Se muestra el tratamiento enzimatico de microalgas

1. Chemosphere 284 (2021) 131264
Available online 21 June 2021
0045-6535/© 2021 Published by Elsevier Ltd.
Enzymatic pretreatment of algal biomass has different optimal conditions
for biogas and bioethanol routes
Shashi Bhushan a,c
, Mohit Singh Rana a,1
, Mamta Bhandari a,1
, Ashwini Kumar Sharma b
,
Halis Simsek c
, Sanjeev Kumar Prajapati a,*
a
Environment and Biofuel Research Lab (EBRL), Department of Hydro and Renewable Energy, Indian Institute of Technology Roorkee (IIT-R), Uttarakhand, 247667,
India
b
Department of Chemical Engineering, Indian Institute of Technology Roorkee (IIT-R), Uttarakhand, 247667, India
c
Department of Agricultural and Biosystem Engineering, North Dakota State University (NDSU), North Dakota, 58102, USA
A R T I C L E I N F O
Handling Editor: Derek Muir
Keywords:
Crude enzyme
Algal biomass
Enzymatic pretreatment
Taguchi method
Biogas
Bioethanol
A B S T R A C T
Enzymatic pretreatment is emerging as an efficient tool for the extraction of biofuel precursors from algal
biomass. However, yardsticks for end-use directed selection of optimal pretreatment conditions are not yet
identified. The present study, for the first time, reveals different optimal conditions for algal biomass solubili­
zation and sugar release. Algal biomass pretreatment optimization was carried out using the Taguchi method.
Crude enzyme from Aspergillus fischeri was found effective for pretreatment of Chlorella pyrenoidosa. Maximum
sugar yield (190 mg g− 1
biomass) from algal biomass was observed at a substrate concentration of 4 g L-1
, with a
5% enzyme load at temperature 60◦
C, pH 5.5, and shaking speed of 80 rpm. In contrast, maximum sCOD (1350
mg g− 1
biomass) was obtained at 2 g L− 1
substrate concentration with enzyme load of 20% v/v, at 60◦
C, pH 4,
and shaking speed of 100 rpm. Hence, the first set of conditions would be more beneficial for bioethanol pro­
duction. Whereas another set of conditions would improve the biofuel production that requires maximum sol­
ubilization of algal biomass, such as fermentative methane production. Overall, the present observations
established that process conditions required for enzymatic pretreatment of algal biomass should be selected
according to the desired biofuel type.
1. Introduction
Algal biomass contains adequate biofuel precursors, i.e., lipid and
carbohydrate, that make it a suitable feedstock for biofuel generation
(Pandey et al., 2019). Biochemical conversion routes using algal
biomass, especially alcoholic fermentation and anaerobic digestion for
the production of fuel-grade ethanol and methane respectively, are
supported by recent reports (Choi et al., 2010; Prajapati et al., 2013).
The biochemical conversions have added advantages over thermo­
chemical conversion routes. The biochemical routes are considered as
environment-friendly and low energy demanding processes compared to
other thermochemical conversion routes. In addition, the use of har­
vested wet biomass through biochemical routes without the
pre-processing (i.e., dewatering and drying) significantly supports the
overall sustainability of the process (Wendt et al., 2019). Furthermore,
the algal biomass can be co-digested with other substrates such as food
waste, municipal sludge, or even agro-residue for enhanced biogas
generation (Kumar et al., 2020). Despite the huge potential, algal biofuel
production at commercial scales fails to achieve sustainability. One of
the major limitations to the commercial sustainability of biofuel is the
complex structure of the algal cell wall (Halim et al., 2019; Rashidi and
Trindade, 2018), which hinders the availability of most of biofuel pre­
cursors for conversion to biofuel. Therefore, the development and
optimization of an efficient, low-cost, and environment-friendly pre­
treatment technique to disrupt the rigid cell wall of algae is essential to
improve the sustainability and commercialization of algal biofuel.
For algal biomass pretreatment, various methods, including me­
chanical, chemical, thermochemical, and biological have been attemp­
ted, as summarized in some recent review articles (Anto et al., 2020;
Bhushan et al., 2020; Yap et al., 2021). However, these methods have
drawbacks. For instance, mechanical methods are energy-intensive
(Hendriks and Zeeman, 2009), and chemical and thermochemical
* Corresponding author. IIT Roorkee, Uttarakhand, 247667, India.
E-mail addresses: sanjukec@hre.iitr.ac.in, sanjukec@gmail.com (S.K. Prajapati).
1
Equal contribution by these authors.
Contents lists available at ScienceDirect
Chemosphere
journal homepage: www.elsevier.com/locate/chemosphere
https://doi.org/10.1016/j.chemosphere.2021.131264
Received 8 April 2021; Received in revised form 27 May 2021; Accepted 15 June 2021

2. Chemosphere 284 (2021) 131264
2
methods are prone to release hazardous chemicals and reaction
by-products into the environment (Kumar et al., 2009). In contrast, some
of the physicochemical methods such as ultrasound and
microwave-based methods do not have such limitations but fall behind
in economy and efficiency on a large scale (Demuez et al., 2015;
Menéndez et al., 2014). However, the biological methods are advanta­
geous over other available methods because of their eco-friendly and
non-toxic nature, and thus they possess immense potential for further
research and developments (Sindhu et al., 2016).
