4. Discussion
Traditional
fermentation process regulation is based on manual experience. The
analysis of relevant parameters can identify connections between
sensitive parameters and key indicators (Chen et al., 2013; Zhang et
al., 2014). With the development of sensor technology, a variety of
advanced on-line sensors have been applied in the fermentation process
to achieve multi-dimensional monitoring of cell metabolism, thus making
the process control more rational and reliable. These sensors have
included process mass spectrometry, near-infrared spectroscopy, raman
spectroscopy, and viable cell
biosensors (Chen et al., 2021a; Iversen, Berg & Ahring, 2014). In the
present study, we developed an on-line monitoring platform for the
fermentation of SLs for the first time. This platform allowed the
simultaneous real-time detection of substrates, products, and cellular
metabolic states, thus creating a significant amount of process data. We
found that the production of SLs under different oil feeding strategies
during the fed-batch fermentation could be predominantly divided into
three stages: the first stage was limited by cell production capacity;
this stage was associated with a relatively higher specific productivity
of SLs (up to 0.15 g/gDCW/h). Then, the cells entered a
stage that was inhibited by high product concentration when the
concentration of SLs reached 75 g/L (non-normalized); at this point, the
SLs productivity decreased, whereas the RQ value stabilized at 0.70.
Finally, when the concentration of SLs exceeded 140 g/L
(non-normalized), the cells entered a third stage which was limited by
oxygen supply. During this stage, the SLs productivity continued to
decline, and the RQ began to rise. Thus, RQ is a key parameter to
characterize the difference of substrate metabolism in SLs fermentation
and can be adopted as a potential control parameter to guide process
optimization.
The method of mining key parameters from a huge amount of process data
by manual analysis is both time-consuming and laborious. With recent
advancements in data science technology, the regulation of fermentation
based on mathematical algorithms is gradually being applied to realize
the processing of many data samples, to identify the relationship
between multiple variables and build association models (Zhu et al.,
2021). Currently, mathematical algorithms predominantly include linear
and non-linear algorithms, such as unary linear models, neural network
models, and support vector machines. A
data model combining a large number
of parameters and
mathematical algorithms could help
us to predict and regulate the fermentation process, thereby
significantly reducing labor costs (Lopez et al., 2013). In a previous
study, Lu et al. (2016) established a continuous fermentation control
system for feedback that adjusted the glucose supplementation rate
during the fermentation process of sodium gluconate by correlating the
changing trend between OUR and DO, thus significantly improving the
fermentation efficiency of sodium gluconate. In another study, Wang et
al. (2020b) proposed a non-linear predictive method for the real-time
control of product concentration during L-lysine fermentation to improve
the efficiency. Data showed that predictive control, as based on
Grey-Wolf Optimization, led to better levels of prediction accuracy,
adaptability, real-time tracking ability, overall error, and control
accuracy. Herein, six common mathematical algorithms, including LR, MLR,
SVMs, PLS, RFs and GBRs, have been used to establish a parameter
correlation model for the first and second stages of SLs synthesis, as
based on the productivity of SLs and using the consumption of glucose
and rapeseed oil as dependent variables. The integrated application of
multiple mathematical algorithms was able to achieve the real-time and
rational control of process feeding based on fermentation process
parameters. Furthermore, the model could accomplish self-optimization
under continuous data iteration. However, it was not possible for the
model to fully understand the physiological state of the cells.
On the other hand, an intracellular metabolic flux model was established
for cells to interpret the mechanism underlying the data model. The
processes involving the two substrates (glucose and rapeseed oil)
included four main pathways: glucose oxidation, β-oxidation of fatty
acids, the synthesis of SLs, and the synthesis of extracellular
by-products. Through structure and component analyses, the main
proportion of the lactone-form of SLs reached 85.0%; of these, the
C18:1 lactone-form of SLs accounted for the highest proportion at
58.2%. This may have been because the fatty acid component in rapeseed
oil was mainly composed of oleic acid and linoleic acid; the proportion
of oleic acid reached 68.5% (Tables S1 and S2). The extracellular
organic acids were mainly citric acid (CIT), pyruvic acid (PYR), malic
acid (MAL), and succinic acid (SUC); these organic acids were present in
only very low concentrations (Fig. S1). The carbon ratio of
extracellular organic acids to the added substrates during the synthesis
of SLs was only 7.7%.
During the fed-batch fermentation of 24-96 h, we calculated the
consumptions of glucose and rapeseed oil, the synthesis of
CO2, SLs, and extracellular organic acids; this allowed
us to calculate that the carbon balance at this stage was 95.8% (Table
S3). Metabolic flux analysis demonstrated that when the RQ was 0.70, the
ratio of glucose and rapeseed oil to SLs was fixed. The synthesis of 1 g
of SLs required 0.824 g and 0.719 g of glucose and rapeseed oil,
respectively. Furthermore, 66.5% of the carbon sources in the substrate
were transferred to SLs, 21.7% to CO2, and 7.7% to
organic acids (Fig. S2). Therefore, there was a strong relationship
between the substrate consumption rate, the productivity of SLs and
respiration metabolism (carbon dioxide production rate, oxygen
consumption rate) during the synthesis stage of SLs. The difference in
respiration metabolism is closely related to the change of culture
conditions. The multi-parameter integrated control platform could
realize precise and intelligent control according to the changes in the
culture environment and cellular metabolism. The mechanism-assisted data
model was applied for the rational and precise control of residual oil
concentration in semi-continuous SLs fermentation. The oxygen limitation
problem encountered during the third stage was effectively alleviated,
thereby further increasing the SLs productivity to 2.30 g/L/h with an
increase of 40.2%.