Multiple linear regression model for predicting biomass digestibility from structural features

Bioresource Technology 101 (2010) 4971–4979

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Bioresource Technology

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Multiple linear regression model for predicting biomass digestibilityfrom structural features

Li Zhu a,*, Jonathan P. O’Dwyer b, Vincent S. Chang c, Cesar B. Granda d, Mark T. Holtzapple d

a Det Norske Veritas (USA) Inc., 1400 Ravello Dr, Katy, TX 77449, USAb Albemarle Corporation, Process Development Center, Gulf States Road, Baton Rouge, LA 70805, USAc Applied Materials Taiwan, Ltd. No. 32, R&D Road II, Science-Based Industrial Park, Hsinchu, Taiwand Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA

a r t i c l e i n f o

Article history:Received 25 August 2009Received in revised form 4 November 2009Accepted 5 November 2009Available online 4 December 2009

Keywords:BiomassCrystallinityEnzymatic hydrolysisLigninPretreatment

0960-8524/$ - see front matter Published by Elsevierdoi:10.1016/j.biortech.2009.11.034

* Corresponding author. Tel.: +1 281 396 1774; faxE-mail address: [email protected] (L. Zhu).

a b s t r a c t

A total of 147 model lignocellulose samples with a broad spectrum of structural features (lignin contents,acetyl contents, and crystallinity indices) were hydrolyzed with a wide range of cellulase loadings during1-, 6-, and 72-h hydrolysis periods. Carbohydrate conversions at 1, 6, and 72 h were linearly proportionalto the logarithm of cellulase loadings from approximately 10% to 90% conversion, indicating that the sim-plified HCH-1 model is valid for predicting lignocellulose digestibility. The HCH-1 model is a modifiedMichaelis–Menton model that accounts for the fraction of insoluble substrate available to bind withenzyme. The slopes and intercepts of a simplified HCH-1 model were correlated with structural featuresusing multiple linear regression (MLR) models. The agreement between the measured and predicted 1-,6-, and 72-h slopes and intercepts of glucan, xylan, and total sugar hydrolyses indicate that lignin con-tent, acetyl content, and cellulose crystallinity are key factors that determine biomass digestibility. The1-, 6-, and 72-h glucan, xylan, and total sugar conversions predicted from structural features usingMLR models and the simplified HCH-1 model fit satisfactorily with the measured data (R2 � 1.0). Theparameter selection suggests that lignin content and cellulose crystallinity more strongly affect ondigestibility than acetyl content. Cellulose crystallinity has greater influence during short hydrolysis peri-ods whereas lignin content has more influence during longer hydrolysis periods. Cellulose crystallinityshows more influence on glucan hydrolysis whereas lignin content affects xylan hydrolysis to a greaterextent.

Published by Elsevier Ltd.

1. Introduction

The bioconversion of lignocellulosic biomass into ethanol is apotential energy source that has become more attractive in recentyears because biomass is renewable and widely available. To makebiomass conversion economical, it is imperative to lower pretreat-ment and enzymatic hydrolysis costs. Pretreatment is an essentialto make biomass digestible by altering biomass structural features.The pretreatment and enzymatic hydrolysis processes are inti-mately coupled; the rate and extent of hydrolysis highly dependon enzyme loadings and structural features resulting from pre-treatment (Chang and Holtzapple, 2000). To some extent, the effectof a poor pretreatment can be overcome by higher enzyme load-ings. Alternatively, an extensive pretreatment, which is costly, ren-ders biomass highly digestible thereby reducing the requiredenzyme. Therefore, an optimal point exists, in which the sum ofpretreatment and enzymatic hydrolysis costs is minimized. Identi-

Ltd.

: +1 281 396 1906.

fying this minimum allows for the design of more effective pre-treatments that reduce the total production cost.

Heterogeneous lignocellulose structural features and enzymecomplexity make it difficult to develop mechanistic models that elu-cidate enzymatic hydrolysis (Drissen et al., 2007; Kadam et al., 2004;Zheng et al., 2008). Therefore, it is desirable to develop an empiricalmodel that describes the structural features that hinder enzymatichydrolysis. Table 1 summarizes the empirical models that have beendeveloped over the decades. The viability of the proposed models isaffected by the small number of tested samples (Fan et al., 1981;Koullas et al., 1992), narrow spectrum of investigated structural fea-tures (Gharpuray et al., 1983; Thompson and Chen, 1992), and theneglected cross effects between structural features that may occurduring pretreatment. In previous studies, the model samples wereonly hydrolyzed for one hydrolysis time with one cellulase loading(Fan et al., 1981; Gharpuray et al., 1983; Kim and Hotlzapple,2006; Thompson and Chen, 1992); consequently, these modelscannot be interpolated to other hydrolysis conditions. To establisha reliable relationship between structural features and enzymaticdigestibility, Chang and Holtzapple (2000) prepared 147 model

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Table 1Summary of empirical models correlating structural features and digestibility.

