Multiple linear regression model for predicting biomass digestibility from structural features.

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 during 1-, 6-, and 72-h hydrolysis periods. Carbohydrate conversions at 1, 6, and 72 h were linearly proportional to the logarithm of cellulase loadings from approximately 10% to 90% conversion, indicating that the simplified HCH-1 model is valid for predicting lignocellulose digestibility. The HCH-1 model is a modified Michaelis-Menton model that accounts for the fraction of insoluble substrate available to bind with enzyme. The slopes and intercepts of a simplified HCH-1 model were correlated with structural features using 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 content, acetyl content, and cellulose crystallinity are key factors that determine biomass digestibility. The 1-, 6-, and 72-h glucan, xylan, and total sugar conversions predicted from structural features using MLR models and the simplified HCH-1 model fit satisfactorily with the measured data (R(2) approximately 1.0). The parameter selection suggests that lignin content and cellulose crystallinity more strongly affect on digestibility than acetyl content. Cellulose crystallinity has greater influence during short hydrolysis periods whereas lignin content has more influence during longer hydrolysis periods. Cellulose crystallinity shows more influence on glucan hydrolysis whereas lignin content affects xylan hydrolysis to a greater extent.

Neural network prediction of biomass digestibility based on structural features.

Plots of biomass digestibility are linear with the natural logarithm of enzyme loading; the slope and intercept characterize biomass reactivity. The feed-forward back-propagation neural networks were performed to predict biomass digestibility by simulating the 1-, 6-, and 72-h slopes and intercepts of glucan, xylan, and total sugar hydrolyses of 147 poplar wood model samples with a variety of lignin contents, acetyl contents, and crystallinity indices. Regression analysis of the neural network models indicates that they performed satisfactorily. Increasing the dimensionality of the neural network input matrix allowed investigation of the influence glucan and xylan enzymatic hydrolyses have on each other. Glucan hydrolysis affected the last stage of xylan digestion, and xylan hydrolysis had no influence on glucan digestibility. This study has demonstrated that neural networks have good potential for predicting biomass digestibility over a wide range of enzyme loadings, thus providing the potential to design cost-effective pretreatment and saccharification processes.

Structural features affecting biomass enzymatic digestibility.

The rate and extent of enzymatic hydrolysis of lignocellulosic biomass highly depend on enzyme loadings, hydrolysis periods, and structural features resulting from pretreatments. Furthermore, the influence of one structural feature on biomass digestibility varies with the changes in enzyme loading, hydrolysis period and other structural features as well. In this paper, the effects of lignin content, acetyl content, and biomass crystallinity on the 1-, 6-, and 72-h digestibilities with various enzyme loadings were investigated. To eliminate the cross effects among structural features, selective pretreatment techniques were employed to vary one particular structural feature during a pretreatment, while the other two structural features remained unchanged. The digestibility results showed that lignin content and biomass crystallinity dominated digestibility whereas acetyl content had a lesser effect. Lignin removal greatly enhanced the ultimate hydrolysis extent. Crystallinity reduction, however, tremendously increased the initial hydrolysis rate and reduced the hydrolysis time or the amount of enzyme required to attain high digestibility. To some extent, the effects of structural features on digestibility were interrelated. At short hydrolysis periods, lignin content was not important to digestibility when crystallinity was low. Similarly, at long hydrolysis periods, crystallinity was not important to digestibility when lignin content was low.

Enzymatic hydrolysis of lime-pretreated corn stover and investigation of the HCH-1 Model: inhibition pattern, degree of inhibition, validity of simplified HCH-1 Model.

The inhibition pattern was identified for a reaction system composed of Trichoderma reesei cellulase enzyme complex and lime-pretreated corn stover. Also, the glucose inhibition effect was quantified for the aforementioned reaction system over a range of enzyme loadings and substrate concentrations. Lastly, the range of substrate concentrations and enzyme loadings were identified in which the linear form of the simplified HCH-1 Model is valid. The HCH-1 Model is a modified Michaelis-Menton Model with non-competitive inhibition and the fraction of insoluble substrate available to bind with enzyme. With a high enzyme loading, the HCH-1 Model can be integrated and simplified in such a way that sugar conversion is linearly proportional to the logarithm of enzyme loading. A wide range of enzyme loadings (0.25-50 FPU/g dry biomass) and substrate concentrations (10-100g/L) were investigated. All experiments were conducted with an excess cellobiase loading to ensure the experimental results were not influenced by cellobiose inhibition. A non-competitive inhibition pattern was identified for the corn stover-cellulase reaction system, thereby validating the assumptions of the HCH-1 Model. At a substrate concentration of 10 g/L, glucose inhibition parameters of 0.986 and 0.979 were measured for enzyme loadings of 2 FPU/g dry biomass and 50 FPU/g dry biomass, respectively. At 5 FPU/g dry biomass, glucose inhibition parameters of 0.985 and 0.853 were measured for substrate concentrations of 10 and 100g/L, respectively. The linear form of the HCH-1 Model predicted biomass digestibility for lime-pretreated corn stover over an enzyme loading range of 0.25-50 FPU/g dry biomass and substrate concentration range of 10-100g/L.

Statistical correlation of spectroscopic analysis and enzymatic hydrolysis of poplar samples.

Spectroscopic characterization of poplar wood samples with different crystallinity indices, lignin contents, and acetyl contents was performed to determine changes in the biomass spectra and the effects of these changes on the hydrolysis yield. The spectroscopic methods used were X-ray diffraction for determining cellulose crystallinity (CrI), diffuse reflectance infrared (DRIFT) for changes in C-C and C-O bonds, and fluorescence to determine lignin content. Raman spectroscopy was also used to determine its effectiveness in the determination of crystallinity and C-C and C-O bond changes in the biomass as a complement to better-known methods. Changes in spectral characteristics and crystallinity were statistically correlated with enzymatic hydrolysis results to identify and better understand the fundamental features of biomass that influence enzymatic conversion to monomeric sugars. In addition, the different spectroscopic methods were evaluated separately to determine the minimum amount of spectroscopic data needed to obtain accurate predictions. The principal component regression (PCR) model with only the DRIFT data gives the best correlation and prediction for both initial rate of hydrolysis and also the 72-h hydrolysis yield. The factor that most affects both the initial rate and the 72-h conversion is the O-H bond content of the sample, which directly relates to the breakage of structural carbohydrates into smaller molecules.