For biological pretreatment of algal biomass, several commercial
enzymes, such as invertase (Khan et al., 2017) or cellulase (Trivedi et al.,
2013), have been tested. Each enzyme possesses specific activity and a
specific target molecule. For example, cellulase acts on the cellulosic
component of the algal cell wall, whereas invertase specifically targets
sucrose compounds and converts them into monomeric sugar molecules
(Kehlbeck et al., 2014; Zhou et al., 2020). In this regard, attempts have
been made to formulate enzyme cocktails having more than one kind of
specific enzyme. For instance, a commercial enzyme cocktail (vis.
Alcalase and Viscozyme) was tested for the pretreatment of Chlorella
vulgaris for biogas production (Mahdy et al., 2016). However, the cost of
pure, commercial-grade enzymes is still an issue. As an alternative to
commercial enzymes, a few reports have highlighted the use of fungal
crude enzymes (Prajapati et al., 2015a). The crude enzymes produced by
most of the fungus on a selected substrate generally have a cocktail of
cellulase, hemicellulose, xylanase (Cekmecelioglu and Demirci, 2020),
which makes them more suitable for algal biomass pretreatment.
As stated earlier, the enzymes are highly specific for their target sites
in the algal cell wall. Further, the effectiveness of the enzymatic pre­
treatment depends on process parameters such as pH, temperature, the
enzyme load, agitation, substrate load, and incubation time (Cekmece­
lioglu and Demirci, 2020). In recent years, a few attempts have been
made to optimize the process parameters for enzymatic pretreatment of
algal biomass (Demuez et al., 2015; Prajapati et al., 2015b; Zhang et al.,
2018b). However, optimization of fungal crude enzyme-based pre­
treatment should be directed by the ultimate use of the pretreated
biomass. For instance, if the pretreated biomass is to be used for bio­
ethanol production, the pretreatment must be optimized for maximum
sugar release. Likewise, if the pretreated biomass is determined for
biogas production, maximum COD solubilization should be concerned
for optimization studies. To the best of the authors’ knowledge, so far,
no such yardsticks for end-use-directed pretreatment of algal biomass
have been established.
In light of the above, the aim of the present work was to identify the
optimal enzymatic pretreatment conditions of algal biomass for
maximum sugar release and maximum biomass solubilization toward
bioethanol and biogas production, respectively. Inhouse algal cultiva­
tion and fungal crude enzyme production were carried out. Chlorella
pyrenoidosa, having a high carbohydrate content (~25.30% of dry cell
weight) compared to other strains, could serve as one of the preferred
feedstocks for bioethanol and biogas production (Kumari et al., 2021;
Megawati et al., 2020; Mohan Singh et al., 2020; Prajapati et al., 2014).
Hence in the present work, based on the biofuel value and industrial
potential, C. pyrenoidosa was selected as a model strain for pretreatment
studies. Based on the cellulase activity, fungal strain that can yield
highly active enzyme, and suitable production routes were identified.
Then an optimization study for algal biomass pretreatment was carried
out with the highly active enzyme. The process parameters were opti­
mized for maximum sugar release and COD solubilization considering
bioethanol and biogas production, respectively.
2. Material and methods
2.1. Algal biomass production
Chlorella pyrenoidosa (NCIM 2738) was procured from the National
Centre for Industrial Microorganism (NCIM), Pune, India. The initial
cultures of the algae were maintained in a 250 mL Erlenmeyer flask
(working volume of 50 mL) using BG11 as a sole growth medium. To
obtain sufficient biomass for pretreatment studies, the algal culture was
sequentially scaled up to 20 L culture in a fabricated tubular photo­
bioreactor under ≈6000 Lux illumination at room temperature. Air at a
flow rate of 1.0 L min− 1
was bubbled in the reactor to avoid settling of
growing algae. After the growth period (≈15 d), air bubbling was
stopped, and the algal biomass was harvested through auto-settling. The
harvested biomass slurry was further concentrated through centrifuga­
tion at 7061g rcf, for 5 min. The pelletized algal biomass was then
washed three times with deionized water through repeating resus­
pension and centrifugation cycles. The harvested algal biomass was then
used for the pretreatment studies.
2.2. Fungal crude enzyme production
Rhizopus oryzae and Aspergillus sp. Have been previously reported as
possessing good cellulase activity (Prajapati et al., 2015a); thus, they
were used for the fungal crude enzyme production. Another environ­
mental strain was isolated from a decaying wood piece collected from a
garden in Roorkee, India (25◦
32′
08.1′′
N 84◦
51′
09.1′′
E). The strain was
identified as Aspergillus fischeri through 18 S rRNA sequencing. Details
related to the molecular identification of the isolated fungus are pro­
vided in the supplementary data file.
All three fungal strains were maintained on potato dextrose agar
(HiMedia, Cat. No. MH096) plates. The crude enzyme production from
all the three tested fungi was carried out following solid-state fermen­
tation, as reported elsewhere (Kaushik and Malik, 2016). The selected
fungal strains were grown on different substrates viz., sugarcane
bagasse, corncob, and rice straw for the effective yield of crude enzyme.
These agro-residues were selected as substrates due to their suitability
for fungal growth and enzyme production, as reported earlier (De Cassia
Pereira et al., 2015; Ire et al., 2018; Kaushik and Malik, 2016).
Furthermore, the agro-residues mentioned above are easily available in
large quantities in India and across the globe (Arumugam and Anan­
dakumar, 2016; Bezerra and Ragauskas, 2012; Satlewal et al., 2018).