Biomass source No. ofsample

Spectrum of structural features Model expression Reference

L (%) CrI (%) SSA(m2/g)

Solka floc 19 n/a 36.5–88.8

1.1–106.2

D = 0.38(SSA)0.195 (100 CrIB)1.04 Fan et al. (1981)

Wheat straw 18 1.33–11.53 13.9–69.6

0.64–2.9 D = 2.044(100 CrIB)0.257(SSA)0.988 (L)�0.388 Gharpuray et al. (1983)

Avicel wheat straw 18 n/a n/a n/a D = 122 – 0.21 CrIB + 0.59DL � 0.013 CrIB2 0.011 DL2 + 0.015 CrIB � DL Koullas et al. (1992)

Mixed hardwoods 13 11.12–24.90

68.4–85.8

14.8–128 D = 0.444(100 � CrIB)0.293(G/L)0.247 (SSA)0.827 Thompson andChen (1992)

L: lignin content; CrIB: biomass crystallinity index; SSA: specific surface area; D: digestibility; DL: extent of delignification; n/a: no reported data.

4972 L. Zhu et al. / Bioresource Technology 101 (2010) 4971–4979

lignocellulose samples with a wide spectrum of structural features(lignin content, acetyl content, and biomass crystallinity). Selectivepretreatment techniques were used to prepare the model lignocellu-lose samples to minimize cross effects between structural features.

Chang and Holtzapple (2000) developed an empirical model tocorrelate digestibility with biomass structural features. It couldonly be used to predict digestibility with one cellulase loading of5 FPU/g dry biomass, which was excessive for low-lignin andlow-crystallinity biomass. To reduce enzyme cost and attain highersugar conversion, it is advantageous to investigate digestibilitiesover a wide range of cellulase loadings. Holtzapple et al. (1984)developed the HCH-1 model describing cellulose hydrolysis ratesas follows:

� dGx

dt¼ jGxEi

aþ /Gx þ eEð1Þ

where Gx is the cellulose concentration (g/L), E is the cellulase con-centration (g/L), / (dimensionless) is the fraction of cellulose sur-face that is free to be hydrolyzed (/ ffi 1), j (h�1), a (g/L), and e(dimensionless) are parameters that describe the degree of sub-strate reactivity, and i (dimensionless) is the inhibition parameter(i ? 1 at high b-glucosidase loading).

With a high enzyme loading, the HCH-1 model can be inte-grated and simplified to the following empirical model (Holtzappleet al., 1994):

Y ¼ A lnðEÞ þ B ð2Þ

where Y is the carbohydrate conversion (%), E is the cellulase load-ing (FPU/g dry biomass), A and B are empirical constants. The linearrelationship between carbohydrate conversion and the natural log-arithm of cellulase loading agrees with others’ observation (Man-dels et al., 1981; Reese and Mandels, 1971). The linearity of Eq.(2) has been observed over a tenfold range in cellulase loading(Mandels et al., 1981; Reese and Mandels, 1971). This is significantbecause the ability to linearly interpolate sugar yields substantiallyreduces the complexity of developing an empirical model to predictbiomass digestibility over a wide range of cellulase loadings. Zhuet al. (2008) showed that the same biomass, subjected to differentextents of pretreatment, alters the slope and intercept in Eq. (2),which are based solely on structural features. By developing a mod-el that predicts the slope and intercept for a given set of biomassstructural features, it is possible to predict digestibility at variouscellulase loadings.

As mentioned, this study employed a total of 147 model ligno-cellulose samples (Chang and Holtzapple, 2000), which werehydrolyzed with a wide range of cellulase loadings for fixed hydro-lysis periods (1, 6, and 72 h). Carbohydrate conversions at a giventime versus the natural logarithm of cellulase loadings were plot-ted to obtain the slopes and intercepts. Multiple linear regression(MLR) were developed to correlate slopes and intercepts with lig-nin content, acetyl content, crystallinity, and carbohydrate content.

By using Eq. (2), the quantity of enzyme required to achieve the de-sired sugar conversion or the sugar conversion for a given enzymeloading on a given biomass can be predicted. Therefore, MLR mod-els correlating the slopes and intercepts with biomass structuralfeatures can predict biomass digestibility and help optimize thedesign of pretreatment and enzymatic hydrolysis processes.

