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\begin{document}

\title{A mechanism-assisted data-driven model to improve the efficiency of
sophorolipids by Candida bombicola}



\author[1]{Yang Chen}%
\author[1]{Xiwei Tian}%
\author[1]{Xiaofeng Zhu}%
\author[1]{Xu Tang}%
\author[1]{Ying-Ping Zhuang}%
\author[2]{Song Zhang}%
\author[3]{Shiyuan Hu}%
\author[4]{Ju Chu}%
\affil[1]{East China University of Science and Technology}%
\affil[2]{Evonik (Shanghai) Investment Management Co., Ltd}%
\affil[3]{Evonik (Shanghai) Investment Management Co., Ltd.}%
\affil[4]{State Key Laboratory of Bioreactor Engineering}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\selectlanguage{english}
\begin{abstract}
In this study, we developed a mechanism-assisted data-driven model to
regulate substrate feedback to improve the production efficiency of
sophorolipids (SLs). First, we used a variety of on-line biosensors to
establish a multi-scale parameter detection system. We found that the
production of SLs by fed-batch fermentation could be divided into three
stages: a stage that was limited by cell production capacity, a stage
that was inhibited by high product concentration, and a stage that was
limited by oxygen supply. Subsequently, we used process parameters to
develop a data-driven model, and this was then combined with the
analysis of cell metabolic mechanisms. The optimal production of SLs was
achieved in the first and second stages by the precise feedback
regulation of substrate feeding, which increased the titer of SLs by
4.9\%. The control error of the substrate was reduced from more than
15\% to less than 5\%. The mechanism-assisted data-driven model was then
applied for semi-continuous fermentation during the production of SLs.
This effectively alleviated the oxygen limitation during the third
stage, and further increased the productivity of SLs to 2.30 g/L/h,
40.2\% higher than the fed-batch fermentation method.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Sophorolipids (SLs) as renewable glycolipid biosurfactants are mainly
produced by microorganisms of the \emph{Saccharomyces} ssp. (Van Bogaert
et al., 2007). Glucose and fatty acids are the main precursors for the
synthesis of SLs. First, fatty acids are catalyzed by P450 monooxygenase
into hydroxy fatty acids, and then UDP-glucoses are sequentially
connected to form non-acetylated acidic SLs \emph{via} the action of
glucose transferase I and II (Lodens et al., 2020). Finally, the
functional activities of acetyltransferase and extracellular lactonase
lead to the acetylation and lactonization of SLs. Up to 20 different
structural forms of SLs are known to exist, and this variation is the
result of differences in acetylation, hydroxyl position, the length of
the fatty acid chain, and the unsaturation degree of fatty acids (Hu \&
Ju, 2001). Thus, the production of SLs is a complex and multiphase
fermentation process, involving a gas phase (air), a solid phase (cells,
SLs crystals), a hydrophilic liquid phase (acidic SLs, glucose) and a
hydrophobic liquid phase (lactonic SLs, oil), which pose a significant
challenge to the efficient and stable production of SLs (Tian, Li, Chen,
Mohsin \& Chu, 2021).

At present, the optimization of microbial fermentation can be divided
into three different aspects: (1) obtain high-performance producers
\emph{via} mutagenic breeding or genetic engineering, (2) develop and
utilize cheap substrates to reduce the costs of fermentation , and (3)
optimize the fermentation process to achieve the efficient synthesis of
product (Dolman, Kaisermann, Martin \& Winterburn, 2017; Li, Chen, Tian
\& Chu, 2020; Tian et al., 2021; Wang et al., 2020a). Of these
processes, the rational and precise regulation of fermentation remains
the most significant factor in achieving high-efficiency production. The
identification of key process parameters form the basis of process
control and optimization. By regulating key process parameters, it is
possible to achieve the regulation of cell metabolism in a flexible
manner, this allowing high titer, productivity, and yield (Wang et al.,
2020c). The continuous development of sensing detection and information
processing technologies, along with the real-time detection of
conventional environmental parameters by on-line sensors, cellular
macro-physiological, and metabolic parameters, has led to the
availability of key parameters on-line, including living cell amount,
oxygen uptake rate (OUR), carbon dioxide evolution rate (CER), and
respiratory quotient (RQ). This information creates a database for the
fermentation process (Chen, Lin, Tian, Li \& Chu, 2019; Feng et al.,
2021).

