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Elucidating the interactive impacts of substrate-related properties on lignocellulosic biomass digestibility: A sequential analysis Liyuan Chai, Mingren Liu, Xu Yan, Xunqiang Cheng, Tingzheng Zhang, Mengying Si, Xiao-Bo Min, and Yan Shi ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.8b00592 • Publication Date (Web): 18 Mar 2018 Downloaded from http://pubs.acs.org on March 19, 2018

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ACS Sustainable Chemistry & Engineering

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Elucidating the interactive impacts of substrate-related properties on

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lignocellulosic biomass digestibility: A sequential analysis

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Liyuan Chai1, 2, Mingren Liu 1, Xu Yan1, 2, Xunqiang Cheng1, Tingzheng Zhang1,

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Mengying Si1, Xiaobo Min1, 2, Yan Shi*1, 2

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1

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School of Metallurgy and Environment, Central South University, Changsha 410083, China

2

Chinese National Engineering Research Center for Control & Treatment of Heavy Metal Pollution, Changsha 410083, China

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*To whom correspondence should be addressed. Y. Shi, E-mail: [email protected]

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(Y. Shi); Fax: +86-0731-88710171; Tel: +86-0731-88830875

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L. Chai, M. Liu, X. Yan, X. Cheng, T. Zhang, M. Si, X. Min, Y. Shi, Mailing address:

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No.932 South Lushan Road, Changsha Hunan 410083, P.R. China

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Key words: Enzymatic hydrolysis; Lignin; Cellulose; NMR spectroscopy;

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Lignocellulose biomass; Physicochemical properties; Statistical analysis

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ABSTRACT

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A lack of insight into interactive effects among substrate-related factors holds

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back the determination of dominant factors in efficient sugar conversion. Herein,

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thirteen factors defining compositional and physicochemical properties of

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lignocellulose pretreated by dilute acid/base and enzymes were analyzed through an

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innovative sequence of correlation analysis, principle component analysis, multiple

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linear regression and multiscale statistical validation. Results showed that the lignin

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content, cellulose content and O/C ratio principally affected enzymatic hydrolysis.

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The dominant role was played by the lignin content due to its major recalcitrance

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providing to biomass and concomitant impacts on surface lignin and porosity

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properties. The structural features of lignin played a less pronounced role with high

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lignin content remained. Besides, the sequential analysis revealed different inhibition

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mechanisms for glucan and carbohydrate conversion, i.e., non-productive binding of

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enzymes and steric hindrance of lignin, respectively. The established weighing order

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of interactive factors enlightens more efficient pretreatment strategies.


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INTRODUCTION

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The efficient bioconversion of lignocellulosic carbohydrates into sugars is

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essential for the valorization of lignocellulose. 1 To overcome the innate recalcitrance

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of lignocellulose, different pretreatment strategies have been developed to open the

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substrate structure for cellulose degradation.2-3 The efforts to elucidate their

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underlying mechanisms have been made by investigating the concomitant changes in

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substrate-related factors, such as biomass porosity, lignin/hemicellulose distribution,

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contents and structural features as well as cellulose crystallinity and accessibility.2-6

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Unfortunately, the interactive changes in the biomass during pretreatments often lead

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to conflicting trends in weighting the dominant factors.1-5,

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influencing mechanisms of these factors thus presents a significant challenge and the

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development of a strategy that optimizes biomass digestibility remains hindered.

7-12

Interpreting the

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The interactions among these substrate factors are mainly affected by the

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distribution, content and composition of lignin and hemicellulose in the highly

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dynamic lignocellulosic structure.4, 6, 12 According to a popular morphological model,

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highly ordered cellulose elemental fibrils are cross-linked with hemicellulose and

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embedded in a non-cellulosic polysaccharide matrix. The lignin, as the plasticizer, is

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partially bonded with hemicellulose via lignin-carbohydrate complexes.1, 7-8 Therefore,

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the negative role of lignin in lignocellulose digestion has been long recognized.

