murthy et al 2011 starch hydrolysis modeling application to fuel ethanol production
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Starchhydrolysismodeling:ApplicationtofuelethanolproductionARTICLEinBIOPROCESSANDBIOSYSTEMSENGINEERINGAPRIL2011ImpactFactor:2DOI:10.1007/s00449-011-0539-6Source:PubMed
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GantiSuryanarayanaMurthyOregonStateUniversity51PUBLICATIONS533CITATIONS
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DavidJohnstonUnitedStatesDepartmentofAgriculture95PUBLICATIONS1,400CITATIONS
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KentRauschUniversityofIllinois,Urbana-Champaign119PUBLICATIONS1,496CITATIONS
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VijaySinghUniversityofIllinois,Urbana-Champaign183PUBLICATIONS1,939CITATIONS
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ORIGINAL PAPER
Starch hydrolysis modeling: application to fuel ethanol production
Ganti S. Murthy David B. Johnston
Kent D. Rausch M. E. Tumbleson
Vijay Singh
Received: 27 November 2010 / Accepted: 22 March 2011 / Published online: 13 April 2011
Springer-Verlag 2011
Abstract Efficiency of the starch hydrolysis in the dry
grind corn process is a determining factor for overall
conversion of starch to ethanol. A model, based on a
molecular approach, was developed to simulate structure
and hydrolysis of starch. Starch structure was modeled
based on a cluster model of amylopectin. Enzymatic
hydrolysis of amylose and amylopectin was modeled using
a Monte Carlo simulation method. The model included the
effects of process variables such as temperature, pH,
enzyme activity and enzyme dose. Pure starches from wet
milled waxy and high-amylose corn hybrids and ground
yellow dent corn were hydrolyzed to validate the model.
Standard deviations in the model predictions for glucose
concentration and DE values after saccharification were
less than 0.15% (w/v) and 0.35%, respectively. Cor-
relation coefficients for model predictions and experimen-
tal values were 0.60 and 0.91 for liquefaction and 0.84 and
0.71 for saccharification of amylose and amylopectin,
respectively. Model predictions for glucose (R2 =
0.690.79) and DP4? (R2 = 0.80.68) were more accurate
than the maltotriose and maltose for hydrolysis of high-
amylose and waxy corn starch. For yellow dent corn,
simulation predictions for glucose were accurate
(R2 [ 0.73) indicating that the model can be used to predictthe glucose concentrations during starch hydrolysis.
Keywords Starch hydrolysis Amylose Amylopectin Liquefaction Saccharification Monte Carlo simulation
List of symbols
Aaa,max Maximum activity of a-amylaseAaa,std Activity of the a-amylase under standard
conditions
Aaa Activity of the a-amylase under operatingconditions
Aavg,dp Average degree of polymerization of amylose
AMW Average molecular weight of molecules in
simulated mash
APavg,dp Average degree of polymerization of
amylopectin
CDP4 Concentration of DP4? (% db) in corn mash
Ceffect Starch composition effect on the enzyme
activities
CGlucose Concentration of glucose (% db) in corn mash
CMaltose Concentration of maltose (% db) in corn mash
CMaltotriose Concentration of maltotriose (% db) in corn
mash
DE Dextrose equivalent of mash (%)
DPsimulated Total number of glucose molecules in
simulated mash
DP4total? Total number of DP4? molecules in
simulated mash
Mention of brand or firm names does not constitute an endorsement
by University of Illinois, Oregon State University or USDA above
others of similar nature not mentioned.
Present Address:G. S. Murthy
Biological and Ecological Engineering, Oregon State University,
122 Gilmore Hall, Corvallis, OR 97331, USA
e-mail: [email protected]
G. S. Murthy K. D. Rausch M. E. Tumbleson V. Singh (&)Department of Agricultural and Biological Engineering,
University of Illinois, 360G AESB, 1304 West Pennsylvania
Avenue, Urbana, IL 61801, USA
e-mail: [email protected]
D. B. Johnston
Eastern Regional Research Center, ARS, USDA, Wyndmoor, PA
19038, USA
123
Bioprocess Biosyst Eng (2011) 34:879890
DOI 10.1007/s00449-011-0539-6
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Em Amount of enzyme (mL) added to the corn
mash
GA Concentration of glucoamylase (g/L)
GAactivity Activity of glucoamylase at given pH and
temperature
Gtotal Total number of glucose molecules in
simulated mash
G Concentration of glucose (g/L)
GMW Molecular weight of glucose (g/mol)
GP Concentration of maltodextrins (g/L)
Mtotal Total number of maltose molecules in
simulated mash
MTtotal Total number of maltotriose molecules in
simulated mash
MW Molecular weight
Nh Number of bonds hydrolyzed by Em (mL) of
enzyme per sec
Na Number of amylose molecules in mash
Nap Number of amylopectin molecules in mash
NhA Number of bonds hydrolyzed per amylose
molecule
NhAP Number of bonds hydrolyzed per amylopectin
molecule
pH Mash/fermenter pH
pHeffect Effect of pH on enzyme activity
pHstability Effect of pH on enzyme stability
PIeffect Effect of product inhibition on enzyme
activity
R2 Coefficient of determination
Rm Mass ratio of amylose to amylopectin in starch
RN Number ratio of amylose to amylopectin in
starch
RNA Relative number of amylose molecules in
starch
RNAP Relative number of amylopectin molecules in
starch
S Solids concentration (wet basis) in corn mash
Starchdp Total degree of polymerization of all starch
molecules in mash
tsim Simulation time (min)
Teffect Effect of temperature on enzyme activity
Tstability Effect of temperature on enzyme stability
T Mash/fermenter temperature (C)Wmash Total weight of the mash (g)
Xstarch Starch content (%) of the corn
Introduction
Cereal grains, such as rice, wheat, corn and millet, contain
more than 60% starch, a polymer of glucose, as an energy
reserve for seedling growth. Starch is the chief energy
source for many higher plants and animals. It is a valuable
raw material used in food processing, paper manufacture
and fuel ethanol industries. It consists of two distinct
polymer types, amylose and amylopectin. Amylose is a
linear polymer of glucose. On average, amylose (MW =
160,000) consists of 50020,000 glucose units joined by
a(14) glycosidic bonds. Amylopectin is a branchedpolymer of glucose, which in addition to a(14) glycosidicbonds has branches (side chains) connected by a(16)glycosidic bonds. Amylopectin (MW = 32,400,000) has a
degree of polymerization (DP) of 200,000 glucose units.
