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|Title: ||Application of class-modelling techniques to near infrared data for food authentication purposes|
|Authors: ||Oliveri, P.|
Di Egidio, V.
|Issue Date: ||2011|
|Citation: ||Oliveri, P.; Di Egidio, V.; Woodcock, T.; Downey, G. Application of class-modelling techniques to near infrared data for food authentication purposes. Food Chemistry, 2011, 125, 1450-1456. DOI: 10.1016/j.foodchem.2010.10.047|
|Abstract: ||Following the introduction of legal identifiers of geographic origin within Europe, methods for confirming any such claims are required. Spectroscopic techniques provide a method for rapid and non-destructive data collection and a variety of chemometric approaches have been deployed for their interrogation. In this present study, class-modelling techniques (SIMCA, UNEQ and POTFUN) have been deployed after data compression by principal component analysis for the development of class-models for a set of olive oil and honey. The number of principal components, the confidence level and spectral pre-treatments (1st and 2nd derivative, standard normal variate) were varied, and a strategy for variable selection was tried. Models were evaluated on a separate validation sample set. The outcomes are reported and criteria for selection of the most appropriate models for any given application are discussed.|
NOTICE: this is the author’s version of a work that was accepted for publication in Food Chemistry. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in FOOD CHEMISTRY 2011, 125, 1450-1456. DOI: http://dx.doi.org/10.1016/j.foodchem.2010.10.047
|Appears in Collections:||Food Chemistry & Technology|
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