Literature DB >> 30580085

Generic gas chromatography flame ionization detection method using hydrogen as the carrier gas for the analysis of solvents in pharmaceuticals.

Frank Bernardoni1, Holst M Halsey2, Robert Hartman3, Timothy Nowak3, Erik L Regalado3.   

Abstract

Within the pharmaceutical industry, the determination of residual solvents by Gas Chromatography Flame Ionization Detection (GC-FID) is a highly utilized analytical test that often employs helium (He) as the carrier gas. However, many do not realize that helium is a non-renewable resource that will eventually become progressively more difficult to source. In recent years, analytical chemists are increasingly adopting hydrogen (H2) in place of helium for routine GC analysis. In this study, a simple and efficient generic/universal GC-FID method using H2 as the carrier gas has been developed with the capability of baseline resolution of over 30 of the most commonly used solvents in development and manufacturing with a method run time of less than eight minutes. The use of this method for the separation and analysis of solvents within a pharmaceutical manufacturing process is demonstrated with additional method validation data presented using five different diluents as a means to increase flexibility for the chromatographer. Furthermore, it is the recommendation of the authors that the current compendia for residual solvent analysis be updated to allow for hydrogen as a carrier gas. The similarity between He and H2 observed within this study supports the use of hydrogen as a suitable replacement for helium, and an update of the EU and USP compendia for residual solvent analysis should be made to reflect this.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Gas chromatography; Green chromatography; High throughput analysis; Hydrogen; Process chemistry; Residual solvent

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Substances:

Year:  2018        PMID: 30580085     DOI: 10.1016/j.jpba.2018.12.006

Source DB:  PubMed          Journal:  J Pharm Biomed Anal        ISSN: 0731-7085            Impact factor:   3.935


  1 in total

1.  Quantitative Analysis of Gas Phase IR Spectra Based on Extreme Learning Machine Regression Model.

Authors:  Tinghui Ouyang; Chongwu Wang; Zhangjun Yu; Robert Stach; Boris Mizaikoff; Bo Liedberg; Guang-Bin Huang; Qi-Jie Wang
Journal:  Sensors (Basel)       Date:  2019-12-14       Impact factor: 3.576

  1 in total

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