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

Autor: M. Doneva, S. Dyankova, Yancho Todorov, Margarita Terziyska, I. Nacheva, P. Metodieva, D. Miteva
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Terziyska, M, Todorov, Y, Miteva, D, Doneva, M, Dyankova, S, Metodieva, P & Nacheva, I 2020, ' Bayesian Regularized Neural Network for Prediction of the Dose in Gamma Irradiated Milk Products ', Cybernetics and Information Technologies, vol. 20, no. 2, pp. 141-151 . https://doi.org/10.2478/cait-2020-0022
ISSN: 1314-4081
1311-9702
DOI: 10.2478/cait-2020-0022
Popis: 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.
Databáze: OpenAIRE