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Cited by 8,170

The DOI matches its Crossref record.

Multivariate Data Analysis

·2008·Environmental Chemometrics
DOI 10.1201/9781420067972-10
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Abstract

Data preprocessing is a very important step in multivariate analysis, which can be used separately (before a method is applied), or as a self-adjusting procedure that forms part of the chemometric approach. Ideally, data preprocessing can be used to remove known interference(s) from data to improve selectivity and enhance more important information to improve robustness techniques such as principal components (see Section 6.4.2), which are scale dependent. If one variable has a much higher variance than the others, it is necessary to scale the original variable before calculating principal components. Common preprocessing scaling techniques include mean centering, autoscaling, column standardization, and autoscaled profi les. Other data preprocessing techniques commonly used in chemometrics include minimum/ maximum transformation, variance normalization, and baseline corrections (1st and 2nd derivative, subtraction).

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  1. Absalan, G. and Nekoeinia, M. 2005. Simultaneous kinetic determination of Fe(II) and Fe(III) based on their reactions with NQT4S in micellar media by using PLS and PCR methods. Analytica Chimica Acta 531: 293–298. · DOI

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Metadata

Venue
Environmental Chemometrics
Publisher
CRC Press
Published
2008-11-21
Pages
225-286
DOI
10.1201/9781420067972-10
OpenAlex
W3012479489
Cited by
8,170
References
0
FWCI
93.60
Type
book chapter
Language
en
Open access
closed

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