Data-driven interpretable analysis for polysaccharide yield prediction  

作　　者：Yushi Tian Xu Yang Nianhua Chen Chunyan Li Wulin Yang 

机构地区：[1]School of Resource and Environment,Northeast Agriculture University,Harbin,150030,PR China [2]College of Environmental Sciences and Engineering,Peking University,Beijing,100871,PR China

出　　处：《Environmental Science and Ecotechnology》2024年第3期91-99,共9页环境科学与生态技术（英文）

基　　金：supported by the Academic Core Project of Northeast Agricultural University Scholars Program(20YJ5B01);Heilongjiang Postdoctoral General Fund Project(LBH-Z21110);Key Laboratory of Swine Facilities Engineering,Ministry of Agriculture and Rural Affairs,Northeast Agricultural University 150030,P.R.China.

摘　　要：Cornstalks show promise as a raw material for polysaccharide production through xylanase.Rapid and accurate prediction of polysaccharide yield can facilitate process optimization,eliminating the need for extensive experimentation in actual production to refine reaction conditions,thereby saving time and costs.However,the intricate interplay of enzymatic factors poses challenges in predicting and optimizing polysaccharide yield accurately.Here,we introduce an innovative data-driven approach leveraging multiple artificial intelligence techniques to enhance polysaccharide production.We propose a machine learning framework to identify highly accurate polysaccharide yield prediction modeling methods and uncover optimal enzymatic parameter combinations.Notably,Random Forest(RF)and eXtreme Gradient Boost(XGB)demonstrate robust performance,achieving prediction accuracies of 93.0%and 95.6%,respectively,while an independently developed deep neural network(DNN)model achieves 91.1%accuracy.A feature importance analysis of XGB reveals the enzyme solution volume's dominant role(43.7%),followed by time(20.7%),substrate concentration(15%),temperature(15%),and pH(5.6%).Further interpretability analysis unveils complex parameter interactions and potential optimization strategies.This data-driven approach,incorporating machine learning,deep learning,and interpretable analysis,offers a viable pathway for polysaccharide yield prediction and the potential recovery of various agricultural residues.

关 键 词：Cornstalk XYLANASE Polysaccharide yield prediction Machine learning Model interpretability 

分 类 号：X712[环境科学与工程—环境工程]