Degree of branching and branch chain length are dependent
on the biological source of amylopectin [1, 2]. Proportion
of amylose to amylopectin also is dependent on starch
source. Yellow dent corn consists of 30% amylose and
70% amylopectin by weight [3]. Waxy corn hybrids have
99% amylopectin, while high-amylose corn hybrids have
30% amylopectin.
There are many naturally occurring enzymes to depo-
lymerize starch into glucose. Endoenzyme a-amylasehydrolyzes a(14) glycosidic bonds while glucoamylase isan exoenzyme that hydrolyzes both a(14) glycosidicbonds and a(16) glycosidic bonds. However, the hydro-lysis rate of each type of bonds is dependent on individual
enzymes. For example, glucoamylase hydrolyzes a(14)glycosidic bonds about 20 times faster than a(16) glyco-sidic bonds. Further, temperature, solution pH, starch
granule structure and starch granule chemical composition
affect enzymatic starch hydrolysis.
Enzymatic hydrolysis is the most common and impor-
tant step for recovery of glucose from starch. Corn is used
as a starch source in the dry grind corn industry to obtain
glucose in a two-step process. Liquefaction is the process
in which starch is hydrolyzed into shorter chain dextrins
(&1000 glucose units) by the action of a-amylase. Partialstarch hydrolysis results in reduced mash viscosity result-
ing in a liquefied mash. Reduction of viscosity is an
important consideration for agitation and pumping mash
for downstream processing. Saccharification is the process
in which maltodextrins are hydrolyzed by glucoamylase to
produce glucose and small amounts of di/trisaccharides.
Glucose produced from starch hydrolysis is fermented by
yeast to obtain ethanol. Understanding liquefaction and
saccharification steps is important; their efficiency partially
determines final ethanol concentration at the end of fer-
mentation and overall production efficiency of the fuel
ethanol process.
There are three main approaches for modeling the liq-
uefaction process. The first approach involves empirical
modeling of sugar concentrations by curve fitting to
experimental data. This approach has been adopted by
Paulocci-Jeanjean et al. [4]. Simplicity of these models is
880 Bioprocess Biosyst Eng (2011) 34:879890
123
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an advantage; limited validity to the range to the calibra-
tion data set is their major disadvantage. These models
cannot be used to explain the complex interactions during
starch hydrolysis.
In the second approach, hydrolysis process is described
using ordinary differential equations (ODE). Rate expres-
sions are described using expressions for enzyme kinetics
with/without temperature, pH, substrate and product inhi-
bition effects. While a more general set of conditions can
be simulated using this approach, the chief difficulty is in
determining parameters for enzymatic reaction kinetics.
While incorporating more parameters can help in achieving
better fit to experimental data, this could lead to over
parametrization problems [5] and a clear physical signifi-
cance of the parameters could be lost. Additionally, com-
putational effort in solving ODE increases exponentially as
one additional ODE is added to equation set for each
oligomer.
The third approach relies on modeling the enzymatic
hydrolysis process at molecular and enzymatic levels. This
modeling approach requires description of starch molecular
structure and simulation of enzymatic hydrolysis of starch
molecules. While this approach is computationally inten-
sive, it is more realistic and can incorporate changes in
starch composition (e.g., amylose:amylopectin ratio) and/
or variable process conditions. As important advantage of
this approach is that the computational complexity does not
increase with number of oligomers considered. Further,
enzyme change or addition of new types of enzymes can be
incorporated without need for extensive experimentation
for all types of substrates. This approach has been used by
several researchers [610]. All these researchers have
modeled liquefaction (a-amylolysis) process only. Due toits complexity, the saccharification process has not been
modeled using a molecular approach to date. The objec-
tives of this study were to
1. Model the structure of amylose and amylopectin
molecules.
2. Simulate a-amylase action on amylose and amylopec-tin (liquefaction) and glucoamylase action on dextrins
(saccharification).
Model formulation
Liquefaction and saccharification processes are affected by
corn starch content, amylose:amylopectin ratio, amylo-
pectin structure and activities of a-amylase and glucoam-ylase enzymes. Hence, a theoretical model for liquefaction
and saccharification processes was formulated in five
principal steps:
1. Starch characterization.
2. Modeling amylose and amylopectin molecules.
3. Characterization of a-amylase and glucoamylaseactivities.
4. Modeling a-amylase action on amylose andamylopectin.
5. Modeling glucoamylase action on amylose and
amylopectin.