The selected agro-residues were dried in a hot air oven at 105◦
C for 24 h
and then grounded to obtain powdered biomass. The grounded biomass
was then sieved through 75 μm pore size mesh. Ultimate analyses of the
obtained powder (particle size ≤75 μm) were done to determine the
elemental (C, H, N) composition of each substrate. Finally, 5.0 g powder
of each substrate was taken in 250 mL Erlenmeyer flask and autoclaved
at 121◦
C and 15 psi for 15 min. Deionized water containing 0.5% (w/v)
yeast extract was aseptically added to the autoclaved substrates for
maintaining the moisture at 60–70%. Each flask was then inoculated
with 5% (w/v) of spore suspension (≈6.0 × 106
spores mL− 1
) and
incubated at 30◦
C for 5 days.
After 5 days of incubation, 30 mL of 0.5 M Na-citrate buffer (pH =
5.1) containing 0.1% tween 80 was added to the culture flask and
agitated at 150 rpm, temperature 30◦
C for 1 h. The contents of the flasks
were then filtered using a muslin cloth followed by subsequent centri­
fugation at 5884g rcf 4◦
C, 10 min, and the supernatant containing crude
enzyme was collected in clean and dry falcon tubes. The extracted crude
enzyme was stored at − 20◦
C till further use.
2.3. Optimization of crude enzyme-based pretreatment of algae
The Taguchi method was employed to investigate the optimum
conditions for enzymatic pretreatment of algal biomass. The Taguchi
method is a popular statistical method that is used in various disciplines,
including bioprocess engineering, to design experiments that can
determine the effects of process parameters on the product or process
performance and, in turn, on the optimal conditions for the process
(Sandhibigraha et al., 2020; Taiwo et al., 2020). The Taguchi method
employs orthogonal arrays to determine the process conditions to be
tested. It reveals the process behavior with a minimal number of
S. Bhushan et al.

3. Chemosphere 284 (2021) 131264
3
experiments, thus saving both time and resources. Substrate load, the
enzyme load, temperature, pH, and shaking speed were identified as
crucial parameters affecting the enzymatic solubilization of algal
biomass. The range for different parameters to be tested was selected
based on previously published reports (Chen et al., 2017; Córdova et al.,
2019; Hom-Diaz et al., 2016). To obtain sufficient information with
minimum number of experiments, 4 levels (values) for each parameter
in the selected range were chosen (Table 1).
In summary, 5 different process parameters, each having 4 different
values were selected. Under such a scenario, more than a thousand ex­
periments would be needed for each possible combination of process
conditions to be evaluated. In contrast, the L16 (45
) orthogonal array
was sufficient based on the Taguchi orthogonal array design. Thus only
16 experiments were required to characterize and optimize the process.
The process performance (or the pretreatment efficiency) was measured
for each experiment in two ways; biomass solubilization, reported as
soluble chemical oxygen demand (sCOD) and sugar (glucose equivalent)
released. All experiments were performed in the 50 mL falcon tubes with
20 mL working volumes and a fixed incubation time of 6 h.
For analyzing the experimental results, the Taguchi method employs
signal-to-noise (S/N) ratio calculations. Here, the larger-the-better cri­
terion was applied for both sCOD and sugar released because their
maximum values were desirable. The S/N ratio for this criterion is
shown below:
S
N
= − 10 log 10
(
1
n
∑
n
i=1
1
yi
2
)
(1)
where yi is the response value for a particular combination of parameters
and n is the total number of runs for this combination. In this way, the S/
N ratio was obtained for each of the 16 experiments or parameter
combinations. Next, the average S/N ratio was calculated for each level
of each parameter. The Taguchi response analysis was then carried out
to arrive at the conclusions and to obtain the optimal process conditions.
2.4. Analytical methods
2.4.1. Activity of fungal crude enzymes
Filter paper assay (FPA) was used as the standard measure of cellu­
lase activity (Eaton, 1988) to estimate the enzymatic activity of fungal
crude enzymes. Briefly, 1.0 mL 0.05 M of Trisodium citrate dihydrate
buffer was mixed with the 0.5 mL of diluted crude enzymes in a glass test
tube, and filter paper strips (6 cm × 1 cm) were used as the substrate for
enzymatic assay. The pH was maintained at 4.8 using Na-citrate buffer
solution. Along with the sample, enzyme blank, reagent blank, and
glucose standards were prepared. Blanks, Glucose standard, and enzyme
assay tubes were incubated at 50◦
C for 60 min. After incubation, a 3.0
mL DNS reagent was added to each tube to stop the reaction. Then all
tubes containing the reaction mixture were incubated for 5 min in a
vigorously boiling water bath. After boiling, the tubes were transferred
to a cold ice-water bath for a few minutes, allowing the pulp to settle
down. Then 0.2 mL of the colour-developed reaction mixture was added
to 1.8 mL of deionized water and pipetted into a quartz cuvette. The
colour intensity was determined by measuring absorbance against the
reagent blank at 540 nm using a spectrophotometer (PerkinElmer,
Lamda 365). Subsequently, the sugar release was computed using the
developed glucose calibration equation (Eq. (2)), and the enzymatic
activity was determined in terms of filter paper unit (FPU) Eq. (3), as
described by Ghose (1987).
G =
A540 + 1.7959
1.622
(2)
Where G is glucose concentration in mgL− 1
and A540 is the absorbance at
540 nm.
FPU = mg of glucose release × 0.185 (3)
The activity of produced enzymes was carefully analyzed and
documented. The enzyme with the highest cellulase activity was
selected for the algal biomass pretreatment studies.
