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 + logsigmoid and GDX + tansigmoid produced > 90% prediction accuracy. Among these models, GDX + tansigmoid exhibited 91.7% accuracy on training, and 96% accuracy on testing using relatively lower number of neurons (10 HLNs), and was therefore selected to predict the quality characteristics of sugarcane juice.
Supplementary information: The online version contains supplementary material available at 10.1007/s13197-024-05994-2.
Keywords: ANN; Artificial intelligence; Enzyme; Food; High pressure; Sugarcane juice.
© Association of Food Scientists & Technologists (India) 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.