2. Methods

2.1. Material

Hybrid poplar (20–40 mesh), mixed with sparse wood chips(about 2 mm � 10 mm), was supplied by the National RenewableEnergy Laboratory (NREL). The original material was ground andsieved through a 40-mesh screen. Poplar wood samples with abroad spectrum of lignin contents, acetyl contents, and crystallin-ity indices were prepared via selective delignification with perace-tic acid, selective deacetylation with potassium hydroxide, andselective decrystallization with ball milling. In multiple-step pre-treatments, delignification was performed first, then followed bydeacetylation, and finally decrystallization (Chang and Holtzapple,2000).

Cellulase enzyme, (Spezyme CP, lot 301-00348-257) was pro-vided by Genencor International Inc. (Palo Alto, CA, USA). Cellulasehad an activity of 65 FPU (Filter Paper Units)/mL using the methoddescribed by Ghose (1984). The activity of b-glucosidase (Sigma, G-0395) was 321 CBU (Cellobiase Unit)/mL based on the company’sassay.

2.2. Enzymatic hydrolysis

Enzymatic hydrolysis of pretreated hybrid poplar was per-formed in 50-mL Erlenmeyer flasks at 50 �C in a shaking air bathagitated at 100 rpm. The hydrolysis experiments were performedat 10-g/L solid concentration in 0.05-M sodium citrate buffer (pH4.8) supplemented with 0.01-g/mL sodium azide to prevent micro-bial contamination; the final volume was 20 mL. Hydrolysis wasinitiated by adding 0.2 mL of appropriately diluted cellulasesupplemented with 0.05 mL of b-glucosidase (i.e., 81.25 CBU/gdry biomass). A low substrate concentration and supplemental b-glucosidase were employed to reduce end-product inhibition bycellobiose (O’Dwyer et al., 2007). The enzymatic hydrolysis of eachsample was performed for three hydrolysis periods (1, 6, and 72 h)and three cellulase loadings for each incubation period. A widerange of cellulase loadings was chosen on the basis of structuralfeatures and hydrolysis periods. Table 2 summarizes enzyme load-ings employed at 1-, 6-, and 72-h hydrolysis for biomass sampleswith various digestibilities. After each hydrolysis period, the sealedErlenmeyer flasks were removed from the shaking air bath andboiled for 15 min to denature enzymes. One-hour samplesindicated the initial hydrolysis rate, 72-h samples indicated the

L. Zhu et al. / Bioresource Technology 101 (2010) 4971–4979 4973

ultimate carbohydrate conversion, and 6-h samples were assumedto indicate approximately the average digestibility at 1 and 72 h.The yield glucose and xylose concentrations were measured usinghigh performance liquid chromatography (HPLC). Total sugar canbe assumed as the summation of glucose and xylose. Glucan, xylan,and total sugar conversion can be expressed as follows:

XG ¼ð½G� � ½G�0Þ � V � 0:9

W � glucan content� 1000 mg=g� 100 ð3Þ

XX ¼ð½X� � ½X�0Þ � V � 0:88

W � xylan content� 1000 mg=g� 100 ð4Þ

XT ¼XG � glucan content

0:9 þ XX � xylan content0:88

glucan content0:9 þ xylan content

0:88

� 100 ð5Þ

where XG = glucan conversion (%); XX = xylan conversion (%);XT = total sugar conversion (%); [G] = glucose concentration inhydrolysis liquid (mg/mL); [G]0 = initial glucose concentration, canbe assumed as 0 (mg/mL); [X] = xylose concentration in hydrolysisliquid (mg/mL); [X]0 = initial xylose concentration, can be assumedas 0 (mg/mL); V = initial volume of biomass slurry (mL); W = initialdry weight of biomass (mg); 0.9 = conversion factor of glucose toequivalent glucan; and 0.88 = conversion factor of xylose to equiva-lent xylan.

2.3. Analytical methods

The hybrid poplar samples were analyzed for the contents ofcarbohydrate, acetyl, and lignin following American Society forTesting and Materials (ASTM) standard E 1758-01 (ASTM, 2003).The samples were taken through a primary 72% (w/w) sulfuric acidhydrolysis at 30 �C for 1 h, followed by a secondary 4% (w/w) sul-furic acid hydrolysis at 121 �C for 1 h. Each was analyzed in dupli-cate. The hydrolysis carbohydrate products (glucose and xylose)were determined by HPLC with a Biorad Aminex HPX-87P columnat 85 �C and 0.2-lm filtered reverse osmosis deionized water asthe mobile phase. Acetyl content was determined using HPLC withBiorad Aminex HPX-87H column at 65 �C and 0.2-lm filtered re-verse osmosis 0.005-M sulfuric acid as the mobile phase. The sam-ples were centrifuged and then filtered through 0.2-lm filters(Fisher, USA) and a volume of 20 lL was loaded using Spectra Sys-tem AS 3500 autoinjector (Spectra-Physics, CA). The flow rate was0.6 mL/min. Elution of the samples was monitored by a refractiveindex detector (Lab Alliance, Series 200).