However, the mining of sensitive process parameters still relies upon
correlation analysis and manual experience and has yet to be studied
from the perspective of big data analysis (Davila, Marchal \&
Vandecasteele, 1997). On the other hand, it is gratifying that various
data processing and analytical methods are gradually being introduced
into the fermentation process. For example, linear and non-linear
algorithms, neural networks, support vector machines, and other
mathematical models, can quickly process many on-line and off-line
parameters, and can therefore be correlated with regulatory processes
during the fermentation process (Safarian, Saryazdi, Unnthorsson \&
Richter, 2021; Zhang et al., 2020). Overall, the current options for
regulating the fermentation process arise predominantly from the process
control perspective and lack rationality (Kim, Yun \& Kim, 2009). Other
options include guidance provided by cellular metabolic characteristics,
but this approach lacks universal applicability.

In terms of SLs fermentation, the supplementation of glucose and oil
substrates is essential for the synthesis of SLs. During the late
fermentation stage, and with the gradual accumulation of SLs, the
rheological properties of the fermentation broth undergoes significant
changes, thus increasing viscosity. These changes exert a key impact on
mass transfer and mixing, thus resulting in a limited oxygen supply,
consequently, the productivity of SLs synthesis decreases notably. The
development of a semi-continuous fermentation process could
significantly alleviate the influence of oxygen limitation on SLs
synthesis (Zhang et al., 2018). It has been found that controlling the
content of oil, and the ratio of oil to SLs, can exert influence on the
morphology of SLs (crystalline or non-crystalline types), thereby
significantly changing the sedimentation characteristics of SLs (Chen et
al., 2021b). In turn, this affects the efficiency of semi-continuous
fermentation. Therefore, it is vital that we are able to precisely
control the process of oil supplementation so that we can achieve the
efficient production of SLs.

In this study, we established a multi-scale parameter detection system
for the SLs fermentation process by applying a range of on-line sensors,
mainly including a near-infrared spectrometer and a process mass
spectrometer. First, we studied the differences in macro-physiological
and metabolic parameters under different rates of oil supplementation.
Subsequently, we used a range of process parameters to construct a
data-mechanism fusion model, which was accomplished by integrating data
modeling with cellular metabolic mechanisms, for feedback feeding of oil
and glucose. Finally, this model was applied to semi-continuous
fermentation to achieve a highly efficient production system for SLs.

\textbf{2 Materials and methods}

2.1 Strain, media, and culture conditions

\emph{Candida bombicola} ATCC 22214 was obtained from the Guangdong
Culture Collection Center (China) and stored at -80 in 20\% glycerol
solution. Seed medium contained 50 g/L of glucose, 1 g/L of
KH\textsubscript{2}PO\textsubscript{4}, 4 g/L of
(NH\textsubscript{4})\textsubscript{2}SO\textsubscript{4}, 0.5 g/L of
MgSO\textsubscript{4}\selectlanguage{ngerman}·7H\textsubscript{2}O, and 10 g/L of corn steep
liquor (CSL). Seed was cultured in a 1 L baffled shake flask with 200 mL
working volume at 200 rpm and 25\selectlanguage{english} for 48 h.

The initial fermentation medium was placed in a 5 L bioreactor (Shanghai
Guoqiang Bioengineering Equipment Co., Ltd., China) and consisted of 100
g/L of glucose, 1 g/L of KH\textsubscript{2}PO\textsubscript{4}, 4 g/L
of (NH\textsubscript{4})\textsubscript{2}SO\textsubscript{4}, 0.5 g/L of
MgSO\textsubscript{4}*7H\textsubscript{2}O, and 10 g/L of CSL. All
culture media were sterilized at 115\selectlanguage{english} for 30 min. An initial volume of
2.5 L was mixed with 3\% of inoculum and cultured at 25\selectlanguage{english} for 168 h.
Aeration was provided at 0.5 vvm and dissolved oxygen (DO) was
maintained above 30\% of the saturation concentration by adjusting
agitation in a stepwise manner. A pH of 3.5 was maintained during the
entire process by the addition of 4 M NaOH solution. The fed-batch
fermentation cycle lasted 168 h. During the fed-batch fermentation
process, the feeding rate for rapeseed oil was 1 g/L/h in the first 24
h, and was then set to 2.0 (Low), 2.6 (Medium), and 3.2 (High) g/L/h.
During the late stage of fermentation, the residual oil concentration of
the fermentation broth was controlled so that it did not exceed 30 g/L.
During semi-continuous fermentation, the concentration of glucose and
rapeseed oil was controlled at 40 g/L and 10 g/L, respectively, and the
glucose concentration was maintained below 30 g/L by controlling the
feeding rate prior to \emph{in-situ} separation.
\emph{In-situ}separation was carried out when the concentration of SLs
in the fermenter exceeded 140 g/L (non-normalized), as determined by the
on-line sensor.