2.4.2. Sugar estimation
Total sugar content was estimated using the phenol sulfuric acid
method (Dubois et al., 1951) after complete hydrolysis of the fresh algal.
Briefly, 100 mg of the powdered algal biomass was hydrolyzed with 5
mL of 2.5 N HCl at 100◦
C for 3 h. The hydrolyzed biomass was cooled at
room temperature and neutralized using solid sodium carbonate. After
neutralization, an aliquot of 0.1 mL was taken as a sample in a clean
glass vial and diluted to 1 mL using deionized water.
In addition, enzyme pretreated algal samples were prepared for
sugar estimation by centrifuging the algal slurry at 7350 g for 10 min at
4◦
C. Then a sample of 1 mL of supernatant was pipetted into a clean glass
vial. Along with samples, a reagent blank and glucose standard was
prepared. Further, the phenol-sulfuric acid method (Dubois et al., 1951)
was followed to quantify the sugar amount. In brief, 1 mL of the sam­
ple/reagent blank/glucose standard was mixed gently with 1 mL phenol
solution (5% of phenol) and 5 mL H2SO4 (98%) in a clean glass vial and
was kept at room temperature for 10 min. Then the vials were incubated
in a water bath at 25◦
C for 20 min. The absorbance of the sample was
recorded at 490 nm using a spectrophotometer (PerkinElmer, Lamda
365). The sugar content was calculated using the developed glucose
calibration equation (Eq. (4)).
G =
A490 − 0.0419
0.0141
(4)
Where G is glucose concentration in mg L− 1
and A490 is the absorbance
at 540 nm.
2.4.3. COD estimation
To estimate the total COD of algal biomass, the fresh and dried algal
biomass (100 mg) was hydrolyzed using 5 mL of 2.5 N HCl at 100◦
C for
3 h. After cooling the reaction mixture to room temperature and
neutralizing it using sodium carbonate pellets, the supernatant was
collected for COD estimation through centrifugation at 7350g for 10 min
at 4◦
C. Similarly, the enzyme pretreated algal biomass was centrifuged
at 7350 g for 10 min at 4◦
C, and the supernatant was collected in cleaned
vials to analyzed the soluble COD (sCOD) as a measure of biomass sol­
ubilization. The colorimetric method was used to estimate the total COD
of the algal biomass and sCOD for the enzyme pretreated algal biomass
(O’Dell, 1996). Briefly, 2.5 mL of supernatant was added to 0.25 mL of
COD solution 1 (HACH, cat. No. 2950830) and 2.8 mL of COD solution 2
(HACH, cat. No. 2950915) in a glass vial. Along with samples, reagent
blank and standard solutions were prepared. The calibration standard
was prepared using potassium hydrogen phthalate standard solution.
The vials containing prepared samples, reagent blank, and calibration
standard were processed in COD digester at a temperature of 150◦
C for
2 h. After digestion, the vials were cooled to room temperature.
Absorbance for all the samples, reagent blank, and calibration standards
were recorded at 600 nm using a spectrophotometer (PerkinElmer,
Lamda 365). The total COD and sCOD were then estimated using the
developed COD calibration equation (Eq. (5)).
Table 1
Selected parameters for the enzymatic pretreatment and their different levels.
Sl. No. Parameter Level 1 Level 2 Level 3 Level 4
1. Substrate load (g L− 1
) 2 4 6 8
2. Enzyme load (%) 5 10 15 20
3. Temperature (◦
C) 30 40 50 60
4. pH 4 5.5 7 8.5
5. Shaking speed (rpm) 60 80 100 120
S. Bhushan et al.

4. Chemosphere 284 (2021) 131264
4
COD
(
mg L− 1
)
=
A600 − 0.0158
0.0393
(5)
Where A600 is absorbance at 600 nm.
2.4.4. Microscopic analyses of algal cells
For the qualitative assessment of algal biomass pretreatment by
fungal crude enzyme, the microscopic analyses of the untreated and
pretreated algal cells were performed. The differential interface contrast
(DIC) microscopic analyses of algal cells were carried out at 40X using
an optical light microscope (Nikon, Eclipse Ci-L). For the scanning
electron microscope (SEM) analyses of the cells, the standard method­
ology reported in a previous study was followed (Prajapati et al., 2015a).
3. Results and discussion
3.1. Fungal crude enzyme activity on different agro-residues
In the present work, Aspergillus fischeri was noticed with the highest
enzymatic activity (0.343 FPU ml− 1
) using corncob as a substrate
(Table 3). Similarly, corncob was observed for the highest enzymatic
activity in Rhizopusoryzae sp. (0.326 FPU ml− 1
). Carbon and nitrogen
quantity and source in the substrate influence the enzyme productivity
of a particular microbial strain (Abdullah et al., 2018). Looking at the
elemental composition (Table 2), corncob can be distinguished with
moderate carbon and nitrogen composition. For both of these microbial
strains, sugarcane bagasse was found with the lowest enzyme produc­
tion. Furthermore, sugarcane bagasse had the least carbon and nitrogen
content (Table 2). On the contrary, rice straw showed the lowest enzyme
production for all employed microbial strains, although it has relatively
higher carbon and nitrogen content. This response to rice straw can be
attributed to its complex biochemical composition. Further, the sugar­
cane bagasse and rice straw showed a reduction in enzyme production,
which could be attributed to the presence of relatively higher silica
content in them (Falk et al., 2019; Satlewal et al., 2018). The corn cob
generally possesses relatively lower silica content among the tested
biomass (Sapawe et al., 2018). The silica content may hinder the
accessibility of microbes toward biomass cell walls (Marizane et al.,
2020; Phitsuwan et al., 2017). Overall, out of the tested fungal strains
and agro-residue, the combination of A. fischeri with corncob had the
highest enzyme (cellulase) yield. Hence, the crude enzyme from
A. fischeri produced on corncob was selected for further algal biomass
pretreatment studies.
