Abstract
Abstract The objective of this study was to predict celeriac drying curves using artificial neural networks (ANNs). The experimental data for vacuum drying kinetics of celeriac slices reported by other researcher in the previously published article was used. The air temperature, chamber pressure and time values were used as ANN inputs. To predict the moisture content, the multilayer feed forward back propagation neural network, as a well-known network, was used. The network with Levenberg-Marquardt learning algorithm, hyperbolic tangent sigmoid transfer function, and 3-6-9-1 topology provided the superior results.
Highlights
Apium graveolens L. var. rapaceum is a root vegetable with a bulbous hypocotyl, growing to 120-200 cm tall
The main objective of the present study was to find the best artificial neural networks (ANNs) topology to predict the kinetics of drying celeriac
The celeriac slices were dried by a vacuum dryer at pressure of 0.1, 3, 7, 10 and 17 kPa and temperatures of 55, 65, 75 °C (Alibaş, 2012)
Summary
Apium graveolens L. var. rapaceum is a root vegetable with a bulbous hypocotyl, growing to 120-200 cm tall. Open sun and shade methods is a traditional drying method that has been employed for the dehydration because of low costs and simplicity. These methods have some problems such as microbial contamination of the dried materials, dust as well as long drying time (Soysal, 2004). To overcome these problems, it is essential to employ artificial dryers for the removal of water from agricultural and food products (Demiray & Tulek, 2012). Food drying is heat sensitive and demands special attention (Mujumdar, 2014)
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