Biomedical Engineering Reference
In-Depth Information
detection of substrates and metabolites in yeast and bacterial cultures, but it has
also been applied to suspended and immobilised animal cell cultures [
129
]. Most
methods use synthetic samples or samples taken from cell cultures to build mul-
tivariate models capable of predicting changing concentration values.
The most common component modelled is glucose. This is the predominant
substrate in cell culturing and, so, is of most interest from a detection and monitoring
point of view [
130
-
132
]. Other substrates detected using online MIR techniques
include fructose, lactose, galactose, ammonia and methyl oleate [
106
,
133
,
134
].
Accuracy values vary between studies, with standard prediction errors ranging from
0.26 to 0.86 g/L for glucose. Subtle differences exist between the various techniques
developed. The sample presentation method is of some importance for this appli-
cation, as many cell cultures require aeration, resulting in gas bubbles forming on the
probe tip. Automated flow systems can help mitigate this problem, while a recessed
geometry of the probe tip can facilitate the formation of pockets on the crystal
surface [
121
]. In addition to the sampling interface, the models employed are specific
to each individual set-up. Although multivariate chemometric modelling is used to
develop these models, each model is unique.
This technique has also been applied to determine the rate of product formation.
Cell culture products that have been successfully detected using MIR include
ethanol, lactic acid and glucuronic acid [
131
,
132
,
135
].
Online MIR measurements have been used not just to detect or monitor cell
culture substrates and metabolites, but also to control cultures. Kornmann et al.
used Gluconacetobactor xylinus to develop a control strategy based on the
depletion of two substrates, fructose and ethanol [
136
]. Real-time spectroscopic
scans were collected every 5 min, concentrations were sent to an adaptive control
algorithm, and fructose and ethanol were fed to the culture in controlled volumes.
Schenk et al. showed that a similar system could be used to control methanol
feeding to Pichia pastoris cultures [
137
].
NIR Applications
NIR spectroscopy can provide online information on substrate, biomass, product
and metabolite concentrations [
138
]. This information can be further used to
control and optimise cell cultures. Extensive work has been carried out in this area
to date. NIR has been used to monitor concentration changes in yeast, bacterial and
even mammalian cell cultures. Crowley et al. used NIR to monitor the main
substrates, glycerol and methanol, as well as biomass, in a Pichia pastoris culture
[
139
], Petersen et al. used NIR to predict the changing concentrations of glucose,
ammonium and biomass in a Streptomyces coelicolor culture [
126
], while Ro-
drigues et al. developed an NIR model to monitor clavulanic acid, the product of a
fed-batch process with S. clavuligerus [
140
].
The technique has also been applied to monitoring of mammalian cell cultures.
Four key analytes of a CHO-K1 mammalian cell culture, i.e. glucose, lactate,
glutamine and ammonia, were monitored by Arnold et al. [
141
], and this work was
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