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Fulltext Evaluation of a suitable thin layer model for drying of pumpkin under forced air convection

Onwude, D. I., Hashim, N., Janius, R. B., Nawi, N., Abdan, K.

International Food Research Journal, 2016;23(3):1173-1181.
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The thin layer drying kinetics of pumpkin slices (Cucurbita moschata) were experimentally
investigated in a convective hot air dryer. In order to select the appropriate model for predicting
the drying kinetics of pumpkin (Cucurbita moschata), twelve thin layer semi theoretical,
theoretical and empirical models, widely used in describing the drying behaviour of agricultural
products were fitted to the experimental data. The Page and Two term exponential models
showed the best fit under certain drying conditions. The Hii et al. (2009) model, which was
adopted from a combination of the Page and Two term models was compared to the other 11
selected thin layer models based on the coefficient of determination (R2
) and sum of squares
error (SSE). Comparison was made between the experimental and model predicted moisture
ratio by non-linear regression analysis. Furthermore, the effect of drying temperature and slice
thickness on the best model constants was evaluated. Consequently, the Hii et al. (2009) model
showed an excellent fit with the experimental data (R2 > 0.99 and SSE < 0.012) for the drying
temperatures of 50, 60, 70 and 80 °C and at different sample thicknesses of 3 mm, 5 mm and
7 mm respectively. Thus, the Hii et al. (2009) model can adequately predict the drying kinetics
of pumpkin.
Fulltext Modelling the convective drying process of pumpkin (Cucurbita moschata) using an artificial neural network

Onwude, D. I., Hashim, N., Janius, R. B., Nawi, N., Abdan, K.

International Food Research Journal, 2016;23(101):237-243.
MyJurnal

This study investigated the drying kinetic of pumpkin under different drying temperatures (50,
60, 70 and 80°C), samples thickness (3, 4, 5 and 7mm), air velocity (1.2m/s) and relative
humidity (40 - 50%). Kinetic models were developed using semi-theoretical thin layer models
and multi-layer feed-forward artificial neural network (ANN) method. The Hii et al. (2009)
semi-theoretical model was found to be the most suitable thin layer model while two hidden
layers with 20 neurons was the best for the ANN method. The selections were based on the
statistical indicators of coefficient of determination (R2), root mean square error (RMSE) and
sum of squares error (SSE). Results indicated that the ANN demonstrated better prediction
than those of the theoretical models with R2, RMSE and SSE values of 0.992, 0.036 and 0.207
as compared to the Hii et al. (2009) model values of 0.902, 0.088 and 1.734 respectively. The
validation result also showed good agreement between the predicted values obtained from
the ANN model and the experimental moisture ratio data. This indicates that an ANN can
effectively describe the drying process of pumpkin.
The potential of computer vision, optical backscattering parameters and artificial neural network modelling in monitoring the shrinkage of sweet potato (Ipomoea batatas L.) during drying

Onwude DI, Hashim N, Abdan K, Janius R, Chen G

J Sci Food Agric, 2018 Mar;98(4):1310-1324.
PMID: 28758207 DOI: 10.1002/jsfa.8595

BACKGROUND: Drying is a method used to preserve agricultural crops. During the drying of products with high moisture content, structural changes in shape, volume, area, density and porosity occur. These changes could affect the final quality of dried product and also the effective design of drying equipment. Therefore, this study investigated a novel approach in monitoring and predicting the shrinkage of sweet potato during drying. Drying experiments were conducted at temperatures of 50-70 °C and samples thicknesses of 2-6 mm. The volume and surface area obtained from camera vision, and the perimeter and illuminated area from backscattered optical images were analysed and used to evaluate the shrinkage of sweet potato during drying.
RESULTS: The relationship between dimensionless moisture content and shrinkage of sweet potato in terms of volume, surface area, perimeter and illuminated area was found to be linearly correlated. The results also demonstrated that the shrinkage of sweet potato based on computer vision and backscattered optical parameters is affected by the product thickness, drying temperature and drying time. A multilayer perceptron (MLP) artificial neural network with input layer containing three cells, two hidden layers (18 neurons), and five cells for output layer, was used to develop a model that can monitor, control and predict the shrinkage parameters and moisture content of sweet potato slices under different drying conditions. The developed ANN model satisfactorily predicted the shrinkage and dimensionless moisture content of sweet potato with correlation coefficient greater than 0.95.
CONCLUSION: Combined computer vision, laser light backscattering imaging and artificial neural network can be used as a non-destructive, rapid and easily adaptable technique for in-line monitoring, predicting and controlling the shrinkage and moisture changes of food and agricultural crops during drying. © 2017 Society of Chemical Industry.

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