Agro-Climatic Information to Enhance the Machine-Learning Classification of Olive Oils from Near-Infrared Spectra.

The integrity of extra virgin olive oil (EVOO) quality markers can be compromised owing to deceptive marketing practices, such as misleading geographical origin claims or counterfeit certification labels, i.e., protected designations of origin (PDO).

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.

Functional approach and agro-climatic information to improve the estimation of olive oil fatty acid content from near-infrared data.

Extra virgin olive oil (EVOO) is very appreciated by its taste, flavor, and benefits for health, and so, it has a high price of commercialization. This fact makes it necessary to provide reliable and cost-effective analytical procedures, such as near-infrared (NIR) spectroscopy, to analyze its traceability and purity, in combination with chemometrics. Fatty acids profile of EVOO, considered as a quality parameter, is estimated, firstly, from NIR data and, secondly, by adding agro-climatic information.

Modeling and partial least squares approaches in OODA.

This is a discussion of the following paper: "Overview of object oriented data analysis" by J. Steve Marron and Andrés M. Alonso.