Starch characterization
Amylose and amylopectin content of starch have an effect
on starch hydrolysis [11]. Hence, starch was characterized
by defining the amylose:amylopectin mass ratio. For
modeling purposes, a mass ratio of amylose:amylopectin
was defined as
Rm Mass of amyloseMass of amylopectin
: 1
Waxy corn hybrids (C99% amylopectin) have Rm B 0.01
while high-amylose corn hybrids have Rm & 2.33. Starchfrom yellow dent corn (70% amylopectin and 30%
amylose) has Rm & 0.429. The mass ratio of amylose:amylopectin was used to determine the relative number
fraction of molecules by defining amylose:amylopectin
number ratio as
RN Number of amylose moleculesNumber of amylopectin molecules
Rm APavg;dp=Aavg;dp
: 2Relative number fraction of amylose molecules to be
simulated, RNA:
RNA RN1RN
Number of amylose moleculesNumber of (amylose + amylopectin) molecules
:
3Relative number fraction of amylopectin molecules to be
simulated, RNAP:
RNAP 11RN
Number of amylopectin moleculesNumber of (amylose + amylopectin) molecules
:
4Total degree of polymerization (DP) of the starch can be
calculated as
Starchdp Wmash S Xstarch100
6:023 1023162
!: 5
Bioprocess Biosyst Eng (2011) 34:879890 881
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From this information, number of amylose and amylopectin
molecules in the mash can be determined as
Na RNAStarchdpAavg;dp
Nap RNAPStarchdpAPavg;dp
:
6
Modeling amylose and amylopectin molecules
Structure of amylose and amylopectin is dependent on
biological origin of starch [1, 12]. Amylose and amylo-
pectin molecules were modeled using a knowledge of the
structure of these molecules obtained from corn starch [8].
Amylose was modeled as a linear molecule of varying DP.
A minimum DP of 390 was considered and a number (0 to
12,710) of glucose units was added randomly to reflect the
DP range of amylose (39013,100) [1].
The cluster model of amylopectin [12] was used in the
modeling of amylopectin. This model has been supported
by other researchers [2, 1315]. In this model, glucose
chains in amylopectin molecule are organized in clusters.
Clusters are defined in terms of their width. A cluster width
of n implies that on average there are n intermediate
branches between the central chain and end chains. A
cluster width of four is assumed for most cereal starches
[12]. Chain lengths and their distribution are dependent on
starch biological source. Chain length distribution for corn
amylopectin were obtained from Bertoft [16].
In addition to the chain length size distribution, amy-
lopectin fine structure is also determined by additional
constraints on structure [15]. Some of the constraints
imposed on the branch locations are (1) no branches in
consecutive locations and (2) no linear chain connected by
an a(16) glycosidic bond.
Characterization of a-amylase and glucoamylase
The third step in modeling is characterization of the
a-amylase and glucoamylase enzymes. Factors affectingenzyme action on substrate can be classified as intrinsic
and extrinsic factors. Intrinsic factors are enzyme charac-
teristics that are not dependent on substrate, while extrinsic
factors depend solely on substrate characteristics.
Intrinsic characteristics such as activity and stability at
different pH and temperatures were obtained from litera-
ture [17]. Enzyme activities are defined according to
varying standards adopted by enzyme manufacturers/
suppliers. In particular, enzyme activity unit for a-amylase(a-amylase solution Bacillus licheniformis, type XII-Asaline solution 5001,000 units/mg protein, 1,4-a-D-glucanglucanohydrolase, 9000-85-5, SigmaAldrich, St. Louis,
MO) is defined as the amount of enzyme solution that will
liberate 1.0 mg maltose from starch in 3 min at pH 6.9 at
20 C. The enzyme activity was 21,390 units/mL. The num-ber of a(14) glycosidic bonds hydrolyzed by enzyme permolecule of amylose and amylopectin can be determined as
follows.
Number of maltose molecules (MW = 342) liberated by
one unit of enzyme solution in 3 min under standard
conditions is
Nh;std 6:023 1023molecules=mole 1:0 mg=3 min342 g/mole 1000 mg/g
:
Bonds hydrolyzed per sec per unit of enzyme
) Nh;std 9:7839 1015
:
Bonds hydrolyzed by Em activity units of enzyme at
specific operating conditions can be determined as
Nh Em AaaAaa;std
9:7839 1015: 7
Some of the important extrinsic factors that influence
enzyme activity are starch gelatinization, starch composition
as defined Rm and product inhibition. Dependence of
hydrolysis on starch composition is due to differential
effects of hydrolytic enzymes on amylose and amylopectin.
Effect of amylose and amylopectin content on starch
hydrolysis was modeled based on the experimental data
from Wu et al. [11] as
Ceffect 91:77 2:845Rm2 6:2619Rm: 8Starch gelatinization determines the accessibility of
starch to enzyme action. Conclusion gelatinization
temperature is a function of starch composition. Extent of
gelatinization is a function of conclusion gelatinization
temperature, temperature and pressure [18]. Based on the
experimental data of Buckow et al. [18], conclusion
gelatinization temperature and gelatinization (%) was
modeled as
Tgelatinization 120Rm 801Rm
Tratio TTgelatinization
Geleffect 1:01 e150:697T2ratio337:307Tratio261:79370:374T1ratio :
9Product inhibition is an important effect in enzymatic
hydrolysis of starch. The product inhibition effect is
the result of competitive binding of the product with the
enzyme resulting in the reduced apparent activity of the
enzyme. This effect is proportional to the relative number
of product molecules at any instant in the hydrolysis. The
effect is also dependent on the duration of hydrolysis.
