Data-driven interpretable analysis for polysaccharide yield prediction

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.

Research on the Extraction Process of Pu-erh Tea Polysaccharide by Response Surface Analysis

[Objective] This study aimed to optimize the extraction process parameters of Pu-erh tea polysaccharide. [Method] Single-factor experiment was carried out to analyze the influences of three main factors, including extraction temperature, ex- traction duration and solid-liquid ratio, on the extraction yield of tea polysaccharide. Box-Behnken central composite design and response surface methodology were adopted to determine the optimal extraction process of Pu-erh tea polysaccharide. [Result] The results of response surface analysis showed that the optimal extraction process was solid-liquid ratio of 1：17, extraction temperature of 80 ℃ and extraction duration of 78.5 min, and the Pu-erh tea polysaccharide yield was 12.72%. [Conclu- sion] Using response surface methodology （RSM） is feasible for the optimization of Pu-erh tea polysaccharide extraction process, and the tea polysaccharide yield increased significantly.

Research progress on Cordyceps militaris polysaccharides

Cordyceps militaris is a famous medicinal and edible fungus,whose polysaccharides are important biologically active substances.In recent years,the molecular structure and pharmacological functions of C.militaris polysaccharides were fully studied.However,the potential mechanism of their pharmacological functions and their chemical modification have not been systematically elucidated.Moreover,the obtention and usage of C.militaris polysaccharides present several issues,including low polysaccharide yields and poor pharmacological functions in industrial production.This review presents the latest results on the pharmacological mechanism of C.militaris,including information on how to improve C.militaris industrial production through stabilization of the strain to prevent degeneration and chemical modifications of its polysaccharides.In addition,this article examines C.militaris fermentation conditions,the polysaccharides extraction methods developed for industrial production,and the strategies to increase the yield of polysaccharides and improve their pharmacological functions.This review could provide a theoretical basis for future research on C.militaris polysaccharides,and also serve as an important reference value in the field of functional foods and medicine.