Literature DB >> 30245073

Influence of charged aerosol detector instrument settings on the ultra-high-performance liquid chromatography analysis of fatty acids in polysorbate 80.

Klaus Schilling1, Ruben Pawellek1, Katherine Lovejoy2, Tibor Muellner2, Ulrike Holzgrabe3.   

Abstract

The analysis of polysorbate 80 is a challenge because all components lack a chromophore. Here, an ultra-high-performance liquid chromatography system equipped with a charged aerosol detector (UHPLC-CAD) was used to study the effect of systematic variation of the CAD settings, namely evaporation temperature, filter constant and power function value (PFV), on the detector response of fatty acid standards and manufacturing batches of polysorbate. Evaporation temperature and filter constant strongly affect the detection limits described by signal-to-noise (S/N) ratios. Although evaporation temperature can be increased to improve signal to noise ratios, analyte volatility at higher temperatures is an important limiting factor. The PFV was found to be a strong tool for optimizing response linearity, but the optimal PFV differed depending on analyte volatility. Because PFV optimization required some additional measurement time and because double-logarithmic transformation at the default PFV of 1.0 yielded satisfying universal results with less measurement time over a range of two orders of magnitude for every homologue fatty acid from C14 to C18, use of the log-log transformation is the favored linearization strategy. Possible optimization procedures for semi volatile substances are presented. Overall, this new UHPLC method method offers improved detection limits, as well as time savings of over 75% and eluent savings of more than 40% compared to the previously published HPLC-CAD method for polysorbate analysis.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Charged aerosol detector; Evaporation temperature; Fatty acids; Filter constant; Power function value; UHPLC method optimization

Mesh:

Substances:

Year:  2018        PMID: 30245073     DOI: 10.1016/j.chroma.2018.09.031

Source DB:  PubMed          Journal:  J Chromatogr A        ISSN: 0021-9673            Impact factor:   4.759


  1 in total

1.  Charged aerosol detector response modeling for fatty acids based on experimental settings and molecular features: a machine learning approach.

Authors:  Ruben Pawellek; Jovana Krmar; Adrian Leistner; Nevena Djajić; Biljana Otašević; Ana Protić; Ulrike Holzgrabe
Journal:  J Cheminform       Date:  2021-07-15       Impact factor: 5.514

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.