Citation
Yousefi A. R., . and Ghasemian N, . Prediction of papaw moisture ratio during hot air-drying: GMDH vs. mathematical modeling. pp. 2347-2352. ISSN 2231-7546
Abstract
The main objective of this work was prediction of the moisture content of papaw during hot air-drying in a cabinet dryer using both mathematical and GMDH (group method of data handling). The influence of air temperatures (40 50 and 60C) and fruit slices thickness (3 5 7 mm) on moisture ratio were investigated. Exactly 50 of the data points were used for training and 50 for testing. Furthermore eight well-known empirical models were subjected to experimental data for modeling of the drying process. The determination coefficient (R ) and root mean square error (RMSE) computed for the GMDH model were 0.9960 and 0.0220. Among the empirical models considered the Two terms model was found to be more suitable for predicting drying of papaw fruit slices with the values of R0.9974 and RMSE0.0123. Thus it was deduced that the estimation of moisture content of papaw fruit could be modelled by GMDH method as good as the best empirical models.
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Abstract
The main objective of this work was prediction of the moisture content of papaw during hot air-drying in a cabinet dryer using both mathematical and GMDH (group method of data handling). The influence of air temperatures (40 50 and 60C) and fruit slices thickness (3 5 7 mm) on moisture ratio were investigated. Exactly 50 of the data points were used for training and 50 for testing. Furthermore eight well-known empirical models were subjected to experimental data for modeling of the drying process. The determination coefficient (R ) and root mean square error (RMSE) computed for the GMDH model were 0.9960 and 0.0220. Among the empirical models considered the Two terms model was found to be more suitable for predicting drying of papaw fruit slices with the values of R0.9974 and RMSE0.0123. Thus it was deduced that the estimation of moisture content of papaw fruit could be modelled by GMDH method as good as the best empirical models.
Additional Metadata
| Item Type: | Article |
|---|---|
| AGROVOC Term: | Papaw |
| AGROVOC Term: | Hot air drying |
| AGROVOC Term: | Drying |
| AGROVOC Term: | Regression analysis |
| AGROVOC Term: | Mathematical models |
| AGROVOC Term: | Models |
| AGROVOC Term: | Air temperature |
| AGROVOC Term: | Prediction |
| AGROVOC Term: | Neural networks |
| AGROVOC Term: | Moisture content |
| Depositing User: | Ms. Suzila Mohamad Kasim |
| Last Modified: | 24 Apr 2025 06:28 |
| URI: | http://webagris.upm.edu.my/id/eprint/23881 |
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