Supplementary material for doctoral dissertation: Prediction of Retention and Ionization Behavior of Selected Analytes in Micellar Liquid Chromatography and Mass Spectrometry Using Machine Learning Algorithm
Само за регистроване кориснике
2023
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Data used in mixed QSPR modeling.
The data table used for modeling includes the following key components:
1. Experimental conditions: The experimental conditions encompass parameters such as methanol (MeOH) content in the mobile phase, flow rate of the mobile phase, discharge current , sheath gas pressure and vaporizer temperature. These parameters are systematically varied within specific ranges according to the experimental plan based on the Box-Behnken design (BBD).
2. Calculated molecular descriptors: An important aspect of the data table is the pool of molecular descriptors calculated by Dragon software. 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 APCI ionization behavior.
3. Observed response: Response (that is, dependent variable used for QSPR modeling) was acquired as signal intensity (cps) of the... m/z signal from the target ionic species in selected ion monitoring (SIM) mode. The responses for each of the eight analytes were measured under 41 different experimental conditions, designed using the BBD. The studied response was sqrt-transformed to eliminate the skewness of the ion signal distribution.
Кључне речи:
Quantitative Structure-Property Relationship / Liquid Chromatography - Mass Spectrometry / Signal Prediction / Molecular Descriptors / Box-Behnken Design / Atmospheric Pressure Chemical IonizationИзвор:
Prediction of Retention and Ionization Behavior of Selected Analytes in Micellar Liquid Chromatography and Mass Spectrometry Using Machine Learning Algorithm, 2023Издавач:
- University of Belgrade - Faculty of Pharmacy
Финансирање / пројекти:
- Министарство науке, технолошког развоја и иновација Републике Србије, институционално финансирање - 200161 (Универзитет у Београду, Фармацеутски факултет) (RS-MESTD-inst-2020-200161)
Напомена:
- Dataset for: https://doi.org/10.1016/j.chemolab.2022.104554
- Related to published version: https://farfar.pharmacy.bg.ac.rs/handle/123456789/4081
- Related to dataset: https://farfar.pharmacy.bg.ac.rs/handle/123456789/4883
- Related to dataset: https://farfar.pharmacy.bg.ac.rs/handle/123456789/4880
Повезане информације:
- Повезани садржај
https://doi.org/10.1016/j.chemolab.2022.104554 - Повезани садржај
https://farfar.pharmacy.bg.ac.rs/handle/123456789/4081 - Повезани садржај
https://farfar.pharmacy.bg.ac.rs/handle/123456789/4883 - Повезани садржај
https://farfar.pharmacy.bg.ac.rs/handle/123456789/4880
Колекције
Институција/група
PharmacyTY - DATA AU - Krmar, Jovana PY - 2023 UR - https://farfar.pharmacy.bg.ac.rs/handle/123456789/4884 AB - Data used in mixed QSPR modeling. The data table used for modeling includes the following key components: 1. Experimental conditions: The experimental conditions encompass parameters such as methanol (MeOH) content in the mobile phase, flow rate of the mobile phase, discharge current , sheath gas pressure and vaporizer temperature. These parameters are systematically varied within specific ranges according to the experimental plan based on the Box-Behnken design (BBD). 2. Calculated molecular descriptors: An important aspect of the data table is the pool of molecular descriptors calculated by Dragon software. 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 APCI ionization behavior. 3. Observed response: Response (that is, dependent variable used for QSPR modeling) was acquired as signal intensity (cps) of the m/z signal from the target ionic species in selected ion monitoring (SIM) mode. The responses for each of the eight analytes were measured under 41 different experimental conditions, designed using the BBD. The studied response was sqrt-transformed to eliminate the skewness of the ion signal distribution. 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 Algorithm 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 Algorithm UR - https://hdl.handle.net/21.15107/rcub_farfar_4884 ER -
@misc{ author = "Krmar, Jovana", year = "2023", abstract = "Data used in mixed QSPR modeling. The data table used for modeling includes the following key components: 1. Experimental conditions: The experimental conditions encompass parameters such as methanol (MeOH) content in the mobile phase, flow rate of the mobile phase, discharge current , sheath gas pressure and vaporizer temperature. These parameters are systematically varied within specific ranges according to the experimental plan based on the Box-Behnken design (BBD). 2. Calculated molecular descriptors: An important aspect of the data table is the pool of molecular descriptors calculated by Dragon software. 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 APCI ionization behavior. 3. Observed response: Response (that is, dependent variable used for QSPR modeling) was acquired as signal intensity (cps) of the m/z signal from the target ionic species in selected ion monitoring (SIM) mode. The responses for each of the eight analytes were measured under 41 different experimental conditions, designed using the BBD. The studied response was sqrt-transformed to eliminate the skewness of the ion signal distribution.", 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 Algorithm", 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 Algorithm", url = "https://hdl.handle.net/21.15107/rcub_farfar_4884" }
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 Algorithm. in Prediction of Retention and Ionization Behavior of Selected Analytes in Micellar Liquid Chromatography and Mass Spectrometry Using Machine Learning Algorithm University of Belgrade - Faculty of Pharmacy.. https://hdl.handle.net/21.15107/rcub_farfar_4884
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 Algorithm. in Prediction of Retention and Ionization Behavior of Selected Analytes in Micellar Liquid Chromatography and Mass Spectrometry Using Machine Learning Algorithm. 2023;. https://hdl.handle.net/21.15107/rcub_farfar_4884 .
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 Algorithm" in Prediction of Retention and Ionization Behavior of Selected Analytes in Micellar Liquid Chromatography and Mass Spectrometry Using Machine Learning Algorithm (2023), https://hdl.handle.net/21.15107/rcub_farfar_4884 .