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Biomass pretreatments that reduce the lignin content are thought to improve the

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saccharification efficiency.3, 5, 9 Contradicting this view, the sugar release from poplar 3 ACS Paragon Plus Environment


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samples with high syringyl /guaiacyl (S/G) ratio shows less dependency on lignin

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content.9 This study reported a tangled interaction between the lignin structural

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features and the lignin content. The increasing S/G ratio implies less cross-linking in

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the triaxial structure and abates the negative role of the lignin content9, 13 whereas it

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was also reported the S/G ratio contributes relatively little to sugar release.

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Moreover, phenolic compounds of lignin should aggravate the non-productive binding

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of cellulase but their counteraction effect with alcoholic and carboxylic hydroxyls

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remains unclear.4, 15 Consequently, no clear picture of lignin inhibition has emerged.

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Similar obscureness was also displayed on the impacts of hemicellulose. Cellulose

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accessibility proves linearly proportional to hemicellulose removal.3,

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cellulase reaction rate is also linearly related to the pore volume of the biomass3, 8, 10

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but followed by a rapid decline in the conversion rate.16 After the initial hydrolysis of

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hemicellulose and amorphous cellulose, biomass porosity increased17 but the

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concomitant increase in crystalline region and lignin content also hinders the

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enzymatic hydrolysis.18-19 Therefore, all substrate-related factors are mechanically

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important, but the factor playing the dominant role remains unidentified.

10

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The initial

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The statistical analyses of these arguments have been developed over many years.

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Pairs of linear relationships between factors and biomass digestibility are commonly

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tested by correlation analysis (CA). Unfortunately, CA alone often leads to conflicting

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or confusing trends.3, 5, 9-10, 16-19 In particular, CA is susceptible to database diversity; a

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small sample size or individual data from a specific pretreatment may undermine the 4 ACS Paragon Plus Environment

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accuracy of the correlation results and the reliability of further interpretation. Worse

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still, CA alone cannot handle the tangled interactions among different factors. In some

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studies, statistical analysis has been complemented with principle component analysis

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(PCA) which reduces the dimensionality of the problem.5, 20 However, current studies

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have focused on a limited scale of factors without removing the irrelevant ones.5, 20

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This reduces the robustness of the total variance interpretation and compromises the

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PCA performance. For a more integrated perspective, a database providing diverse

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factors after pretreatments should be combined with a systematic and mechanical

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statistical method.

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In this study, thirteen factors were comprehensively collected after dilute

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acid/base and combined enzyme-chemical pretreatments. These factors included the

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compositional variances, substrate surface properties, cellulose crystallinity, biomass

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porosity and structural features of lignin. The latter pretreatment introduced laccase

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and endo-xylanase for facile bioprocessing and diversified the physicochemical

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changes.21 To identify the dominant factors of biomass digestibility, the interactions

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between the thirteen factors were analyzed through a sequential analysis which

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integrates CA, PCA and stepwise multiple linear regression (MLR).22-23 Analogous to

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impurity removal in cascade filtration, the method excludes the factors that are

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irrelevant or subjected to the collinearity problem, thus guaranteeing that no chance

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correlations or collinearity will undermine the accuracy of the MLR results. To our

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knowledge, our study is the first sequential study on the complex influence of 5 ACS Paragon Plus Environment


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interacting factors on enzymatic hydrolysis. We thus provide a systematic and reliable

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approach for optimizing pretreatment strategies.

