnitude of absorbance to a pure component spectrum of glucose at 10 g/l. It is shown that the drift can be significantly reduced using techniques such as spectrum derivation, spectral anchoring and Orthogonal Signal Correction (OSC). Proposed next is a technique to generate on-line reference standards for the FTIR without the need of sampling. The method involves the periodic injection of small amounts of the monitored metabolites into the culture medium. The corresponding measured differences in the spectra are used as reference measurements for recalibrating the model in real-time based on the technique of Dynamic Orthogonal Projection (DOP). Applying this approach leads to a decrease, ranging from 25 to 50 %, in the standard error of prediction of metabolite concentrations. The following study compares three distinct methods of calibrating a dielectric spectrometer: fitting capacitance data to the theoretical Cole-Cole equation, correlating capacitance measurements linearly to biomass concentration and the modeling of scanning capacitance spectra using multivariate (PLS) analysis. The performance and robustness of each calibration technique is assessed during a sequence of validation batches in two experimental settings differing in the level of signal noise. The linear and PLS models outperform the Cole-Cole model in terms of biomass concentration prediction error, particularly in the more noisy conditions. The PLS model proves to be the most robust in rejecting the signal variability. Estimates of the mean cell size are additionally done using the Cole-Cole and PLS models, the latter technique giving more precise results. Finally, in a study involving the simultaneous use of the FTIR and capacitance spectrometers, data reconciliation is shown to improve the on-line prediction of process analytes and biomass. The concentrations predicted by both spectrometers are reconciled in real-time based on mass and elemental balances involving off-gas analysis and measurements of base addition. A statistical test is used to confirm the integrity of the balances before the reconciliation. The technique leads to a significant reduction in the standard error of prediction for all the components involved.