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370 results on '"Płotka-Wasylka, Justyna"'

37. History and Milestones of Green Analytical Chemistry

Author

Płotka-Wasylka, Justyna, Fabjanowicz, Magdalena, Kalinowska, Kaja, Namieśnik, Jacek, He, Liang-Nian, Series Editor, Rogers, Robin D., Series Editor, Su, Dangsheng, Series Editor, Tundo, Pietro, Series Editor, Zhang, Z. Conrad, Series Editor, Płotka-Wasylka, Justyna, editor, and Namieśnik, Jacek, editor

Published
2019
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40. The Use of Ultra-Fast Gas Chromatography for Fingerprinting-Based Classification of Zweigelt and Rondo Wines with Regard to Grape Variety and Type of Malolactic Fermentation Combined with Greenness and Practicality Assessment.

Author

Stój, Anna, Wojnowski, Wojciech, Płotka-Wasylka, Justyna, Czernecki, Tomasz, and Kapusta, Ireneusz Tomasz

Subjects
GAS chromatography/Mass spectrometry (GC-MS), SUPPORT vector machines, PRINCIPAL components analysis, GAS chromatography, GRAPES
Abstract

In food authentication, it is important to compare different analytical procedures and select the best method. The aim of this study was to determine the fingerprints of Zweigelt and Rondo wines through headspace analysis using ultra-fast gas chromatography (ultra-fast GC) and to compare the effectiveness of this approach at classifying wines based on grape variety and type of malolactic fermentation (MLF) as well as its greenness and practicality with three other chromatographic methods such as headspace solid-phase microextraction/gas chromatography-mass spectrometry with carboxen-polydimethylosiloxane fiber (SPME/GC-MS with CAR/PDMS fiber), headspace solid-phase microextraction/gas chromatography-mass spectrometry with polyacrylate fiber (SPME/GC-MS with PA fiber), and ultra performance liquid chromatography–photodiode array detector-tandem mass spectrometry (UPLC-PDA-MS/MS). Principal Component Analysis (PCA) revealed that fingerprints obtained using all four chromatographic methods were suitable for classification using machine learning (ML). Random Forest (RF) and Support Vector Machines (SVM) yielded accuracies of at least 99% in the varietal classification of Zweigelt and Rondo wines and therefore proved suitable for robust fingerprinting-based Quality Assurance/Quality Control (QA/QC) procedures. In the case of wine classification by the type of MLF, the classifiers performed slightly worse, with the poorest accuracy of 91% for SVM and SPME/GC-MS with CAR/PDMS fiber, and no less than 93% for the other methods. Ultra-fast GC is the greenest and UPLC-PDA-MS/MS is the most practical of the four chromatographic methods. [ABSTRACT FROM AUTHOR]

Published
2024
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41. Modified GAPI (MoGAPI) Tool and Software for the Assessment of Method Greenness: Case Studies and Applications.

Author

Mansour, Fotouh R., Płotka-Wasylka, Justyna, and Locatelli, Marcello

Subjects
*SUSTAINABLE chemistry, *SOFTWARE development tools, *ANALYTICAL chemistry, *CHEMICAL processes, *RESEARCH personnel
Abstract

Abstract: The green analytical procedure index (GAPI) has been widely used to assess the greenness of different steps in analytical methodology. In the GAPI index, the different stages of the chemical analysis process are depicted using five pentagrams divided in subsections, labeled green, yellow, or red according to the degree of greenness. This GAPI tool provides a quick overview of the environmental impact and safety of the procedure. However, there is no total score that can be calculated from the GAPI metric to enable comparison between methods. In this work, a modified GAPI tool (MoGAPI) and software have been developed and applied to address the limitations of the current GAPI metric. The presented tool offers a more precise assessment of greenness, while the software simplifies and expedites its application. It also combines the advantages of the analytical Eco-Scale with the merits of the widely used GAPI metric. This tool was successfully applied to calculate the greenness of a few case studies and applications to show the applicability of this modified metric. The software for the MoGAPI tool is also freely available (open source) at bit.ly/MoGAPI to facilitate application and method comparison. The MoGAPI tool and its software represent a significant advancement in greenness assessment, providing researchers with a robust and user-friendly means to evaluate and compare analytical methods. [ABSTRACT FROM AUTHOR]

Published
2024
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47. Deep eutectic solvent-based shaking-assisted extraction for determination of bioactive compounds from Norway spruce roots

Author

Kalyniukova, Alina, primary, Várfalvyová, Alica, additional, Płotka-Wasylka, Justyna, additional, Majchrzak, Tomasz, additional, Makoś-Chełstowska, Patrycja, additional, Tomášková, Ivana, additional, Pešková, Vítězslava, additional, Pastierovič, Filip, additional, Jirošová, Anna, additional, and Andruch, Vasil, additional

Published
2024
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