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EXPERIMENTAL SECTION

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Pretreatment and enzymatic hydrolysis

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Corn stover ( 0.6, p < 0.05, variance inflation factor < 10 (for evaluating

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the collinearity level) and F-statistic > 15.22 The error in the model during its

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application was then estimated by both internal validation (leave-one-out method,

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LOO; bootstrap method, BOOT) and experimental test (Table S11). The

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cross-validation coefficient Q2 was defined as follows:

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Q2 =1 −

∑ (yi ŷi )2

(2)

2 ∑ (yi y) i

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where yi and ŷi were the detected and predicted glucan/carbohydrate conversion,

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respectively. yi was the average value in dataset. The BOOT was performed on 5000

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randomly sampled subsets. The Q2 and the root mean square error (RMSE) of LOO

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2 and BOOT method were also calculated. A model was considered robust if its QLOO

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2 and QBOOT exceeded 0.5 and RMSE less than 0.3. All statistical analyses were

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performed in SPSS software (Version 21.0, SPSS Inc., USA), the R programming

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language (3.4.1, USA) and MATLAB (2014a, MathWorks, USA).

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RESULTS AND DISCUSSION 10 ACS Paragon Plus Environment

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Correlation analysis of thirteen substrate-related properties with potential

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influence on biomass digestibility

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Thermochemical pretreatment with dilute acid/base as well as facile bioprocessing

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with laccase and xylanase digestion were employed in present study, providing

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diverse changes in the substrate-related factors. 21 For simplicity, we hereafter refer to

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the dilute acid and acid-enzyme pretreatments as acid pretreatment (a similar

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terminology is adopted for base pretreatment; Table S1). Quantile−quantile plots of

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glucan and carbohydrate conversions were performed. All plots were distributed in

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the diagonal line, confirming the validity of a random normal distribution assumption

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(Figure S1).22 Figures 1 and 2, respectively, present the matrix of correlation

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coefficients among factors and the relations between glucan/carbohydrate conversions

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and the thirteen factors. The data falls into three classes: acid pretreated data, base

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pretreated data and untreated data.

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Compositional analysis

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The acid/base pretreatment induced delignification and hemicellulose removal,

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with concomitant changes in the physicochemical factors of the biomass substrate.

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Herein, the lignin content varied from 1.61% to 26.2% and was strongly negatively

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correlated with both glucan (r = −0.830, P < 0.05) and carbohydrate conversions (r =

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−0.866, P < 0.05, Figures 1 and 2a). It’s observed that the glucan and carbohydrate

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conversion of untreated and acid-pretreated samples were less than 50%, whereas 11 ACS Paragon Plus Environment


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those of base-pretreated samples exceeded 50% (Table S2). Lignin forms not only

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hydrophobic networks that restrict the entry of cellulases and hemicellulases into the

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polysaccharide1, 3 but also LCCs that non-productively bind with the enzymes.10, 32

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Residual lignin after pretreatment could further block the progress of cellulase down

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the cellulose chain.33 Therefore, the lignin content exerted a pronounced adverse

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effect on the enzymatic digestibility.

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By contrast, the hemicellulose content was not significantly correlated with glucan

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(r = 0.163, P > 0.05) or carbohydrate conversions (r = 0.151, P > 0.05, Figures 1 and

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2b); this finding is consistent with a previous CA of diversely pretreated samples.20

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Although hemicellulose removal improved the access of cellulases to cellulose, its

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impact on biomass digestibility might be less important. Hemicellulose obstacles are

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frequently overcome by adding accessory enzymes such as xylanase in Cellic®

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CTec2.34 With deacetylation during lignin removal, the inhibition effect of acetyl

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groups on endo-xylanases is also reduced.

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hydrolysis of hemicellulose is thus achieved. Due to the low correlation coefficients,

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the hemicellulose content was excluded from the PCA step in the sequential study.

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A more efficient enzymatic

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The cellulose content was positively correlated with glucan (r = 0.636, P < 0.05)

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and carbohydrate conversion (r = 0.638, P < 0.05, Figures 1 and 2c). Given that the

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acid/base pretreatment only moderately affects the cellulose itself, cellulose content

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mainly varies by concomitant mass reduction during lignin and/or hemicellulose

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removal.

2, 5, 36

Specifically, closed regression coefficients of lignin and hemicellulose 12 ACS Paragon Plus Environment

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contents to the increase in cellulose contents were obtained (-0.819 vs. -0.890, Table

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S9). Therefore, the positive relation between cellulose content and glucan and

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carbohydrate conversions resulted from a combined effect of lignin and hemicellulose

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removal.