3.2. Pretreatment conditions optimization using taguchi method
The pretreatment performance measured in terms of sCOD and sugar
release is presented in Table 4 along with the parameter combinations,
as per the pre-designed experimental runs. Fig. 1 represents the main
effects plot for the S/N ratio of sugar/COD against five parameters, i.e.,
substrate load, enzyme load, temperature, pH, and shaking speed. From
the main effect plots, the relative dominance of the parameters as well as
their optimal levels was determined. The relative dominance of pa­
rameters was determined by analyzing the variation in the response
value (here, sCOD and sugar) corresponding to variation in the param­
eter values on different levels. In the case of sugar release (Fig. 1A),
substrate load was the most dominant parameter, followed by shaking
speed and pH. Enzyme load and temperature did not cause a significant
variation in process performance. This significance or dominance of
parameters changed if biomass solubilization was measured in terms of
sCOD (Fig. 1B). After substrate load, temperature and enzyme load were
other two significant parameters for pretreatment performance.
In the main effects’ plots, parameter values corresponding to the
highest value of mean S/N ratios show the optimal conditions for
maximizing the process performance. For maximizing the sugar release,
moderate values of substrate load (4 gL-1
), pH (5.5), and shaking speed
(80 rpm) were found suitable. An optimal value of substrate load is
required for effective cell wall degradation as the higher substrate loads
may impose mass transfer limitations during the reaction and inhibit the
enzymatic activity (Du et al., 2017; Kristensen et al., 2009). The lowest
enzyme load of 5% was found the most effective for sugar release.
Considering the temperature, the sugar release does not vary signifi­
cantly at lower values of temperature but increases sharply at 60◦
C.
Overall, substrate load of 4 g L-1
, enzyme load of 5%, temperature 60◦
C,
pH 5.5, and shaking speed of 80 rpm showed the maximum sugar release
in the enzymatic pretreatment being studied.
The identified optimal conditions for maximizing the COD release
were different from that for sugar release. For maximum COD solubili­
zation, low substrate load (2 g L-1
) and high enzyme load (20%) were
found optimal. This is quite contrasting to the optimal values for sugar
release where moderate substrate load at low enzymatic load was found
suitable. Moreover, the lowest pH value of 4 was found optimum for the
highest COD recovery. Further, the COD solubilization was found to
decrease with an increase in pH. Similarly, high shaking speed led to a
reduction in the COD solubilization. The temperature has similar effects
on sugar and COD release; a higher temperature is desired for maxi­
mizing both. In summary, substrate load of 2 gL-1
, enzyme load of 20%,
temperature 60◦
C, pH of 4, and shaking speed of 100 rpm resulted in
maximum COD solubilization (1350 mg g− 1
).
Based on the results, it can be concluded that COD solubilization and
sugar release are the results of two different mechanisms of enzymatic
pre-treatment. This difference is reflected when the dominant parame­
ters and optimal conditions were determined. Although the substrate
load was the most dominant parameter for both COD and sugar release,
the significance of other parameters (enzyme load, shaking rpm, tem­
perature, and pH) was dependent on the product of interest, i.e., COD or
sugar release. Similarly, optimal conditions for the enzymatic treatment
also vary, again depending on the interest of the product. It is worth
noting, the optimum conditions for maximum sugar solubilization and
COD solubilization are not correlated to each other. For example, run 2
yielded the highest sugar solubilization, whereas, highest COD solubi­
lization was observed in run 4 (Table 4).
3.3. Validation of optimal conditions for enzymatic pretreatment
As discussed in the previous subsection, two sets of optimal condi­
tions depending on the desired output of enzymatic pretreatment, i.e.,
COD solubilization or sugar release, were determined. Further, experi­
ments in the lab were conducted at these optimal conditions to validate
optimal conditions obtained from the Taguchi method. The qualitative
analyses of crude enzyme’s effect on algal biomass were carried out
through DIC and SEM images (Fig. 2). Whereas the sCOD as the sugar
Table 2
Ultimate analysis of substrates used for the production of crude enzyme.
Sl.
No.
Substrate C (%) H
(%)
N
(%)
S (%) Volatile solid
(%)
1. Sugar Cane
Bagasse
24.13 3.69 2.84 0.325 98.74
2. Corncob 24.79 3.98 3.37 0.447 97.35
3. Rice Straw 36.10 5.51 7.26 0.505 82.78
Table 3
Cellulase activity of the fungal crude enzymes produced during solid state
fermentation of three fungal strain on three different substrates.
Sl.
No.
Agro-residue used as
substrate
Cellulase activity (FPU mL− 1
)
Aspergillus
sp.
