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Critical Evaluation of Models Developed for Monitoring an Industrial Submerged Bioprocess for Antibiotic Production Using Near‐Infrared Spectroscopy
Author(s) -
Vaidyanathan Seetharaman,
Arnold Alison,
Matheson Liliana,
Mohan Pankaj,
Macaloney Graeme,
McNeil Brian,
Harvey Linda M.
Publication year - 2000
Publication title -
biotechnology progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 129
eISSN - 1520-6033
pISSN - 8756-7938
DOI - 10.1021/bp0000656
Subject(s) - bioprocess , context (archaeology) , process engineering , biochemical engineering , tylosin , computer science , biological system , analyte , environmental science , chemistry , chromatography , engineering , chemical engineering , paleontology , biochemistry , biology , antibiotics
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.