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ZDD - [W] Generalized Error, Linear Gradient (HPLC Gradient Peaks Only)

This is the Zero Distortion Density (ZDD) used in the GenHVL[W] models.

The [W] model is offered as a ZDD option for HPLC gradient peaks. In most cases, the Gen2HVL or GenHVL[Y], which use a logarithmic gradient, will be the models of choice for fitting HPLC gradient peaks. If the gradient is particularly strong, however, and a peak is dramatically impacted across the span of its specific elution by a linear gradient, and if there is very little intrinsic fronting or tailing arising from the chromatographic distortion, the peak decay may be better described by a linear rather than a logarithmic asymmetry. This model should be deemed experimental, and should always be evaluated alongside either the Gen2HVL or GenHVL[Y] model.

You will not want to use the GenHVL[W] for analytic non-gradient peaks. The linear gradient in the [W] density will actually work against the strongly fronted and tailed shapes arising from high intrinsic chromatographic distortions.

This generalized error's a₁ center parameter is the mean of the underlying Normal. The a₂ width parameter is the Gaussian width used in the model. The a₃ is the power of the decay, 2.0 for Gaussian, >2 for the compaction arising from the gradient. The a₄ parameter in the ZDD nomenclature specifies the linear gradient asymmetry, negative for fronting, positive for tailing.

There is no GenNLC[W] model since this [W] ZDD cannot replicate a Giddings decay.

[W] ZDD

a₀ = Area

a₁ = Center (as mean of underlying Gaussian)

a₂ = Width (as SD of underlying Gaussian)

a₃= Power of decay (2.0=Gaussian)

a₄= Asymmetry (fronted -1 > a₄ > 1 tailed)

Built-In Peak Model

  GenErrorLin (Statistical family)

User-Defined Peaks and View Functions

 GenErrorLin(x,area,median,width,shape,power) LinAsym Generalized2 Normal Area

 GenErrorLin_C(x,area,median,width,shape,power) LinAsym Generalized2 Normal Cumulative

 GenErrorLin_CR(x,area,median,width,shape,power) LinAsym Generalized2 Normal Rev. Cumulative

This Generalized Error with Linear Gradient model is part of the unique content in the product covered by its copyright.