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Surface O/C ratio

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Besides the lignin in bulk substrate, the surface lignin increases the adhesion

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forces to cellulase, thus aggravating non-productive binding of enzymes.27, 37 Herein,

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the impact of the surface-lignin coverage was identified from the ratio of surface

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oxygen (O1s) to carbon (C1s) obtained by XPS.38 Generally, a higher O/C ratio

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indicates that the reduced-oxygen components (such as lignin, 0.33) are less

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distributed than the oxygen-rich components (such as cellulose, 0.8339).15 For

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confirmation, XPS C1s peaks were deconvoluted. The dominant peak at 284.8 ± 0.1

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eV (C1) was corresponded to the C-C bonds from lignin structure.27 Notably, the O/C

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ratio was negatively correlated with the content of C1 (r = −0.581, P < 0.05, Table S4),

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confirming that lowering surface lignin coverage could increase the surface O/C ratio.

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As presented in Table S4 and Figure 2d, the O/C ratio of lignocellulosic biomass

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varied from 0.371 to 0.658 and was positively correlated with both glucan (r = 0.783,

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P < 0.05) and carbohydrate conversion (r = 0.742, P < 0.05), revealing that lower

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surface lignin coverage contributed to biomass enzymatic digestibility. Indeed, a low

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surface lignin coverage was highly correlated with lignin content (r= −0.666, P
0.05, respectively; Figures 1 and 2e). In fact, cellulose

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crystallinity affected by pretreatment is often less straightforward to enzymatic

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hydrolysis in a complicated biomass structure.2, 40-41 The good correlation between CrI

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and enzymatic digestibility is often obtained from nearly pure cellulose.42 Therefore,

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some studies have associated the CrI with actual cellulose content and constructed a

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new parameter (CrI/cellulose) to assess the actual changes in crystallinity.2,

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Interestingly, the untreated biomass possessed the lowest CrI value but the highest

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CrI/cellulose (1.411, Table S3) and the Cr/cellulose values were lower in the

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base-pretreated samples. The base pretreatment led to cellulose swelling which


5

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decreased the crystallinity and partially transformed the recalcitrant cellulose Iα to

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amorphous-like cellulose IIII, thus boosting the enzymatic hydrolysis.5,

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suggests that the total crystallinity is mainly increased with the increase in the

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cellulose content, although the crystallinity of the cellulose itself deceases.44-45

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Correspondingly, the correlation between the CrI/cellulose and glucan/carbohydrate

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conversions was improved (r = −0.548/−0.483, P < 0.05, Figures 1 and 2f). In the

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next stage of PCA, CrI/cellulose was preserved as the glucan-conversion-relevant

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factor.

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Biomass Porosity

7, 43

This

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Considering that hydrolysis requires intimate contact between the enzymes and a

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valid polysaccharide surface, biomass porosity should be considered.7 Herein, the

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biomass porosity was estimated from the specific surface area (SSA), pore volume

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(PV) and average pore sizes (APS) determined by nitrogen adsorption. Unexpectedly,

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the glucan and carbohydrate conversions were poorly correlated with SSA (r =−0.218

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and −0.268, P > 0.05, respectively; Figures 1 and 2g) and with PV (r = −0.270 and

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−0.302, P > 0.05, respectively; Figures 1 and 2h). We then separately analyzed the

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acid and base pretreatment data and yielded very different results. In acid pretreatment,

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the porosity of biomass substrate is significantly promoted by hemicellulose removal

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and lignin rearrangement.3 In present study, the acid pretreatment increased the SSA

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from 0.623 m2 g-1 (untreated) to 3.41–7.72 m2 g-1, and the PV from 0.00242 m3 g-1 to



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0.0147–0.0280 m3 g-1 (Figure 2g, h). The larger SSA and PV indicates a more porous

