A machine learning method for juice human sensory hedonic prediction using electronic sensory features.

ABSTRACT: This study proposed a method that combines fused electronic sensory analysis technology with artificial neural network to predict the human sensory hedonic of fruit juice. Quantitative descriptive analysis (QDA) and the scoring test method were utilized for human sensory evaluation. The first step involved modeling the fused e-sensory features with human sensory attributes, followed by establishing a fitting model of human sensory attributes and acceptance. The R² and RMSE values obtained were 0.77 and 0.42 (QDA method), and 0.63 and 0.63 (scoring test method). Finally, the relationship between the fusion e-sensory features and the human sensory hedonic was established. Model-1 achieved an R² of 0.95 and an RMSE of 0.04, while model-2 achie

SUBMITTER: Yang H 

PROVIDER: S-EPMC10485034 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature