Redundancy Analysis to Reduce the High-Dimensional Near-Infrared Spectral Information to Improve the Authentication of Olive Oil

The high price of
marketing of extra virgin olive oil
(EVOO) requires
the introduction of cost-effective and sustainable procedures that
facilitate its authentication, avoiding fraud in the sector. Contrary
to classical techniques (such as chromatography), near-infrared (NIR)
spectroscopy does not need derivatization of the sample with proper
integration of separated peaks and is more reliable, rapid, and cost-effective.
In this work, principal component analysis (PCA) and then redundancy
analysis (RDA)which can be seen as a constrained version of
PCAare used to summarize the high-dimensional NIR spectral
information. Then PCA and RDA factors are contemplated as explanatory
variables in models to authenticate oils from qualitative or quantitative
analysis, in particular, in the prediction of the percentage of EVOO
in blended oils or in the classification of EVOO or other vegetable
oils (sunflower, hazelnut, corn, or linseed oil) by the use of some
machine learning algorithms. As a conclusion, the results highlight
the potential of RDA factors in prediction and classification because
they appreciably improve the results obtained from PCA factors in
calibration and validation.