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Chemical Product and Process Modeling

Ed. by Sotudeh-Gharebagh, Rhamat / Mostoufi, Navid / Chaouki, Jamal


CiteScore 2017: 0.96

SCImago Journal Rank (SJR) 2017: 0.295
Source Normalized Impact per Paper (SNIP) 2017: 0.347

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1934-2659
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Estimation of Moisture Ratio of a Mushroom Undergoing Microwave-vacuum Drying Using Artificial Neural Network and Regression Models

Poonpat Poonnoy / Ampawan Tansakul / Manjeet Chinnan
Published Online: 2007-10-31 | DOI: https://doi.org/10.2202/1934-2659.1057

The drying rate of a mushroom undergoing microwave-vacuum (MV) drying (MVD) was controlled by moisture dissipation and was dependent on vacuum pressure levels. The main objective of this work was to develop artificial neural network (ANN) model to predict moisture ratio of MV-dried mushrooms. One-hidden-layer feed-forward ANN models were trained and validated with experimental data. The Levenberg-Marquardt algorithm was utilized in regulating the ANN model weights and biases. Inputs for ANN models were vacuum pressure and drying time. Output from ANN models was moisture ratio at a given drying time. Reduced chi-square (X 2) and root mean square error (RMSE), and residual sum of squares (RSS) of the results from ANN models were calculated and compared with those of a modified Page's model (an experimental-based mathematical model), which is commonly used in the literature. The X 2, RMSE, and RSS of the ANN model (2.272 x 10 -5, 4.023 x 10 -3, and 3.204 x 10 -3, respectively) were found to be lower than those of the modified Page's model (6.692 x 10 -4, 2.561 x 10 -2, and 12.98 x 10 -2, respectively). These results indicate that the feed-forward ANN model represented the drying characteristics of mushrooms better than the modified Page's model. Therefore, the ANN model could be considered as a better tool for estimation of the moisture content of mushrooms than by the modified Page's model.

Keywords: drying; microwave-vacuum; modeling; neural network; mushroom

About the article

Published Online: 2007-10-31


Citation Information: Chemical Product and Process Modeling, Volume 2, Issue 3, ISSN (Online) 1934-2659, DOI: https://doi.org/10.2202/1934-2659.1057.

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