882 Bioprocess Biosyst Eng (2011) 34:879890
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It was assumed that the inhibition effect of glucose,
maltose and maltotriose would be identical. A similar
approach considering the hydrolysis time also leads to a
similar exponential function [10].
DPMol:Ratio
No:of DP4 molecules
No. of (DP4MaltotrioseMaltoseGlucose) molecules
PIeffecte0:001tsim
DPMol:Ratio
:
10Enzyme activity at a particular process condition (pH
and temperature) was determined by interpolating values
from Ivanova et al. [17]. The regression equations obtained
from the data presented in Ivanova et al. [17] are
Aaa;std Aaa;max TeffectTstd pHeffectpHstdAaa Aaa;max Teffect pHeffect Ceffect Geleffect PIeffect: 11
Where
T [ 87 C Teffect 2:857 log T 13:903T\87 C Teffect 0:0603 log T3 0:603 log T2 1:611 log T 1:533
T [ 73 C Tstability 0:010:149 T2 27:561 T 1305:1T\73 C Tstability 1:00:
12pH\5:17 pHeffect 0:035 pH2 0:041 pH 0:2915:17\pH\5:78 pHeffect 1:00pH [ 5:78 pHeffect 0:021 pH4 0:614 pH3 6:619 pH2 31:061 pH 52:493
pH\5:31 pHstability 0:012:174 pH2 29:08 pH 4:937
5:31\pH\7:69 pHstability 1:00pH [ 7:69 pHstability 0:0113:658 pH 203:77:
13The number of bonds hydrolyzed per amylose molecule
NhA Nh NaNa Nap
NhRN
1 RN : 14
The number of bonds hydrolyzed per amylopectin
molecule:
NhAP Nh NapNa Nap
Nh
1 RN : 15
Glucoamylase characteristics were obtained from data
sheets published by the manufacturer (Distillase 400L,
Genencor, Palo Alto, CA). One Novo amyloglucosidase
(AMG) unit was defined as the amount of enzyme that
releases one micromole maltose/min under standard
conditions. Similar analyses were performed (Eqs. 7 to
15) to obtain number of bonds hydrolyzed by glucoamylase
for each amylose and amylopectin molecule. Effect of
amylose and amylopectin content on starch hydrolysis was
incorporated into the model based on Wu et al. [11] as
described earlier (Eq. 8). Corresponding equations (Eq. 11)
for glucoamylase are
Aga;std Aga;max TeffectTstd pHeffectpHstdAga Aga;max Teffect pHeffect Ceffect Geleffect PIeffect: 16
Where: characteristics of glucoamylase from Sigma
Aldrich (St. Louis, MO) in the operating range of
T \ 67 C and pH \4.8 were obtained from data sheetspublished by the manufacturer.
Teffect 4:49 104 T3 0:066 T2 1:384 T 31:698Tstability 100 if T\65 C: 17pH\3:7
pHeffect 16:284 pH2 113:848 pH 99:305pH [ 3:7
pHeffect 0:874 pH3 9:709 pH2 2:734 pH 203:088pH\4:84
pHstability 6:920 pH3 94:651 pH2 444:991 pH 619:222: 18Characteristics of glucoamylase from Genencor (Palo
Alto, CA) in the operating range of T \ 62 C andpH \ 6.5 were obtained from data sheets published by themanufacturer (Distillase 400L, Genencor, Palo Alto, CA).
Teffect 3 104 T3 0:0307 T2 2:2349 T 41:186;19
pHeffect 0:78514 pH4 14:074 pH3 80:405 pH2 167:01 pH 179:18: 20
Modeling a-amylase action: liquefaction
In the final step in modeling the liquefaction process a
Monte Carlo simulation method [8] was used for enzymatic
hydrolysis of amylose and amylopectin. Hydrolysis was
performed on each molecule. Briefly, in the Monte Carlo
method, a sequence of random numbers, less than or equal
to DP of the starch molecule, with uniform probability
distribution was generated. Since a-amylase is a endoen-zyme and hydrolyzes the starch molecules at random
locations, these random numbers indicate possible
Bioprocess Biosyst Eng (2011) 34:879890 883
123
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locations for hydrolysis by the enzymes. Bonds at the
locations indicated by generated random numbers were
hydrolyzed depending on the rules for enzymatic action,
namely (1) glucose units that had associated branch chains
could not be hydrolyzed, (2) a branch location to be
hydrolyzed should be at least two units away from the end
of the chain and (3) a bond could not be broken twice. At
the end of simulation of all amylose and amylopectin
molecules, dextrose equivalent (DE) and average molecu-
lar weight (AMW) were calculated as
DE
100180 No: bonds hydrolyzed1 162Total DP18 No. bonds hydrolyzed1
%;
21
AMW 162 Total DPNo. bonds hydrolyzed
18
g/mol: 22
Inputs to the model were weight of mash, mash moisture
content, starch content of solids, number of amylose and
amylopectin molecules to be simulated, enzyme present
(activity units), time of simulation and temperature and pH
profiles for the simulation time. The model output included
detailed structure of maltodextrin molecules, concentration
of sugars such as glucose, maltose and maltotriose and
mash DE.
