Supervised pattern recognition in food analysis

Data analysis has become a fundamental task in analytical chemistry due to the great quantity of analytical information provided by modern analytical instruments. Supervised pattern recognition aims to establish a classification model based on experimental data in order to assign unknown samples to...

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Bibliographic Details
Published in:Journal of Chromatography A 2007-07, Vol.1158 (1), p.196-214
Main Authors: Berrueta, Luis A., Alonso-Salces, Rosa M., Héberger, Károly
Format: Article
Language:English
Subjects:
Chemometrics
classification
color
data analysis
discriminant analysis
electronic nose
Food analysis
Food Analysis - methods
linear models
literature reviews
model validation
multivariate analysis
Multivariate data analysis
Pattern Recognition, Automated
Supervised pattern recognition
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Summary:Data analysis has become a fundamental task in analytical chemistry due to the great quantity of analytical information provided by modern analytical instruments. Supervised pattern recognition aims to establish a classification model based on experimental data in order to assign unknown samples to a previously defined sample class based on its pattern of measured features. The basis of the supervised pattern recognition techniques mostly used in food analysis are reviewed, making special emphasis on the practical requirements of the measured data and discussing common misconceptions and errors that might arise. Applications of supervised pattern recognition in the field of food chemistry appearing in bibliography in the last two years are also reviewed.
ISSN:0021-9673
DOI:10.1016/j.chroma.2007.05.024