Modeling the Physiological Parameters of Brewer’s Yeast during Storage with Natural Zeolite-Containing Tuffs Using Artificial Neural Networks
نویسندگان
چکیده
Various methods are used to prevent the deterioration of biotechnological properties brewer’s yeast during storage. This paper studied use artificial neural networks for mathematical modeling correcting biosynthetic activity seed C34 race storage with natural minerals. The input parameters were suspending medium (water, beer wort, or young beer); type zeolite-containing tuff from Siberian deposits; content (0.5–4% total volume suspension); and duration (3 days). output number cells glycogen, budding cells, dead cells. In stored tuffs, increased by 1.2–2.5 times, glycogen 9–190% compared control sample (without tuff). presence kholinskiy zeolite shivyrtuin tuffs resulted in a significant effect. required solving regression tasks predicting based on parameters. Four created: ANN1 (mean relative error = 4.869%) modeled values all parameters; ANN2 (MRE 1.8381%) glycogen; ANN3 6.2905%) cells; ANN4 4.2191%) optimal then determined. As result, possibility using ANNs undesired deviations physiological minerals was proven.
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ژورنال
عنوان ژورنال: Information
سال: 2022
ISSN: ['2078-2489']
DOI: https://doi.org/10.3390/info13110529