2.2 Determination of fermentation process parameters

During the fermentation process, the concentration of SLs, rapeseed oil,
and glucose, were determined by using a real-time on-line detection
platform involving a near-infrared spectrum (Chen et al., 2021a). The
relationship between off-line data and near-infrared spectrum data was
established during the early stages to achieve the real-time detection
of substance concentration in the fermentation broth. OUR, CER, and RQ
were calculated on-line through detecting the oxygen and carbon dioxide
proportion in the inlet air and off-gas by a process mass spectrometer
as described previously by Chen et al. (2019). For dry cell weight (DCW)
determination, 2 mL of fermentation broth was sampled, washed three
times with an equal volume of 70\% ethanol (v/v) solution, and then
dried in an oven at 80degC. Next, the sample was weighed. SLs structure
was determined by LC-MS as described previously by Chen et al. (2020).

Extracellular organic acids were analyzed by the HPLC method. A 2 mL
sample of fermentation broth was taken at 24 h, 48 h, 72 h, and 96 h.
The samples were then centrifuged to isolate the supernatant. The
supernatant was filtered into a liquid phase vial with a 0.22 \selectlanguage{greek}μ\selectlanguage{english}m filter
and then analyzed by HPLC, including a VARIAN Metacarb-H plus
chromatographic column, a RID detector, 0.01 mol/L dilute sulfuric acid
as the mobile phase, a flow rate of 0.4 mL/min, an injection volume of
10 \selectlanguage{greek}μ\selectlanguage{english}L, a column temperature of 50\selectlanguage{ngerman}°C, and a detection temperature of
35°C.

The composition of rapeseed oil was determined by the GC method, as
described previously by Gao, Liu, Jin \& Wang (2019).

2.3 The influence of SLs concentration on SLs synthesis

First, we added 200 mL of fermentation broth (the concentration of the
mixture of glucose and rapeseed oil was 50 g/L) into a 1 L baffled shake
flask. Next, we added SLs to the shake flask to prepare the following
concentrations: 0, 15, 30, 45, 60, 75, 90, and 105 g/L. Then, we took 50
mL of the fermentation broth and cultured this sample in a 5 L fermenter
for 30 h; this was followed by centrifugation to remove the supernatant.
After washing, the cells were resuspended in 20 mL of sterile water and
inoculated in a shake flask. This was then cultured for 24 h at 200 rpm
and 25°C.