Rhizopus
oryzae
Aspergillus
fischeri
1. Sugarcane bagasse 0.301 0.301 0.261
2. Corncob 0.250 0.326 0.343
3. Rice straw 0.219 0.217 0.247
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5. Chemosphere 284 (2021) 131264
5
concentration were used as quantitative measures of algal biomass
pretreatment efficiency of selected fungal crude enzyme. C. pyrenoidosa
showed total carbohydrate content as 249 mg g− 1
dry algae and total
COD as 2200 mg g− 1
dry algae. In the present enzymatic pretreatment
study as per the designed experiments, for the first set of optimal con­
ditions, about 76.30% sugar release (190 mg g− 1
algae) along with 480
mg g− 1
of sCOD was observed (Table 5). This is indeed the maximum
value of sugar release in all the experiments performed. Based on the
sugar release and the COD solubilization data, the theoretical bioethanol
(mg g− 1
dry algae) and biomethane yield (mL g− 1
dry algae) were
estimated to 96.9 and 0.17, respectively (Table 5). The disintegration of
the algal cell wall due to the action of the crude enzyme was further
confirmed through microscopic images (Fig. 2B). It is clear from the
results that simultaneous optimization of influencing parameters is more
effective rather than optimizing one parameter at a time. With simul­
taneous optimization, it is found that effective pretreatment can be
carried out even at a low level of enzyme loads. In general, one would
seek to increase the enzyme load to maximize the biomass pretreatment.
For example, Prajapati et al. (2015a) observed that increase in enzyme
loading from 10% to 20% is required for enhanced soluble sugar yield.
Notably, the observed sugar released within 6 h of incubation under
optimal conditions in the present study are either higher or at par with
the values reported in the literature during enzymatic pretreatment of
different microalgal biomass (Table 6). Further, the optimized condi­
tions resulted in a relatively higher soluble sugar yield than the previ­
ously reported values with other non-enzymatic pretreatments. For
Table 4
Orthogonal array for L16 with five parameters and four levels experimental design and the corresponding numerical results.
Experimental run Substrate load (gL− 1
) Enzyme load (%) Temp. (◦
C) pH Shaking (rpm) Soluble sugar (mg g− 1
dry algae) sCOD (mg g− 1
dry algae)
1 2 5 30 4 60 43.87 301.35
2 2 10 40 5.5 80 172.41 598.58
3 2 15 50 7 100 21.46 796.73
4 2 20 60 8.5 120 39.15 1193.03
5 4 5 40 7 120 29.01 117.65
6 4 10 30 8.5 100 56.72 150.68
7 4 15 60 4 80 116.86 530.47
8 4 20 50 5.5 60 126.89 315.80
9 6 5 50 8.5 80 36.24 45.41
10 6 10 60 7 60 33.49 199.53
11 6 15 30 5.5 120 23.27 23.39
12 6 20 40 4 100 11.87 188.52
13 8 5 60 5.5 100 35.44 149.64
14 8 10 50 4 120 3.89 124.88
15 8 15 40 8.5 60 14.21 133.13
16 8 20 30 7 80 12.44 108.36
Fig. 1. Main effect plots for the signal to noise ratios of various parameters with respect to (a) sugar and (b) sCOD.
S. Bhushan et al.

6. Chemosphere 284 (2021) 131264
6
instance, Giang et al. (2019) reported 151.8 mg sugar g− 1
algae through
acidic pretreatment of Chlorella sp. Similarly, Möllers et al. (2014)
documented only 30% sugar solubilization from enzymatic pretreat­
ment of cyanobacterial biomass using the alpha-glucanases enzyme.
For the second set of optimal conditions for COD release, disruption
of the algal cells was observed (Fig. 2C) along with ≈60 mg g− 1
sugar
release and up to 61.36% COD (1350 mg g− 1
algae) solubilization from
algal biomass (Table 5). The corresponding theoretical bioethanol (mg
g− 1
dry algae) and biomethane yield (mL g− 1
dry algae) were estimated
to 96.9 and 0.17, respectively. The observed biomass solubilization
(even with the short incubation time of 6 h), was relatively higher than
the previously reported values for enzymatic pretreatment (Table 6) as
well as non-enzymatic pretreatment. For instance, Rincón-Pérez et al.
(2020) recently reported about 60% COD solubilization of Scenedesmus
obtusiusculus by energy-intensive thermochemical pretreatment method.
Similarly, Marques et al. (2018) performed pretreatment of Scenedesmus
sp. using carbonic acid and reported up to 41.6% COD solubilization.
The corresponding optimal conditions for maximum COD solubili­
zation and sugar release can be considered for the production of desired
biofuel i.e., biogas and bioethanol, respectively. Fig. 3 depicts the
relation between sugar release and COD solubilization. The data illus­
trates substrate load 4g L-1
, enzyme load 5%, temperature 60◦
C, pH 5.5,
and shaking speed 80 rpm is optimum for maximal sugar release (190
mg g− 1
algae). Whereas, the combination of parameters having substrate
load 2g L-1
, enzyme load 20%, temperature 60◦
C, pH of 4, and shaking
speed 100 rpm was found optimal form maximal COD solubilization
(1350 mg g− 1
dried algae). The three different circles in Fig. 3 show the
clusters of values where the tested results fall. However, there is no
linear relationship observed between sugar release and COD solubili­
zation. Cluster 2 shows that at specific conditions, sugar may get loss and
thus advocating only high COD solubilization due to the presence of
other compounds such as lipids (Angelidaki and Sanders, 2004).
