Three-way principal component analysis applied to food analysis: an example

被引：53

作者：

Pravdova, V

Boucon, C

de Jong, S

Walczak, B

Massart, DL

机构：

[1] Free Univ Brussels, ChemoAC, FABI, B-1090 Brussels, Belgium

[2] Unilever Res Labs Vlaardingen, NL-3130 AC Vlaardingen, Netherlands

来源：

ANALYTICA CHIMICA ACTA | 2002年 / 462卷 / 02期

关键词：

n-way PCA; Tucker3; model; PARAFAC model; Maillard reaction;

D O I：

10.1016/S0003-2670(02)00318-5

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

The purpose of the study is to show how the interpretation of a complex multivariate data array can significantly be improved by the application of N-way principal component analysis (PCA). Two food related three-way data sets were studied; a sensory and a chromatographic data array. The Parafac and the Tucker3 models were applied and results were compared. Both N-way models presented here allow visualization of the data structure and give detailed information about the data set, notably allowing to understand relationships between objects and variables. (C) 2002 Elsevier Science B.V. All rights reserved.

引用

页码：133 / 148

页数：16

共 16 条

[1] Analysis of N-dimensional data arrays from fluorescence spectroscopy of an intermediary sugar product [J].