Inline noninvasive Raman monitoring and feedback control of glucose concentration during ethanol fermentation. (18th June 2019)

• Record Type: Journal Article
• Authors: Hirsch, Edit; Pataki, Hajnalka; Domján, Júlia; Farkas, Attila; Vass, Panna; Fehér, Csaba; Barta, Zsolt; Nagy, Zsombor K.; Marosi, György J.; Csontos, István
• Is Part Of: Biotechnology progress. Volume 35:Number 5(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-06-18
• Subjects: bioprocess control -- closed‐loop feedback control -- ethanol fermentation -- industry 4.0 -- inline noninvasive Raman spectroscopy -- PAT; Biotechnology -- Periodicals; Food industry and trade -- Periodicals; Bioengineering -- Periodicals; 660.6
• DOI: 10.1002/btpr.2848
• Languages: English
• ISSNs: 8756-7938

Abstract

Abstract: Raman spectroscopy as a process analytical technology tool was implemented for the monitoring and control of ethanol fermentation carried out with Saccharomyces cerevisiae . The need for the optimization of bioprocesses such as ethanol production, to increase product yield, enhanced the development of control strategies. The control system developed by the authors utilized noninvasive Raman measurements to avoid possible sterilization problems. Real‐time data analysis was applied using partial least squares regression (PLS) method. With the aid of spectral pretreatment and multivariate data analysis, the monitoring of glucose and ethanol concentration was successful during yeast fermentation with the prediction error of 4.42 g/L for glucose and 2.40 g/L for ethanol. By Raman spectroscopy‐based feedback control, the glucose concentration was maintained at 100 g/L by the automatic feeding of concentrated glucose solution. The control of glucose concentration during fed‐batch fermentation resulted in increased ethanol production. Ethanol yield of 86% was achieved compared to the batch fermentation when75 % yield was obtained. The results show that the use of Raman spectroscopy for the monitoring and control of yeast fermentation is a promising way to enhance process understanding and achieve consistently high production yield.