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Raman spectroscopy and chemometrics for on‐line control of glucose fermentation by Saccharomyces cerevisiae
Author(s) -
Ávila Thiago C.,
Poppi Ronei J.,
Lunardi Inês,
Tizei Pedro A. G.,
Pereira Gonçalo A. G.
Publication year - 2012
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 - 10.1002/btpr.1615
Subject(s) - chemometrics , partial least squares regression , principal component analysis , multivariate statistics , fermentation , control chart , calibration , raman spectroscopy , process control , mean squared error , analytical chemistry (journal) , chemistry , computer science , chromatography , mathematics , biological system , artificial intelligence , process (computing) , statistics , machine learning , food science , biology , physics , optics , operating system
This work presents the use of Raman spectroscopy and chemometrics for on‐line control of the fermentation process of glucose by Saccharomyces cerevisiae. In a first approach, an on‐line determination of glucose, ethanol, glycerol, and cells was accomplished using multivariate calibration based on partial least squares (PLS). The PLS models presented values of root mean square error of prediction (RMSEP) of 0.53, 0.25, and 0.02% for glucose, ethanol and glycerol, respectively, and RMSEP of 1.02 g L −1 for cells. In a second approach, multivariate control charts based on multiway principal component analysis (MPCA) were developed for detection of fermentation fault‐batch. Two multivariate control charts were developed, based on the squared prediction error (Q) and Hotelling's T 2 . The use of the Q control chart in on‐line monitoring was efficient for detection of the faults caused by temperature, type of substrate and contamination, but the T 2 control chart was not able to monitor these faults. On‐line monitoring by Raman spectroscopy in conjunction with chemometric procedures allows control of the fermentative process with advantages in relation to reference methods, which require pretreatment, manipulation of samples and are time consuming. Also, the use of multivariate control charts made possible the detection of faults in a simple way, based only on the spectra of the system. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012