Computational procedure chromatograms

The above procedure has been verified for computer-generated chromatograms (39). For real separations, series of chromatograms acquired at different column efficiencies (thus different values of ) are rarely available, as required by the procedure. Fortunately, a simpler procedure has been developed to deal with single chromatograms [34]. For this, Eq. 6.54 is written in the form... 

A series of computer-simulated chromatograms has been generated to test the validity of a procedure derived from the statistical model for calculating the number of randomly distributed components when many of them are obscured by overlap. Plots of the logarithm of the peak count versus reciprocal peak capacity are used for this purposTI TRese plots are shown to provide reasonable estimates of the total number of components In the synthetic chromatograms. 

One of the major objectives of this paper Is to test the above procedure for obtaining m using computer-generated chromatograms. 

The second level of computer utilization in HPLC is extraction of valuable analytical and physicochemical information from the chromatogram. This includes standard analytical procedures of peak integration, calibration and quantitation, and more complex correlation of the retention dependencies with variation of selected parameters. 

It is possible in principle to derive K and a from a single whole polymer sample for which [ ] in the GPC solvent and M are known [21], This method is less reliable than the preceding procedure which involved intrinsic viscosities of two samples because the computations of M can be adversely affected by skewing and instrumental broadening of the GPC chromatogram. 

Headspace peaks from the wastewaters were correlated with one another and identified using the reference compounds by a statistical comparison of their retention indices, using an in-house computer program, Chromscan. Peak retention indices were calculated using two NHCs, pyridine and quinoline, as reference points with index values of 100 and 200, respectively [8 ]. The peak comparison procedure was based on repetitive calculation of analysis of variance (anova) in which the retention index of each peak in triplicate chromatograms of one of the samples was compared with the 3 nearest peaks in each of the other samples. Peaks were considered correlated, and hence probably identical, if the 95% F-statistic appropriate to the number of samples analyzed was not exceeded. For most correlations, the F values were much smaller... 

The calculation of the relative characteristic peak areas on the chromatograms of the volatile pyrolysis products, using an external standard irrespective of the pyrolysis procedure, permits one to take into account the sensitivity of the detector, with easy computation of the ratio between the peak areas of the component of interest and the standard which, under normal conditions (sample size, carrier gas flow-rate, pyrolysis temperatures, etc.) are proportional to the absolute amounts of the pyrolysis products. This method of calculation is essentially a modification of the absolute calibration method in gas chromatography, which had never been used before in Py—GC.To facilitate comparison of the results obtained at different times or on different instruments, the results of individual measurements should preferably be presented in terms of specific yields (or relative characteristic peak areas), i.e., the yield of the volatile pyrolysis products must be calculated per 1 mg (or g or ng) of the pyrolysed sample with respect to 1 mg (or g or Mg) of the external standard. Such a calculation makes sense in the range of sample sizes which affect only insignificantly the specific yield of light pyrolysis products. 

The experimental optimization procedures outlined above can be replaced with others based on computer simulations [64,65], which make use of the chromatographic theory and of one or two prior experiments intended to define critical parameters such as the sample, mobile phase, column, temperature, flow-rate and pressure. Simulated chromatograms are obtained for different experimental conditions (column dimensions, particle size, mobile phase composition, flow-rate, temperature, etc.) until the required resolution is achieved. In essence, the procedure is similar to experimental optimization, although the chromatograph functioning is replaced with programming. The information obtained can be checked experimentally or be used for designing new approached to experimental optimization. 

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