Bayesian Regularized Neural Network for Prediction of the Dose in Gamma Irradiated Milk Products

Gamma irradiation is a well-known method for sterilizing different foodstuffs, including fresh cow milk. Many studies witness that the low dose irradiation of milk and milk products affects the fractions of the milk protein, thus reducing its allergenic effect and make it potentially appropriate for...

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Bibliographic Details
Published in:Cybernetics and information technologies : CIT 2020-06, Vol.20 (2), p.141-151
Main Authors: Terziyska, M., Todorov, Y., Miteva, D., Doneva, M., Dyankova, S., Metodieva, P., Nacheva, I.
Format: Article
Language:English
Subjects:
Bayesian neural network
milk allergy
milk products
protein fraction
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Summary:Gamma irradiation is a well-known method for sterilizing different foodstuffs, including fresh cow milk. Many studies witness that the low dose irradiation of milk and milk products affects the fractions of the milk protein, thus reducing its allergenic effect and make it potentially appropriate for people with milk allergy. The purpose of this study is to evaluate the relationship between the gamma radiation dose and size of the protein fractions, as potential approach to decrease the allergenic effect of the milk. In this paper, an approach for prediction of the dose in gamma irradiated products by using a Bayesian regularized neural network as a mean to save recourses for expensive electrophoretic experiments, is developed. The efficiency of the proposed neural network model is proved on data for two dairy products – lyophilized cow milk and curd.
ISSN:1314-4081
1314-4081
DOI:10.2478/cait-2020-0022