Deconvolution of near-infrared spectral information for monitoring mycelial biomass and other key analytes in a submerged fungal bioprocess

Abstract

Near-infrared spectroscopy is a promising technique for the rapid monitoring of submerged culture bioprocesses. However, despite the key role of mycelial (filamentous fungal and bacterial) micro-organisms in the manufacture of antibiotics and other valuable therapeutics, there is little information on the application of the technique to monitor mycelial bioprocesses. In part, this is due to the complex and spectroscopically challenging matrices, which result from the growth of these micro-organisms. Moreover, there is a particular lack of any detailed mechanistic information on how models for the prediction of the concentration of key analytes (e.g. biomass, substrates, product) can be constructed, evaluated and improved using the spectral data arising from such complex matrices. We investigated the near-infrared spectra of culture fluid from a submerged fungal bioprocess, for monitoring the concentrations of mycelial biomass and other key analytes. Several empirical models were developed for predicting the concentration of the analytes, using multivariate statistical techniques. Despite the filamentous nature of the biomass and the resulting complexity of the spectral variations, empirical models could be developed for the prediction of this analyte, using biomass ‘specific’ information. SEP values of <1 g/l could be achieved on external validation, for models developed in the concentration range of 0–20 g/l. The concentrations of the substrates, total sugars (as glucose equivalents) and ammonium, could also be predicted, simultaneously. However, the product (penicillin) and by product (extracellular proteins) levels had to be monitored on the cell free culture fluid, due to their relatively low concentration. Here we report upon how the spectral information can be deconvoluted for predicting the levels of the analytes and upon how the ‘analyte specific’ information in the spectral data can be used to inform and assist the modelling process, in order to increase confidence in exactly what is being modelled.