cellulose


  The data were collected at Akzo Nobel, Ornkoldsvik (Sweden). The raw
  material for their cellulose derivative process is delivered to the
  factory in form of cellulose sheets. Before entering the process the
  cellulose sheets are controlled by a viscosity measurement, which
  functions as a steering parameter for that particular batch.
  In this data set NIR spectra for 180 cellulose sheets were collected
  after the sheets had been sent through a grinding process. Hence the
  NIR spectra were measured on the cellulose raw material in powder
  form. Data are divided in two parts, one used for modeling and one
  part for testing.


datasets

Latent variable modeling with Principal Component Analysis
(PCA) and Partial Least Squares (PLS) are powerful methods for
visualization, regression, classification, and feature
selection of omics data where the number of variables exceeds
the number of samples and with multicollinearity among
variables. Orthogonal Partial Least Squares (OPLS) enables to
separately model the variation correlated (predictive) to the
factor of interest and the uncorrelated (orthogonal) variation.
While performing similarly to PLS, OPLS facilitates
interpretation. Successful applications of these chemometrics
techniques include spectroscopic data such as Raman
spectroscopy, nuclear magnetic resonance (NMR), mass
spectrometry (MS) in metabolomics and proteomics, but also
transcriptomics data. In addition to scores, loadings and
weights plots, the package provides metrics and graphics to
determine the optimal number of components (e.g. with the R2
and Q2 coefficients), check the validity of the model by
permutation testing, detect outliers, and perform feature
selection (e.g. with Variable Importance in Projection or
regression coefficients). The package can be accessed via a
user interface on the Workflow4Metabolomics.org online resource
for computational metabolomics (built upon the Galaxy
environment).

Etienne A Thevenot

ropls

PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and
feature selection of omics data

cellulose function

A list with the following elements: 1) nirMN: a matrix of 180 samples x 1201 wavelengths in the VIS-NIR
  region, 2) viscoVn: a vector (length = 180) of viscosity of cellulose
  powder, and 3) classVn: a vector (length = 180) of class membership (1 or 2)

Format

The data were collected at Akzo Nobel, Ornkoldsvik (Sweden). The raw
  material for their cellulose derivative process is delivered to the
  factory in form of cellulose sheets. Before entering the process the
  cellulose sheets are controlled by a viscosity measurement, which
  functions as a steering parameter for that particular batch.
  In this data set NIR spectra for 180 cellulose sheets were collected
  after the sheets had been sent through a grinding process. Hence the
  NIR spectra were measured on the cellulose raw material in powder
  form. Data are divided in two parts, one used for modeling and one
  part for testing.

cellulose: NIR-Viscosity example data set to illustrate multivariate calibration using PLS, spectral filtering and OPLS

Description

Usage

Arguments

Value

Format

References