Modeling glucoamylase action: saccharification
Maltodextrin profiles obtained from liquefaction simulation
were used as an input for saccharification simulation. A
nonreducing end was selected randomly from available
nonreducing ends of maltodextrin molecules. Bonds were
hydrolyzed depending on the rules for glucoamylase action
[19] defined as (1) a glucose unit that had a branch chain
associated with it had 20 times lower probability to be
hydrolyzed and (2) for molecules with DP B 5, probability
of hydrolysis decreased with decrease in DP of the mole-
cule. At the end of simulation, DE and AMW were
calculated using Eqs. 21 and 22. From the model, we
predicted sugar concentration profiles at various sacchari-
fication times for glucose, maltose, maltotriose and DP4?
molecules in the mash. Concentrations of various sugars
were calculated based on sugars produced during hydro-
lysis as follows:
CGlucose 100 Gtotal StarchdpDPsimulated 6:023 1023
180Wmash 1 S
; 23
CMaltose 100 Mtotal StarchdpDPsimulated 6:023 1023
342
Wmash 1 S
;
24
CMaltotriose 100 MTtotal StarchdpDPsimulated 6:023 1023
522Wmash 1 S
;
25
CDP4 100 DP4total Starchdp
DPsimulated 6:023 1023
162
Wmash 1 S
:
26
Model implementation
The computer algorithms implementing the five-step
modeling process were written in C?? language. Starch
content of the ground corn was obtained using Fourier
Transform Near Infra Red (FT-NIR) analysis [20] and the
amylose:amylopectin ratio was assumed as 0.429 for yel-
low dent corn. The a-amylase and glucoamylase activitieswere obtained from manufacturers specifications. Enzyme
activity dependence on pH and temperature was obtained
from data sheets published by the manufacturer.
Simulations were performed for the conditions used in
model validation experiments described below and the user
defined characteristics of starch or ground corn and
enzymes. Random number generators were used in simu-
lation of starch molecule structure. Similarly, the Monte
Carlo method used randomization to simulate enzymatic
hydrolysis; hence, there was a variation in predictions
among multiple simulations performed with the same ini-
tial parameters. Precision of simulation was evaluated by
performing repeated simulations with the same initial
parameters and calculating standard deviations of model
predictions. High precision, indicated by low standard
deviation from repeated model runs was a requirement for
a reliable model.
Model validation
Model was validated using two data sets: first validation
data set describing hydrolysis of pure amylose, amylo-
pectin and corn starches was obtained from literature [21];
for obtaining the second validation data set, experiments
were performed using pure waxy and high-amylose corn
starch and ordinary whole corn flour at three different
enzyme dosages using industrially relevant experimental
conditions.
884 Bioprocess Biosyst Eng (2011) 34:879890
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Validation data set 1: starch liquefaction
Inglett [21] conducted experiments to describe the action
pattern of a-amylase from Bacillus licheniformis on ordinary,waxy and high-amylose corn starches. Action pattern of
amylase was inferred based on the oligosaccharide composi-
tions measured using High-Pressure Liquid Chromatography
(HPLC) methods. Using the model described above, simula-
tions were performed for the exact experimental conditions
used in [21]. The results from the simulations were compared
with the experimental data reported in the paper. The data
reported in this paper were only for starch liquefaction. The
experiments were performed on pre-gelatinized starch; hence
gelatinization effects were ignored when simulating this
experiment. However, gelatinization is not always complete
during starch hydrolysis, therefore there was a need for
additional data to validate the model. Saccharification
experiments were also not performed in this paper. Hence, an
additional set of experiments was conducted to validate the
model for both liquefaction and saccharification.
Validation data set 2: liquefaction and saccharification
Two sets of experiments were performed to obtain second
data set to validate the liquefaction and saccharification
models. Pure starches from wet milled waxy and high-
amylose corn hybrids were liquefied followed by sacchar-
ification. Yellow dent corn was liquefied and saccharified
using three combinations of a-amylase and glucoamylaseconcentrations. Combinations of temperatures and enzyme
activity units were chosen to reflect the range of conditions
encountered in industry.
The a-amylase (a-amylase solution Bacillus licheni-formis, type XII-A saline solution 5001000 units/mg
protein, 1,4-a-D-glucan-glucanohydrolase, 9000-85-5,SigmaAldrich, St. Louis, MO) and glucoamylase (amylo-
glucosidase from Aspergillus niger, glucoamylase, 1,4-a-D-glucan glucohydrolase, exo-1,4-a-glucosidase, 9032-08-0,SigmaAldrich, St. Louis, MO) with activities of 21,390 and
300 units/mL, respectively, were used for liquefaction and
saccharification, respectively, in the first experiment. The
a-amylase (SpezymeFredr, Genencor, Palo Alto, CA) andglucoamylase (Distillase 400L, Genencor, Palo Alto, CA)
with activities of 21,390 and 315 units/mL, respectively,
were used for liquefaction and saccharification, respectively,
in the second experiment.
Experiment 1: starch liquefaction and saccharification
Starches from waxy and high-amylose corn hybrids were
obtained from a 1-kg laboratory wet milling process [22].
Starch samples (50 g db) were mixed with tap water to
obtain 20% solid slurry. Starch slurry was liquefied using
0.56% (w/w) a-amylase at 90 C for 90 min. Liquefactionwas performed in a rotating air bath (Mathis Labomat,
Werner Mathis AG, Zurich, Switzerland) under a con-
trolled temperature of 90 C for 90 min. Samples (9 mL)were drawn at 0, 15, 30, 60 and 90 min. Addition of 1.25
mL 0.5 M NaOH to each sample inactivated a-amylase.Starch slurry after liquefaction was cooled to 60 C, wasadjusted to 4.0 pH using 1 NH2SO4 and 0.2% (w/w) glu-
coamylase was added. Saccharification was performed at
60 C for 120 min in the same rotating air bath. Samples(9 mL) were drawn at 0, 15, 30, 60, 90 and 120 min.
Addition of 1.25 mL of 0.5 M NaOH to each sample
inactivated glucoamylase. Sugar concentrations were
determined using an HPLC method described below.
