↓ Skip to main content

Comprehensive, quantitative bioprocess productivity monitoring using fluorescence EEM spectroscopy and chemometrics

Overview of attention for article published in Analyst, January 2014
Altmetric Badge

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
20 Dimensions

Readers on

mendeley
33 Mendeley
Title
Comprehensive, quantitative bioprocess productivity monitoring using fluorescence EEM spectroscopy and chemometrics
Published in
Analyst, January 2014
DOI 10.1039/c4an00007b
Pubmed ID
Authors

Boyan Li, Michael Shanahan, Amandine Calvet, Kirk J. Leister, Alan G. Ryder

Abstract

This study demonstrates the application of fluorescence excitation-emission matrix (EEM) spectroscopy to the quantitative predictive analysis of recombinant glycoprotein production cultured in a Chinese hamster ovary (CHO) cell fed-batch process. The method relies on the fact that EEM spectra of complex solutions are very sensitive to compositional change. As the cultivation progressed, changes in the emission properties of various key fluorophores (e.g., tyrosine, tryptophan, and the glycoprotein product) showed significant differences, and this was used to follow culture progress via multiple curve resolution alternating least squares (MCR-ALS). MCR-ALS clearly showed the increase in the unique dityrosine emission from the product glycoprotein as the process progressed, thus provided a qualitative tool for process monitoring. For the quantitative predictive modelling of process performance, the EEM data was first subjected to variable selection and then using the most informative variables, partial least-squares (PLS) regression was implemented for glycoprotein yield prediction. Accurate predictions with relative errors of between 2.3 and 4.6% were obtained for samples extracted from the 100 to 5000 L scale bioreactors. This study shows that the combination of EEM spectroscopy and chemometric methods of evaluation provides a convenient method for monitoring at-line or off-line the productivity of industrial fed-batch mammalian cell culture processes from the small to large scale. This method has applicability to the advancement of process consistency, early problem detection, and quality-by-design (QbD) practices.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 3%
Germany 1 3%
Switzerland 1 3%
Unknown 30 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 36%
Student > Master 8 24%
Student > Ph. D. Student 7 21%
Professor 2 6%
Student > Bachelor 1 3%
Other 2 6%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 27%
Biochemistry, Genetics and Molecular Biology 7 21%
Chemistry 4 12%
Engineering 3 9%
Chemical Engineering 2 6%
Other 5 15%
Unknown 3 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 September 2014.
All research outputs
#9,860,080
of 12,346,805 outputs
Outputs from Analyst
#2,743
of 3,709 outputs
Outputs of similar age
#145,114
of 217,221 outputs
Outputs of similar age from Analyst
#54
of 79 outputs
Altmetric has tracked 12,346,805 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,709 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 13th percentile – i.e., 13% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 217,221 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 79 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.