Literature DB >> 11355816

Validation of a chromatography data analysis software.

A Felinger1, G Guiochon.   

Abstract

The performance of chromatography data analysis software packages is of cardinal importance when the precision and the accuracy of a chromatographic system are evaluated. Users cannot rely on a procedure generating chromatographic data of known accuracy. Holistic approaches cannot always be entirely trusted. We propose a new method consisting in validating a data analysis package against computer generated chromatograms of exactly known characteristics by feeding these chromatograms into the vendor supplied software and comparing the results supplied by the software and the exact answers. We simulated symmetrical and tailing chromatograms and processed these signals with the Agilent Technologies (formerly Hewlett-Packard) ChemStation software. The noise profile (i.e. the power spectrum of the baseline) was determined for a HPLC UV detector prior to the calculations, and chromatograms of different signal-to-noise ratios were used for the analysis. For every chromatogram, we simulated 25 replicates with identical signal-to-noise ratios but different noise sequences. In this manner, both the random and the systematic errors of the retention data and peak shape characteristics can be evaluated. When analyzing tailing peaks, we simulated the effects of extra-column band broadening and those of column overload. Our calculations show that the general performance of the data analysis system studied is excellent. The contribution of the random error originating from the data analysis procedure is in most cases negligible compared to the repeatability of the chromatographic measurement itself.

Mesh:

Year:  2001        PMID: 11355816     DOI: 10.1016/s0021-9673(00)00979-1

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


  1 in total

1.  Improving Fab' fragment retention in an autonucleolytic Escherichia coli strain by swapping periplasmic nuclease translocation signal from OmpA to DsbA.

Authors:  Desmond M Schofield; Ernestas Sirka; Eli Keshavarz-Moore; John M Ward; Darren N Nesbeth
Journal:  Biotechnol Lett       Date:  2017-09-05       Impact factor: 2.461

  1 in total

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