首页|Researchers’ from Kuban State University Report Details of New Studies and Findi ngs in the Area of Machine Learning (Authentication of selected white wines by g eographical origin using ICP spectrometric and chemometric analysis)
Researchers’ from Kuban State University Report Details of New Studies and Findi ngs in the Area of Machine Learning (Authentication of selected white wines by g eographical origin using ICP spectrometric and chemometric analysis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting from Krasnodar, Russia, by Ne wsRx journalists, research stated, “An important aspect of assessing the authent icity of wines is its geographical origin.” Funders for this research include Russian Science Foundation. Our news editors obtained a quote from the research from Kuban State University: “The aim of the work is to authenticate by geographical origin according to the data of the ICP-spectrometric and chemometric analysis of elemental “images” of wines produced from white grape varieties Chardonnay, Riesling and Muscat grown in four regions of the Krasnodar Territory, Russia. The difference in the conte nts of Al, Ba, Ca and Rb in wines was found depending on the variety, and Al, Ba , Rb, Fe, Li, Sr - depending on the region of grape growth. Different models of the experimental data processing were used for attribution of the produced varie ties of wine to the area of the grape’s growth. The criterion for the quality of the constructed models was the accuracy of the attribution of a wine variety to the area of the grape’s growth (%). Analysis of the elemental anal ysis data of 153 wine samples showed that in terms of attribution accuracy, auto mated neural networks (100 %) are preferred among machine learning methods, followed by support vector machines (98.69 %) and general discriminant analysis (94.77 %). The applied mathematical models en abled the revealing of the cluster structure of the analyzed wine varieties and their attribution to the area of a grape growth with high accuracy.”
Kuban State UniversityKrasnodarRussi aEurasiaChemometricCyborgsEmerging TechnologiesMachine Learning
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