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structure, facilitating enzyme access to the embedded cellulosic microfibrils.2,

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Therefore, the glucan and carbohydrate conversions were strongly positively

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correlated with SSA (r = 0.861 and 0.754, P < 0.05, respectively) and with PV (r =

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0.848 and 0.814, P < 0.05, respectively). However, the base-pretreated biomass was

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less porous (with SSA and PV ranging from 1.22 to 1.97 m2 g-1 and from 0.00335 to

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0.00715 m3 g-1, respectively; Figure 2g, h). The effect of delignification on cellulose

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accessibility is often limited.3, 46 By partially reducing the mechanical support of the

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whole biomass, delignification might induce the partial aggregation of adjacent

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cellulose microfibrils.2,

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between glucan conversion/carbohydrate conversion and SSA (r = −0.101/−0.107, P >

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0.05) and PV (r = −0.129/−0.135, P > 0.05) after the base pretreatment. Notably,

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although polysaccharide digestion increases the structural porosity, the enzymatic

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hydrolysis rate still drops.45, 46 As enzymatic hydrolysis slightly increases the CrI,

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crystalline regions of cellulose cannot tell the whole story.42, 48 Therefore, in addition

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to enzyme factors (including traffic jams),49 the subsequently increased lignin content

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might play more important roles. This suggests that biomass porosity does not play

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the dominant role.17, 19, 46, 50

47

15

Corroborating this view, we observed poor correlations

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The APS is also considered as a positive factor for enzyme invasion.32 However,

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the glucan and carbohydrate conversions were significantly negatively correlated with

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APS (r = −0.634 and −0.561, P < 0.05, respectively; Figures 1 and 2i). The APS 16 ACS Paragon Plus Environment

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decreased with the increase of the severity in pretreatments, while adverse trends were

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observed in both SSA and PV. This indicated more nano-pores (>10nm in present

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study, consistent with the reported pore size to enable essential accessibility of

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cellulase protein molecule40, 51) was formed during the ‘peeling away’ of the cell wall

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components in pretreatments.3, 52 At the most fundamental level, these nano-pores

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provides more reaction volumes for the synergistical enzymes, thus facilitating

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enzymatic hydrolysis.53 Consequently, in the next stage of PCA, the APS was

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preserved in the sequential study, whereas the SSA and PV were excluded.

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Lignin structural features

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S/G ratio. Lignin subunits were identified by 2D-HSQC spectra. The S/G ratio varied

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from 0.59 to 6.39 (Figure 2j; for relative contents of syringyl (S), guaiacy (G) and

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p-hydroxyphenyl (H) units, see Table S5). The S/G ratio was positively correlated

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with the glucan and carbohydrate conversions (r = 0.651 and 0.712, P < 0.05,

333

respectively; Figures 1 and 2j). This result revealed that a higher S/G ratio facilitated

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sugar release, consistent with the trend observed in a large-scale poplar study.9 Due to

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the open C-5 position, G units not only feature higher affinity to enzymes than S units

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and thus aggravate the non-productive binding of cellulases.11 They could further lead

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to more stable 5-5 and β-5 linkages and form a more cross-linked and recalcitrant

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structure of lignin.13, 54 By contrast, the methoxylated and blocked C-5 position in S

339

units preferentially forms β-β linkages, which contributes to the linearity of lignin.9, 55



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Considering G-rich lignin with lower reactivity is also related to less labile β-O-49,

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the relative content of lignin linkages was investigated. The S/G ratio was positively

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correlated with the relative content of β-O-4 (r =0.458, P < 0.05, Table S5), but

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negatively correlated with the relative content of β-5 (r = −0.413, P < 0.05, Table S5).

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These results confirmed the negative role of G-rich lignin.

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The negative influence of lignin content reportedly decreases at higher S/G ratios

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(≥2.0).9 However, the interaction effect between the lignin content and S/G ratio

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remains nonquantitative. Therefore, the relative contribution of these two factors in

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biomass digestibility was additionally weighed by standardised MLR, wherein the

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coefficients of the lignin content were much higher than those of the S/G ratio (glucan

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conversion regression: −0.679 vs. 0.263, carbohydrate conversion regression: 0.681 vs.

