Screening of the aerodynamic and biophysical properties of barley malt
 
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1
Department of Agricultural Engineering, Golestan Agricultural and Resources Research, Center, Gorgan, Iran
2
These authors contributed equally to this work as first authors
3
MeditBio, Faculty of Sciences and Technology, University of Algarve, Campus de Gambelas, 8005-139 Faro, Portugal
4
Young Researchers and Elites Club, Gorgan Branch, Islamic Azad University, Gorgan, Iran
5
Young Researchers and Elites Club, Shahre Qods Branch, Islamic Azad University, 37541-374 Shahre Qods, Iran
6
Sanandaj Branch, Faculty of Agriculture, Islamic Azad University, Iran
7
Young Researchers and Elite Club, Sabzevar Branch, Islamic Azad University, Sabzevar, Iran
Publication date: 2016-10-18
 
Int. Agrophys. 2016, 30(4): 457–464
KEYWORDS
ABSTRACT
An understanding of the aerodynamic and biophysical properties of barley malt is necessary for the appropriate design of equipment for the handling, shipping, dehydration, grading, sorting and warehousing of this strategic crop. Malting is a complex biotechnological process that includes steeping; germination and finally, the dehydration of cereal grains under controlled temperature and humidity conditions. In this investigation, the biophysical properties of barley malt were predicted using two models of artificial neural networks as well as response surface methodology. Stepping time and germination time were selected as the independent variables and 1 000 kernel weight, kernel density and terminal velocity were selected as the dependent variables (responses). The obtained outcomes showed that the artificial neural network model, with a logarithmic sigmoid activation function, presents more precise results than the response surface model in the prediction of the aerodynamic and biophysical properties of produced barley malt. This model presented the best result with 8 nodes in the hidden layer and significant correlation coefficient values of 0.783, 0.767 and 0.991 were obtained for responses one thousand kernel weight, kernel density, and terminal velocity, respectively. The outcomes indicated that this novel technique could be successfully applied in quantitative and qualitative monitoring within the malting process.
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