Experiment 2: whole corn liquefaction and saccharification
Yellow dent corn grown during the 2005 crop season at the
Agricultural and Biological Engineering Research Farm,
University of Illinois at Urbana-Champaign was used. Corn
was hand cleaned and moisture content was determined
using a standard two stage convection oven method [23].
Corn was ground in a cross beater mill (model MHM4,
Glen Mills Inc., Clifton, NJ). Ground corn samples (75 g
db) were mixed with tap water to obtain 25% solid slurry.
Three a-amylase levels (Table 1) were added and sampleswere liquified in a rotating air bath (Mathis Labomat,
Werner Mathis AG, Zurich, Switzerland) under a con-
trolled temperature of 90 C for 90 min. Samples (9 mL)were drawn at 0, 15, 30, 60 and 90 min. Addition of 1.25
mL 0.5 M NaOH to each sample inactivated a-amylase.After liquefaction, slurry was cooled to 30 C and wasadjusted to 4.0 pH using 1 N H2SO4. Three corresponding
levels of glucoamylase (Table 1) were added to the slurry
samples. Saccharification was performed in the rotating air
bath for 18 h. Samples (9 mL) were drawn at 0, 6 and 18 h.
Addition of 1.25 mL 0.5 M NaOH to each sample inacti-
vated glucoamylase. Sugar concentrations were determined
using an HPLC method described below.
HPLC analyses
Samples (2 mL) drawn from fermentation vessels were
centrifuged (model 5415 D, Brinkmann-Eppendorf,
Hamburg, Germany) at 16,110 9 g for 5 min to obtain
Table 1 The a-amylase and glucoamylase dosages
Enzyme Treatment (mL/100 g corn)
Low Medium High
a-Amylase 0.093 0.186 0.280
Glucoamylase 0.093 0.200 0.330
Bioprocess Biosyst Eng (2011) 34:879890 885
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supernatant which was filtered through a 0.2 lm filter.Filtered supernatant liquid (5 lL) was injected into an ionexclusion column (Aminex HPX-87H, Bio-Rad, Hercules,
CA) maintained at 50 C. Sugars (glucose, fructose,maltose and maltotriose), organic acids (lactic, succinic
and acetic) and alcohols (ethanol, methanol and glycerol)
were eluted from the column with HPLC-grade water
containing 5 mM H2SO4. Separated components were
detected with a refractive index detector (model 2414,
Waters Corporation, Milford, MA). The elution rate was
0.6 mL/min; a calibration standard (DP4?, 0.44% w/v;
maltotriose, 0.5% w/v; maltose, 2% w/v; glucose, 2% w/v;
fructose, 1% w/v; succinic acid, 0.5% w/v; lactic acid, 1%
w/v; glycerol, 2% w/v; acetic acid, 0.5% v/v; methanol, 1%
v/v and ethanol, 20% v/v) was used to calibrate the HPLC
prior to each set of samples. Calibration standards were used
as unknown secondary standards to check the consistency of
the HPLC measurements. Data were processed using HPLC
software (version 3.01, Waters, Milford, MA). Three repli-
cate liquefactions and saccharifications were conducted for
high-amylose and waxy corn starches. Data were analyzed
using a mean of two values from HPLC analyses.
Results and discussion
Amylopectin structure simulation
Chain length distribution (CLD) for amylopectins from
different biological sources will be different [16]. Based on
the experimentally determined CLD for amylopectin, the
CLD for the simulated amylopectin can be specified in the
model. Using the CLD for corn amylopectin [16], amylo-
pectin molecules were simulated. Average CLD for the
simulated amylopectin was similar to the experimental
CLD for corn amylopectin Fig. 1. Since the structure of
amylopectin is generated using experimental data [16], the
structure is guaranteed to be statistically similar to the
amylopectin structure of starch. For any Monte Carol
simulation-based model, there is a small variation in results
even from consecutive runs with same set of input data.
However, for the results to be acceptable, it is important
that the standard deviation of the model simulations be less
than the sensitivity of the experimental method used to
validate the model. The standard deviations in the model
predictions for DP4?, maltotriose, maltose, glucose con-
centrations and DE values after hydrolysis (liquefaction
and saccharification) were\0.08%,\0.65%,\0.67%,\0.15% and\0.09%, respectively. This level of precisionin the model was acceptable as the HPLC method used
to determine the sugar concentrations has a sensitivity
of 0.3 g/L (&3%) for maltotriose, maltose and glucoseconcentrations.
Validation data set 1: starch liquefaction
Model predictions for pregelatinized ordinary, waxy and
high-amylose corn starches (Figs. 2, 3, 4) agree with the
experimental trends observed by Inglett [21]. Since the
starches were pregelatinized in the experiments, no effects of
gelatinization were considered in the model simulations.
Final glucose, maltose and maltotriose values were in close
qualitative agreement with the experimental values for all
three types of starches. Since the number of data points in this
study was not adequate for validation of the current model,
only qualitative agreement can be noted. The reason for
using this data set was to compare the model predictions to a
completely different set of data not generated by us. For
performing a more quantitative validation of the model
predictions, additional experiments 1 and 2 were performed.