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0.323, Table S9). These results implied that enzymatic digestibility might be affected

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by both the lignin content and composition in untreated biomass, but only dominated

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by lignin content in pretreated biomass.56 Regardless of the S/G ratio effect, lowering

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lignin content is preponderant in improving the digestibility.11

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Hydroxyl Features. The non-productive binding of cellulase is often relevant to the

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hydroxyl groups of lignin8, which could be quantified by

357

(Figure S3, Table S6). Among the phenolic hydroxyls (PhOH), we detected C5

358

substituted condensed phenolic OH, guaiacyl phenolic OH, catechol type OH and

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p-Hydroxyl-phenyl-OH.69 Aliphatic (AOH) and carboxylic acid hydroxyls (COH)

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were summed as a single factor ACOH (aliphatic + carboxylic acid hydroxyls). 4, 57-58 18 ACS Paragon Plus Environment

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P NMR spectroscopy

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The PhOH and ACOH were then summed to give the TOH. Interestingly, the PhOH

362

was significantly negatively correlated with carbohydrate conversion (r = −0.509, P
0.05, Figures 1

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and 2 m). PhOH in residual lignin could interact with the amide groups in enzymes

365

through hydrogen bond and strengthen the hydrophobic interactions between aromatic

366

amino acids in enzymes and the aromatic rings of PhOH. Thus, phenolic compounds

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became disruptive in enzymatic functions.28 This result confirmed the negative role of

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PhOH in cellulase inactivation. However, given the abovementioned structural model

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of lignocellulose (in which lignin is directly linked to hemicellulose), PhOH should

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less strikingly affect the glucan conversion59 and was thus excluded from the

371

glucan-conversion-relevant factors in the following PCA stage.

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Unexpectedly, glucan/carbohydrate conversion was negatively correlated with

373

ACOH (r = −0.500/−0.580, P < 0.05, Figure 2k) and TOH (r =−0.509/−0.632, P
15). Therefore, it was highly unlikely that the results were observed under the

437

null hypothesis.22 All variance inflation factors (VIF) were below 10.0, indicating

438

insignificant correlations among the factors. These results guaranteed reliability of the

439

regression. In the next internal validation by LOO and BOOT, the cross-validation

440

2 2 coefficients were relatively high (QLOO-Glucan = 0.672 and QLOO-Carbohydrate = 0.654). The

441

2 BOOT step also yielded sufficiently high coefficients ( QBOOT-Glucan = 0.692 and


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2 QBOOT-Carbohydrate = 0.698). Their RMSELOO-Glucan, RMSELOO-Carbohydrate, RMSEBOOT-Glucan ,

443

RMSEBOOT-Carbohydrate were 0.109, 0.195, 0.096, 0.171, respectively. The validation

444

coefficients (> 0.5) and RMSEs (< 0.3) confirmed the robustness of the regressions.

445

The experimental validation involved in both mild and severe pretreatments was

446

further conducted on four additionally-tested samples (Table S11). The errors between

447

the predicted and measured sugar conversions were less than 10% and their

448

2 cross-validation coefficient QExt also exceeded 0.5 (Table S11), confirming the

449

validity of the statistical analysis.

450

Eqs. I1 and I2 with sufficient predictability (R2 = 0.784 and 0.832 for glucan and

451

carbohydrate conversion, respectively) can be highlighted for promoting enzymatic

452

digestibility by delignification. However, the major inhibition mechanism of lignin

453

might differ in glucan and carbohydrate conversions. The glucan conversion was

454

mainly affected by O/C ratio and lignin content, indicating that its underlying

455

mechanism involves surface-lignin coverage. Specially, during acid pretreatment,

456

lignin migrates to the biomass surface and aggregates into droplets with a low O/C

457

ratio,61 while the largely dissolved surface lignin in base pretreatment increases the

458

O/C ratio and improves the biomass digestibility.27 Given that a significantly

459

heterogeneous and hydrophobic surface with high lignin content impedes the glucan

460

conversion, we inferred that lignin negatively impacts the glucan conversion mainly

461

through the non-productive binding of cellulase.

