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Sabina Malovaná

Researcher at Masaryk University

Publications -  5
Citations -  481

Sabina Malovaná is an academic researcher from Masaryk University. The author has contributed to research in topics: High-performance liquid chromatography & Wine. The author has an hindex of 5, co-authored 5 publications receiving 436 citations.

Papers
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Separation of phenolic compounds by high-performance liquid chromatography with absorbance and fluorimetric detection.

TL;DR: Phenolic compounds including phenolic aldehydes, acids and flavonoids are separated by high-performance liquid chromatography (HPLC) with analysis time shorter than described in the literature.
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Optimisation of sample preparation for the determination of trans-resveratrol and other polyphenolic compounds in wines by high performance liquid chromatography

TL;DR: In this article, different methods for sample preparation as a preliminary stage for the simultaneous determination of trans-resveratrol and other polyphenolic compounds are compared with the aim to establish the best conditions for the determination of these compounds in wine samples by high performance liquid chromatography (HPLC).
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Fast analysis of proteins in wines by capillary gel electrophoresis

TL;DR: In this paper, a new and fast method for carrying out the analysis of the protein fraction of wines is proposed, which consists of direct treatment of wine using a centrifugal filter device (CFD), denaturation of proteinaceous fraction with sodium dodecyl sulfate (SDS) and 2-mercaptoethanol, and subsequent CGE analysis of SDSproteins.
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Determination of esmolol in serum by capillary zone electrophoresis and its monitoring in course of heart surgery.

TL;DR: A new Capillary Zone Electrophoresis procedure for determination of esmolol, an ultra-short-acting beta-blocker, in serum was developed and it was applied in an extensive heart surgery experiment on pigs (Sus scrofa).
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Artificial neural networks for modeling electrophoretic mobilities of inorganic cations and organic cationic oximes used as antidote contra nerve paralytic chemical weapons.

TL;DR: The number of experiments necessary to find optimal separation conditions can be reduced significantly and ANNs combined with experimental design were shown to be efficient for modeling and prediction of optimalseparation conditions.