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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. However, identification of these analytes can be quite costly or taxing without proper analytical standards. To circumvent this problem we utilize Quantitative structure-retention relationships (QSRR) models to predict elution order and retention times. Properties calculated from density functional theory (DFT) and the conductor-like screening model for real solvents (COSMO-RS) theory are used to produce our QSRR models, which can be calculated for virtually any analyte. We show that this methodology has been successful in identification of per- /poly-fluoroalkyl substances (PFAS) and other contaminants. 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. However, identification of these analytes can be quite costly or taxing without proper analytical standards. To circumvent this problem we utilize Quantitative structureretention relationships (QSRR) models to predict elution order and retention times. Properties calculated from density functional theory (DFT) and the conductor-like screening model for real solvents (COSMO-RS) theory are used to produce our QSRR models, which can be calculated for virtually any analyte. We show that this methodology has been
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Matrix effects demystified: Strategies for resolving challenges in analytical separations of complex samples
Matrix effects can significantly impede the accuracy, sensitivity, and reliability of separation techniques presenting a formidable challenge to the analytical process. It is crucial to address matrix effects to achieve accurate and precise measurements in complex matrices. The multifaceted nature of matrix effects which can be influenced by factors such as target analyte, sample preparation protocol, composition, and choice of instrument necessitates a pragmatic approach when analyzing complex matrices. This review aims to highlight common challenges associated with matrix effects throughout the entire analytical process with emphasis on gas chromatography‐mass spectrometry, liquid chromatography‐mass spectrometry, and sample preparation techniques. These techniques are susceptible to matrix effects that could lead to ion suppression/enhancement or impact the analyte signal at various stages of the analytical workflow. The assessment, quantification, and mitigation of matrix effects are necessary in developing any analytical method. Strategies can be implemented to reduce or eliminate the matrix effect by changing the type of ionization, improving extraction and clean‐up methods, optimization of chromatography conditions, and corrective calibration methods. While development of an effective strategy to completely mitigate matrix effects remains elusive, an integrated approach that combines sample preparation, analytical extraction, and effective instrumental analysis remains the most promising avenue for identifying and resolving matrix effects.
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- Award ID(s):
- 2144591
- PAR ID:
- 10473572
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Journal of Separation Science
- Volume:
- 46
- Issue:
- 23
- ISSN:
- 1615-9306
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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