RpeakChrom (version 1.1.0)

vanDeemterAlternative:

Description

Characterization of chromatographic columns using a new aproximation to vanDeemter equations.

Usage

vanDeemterAlternative(col, ext, dead, length, approachI=FALSE, A, B, C,
approachII=FALSE)

Arguments

col

data frame of the columnar measurements obtained using processPeak function.

ext

data frame of the extracolumnar measurements obtained using processPeak function.

dead

data frame of the dead marker measurements obtained using processPeak function.

length

numeric value indicating the column length in mm.

approachI

If TRUE approach I is performed.

A

numeric value indicating the initial value of the parameter A from the van Deemter equation.

B

numeric value indicating the initial value of the parameter B from the van Deemter equation.

C

numeric value indicating the initial value of the parameter C from the van Deemter equation.

approachII

If TRUE approachII is performed.

Value

For Approach I: list containing 6 items. Table: a summary of slope estimated values at several flows. Coefficients: A, B and C coefficients already fitted. Step I: coefficient of the linear fitting for the first step (R2). Correlation: R for the second step of the graphic approach (H vs u, non-linear fitting). Mean error: for the graphic (H vs u). RSE: square root of the estimated variance of the random error for the nls graphic. For Approach II: list containing 8 items Table: a summary of slope estimated values at several flows. Coefficients: A, B and C coefficients already fitted. r2A: R2 for the coefficient A. r2B: R2 for the coefficient B. r2C: R2 for the coefficient C. MREA: mean relative prediction error for the coefficient A. MREB: mean relative prediction error for the coefficient B. MREC: mean relative prediction error for the coefficient C.

Details

In the ApproachI the parameters A, B and C from the Van Deemter equation are obtained in two steps. First the variance in volume for a set of compounds eluted a several flows are linearly fitted versus the retention volume to obtain the plate height. In the second step, the obtained slopes at several flow rates are non-linearly correlated with the linear mobile phase velocity. In the ApproachII in the first step the parabolic behavior for the variance in volume units for each compound in the set is fitted against the flow rate. In the second step, the A, B and C coefficients for the different compounds are linearly correlated with their retention volume. The slopes in the straight-lines are the model parameters A, B and C in the Van Deemter equation.

References

J. Baeza-Baeza, J. Torres-Lapasio, and M. Garcia-Alvarez-Coque. Approaches to estimate the time and height at the peak maximum in liquid chromatography based on a modified gaussian model. J.Chromatography A, 1218(10):1385-1392, 2011. R. Caballero, M. Garcia-Alvarez-Coque, and J. Baeza-Baeza. Parabolic-lorentzian modified gaussian model for describing and deconvolving chromatographic peaks. J. Chromatography A, 954:59-76, 2002. J. Foley and J. Dorsey. Equations for calculation of chromatographic figures of merit for ideal and skewed peaks. Analytical Chemistry, 55:730-737, 1983. E. Grushka, M. Meyers, and J. Giddings. Moment analysis for the discernment of overlapping chromatographic peaks. Analytical Chemistry, 42:21-26, 1970. L. He, S. Wang, and X. Geng. Coating and fusing cell membranes onto a silica surface and their chromatographic characteristics. Chromatographia, 54:71-76, 2001. T. Pap and Z. Papai. Application of a new mathematical function for describing chromatographic peaks. J. Chromatography A, 930:53-60, 2001. J. van Deemter, F. Zuiderweg, and A. Klinkenberg. Longitudinal diffusion and resistance to mass transfer as causes of nonideality in chromatography. Chemical Engineering Science, 5(6):271-289, 1956. V.B. Di Marco and G.G. Bombi. Mathematical functions for the representation of chromatographic peaks. Journal of Chromatography A, 931:1-30, 2001.

See Also

readChrom, processPeak, vanDeemter

Examples

coeff1 <- vanDeemterAlternative(col = col, ext = parameters_ext,
    dead = parameters_dead, length = 150, approachI = TRUE, A = 6, B = 200,
    C = 0.04, approachII = FALSE)

coeff2 <- vanDeemterAlternative(col = col, ext = parameters_ext,
    dead = parameters_dead, length = 150, approachI = FALSE, A = 6, B = 200,
    C = 0.04, approachII = TRUE)