Supplementary material for doctoral dissertation: Prediction of Retention and Ionization Behavior of Selected Analytes in Micellar Liquid Chromatography and Mass Spectrometry Using Machine Learning Algorithms
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2023
Skup podataka (Objavljena verzija)
Metapodaci
Prikaz svih podataka o dokumentuApstrakt
Data table for model building.
The data table used for modeling includes the following key components:
1. Chromatographic conditions: The chromatographic conditions encompass parameters such as the concentration of Brij L23, the pH of the micellar component of the mobile phase, and the volume fraction of acetonitrile. These parameters are systematically varied within specific ranges according to the experimental plan based on the Box-Behnken design.
2. Calculated molecular descriptors: An important aspect of the data table is the pool of calculated molecular descriptors. These descriptors provide valuable insights into the molecular characteristics of the compounds under investigation. They aid in understanding the relationship between the compounds' structural properties and their observed retention behavior.
3. Observed response: The observed response parameter consists of retention factors, a fundamental measure in chromatography. These retention factors were measured an...d recorded during the experimental process. They offer critical information about the compounds' affinity for the stationary phase and their overall behavior in the MLC system.
Ključne reči:
Box-Behnken Design / Aripiprazole / Quantitative Structure-Retention Relationship / Micellar Liquid ChromatographyIzvor:
Prediction of Retention and Ionization Behavior of Selected Analytes in Micellar Liquid Chromatography and Mass Spectrometry Using Machine Learning Algorithms, 2023Izdavač:
- University of Belgrade - Faculty of Pharmacy
Finansiranje / projekti:
- Sinteza, kvantitativni odnos između strukture i dejstva, fizičko-hemijska karakterizacija i analiza farmakološki aktivnih supstanci (RS-MESTD-Basic Research (BR or ON)-172033)
Napomena:
- Dataset for: https://doi.org/10.1016/j.chroma.2020.461146
- Related to dataset: https://farfar.pharmacy.bg.ac.rs/handle/123456789/4883
- Related to published version: https://farfar.pharmacy.bg.ac.rs/handle/123456789/3585
- Related to dataset: https://farfar.pharmacy.bg.ac.rs/handle/123456789/4884
Povezane informacije:
- Povezani sadržaj
https://doi.org/10.1016/j.chroma.2020.461146 - Povezani sadržaj
https://farfar.pharmacy.bg.ac.rs/handle/123456789/4883 - Povezani sadržaj
https://farfar.pharmacy.bg.ac.rs/handle/123456789/3585 - Povezani sadržaj
https://farfar.pharmacy.bg.ac.rs/handle/123456789/4884
Kolekcije
Institucija/grupa
PharmacyTY - DATA AU - Krmar, Jovana PY - 2023 UR - https://farfar.pharmacy.bg.ac.rs/handle/123456789/4880 AB - Data table for model building. The data table used for modeling includes the following key components: 1. Chromatographic conditions: The chromatographic conditions encompass parameters such as the concentration of Brij L23, the pH of the micellar component of the mobile phase, and the volume fraction of acetonitrile. These parameters are systematically varied within specific ranges according to the experimental plan based on the Box-Behnken design. 2. Calculated molecular descriptors: An important aspect of the data table is the pool of calculated molecular descriptors. These descriptors provide valuable insights into the molecular characteristics of the compounds under investigation. They aid in understanding the relationship between the compounds' structural properties and their observed retention behavior. 3. Observed response: The observed response parameter consists of retention factors, a fundamental measure in chromatography. These retention factors were measured and recorded during the experimental process. They offer critical information about the compounds' affinity for the stationary phase and their overall behavior in the MLC system. PB - University of Belgrade - Faculty of Pharmacy T2 - Prediction of Retention and Ionization Behavior of Selected Analytes in Micellar Liquid Chromatography and Mass Spectrometry Using Machine Learning Algorithms T1 - Supplementary material for doctoral dissertation: Prediction of Retention and Ionization Behavior of Selected Analytes in Micellar Liquid Chromatography and Mass Spectrometry Using Machine Learning Algorithms UR - https://hdl.handle.net/21.15107/rcub_farfar_4880 ER -
@misc{ author = "Krmar, Jovana", year = "2023", abstract = "Data table for model building. The data table used for modeling includes the following key components: 1. Chromatographic conditions: The chromatographic conditions encompass parameters such as the concentration of Brij L23, the pH of the micellar component of the mobile phase, and the volume fraction of acetonitrile. These parameters are systematically varied within specific ranges according to the experimental plan based on the Box-Behnken design. 2. Calculated molecular descriptors: An important aspect of the data table is the pool of calculated molecular descriptors. These descriptors provide valuable insights into the molecular characteristics of the compounds under investigation. They aid in understanding the relationship between the compounds' structural properties and their observed retention behavior. 3. Observed response: The observed response parameter consists of retention factors, a fundamental measure in chromatography. These retention factors were measured and recorded during the experimental process. They offer critical information about the compounds' affinity for the stationary phase and their overall behavior in the MLC system.", publisher = "University of Belgrade - Faculty of Pharmacy", journal = "Prediction of Retention and Ionization Behavior of Selected Analytes in Micellar Liquid Chromatography and Mass Spectrometry Using Machine Learning Algorithms", title = "Supplementary material for doctoral dissertation: Prediction of Retention and Ionization Behavior of Selected Analytes in Micellar Liquid Chromatography and Mass Spectrometry Using Machine Learning Algorithms", url = "https://hdl.handle.net/21.15107/rcub_farfar_4880" }
Krmar, J.. (2023). Supplementary material for doctoral dissertation: Prediction of Retention and Ionization Behavior of Selected Analytes in Micellar Liquid Chromatography and Mass Spectrometry Using Machine Learning Algorithms. in Prediction of Retention and Ionization Behavior of Selected Analytes in Micellar Liquid Chromatography and Mass Spectrometry Using Machine Learning Algorithms University of Belgrade - Faculty of Pharmacy.. https://hdl.handle.net/21.15107/rcub_farfar_4880
Krmar J. Supplementary material for doctoral dissertation: Prediction of Retention and Ionization Behavior of Selected Analytes in Micellar Liquid Chromatography and Mass Spectrometry Using Machine Learning Algorithms. in Prediction of Retention and Ionization Behavior of Selected Analytes in Micellar Liquid Chromatography and Mass Spectrometry Using Machine Learning Algorithms. 2023;. https://hdl.handle.net/21.15107/rcub_farfar_4880 .
Krmar, Jovana, "Supplementary material for doctoral dissertation: Prediction of Retention and Ionization Behavior of Selected Analytes in Micellar Liquid Chromatography and Mass Spectrometry Using Machine Learning Algorithms" in Prediction of Retention and Ionization Behavior of Selected Analytes in Micellar Liquid Chromatography and Mass Spectrometry Using Machine Learning Algorithms (2023), https://hdl.handle.net/21.15107/rcub_farfar_4880 .