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

Margarita Terziyska, Yancho Todorov, Daniela Miteva, Maria Doneva, Svetla Dyankova, Petya Metodieva, Iliana Nacheva

    Research output: Contribution to journalArticleScientificpeer-review

    1 Citation (Scopus)
    35 Downloads (Pure)

    Abstract

    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.
    Original languageEnglish
    Pages (from-to)141-151
    JournalCybernetics and Information Technologies
    Volume20
    Issue number2
    DOIs
    Publication statusPublished - 12 Jun 2020
    MoE publication typeA1 Journal article-refereed

    Keywords

    • protein fraction
    • milk products
    • milk allergy
    • Bayesian neural network

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