The liquor and solid residue after a two-stage sulfuric acidhydrolysis were used to determine acid-soluble and acid-insolublelignin, respectively. The acid-soluble lignin was measured by UV–vis spectroscopy (Milton Roy Company, PA, USA) at 205 nm, usingdistilled water as blank. The acid-insoluble lignin was determinedusing gravimetric analysis at 105 �C and 575 �C. The total lignincontent was the summation of acid-insoluble lignin and acid-solu-ble lignin.

Biomass crystallinity was measured by X-ray diffraction using aD8 Advance Powder X-ray Diffractometer (Bruker AXS Inc., Madi-son, Wisconsin, USA) operated at 40 kV and 200 mA. The sample(40 mesh) was scanned at 2�/min from 2h = 10� to 26� with a stepsize of 0.05�. Biomass crystallinity index (CrIB) was determinedusing the method by Segal et al. (1959).

2.4. Modeling approach

The slopes and intercepts in Eq. (2) can be correlated with lignincontent, acetyl content, cellulose crystallinity, and carbohydratecontent, as expressed in Eqs. (6) and (7), respectively.

Slope ¼ f ðL; AC; CrIC; CÞ ð6Þ

Intercept ¼ f ðL; AC; CrIC; CÞ ð7Þ

where L is lignin content, AC is acetyl content, C is carbohydrate con-tent (glucan, xylan, or total sugar), and CrIC is cellulose crystallinity.Because lignin content, acetyl content, and crystallinity show differ-ent effects on glucan and xylan digestibilities (Chang and Holtzap-ple, 2000), separate models are developed for glucan, xylan, andtotal sugar hydrolyses at 1, 6, and 72 h.

Cellulose crystallinity is defined as the mass fraction of crystal-line cellulose in cellulose, and it is calculated from biomass crystal-linity CrIB and xylan content as follows (O’Dwyer, 2005):

CrIC ¼ 1:097 CrIB þ 0:939 XC � 11:433 ð8Þ

where CrIB is biomass crystallinity and XC is xylan content. The pres-ence of amorphous lignin and xylan reduces biomass crystallinityand masks the effect of cellulose crystallinity; therefore, rather thanusing biomass crystallinity as one of the structural features, cellu-lose crystallinity is used instead. Biomass crystallinity measuredby XRD is the mass fraction of crystalline material in the whole lig-nocellulosic biomass sample including the amorphous characteris-tics of hemicellulose and lignin (Andersson et al., 2004). Lee andFan (1982) showed that cellulose crystallinity is a major limitingfactor in pure cellulose digestibility; therefore, it would be advanta-geous to separate the crystallinity of cellulose from biomass crystal-linity as a whole, which is what Eq. (8) accomplishes.

2.5. Multiple linear regression models

MLR models are often used as empirical models or approximat-ing functions when more than one independent variable is in-volved. The true functional relationship between the dependentvariable and independent variables is unknown, but by utilizingcomplex forms of independent variables, the MLR model ade-quately approximates the true unknown functions. In this study,MLR models that include the linear and quadratic terms of eachindependent variable and the interaction terms between the threestructural features may take the following form:

A=B ¼ b0 þ b1Lþ b2AC þ b3CrIC þ b4C þ b11L2 þ b22A2C

þ b33CrI2C þ b44C2 þ b12LAC þ b13LCrIC þ b23ACCrIC þ e ð9Þ

where A/B = slope or intercept; L = lignin content (%); AC = acetylcontent (%); CrIC = cellulose crystallinity (%) ; C = carbohydrate con-tent (%); b0–b44 are correlation parameters, and e is random error.

To make the values of correlation parameters fall in a reason-able range, a scale factor of 0.1 was applied for lignin content, crys-tallinity, and carbohydrate content, acetyl content was useddirectly.

Although Eq. (9) includes all the influential factors, not all ofthese variables may be statistically significant for the 18 differentdependent variables. An appropriate subset of variables for eachmodel should be determined. GLMSELECT procedure in SAS/STATsoftware was used to perform model selection in the frameworkof general linear models. PROC GLMSELECT employed CVMETHO-D = RANDOM(5) for cross validation to assess the predictive perfor-mance of a model.

3. Results and discussion

3.1. Structural features of model lignocellulose samples

Fig. 1 illustrates distributions of three structural features for147 model lignocellulose samples. Lignin content and acetyl con-tent ranged from 0.7% to 26.3% and 0.1% to 3.1%, respectively.

Table 2Summary of enzyme loading for biomass samples with various digestibilities.