2.4 Feedback feeding model

The feedback feeding model achieved stable, real-time, and rational
regulation of fermentation process feeding based on on-line parameters
and fermentation control. This model was mainly divided into an on-line
parameter acquisition module, a parameter analysis module, and a
feedback feeding module. First, we established a real-time detection
system for fermentation process parameters (independent variables: OUR,
CER, RQ, pH, DO, temperature, agitation, aeration, and DCW). This was
achieved by a variety of on-line sensors (near infrared spectroscopy, a
process mass spectrometer, a dissolved oxygen electrode, temperture
electrode and pH electrode ). We also monitored a range of dependent
variables, including SLs productivity, glucose consumption rate, and
rapeseed oil consumption rate. Then, we used six algorithms to fit these
key parameters, including multiple linear regression, partial least
squares, a support vector machine, random forest, and gradient boosting
regression. The correlations between parameters (R\textsuperscript{2})
were then used to construct a data correlation model. Next, we used the
data correlation model to select the optimal feedback value for feed
output in accordance with the on-line parameters and fermentation
control. Finally, we used the output correlation (R\textsuperscript{2})
for multiple algorithm models to achieve real-time control over the feed
module. The model could read a range of detection data (SLs
productivity, fermenter volume, DCW, residual oil concentration,
residual glucose concentration, OUR, and other parameters) every 1 h.
The control of oil and glucose concentrations enabled real-time feedback
and regulation of oil and glucose supplementation. During the
verification process, fed-batch fermentation was performed to control
the residual oil concentration of the fermentation broth to 2 g/L and 10
g/L; the glucose concentration was controlled at 40 g/L. During
semi-continuous fermentation, the residual oil concentration was
controlled at 10 -15 g/L, and the glucose concentration was controlled
at 30-40 g/L. We used gravity sedimentation separation and washing
recovery to achieve \emph{in-situ} separation and fermentation for 234
h. The fermentation control platform and feedback feeding model are
shown in Fig. 1.

2.5 Data analysis

Due to the consideration of changes in working volume during feeding,
all of the data presented in the figures and tables were normalized to
the initial volume (except those marked `non-normalized in part'), and
represent the real-time detection of SLs concentration in the fermenter.
All experiments were performed in triplicate. The data shown in the
tables and figures represent the mean ± standard deviation of three
experiments. One-way analysis of variance (ANOVA) and the T-test (P
\textless{} 0.05) were used to identify significant differences between
treatments (GraphPad Prism 8.3.0, GraphPad Software Inc., USA).

\textbf{3 Results}

3.1 The effect of oil feeding rate on SLs production during fed-batch
fermentation

During 5 L fed-batch fermentations, SLs were synthesized with different
rapeseed oil feeding rates (high, medium, and low modes) for 168 h.
There were no significant differences between the three modes with
respect to the titer of SLs, total glucose consumption, and total oil
consumption (Fig. 2A, Table 1), however, the phased synthesis rate of
SLs differed substantially during the fermentation process. The higher
the oil feeding rate, the faster the accumulation of SLs in the early
stages of fermentation, although the accumulation rate decreased during
the middle and late stages. We also found that the phased productivity
of SLs differed significantly between the three modes (Fig 2B). The
highest productivity of SLs was 3.53 g/L/h under the high mode, while
the highest productivity in the medium and low modes was 2.63 and 2.09
g/L/h, thus representing reductions of 25.5\% and 40.8\%, respectively.
Notably, after 48 h, the productivity of SLs declined rapidly under the
high mode until 110 h and was then maintained at 0.98 g/L/h to the end
of the fermentation process. In contrast, the productivity of SLs under
the low mode was maintained at an average of 2.04 g/L/h until 120 h and
then began to decrease. As shown in Fig 2C, the difference between the
high and low modes reached a maximum of 139.7 g at around 87 h, while
the maximum difference between the high and medium modes was 85.7 g at
73 h. Otherwise, there were significant differences in the OUR and CER
profiles when compared between the three modes during the fermentation
process. In high mode, the maximum OUR reached up to 64 mmol/L/h,
gradually decreased after 48 h, and was then maintained at 47 mmol/L/h
after 110 h. In contrast, the OUR in low mode was maintained at
approximately 54 mmol/L/h for 120 h. The CER showed a similar trend to
OUR (Fig 2D).

