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

Abstract

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.

Mathematical Models for Vacuum‐microwave Concentration Behavior of Pineapple Juice

“…Therefore, at the same microwave power density, high vacuum pressure provides faster speed of evaporation than low vacuum pressure. These reasons were similar with microwavevacuum-drying principle of grape (Clary et al 2007), mushroom (Poonnoy et al 2007), carrot (Cui et al 2004) and banana slices (Mousa and Farid 2002).…”

“…These reasons were similar with microwave‐vacuum‐drying principle of grape (Clary et al. 2007), mushroom (Poonnoy et al. 2007), carrot (Cui et al.…”

Drying characteristics and quality of shiitake mushroom undergoing microwave-vacuum drying and microwave-vacuum combined with infrared drying

“…In case of absolute pressure, the lowest level of absolute pressure was set based on the efficiency of the vacuum pump. However, the range of absolute pressure employed in this work covered the range of the microwave vacuum drying of other agricultural products as shown in the works of Giri and Prasad (2006), Sutar and Prasad (2007), Poonnoy et al (2007) and Motavali et al (2011).…”

Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network

“…In addition, Zbysinsky et al [3] worked on an investigation for modeling the moisture evaporation process in a fluid bed dryer with the help of ANN. Also, Zubisinsky et al [24] to predict the heat transfer coefficient of various materials; Mittal and Zang [25] to estimate moisture and temperature in thermal processes; Brüyart et al [26] to model the heat and mass transfer phenomenon, and to study the process of qualitative changes in biscuit processing; Hernandez et al [27] for estimating heat and mass transfer in the process of drying starch and mango; and Poonnoy et al [28,29] to model the prediction of the moisture content of the fungus, and predict the temperature and moisture content of the thin layer of tomato in the microwave-vacuum dryer used the neural network (ANN). All of the above studies show the effectiveness of thin-layer models, as well as the neural network model for determining the kinetics of drying agricultural products.…”