Digestibility Enzyme loading for various incubation periods(FPU/g dry biomass)

1 h 6 h 72 h

High 1, 3, 10 1, 3, 10 0.25, 0.75, 2Medium 1, 3, 10 1, 3, 10 0.5, 1.5, 5Low 1, 5, 30 1, 5, 30 1, 5, 30

0

0.5

1

1.5

2

2.5

3

0 5 10 15 20 25 30Lignin content (%)

Ace

tyl c

onte

nt (%

)

0

20

40

60

80

100

0 5 10 15 20 25 30Lignin content (%)

Cry

stal

linity

inde

x (%

)

0

20

40

60

80

100

0 0.5 1 1.5 2 2.5 3Acetyl content (%)

Cry

stal

linity

inde

x (%

)

Fig. 1. Distributions of structural features of model lignocellulose samples.


Increase in biomass crystallinity (i.e., from 55.4% to 68.8%) wereobserved with the increase delignification and deacetylation be-cause amorphous materials, such as lignin and acetyl groups, were

removed. The wide ranges of structural features make it possible todevelop empirical models to predict biomass digestibility fromthese structural features.

3.2. Validity of simplified HCH-1 model

To verify the simplified HCH-1 model described in Eq. (2), themodel lignocellulose samples were hydrolyzed with cellulase load-ings from 0.25 to 100 FPU/g dry biomass. The 1-, 6-, and 72-h sugarconversions (glucose, xylose, or total sugar) were plotted againstthe natural logarithm of cellulase loadings. Fig. 2a–c show thatthe 1-, 6-, and 72-h glucan, xylan, and total sugar conversionsare proportional to the natural logarithm of cellulase loadings from�10% to �90% sugar conversion, thus the simplified HCH-1 modelis valid for predicting biomass digestibility. For high-digestibilitybiomass samples, three distinct regions may exist in the plot of72-h glucan conversion versus the logarithm of cellulase loading,as shown in Fig. 2d. The valid region for the simplified HCH-1 mod-el (�10% to �90% sugar conversion) is of particular interest. Theextremely low (<10%) and high (>90%) conversions are not of inter-est due to low sugar conversions or inefficient enzyme utilization,respectively. It was verified that enzymatic hydrolysis of biomasswith various structural features followed the simplified HCH-1model over a wide range of cellulase loadings (Zhu et al., 2008),identified with the slopes and intercepts in Eq. (2). As a result, itis possible to correlate the slopes and intercepts that characterizethe pretreatment effects on digestibility with structural features.

3.3. Correlation of slopes and intercepts with structural features

Table 3 summarizes the correlation parameters for the slopesand intercepts of glucan hydrolysis. The parameter selections sug-gest that linear and quadratic terms of lignin content have signifi-cant effects on the 6- and 72-h slopes and intercepts, whereas onlythe quadratic term of lignin content shows effects on the 1-h slopeas lignin content did not show any effect on the 1-h intercept. Thevariable selection indicates that lignin content greatly affects glu-can digestibility during longer hydrolysis periods. The terms ofacetyl contents show impacts on the 1-h slope and 1- and 6-hintercepts whereas the effects of crystallinity on the 1-, 72-h slopesand 1-, 6-, and 72-h intercepts are statistically significant. Com-pared to lignin content, cellulose crystallinity has more significantinfluence on the initial hydrolysis rate. It is conclusive that lignincontent and cellulose crystallinity have more significant effectson glucan hydrolysis than acetyl content, which agrees well withChang’s conclusion (Chang and Holtzapple, 2000). The parametersof glucan content show significant influence on the intercepts thanthe slopes of glucan hydrolysis. The R2 values varied from 0.9 to0.96 for the slopes and intercepts of glucan hydrolysis, except forthe 1-h intercept (i.e., 0.75); thus, Eq. (9) predicted the 1-, 6-,and 72-h slopes and intercepts of glucan hydrolysis well. The meansquare errors (MSEs) were in the range of 0.9–22.5.

The correlation parameters for the slopes and intercepts of xy-lan hydrolysis are listed in Table 4. The slopes and intercepts canbe predicted using 2–9 variables. Similar to glucan hydrolysis,the linear and quadratic terms of lignin content show more effectson the 1-, 6-, and 72-h slopes, 6- and 72-h intercepts than the 1-hintercept, meaning that lignin content greatly affects xylan hydro-lysis during longer hydrolysis periods. The interaction term of lig-nin content and acetyl content (b12) demonstrates more impactson the slopes and intercepts of xylan hydrolysis; therefore, lignincontent and acetyl content have a greater impact on xylan hydro-lysis than on glucan hydrolysis. The parameter selection suggestthat the effects of cellulose crystallinity on the 1- and 6-h inter-cepts and 6-h slope are statistically significant, implying that cellu-lose crystallinity has less effects on xylan hydrolysis than on glucan

0

20

40

60

80

100

0.1 1 10 100 1000Cellulase loading (FPU/g dry biomass)

