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GenHVL[T]

GenHVL[T] - [T] Generalized Student's t ZDD

The GenHVL[T] model is a further generalization of the GenHVL[S] model. The [T] ZDD allows both the skew, the third moment, and the kurtosis, the fourth moment or 'fatness' of the tails to vary. The GenHVL[T] model may be of value in fitting preparative peaks with their high overload shapes.

By inserting the [T] Generalized Student's t (Asymmetric) ZDD for the PDF, CDF, and CDFc in GenHVL template, we produce the GenHVL[Y] model:

a_{0} = Area

a_{1} = Center (as mean of underlying normal ZDD)

a_{2} = Width (SD of underlying normal ZDD)

a_{3} = HVL Chromatographic distortion ( -1 > a_{3}
> 1 )

a_{4} = Student's
t tailing, the nu or DOF ( 1 > a_{4} > 1,000,000 ) (fourth moment)

a_{5} = ZDD asymmetry ( -1 > a_{5} > 1 ), adjusts skew (third moment)

Built in model: GenHVL[T]

User-defined peaks and view functions: GenHVL[T](x,a_{0},a_{1},a_{2},a_{3},a_{4},a_{5})

The GenHVL[T] model with a_{5}=0
reduces to the GenHVL[S]
model.

The GenHVL[T] model with a_{5}=0
and a_{4}=infinity
reduces to the HVL
model.

Inapplicability of the GenHVL[T] for Gradient HPLC Peaks

In a gradient separation, the tailing of a peak is typically more compact than that of a Gaussian. Since the GenHVL[T]'s ZDD cannot produce such compaction, the GenHVL[T] model will not be useful for gradient peaks.

Using the GenHVL[T] for Preparative Peaks

Although the GenHVL[T] can approximate certain overload shapes, you should
regard this as an empirical model only. If you can realize a strong fit of an overload shape using this
model, the a_{4} and a_{5}
will give you useful estimates of the higher moments of such peaks, absent the overload, which you may
be able to tie to column health.

GenHVL[T] Considerations

When a_{4} approaches infinity and a_{5}=0, the ZDD becomes a Gaussian and the model reduces
to the HVL.

This a_{4} kurtosis adjustment in the ZDD manages the deviations only from a Gaussian tail decay.

This a_{5} skew adjustment
in the ZDD manages the deviations from the Gaussian ideality assumed in the theoretical infinite dilution
HVL. This is the statistical asymmetry parameter; small differences in values produce large deviations
in analytic shapes. For most IC and non-gradient HPLC peaks, you should expect an a_{5}
between +0.01 and +0.03 (the deviation from non-ideality is a right skewed or tailed).

We have often observed a small modeling power improvement when using the GenHVL[T] model with non-gradient analytic peaks. The nu will typically fit to 50-500, and as such the benefit of adding the kurtosis to the modeling will be small. You should use the GenHVL[T] model cautiously for fitting analytic peaks. Use the F-statistic of the fit of the GenHVL[T] model against the F-statistic for the GenHVL or GenHVL[Z] models to ensure there is an actual improvement in the modeling. The GenHVL[T] F-statistic will increase in contrast with the GenHVL or GenHVL[Z] model when this adjustment to the fourth moment is statistically beneficial. A high S/N will definitely be needed to even see this benefit.

In most instances, a_{4}
and a_{5} can be assumed constant (shared) across all
peaks in the chromatogram. It is strongly recommended that a_{4}
and a_{5} be shared across all peaks.

The addition of a shared a_{4}
and a_{5} parameter to an overall fit can result in orders
of magnitude improvement in the goodness of fit.

Both a_{4} and a_{5}
are measures of the deviation from ideality. Changes in either,
in fitting a given standard, may well be indicative of column health. The greater the a_{5}
value, the more the skew is deviating from this Gaussian ZDD assumption of the HVL. For the GenHVL[T]
model, the a_{4}
may be of equally or even greater importance since additional tailing represents a drizzle of sorts that
can impact adjacent peaks. You may wish to use the GenHVL[T] with a standard and watch for any unexpected
changes in either a_{4}
or a_{5}.

Note that the a_{5}
will be most effectively estimated and fitted when the peaks are skewed with some measure of fronting
or tailing. Higher concentrations are very good for fitting analytic peaks with this model, assuming that
one does not enter into a condition of overload that impacts the quality of the fit.

This model will probably not be effective at all in highly dilute samples with a poor S/N ratio since such peaks will generally have much less intrinsic skew and the tailing will be poorly defined due to inaccuracies in the baseline subtraction.

The GenHVL[T]<irf> composite fits, the model with a convolution integral describing the instrumental distortions, isolate the intrinsic chromatographic distortion from the IRF instrumental distortion only when the data are of a sufficient S/N and quality to realize independent deconvolutions within the fitting. For very dilute and noisy analytic samples, you will probably have to remove the IRF prior using independent determinations of the IRF parameters.

The GenHVL[T]<ge> model uses the <ge>IRF, consistently the
best of the convolution models as it fits both kinetic and probabilistic instrument distortions. Bear
in mind, however, that this fit must extract the kinetic instrumental distortion, the probabilistic instrumental
distortion, the a_{5}
intrinsic skew to the chromatographic distortion, the a_{4}
intrinsic tailing in the ZDD, and the primary a_{3}
chromatographic distortion (very possibly for for each peak). It is recommended the IRF parameters be
determined by fits of a clean standard, and the instrumental distortions removed by deconvolving the known
IRF prior to fitting production peak data.

Since peaks often increase in width with retention time, the a_{2}
will probably be varied (independently fitted) for each peak.

Since peaks often evidence increased tailing with retention time, the
a_{3} will probably be varied (independently fitted)
for each peak.

If you are dealing with a small range of time, however, or of you are dealing with overlapping or hidden
peaks in a narrow band, a_{2} and/or a_{3} can be held constant across the peaks in this
band.

The GenHVL[T] model is part of the unique content in the product covered by its copyright.