462

conversion was mainly influenced by cellulose content besides lignin content. Given

62-63

By contrast, the carbohydrate



463

that cellulose is minimally depolymerised in acid/base pretreatment, the cellulose

464

content is indirectly varied by both lignin and hemicellulose removal. The removal of

465

the recalcitrant components unblocked the initial availability of the biomass substrate.

466

This also implied that, for carbohydrate conversion, the steric hindrance might be the

467

major negative factor. To avoid these two inhibitors, removing lignin to a low content

468

should be the most direct and efficient approach to optimise the amenability of the

469

biomass to enzymatic hydrolysis.

470

CONCLUSION

471

In this work, the substrate-related factors of corn stover pretreated by acid/base

472

and enzymes were comprehensively characterised. A statistically-designed sequence

473

was employed to account for the interaction effects of multivariate and multiscale

474

factors involved in biomass recalcitrance. Among thirteen factors, lignin content,

475

CrI/cellulose, APS, TOH, ACOH, PhOH were significantly negative factors, whereas

476

cellulose content, O/C ratio and S/G ratio played the positive roles. The hemicellulose

477

content, CrI, SSA, and PV proved irrelevant to biomass digestibility. Three

478

representative factors (i.e., lignin content, O/C ratio and cellulose content) were then

479

selected by PCA. Finally, a standardised model was constructed by stepwise MLR,

480

which mechanically interpreted the impacts of these three factors. The equations were

481

determined as Glucan Conversion = 0.225 − 1.38 × Lignin Content + 0.911 × O/C

482

ratio and Carbohydrate Conversion = 0.261 − 3.142 × Lignin Content + 1.488 ×

483

Cellulose Content. Lignin content was systematically and quantitatively proved most 24 ACS Paragon Plus Environment

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484

dominant. The influence of the lignin structural features (S/G ratios and hydroxyl

485

groups) were important but less pronounced when high lignin content remained. More

486

importantly, the sequential analysis revealed different inhibition mechanisms for

487

glucan and carbohydrate conversions, i.e., non-productive binding of cellulase to

488

lignin and steric hindrance of lignin, respectively. Further sequential analysis on

489

larger-scale samples would provide deeper insights into biomass digestibility, thus

490

enabling the optimisation of the bioconversion process.

491

ASSOCIATED CONTENT

492

Supporting Information

493

The Supporting Information is available free of charge on the ACS Publications

494

website. 31

495

Compositional analysis and lignin extraction methods; Q-Q plots, XRD and

496

NMR spectra (Figures S1-3); Pretreatment details, compositional analysis and

497

mass recovery of samples, XPS, CrI and CrI/cellulose, 2D NMR, 31P NMR, PCA

498

results, standardized MLR results and experimental test (Tables S1-11, PDF).

499

AUTHOR INFORMATIN

500

Corresponding Author

501

Yan Shi

502

*E-mail: [email protected]; Fax: +86-0731-88710171; Tel: +86-0731-88830875

503

Notes

504

The authors declare no competing financial interest.

505

ACKNOWLEDGMENTS


P


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506

This work was supported by key project of National Natural Science Foundation

507

of China (51634010), National Natural Science Foundation of China (31400115,

508

51474102), China Postdoctoral Science Foundation (2017M612594).

509

TABLES AND FIGURES

510

Table 1. Development of regression equations with the factors selected by PCA using

511

stepwise MLR method.

512 #

Equation

R2

F

p

VIF

I1

Glucan Conversion =0.225−1.38×LigninContent + 0.911×O/C ratio

0.784

30.8

Elucidating the Interactive Impacts of Substrate ... - ACS Publications

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