Glu

can

co

nver

sio

n (

%)

72 h

6 h

1 h

0

20

40

60

80

100

0.1 1 10 100 1000

Cellulase loading (FPU/g dry biomass)

Xyl

an c

onve

rsio

n (%

)

72 h

6 h

1 h

0

20

40

60

80

100

0.1 1 10 100 1000Cellulase loading (FPU/g dry biomass)

Tota

l su

gar

co

nver

sio

n (

%) 72 h

6 h

1 h

0

20

40

60

80

100

0.1 1 10 100


Glu

can

co

nver

sio

n (

%)

72 h

Low conversion

Interesting region

Inefficient enzyme

Glucan

Xylan

Total sugar

Glucan

a b

c d

Fig. 2. Relationship between sugar conversion and cellulase loadings: (a) glucan; (b) xylan; (c) total sugar; and (d) glucan at 72 h. Hydrolysis conditions: 81.2 CBU/g drybiomass, substrate concentration: 10 g/L.

Table 3Correlation parameters for slopes and intercepts of glucan hydrolysis.

Parameters 1 h 6 h 72 h

Slope Intercept Slope Intercept Slope Intercept

b0 35.93 �8.16 26.92 59.9 30.33 116.8b1 – – 6.67 3.96 3.28 11.41b2 �1.3 �2.64 – �3.46 – –b3 – 1.33 – �1.53 �1.77 –b4 – 5.88 – – – –b11 �1.97 – �3.35 �4.39 �3.4 �9.91b22 – 0.35 – – – –b33 �0.23 �0.19 – �0.33 0.23 �0.39b44 �0.26 �0.59 – �0.44 – �0.6b12 – – – – – –b13 �0.13 �0.25 �0.97 – �0.53 �1.95b23 �0.15 0.11 �0.32 – �0.21 �1.13R2 0.95 0.75 0.92 0.94 0.89 0.96MSE 2.4 0.9 4.7 7.1 7.1 22.5No. of variables 6 8 4 6 6 6

Correlations use Eq. (9) with the following definitions: L = Lignin content (%) � 0.1,AC = Acetyl content (%), CrIC = Cellulose crystallinity (%) � 0.1, C = Glucan content(%) � 0.1.

Table 4Correlation parameters for slopes and intercepts of xylan hydrolysis.



b0 15.23 4.71 21.49 55.3 16.2 80.08b1 3.23 – 15.10 18.54 – 42.26b2 1.23 – 1.79 – �0.68 –b3 – 0.65 1.93 2.44 – –b4 �5.53 – �9.57 �13.65 – –b11 �0.58 – �4.47 �5.93 �1 �14.35b22 – – 0.46 0.79 – 1.55b33 – �0.09 – �0.46 – –b44 – – – – – –b12 �0.84 �0.57 �1.01 �4.18 – �4.91b13 �0.27 �0.15 �1.23 �1.39 – �3.86b23 �0.12 – �0.63 – – �0.80R2 0.69 0.69 0.87 0.91 0.37 0.95MSE 1.1 1.3 2.9 14 10.1 32.7No. of variables 7 4 9 8 2 6

Correlations use Eq. (9) with the following definitions: L = lignin content (%) � 0.1,AC = acetyl content (%), CrIC = cellulose crystallinity (%) � 0.1, C = xylan content(%) � 0.1.


hydrolysis. The R2 values for the regressions of the 1-h slope (0.69),1-h intercept (0.69), and 72-h slope (0.37) were much lower thanthose of 6-h slope (0.87), 6-h intercept (0.91), and 72-h intercept(0.95). By comparing the corresponding R2 values, the regressions

for the slopes and intercepts of glucan hydrolysis were much betterthan those of xylan hydrolysis. This may result from two possiblecauses: (1) the xylan content in the model lignocellulose samplesis only 1/3–1/5 of the glucan content; it is difficult to determine

Table 5Correlation parameters for slopes and intercepts of total sugar hydrolysis.



b0 35.02 �24.93 19.63 �58.30 �14.70 138.22b1 – – 7.44 – 11.64 12.63b2 �1.79 �2.66 – �7.14 – �2.7b3 – 1.19 2.12 – �1.83 –b4 – 9.16 – 33.96 4.68 –b11 �1.69 – �3.26 �2.51 �3.96 �10.93b22 – 0.38 0.28 1.08 – –b33 �0.21 �0.17 �0.14 �0.40 0.22 �0.35b44 �0.19 �0.67 – �2.62 0.65 �0.96b12 – – – – �0.29 –b13 �0.20 �0.25 �1.09 �0.47 �0.63 �2.46b23 – 0.1 �0.49 – – �0.77R2 0.95 0.76 0.93 0.96 0.89 0.96MSE 1.7 0.8 3.5 5.51 5.2 22.7No. of

variables5 8 7 7 7 7

Correlations use Eq. (9) with the following definitions: L = lignin content (%) � 0.1,AC = acetyl content (%), CrIC = cellulose crystallinity (%) � 0.1, C = total sugar content(%) � 0.1.

