Application of class-modelling techniques to near infrared data for food authentication purposes
MetadataShow full item record
StatisticsDisplay Item Statistics
CitationOliveri, 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
AbstractFollowing 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.