Abstract
This chapter gives an introduction to the world of multiway chemometrics, as well as showing how it can be applied to different types of multidimensional data related to food science and technology. It describes how information can be extracted from multiway data in a meaningful way by keeping the multiway dimensionality, or even by adding a new dimensionality to traditional two-dimensional data (data tables). The chapter mainly focuses on fluorescence and gas chromatography mass spectrometry (GC-MS) data. However, the methods discussed are not limited to the few cases shown, but rather to any multiway data which contain (or can be transformed to contain) a trilinear data structure.
| Original language | English |
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| Title of host publication | Mathematical and statistical methods in food science and technology |
| Editors | Daniel Granato, Gastón Ares |
| Number of pages | 32 |
| Publisher | Wiley |
| Publication date | 27 Dec 2013 |
| Pages | 143-174 |
| Chapter | 9 |
| ISBN (Print) | 9781118433683 |
| ISBN (Electronic) | 9781118434635 |
| DOIs | |
| Publication status | Published - 27 Dec 2013 |