Author_Institution :
Fac. de Eng., Univ. Lusofona de Humanidades e Tecnol., Lisbon, Portugal
Abstract :
Infrared spectroscopy, either in the near and mid (NIR/MIR) region of the spectra, has gained great acceptance in the industry for bioprocess monitoring according to Process Analytical Technology, due to its rapid, economic, high sensitivity mode of application and versatility. Due to the relevance of cyprosin (mostly for dairy industry), and as NIR and MIR spectroscopy presents specific characteristics that ultimately may complement each other, in the present work these techniques were compared to monitor and characterize by in situ and by at-line high-throughput analysis, respectively, recombinant cyprosin production by Saccharomyces cerevisiae. Partial least-square regression models, relating NIR and MIR-spectral features with biomass, cyprosin activity, specific activity, glucose, galactose, ethanol and acetate concentration were developed, all presenting, in general, high regression coefficients and low prediction errors. In the case of biomass and glucose slight better models were achieved by in situ NIR spectroscopic analysis, while for cyprosin activity and specific activity slight better models were achieved by at-line MIR spectroscopic analysis. Therefore both techniques enabled to monitor the highly dynamic cyprosin production bioprocess, promoting by this way more efficient platforms for the bioprocess optimization and control.
Keywords :
biological techniques; biotechnology; dairy products; infrared spectroscopy; least squares approximations; microorganisms; regression analysis; sugar; MIR-spectral features; NIR-spectral features; Process Analytical Technology; Saccharomyces cerevisiae; acetate concentration; at-line MIR spectroscopic analysis; at-line high-throughput analysis; biomass; bioprocess control; bioprocess monitoring; bioprocess optimization; cyprosin activity; dairy industry; dynamic cyprosin production bioprocess; ethanol; galactose; glucose; in situ NIR spectroscopic analysis; mid-infrared spectroscopy; near infrared spectroscopy; partial least-square regression models; recombinant cyprosin production; regression coefficients; specific activity; Biological system modeling; Biomass; Ethanol; Monitoring; Predictive models; Spectroscopy; Sugar; Mid infrared spectroscopy; Near Infrared spectroscopy; Recombinant cyprosin; Saccharomyces cerevisiae;