Critical Evaluation of Models Developed for Monitoring an Industrial Submerged Bioprocess for Antibiotic Production Using Near-Infrared Spectroscopy

Abstract

Near-infrared spectroscopy (NIRS) is known to have potential for cost-effective monitoring of bioprocesses. Although this has been demonstrated in many instances and several models have been reported, information regarding the complexity of models required and their utility over extended periods of time is lacking. In the present study, the complexity of the models required for the NIRS prediction of substrate (oil) and product (tylosin) concentration in an industrial bioprocess that employs a physicochemically heterogeneous medium for antibiotic production was assessed. Measurements made by both the diffuse reflectance and transmittance modes were investigated. SEP values for the prediction of the analytes averaged 5% or less, for the successful models, when evaluated using an external validation set, 2 years after the initial model development exercise. Diffuse reflectance measurements showed poorer results, compared to transmittance measurements, especially for monitoring tylosin. In general, this investigation provides evidence to support the fact that models built for the prediction of analytes in a commercial bioprocess that employs a physicochemically complex production medium can be robust in performance over an extended period of time and that simple models based on fewer terms or latent variables can perform well, even in the context of matrices that are relatively complex. It also indicates that sample presentation is likely to be a critical factor in the successful application of NIRS in bioprocess monitoring, which merits further detailed investigation.

Application of near‐infrared spectroscopy for monitoring and control of cell culture and fermentation

“…10 Research in NIR spectroscopy applied to cell culture and fermentation has evolved from less challenging systems, with anaerobic conditions and/or low agitation, to more complex ones, characterized by vigorous agitation and aeration. Likewise, there has been a tendency to first implement atline [11][12][13][14][15][16] or ex situ systems, [17][18][19][20] followed by the more challenging in situ 7,10,[21][22][23][24][25][26][27][28] configurations when the technology and process knowledge became more mature.…”

“…Vaidyanathan et al 12 explained that over an extended period of time, factors such as batch-to-batch variability in substrate feedstock, for instance, could contribute to spectral variations. The ability of the models to cope with such sample variations is critical.…”

“…The ability of the models to cope with such sample variations is critical. 12 NIR spectroscopy was used to predict substrate (oil) and product (tylosin) concentration in an industrial bioprocess that employed a heterogeneous medium for antibiotic production, using S. fradiae.…”

Use of at‐line and in‐situ near‐infrared spectroscopy to monitor biomass in an industrial fed‐batch Escherichia coli process

“…The region was manually chosen based on the chemistry of the system rather than allowing the computer to simply build a model with no analytical basis. The purpose of this article is to discuss the route from at-line to in-situ application of NIRS and simply present a successful model rather than discuss the different models and critically evaluate their performance relative to their complexity, since this topic has been addressed in the recent publications by Vaidyanathan et al (2000Vaidyanathan et al ( , 2001.…”

Chemometric analysis of in‐line multi‐wavelength fluorescence measurements obtained during cultivations with a lipase producing Aspergillus oryzae strain

“…Especially the use of NIR-and MIR-spectroscopy for monitoring of cell mass, product and substrate concentration has been studied intensively in recent years (Arnold et al, 2002;Blanco et al, 2004;Kornmann et al, 2003Kornmann et al, , 2004Pollard et al, 2001;Tosi et al, 2003;Vaidyanathan et al, 1999Vaidyanathan et al, , 2000. Multiwavelength fluorescence has been applied as a method for estimation of cell mass during cultivations with Saccharomyces cerevisiae, Pseudomonas fluorescens and Claviceps purpurea (Boehl et al, 2003;Haack et al, 2004;Skibsted et al, 2001;Solle et al, 2003).…”