# Multivariable Analysis Reveals the Key Variables Related to Lignocellulosic Biomass Type and Pretreatment before Enzymolysis

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## Abstract

**:**

## 1. Introduction

## 2. Results and Discussion

#### 2.1. Effects of Three Pretreatments on the Sugar Yield in the Enzymolysis

#### 2.2. Influence and Estimation of Factors on Enzymolysis

#### 2.2.1. Establishment of PLS Models for the Analysis of Enzymolysis

#### 2.2.2. Comparison of Different Lignocellulosic Biomass and Pretreatment Methods on ERSY

#### 2.3. PLS Analysis for the Effects of the Biomass Compositions and Conditions on the ERSY under Three Pretreatments

## 3. Materials and Methods

#### 3.1. Materials and Preparation

#### 3.2. Pretreatment

_{2}SO

_{4}(0.25%, 1.00% and 2.13% v/v, respectively) were put into the micro-polymerization reactors (250 mL). Then the samples were performed at 30 °C, 105 °C and 121 °C for 30 min.

#### 3.3. Enzymolysis

#### 3.4. The Reducing Sugar Analysis and Calculation

#### 3.5. Multivariate Data Analysis and Statistical Analysis

## 4. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Distribution of enzymatic reducing sugar yield (ERSY) under different pretreatment methods. (

**a**). Poplar. (

**b**). Salix. (

**c**). Corncob. All boxplot distributions are tested using Independent-Samples t-test at the 95% level compared with each other; *, p < 0.05; **, p < 0.01. Note: Each box represents a distribution of one pretreatment method containing a nine-condition dataset.

**Figure 2.**PLS model 1 analysis for the effects of biomass type and pretreatments on the ERSY. (

**a**) Biplot. (

**b**) Regression coefficients of X variables on the Y variable. (

**c**) Importance of X variables on the Y variables for the projection. Note: (

**a**) The poplar, salix and corn cob were the pretreated samples under different experimental conditions. The scores t[1] and t[2] summarize the X variables, which are orthogonal and completely independent of each other. The score t[1] (first component) explains the largest variation of the X space, followed by t[2]. The regular loading values p correspond to the covariances between the X variables and the score vectors (t) in question, which mean that the magnitude (numerical range) of each X variable is reflected in the loading value. The weights of the variables were indicated by pc[1] (for the first component) and pc[2] (for the second component). The error bars mean the confidence intervals of the coefficients, which indicate the influence degree of each variable, and the influence is significant when the confidence intervals do not include zero.

**Figure 3.**PLS model 2 analysis for the alkaline pretreatment followed by enzymolysis. (

**a**) Biplot. (

**b**) Regression coefficients of X variables on the Y variable. (

**c**) Importance of X variables on the Y variables for the projection.

**Figure 4.**PLS model 3 analysis for the hot water pretreatment followed by enzymolysis. (

**a**) Biplot. (

**b**) Regression coefficients of X variables on the Y variable. (

**c**) Importance of X variables on the Y variables for the projection.

**Figure 5.**PLS model 4 analysis for the acid pretreatment followed by enzymolysis. (

**a**) Biplot. (

**b**) Regression coefficients of X variables on the Y variable. (

**c**) Importance of X variables on the Y variables for the projection.

Lignocellulose Biomass | NDF (%) | Hemicellulose (%) | Cellulose (%) | Lignin (%) | Ash (%) |
---|---|---|---|---|---|

Poplar | 22.45 ± 0.62 | 25.72 ± 0.93 | 34.89 ± 0.32 | 16.59 ± 0.09 | 0.34 ± 0.02 |

Salix | 28.43 ± 1.13 | 25.43 ± 0.17 | 31.54 ± 0.47 | 13.94 ± 1.25 | 0.65 ± 0.06 |

Corncob | 31.32 ± 0.71 | 37.23 ± 0.54 | 24.09 ± 0.12 | 7.29 ± 0.26 | 0.07 ± 0.01 |

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**MDPI and ACS Style**

Wang, X.; Fan, D.; Han, Y.; Xu, J.
Multivariable Analysis Reveals the Key Variables Related to Lignocellulosic Biomass Type and Pretreatment before Enzymolysis. *Catalysts* **2022**, *12*, 1142.
https://doi.org/10.3390/catal12101142

**AMA Style**

Wang X, Fan D, Han Y, Xu J.
Multivariable Analysis Reveals the Key Variables Related to Lignocellulosic Biomass Type and Pretreatment before Enzymolysis. *Catalysts*. 2022; 12(10):1142.
https://doi.org/10.3390/catal12101142

**Chicago/Turabian Style**

Wang, Xiujun, Deliang Fan, Yutong Han, and Jifei Xu.
2022. "Multivariable Analysis Reveals the Key Variables Related to Lignocellulosic Biomass Type and Pretreatment before Enzymolysis" *Catalysts* 12, no. 10: 1142.
https://doi.org/10.3390/catal12101142