Simultaneous Prediction of Retention Times and Peak Shapes of Sulfonamides in Reversed-Phase High-Performance Liquid Chromatography

Wei, Xiaobing; Pang, Zhengji; Fan, Guoliang; Xu, Xinhua; Wang, Lihong

doi:10.1007/s12209-017-0110-x

Cited by 3 publications

(5 citation statements)

References 12 publications

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“…80 When simulations were performed for 101 different conditions, including variation in injection volume, loop filling level, and degree of mismatch between the sample solvent and the starting composition of the gradient, the median prediction error in retention time was −1.0%. In a recent related study, 81 plate theory has been combined with a third-order modification of the LSSM to predict the retention times of 8 sulfonamides on a C18 column using gradient elution conditions. Prediction errors over 18 gradient conditions and 4 flow-rates averaged 0.70%.…”

Section: ∫ =mentioning

confidence: 99%

“…An alternative way to approach retention time prediction is the use of the Craig counter-current distribution model (i.e., the plate model), where the analyte is considered to propagate through a discrete space and time grid. This approach, coupled with the LSSM, has been used to predict retention times and peak shapes in gradient elution RPLC. − In the initial study, both the LSSM and its nonlinear form were used to simulate retention times and peak widths for separations of alkylbenzenes and also for amphetamines. The simulation was extended to analytes eluted under conditions where the MP and sample solvent differ.…”

Section: Prediction Of Retention In Rplcmentioning

confidence: 99%

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Prediction of Analyte Retention Time in Liquid Chromatography

2020

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Section: ∫ =mentioning

confidence: 99%

Section: Prediction Of Retention In Rplcmentioning

confidence: 99%

Prediction of Analyte Retention Time in Liquid Chromatography

2020

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“…The flow rate of the mobile phase affects the retention time and peak shape [24]. On the process development, the best result for the flow rate was 1 mL/min as showed better retention time and resolution.…”

Section: Development Of Chromatographic Conditionmentioning

confidence: 99%

Validation and Quantification of Domperidone in Spiked Plasma Matrix Using Reversed Phase HPLC-UV Method

et al. 2021

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Pharmacokinetics studies of domperidone generally analyze plasma matrix samples. The present work aimed to develop and validate a rapid and simple reversed phase-HPLC method for quantifying domperidone in plasma matrices. The chromatographic method implemented: 1. Luna Phenomenex® C18 (250 mm × 4.6 mm i.d; 5 µm) column, 2. isocratic mobile phase mixture of phosphate buffer 0.02 M:acetonitrile (70:30, v/v) with a flow rate of 1 mL/min, 3. UV detection at 285 nm. Domperidone and propranolol hydrochloride (as internal standard) were extracted from the deproteinated plasma sample. The method linearity was 0.998 in the range concentration of 15–200 ng/mL. The percentage of accuracy error was between -8.49–4.31%, while the percentage coefficient variation of precision ranged between 5.11–14.24%. This proposed method was simple, rapid (separation time less than 10 min), and selective. The validation parameters responses satisfied the method's requirements to determine domperidone in a plasma sample.

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“…25,26 Wang et al have also addressed prediction of retention time by examining different models for a set of sulfonamides with 0.70% prediction error average for multiple gradient conditions and flow rates. 27 Quantitative structure-retention relationships (QSRR) models have been used to predict elution order and retention times. The general procedure for QSRR models includes preparing a training set where the retention time/factor of a known analyte is fitted to "molecular descriptors" via a mathematical model.…”

Section: Introductionmentioning

confidence: 99%

“…Among different applications, physicochemical retention models are often studied which utilizes physical and chemical properties of targeted analytes, chromatographic stationary phases, and mobile phases to relate to retention parameters to derive mathematical expressions . One study has investigated a range of retention models to predict retention time with chromatographic gradient elution programs, and are able to achieve prediction errors of below 1–2%. , Wang et al have also addressed prediction of retention time by examining different models for a set of sulfonamides with 0.70% prediction error average for multiple gradient conditions and flow rates …”

Section: Introductionmentioning

confidence: 99%

In Silico Supported Nontarget Analysis of Contaminants of Emerging Concern: Increasing Confidence in Unknown Identification in Wastewater and Surface Waters

et al. 2021

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Nontarget analysis using liquid chromatography–high resolution mass spectrometry (LC–HRMS) is a valuable approach in characterizing for contaminants of emerging concern (CECs) in the environment. In this study, wastewater and surface water samples from three locations in Manila, Philippines were analyzed for CECs using a nontarget analysis approach with an LC-Orbitrap. A previously optimized semiautomated workflow was used for data processing with Compound Discoverer. A total of 157 compounds were identified, with 21 confirmed with reference standards, 83 confirmed with evidence from a mass spectral library (mzCloud), and 53 tentatively identified using in silico fragmentation (MetFrag). These compounds include pharmaceuticals such as antibiotics, antifungal, and antihypertensive compounds, human metabolites, natural products, pesticides, and industrial chemicals. Compounds confirmed with reference standards include antibiotics ciprofloxacin, clarithromycin, acetyl-sulfamethoxazole, and trimethoprim (2 to 19 ng/L), and antifungal compounds carbendazim and climbazole (3–47 ng/L). The pesticides diethyltoluamide (DEET) and diuron were also detected (37 ng/L). The utility of a preliminary multivariable linear regression quality structure-retention relationship (QSRR) model based on quantum chemical molecular descriptors is demonstrated. This study demonstrates the importance of using tools and software that are helpful for annotating HRMS data and reporting detections according to a standardized classification system. The detection of several CECs in wastewater and surface water samples show the importance of performing nontarget analysis in determining occurrence of CECs in the environment.

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Simultaneous Prediction of Retention Times and Peak Shapes of Sulfonamides in Reversed-Phase High-Performance Liquid Chromatography

Cited by 3 publications

References 12 publications

Prediction of Analyte Retention Time in Liquid Chromatography

Prediction of Analyte Retention Time in Liquid Chromatography

Validation and Quantification of Domperidone in Spiked Plasma Matrix Using Reversed Phase HPLC-UV Method

In Silico Supported Nontarget Analysis of Contaminants of Emerging Concern: Increasing Confidence in Unknown Identification in Wastewater and Surface Waters

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