3.2 Cellular metabolic characteristics in different stages of SLs
production

The fed-batch fermentation process underlying the production of SLs
predominantly consisted of two phases: the primary metabolism of cell
growth and the secondary metabolism of SLs synthesis. We found that the
maximum productivity of SLs was related to the oil feeding rate and that
the highest value was 3.53 g/L/h. However, a further increase in the oil
feeding rate did not enhance SLs productivity, meaning that the maximum
specific productivity for SLs reached 0.15 g/g\textsubscript{DCW}/h due
to limitations imposed by the production capacity of the cells.
Moreover, we observed that the production of SLs in the shake flask
began to decrease significantly (by 15.6\%) when the concentration of
SLs exceeded 75 g/L (non-normalized) (Fig. 3A); at this point, the
productivity of SLs in the fermenter under the three modes also
gradually decreased, thus inferring that product inhibition may have
occurred. Furthermore, the degree of inhibition increased with an
increase in the concentration of SLs. Interestingly, although the OUR
and CER decreased during this phase, the RQ remained unchanged, thus
showing that product inhibition only limited the metabolic capacity of
the cells, while the metabolic pathway remained unchanged. Notably, when
the concentration of SLs in the fermentation broth exceeded 140 g/L
(non-normalized), although the OUR and CER continued to decrease with
the decline of SLs productivity, the RQ began to increase from
approximately 0.70 to 0.76 (Fig. 3B). This result demonstrated that the
metabolic pathways of cells underwent significant changes at this time.
By further analyzing the viscosity of the fermentation broth, we found
that the viscosity of the fermentation broth would rise sharply during
the middle and late phases of fermentation, and when the concentration
of SLs exceeded 140 g/L (non-normalized), the viscosity exceeded 20 cP
(Fig. 3C).

The fermentation of SLs is a high oxygen consumption process. High
viscosity can exert serious effects on oxygen supply, thus resulting in
alterations in cell metabolism and the synthesis of SLs. Further
analysis of the RQ profiles demonstrated that when the average RQ was
approximately 0.70 (high mode: 24-96 h; medium mode: 24-110 h; low mode:
24-120 h), the average yields of SLs to glucose and rapeseed oil were
1.21 g/g and 1.39 g/g, respectively. However, when the concentration of
SLs exceeded 140 g/L (non-normalized) (high mode: after 96 h; medium
mode: after 110 h; low mode: after 120 h), the average yields of SLs to
glucose and rapeseed oil changed to 0.82 g/g and 1.46 g/g, respectively,
and the average RQ reached 0.76 (Table 2). This implied that once the
oxygen supply became limited, the yield of SLs to glucose was reduced,
while the yield to rapeseed oil was increased, thus leading to an
increase in RQ value.

In summary, the synthesis of SLs during fed-batch fermentation could be
divided into three phases (Fig. 4). During the initial phase, the
synthesis of SLs was mainly limited by the production capacity of the
cells as well as the oil supply. The maximum specific SLs productivity
could reach up to 0.15 g/g\textsubscript{DCW}/h but was affected by the
oil feeding rate. During the second phase, when the concentration of SLs
in the fermentation broth exceeded 75 g/L (non-normalized), the
production capacity of the cells began to be inhibited by high product
concentration. The higher the concentration of SLs, the stronger the
degree of inhibition. However, cellular metabolic pathways remained
unchanged during this phase. During the last phase, the concentration of
SLs reached 140 g/L (non-normalized) and the production capacity of the
cells became significantly restricted by oxygen supply. On the basis of
these analyses, the first and second phases were considered to represent
the main stages of SLs accumulation during fed-batch fermentation.
Increasing the productivity of SLs as much as possible during these
phases \emph{via} the fine control of substrate feeding strategy was of
great significance to the improvement of production efficiency. In terms
of the third phase, although it was difficult to improve production
efficiency without increasing oxygen supply during the conventional
fermentation mode, the semi-continuous fermentation mode, involving
the\emph{in-situ} separation of SLs, which was regulated by the
concentration of oil in the fermentation broth (Chen et al., 2021b),
could effectively alleviate product inhibition and significantly reduce
the viscosity of the fermentation broth to eliminate oxygen limitation
(Liu et al., 2019).