0

20

40

60

80

100

0 20 40 60 80 100Predicted Glucan Conversion (%)

Mea

sure

d G

luca

n C

on

vers

ion

(%

) Glucan 1 h

R2 = 0.95 MSE = 3.6---95% Prediction Interval

0

20

40

60

80

100

0 20 40 60 80 100Predicted Glucan Conversion (%)

Mea

sure

d G

luca

n C

on

vers

ion

(%

) Glucan 6 h

---95% Prediction IntervalR2 = 0.97 MSE = 5.2

0

20

40

60

80

100

0 20 40 60 80 100

Predicted Glucan Conversion (%)

Mea

sure

d G

luca

n C

on

vers

ion

(%

) Glucan 72 h


Fig. 3. Graphs of experimentally measured glucan conversions versus predictedvalues calculated from MLR and HCH-1 models.


an accurate xylose concentration in the enzymatic hydrolyzate at10-g/L substrate concentration, low enzyme loadings, and shorthydrolysis periods, and (2) the enzyme loading is expressed as cel-lulase, not xylanase. Furthermore, the xylanase activity in the sup-plemental b-glucosidase should be quantified if a large amount ofb-glucosidase is supplemented (Lu et al., 2002).

Table 5 summarizes the selected correlation parameters for theslopes and intercepts of total sugar hydrolysis. Because total sugarconversion is the summation of glucan and xylan conversions, theeffects of structural features on total sugar hydrolysis are similar tothose on glucan and xylan hydrolyses. The R2 values and MSEs oftotal sugar hydrolysis were similar to those of glucan hydrolysis,ranging from 0.89 to 0.96 for the slopes and intercepts, exceptfor the 1-h intercept (i.e., 0.76), indicating that Eq. (9) describesthe 1-, 6-, and 72-h slopes and intercepts of total sugar hydrolysiswell. The MSEs were in the range of 0.8–23.

3.4. Prediction of carbohydrate conversions

The primary goal of developing MLR models is to predict bio-mass digestibility, which can be calculated from the slopes andintercepts of the simplified HCH-1 model predicted by MLR mod-els. Using various enzyme loadings, Figs. 3–5 compare the mea-sured 1-, 6-, and 72-h glucan, xylan, and total sugar conversionsand the corresponding calculated data, respectively. Most of pointsin each plot fall within the 95% prediction interval. The narrownessof 95% prediction interval means prediction errors are small. The R2

values were in the range of 0.9 and 0.97 and the MSE values variedfrom 2.7 to 7.0. Therefore, the MLR models and simplified HCH-1model can predict biomass digestibility from structural features(R2 � 1.0).

4. Implications

The use of these models is to predict sugar conversions solelyfrom structural features, which may be applied to a variety of bio-mass feedstocks and pretreatment techniques. This approach pro-vides insights into the design of effective pretreatment systemsthat reduce lignin content, cellulose crystallinity, and acetyl con-tents thereby allowing the design of effective and economical pre-treatment and hydrolysis systems.

Fig. 6 illustrates the total sugar conversions calculated fromstructural features as a function of the logarithm of cellulase load-

ings at various lignin contents for 1-, 6-, and 72-h hydrolysis peri-ods. The sugar conversions were calculated using the MLR modelwith a typical pretreated biomass composition (glu-can:xylan = 45:20; CrIB = 55%; acetyl content = 0%) and a ball-milled biomass composition (glucan: xylan = 45:20; CrIB = 15%;acetyl content = 0%). Actually, these figures are only illustrations;the composition changes during a chemical pretreatment are verycomplex, causing the ratio of glucan to xylan and biomass crystal-linity to vary with the extent of delignification.

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Fig. 4. Graphs of experimentally measured xylan conversions versus predictedvalues calculated from MLR and HCH-1 models.

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ugar

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Fig. 5. Graphs of experimentally measured total sugar conversions versus predictedvalues calculated from MLR and HCH-1 models.


Compared to Fig. 6a, the larger slopes in Fig. 6b demonstratethat decrystallization has more significant effects on the initialhydrolysis rate. With a cellulase loading of 70 FPU/g dry biomass,the 1-h total sugar conversion of low-crystallinity (i.e., 15%) andhigh-crystallinity (i.e., 55%) biomass with the same lignin content(i.e., 10%) were 85% and 55%, respectively. For low-lignin andhigh-crystallinity biomass sample, Fig. 6a shows that theoreticalcellulase loading required to attain 1-h total sugar conversion of85% is more than 500 FPU/g dry biomass; thus, decrystallization

significantly reduces the amount of enzyme required to achieve agiven sugar conversion.

