Artificial neural network in response of mass transfer parameters predictions (moisture loss and solid gain) during osmotic dehydration of fruits

Acta Agronómica

Ochoa Martinez, Claudia Isabel¹ 

[1] Universidad del Valle, Cali, Colombia

关键词: Agroindustrial applications;    ANN model;    drying;    fruits;    moisture;   

DOI  :  10.15446/acag.v65n4.50382

学科分类：农业科学（综合）

来源: Universidad Nacional de Colombia * Facultad de Ciencias Agropecuarias Palmira

【 摘 要 】

Models for the prediction of water loss (ML) and solid gain (SG) in osmotic dehydration process based on artificial neural network (ANN) perform better as compared to other models developed for osmotic dehydration, because these models mathematically correlate a wide quantity of processing variables with ML and SG. The main advantage of these models is that they are predictive rather than correlative, also these models can be easily implemented in a spreadsheet, and they are very useful and practical for process design and control. The aim of this work is to use a developed model based on ANN to predict outcomes in osmotic dehydration processes. Predictions were made with different process conditions and were validate by using experimental data reported in literature. Good predictions of ML (MRE 19%) and variable behavior for SG (MRE: 62%) were obtained.

【 授权许可】

CC BY-NC-SA