3.3 Construction of an intelligent feedback feeding model based on
multi-parameter process data

Next, we constructed and optimized an efficient, stable, and rational
fermentation process for SLs production by regulating feeding during the
first and second phases. First, we established correlations between the
process macroscopic parameters and the production capacity of cells. All
the on-line parameters, including agitation, temperature, pH, DO,
glucose concentration, oil concentration, SLs concentration, OUR, CER,
and RQ were applied as independent variables. The oil consumption rate,
glucose consumption rate, and SLs productivity were set as dependent
variables. Then, six linear and non-linear mathematical equations were
adopted to establish the correlation between independent and dependent
variables, including unary linear regression equations (LR), multiple
linear regression equations (MLR), support vector machines (SVMs),
partial least squares (PLS), random forest algorithms (RFs) and gradient
boosting regression algorithms (GBRs). Finally, the pros and cons of the
mathematical equations were compared by the correlation coefficient
(R\textsuperscript{2}) between the predicted values and the real values
(Fig. 5). Approximately 110 sets of data were used as the training set,
and 20 sets of data were used as the testing set. Three dependent
variables relating to SLs (productivity, oil consumption rate, and
glucose consumption rate) were all strongly correlated with the actual
values when applying the six equations
(R\textsuperscript{2}\textgreater{} 0.96), thus showing that these six
algorithms accurately reflected the relationship between the consumption
of substrates and the synthesis of product and fermentation process
parameters (Table 3). Notably, the R\textsuperscript{2} for the RF and
GBR algorithms decreased slightly after the testing set was added, while
the R\textsuperscript{2} for the other four equations increased as the
number of parameters increased. It is possible that with the continuous
input of parameters, the database of equations also increased;
consequently, the accuracy of the predicted value would also be
improved. Non-linear equations might be associated with over-fitting
problems; therefore, when there is less data, the predicted values of
ULR, MLR, SVM, and PLS, were more reliable. In contrast, with a
continuous increase in sample number, the prediction values for RF and
GBR became more accurate.

When the model produced a predicted value (SLs productivity, glucose
consumption rate, and oil consumption rate) with an optimal
R\textsuperscript{2}, then the model would add data to the database,
thus increasing the data sample. This feedback feeding model was
combined with the data provided by on-line sensors. This meant that the
model could read the on-line data, perform equation fitting, and output
the optimal predicted value by the computer. By integrating the working
volume, the required concentrations of glucose and oil, as well as the
amounts of consumed glucose and oil, the model could automatically guide
the precise feeding rate \emph{via} a computer-controlled feedback
module. Simultaneously, the model performed data overlay and
self-learning based on the continuous accumulation of data, thereby
further improving the accuracy of the model.

The feedback feeding model was validated in a 5 L fermenter. As shown in
Fig. 6A and B, the concentration of residual oil was well controlled at
2 g/L and 10 g/L respectively, and the concentration of residual glucose
was maintained at 40 g/L from 24 h until the RQ was increased to end the
fermentation process. In addition, by applying the feedback feeding
model, the SLs titer with a residual oil concentration at 2 g/L was
improved by 4.9\% during the first and second phases of fed-batch
fermentation, when compared to the high oil feeding mode. More
importantly, the feedback feeding model shortened the feeding interval
from 6 to 12 h by conventional manual intervention to 1 h by automatic
feeding. The model also reduced the error related to feeding control
from more than 15\% to less than 5\%, thus providing significant savings
in terms of labor and time costs.

3.4 An intelligent feedback feeding model facilitated semi-continuous
fermentation for the efficient production of SLs

To alleviate oxygen limitation in the late phase of fed-batch
fermentation, we used gravity sedimentation for the \emph{in-situ}
separation of SLs, thus permitting semi-continuous fermentation. When
the concentration of SLs reached 140 g/L (non-normalized), we began the
\emph{in-situ} separation process and allowed this to proceed until the
concentration of SLs fell below 60 g/L, thus alleviating the influence
of a high SLs concentration on the rheological properties, as well as
relieving product inhibition. As illustrated in Fig. 7A, the feedback
feeding model could stably control the concentrations of residual
glucose and residual oil at appropriate levels during the entire
fermentation process, so as to ensure the efficient in-situ separation
of SLs (Chen et al., 2021b). After two rounds of \emph{in-situ}
separation of product, the total production of SLs reached 1348.3 g
within 234 h. Moreover, the overall SLs productivity and yield were 2.30
g/L/h and 0.57 g/g, respectively, thus representing increases of 40.2\%
and 18.7\% in comparison to the fed-batch fermentation with a high oil
feeding mode (Fig. 7B). Notably, the productivity of SLs did not exhibit
a downwards trend at the end of semi-continuous fermentation (Fig. 7C).
Therefore, it was reasonable to speculate that the production efficiency
could be further improved by performing longer operations with the
assistance of an intelligent feedback feeding model.

\textbf{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/g\textsubscript{DCW}/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, \selectlanguage{greek}β-\selectlanguage{english}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
CO\textsubscript{2}, 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 CO\textsubscript{2}, 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\%.

\textbf{5. Conclusion}

According to the physiological metabolic state of cells, the fed-batch
fermentation process for SLs synthesis can be divided into three stages:
a stage that is limited by cell production capacity, a stage that is
inhibited by high product concentration, and a stage that is limited by
oxygen supply. Based on the detection of process multi-parameters, we
established a data-driven feedback feeding model. This was combined with
analyses of cellular metabolic mechanisms such that an optimal
production state was achieved in the first and second stages,in which
there was a 4.9\% increase in SLs production. Furthermore, the
mechanism-assisted data model was applied to the rational and precise
control of residual oil concentration during semi-continuous SLs
fermentation. The problem associated with oxygen limitation that was
encountered during the third stage was effectively alleviated, thereby
further increasing the SLs productivity to 2.30 g/L/h. The
mechanism-assisted data modeling method developed in this study could be
easily extended to other fermentation processes. We also expect our new
process strategy for the production of SLs to be applied on an
industrial-scale.

\textbf{Author Contribution Statement}

Chen Y: Conceptualization, Methodology, Validation, Formal analysis,
Investigation, Writing - original draft \& editing, Visualization.

Tian XW: Conceptualization, Methodology, Validation, Formal analysis,
Writing - review \& editing, Supervision,

Zhu XF, Tang X, Zhang S, Hu SY: Conceptualization, Methodology,
Supervision.

Chu J, Zhuang YP: Conceptualization, Methodology, Validation, Writing -
review \& editing, Supervision, Funding acquisition.

\textbf{Competing interests}

The authors declare that they have no competing interests.

\textbf{Supplementary data}

E-supplementary data of this work can be found in online version of the
paper.

\textbf{Ethical approval}

This article does not contain any studies with human participants or
animals performed by any of the authors.

\textbf{Acknowledgement and Funding}

This work was financially supported by the National Key Research
Development Program of China (2019YFA0904803), the Scientific Research
Think Tank of Biological Manufacturing Industry in Qingdao
(QDSWZK202004), the Fundamental Research Funds for the Central
Universities (JKF01211116, JKVF1211036, JKF01211708). The authors thank
Research Center of Analysis and Test of East China University of Science
and Technology for the help on the characterization.

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\textbf{Figure caption}

\textbf{Fig. 1.} Feedback feeding platform in SLs fermentation process.
The solid lines represent material pipelines, dotted lines represent
signal pipelines.

\textbf{Fig. 2.} Process parameters of SLs fermentation with different
oil feeding modes. A: SLs titer; B: phased SLs productivity; C:
difference in total SLs production; D: process CER and OUR profilings

\textbf{Fig. 3.} Metabolic characteristics of SLs at different
production stages. A: The effect of the product on the synthesis of SLs;
B: Changes in RQ during SLs synthesis; C: Relation between SLs
concentration and viscosity.

\textbf{Fig. 4.} Analysis of SLs synthesis stage.

\textbf{Fig. 5.} Intelligent feedback feeding model. OUR for the oxygen
uptake rate (mmol/L/h), CER for the CO\textsubscript{2} release rate
(mmol/L/h), RQ for the respiratory quotient, RPM for the fermentation
speed (rpm), DO for the dissolved oxygen (\%), Temp for the fermentation
temperature (), pH for the fermentation pH. SLs represents the
productivity of SLs (g/L/h), Oil represents the consumption rate of
rapeseed Oil (g/L/h), Glc represents the consumption rate of glucose
(g/L/h), V represents the working volume (L), c1 and c3 represent the
concentrations of rapeseed Oil and glucose in the actual fermentation
broth (g/L), c2 and c4 represent the controlling concentrations of
rapeseed oil and glucose in fermentation broth (g/L).

\textbf{Fig. 6.} Feedback feeding control. A: Glucose concentration; B:
Rapeseed oil concentration.

\textbf{Fig. 7.} Semi-continuous fermentation of SLs production with the
assistance of intelligent feedback feed model. A: Rapeseed oil and
glucose concentration as well as DCW; B: SLs titer; C: SLs productivity
and yield.

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