Abstract
Artificial neural networks (ANNs) constitute a promising modeling approach that may be used in control systems for postharvest preservation and storage processes. The study investigated the ability of multilayer perceptron and radial-basis function ANNs to predict fungal population levels in bulk stored rapeseeds with various temperatures (T = 12–30 °C) and water activity in seeds (aw = 0.75–0.90). The neural network model input included aw, temperature, and time, whilst the fungal population level was the model output. During the model construction, networks with a different number of hidden layer neurons and different configurations of activation functions in neurons of the hidden and output layers were examined. The best architecture was the multilayer perceptron ANN, in which the hyperbolic tangent function acted as an activation function in the hidden layer neurons, while the linear function was the activation function in the output layer neuron. The developed structure exhibits high prediction accuracy and high generalization capability. The model provided in the research may be readily incorporated into control systems for postharvest rapeseed preservation and storage as a support tool, which based on easily measurable on-line parameters can estimate the risk of fungal development and thus mycotoxin accumulation.
Highlights
Rapeseed (Brassica napus L.) is one of the most important species among protein-oil crops grown in regions of moderate climates
For this purpose data covering a wide range of storage parameters that may be encountered in agricultural practice (i.e., from low values of water activity in seeds and low temperature (T = 12 ◦ C), at which seeds can be stored without considerable seed quality losses for a long time to very adverse conditions, i.e., high values of water activity in seeds and high temperature (T = 30 ◦ C), at which lack of proper postharvest treatments almost immediately leads to seed quality deterioration [1,3,6]) were modeled using two approaches based on the neural network technique (MLP and radial basis function (RBF))
The capability of the network to approximate the analyzed data largely depends on the network structure, which includes among others the number of hidden layers, the size of hidden layers, and the type of transfer function in hidden and output layer neurons
Summary
Rapeseed (Brassica napus L.) is one of the most important species among protein-oil crops grown in regions of moderate climates. Rapeseed plays an essential role in global agriculture and constitutes an important crop for many industries. It is one of the main raw materials, and its usefulness results, among others, from the fact that it contains a high level of oil characterized by a favorable fatty acid composition. Rapeseed oil is a rich source of bioactive components with pro-health properties [1,2,3], which opens the possibility of its use in functional food production. In addition to oil that is a part of a healthy human diet, rapeseed residue (after oil extraction) is a high-protein alternative to other raw materials in animal nutrition
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