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.

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.

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. NIR and agro-climatic data sets are summarized by using principal component analysis (PCA) and treated by both scalar and functional approaches. The corresponding PCA and FPCA are progressively introduced in regression models, whose goodness of fit is evaluated by the dimensionless root-mean-square error. In general, SFAs, MUFAs, and PUFAs (and disaggregated fatty acids) estimations are improved by adding agro-climatic besides NIR information (mainly, temperature or evapotranspiration) and considering a functional point of view for both NIR and agro-climatic data.