5. Multivariate Techniques

  1. Kevin Goodner2 and
  2. Russell Rouseff3
  1. Vanessa Kinton

Published Online: 21 JUN 2011

DOI: 10.1002/9781444343137.ch5

Practical Analysis of Flavor and Fragrance Materials

Practical Analysis of Flavor and Fragrance Materials

How to Cite

Kinton, V. (2011) Multivariate Techniques, in Practical Analysis of Flavor and Fragrance Materials (eds K. Goodner and R. Rouseff), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9781444343137.ch5

Editor Information

  1. 2

    Sensus, LLC, Hamilton, USA

  2. 3

    IFAS, Citrus Research and Education Center, University of Florida, USA

Author Information

  1. Alcohol and Tobacco Tax and Trade Bureau (TTB), Ammendale, USA

Publication History

  1. Published Online: 21 JUN 2011
  2. Published Print: 15 JUL 2011

ISBN Information

Print ISBN: 9781405139168

Online ISBN: 9781444343137



  • multivariate techniques - chemometric analysis, tool for analyzing complex data;
  • International Chemometrics Society - chemometrics, mathematical and statistical methods;
  • chemometrics, in analytical method development - selecting variables of analytical signal;
  • multivariate techniques, and analyst treating spectrum - or chromatogram as a pattern;
  • sample-preprocessing techniques - normalization, weighting, smoothing and baseline corrections;
  • variable-preprocessing tools - centering and variable weighting;
  • Savitzki–Golay polynomial filter;
  • unsupervised learning techniques - principal component analysis (PCA) and hierarchical cluster analysis (HCA);
  • classification models, build and validate - new samples compared, using KNN and SIMCA;
  • SIMCA model, diagnostic - for examining SIMCA model, interclass distance


This chapter contains sections titled:

  • Introduction

  • Hierarchical Cluster Analysis (HCA)

  • Principal Component Analysis (PCA)

  • Classification Models

  • Principal Component Regression

  • Example of Data Analysis for Classification Models

  • References