TY  - JOUR
T1  - Prediction of Solubility of Food Oleoresins during Supercritical CO2 Extraction using Artificial Neural Networks
AU - , A. Segura Juan AU - , Purlis Emmanuel 
JO  - Journal of Food Technology
VL  - 2
IS  - 4
SP  - 320
EP  - 325
PY  - 2004
DA  - 2001/08/19
SN  - 1684-8462
DO  - jftech.2004.320.325
UR  - https://makhillpublications.co/view-article.php?doi=jftech.2004.320.325
KW  - 
AB  - In this paper a backpropagation artificial neural network was implemented in order to predict the solubility of several chemical substances during supercritical extraction processes. The predictive models reported in literature can be classified into two groups: a) semi-empirical models, and b) empirical models. The semi-empirical models are derived from thermodynamic laws and take into account the non-linear behavior of the fluids; whereas the empirical models are based on non-linear regression in least square sense from experimental data sets. Due to its simplicity, the most common empirical model applied in literature is the Chrastil density-based model. However, this model produces relatively high errors when it is used to predict solubility of food oleoresins. In this work we have developed a predictive neural-network-based model and have concluded that the error in prediction is significantly low with respect to the error of both semi-empirical and empirical mathematical models.
ER  -