Critical evaluation of models developed for monitoring an industrial submerged bioprocess for antibiotic production using near-infrared spectroscopy

Near-infrared spectroscopy (NIRS) is known to have potential for cost-effec tive monitoring of bioprocesses. Although this has been demonstrated in man y instances and several models have been reported, information regarding th e complexity of models required and their utility over extended periods of time is lacking. In the present study, the complexity of the models require d for the NIPS prediction of substrate toil) and product (tylosin) concentr ation in an industrial bioprocess that employs a physicochemically heteroge neous medium for antibiotic production was assessed. Measurements made by b oth the diffuse reflectance and transmittance modes were investigated. SEP values for the prediction of the analytes averaged 5% or less, for the succ essful models, when evaluated using an external validation set, 2 years aft er 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 com mercial bioprocess that employs a physicochemically complex production medi um can be robust in performance over an extended period of time and that si mple 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 successfu l application of NIPS in bioprocess monitoring, which merits further detail ed investigation.