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Optimization based on retention prediction and information theory for liquid-chromatographic analysis of alkylbenzenes

Journal Article · · Analytical Letters; (United States)
; ; ;  [1];  [2]
  1. National Inst. of Hygienic Sciences, Tokyo (Japan)
  2. Toyohashi Univ. of Technology (Japan)

The mobile phase composition and column length are optimized for analyses of six alkylbenzenes in reversed-phase liquid chromatography with the aid of retention prediction and information theory. Optimal conditions selected according to the resolution Rs and information theory are evaluated from the viewpoint of the precision and analytical efficiency (rapidity) of chromatography. The combination of the information-theoretical optimization with the retention prediction will accelerate the development in the automation of liquid-chromatographic analysis.

OSTI ID:
5262018
Journal Information:
Analytical Letters; (United States), Journal Name: Analytical Letters; (United States) Vol. 24:11; ISSN ANALB; ISSN 0003-2719
Country of Publication:
United States
Language:
English