Fig. 6d demonstrates that a cellulase loading of 8 FPU/g dry bio-mass is sufficient for low-lignin and low-crystallinity biomasssamples to achieve 6-h total sugar conversion of 85% whereasthe 6-h total sugar conversion of low-lignin and high-crystallinitybiomass sample is �60% with the same cellulase loading. With thesame enzyme loading, greater changes in biomass digestibility ofthe high-crystallinity biomass sample indicate that delignification

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b a

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Fig. 6. Calculated total sugar conversions predicted from MLR model as a function of cellulase loading at various lignin contents at (a) 1 h, (c) 6 h and (e) 72 h; low-crystallinity samples at (b) 1 h, (d) 6 h and (f) 72 h.

Table 6Optimization of enzyme efficiency.

Hydrolysisperiod (h)

Enzyme loading(FPU/g dry biomass)

Total sugar conversion (%) Efficiency

CrIB = 55% CrIB = 15%

1 70 55 85 0.01216 8 59 85 0.017772 1 64 85 0.0118

For biomass with 10% lignin content and 0% acetyl content

Enzyme efficiency ¼ total sugar conversionhydrolysis period� enzyme loading

:


has more impacts on the digestibility of high-crystallinity biomassthan on low-crystallinity biomass.

Fig. 6e shows that 72-h enzymatic hydrolysis of low-lignin andhigh-crystallinity biomass samples are nearly complete with a cel-lulase loading of 5 FPU/g dry biomass, indicating that only exten-sive delignification is sufficient to achieve nearly completehydrolysis regardless of biomass crystallinity. Compared to 1-and 6-h hydrolysis, the larger slopes resulting from delignification(Fig. 6e) indicate that decrystallization has less effect at longerhydrolysis times. With a cellulase loading of 1 FPU/g dry biomass,the 72-h total sugar conversion of low-crystallinity and high-crys-tallinity biomass with the same lignin content (i.e., 10%) were 85%and 65%, respectively.

In summary, both delignification and decrystallization caneffectively enhance biomass digestibility, however, their effective-


ness is quite different (Zhu et al., 2008). Therefore, optimal deligni-fying or decrystallizing pretreatment and enzymatic hydrolysisconditions depend on economics. Table 6 shows the enzyme re-quired for low-lignin and low-crystallinity biomass to achieve su-gar conversion of 85% at 1-, 6-, and 72-h hydrolysis periods are70, 8, and 1 FPU/g dry biomass, respectively. Considering the en-zyme efficiency related to sugar conversion and hydrolysis time,the optimal enzymatic hydrolysis condition is at 6 h with a cellu-lase loading of 8 FPU/g dry biomass. Enzyme efficiency in Table 6is only an indication without considering enzyme cost. It mightbe ideal to consider the enzyme cost as one factor for the economicoptimum. If the enzyme cost is too high, longer hydrolysis time(72 h) is preferred; if the enzyme price is very low, fast reactionis desired. Thus, the MLR model provides the potential to optimizepretreatment and enzymatic hydrolysis processes. From a practicalviewpoint, high substrate concentration (i.e., >10%) would be pre-ferred in industrial enzymatic hydrolysis/fermentation. Conse-quently, reaction rate and carbohydrate conversions would belower if sugars were allowed to accumulate. These models havegreater predictive ability if the sugars are fermented immediatelyto ethanol, which is significantly less inhibitory than sugars.

5. Conclusions

The slopes and intercepts in the plots of sugar conversions ver-sus the natural logarithm of cellulase loadings can be correlatedwith biomass structural features. The measured 1-, 6-, and 72-hglucan, xylan, and total sugar conversions agreed well with pre-dicted values calculated from structural features, indicating thatthe simplified HCH-1 model and MLR models can predict digest-ibilities of biomass from structural features with a given enzymeloading. The variable selection implied that lignin content and cel-lulose crystallinity had more effects on digestibility than acetylcontent. Cellulose crystallinity showed more influence duringshort hydrolysis periods whereas lignin content had greater impactduring long hydrolysis periods. These results are important be-cause it suggests that decreasing crystallinity accelerates biomasshydrolysis reaction and thus decrease the residence time necessaryto achieve a given conversion. On the other hand, removing lignininfluences the extent of final conversion allowing for a high bio-mass conversion to be achieved. Therefore, pretreatments that de-crease crystallinity and remove lignin are complementary to eachother in increasing biomass digestibility.

Acknowledgements

The authors would like to thank Dr. Changchun Li for helping uswith SAS programming.

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Multiple linear regression model for predicting biomass digestibility from structural features

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