Generally, a relatively higher level of substrate load and lower level of
enzyme concentration imparts substrate inhibition and affect the ac­
tivity of enzymes (Xue et al., 2015). On the contrary, the obtained
optimized conditions in the present work showed maximal sugar release
at a lower level of enzyme load and a higher level of substrate load
compared to that required for maximal COD solubilization. Addition­
ally, both the maximal sugar release and maximal COD solubilization
required an elevated level of temperature (60◦
C). Along with the tem­
perature, shaking speed and pH might be crucial parameters to cause
maximum sugar release from the biomass, as can be inferred from
parameter dominance level (as expressed in section 3.2). Moreover, as
visualized from the interaction plot (Fig. 1), one of the parameters is
influencing the effect of others. Therefore, it is indispensable to optimize
the biomass pretreatment conditions based on the desired application.
The cellular structure of algae is similar to the of higher terrestrial
Fig. 2. Differential interface contrast (DIC) and scanning electron microscope (SEM) images of untreated algal biomass (a), enzymatically pretreated algal biomass at
optimal conditions for maximum biomass solubilization (b), and optimal conditions for maximum biomass sugar release (c). The image from DIC and SEM were
captured at 40X and 15000X, respectively.
Table 5
Optimized conditions for sCOD and sugar release.
Sl.
No.
Parameters Optimal conditions
for sugar release
Optimal conditions for
COD solubilization
1. Substrate load (g L− 1
) 4 2
2. Enzyme load (%) 5 20
3. Temperature (◦
C) 60 60
4. pH 5.5 4
5. Shaking (rpm) 80 100
6. Sugar release (mg g− 1
algae)
190 60
7. COD solubilization (mg
g− 1
algae)
480 1350
8. Theoretical bioethanol
yield (mg g− 1
algae)a
96.9 30.6
9. Theoretical biomethane
yield (mL CH4 g− 1
algae) b
0.17 0.47
a
Bioethanol yield based on sugar-to-ethanol conversion factor = 0.51 (Dong
et al., 2016).
b
Biomethane yield based on COD-to-biomethane conversion factor = 0.35 L
CH4 g-1 COD (Angelidaki and Sanders, 2004).
S. Bhushan et al.

7. Chemosphere 284 (2021) 131264
7
plants. The algal-derived precursors required for biofuel production are
intact inside the intracellular matrix. It is very hard to penetrate the cell
and recover the precursors due to the cell wall. The cell wall is mainly
made up of cellulose and hemicellulose. For the effective enzymatic
digestion of the cell wall, it is advised to use more than one enzyme type
because of the structural complexity. To address this structural
complexity, the deployment of fungal crude enzymes seems promising.
The future attempts to scaleup the investigated fungal crude enzyme-
based pretreatment will substantially contribute to the commercializa­
tion of algal biofuel, including bioethanol and biogas. Usually, the
application of enzymes is restricted to high-value, low-volume products
obtained through highly selective reactions. However, due to the spec­
ificity requirement, the application of enzymes has been extended for
the production of high-volume bulk chemicals and biofuels from various
organic materials. In such circumstances, cost reduction, and therefore
process intensification becomes a major focal point of research during
the scale-up of crude enzymes. Further, process scale-up faces several
technical challenges such as, difficulty in maintaining the desired
environmental conditions, like temperature and pH, increased reaction
time required, and decreased oxygen transfer rates at elevated scale
(Tufvesson et al., 2010). Therefore, considerable attention is needed
from the point of process engineering, design, and economics.
3.4. Possible underlying mechanism of fungal crude enzyme action on
algal cells
The present study reveals that the process conditions significantly
affect the performance and activity of the fungal crude enzyme for algal
biomass pretreatment. Therefore, under one set of process conditions
(Run 2, i.e., substrate load 4 g L-1
, enzyme load 5% (v/v), temperature
60◦
C, pH 5.5, and shaking speed 80 rpm), high sugar release was
observed. Whereas, another optimized set of conditions (Run 8, i.e.,
substrate load 2 g L-1
, enzyme load 20% (v/v), temperature 60◦
C, pH
4.0, and shaking speed 100 rpm), showed maximum algal biomass sol­
ubilization as evident from the soluble COD data. The entirely different
action of the tested fungal crude enzyme on algal biomass could be
attributed to the changing behaviors of the enzyme under different
process conditions. Based on the present observations, the possible un­
derlying mechanism of the fungal crude enzyme’s action of algal
biomass is presented in Fig. 4.
It is well reported that the cell wall of algae is mainly composed of
cellulose (Passos et al., 2016). As per the observation, run 2 with high
substrate load and low enzyme dose result in the higher sugar release
from the algal cell wall. Hence, it can be postulated that under optimal
conditions (run 2), the enzyme molecules may distribute evenly
throughout the cell surface (Fig. 4). As the cellulase acts on the β-1,4
glycosidic bond between glucose molecules in the cellulose, this phe­
nomenon facilitates the partial degradation of cell wall throughout the
cell surface and subsequently sugar release, even at reduced enzyme
loading. In contrast, for another set of optimized conditions (run 8) with
low pH (4.40), the enzyme molecules may agglomerate together
(Nguyen and Yang, 2014) and attach to the cell surface in the form of
uneven aggregates (Fig. 4). Li et al. (2016) expressed a similar kind of
phenomenon by using bovine serum albumin as a model protein. The
Table 6
A comparison of present results with the some of the previous reports on enzymatic pretreatment of algal biomass.
Sl.
No.
Algae Enzyme used Pretreatment conditions Pretreatment
efficiency
References
E S T pH Ti Shaking Biomass
solubilization
Sugar
1. Chlamydomonas
reinhardtii
α-Amylase and
amyloglucosidase
0.001–0.3%
(v/w)
5 g
L–1
25–100◦
C 6.0 10–60
min
– – 94% Choi et al.
