Murthy Et Al 2011 Starch Hydrolysis Modeling Application to Fuel Ethanol Production

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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

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

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

123

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.


123

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


123

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.


123

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


123

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


123

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+


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+


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


123

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


123

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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0 2 4 6 8 10 12 14 16 18 20 220

10

20

30

40

50

60

70

80

90

100

Time (hr)

DE

(Mod

el)

High EnzymeMedium Enzyme Low Enzyme

Saccharification

Fig. 10 Modeled dextrose equivalent (DE) of whole corn at differentenzyme dosages


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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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Murthy Et Al 2011 Starch Hydrolysis Modeling Application to Fuel Ethanol Production