Fig. 1 Corn amylopectin chain length distribution
0 50 100 150 2000
10
20
30
40
50
60
70
80
90
100
Time (min)
Com
posit
ion
by W
eigh
t (%
w/w)
DP4+
DP4+ (Inglett, 1987)MaltotrioseMaltoriose (Inglett, 1987)MaltoseMaltose (Inglett, 1987)GlucoseGlucose (Inglett, 1987)
Fig. 2 Liquefaction of high-amylose starch: predictions andexperiments
886 Bioprocess Biosyst Eng (2011) 34:879890
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Validation data set 2: liquefaction and saccharification
Experiment 1: starch liquefaction and saccharification
The first set of experiments were conducted to study the
hydrolysis characteristics of waxy and high-amylose star-
ches in the absence of interference from corn proteins,
lipids and other constituents of corn kernel. In addition, the
starches were not pregelatinized so as to study the effect of
gelatinization temperature on the hydrolysis characteristics.
Model predictions of glucose, maltose, maltotriose and
DP4? sugars are shown in Figs. 5 and 6. Agreement
between the model predictions and experimental values
were measured by coefficient of determination (R2). Values
of R2 = 1 indicates a perfect prediction of the experimental
results using the model, while negative values indicate
deviation of the model predictions from experimental
values. The model prediction for glucose concentrations at
the end of liquefaction was 0.59 0.0017 and
1.57 0.014% (w/v) for high-amylose and waxy corn
starch, respectively. The values of R2 for DP4?, maltotri-
ose, maltose and glucose were 0.68, -1.8, -0.42 and 0.79,
respectively, for high-amylose starch. Similarly, the R2 for
DP4?, maltotriose, maltose and glucose was 0.58, -1.62,
0.26 and 0.69, respectively, for waxy corn starch. While
this indicates that the model is effective in predicting the
DP4? and glucose concentrations, the negative values of
R2 indicate that the model predictions for maltose and
0 50 100 150 2000
10
20
30
40
50
60
70
80
90
100
Time (min)
Com
posit
ion
by W
eigh
t (%
w/w)
DP4+
DP4+ (Inglett, 1987)MaltotrioseMaltoriose (Inglett, 1987)MaltoseMaltose (Inglett, 1987)GlucoseGlucose (Inglett, 1987)
Fig. 3 Liquefaction of waxy (amylopectin) starch: predictions andexperiments
0 50 100 150 2000
10
20
30
40
50
60
70
80
90
100
Time (min)
Com
posit
ion
by W
eigh
t (%
w/w)
DP4+
DP4+ (Inglett, 1987)MaltotrioseMaltoriose (Inglett, 1987)MaltoseMaltose (Inglett, 1987)GlucoseGlucose (Inglett, 1987)
Fig. 4 Liquefaction of ordinary corn starch: predictions andexperiments
0 50 100 150 200 2500
5
10
15
20
25
Time (min)
Com
posit
ion
by W
eigh
t (%
w/w)
DP4+
DP4+ (Experiment)MaltotrioseMaltoriose (Experiment)MaltoseMaltose (Experiment)GlucoseGlucose (Experiment)
Saccharification
Fig. 5 Hydrolysis of high-amylose starch: predictions andexperiments
0 50 100 150 200 2500
5
10
15
20
25
Time (min)
Com
posit
ion
by W
eigh
t (%
w/w)
DP4+
DP4+ (Experiment)MaltotrioseMaltoriose (Experiment)MaltoseMaltose (Experiment)GlucoseGlucose (Experiment)
Saccharification
Fig. 6 Hydrolysis of waxy (amylopectin) starch: predictions andexperiments
Bioprocess Biosyst Eng (2011) 34:879890 887
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maltotriose have larger deviations from experimental val-
ues. As sugar DP decreases, variation between model
predictions and experimental values decreased. Similar
difficulties in predicting the maltose and maltotriose were
also observed by other researchers [9, 10]. These deviations
could be due to the differences in the action pattern of the
a-amylase enzyme [24]. The variation in the action patternscan be attributed to the strain differences in the enzyme-
producing microbes [25]. One of the additional reasons for
discrepancy in model results could be formation of lipid
complexes by amylose molecules that are reported to
inhibit liquefaction and saccharification enzymes [1].
Model predictions for the saccharification process also
follow the same overall trends observed for liquefaction.
Deviations from experimental data were larger for high-
amylose starches, which could be due to formation of lipid
complexes by amylose molecules [1].
Experiment 2: whole corn liquefaction and saccharification
Simulations for whole corn at various enzyme levels cor-
responded well to experimentally determined values of
glucose for all levels of a-amylase (Figs. 7, 8, 9). Thevalues of R2 for DP4?, maltotriose, maltose and glucose
were -0.513, -0.618, -0.322 and 0.966, respectively, for
whole corn starch with low enzyme dosage. The R2 for
DP4?, maltotriose, maltose and glucose was 0.589, -0.58,
-0.512 and 0.89, respectively, for whole corn starch with
medium enzyme dosage. Similarly, the R2 for DP4?,
maltotriose, maltose and glucose was 0.75, 0.32, -0.529
and 0.73, respectively, for whole corn starch with high
enzyme dosage. It is observed that the model prediction
accuracy decreases with increasing enzyme levels. In
addition to product inhibition [26, 27] which was accoun-
ted in the model, inhibition effects of other components in
mash, such as proteins, free amino acids and lipids, could
have resulted in the observed lower activity levels of glu-
coamylase. Accurate prediction of glucose, the primary
fermentable sugar, is more important compared with non-
fermentable sugars such as maltotriose and DP4?. The
model predictions for glucose are accurate (R2 = 0.73-
0.966) at the end of saccharification process (30 h) for all
the enzyme levels. Agreement between model predictions
and experimental data, as indicated by high R2 [ 0.73 forglucose, validates the use of model estimates for lique-
faction and saccharification processes. Further, the quali-
tative trends of predictions follow the experimentally
observed values and thus validate the model. Dextrose
equivalent (DE) is an industrially relevant measure of
progress of starch hydrolysis. The model DE predictions
for all three levels of enzyme dosages are shown in Fig. 10.
