dorrit: Dorrit fluorescence data.

In rrcov3way: Robust Methods for Multiway Data Analysis, Applicable also for Compositional Data

Dorrit fluorescence data.

Description

A data set with 27 synthetic samples containing different concentrations of four analytes (hydroquinone, tryptophan, phenylalanine and dopa) measured in a Perkin-Elmer LS50 B fluorescence spectrometer.

Usage

data(dorrit)

Format

A three-way array with dimension 27 x 116 x 18. The first dimension refers to the 27 samples. The second dimension refers to the emission measurements (251-481nm, 2nm intervals). The third dimension refers to the excitation (230-315nm, 5nm intervals).

Details

Each fluorescence landscape corresponding to each sample in the original data set consists of 233 emission wavelengths (250-482 nm) and 24 excitation wavelengths (200-315 nm taken each 5 nm). The fluorescence data is three-way. Ideally, the data is trilinear, the components of the modes corresponding to concentrations (27), emission spectra (233) and excitation spectra (24). Hence a three-way PARAFAC model should be capable of uniquely and meaningfully describing the variation in the data set.

The data set is modified as described in Engelen and Hubert (2011): the emission wavelengths are taken at 2 nm, noisy parts situated at the excitation wavelengths from 200 to 230 nm and at emission wavelengths below 250 nm are excluded. The severe Rayleigh scattering areas present in all samples are replaced by interpolated values. Thus we end up with a (27 x 116 x 18) data array.

Source

https://ucphchemometrics.com/datasets/.

References

Bro, R, Sidiropoulos, ND and Smilde, AK (2002). Maximum likelihood fitting using ordinary least squares algorithms. Journal of Chemometrics, 16(8–10), 387–400.

Riu, J and Bro, R (2003) Jack-knife estimation of standard errors and outlier detection in PARAFAC models. Chemometrics and Intelligent Laboratory Systems, 65(1), 35–49

Engelen, S and Hubert, M (2011) Detecting outlying samples in a parallel factor analysis model, Analytica Chemica Acta 705 155–165.

Baunsgaard, D (1999) Factors Affecting 3-way Modelling (PARAFAC) of Fluorescence Landscapes, Royal Veterinary and Agricultural University, Department of Dairy and Food Science, Frederiksberg, Denmark.

Examples

 ## Not run: 

 data(dorrit)
 ##  Plotting Emission spectra
 oldpar <- par(mfrow=c(2,1))
 matplot(t(dorrit[,,1]), type="l",
     xlab="Wavelength/nm", ylab="Intensity",
     main="Fluorescence emission spectra")
 matplot(t(dorrit[,,5]), type="l",
     xlab="Wavelength/nm", ylab="Intensity",
     main="Fluorescence emission spectra")
 par(oldpar)

 ##  Plotting excitation spectra
 oldpar <- par(mfrow=c(2,1))
 matplot(t(dorrit[,1,]), type="l",
     xlab="Wavelength/nm", ylab="Intensity",
     main="Fluorescence excitation spectra")
 matplot(t(dorrit[,30,]), type="l",
     xlab="Wavelength/nm", ylab="Intensity",
     main="Fluorescence excitation spectra")
 par(oldpar)

 persp(as.numeric(dimnames(dorrit)[[2]]),
     as.numeric(dimnames(dorrit)[[3]]), dorrit[4,,],
     xlab="Emission", ylab="Excitation", zlab="Intensity",
     theta = 30, phi = 30, expand = 0.5, col = "lightblue",
     ticktype="detailed")

 pp <- Parafac(dorrit, ncomp=4, robust=TRUE)
 plot(pp)


## End(Not run)

rrcov3way documentation built on July 9, 2023, 7:44 p.m.