scholarly journals Data sharing in PredRet for accurate prediction of retention time: application to plant food bioactive compounds

2021 ◽  
pp. 129757
Author(s):  
Dorrain Yanwen Low ◽  
Pierre Micheau ◽  
Ville Mikael Koistinen ◽  
Kati Hanhineva ◽  
László Abrankó ◽  
...  
2014 ◽  
Vol 64 (1) ◽  
pp. 49-57 ◽  
Author(s):  
Sahar Al-Okbi ◽  
Doha Mohamed ◽  
Thanaa Hamed ◽  
Reham Esmail ◽  
Souria Donya

2021 ◽  
pp. 501-506
Author(s):  
Massimo Lucarini ◽  
Alessandra Durazzo ◽  
Antonio Raffo ◽  
Amirhossein Nazhand ◽  
Eliana B. Souto ◽  
...  

1997 ◽  
Vol 788 (1-2) ◽  
pp. 207-211 ◽  
Author(s):  
Megan C. Frost ◽  
Tom Lahr ◽  
Robert M. Kleyle ◽  
David Nurok

2015 ◽  
Vol 1425 ◽  
pp. 258-264 ◽  
Author(s):  
Carlos Alberto Claumann ◽  
André Wüst Zibetti ◽  
Ariovaldo Bolzan ◽  
Ricardo A.F. Machado ◽  
Leonel Teixeira Pinto

2020 ◽  
Vol 85 (1) ◽  
pp. 9-23
Author(s):  
Branimir Pavlic ◽  
Nemanja Teslic ◽  
Predrag Kojic ◽  
Lato Pezo

This work aimed to obtain a validated model for prediction of retention time of terpenoids isolated from sage herbal dust using supercritical fluid extraction. In total 32 experimentally obtained retention time of terpenes, which were separated and detected by GC?MS were further used to build a prediction model. The quantitative structure?retention relationship was employed to predict the retention time of essential oil compounds obtained in GC?MS analysis, using six molecular descriptors selected by a genetic algorithm. The selected descriptors were used as inputs of an artificial neural network, to build a retention time predictive quantitative structure?retention relationship model. The coefficient of determination for training cycle was 0.837, indicating that this model could be used for prediction of retention time values for essential oil compounds in sage herbal dust extracts obtained by supercritical fluid extraction due to low prediction error and moderately high r2. Results suggested that a 2D autocorrelation descriptor AATS0v was the most influential parameter with an approximately relative importance of 25.1 %.


Metabolites ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 46 ◽  
Author(s):  
Ville Mikael Koistinen ◽  
Andreia Bento da Silva ◽  
László Abrankó ◽  
Dorrain Low ◽  
Rocio Garcia Villalba ◽  
...  

Bioactive compounds present in plant-based foods, and their metabolites derived from gut microbiota and endogenous metabolism, represent thousands of chemical structures of potential interest for human nutrition and health. State-of-the-art analytical methodologies, including untargeted metabolomics based on high-resolution mass spectrometry, are required for the profiling of these compounds in complex matrices, including plant food materials and biofluids. The aim of this project was to compare the analytical coverage of untargeted metabolomics methods independently developed and employed in various European platforms. In total, 56 chemical standards representing the most common classes of bioactive compounds spread over a wide chemical space were selected and analyzed by the participating platforms (n = 13) using their preferred untargeted method. The results were used to define analytical criteria for a successful analysis of plant food bioactives. Furthermore, they will serve as a basis for an optimized consensus method.


2018 ◽  
pp. 55-73
Author(s):  
L. A. Ortega-Ramirez ◽  
G. A. Gonzalez-Aguilar ◽  
J. F. Ayala-Zavala ◽  
M. R. Cruz-Valenzuela

2016 ◽  
Vol 916 ◽  
pp. 8-16 ◽  
Author(s):  
Giuseppe Marco Randazzo ◽  
David Tonoli ◽  
Stephanie Hambye ◽  
Davy Guillarme ◽  
Fabienne Jeanneret ◽  
...  

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