(2010)
2. Wastewater
microalgae
Commercial enzyme
mixture
1% (w/w) – 37◦
C 48 h – 680 mg VS
soluble L− 1
– Passos et al.
(2016)
3. Chlorella sp. Mixture of cellulase,
xylanase and
pectinase
0.6% w/v 0.1 g
dry
wt.
55◦
C 4.8 24 h 250
rpm
54.45%
enhancement
in lipid
extraction
– Zhang et al.
(2018a)
4. Porphyridium
cruentum
Enzyme mix 0.1, 0.3, and
0.5 mL/g dry
biomass
55◦
C 8.0–8.5 (at
first 4.5 h),
4.0–4.5
(after 4.5 h)
9 h 100
rpm
30.5% sCOD 24% Kendir et al.
(2020)
5. Chroococcus sp. Crude enzyme from
A. lentulus
20% (v/v) 2 g
L–1
60◦
C 5.0 2.5 h 150
rpm
50% sCOD 26% Prajapati
et al.
(2015b)
6. Spirulina subsalsa Mixture of Cellulase,
Hemicellulose and
protease
10% (w/w) 2.5 g
L–1
37◦
C – 24 h – 42.22% sCOD – Dar et al.
(2020)
7. Chlorella
pyrenoidosa
Crude enzyme from
A. fischeri
5% (v/v) 4 g
L–1
60◦
C 5.0 6 h 80 rpm 21.81% 76.30% Present
study
20% (v/v) 2 g
L–1
60◦
C; 4.0 6 h 100
rpm
61.36% sCOD 24.10% Present
study
Abbreviations: E = Enzyme load, S = Substrate load, T = Temperature, Ti = Incubation time, sCOD = Biomass COD solubilization and Sugar = sugar released.
Fig. 3. Representation of relation between sugar release vs sCOD at different
pretreatment conditions under three clusters. Dots in red colour ( ) showed the
maximum values of sugar release and sCOD. (For interpretation of the refer­
ences to colour in this figure legend, the reader is referred to the Web version of
this article.)
S. Bhushan et al.

8. Chemosphere 284 (2021) 131264
8
author reported an increased aggregation level for BSA as the pH of the
solution decreased from 7.0 to 3.0. The enzyme aggregates may have
promoted uneven degradation of the algal cell wall. Despite the collec­
tive action of the enzyme at target sites on the cell, the complete cell wall
disruption may trigger the release of intracellular components. This
phenomenon leads to the optimal recovery of sCOD from the pretreated
algal biomass.
It is noteworthy that the enzymatic pretreatment of algal biomass is
carried out in a heterogeneous environment, having an interface be­
tween water, protein (enzyme), and insoluble substrate (algal biomass).
Also, the interaction of the enzyme with the insoluble substrate (in
water) is drastically affected by the process conditions, including pH
(Røjel et al., 2020). Unfortunately, the actual mode of action and ki­
netics of cellulase, particularly in the heterogeneous environment hav­
ing an insoluble substrate (as in the case of algal biomass pretreatment),
is not well understood. Hence, further in-depth mechanistic studies
targeting the behavioral changes in cellulase action due to changing
process conditions are warranted for a proper understanding of the
enzymatic pretreatment of algal biomass.
4. Conclusions
The crude enzyme produced by Aspergilus fischeri grown in corn cub
was found effective for the pretreatment of Chlorella pyrenoidosa
biomass. Strategically designed Taguchi experimentation revealed
different optimal conditions for maximum sugar release and COD solu­
bilization. Substrate load was the most peculiar parameter to be
considered in enzymatic digestion. Further, the study revealed that
uniform distribution of enzyme molecules throughout the cell wall may
lead to cell breakage evenly, yielding optimal sugar release. On the other
hand, enzyme agglomeration at the cell wall may subsequently result in
whole-cell disruption, leading to maximal COD solubilization. By
applying the suitable pretreatment conditions revealed in the present
work, it will be possible to obtain improved yield in the desired biofuel
types. However, further in-depth studies are crucial for understanding
the mode of action of the fungal crude enzyme on algal biomass
pretreatment.
Fig. 4. Schematic representation of a possible mode of action of the fungal crude enzyme on algal cells under different optimal conditions; S = Substrate load, E =
Enzyme load, T = Temperature and COD = Chemical Oxygen Demand.
S. Bhushan et al.

9. Chemosphere 284 (2021) 131264
9
Credit author statement
S.B.- Conceptualization, Methodology, Investigation, Formal anal­
ysis and interpretation, Writing- Original draft; M.S.R.- Methodology,
Formal analysis and interpretation, Writing- Original draft; M.B.-
Methodology, Investigation, A.K.S.- Design of experiment, Writing–
Reviewing and Editing; H.S.- Supervision, Writing– Reviewing and
Editing, S.K.P. -Supervision, Funding acquisition, Conceptualization,
Data interpretation, Writing– Reviewing and Editing
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Acknowledgments
The present research work was financially supported by the Science
and Engineering Research Board (SERB) under the IMPRINT- IIc scheme
of the Department of Science and Technology (DST) and Ministry of
Human Resource Development (MHRD), Govt. of India (IMP/2019/
000089). Further, the authors are thankful to the Center for Writers,
North Dakota State University, USA, for the language corrections.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.chemosphere.2021.131264.
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