R/authen_NMR.R

In ClustVarLV: Clustering of Variables Around Latent Variables

#'  Authentication data set/ NMR spectra
#'
#' @description
#' Discrimination between authentic and adulterated juices using 1H NMR spectroscopy.
#' 150 samples were prepared by varying the percentage of co-fruit mixed with the fruit juice of interest. The two first characters in the row names represent this percentage. Authentic juice names begin with "00". Samples prepared with the co-fruit alone are identified by "99" (rather than 100). 
# 'Measurements were done for two spectral ranges. All Spectral values were log-transformed.
#'
#' @docType data
#'
#' @usage data(authen_NMR)
#'
#' @format  150 observations and 2 blocks of variables.
#' \describe{
#'     \item{authen_NMR$Xz1}{spectral range from  6 to 9 ppm (300 variables)}
#'     \item{authen_NMR$Xz2}{spectral range from  0.5 to 2.3 ppm (180 variables)}
#' }
#' @references Vigneau E, Thomas F (2012). Model calibration and feature selection for orange juice authentication by 1H NMR spectroscopy.Chemometrics and Intelligent Laboratory Systems, 117, 22:30.
#' @keywords datasets
#'
#'
#' @examples
#'   data(authen_NMR)
#'   xlab=as.numeric(colnames(authen_NMR$Xz2))
#'   plot(xlab, authen_NMR$Xz2[1,], type="l", xlab="ppm",ylab="", ylim=c(14.8,15.8), 
#'   xlim=rev(range(xlab)))
#'   for (i in (1:nrow(authen_NMR$Xz2))) lines(xlab,authen_NMR$Xz2[i,])
"authen_NMR"