# The Effect of Enzyme Synergism on Generation of Fermentable Sugars After Alkali Pretreatment of Wheat Straw, Assessed and Predicted Using Multivariate Analysis

## TL;DR

This study uses multivariate analysis to understand how enzyme combinations affect sugar production from wheat straw after alkali pretreatment.

## Contribution

A predictive model using PCA and PLS is developed to optimize enzyme synergism in lignocellulosic biomass conversion.

## Key findings

- Cellulase dosage, mechanical refining, dye adsorption, and solid content are key variables in the process.
- Combining cellulase and xylanase significantly improves sugar yields and carbohydrate conversion rates.
- The PLS model accurately predicts enzymatic hydrolysis outcomes, aiding industrial bioprocessing optimization.

## Abstract

Alkaline pretreatment of wheat straw could significantly augment enzymatic hydrolysis for producing fermentable sugars, which is a pivotal process for the conversion of lignocellulosic biomass into advanced biofuels, biomaterials, or biochemicals. Yet, the enzymatic conversion process system is complex and multivariate, and study on the interaction mechanism of the key parameters in enzymatic hydrolysis is still lacking. Therefore, in this work, multivariate data analysis (MDA) (i.e., principal component analysis (PCA) and partial least square (PLS)) was conducted to reveal the inherent relationship and the significance of these factors in a modified alkali pretreatment system. A robust model, developed from 140 enzymatic hydrolysis datasets, was validated with an additional 20 datasets, demonstrating the predictive prowess of the PLS model. MDA identified that cellulase dosage, mechanical refining, dye adsorption value, and solid content were paramount variables. The integration of cellulase and xylanase notably elevated sugar yields and the conversion rates of carbohydrates, surpassing those of single enzyme treatments. The model’s predictive accuracy, reflected in the close alignment between observed and predicted data, underscores its suitability for optimizing and controlling the enzymatic hydrolysis process. This study paves a way for data-driven strategies to enhance industrial bioprocessing of lignocellulosic feedstocks.