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 230
1
2
3
4
5
6
7
8
9
10
Time (hr)
Conc
entra
tion
(% w
/v)
MaltotrioseMaltoriose (Experiment)MaltoseMaltose (Experiment)GlucoseGlucose (Experiment)
Saccharification
Fig. 7 Liquefaction of whole corn (low enzyme: 0.093 mL/100 gcorn): predictions and experiments
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 220
5
10
15
Time (hr)
Conc
entra
tion
(% w
/v)
MaltotrioseMaltoriose (Experiment)MaltoseMaltose (Experiment)GlucoseGlucose (Experiment)
Saccharification
Fig. 8 Liquefaction of whole corn (medium enzyme: 0.186 mL/100 g corn): predictions and experiments
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 220
2
4
6
8
10
12
14
16
Time (hr)
Conc
entra
tion
(% w
/v) MaltotrioseMaltoriose (Experiment)MaltoseMaltose (Experiment)GlucoseGlucose (Experiment)
Saccharification
Fig. 9 Liquefaction of whole corn (high enzyme: 0.280 mL/100 gcorn): predictions and experiments
888 Bioprocess Biosyst Eng (2011) 34:879890
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During simultaneous saccharification and fermentation
in the dry grind corn process, glucose is produced by action
of glucoamylase and simultaneously consumed by yeast
producing ethanol. The HPLC method only measures net
glucose concentration. Therefore, HPLC measurements
cannot be used to estimate glucose production and con-
sumption rates separately. Knowledge of sugar concentra-
tions and production rates at various times during SSF
process is critical for process control as this information
could be used to estimate the yeast cell mass. Use of starch
hydrolysis models that predict DE and sugar concentrations
in mash is important for fuel ethanol production.
Conclusions
A model, based on a molecular approach, was developed to
simulate structure and hydrolysis of starch. Starch structure
was modeled based on a cluster model of amylopectin.
Liquefaction modeling was based on a Monte Carlo sim-
ulation method for enzymatic hydrolysis of amylose and
amylopectin. Saccharification modeling was developed, for
the first time using a Monte Carlo method. The model
included the effects of process variables such as tempera-
ture, pH, enzyme activity and enzyme dose. Effect of
starch composition, gelatinization and product inhibition
were included in the model. The model results were eval-
uated by comparing simulated values with experiments,
using waxy, high-amylose corn starches and ground yellow
dent corn with varying enzyme loadings. Precision of
model was acceptable since standard deviation of model
results (\0.08%,\0.65%,\0.67% and\0.15% forDP4?, maltotriose, maltose and glucose) was lower than
the detection limits for the HPLC methods to determine the
sugar concentrations (0.3 g/L &3% for maltotriose,maltose and glucose).
The model prediction for glucose concentrations at the
end of liquefaction was 0.59 0.0017 and 1.57 0.014%
(w/v) for high-amylose and waxy corn starch, respectively.
Agreement between the model predictions and experi-
mental values were measured by coefficient of determina-
tion (R2). The values of R2 for DP4?, maltotriose, maltose
and glucose were 0.68, -1.8, -0.42 and 0.79, respectively,
for high-amylose starch. Similarly, the R2 for DP4?, mal-
totriose, maltose and glucose was 0.58, -1.62, 0.26 and
0.69, respectively, for waxy corn starch. This indicates that
the model is effective in predicting the DP4? and glucose
concentrations, while the negative values of R2 indicate
that the model predictions for maltose and maltotriose have
larger deviations from experimental values. As sugar DP
decreases, variation between model predictions and
experimental values decreases.
Model predictions for glucose (R2 = 0.69-0.79) and
DP4? (R2 = 0.8-0.68) were more accurate than the mal-
totriose and maltose for hydrolysis of high-amylose and
waxy corn starch. Coefficient of determination (R2) was
[0.73 for all enzyme loading indicating that the model canbe used to predict the glucose concentrations during starch
hydrolysis. Some of the sources of error in the model
predictions were attributed to differences in the action
pattern of the enzymes and formation of amylose-lipid
complexes. Model predictions for glucose were more
accurate than those for sugars with higher DP. Therefore,
this model can be used to predict glucose profiles during
liquefaction and saccharification processes.
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DE
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Starch hydrolysis modeling: application to fuel ethanol productionAbstractIntroductionModel formulationStarch characterizationModeling amylose and amylopectin moleculesCharacterization of alpha -amylase and glucoamylaseModeling alpha -amylase action: liquefactionModeling glucoamylase action: saccharification
Model implementationModel validationValidation data set 1: starch liquefactionValidation data set 2: liquefaction and saccharificationExperiment 1: starch liquefaction and saccharificationExperiment 2: whole corn liquefaction and saccharification
HPLC analyses
Results and discussionAmylopectin structure simulationValidation data set 1: starch liquefactionValidation data set 2: liquefaction and saccharificationExperiment 1: starch liquefaction and saccharificationExperiment 2: whole corn liquefaction and saccharification
ConclusionsReferences
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