Intelligent modelling of sugarcane juice quality characteristics based on microfluidization processing conditions.

ABSTRACT: This investigation employed different ANN infrastructures for predicting the quality of sugarcane juice under varying microfluidization pressures (50-200 MPa) and cycles (1-7) which was previously unexplored. Two hidden layer (HL) activation functions (tansigmoid, logsigmoid) and learning algorithms (LM, GDX) with varying hidden layer neurons (HLNs) were tested to predict the color, total phenol content, total flavonoid content, chlorophyll content, total and reducing sugars, polyphenol oxidase activity, peroxidase activity, sucrose neutral invertase activity, aerobic plate count, yeast and mold count, particle size, sensory score and sedimentation rate of sugarcane juice under different microfluidization processing conditions. Results showed that the combination of LM + logsigmoid, GDX + 

SUBMITTER: Tarafdar A 

PROVIDER: S-EPMC11464965 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature