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.
['Sánchez-Rodríguez|María Isabel|MI|https://orcid.org/0000-0003-0657-7823', 'Sánchez-López|Elena M|EM|https://orcid.org/0000-0002-4885-6065', 'Marinas|Alberto|A|https://orcid.org/0000-0002-2693-0711', 'Urbano|Francisco José|FJ|https://orcid.org/0000-0002-3489-1601', 'Caridad|José M|JM|https://orcid.org/0000-0003-4558-6618']
extra virgin olive oil regression models NIR spectra agro‐climatic curves functional data analysis